For this Research Topic, we solicited forward-looking contributions on current and prospective use of methodology and models organisms in the field of molecular neuroscience. Indeed, we received a thought-provoking mix of review and original research articles that are briefly summarized in this editorial.Baratta et al. is written very much the same vein of this Research Topic in reporting on advances in genomic and behavioral analysis to understand normal and pathological brain function. Both approaches are the focus of intense research activities, taking advantage of both new instrumentation and increasingly more important data analysis tools. The complex and exciting relationship between genes and behavior necessitates such multipronged experimental approaches.The review by Arias et al. presents a much-needed guide to recent advances in the development of fluorescent transgenic mouse models with an eye to their use in understanding disease mechanisms.Enormous advances in imaging technology (including sample preparation and microcopy) and genetic engineering technologies have revolutionized the neurosciences. This is particularly apparent in the impressive number of studies published in the last few years using techniques that are ever more sophisticated to label specific cell types and their connections in the nervous system of mice. The review article by Goel and Ploski discuss in their review the merits of short hairpin RNA-mediated (hRNA) gene silencing in light of the more recent gene editing tools provided by the CRISPR/Cas system. A particular concern is the chance of off-target effects of shRNA that may limit its utility in comparison to alternative CRISPR/Cas approaches.Deng et al. describe a patient with a de novo non-sense mutation in the BRSK2 gene exhibiting characteristics of autism spectrum disorder (ASD). They went on to create a zebrafish mutant model using genome editing. BRSK2 deficient fish displayed neurodevelopmental deficits consistent with altered expression level of neurogenesis-related genes. Behaviorally these fish display impaired social preferences and shoaling behavior, establishing a new ASD animal model.In an original research article, Yokoyama et al. discuss a variety of mouse models, including transgenic, knock-in, and injection based model, aiming to recapitulate aspects of the pathophysiological processes in AD. Other mouse models based on neuroinflammation and the involvement if microglia are also discussed.Alzheimer's disease (AD) is a recognized public health issue commanding a huge amount of research funding, predominantly using mice to model the disease. Tello et al. address another approach to the study of AD in their review by reporting on recent advances on drug discovery using the fruit fly Drosophila melanogaster, a prominent invertebrate model for drug discovery. They argue that the most useful approach is to combine multiple screening methods such as morphological, behavioral and biomolecular analyses.Ferguson et al. describe the latest research on using transcriptomic data and their analysis using artificial intelligence to identify molecular biomarker aiming at improving clinical management of AUD.It is an important quest of diagnostic medical research to identify molecular biomarkers that are able to assist in diagnosis, patient stratification and prediction of disease outcome. Studies on the highly prevalent alcohol use disorder (AUD) are no exception. Neuwirth et al. address the reporting of light conditions in their systematic review in publications on a number of standard behavioral neuroscience tests. They found that only a minority of reports state the exact lighting conditions under which the tests have been performed. The authors provide suggestions on how the behavioral neuroscience community should address this untenable situation helping efforts to booster validity of behavioral experiments.The reproducibility of any scientific experiment is one of the basic tenants of science. Ideally, any publication should provide all the information needed to repeat the describe experiments in any laboratory yielding comparable results. Much has been written about the current reproducibility crisis in Science with some of the discussions centering on the detailed description of experimental procedures. This is particularly true for behavioral experiments with its abundant ambient parameters affected outcome. The author confirms being the sole contributor of this work and has approved it for publication.


Data-driven rolling-bearing fault diagnosis methods are mostly based on deep-learning models, and their multilayer nonlinear mapping capability can improve the accuracy of intelligent fault diagnosis. However, problems such as gradient disappearance occur as the number of network layers increases. Moreover, directly taking the raw vibration signals of rolling bearings as the network input results in incomplete feature extraction. In order to efficiently represent the state characteristics of vibration signals in image form and improve the feature learning capability of the network, this paper proposes fault diagnosis model MTF-ResNet based on a Markov transition field and deep residual network. First, the data of raw vibration signals are augmented by using a sliding window. Then, vibration signal samples are converted into two-dimensional images by MTF, which retains the time dependence and frequency structure of time-series signals, and a deep residual neural network is established to perform feature extraction, and identify the severity and location of the bearing faults through image classification. Lastly, experiments were conducted on a bearing dataset to verify the effectiveness and superiority of the MTF-ResNet model. Features learned by the model are visualized by t-SNE, and experimental results indicate that MTF-ResNet showed better average accuracy compared with several widely used diagnostic methods. Rolling bearings are critical components in rotating machinery, and their operating conditions under various loads directly impact their performance, stability, and endurance. More specifically, rolling bearings are vital in mechanical equipment. To maintain the normal operation of mechanical equipment, it is necessary to monitor the vibration signals generated by the rotating mechanism in real time . Many scThe diagnosis of rolling bearings generally consists of two stages: feature extraction and classification. Signal processing approaches that are widely employed to extract features from a raw signal include short-time Fourier transform (STFT) , waveletIn recent years, deep-learning models have grown in popularity in the field of machine learning, which uses the deep network structure to achieve more efficient and reliable feature extraction. Deep learning disposes of the dependence on manually extracting features and expert experience, which has achieved breakthroughs in many pattern recognition tasks such as natural-language processing , automatIn order to efficiently represent the state characteristics of vibration signals in image form and improve the feature learning capability of the network, a new intelligent bearing fault diagnosis method (MTF-ResNet) is proposed in this paper. The main contributions of this paper are summarized as follows.A novel two-step fault diagnosis method is proposed that converts raw vibration signals into images through the Markov transition field, and adopts the residual network for feature extraction and fault identification.The signal-to-image conversion preserves the time dependence of the raw vibration signals and retains sufficient temporal features without setting parameters involving expert knowledge. Residual learning is applied to effectively address degradation problems in the deep neural network.The effectiveness of the proposed model was verified on a popular bearing dataset. Compared with some existing methods, the MTF-ResNet method achieved better accuracy in bearing fault diagnosis.To further demonstrate the performance of the proposed method and investigate the intrinsic mechanism of the CNN model in bearing fault diagnosis, t-SNE was used to visualize the feature maps learned by the model.The remainder of this paper is organized as follows. Motivated by the concept of various cells in the visual cortex of the brain, and some cells that are exclusively responsive to the local receptive field , convoluThe convolutional layer performs convolutional operations on local regions of the input data (or features) with the use of the convolutional kernel. Weight sharing is the most essential characteristic of the convolutional layer, since the input is traversed once by the same convolutional kernel at a set stride which can minimize the parameters and alleviate overfitting to some extent. In general, the mathematical model of the convolutional layer can be described as:The main function of the pooling layer is to reduce the dimensionality of the data after convolutional operations. Average and maximal pooling are two commonly used pooling methods. The pooling layer performs a downsampling operation on the feature map, which avoids overfitting to a certain extent while retaining key features. The pooling layer transformation can be described as:Traditional deep convolutional neural networks are difficult to train as the network deepens because of problems of vanishing and exploding gradients. To address the degradation problem, He et al.  proposedResidual building blocks are the basic components of a residual network. As shown in This section presents the proposed MTF-ResNet fault diagnosis method. First, data augmentation is used to increase the training data. Then, the conversion method of the vibration signals into images is presented. Lastly, the network architecture based on MTF and ResNet for rolling bearing fault diagnosis is established.An effective technique to improve the generalization capabilities of machine-learning models is to use additional training samples. In computer vision tasks, horizontal flips, random crops or scales, and color jitter are commonly utilized to increase the data to train the model. Data augmentation is also required in fault diagnosis for deep convolutional neural networks to achieve high classification accuracy and avoid overfitting. The data augmentation method used in this paper is overlapping samples from raw one-dimensional sequences. Augmented samples were all allocated the same fault label as that of the raw sequence, since each input sequence was obtained under a single fault state. The data augmentation process is shown in When diagnosing and analyzing bearing faults, the accelerometer is one of the most frequently used sensors in modern research, which can directly collect the original vibration signal of the target object. Collected data from industrial processes are continuous time series, and have the characteristics of nonlinearity and nonstationary caused by high coupling in the system.Assume a time series The Markov transition field (MTF) then can be defined as follows:In the MTF matrix, the Once the raw vibration signals are converted into MTF images and formed into the image dataset, a CNN model can be trained to classify these images. In this paper, we applied the ResNet-34 network to extract 2D image features. A softmax layer was employed at the end of the network to classify the rolling-bearing health condition on the basis of the learned features. The proposed MTF-ResNet model architecture is demonstrated in To validate the performance of the proposed MTF-ResNet, the Case Western Reserve University (CWRU)  bearing In this paper, we used raw vibration signal sample at 12 kHz from the drive end accelerometer (DE). The training data were generated from half of the raw vibration sequence by overlapping samples through a sliding window length of 2048 with a step size of 80, while the test data were generated by the same window length from the other half without data augmentation. According to the working conditions, datasets under a single working condition and variable working conditions are considered in this study. The bearing fault datasets under a single working condition are shown in All samples were then converted into MTF images. In order to show the detailed identification effect of the model for each fault type in the test set, a confusion matrix was introduced for more accurate and comprehensive analysis of the experimental results. The vertical axis of the confusion matrix represents the true labels of the classification, and the horizontal axis demonstrates the predicted labels. The confusion matrix shows the classification results of all fault types, containing both correct and incorrect classification information. The confusion matrices of the MTF-ResNet prediction results are shown in In order to qualitatively illustrate the effectiveness of the proposed model and judge the separability of the data on the basis of the visualization of learned representation, nonlinear dimensionality reduction algorithm t-SNE was employed to project the data into a 2-dimensional space. The model had powerful feature extraction and classification capability, samples of different fault types were almost perfectly separated, and samples within the same type were intuitively clustered. The results of feature visualization are consistent with the confusion matrices and demonstrate that the fault diagnosis problem can be successfully addressed by the proposed MTF-ResNet model.To better understand the effect of convolutional layers of the model in fault diagnosis, the features extracted from the four convolutional layers are visually mapped into a two-dimensional distribution by t-SNE, as shown in In this section, the performance of the MTF-ResNet model with different residual network structures is investigated. The same 10-category dataset was adopted, and the encoded MTF images were applied as input in ResNet-18 and ResNet-50 for feature extraction and classification. The average classification accuracy of different residual structures is shown in In recent years, much research has been conducted for rolling-bearing fault diagnosis problems. In order to further prove the superiority of the MTF-ResNet method proposed in this paper, we compared it with some commonly used methods. The detailed comparison results are shown in In this work, we proposed a novel intelligent rolling-bearing fault diagnosis method based on the Markov transition field (MTF) and residual network. Encoding one-dimensional time-series signals into two-dimensional images by Markov transition field preserves the time dependence of the raw signals and discards the prior knowledge to set parameters during the conversion. On this basis, a residual network is applied to identify the fault types through image classification. Experiments conducted on the CWRU bearing dataset indicate that MTF-ResNet achieved prominent performance on the identification of rolling bearings faults with various degrees of severity and locations, the proposed model achieves an average accuracy of 100% and 98.52% in the 4- and 10-category datasets, respectively. Compared with other intelligent bearing-fault diagnosis methods, the proposed MTF-ResNet method offers a better performance of feature extraction and classification in the fault diagnosis.While the MTF-ResNet method can achieve good performance for fault diagnosis, it has the disadvantage of requiring a longer training period than other shallow neural network-based methods do, as the residual network in this study was trained from scratch. Deep-learning algorithms are frequently hampered by a high computational burden. In further work, the transfer-learning approach, which showed promising results in reducing training time and computational cost , will be


Lead–halide perovskite nanocrystals are an attractive class of materials since they can be easily fabricated, their optical properties can be tuned all over the visible spectral range, and they possess high emission quantum yields and narrow photoluminescence linewidths. Doping perovskites with lanthanides is one of the ways to widen the spectral range of their emission, making them attractive for further applications. Herein, we summarize the recent progress in the synthesis of ytterbium-doped perovskite nanocrystals in terms of the varying synthesis parameters such as temperature, ligand molar ratio, ytterbium precursor type, and dopant content. We further consider the dependence of morphology (size and ytterbium content) and optical parameters  on the synthesis parameters. The developed open-source code approximates those dependencies as multiple-parameter linear regression and allows us to estimate the value of the photoluminescence quantum yield from the parameters of the perovskite synthesis. Further use and promotion of an open-source database will expand the possibilities of the developed code to predict the synthesis protocols for doped perovskite nanocrystals. Both Vb] = 0.4 e.3 based on the data shown in https://github.com/VladislavNexby/ML (accessed on 15 February 2023).From the above discussion of the literature data, dependencies of structural and optical properties of Yb-doped pNCs on each synthesis parameter seems to be not that univocal. This inspired us to reveal the multiple linear regression model which could predict the NIR PL QY from the initial synthesis parameters for Yb-doped CsPbClThe following parameters were considered: the amount of Pb and Yb salts in mmol, the amount of ligands (OlAm and OlAc) in mL, the reaction temperature in °C, and the reaction time in seconds . Table S3+ ions results in an increase in PL QY up to 200% via the efficient energy transfer from the perovskite host and quantum cutting. The literature analysis shows that the process of the quantum cutting depends on the bandgap of the perovskite host which can be varied by a change of X site from Cl to different ratios of Cl/Br anions in CsPbX3 pNCs. The analysis of reported data also shows dependencies of NIR PL QY on the synthetic parameters of Yb-doped CsPbX3 pNCs such as ligands ratio, reaction temperature, and the molar ratio of Yb and Pb precursors. For instance, a distinct maximum was observed for NIR PL QY at equal molar amounts of Yb and Pb precursors in the synthesis. The optimization of synthetic procedures may lead to a better performance of perovskite-based devices. Obviously, extended use of the existing and emerging instruments of data analysis and machine learning will play an important role in improving the synthetic protocols and developing novel lanthanide-doped perovskite materials.To date, perovskite materials, and in particular all-inorganic lead–halide perovskite nanocrystals, attract a lot of scientific attention due to the wide field of their potential applications. However, these materials are still far from industrial use since they have several drawbacks such as fast oxidation and poor photostability in the UV spectral region. An important target is the development of simple, scalable, and reliable synthetic procedures for producing perovskite materials with well-controlled properties such as bright emission and tunable energy structure. The doping of pNCs with lanthanides, along with expanding the spectral range of their optical transitions to NIR, allows us to overcome some existing issues of perovskite materials, by improving their environmental stability, decreasing the intrinsic defects and, hence, improving the emissive properties in the visible spectral region. Furthermore, doping with Yb3 pNCs and it can be considered as a base for further improvements, which may be accomplished in several possible ways:(i)Development of a standardized template for data collection and further expansion of the dataset based on available experimental data will improve the accuracy of the model prediction;(ii)Other machine learning models, regularization techniques, and other methods of preprocessing can be employed and compared for better model performance;(iii)Improvement of mathematical models may include such functions as a prediction of maximal values, sorting by type of precursors and/or resulting materials, graphical representation of derived dependencies of optical characteristics on synthesis parameters, and many more.Based on the literature data, an open-source code for the approximation of NIR PL QY dependence on synthetic parameters was proposed. This model can solve the direct problem of predicting the values of the PL QY in the near-IR region of the spectrum from the synthesis parameters of CsPbClProperly performed data analysis will facilitate the optimization of existing synthesis methods together with the possibility to predict the optical properties of doped perovskite materials by setting the synthesis parameters. Thus, the employment of machine learning methods offers a great potential for improvement of the doped perovskite materials in particular and plenty of novel nanostructured materials in general.


Sindbis virus (SINV) caused a large outbreak in Finland in 2021 with 566 laboratory-confirmed human cases and a notable geographical expansion. Compared with the last large outbreak in 2002, incidence was higher in several hospital districts but lower in traditionally endemic locations in eastern parts of the country. A high incidence is also expected in 2022. Awareness of SINV should be raised in Finland to increase recognition of the disease and prevent transmission through the promotion of control measures. Togaviridae family, Alphavirus genus) is the causative agent of Pogosta disease, a typically self-limited disease with common symptoms of rash, arthralgia, myalgia and fever [Sindbis virus (SINV)  and August (n = 175) . A consi Incidence rates of Pogosta disease in 2021 ranged in the different hospital districts from 0 (Åland Islands) to 40.6 (North Savo) per 100,000 residents . The hosAlthough some hospital districts reported large numbers of cases in both outbreaks (2002 and 2021), there were also clear differences . The SINThe 2021 SINV outbreak was, to the best of our knowledge, the largest mosquito-borne viral disease outbreak in Europe that year. In comparison, West Nile virus caused 159 documented cases in the European Union and European Economic Area during the same mosquito season . The groThe NIDR data for Pogosta disease cases allowed a comparison between different years by hospital district in Finland. However, the data were based on place of residence of the patient, which may not have reflected the location where the patient was infected. Despite this potential limitation, the available data suggested an expansion of the geographical range of Pogosta disease in 2021. We observed a considerably higher incidence in eight hospital districts compared with the previous outbreak in 2002. The positive change in incidence was highest in northern parts of the country and in four hospital districts with previously low incidence in southern Finland. The spatial pattern of highest incidences remained similar, which is in line with the spatial modelling results of the current SINV infection risk in Finland . The typThe reasons for the observed geographical shift in the high incidence areas between the latest two Pogosta disease outbreaks are currently not known. The remaining immunity in the human population after the 2002 outbreak may have lowered the number of cases in the traditional endemic areas in 2021 but does not explain the spread to new areas. Further information would be needed on the factors affecting SINV emergence, including a possible effect of virus strain variation . The lonAlthough the large outbreaks in 2002 and 2021 have probably increased awareness of the disease since then, it is likely that Pogosta disease is underdiagnosed in Finland, especially in areas where the disease is not common. The correct diagnosis of SINV patients is important because of the potential burden of persistent joint symptoms that can last for years. These symptoms have been reported in Finland in 24.5%, and more recently in Sweden up to 39%, of the diagnosed patients [Sindbis virus caused the largest outbreak of mosquito-borne viral disease in the EU in 2021, with 566 diagnosed cases in a single country. The factors contributing and enabling SINV outbreaks are currently poorly understood. Raising public awareness of the disease and the ways of preventing mosquito bites would be important, especially in current high incidence and predicted risk areas in central, eastern and western Finland. One year after the 2002 outbreak year, elevated numbers of cases were reported in Finland and, therefore, we consider SINV transmission potential increased also for the 2022 mosquito season. The observed regional shift of reported cases to new areas poses challenges for the recognition of the disease and highlights the need for using virus-specific diagnostic testing of febrile patients with compatible symptoms.


The in vitro assessment focuses on MIC determination, static time-kill kinetics, and dynamic hollow fibre infection models to investigate acquisition of resistance and killing of Mycobacterium tuberculosis populations in various metabolic states. The in vivo assessment focuses on the various animal models, routes of infection, PK at the site of infection, PD read-outs, biomarkers and differences in treatment outcome evaluation (relapse and death). For human PKPD we focus on early bactericidal activity studies and inclusion of PK and therapeutic drug monitoring in clinical trials. Modelling and simulation approaches that are used to evaluate and link the different data types will be discussed. We also describe the concept of different studies, study design, importance of uniform reporting including microbiological and clinical outcome assessments, and modelling approaches. We aim to encourage researchers to consider methods of assessing and reporting PKPD of anti-tuberculosis drugs when designing studies. This will improve appropriate comparison between studies and accelerate the progress in the field.There has been an increased interest in pharmacokinetics and pharmacodynamics (PKPD) of anti-tuberculosis drugs. A better understanding of the relationship between drug exposure, antimicrobial kill and acquired drug resistance is essential not only to optimize current treatment regimens but also to design appropriately dosed regimens with new anti-tuberculosis drugs. Although the interest in PKPD has resulted in an increased number of studies, the actual bench-to-bedside translation is somewhat limited. One of the reasons could be differences in methodologies and outcome assessments that makes it difficult to compare the studies. In this paper we summarize most relevant  Mycobacterium tuberculosis to the drug are the area under the unbound drug concentration-time curve over the minimal inhibitory concentration (fAUC/MIC), the maximum concentration over the MIC (Cmax/MIC) and the time the unbound drug concentration exceeds the MIC (%fT > MIC). There is a broad range in PKPD studies which are ideally complementary in providing relevant data to optimize the drug dose and regimens for treatment of TB.After diagnosis, providing each tuberculosis (TB) patient with the right drugs at the right dose for the right duration in the right combination is important to effectively reduce transmission, prevent relapse and control the risk of development of drug resistance . FollowiIn vitro studies are focused on MIC determination, static time-kill kinetics and/or dynamic models that allow to mimic a pharmacokinetic profile in vitro such as the hollow fibre infection model  studies and should be part of Phase 2b-3 studies to further refine the understanding of the exposure-response relationship  and/or colony forming units (CFU). The STK studies are commonly performed using the actively replicating logarithmic phase bacteria in cultures and based on the extent of kill drugs are commonly classified as bactericidal or bacteriostatic   can be varied to mimic human-like PK as well as the dosing frequency. Further, the fresh media is continuously infused into the systems using peristaltic pumps and at the same time the media coming out of the cartridge is pumped out using a second set of peristaltic pumps. The dilution rate, using the peristaltic pumps, determines the half-life of the drug in the HFS-TB. Multiple half-lives of the drugs in a combination regimen can be achieved in the HFS-TB, by adjusting the parameters on the programable syringe pumps , as well as intrabacterial drug concentrations  . The HFSge pumps . Anothertrations . Thus, tM. tuberculosis metabolic populations  of the drugs in the regimen. In addition, data from static and dynamic hollow-fibre infection models can be used to develop model-based PKPD relationships linked to human population PK models to support dose selection for first-in-man studies and/or selection of optimized drug combinations.  zebrafish can provide different steady state exposure results, but the limited duration of the experiments and the completely different PK, makes these models complementary to the mouse TB models  compound and the (active) metabolites, Preclinical murine TB models come in many different forms. The route of infection  , the inoRegarding the PD (effect side) of the PKPD relationship, there are different outcome parameters that can be used. Traditionally, the number of colony forming units (CFU) are counted to the determine the mycobacterial load at specific that time-points and used as a biomarker to assess the PD in different organs. PD can also be assessed by time-to-positivity (TTP) measurement, using liquid cultures and automated hourly detection of growth or molecular load determination targeting rRNA, as read-out . Irrespein vivo experimental data, there should be gained insight in the exposure of the parent compound and the different metabolites and subsequently the contribution to the effect of these metabolites should be assessed. Although activity of metabolites has been tested in vitro experiments, the in vivo studies add additional information as in these studies activity of metabolites is included by nature. Given the different mode of action of the different compounds, the different metabolic states within which the different Mycobacteria are and the different immunological reactions to this infection, the diversity and complexity of these in vivo models are one step closer to that of TB patients. Bundling of the information of the different in vivo models in a model-based analysis is essential to strengthen the translational value of the different in vivo models  studies have been conducted since the landmark publication of It is generally accepted that patients with active pulmonary TB harbor a spectrum of bacterial phenotypes ranging from metabolically very active, fast growing, extracellular bacteria expectorated in sputum to more dormant, intracellular bacteria situated within granulomatous lesions. This requires different antibiotics with complementary characteristics to be part of a successful treatment regimen that can kill all these phenotypes . In the The role of 14-day EBA studies in the clinical evaluation of novel antibiotics today is to determine whether a drug is active in humans, and to establish relationships between dose, drug exposure and bactericidal activity in small groups of patients . EBA stuQuantification of viable bacteria by CFU counting is hazardous, laborious, costly and relatively slow. Besides the recent introduction of TTP in liquid media, which is easier to standardize and more sensitive than CFU , a range10 CFU per ml of sputum between two study days  is used to assess the drug exposure and adjust the dose in individual patients.max is not sufficient as several studies have demonstrated that the time the Cmax is achieved has a range of several hours . When using population PK models in combination with a limited sampling strategy, 1-3 samples result in adequate accuracy of the AUC calculation (Model-informed precision dosing (MIPD) is an approach where information from a population pharmacokinetic (POPPK) or physiology-based pharmacokinetic (PBPK) model in combination with individually observed plasma drug concentrations is utilized to forecast the dose that leads to the most optimal exposure in an individual patient  Further,interval . Using aculation . It is eculation . The MIPculation .The opportunities in routine care to assess the PD are often limited as bacteriological read outs are collected less frequently as in EBA studies. PD include sputum smear and sputum culture. In high incidence setting sputum is performed at start of treatment, after 2 months and close to the end of treatment. If sputum culture is performed, it is often limited to start of treatment and occasionally during treatment when suboptimal treatment is suspected. This limits the opportunity to assess the effect of differences in drug exposure on treatment response. The most frequently used PD endpoints are sputum culture conversion after 2 months of treatment (drug susceptible TB), and 6 months of treatment (drug resistant TB). Time to sputum conversion is relevant as it presents the same data more continuously than the fixed time points. When using the TTP, the bacterial load can be accounted for as well. The end of treatment is used to quantify treatment outcome . Subsequently the PK/PD parameter, taking into account drug exposure and pathogen susceptibility, are then correlated with the outcome measures. The simplest analysis includes the analysis of differences in treatment response between the different quartiles of drug exposures. Due to the limited sample size a statistically significant difference between the first and fourth quartile is often considered a proof of principle. More sophisticated regression methods like classification and regression tree analysis (CART) allow for the determination of thresholds associated with better (or worse) treatment outcomes , 2022. IImplementing TDM or MIPD poses significant challenges for programmatic care as measurements of drug concentrations need to be made available in a timely manner . Multi-aModelling and simulations are frequently used in the preclinical and clinical development, evaluation and optimization of therapeutic regimens and dosing schedules .etc.,). Separation of PK variability, and in particular knowledge about potentialinter-occasion variability is key when the models should be used to calculate personalized doses using model-informed precision dosing, e.g., as shown for rifampicin by Keutzer et al.  as they come with several advantages: pharmacometric models can handle sparse and/or imbalanced data. Thereby even PK data generated in routine TB care can be leveraged . Moreover et al. . Likewisr et al. . Also, pr et al.  in the cSecondly, modelling approaches are used to characterize the exposure-response relationship of TB drugs . For thiIn case of safety concerns, pharmacometric models can quantify relationships between a variable that quantifies the side effect and PK. For example, in vitro studies using time-kill curve studies, or the hollow-fiber infection model allow for a detailed characterisation of the PK/PD of TB drugs alone and in combination. For example, in vivo studies  can be used for drug screening studies while rodent models can help to understand PKPD relationships of the drugs in relation to treatment outcome and relapse. Results from adequate in vitro and in vivo studies can help to guide dose selection for 14-day EBA studies. These studies will determine drug activity and PKPD relationships and safety in humans. Further confirmation of PKPD in large scale phase 3 studies will help to create a better understanding of variability in drug exposure and implications for treatment outcome and hence can contribute to further dose optimization in subpopulations or personalized dosing via TDM or MIPD. Modelling and simulations provide the opportunity to integrate data from in vitro, in vivo and clinical studies, which contributes to a better understanding of the PKPD of a drug and has the opportunity to accelerate the clinical development pathway.In this review we provided an overview of pre-clinical and clinical methods to assess the PKPD of anti-TB drugs . By in-d


Arabidopsis thaliana.Seed dormancy determines the environmental niche of plants in seasonal environments, and has consequences for plant performance that potentially go far beyond the seed and seedling stages. In this study, we examined the cascading effects of seed dormancy on the expression of subsequent life-history traits and fitness in the annual herb We planted seeds of >200 recombinant inbred lines (RILs) derived from a cross between two locally adapted populations , and both parental genotypes at the native site of the Swedish population in three consecutive years. We quantified the relationship between primary seed dormancy and the expression of subsequent life-history traits and fitness in the RIL population with path analysis. To examine the effects of differences in dormancy on the relative fitness of the two parental genotypes, we planted dormant seeds during the seed dispersal period and non-dormant seeds during the germination period of the local population.In the RIL population, strong primary dormancy was associated with high seedling survival, but with low adult survival and fecundity, and path analysis indicated that this could be explained by effects on germination timing, rosette size and flowering start. The relationship between primary seed dormancy and germination proportion varied among years, and this was associated with differences in seasonal changes in soil moisture. The planting of dormant and non-dormant seeds indicated that the lower primary dormancy of the local Swedish genotype contributed to its higher germination proportion in two years and to its higher fecundity in one year.Our results show that seed dormancy affects trait expression and fitness components across the life cycle, and suggest that among-year variation in the incidence of drought during the germination period should be considered when predicting the consequences of climatic change for population growth and evolution. Early life stages can have long-lasting consequences for performance across the life cycle. Conditions early in life can influence the expression of later life-history traits, as shown in a wide range of organisms including mammals, birds, reptiles and plants . MoreoveIn plants, conditions experienced during the early life stages are to a large degree set by seed dormancy. Seed dormancy is a temporary block to germination in response to germination cues in viable seeds, and determines under which conditions or season a seed can germinate . DormancArabidopsis thaliana in field experiments  derived from a cross between these genotypes  technique that tests a series of hypothesized cause–effect relationships between observed variables as a composite hypothesis . This maMany life-history traits such as growth and survival are strongly influenced by seasonal changes in environmental conditions (e.g. temperature and precipitation), and among-year variation in these conditions can therefore be expected to affect both estimates of relative fitness and selection on specific traits (We asked the following questions: (1) What is the effect of genetic differences in primary seed dormancy on fitness and its components? (2) What are the cascading effects of seed dormancy on the expression of subsequent life-history traits  and how do they influence fitness? (3) How do the direct and indirect effects of genetic variation in seed dormancy on later life-history traits and fitness vary among years, and how is this related to variation in temperature and soil moisture during the germination period?Arabidopsis thaliana (L.) Heynh., and RILs derived from a cross between the two populations , with two temperature sensors placed ~1 cm below the soil surface. We obtained hourly recordings of soil moisture measured as water potential with four sensors located ~1 cm below the soil surface .A. thaliana life cycle.In three consecutive years (2012–2014), we established experiments by planting primary dormant seeds of RILs and the parental genotypes during the natural seed dispersal period in early summer, and non-dormant seeds of only the parental genotypes during the natural germination period at the end of summer. The fate of the seeds was monitored from germination until the time of fruit maturation the following year, thus covering the full The primary dormant seeds  were produced at the Swedish field site and harvested at fruit maturation. The seeds were pooled by genotype, and sown during the natural seed dispersal period . We planted 80 replicates per parental genotype of the dormant field-produced seeds in all three experiments , with each replicate consisting of 40 seeds sown in one cell of a plug tray. In addition, we randomly selected field-matured seeds of 219 (2012 experiment), 220 (2013 experiment) and 204 (2014 experiment) RILs to be included in the experiment, of which 164 RILs were planted in all three years. In the 2012 and 2013 experiments, we planted up to ten replicates per RIL, with each replicate consisting of 40 seeds sown in one cell of a plug tray. In the 2014 experiment, we planted five replicates per RIL, with 40 seeds per replicate for 180 RILs and between 20 and 35 seeds per replicate for 40 RILs  were produced in the glasshouse, using standard glasshouse conditions . The trays were filled with an equal mixture of local sand, gravel and unfertilized peat and sunk into the ground. Both dormant  and non-dormant  seeds were sown in the same randomized block design which consisted of two plug trays per block, with ten blocks in the 2012 and 2013 experiments, and five blocks in the 2014 experiment. In total, more than 230 000 seeds of the parental lines and the RILs were planted in the three experiments.Data on germination timing, total fitness, and the fitness components seedling establishment, adult survival and fecundity in experiments planted with dormant seeds at the time of seed dispersal were analysed in From seed planting until the end of the germination period , we monitored the number of seedlings within each cell once or twice per week (12 observations) in the 2012 experiment and once or twice per month (four observations) in the 2013 and 2014 experiments. To reduce the risk that seedling turnover between censuses would go unnoticed, we noted for each seedling its developmental stage (seedling with cotyledons only or seedling with true leaves developed) and position within the cell. From these recordings, we estimated for each cell the total germination proportion  or in early spring (April) well before flowering start (2013 and 2014 experiments). In the 2013 and 2014 experiments, seeds germinated very late, and seedlings were very small in mid-October. Because, at this site, survival across the winter is negatively related to rosette size , thinnine months .At the end of the germination period (mid-October), we estimated the rosette sizes of seedlings . In the 2012 experiment, we measured the widest diameter of the focal seedling in each cell directly after thinning. In the 2013 and 2014 experiments (thinning in spring), we calculated the average rosette size per cell based on measurements of the widest diameter of each seedling with true leaves and a set diameter of 3.0 mm for seedlings with cotyledons only. A very large number of seedlings with only cotyledons were observed in 2013 and 2014 (>15 000) making it impractical to measure all of them. However, subsampling indicated that diameter varied very little in this category.We monitored flowering start for each plant (the day when the first open flower was observed) every 1–5 d during the flowering period (end of April until mid-June) in the 2013 and 2014 experiments. Flowering start was not quantified in the 2012 experiment.At fruit maturation (end of June), we recorded adult survival  and fecundity (number of fruits with viable seeds per surviving plant). We calculated total fitness as the number of fruits produced per planted viable seed , using the focal plant as a proxy for the adult survival and fecundity of removed seedlings , 2018.Contamination of the sowing experiment with non-experimental seeds was low: at the end of the natural germination period, the cells where seeds were planted had on average 19 times (2012 experiment), nine times (2013 experiment) and 16 times (2014 experiment) more plants than the control cells where no seeds had been planted .A. thaliana from which the Swedish genotype originates, located ~100 m from the experimental plot. In 25 squares of 10 × 10 cm, we recorded the number of seedlings, their developmental stage and their position in the square on the same days as germination was monitored in the sowing experiment.We monitored germination in the natural population of We conducted all statistical analyses in R version 3.5.0 . For eacs) was calculated as s = 1 − [mean fitness of the less fit genotype/mean fitness of the fittest genotype]. We multiplied the selection coefficient by −1 when selection favoured the non-local, Italian genotype. In this way, positive selection coefficients indicate selection against the non-local Italian genotype and negative values indicate selection against the local Swedish genotype. The difference in selection against the non-local genotype between dormant seeds planted during the natural dispersal period vs. non-dormant seeds planted during the natural germination period was calculated as sdiff = sdormant − snondormant, where positive values indicate that genetic differences in primary dormancy increase the strength of selection favouring the Swedish genotype.The selection coefficient  to examine the effect of genotype , dormancy category  and year  on total fitness, its components, and the traits germination timing (day of year), rosette diameter, flowering start (day of year) and number of days from germination until flowering start.In the analyses of variation in fitness and its components, we used a negative binomial error distribution with log link function for total fitness and fecundity, a quasibinomial error distribution with logit link function for germination proportion and seedling survival, and a binomial error distribution with logit link function for adult survival. In the analyses of trait variation, we used a Poisson error distribution with log link function for germination timing, flowering start and number of days from germination until flowering start, and a quasipoisson error distribution with log link function for rosette size.2-test  or F-test .We tested the effect of genotype, dormancy category, year, and all second- and third-order interactions by comparing the change in deviance between the full model and a reduced model in which one term is dropped at a time  with a χ We tested the effect of genotype on response variables within dormancy category in each of the three experimental years with contrasts using the CONTRAST package in R , and the fitness components germination proportion, seedling survival, adult survival and fecundity. We used estimates of the mean dormancy levels of seeds produced at the Swedish field site in 2011, obtained for the same RILs in a previous experiment . Seed doA. thaliana (see Introduction). We used RIL mean values of the variables, as this reduces both environmentally induced covariances between traits and fitness . We therefore assumed linear relationships in all models. Germination timing and flowering start were quantified as day of the year. To reduce differences in scale between variables , CFI and TLI > 0.95, and RMSEA and SRMR < 0.05 (2 goodness-of-fit statistic), and that were biologically relevant (P < 0.05) based on the Z-test  with the Satorra–Bentler scaled χ ormality . In addie Z-test .Comparisons of selection among the primary dormant seeds planted at the time of seed dispersal (henceforth ‘the dormant seeds’) and among the non-dormant seeds planted at the time of seed germination in the local population (‘the non-dormant seeds’) showed that genetic differences in primary dormancy affected the relative fitness of the Italian and Swedish genotypes because of effects on fitness components expressed throughout the life cycle.Among the primary dormant seeds, there was strong selection against the non-local Italian genotype in all three years . GeneticThe increased overall selection among dormant seeds favouring the local genotype was mainly the result of increased selection via fecundity (number of fruits produced per surviving plant) in the 2013 experiment, and via germination proportion (proportion of viable seeds that germinated) in the 2014 experiment . SelectiAmong plants developing from the dormant seeds, there was selection via seedling survival  favouring the Italian genotype in two of three years . Among pDifferences in primary dormancy affected selection through both adult survival and fecundity, but effects varied among years. Selection expressed as differences in adult survival favoured the Swedish genotype in all three years, among both dormant and non-dormant seeds . GeneticGermination proportion and seedling survival were lower, whereas adult survival  and fecundity were higher among plants developing from dormant seeds compared to plants developing from non-dormant seeds . Among tGenetic variation in primary dormancy contributed to differences in germination timing, rosette diameter and flowering start between the parental genotypes . When plP ≤ 0.001), whereas estimates of the effect of germination proportion on rosette size were as often negative as positive, and statistically significant only for dormant seeds planted in 2013 when the effect was positive . GLMs detected significant negative relationships between germination timing and rosette size in all three years for dormant as well as non-dormant seeds  the comparison between the Swedish and Italian genotypes planted as primary dormant seeds and as after-ripened non-dormant seeds, and (2) the path model based on seed fates in the RIL population show that loci affecting seed dormancy influence several life-history traits and fitness components across the whole life cycle. Genetic differences in primary dormancy contributed to the fitness advantage of the local genotype, and the path models showed that genetic variation in primary dormancy affected fitness components via germination timing, rosette size and flowering start. Moreover, data on seasonal changes in soil moisture and temperature suggest that differences in soil conditions during the germination period contribute to among-year variation in the strength and direction of selection during the germination phase. Below, we discuss our results in relation to previous studies on the cascading effects of seed dormancy, and the effect of environmental conditions on dormancy and germination behaviour.In previous studies with the same study system, we have shown that selection during the seedling establishment phase contributes strongly to adaptive differentiation , but thaA. thaliana from which it originates (Differences in primary dormancy contributed to differences in germination timing and rosette size between the Italian and Swedish genotype. Path analysis indicated that primary dormancy was positively associated with germination timing, which in turn was negatively related to rosette size, and these relationships were consistent in the RIL population across the three years of study . Moreoveiginates , demonstAmong plants developing from seeds planted in a dormant state at the time of seed dispersal, the Italian genotype tended to start flowering a few days later than the Swedish genotype (statistically significant difference in the 2014 experiment; When planted as dormant seeds, the difference in flowering start between the Italian and Swedish genotype was rather small, but because of their different germination times, the two genotypes differed markedly in number of days from germination to flowering . The latA. thaliana in a field experiment conducted in the introduced range in North America (A. thaliana (cf. Analysis of the relationships between primary seed dormancy, life-history traits and fitness components in the RIL population demonstrated that seed dormancy influenced all fitness components, directly by influencing the proportion of seeds germinating, and indirectly via effects on the timing of germination, rosette size and flowering start. Consistent with the results of the present study, seed dormancy was found to affect germination timing and germination proportion of iana cf. .The present results provide further insight into selection on primary seed dormancy at the study site and how it differs from that at the native site of the Italian genotype. In the present study, strong dormancy and late germination were correlated with high seedling survival before winter. However, late germination provided little time and poor conditions for growth before the onset of winter, resulting in small leaf rosettes, and was associated with low adult survival and fecundity. Similarly, a previous field experiment in which the timing of germination was manipulated indicated that germination time was subject to conflicting selection at this site: late germination was associated with increased seedling survival until the onset of winter, but this was more than balanced by reduced adult survival and fecundity . In thatSeed dormancy influenced flowering time indirectly via rosette size, but path analysis did not detect any direct effect of differences in seed dormancy on flowering time , D. The The effects of genetic differences in seed dormancy on fitness showed particularly high among-year variation during the seed germination stage. First, the effect of primary dormancy on germination proportion in the RIL population varied not only in strength but also in direction among the three experimental years . Second,Among-year variation in the match between annual seed dormancy cycle and seasonal changes in soil conditions could potentially explain why the relative germination success and seedling survival of the two genotypes varied among years (illustrated with a conceptual model in For a given year and genotype, the seasonal pattern of germination will depend on the timing of seed dormancy release and during which periods conditions are conducive for germination. Temperature affects the rate of seed dormancy loss, while drought may prevent the germination of non-dormant seeds. High temperatures during seed after-ripening decrease the primary dormancy level of dormant seeds . Soil teAfter primary dormancy has been released by after-ripening, seeds can subsequently acquire secondary dormancy preventing response to germination cues . AlthougA. thaliana by low water potential, and by wet incubation at both low and high temperatures (Among the non-dormant seeds planted at the time of seed germination, the direction of the difference in germination proportion between the Swedish and Italian genotype varied among years. In the 2012 experiment, the germination proportion of the Swedish genotype was more than twice as high as that of the Italian genotype, whereas in the 2013 and 2014 experiments the germination proportion of the Italian genotype was higher than that of the Swedish genotype . This steratures , 5 appeaWe have not included potential recruitment from the seed bank in our fitness estimates, but the secondary dormancy cycles observed in previous seed burial experiments at the field site suggest that potential recruitment from the seed bank would increase the advantage of the local genotype .In this study genetic differences in primary seed dormancy influenced plant fitness both directly by affecting the proportion of seeds germinating and indirectly by having cascading effects on other life-history traits, influencing plant survival and fecundity. We conclude that it is vital to include the very early life stages in field experiments to gain a full understanding of the evolution of life-history traits and local adaptation. This is true, not least when assessing the effects of climatic change on population growth and evolution . In the https://academic.oup.com/aob and consist of the following. Fig. S1: germination proportion of seeds of the Italian and Swedish genotypes produced at the field site as a function of seed after-ripening. Fig. S2: fitness and fitness components of the Italian and Swedish genotypes when planted as dormant seeds shortly after maturation at the field site and as non-dormant seeds during the germination period of the local population. Fig. S3: frequency distributions of mean proportion of viable seeds germinating and mean seedling survival in the RIL population. Fig. S4: frequency distributions of mean timing of germination, rosette size, flowering start and number of days from germination until flowering start in the RIL population. Fig. S5: mean number of living seedlings expressed as a percentage of total germination of the Italian genotype, the Swedish genotype and the RIL population planted as primary dormant seeds shortly after maturation at the field site. Table S1: fitness and fitness components of the Italian and Swedish genotypes planted as primary dormant and as after-ripened non-dormant seeds, and of the RIL population planted as primary dormant seeds. Table S2: effects of year, genotype and dormancy category on total fitness and its components. Table S3: life-history traits of the Italian and Swedish genotypes planted as primary dormant seeds, and as after-ripened non-dormant seeds, and of the RIL population planted as primary dormant seeds. Table S4: effects of year, genotype and dormancy category on life-history traits. Table S5: effects of germination timing and germination proportion on rosette size, analysed separately by year and dormancy category. Table S6: sequential model evaluation in the path analysis of effects of primary dormancy on life-history traits and fitness in the RIL population. Table S7: unstandardized path coefficients in the final models of the path analysis for the 2012, 2013 and 2014 experiments.Supplementary data are available online at mcac010_suppl_Supplementary_MaterialClick here for additional data file.


Identifying tomorrow's “Systems Microbiology” front-runners is critical for promoting and stimulating future research and innovation in biology. This collection highlights the high-quality research work of globally known scientists in their early academic careers. We have aimed to highlight the research conducted by the future leaders in the area of “Systems Microbiology”, as well as their theoretical, experimental, and methodological advances for solving exciting research problems.Li M. et al. studied the association between the composition of the rumen bacteriome, the concentration of rumen metabolites, and nitrogen utilization efficiency in dairy cows. Cows utilizing nitrogen more efficiently had higher propionate molar percentage, lower rumen bacterial diversity, and higher relative abundances of three bacterial taxa . This study generates significant insights toward the improvement of nitrogen utilization efficiency in dairy cows, which has important consequences for production, cost-effectiveness, and the environment.Burkholderia pseudomallei and its resistance and virulence factors by Shafiq et al. showed B. pseudomallei to be a highly-infectious microbe in humans and animals causing melioidosis. The researchers studied the phylogenetic and evolutionary relationships among different strains of B. pseudomallei using genomic diversity, resistance determinants, and molecular epidemiology. The researchers discovered new virulence factors and mutations among different strains of B. pseudomallei causing pneumonia, neurological diseases, abscesses and lesions in the skin. The researchers also reported two novel sequences  that do not match other strains of B. pseudomallei.The paper on the genomic diversity of the clinical strains of Romero et al. on the reconstruction of archaeal and bacterial genomic regulatory networks on the basis of homology is important to understand the effects of various environmental stimuli on gene expression, as a key metabolic activity for microbes to adapt to environmental changes. The authors inferred the gene regulatory networks of archaeal and bacterial genomes by comparing them with the six organisms with known regulatory interactions. The gene regulatory network of these six model organisms was expanded using transcription-unit assignments and orthologs inferences. They demonstrated the most significant findings from these inferences through several examples and also by calculating the topological metrics of such networks. As a result, this approach is a valuable resource for reconstructing the regulatory network of interactions occurring within archaeal and bacterial domains which can be further combined with the globally available expression data for these organisms to improve global interaction data models. Inferences of gene regulatory networks are critical to understanding bacterial metabolism. Finally, the authors provided readers with a website to analyze and download all the network data.The study by Li G. et al. examined the differences in composition of gut microbiota fecal fatty acids in patients suffering from polycystic ovary syndrome (PCOS) compared to healthy subjects. Specific metabolites present in the serum and fatty acids produced by the intestinal fecal microflora may modulate the onset and progression of PCOS. The specific intestinal microbiota in PCOS patients has been reported to vary by body mass. By using clinical-index detection techniques of stool and serum samples from PCOS patients, it was possible to elucidate potential correlations between altered gut microbiota and inflammatory markers, as well as short-chain fatty acid metabolism and gut permeability markers. The gut microbiota of patients with different body types and PCOS has never been thoroughly characterized before this study. These discoveries will provide a strong framework for understanding the etiology of PCOS and potentially guide future medical interventions in its treatment, for example fecal bacteria transplantation.The paper by Rising stars in systems microbiology: 2021” are yet to reach fruition; however, the articles published in this Research Topic point toward exciting and promising future directions. We hope that the topics will inspire future generations of scientists to continue advancing these areas of research.Research innovations resulting from research presented in “AP, GG, and EU contributed to the writing and editing of the manuscript. All authors contributed to the article and approved the submitted version.


Physiology and biological sciences in general, are still dominated by contributions from male researchers worldwide, with less than 30% of scientists being women. The Women in Invertebrate Physiology Research Topic is part of a series of Frontiers Research Topics highlighting contributions of female scientists to the respective fields. These showcases are meant to encourage female scientists to pursue scientific careers in spite of often unfavorable working conditions for women, especially with young children at key moments of career decisions. The Research Topic also intends to support the battle against gender discrimination, which has still a long way to go within the scientific world.Uebi et al. describe the neural mechanisms involved in the aversive effects of cuticular hydrocarbons of a native Japanese ant species on invasive ant species. Interestingly a specific nest-mate recognition compound of the native species causes indiscriminate activation within the primary olfactory center of the invasive species, creating neural disarray. The authors discuss the importance of their findings for the development of control measures for invasive ant species. Damulewicz et al. report important influences of light exposure during development on sleep regulation and underlying physiological mechanisms in adult Drosophila melanogaster. A lack of light exposure in larvae affected the expression of genes coding for neurotransmitters and neuropeptides, as well as for their receptors and reduced the size of the mushroom bodies significantly. The authors show furthermore the importance of chryptochrome-dependent photoreception in the brain for the observed behavioral and physiological effects. Physiological mechanisms involved in the resistance of mosquito larvae to the neonicotinoid insecticide acetamiprid were investigated by Samal et al. Using a laboratory selection approach with acetamiprid-exposed yellow fever mosquito larvae, the authors evaluate levels of detoxification enzymes and mutations in an acetylcholinesterase gene in susceptible and resistant strains. Their results show that a combination of target site mutation and metabolic detoxification cause acetamiprid resistance.In this Research Topic of five original research articles, with female first-and/or lead authors, various aspects of invertebrate physiology in different model organisms are covered and illustrate the creativity and innovation achieved by female researchers. Three articles present different approaches to insect physiology and two articles investigate the role of specific genes in the physiology of annelids and nematodes. De la Fuente and Novo present an in silico analysis of small heat shock proteins, i.e., proteins with a critical role in stress physiology throughout the animal kingdom, in annelid worms, a taxon including species with widely varying life styles and habitats. This study identifies and characterizes for the first time this type of protein in annelids and reveals a large diversity throughout the phylum. Both monomeric and dimeric α-crystallin domains were frequently found. Physicochemical analysis indicates that monomeric small heat shock proteins cluster in two different subgroups, one close to the same type of proteins in metazoans, the other closer to proteins in plants and bacteria. Thus annelids provide interesting models to further understand the evolution and highly divergent functions of small heat shock proteins. Samaro et al. investigated the mechanisms underlying the inability to enter dauer diapause under environmental stress of unc-33 mutants of the nematode Caenorhabditis elegans. Dauer diapause is characterized by long survival without feeding and uncoordinated locomotion. The unc-33 gene is among others involved in axonal outgrowth through microtubule orientation in neuronal processes. The authors show that the dauer defects in unc-33 mutants can be rescued by the introduction of the daf-7(e1372) mutation, but uncoordinated locomotion in dauers is not restored. This indicates that the DAF-7 pathway, regulating growth and development and allowing dauer arrest in C. elegans, might be defect in unc-33 mutants, but that DAF-7 seems not to impact regulation of axonal outgrowth in motor neurons.


Pure and viable strains of microorganisms identified and characterized as probiotic strains are used in the fermentation process to prepare probiotic food and beverages. These products are sources of nutrition and help in the maintenance of gut microflora. The intake of food products prepared with the use of probiotic microorganisms and containing their metabolites and whole microbial cells can be considered as a natural formulation of synbiotic products with prebiotic substrates and culture. Other than through the intake of fermented food and beverages, probiotic microorganisms can be taken through a supplement, which is a complementary form prepared by combining separate sources of prebiotic substrates and specific probiotic cultures. Whether a fermented solid food or beverage, both the components in the product are in a synergistic relationship and contribute to several health benefits at a lower cost. The aim of this article is to review the relevant literature and present the outcomes of recent studies which have been conducted to explore the clinical potential of probiotic strains and their effect on psychological conditions. Studies have shown the relationship between gut microbiota and the brain, and their interaction through signaling. The studies have concluded that the gut–brain axis can be manipulated with the intake of probiotic foods or synbiotic supplements containing specific probiotic strains accompanied with their complementary prebiotics for the enhanced sustainability of healthy GIT microflora. The constituents in the intake of food and nutrients affect the development of different microbial strains in the gastrointestinal tract (GIT). Hence, it is sensible to consider the fact that the nutritional and bioactive components could be the regulators and the main determinants of the gut microbiota composition. Research has confirmed that the gut microbiota significantly contribute to our general well-being, and their potential can also be explored to help in the remediation of psychological issues . An imprThe definition of probiotics given by the Food and Agriculture Organization and the World Health Organization explains that the live microorganisms, when administered in adequate amounts, confer a health benefit on the host ,8. ProbiLactobacillus casei and Bifidobacterium bifidum [Probiotics usually comprise bacteria, including Lactobacillus, Bifidobacterium, and Bacillus, although a few strains of yeast, Saccharomyces, have also been included in probiotic cultures. According to the consensus panel of the International Scientific Association for Probiotics and Prebiotics, probiotic practices are only delivered by some specific strains of a certain class of bacteria, for instance,  bifidum . However bifidum ,12.The gut microbiota is composed of diverse and variable combinations of several microbial strains. Healthy stability of gut microorganisms must necessarily be maintained to perform everyday functions related to physiological and metabolic activities and sustain immunity to prevent disease development. If the balance of the gut microbiota is disturbed for different reasons, it could initiate a number of health concerns and cause the progression of certain ailments ,14. PersThere are several scientific reports with evidence supporting the inclusion of probiotics in nutrition as a resource to gain other health benefits. The probiotic products confer a number of health-related benefits such as increasing GIT control and inhibiting infections, tumors, and diseases by stimulating immunity and improving vitamin B production . Recent This review article is based on relevant information collected from 120 selected published reports. Appropriate references have been cited supporting all information presented in separate sections, e.g., on the contribution of probiotic microorganisms to gut health and their potential in the treatment of psychological diseases and improvement in cognitive skills. The information presented in this article is only based on those GRAS strains used in the food industry for the production of yogurt, fermented milk, fermented functional foods, and beverages. For vegans and persons allergic to dairy food products, probiotic culture/s in the form of formulated supplements can be taken to maintain healthy GIT microbiota. Freeze-dried preparations of live cells of probiotic cultures are available as commercial products and in different forms to cater to different requirements of consumers. The health benefits of probiotic microorganisms and their role in the management of specific psychological disorders are discussed further in Several reports have presented their outcome that the gut microbiota can be affected and influenced by the intake of suitable diets and supplements ,22. ResuA probiotic milk product, yogurt, is the best-known carrier for the efficient transfer of beneficial microbes into the gut ,31. ReceFermentation by lactic acid bacteria also produces bactericidal proteins in dairy products, which are useful for the longer shelf life of these products ,34. SynbSaccharomyces boulardii has been characterized as a probiotic yeast strain [S. boulardii. This probiotic strain was clinically tested through its oral administration to treat diarrhea developed after prolonged antibiotic treatment and recurrent illness associated with the infections caused by Clostridium difficile [Lactococcus lactis ssp. cremoris, on the alleviation of infection caused by the invasion of intestinal epithelial cells by Salmonella [The consumption of products containing probiotics may provide an encouraging approach to the prevention of microbial dysbiosis . The intt strain . A bio-tifficile ,43,44. Difficile ,46. The lmonella . Table 1It is not always possible to get the required numbers of live cultures available in the consumed portion size of fermented probiotic foods and beverages. For the requirement of sustaining a healthy gut microbiome, the resident population of GIT microflora can be improved by the intake of suitable probiotic supplements, containing the relevant viable strains and their required population, measured in the form of CFU. The formulated commercial products also indicate the number of probiotic cells and their dosage. Psychobiotics have been defined as probiotics that, when consumed, might produce benefits to mental health through the interactions of commensal gut bacteria with the brain . It is iThese psychobiotics can produce positive psychiatric effects in psycho-pathology if taken through appropriate formulations and in correct quantities ,77. The All microbial strains and their genomes inhabiting the GIT are included in the gut microbiome. Through the healthy and sustained colonization of the gut microbiome, a reciprocal gut-brain partnership develops, which is important for the collaborative efforts of the brain and gut in the maintenance of an individual’s health . The sysThe evidence was reported after a decisive study where mice were raised under sterile growth conditions to develop a complete absence of indigenous microflora. The microbe-free mice were found to be sensitive to stress and showed exaggerated physiological reactions compared to normally grown mice with a developed gut microflora. The unusual reactions toward the stress could be reverted through the development of probiotic-induced bacterial recolonization in initially germ-free mice . This obThese reports have confirmed that psychobiotics are beneficial microbial strains, when dispensed and propagated in GIT supported by appropriate prebiotics, and influence the complex microbiome-brain relationship. The term should more precisely address psychobiotics as those synbiotics (probiotics + prebiotics), which can exert anti-anxiety results and act as an anti-depressant, distinguished through the changes in cognitive, emotional, and neural indicators . Figure Psychobiotics affect psychophysiological markers of anxiety and depression. Most of the research on psychobiotics has been centered on rodent models, where studies have used behavioral tests and stress inductions to measure the levels of motivation, anxiety, and depression. The results of psychobiotic application to rodent models of neurodegeneration might also provide an experimental clinical understanding of human illnesses. Investigations with human volunteers represent a conclusive approach to the use of psychobiotics.The psychophysiological effects of psychobiotics have been mainly analyzed in three ways. (1) Through the results gathered on cognitive and emotional processes. (2) Through the ystemic impacts on the hypothalamic–pituitary–adrenal–axis, glucocorticoid stress response, and the degree of inflammation indicated by irregular cytokine concentrations. (3) Through the effects measured through the observation of neural effects on neurotransmitters and proteins.Pro-inflammatory cytokines have been reported for their positive association with depression and other psychiatric conditions . In a stLactobacillus rhamnosus R0011 and Lactobacillus helveticus R0052, and produced results showing downregulation of hypothalamic–pituitary–adrenal axis activity [The benefits noticed from the consumption of some probiotics were found to be similar to antidepressant effects. These findings were indicative of an earlier study, which used a mixture of two probiotic strains, activity .Lactobacillus rhamnosus JB-1 or a vehicle in healthy adult male mice. The results indicated raised concentrations of N-acetylaspartate glutamic acid, γ-aminobutyric acid, and glutamate in the probiotic-fed mice. Glutamate, the main excitatory neurotransmitter in the central nervous system, was found to be sensitive to probiotics [In one study including in vivo research work, magnetic resonance spectroscopy  was usedobiotics . This coobiotics .Lactobacillus casei Shirota, and the comparison group received a placebo drink. The outcome of the intervention study was very interesting; participants that consumed probiotic drinks self-rated as happier rather than depressed, compared to those participants who consumed a placebo drink. Another study has provided evidence of improved mood in trials with a generally healthy group [In a research project, the impact of consumption of a probiotic milk was observed on the status of moods and cognition ; particihy group .Lactobacillus helveticus and Bifidobacterium longum to consume over a period of 30 days; the comparison group received a placebo sample. A self-report completed by participants after the intervention period was based on a range of measures of mood and distress. Significant declines in negative mood and distress were reported by participants treated with probiotics compared to those who received a placebo supplement. These self-reported results were further confirmed by an analysis of cortisol levels [A randomized double-blind design project included male and female participants with no health problems who were given a test sample of probiotics consisting of two strains l levels . The stuBifidobacterium bifidum W23, B. lactis W52, Lactobacillus acidophilus W37, Lactobacillus brevis W63, Lactobacillus casei W56, Lactobacillus salivarius W24, Lactococcus lactis W19, and L. lactis W58. Participants with probiotic treatment exhibited substantially reduced reactivity to sad moods, relative to participants who consumed a placebo supplement [In another randomized controlled project, studies were conducted over a period of 4 weeks of intervention. A group of 40 participants including healthy males and females was given a product for consumption containing a mixture of several probiotic strains, including pplement .Lactobacillus casei Shirota has also been tested in a fascinating study with volunteers having academic stress prior to their assessment period. The study reported the consumption of milk fermented with Lactobacillus casei strain Shirota supported the preservation of the diversity of the gut microbiota, and it relieved the abdominal dysfunction in healthy medical students during the period of academic stress [The cognitive enhancement effect of c stress .Lactobacillus gasseri OLL2809 LG2809. The performance was noticed as an elevation in their mood and a reduction in natural killer cell activity after vigorous physical exercises, relative to those students receiving a supplement of a placebo sample [Another study was performed with a group of 44 student athletes, who were given a supplement containing the probiotic strain o sample . These rBifidobacterium longum (B.)1714 was effective in reducing the depressive behavior in tail-suspension tests, although Bifidobacterium breve 1205 reduced anxiety in the tests of elevated-plus mazes [Supplements containing prebiotics reduced the waking cortisol response and altered the emotional bias in healthy volunteers; prebiotic feeding elevated central-brain-derived neurotrophic factor, N-methyl-D-aspartate receptor subunits, and D-serine ,106. Theus mazes .Bifidobacterium longum (B.)1714 created desired cognitive advances, although the same results were not delivered by Bifidobacterium breve 1205 [Results of another study showed that eve 1205 . Despiteeve 1205 ,110,111.Lactobacillus helveticus R0052 on inflammation and anxiety were reported to be associated with diet and genotype [Clostridium difficile concentrations decreased [Effects exerted by the strain genotype . The adugenotype , and alsgenotype . The rolecreased ,115,116.Several studies performed in recent years have demonstrated that the normal gut microbiota modulates brain development and behavior; however, there are challenges in manipulating the gut microbiome for clinical studies . ThroughA recent research project has studied the development of a 3D-printed high-temperature resin cecal fistula implant in nine male Sprague–Dawley rats. The results present the most recent method of cecal fistula as an effective procedure that allows long-term and minimally invasive access to the gut microbiome. The feasibility of 3D-printed fistula implants through the body wall and into the cecum of rats to obtain long-term access to gut microbiome was evaluated . The resIn the last few decades, a number of studies have been performed on the effect of probiotics, beverages, and fermented foods as potential synbiotics on the gut microbiome. The interest of research projects has moved in the direction of clinical studies to identify how the GIT-microbiome could be manipulated for establishing and maintaining a healthy gut. The intake of food prepared with probiotic microbial strains has been recommended through the positive outcome of several studies that have reported that they have an effect on health and for the relief of a number of ailments. Probiotic food, fermented milk beverages, and synbiotic supplements have shown clinical potential for their use as psychobiotics. Several studies have proved their effectiveness in the performance of the gut–brain axis in psychiatric disorders. In conclusion, it is beneficial to explore the clinical potential of microbial strains, which are normally used in the preparation of probiotic food and beverages products or synbiotic supplements, as psychobiotics for cognitive treatment through gut–brain signalling-mechanism regulated by the vagus nerve system.


Coronavirus disease (COVID-19) has created an unprecedented devastation and the loss of millions of lives globally. Contagious nature and fatalities invariably pose challenges to physicians and healthcare support systems. Clinical diagnostic evaluation using reverse transcription-polymerase chain reaction and other approaches are currently in use. The Chest X-ray (CXR) and CT images were effectively utilized in screening purposes that could provide relevant data on localized regions affected by the infection. A step towards automated screening and diagnosis using CXR and CT could be of considerable importance in these turbulent times. The main objective is to probe a simple threshold-based segmentation approach to identify possible infection regions in CXR images and investigate intensity-based, wavelet transform (WT)-based, and Laws based texture features with statistical measures. Further feature selection strategy using Random Forest (RF) then selected features used to create Machine Learning (ML) representation with Support Vector Machine (SVM) and a Random Forest (RF) to make different COVID-19 from viral pneumonia (VP). The results obtained clearly indicate that the intensity and WT-based features vary in the two pathologies that are better differentiated with the combined features trained using SVM and RF classifiers. Classifier performance measures like an Area Under the Curve (AUC) of 0.97 and by and large classification accuracy of 0.9 using the RF model clearly indicate that the methodology implemented is useful in characterizing COVID-19 and Viral Pneumonia. The extremely infectious nature of coronavirus disease COVID-19), which has been declared a pandemic, has paved the way for a phenomenally high infection rate, leading to overburdened healthcare systems globally 9, which . COVID-1Some of the CXR characteristics pertaining to COVID-19 pneumonia encompass consolidation, ground glass opacity and spread across peripheral and lower zones with bilateral involvement . CXR wasProcessing and reproducing these human features using computer systems has been a much-researched topic in the current era , 21. LawThe Group Search Optimization Algorithm depicted for optimization to optimize the sequences obtained from the mining process was discussed by Lakshmanna and Khare . The tecDourado et al. deduced an approach and is validated among three medical databases. The cerebral vascular accident images, lung nodule image data set, and skin image data set for stroke type, malignant, and melanocytic lesions classifications, respectively, . KrishnaSusaiyah et al. implemented image segmentation by means of multilevel Otsu thresholding. This was validated through using Dice's coefficient; the Jaccard index and accuracy recommend the first amongst four levels in Otsu . ParvazeAlong with preprocessing, segmentation of the region of interest is necessary for designing effective strategies to delineate the tissue of interest , 49, 50.After a widespread literature survey, we derived a technique made to formulate a comprehensive strategy to distinguish COVD-19 and VP with the aid of CXR by computing first-order statistical features, wavelet-based features, and laws texture features. The extracted features were subjected to feature selection using Random Forest (RF) and finally training the above features using a support vector machine (SVM) and RF classifiers. The salient features of the work is to use the fusion of statistical features, Wavelet features, and Laws texture features within the threshold region.Utilization of the multithreshold approach to segment and thereby extract the texture featuresVisualization of the feature mapsInvestigating the handcrafted features that could identify the desired pathologyThe main contributions are as follows:Thus, the novelty in our work is the utilization of the most significant features to construct the machine learning model.The block diagram representing the step-by-step computation is shown in The mathematical representation of the wavelet transform is shown in the , where “The Random Forest based classifier model functionality is to put up a strong learner from an ensemble of learners, by partitioning the data into individual trees in the forest as shown in the equation.The 72 extracted features were then subjected to critical statistical analysis to measure the extent of the significance of the features. Subsequently, the features were subjected to Random Forest based feature selection to extract the most useful and viable features which were employed in building the classifier model using SVM with linear kernel and RF classifier with 60% of data retained for training and 40% of data for purpose of evaluation. The classifier models are then subjected to validation using 40% of the data. Furthermore, for validation purpose the performance measures were computed including receiver operating characteristics (ROC) from which AUC is computed . The claThe computation is performed using Python compiler 3.6 and packages including Scikit-learn. (Algorithm 1)e) represent the respective histograms, while (c) and (f) depict the respective gradient images. Both pathologies demonstrate the presence of infection spread but with varying intensities and possible density of high intensities observed in COVID-19 that could also be observed from the histogram. The gradient images reveal the edge information pertaining to various anatomical structures.Representative CXR images of COVID-19 and VP are shown in The median-filtered images of the representative COVID-19 and VP along with histograms and gradient images are shown in The median-filtered image and multithreshold-based Otsu segmentation outputs are depicted in The segmented regions representing the high mean intensities pertain to label three in both COVID-19 and VP, as represented in Figures The wavelet-based decomposed images and histograms of the representative COVID-19 and VP are shown in The feature maps obtained using Laws texture computation are depicted in First-order, Wavelet features, and texture features were obtained directly from the segmented ROI-CXR images that were further subjected to statistical significance and to build an ML model.The RF-based feature selection algorithm was able to pick 39 features from 72 feature vector as important features, which were further used for formulating the classifier model. The representative 8 feature values computed from HH decomposition of Wavelet transformed images were incorporated in The ROC plot generated using the SVM and RF classifier model for the test data is shown in The performance measures obtained using the two classifier models are listed in Efficient screening and diagnosis of COVID-19 methods executed with the advent of state-of-the-art image processing and ML-based approaches is needed. CXR images used by physicians for screening purposes provide information about the presence of the infection region and the spread of the infection. The median filter is a standard filtering process to reduce the variations in pixel intensities while preserving the edgelike information. A mask size of 3 was selected in this study to preserve the local morphology of the anatomical structures. In this work, the preprocessed images subjected to the generation of segmented masks were observed to be effective in segmenting the infection region; however, a threshold-based approach over segmentation resulting in noninfection regions was also observed. This might be due to the resemblance of the infection regions and certain anatomical regions with respect to the pixel intensities. The analysis of the histograms is deliberately attempted as the features obtained from the preprocessed images and the wavelet-decomposed images are histogram-based features.Hence, the morphology of the histograms was of assistance in comparative analysis. The certain intensity and WT-based feature values were observed to be more effective for differentiating COVID-19 and VP. Even though both pathologies seem to be represented by bright regions, there exist subtle variations which are picked up by most features. The differentiation of COVID-19 from VP and other CXR images has been attempted with DL and other artificial-based methods in a broad range of studies . SekerogRodrigues et al., feature extractors were applied to the Region of Interests (ROI) that includes nodules. The analysis of malignancy of the nodules can be studied at some stage in the classification step by incorporating ML techniques . The resIn this work, handcrafted features were investigated from preprocessed, Wavelet-decomposed images and Laws texture maps to understand the local inherent intensity variations. The combined features and the feature selection from the set were important to formulate the most feasible feature vector to build the ML model. ML-based results lay further stress on the comprehensive pipeline that is necessary to address the challenges of categorising the pathologies. Even though the two ML approaches provided better results, the RF-based model outperformed the overall results. In comparison with the DL approaches, the RF model with the extracted features herein seems to have outperformed some of the studies as depicted with the accuracy parameter in The meticulous design encompassing a comprehensive methodology utilising image analysis techniques and ML models can aid physicians and radiologists in performing efficient and accurate screening for COVID-19. Preprocessing and determining the effective region for extracting features might be essential, as comprehended from the study. In particular, the segmentation mask, even though not robust, can locate the majority of the local infection region, which might be critical in the pipeline design. The study with first-order features from preprocessed, Wavelet sub-bands and Laws texture maps integrated with the ML approach serves to discriminate the effects of COVID-19 from Viral Pneumonia for effective and exact diagnosis to mitigate the spread of the infection. Identifying the handcrafted features is a very exhaustive process and is the main limitation of the work. In the future, researchers can incorporate texture features and other forms of features including morphological features to distinguish the pathologies.


A reserve of primordial follicles is set up in the ovaries of fetuses or neonates, depending on the species, and serves as the source of developing follicles throughout the reproductive lifespan. This review focuses on the cellular and molecular mechanisms currently known to control the establishment of this reserve, and their regulation by environmental factors. Most mutations in genes controlling germ cell proliferation and survival, meiosis or follicle assembly lead to the absence of primordial follicles or a sharp reduction in their number, incompatible with fertility in adults. Inadequate maternal nutrition affects the cellular metabolism, increases the oxidative stress and delays follicle formation in fetal ovaries. Despite the existence of compensation mechanisms of some developmental processes, the early-life nutritional environment imprints the long-term ability of follicles to enter growth and develop in adult ovaries. However, maternal undernutrition, overfeeding or high-fat diet during the establishment of the ovarian reserve does not seem to affect the fertility of the female offspring, unless their metabolism or neuroendocrine status is altered. Exposure of fetal or neonatal ovaries to excess steroids inhibits or stimulates follicle formation in a complex manner depending on the nature of the steroid, the dose and the animal species. Estrogens can control follicle formation through intra-ovarian mechanisms involving members of the TGF-beta family such as activin and BMP2. Early-life exposure to synthetic estrogens or environmental pollutants with estrogen-like activity impairs meiotic progression and follicle assembly, and affects long-term primordial follicle activation in adult ovaries. The effects of compounds with estrogen-like activity on the ovarian reserve can be transmitted to several generations through the female germline. Further investigations are needed to establish the early-life effects of the environmental factors on the female reproductive lifespan and decipher the mechanisms of their epigenetic effects on the size and quality of the ovarian reserve. In the ovary, the growing follicles develop from a reserve of primordial follicles constituted early in life and gradually emptied by both follicle growth activation and follicle degeneration. From this first reserve of primordial follicles, a second ovarian reserve is formed, which consists of gonadotropin-responsive small antral growing follicles and is a dynamic reserve for ovulation. The mechanisms regulating the transitions between reserves have been recently reviewed  and is located in oocytes  and germ cell cyst breakdown  and impairs the initiation of meiosis and follicle assembly in the fetal offspring ovaries , androgens (AR), as well as progesterone (PGR), are all expressed in the surface epithelium and ovarian stroma of the fetal ovaries . HoweverFusarium graminearum) to pregnant mice impairs germ cell meiotic progression, decreases the expression of the meiosis-specific genes Dazl, Stra8, Scp1 and Scp3, increases DNA double-strand breaks at the diplotene stage and affects primordial follicle assembly in the fetal ovaries (Stra8 and a decrease in its expression levels (Increasing evidence indicate that environmental factors with estrogen-like activity modulate importantly follicle formation. Mice treated neonatally with genistein, the primary soy phytoestrogen, have multi-oocyte follicles in ovaries; genistein was found to inhibit germ cell cyst breakdown and attenuate oocyte cell death, and these effects are mediated by the estrogen receptor Esr2 . Oral adEstradiol is not required for the initiation of follicle growth in mice (Steroid excess during follicle formation can be deleterious for subsequent follicular development. The exposure of ewes between 60 and 80 days of pregnancy to a mixture of environmental chemicals with estrogen-like activity decreases primordial follicle activation in fetal ovaries near term and increases atresia rate in activated follicles  and simiEarly exposure to environmental factors with estrogen-like activity affects long-term primordial follicle activation and the observed effects are transgenerational. For instance, maternal DEHP exposure significantly accelerates the recruitment of primordial follicles in the F1 and F2 generations of mice (During the last decades, our knowledge of the cellular mechanisms and the molecular and environmental control of the establishment of the ovarian reserve of the primordial follicles has made significant progress. Some of the intra-ovarian mechanisms currently known to control germ cell cyst breakdown and primordial follicle assembly are illustrated in The size of the initial follicular reserve is primarily dependent on genetic determinants, and mutations affecting factors controlling the different steps of ovarian reserve establishment can lead to the absence of primordial follicles, or a sharp reduction in their numbers, incompatible with fertility in the adult. However, having two- or three-fold more or less follicles than the mean number characteristic of the species at birth is not a determinant for fertility and reproductive longevity in adulthood, as demonstrated in mice (From the data available, the ovarian changes induced by environmental factors present during the fetal or neonatal life do not seem to alter the fertility of young females, unless their metabolism or neuroendocrine status is affected, but their consequences on the reproductive lifespan have not yet been established. To understand the impact of the early-life environment on fertility of both young and older adults, there is now a significant need to consider the different types of populations of primordial follicles, which have distinct developmental dynamics and contribute differently to ovarian functionality (The early-life ovarian environment modulates in the long term the rate of primordial activation and the dynamics of the small growing follicles in adult ovaries. As these processes are little or not influenced by the gonadotropins, the observed changes result likely from direct imprinting effects of environmental factors on the germ and/or somatic ovarian cells, occurring before and during follicle formation. Increasing evidence indicate that environmental chemicals with estrogen-like activity leave epigenetic marks on the germ cells, and their effects are transmitted through the female germline up to the third generation in mice (