Background and Objectives: We performed anterolateral total hip arthroplasty (ALS THA) with the purpose of complete muscle-tendon preservation without muscle-tendon dissection. This study aimed to evaluate muscle damage in the periprosthetic hip joint muscles of patients undergoing ALS THA at 1-year post-operative hip magnetic resonance imaging (MRI). Materials and Methods: We evaluated changes in the muscle cross-sectional area (M-CSA) and fatty atrophy of the periprosthetic muscles. We also assessed the Harris hip score on pre-operative and 12-month post-operative MRI in 66 patients who underwent ALS THA. The grade of M-CSA atrophy was classified into no atrophy, slight atrophy, moderate atrophy, and severe atrophy. Fatty atrophy was classified as improved, no change, and worsened using the Goutallier classification. Results: More than 90% of patients’ M-CSA had no atrophy in the obturator internus (Oi), obturator externus (Oe), gluteus medius (Gmed), and gluteus minimus (Gmin), and some improvement was observed in terms of fatty atrophy. In contrast, M-CSA of the tensor fascia latae (TFL) muscle was clearly decreased, and there was no improvement in the TFL fatty atrophy. However, the presence or absence of TFL atrophy did not affect clinical outcome. Conclusions: We performed the complete muscle preserving procedure, ALS THA, with attention to preserving the Oi and Oe by direct visual confirmation and gentle treatment of the Gmed and Gmin with effective retraction. Post-operative M-CSA atrophy evaluation on MRI showed that the Oi, Oe, Gmed, and Gmin were satisfactorily preserved; however, the TFL was clearly atrophic. In the ALS approach, where entry is made between Gmed and TFL, atrophy of the TFL due to superior gluteal nerve injury must be tolerated to some extent. Anterolateral total hip arthroplasty, based on the Watson-Jones approach, is a technique that reduces soft-tissue involvement by entering through the intermuscular space between the gluteus medius (Gmed) and tensor fascia latae (TFL) muscles, thereby reducing soft-tissue invasion and allowing for a quicker recovery. The anterolateral approach is performed in the supine or lateral decubitus position. The approach performed in the supine position is called the anterolateral supine approach (ALS), and the one performed in the lateral position is called the Orthopädische Chirurgie München (OCM) approach, both of which involve entry from between the muscles of the Gmed and TFL. In the anterolateral approach, the superior gluteal nerve (SGN) runs between the Gmed and TFL, and SGN nerve damage may occur. The advantages of ALS THA include less post-operative pain, a lower dislocation rate, and easier intraoperative fluoroscopy. The disadvantages include tip fracture of the greater trochanter, the presence of a learning curve, and SGN injury. In addition, ALS THA has a learning curve, especially in femoral manipulation. Some centers that are unskilled in the technique may add a partial dissection of the short external rotator muscle for greater exposure. Therefore, there have been scattered reports of atrophy and fatty degeneration of the joint tendon on post-operative magnetic resonance imaging (MRI) evaluation after ALS THA ,2. In ouIn this study, 66 patients with ALS THA at the same institution with 66 joints and unilateral osteoarthritis with normal contralateral sides were enrolled. Of these, 14 were men and 52 women, there were 35 right hip joints and 31 left hip joints, mean age (SD) was 68.5 (9.7) years, mean Body Mass Index (BMI) (SD) was 24.9 (1.8), and all patients had Crowe classification type I.Total hip arthroplasty (THA) was performed in all patients under general anesthesia with complete muscle-tendon-sparing ALS THA by a single experienced surgeon using a cementless stem and cup with a tapered-wedge short stem. Intraoperative fluoroscopy was used to ensure accurate cup and stem placement. After surgery, all patients were allowed full weight-bearing the day after surgery and were discharged home when they could ambulate, ascend, and descend stairs. MRI was performed pre-operatively and 12 months post-operatively, and M-CSA of each muscle (obturator internus (Oi), obturator externus (Oe), Gmed, gluteus minimus (Gmin), and TFL) was measured on T1 imaged axial MRI, and the affected side/unaffected side ratio (A/N ratio) was calculated for each muscle. Atrophy was calculated using this formula: (post-operative patient tendon ratio/pre-operative patient tendon ratio) × 100%, where 75% or more was no atrophy, 50–75% was slight atrophy, 25–50% was moderate atrophy, and 25% or less was severe atrophy.p < 0.01 was considered statistically significant.Fatty atrophy of the following muscles was assessed using the Goutallier classification  [The surgery was performed with attention to the anatomical characteristics of muscles and ligaments, effectively retracting the muscles and confirming their preservation under direct vision. The surgeon also preserved and repaired articular capsule ligaments as much as possible to achieve appropriate muscle tension. The surgical technique is described in detail: the anterior articular capsule ligament was exposed, and the lateral ridge to which the superior band of the iliofemoral ligament (SBILFL) was attached was identified. During surgery, it was important to dissect the tight capsular attachment between the Gmin and bursa ligament. The capsular ligament was dissected through an inverted T-shaped incision while ensuring the preservation of the femoral attachment of the inferior band of iliofemoral ligament (IBILFL) . After rp < 0.0001) (p = 0.0063) (p = 0.95) (p < 0.0001). The TFL patients were divided into two groups—one with more than slight atrophy and one without atrophy—and were compared in terms of age, gender, BMI, and pre- and post-operative Harris hip scores. No significant differences were observed between both groups (The affected side/unaffected side ratio (A/N) ratio of the M-CSA showed no obvious pre- and post-operative changes in the Oi and Oe; however, there was a significant post-operative decrease in the Gmed, Gmin, and TFL . On the  0.0001) . Fatty a 0.0063) . Fatty a = 0.95) . The Harh groups .Posterior, direct lateral, anterolateral, and direct anterior approaches have been established for THA. Damage to Gmed, Gmin, short external rotator muscles, and TFL usually depends on the surgical approach. In the posterior approach, the short external rotator is usually detached from its attachment and reattached to the non-anatomic insertion. However, a high detachment rate of the attachment site has been reported .PellicciThe ALS THA is an approach which utilizes the interval between the Gmed and TFL; however, this muscle space is not a true internervous plane. It has long been noted that entry from this intermuscular space results in SGN injury. Several travel patterns of the descending branches of the superior gluteal nerve have been described . In a stThis study had some limitations. In our study, we evaluated MRI at 1 year post-operatively, and we were unable to track MRI changes beyond that time. Ismailidis et al.  stated tPost-operative muscle damage in ALS THA was evaluated by MRI. ALS THA with a complete muscle-preserving procedure performed under direct visualization with OI and OE preservation was observed to reduce muscle damage to OI and OE. The retraction maneuver during femoral elevation caused minor damage to the Gmed and Gmin; however, muscle volume was preserved in most patients. Furthermore, 29% of TFLs had muscle atrophy thought to be caused by SGN injury. In the anterolateral approach, TFL atrophy due to SGN injury may occur to some extent.


Autophagy and immunity play critical roles in various cancers, but the prognostic impact of autophagy and immunity for uveal melanoma (UM) remains lacking. Therefore, the RNA sequencing of data in the TCGA-UVM dataset was downloaded from UCSC Xena database. The prognostic autophagy- and immunity-related genes (AIRGs) were selected via univariate Cox regression. Next, we applied LASSO method to construct four genes of signature in the TCGA-UVM and verified in another two GEO datasets (GSE84976 and GSE22138). This signature intimately associated with overall survival (OS) time and metastasis-free survival (MFS) time of UM, which could be considered as a prognostic indicator. Besides, by applying risk assessment, the patients of UM can be divided into two subgroups (high/low risk) with different survival time, distinct clinical outcomes, and immune microenvironments. Gene set enrichment analysis (GSEA) manifested that cancer hallmark epithelial-mesenchymal transition and KRAS pathways were positively activated in the high-risk group. Moreover, the high-risk group could be more sensitive to chemotherapies than the low-risk group. Thus, our finding suggested that the four genes of signature closely linked with UM risk and survival can afford more accurate survival prediction and potential therapeutic targets for clinical application. Uveal melanoma (UM) is the most common primary intraocular malignancy which accounts for 70% of all ocular cancers. It has become a growing global public health concern with about 50% of UM patients dying of metastatic disease –3. DespiAutophagy is an important process mediating intracellular degradation which regulates cellular and biological homeostasis. Deregulation of autophagy has been proven in various pathological and disease processes, including differentiation, development, and tumorigenesis . As for Luckily, large-scale transcriptome datasets are deposited in accessible repositories, such as UCSC Xena website and GEO database, which afforded valuable resources to explore potential signatures in various tumors for different organisms and biological conditions .Therefore, in our research, AIRGs were firstly downloaded from the ImmPort Database website and the Human Autophagy Database. Next, the univariate Cox analysis and LASSO model were performed to construct a robust four genes of signature, which can distinguish different clinical outcomes, immune microenvironment, and chemotherapy response of UM. The present study indicated that the expression of AIRGs takes pivotal roles in the prognosis of UM and could be considered as a prognostic marker for UM therapy.The RNA sequencing data of 80 UM samples as well as clinical characteristics in the TCGA-UVM dataset were downloaded from UCSC Xena website, which was used for training set. Another two gene expression datasets (GSE84976 and GSE22138) contained 91 UM patients, which was acquired from GEO database and used as outside validation sets. The workflow of overall bioinformatic analysis in this study is shown in http://www.autophagy.lu/), which has been demonstrated to take part in the process of autophagy according to the previous studies. Moreover, the list of immunity-related genes was derived from the ImmPort Database (https://immport.niaid.nih.gov), which was the largest accessible human immunology database.The autophagy-related genes were collected from the Human Autophagy Database . Next, we used LASSO algorithm to develop risk model via the prognostic AIRGs. The qualified prognostic AIRGs were selected out to construct the risk system and generate risk scores for UM patients based on the corresponding coefficients. Afterwards, the patients of UM were classified into two subgroups (high or low risk) by the median cutoff value of risk score. The Kaplan-Meier survival curve (log-rank test) was applied to estimate the prognosis of the high-risk and low-risk groups. The sensitivity and specificity of risk model were assessed by receiver operating characteristic (ROC) analysis with area under the curve (AUC).The overall survival (OS) time was applied to assess the prognostic value of AIRGs in the TCGA-UVM. The univariate cox regression and Benjamini and Hochberg (BH) correcting methods were used to select prognostic AIRGs . To explore the likelihood of chemotherapeutic drugs, the “pRRophetic” algorithm was used to predict the chemotherapeutic response for each sample, and the significant chemo drugs were selected by p < 0.05 [Drug information data was extracted from the Genomics of Drug Sensitivity in Cancer (GDSC) database  and corresponding statistical packages.A number of 222 autophagy-related genes and 1793 immunity-related genes were collected for univariable Cox regression in the TCGA-UVM dataset. According to the selection standard, 44 autophagy-related genes were significantly associated with survival of UM patients, which contained 27 risk genes and 17 protect genes . In addii=1N(coefi × expri). The coefi means the Cox coefficient of gene and N is the number of gene. The expri represents the relative expression of the gene in risk model. The risk scores of UM patients were calculated by risk formula and next scaled range from 0 to 1. Then, UM patients were divided into high-risk (n = 40) and low-risk groups (n = 40) by the median cutoff value. The curves of the Kaplan-Meier (KM) survival analysis manifested that high-risk patients have worse prognosis than those in the low-risk with log-rank test p value < 0.001  , GSE22138 dataset , and GSE84976 dataset . Compared to the time-dependent AUC estimation of traditional clinical variables, the risk score of four genes signature was close to chromosome 3 status and even superior than other variables regardless in the TCGA-UVM  time in multiple datasets . The resTCGA-UVM , GSE2213TCGA-UVM , and GSETCGA-UVM . The 5-yTCGA-UVM . In GSE2TCGA-UVM . In addiTCGA-UVM .The correlation between risk score of signature and clinical features was evaluated, and the results of stratified analyses showed that metastasis , vital sThrough the CIBERSORT algorithm, 22 kinds of immunity cells were selected for the stratifying analysis. The box plots showed that the fractions of T cell CD8, T cells CD4, T cells regulatory, NK cells activated, monocytes, macrophages M1, and mast cells in the high-risk subgroup significantly different from those in the low-risk group . Next, tAccording to selected criterion, we confirmed that six positive correlated pathways were enriched in the high-risk group, including epithelial-mesenchymal transition (EMT), estrogen response late, myogenesis, estrogen response early, apical junction, and KRAS signaling. Moreover, six cancer hallmark pathways contained interferon gamma response, protein secretion, interferon alpha response, MTORC1 signaling, JAK STAT3 signaling, and inflammatory response actively associated with the low-risk group .Due to the resistance for regular chemotherapeutics, scientists are struggling to discover new potential compounds for UM. Therefore, the GDSC database was used to predict sensitivity of chemo drugs. Based on the selecting criteria, 45 kinds of drugs were screened out in the TCGA-UVM set. 34 and 14 kinds of drugs were identified in the GSE22138 and GSE84976, respectively . The VenLike many kinds of tumor, the prognosis of uveal melanoma is largely dependent on the early diagnosis and treatment . With thIn this research, we developed a prognostic biomarker with four selected AIRGs , which classified the UM patients into high- and low-risk groups. The Kaplan-Meier curves revealed that patients in the high-risk group have a poor survival. Among the four AIRGs, almost all of the genes have been reported to be associated with prognosis in other malignancies. For instance, PRKCD (protein kinase C delta) is a member of protein kinase C (PKC) family which is a critical regulator of the chemosensitivity in cancers such as non-small-cell lung cancer, ovarian cancer, and prostate cancer –38. AddiTo better understand the underlying molecular functions in the subtypes of UM, the GSEA analyses were conducted to find the potential 4 genes of signature-related pathways in cancer hallmarks database. Cancer hallmark pathways such as epithelial-mesenchymal transition (EMT) and KRAS signaling were positively enriched in the high-risk expression subgroup. It is generally recognized that EMT pathway is a crucial factor to control the clinical outcome of patients with various tumors. Asnaghi et al. proven that EMT-related factors will promote invasive properties of uveal melanoma cell and finally lead to poor prognosis . BesidesBy using various TME-decoding algorithms, we firstly observed that the high-risk group has a significant higher microenvironment score, immune score, and stromal score than the low-risk group, which indicated that a distinct TME contexture exists between the two subtypes. Next, via CIBERSORT assessment, we found that the infiltration of CD8+ T cells and M1 macrophages were highly expressed in the high-risk group. What is more, immunity-related pathways including MHC class-II, MHC class-I, immune checkpoint, CD8 T effector, ICB resistance, and tumor-associated pathways containing cell cycle, EMT, and ferroptosis were all positively enriched in the high-risk group. These observations appear counterintuitive because high infiltration of immune cells and activation of immune-related pathways are usually correlated with good prognosis in many solid tumors . HoweverFurthermore, in order to explore the different chemotherapeutic response between the high- and low-risk subgroups, GDSC drug database was performed to explore the candidate drugs, and the results showed that the high-risk subgroup is more sensitive to chemo drugs. Moreover, the three datasets showed that the AMG.706 and JNK.Inhibitor.VIII were the commonly enriched drugs. The estimated IC50 of the AMG.706 and JNK.Inhibitor.VIII in the high-risk group is significantly lower than the low-risk groups. The AMG.706 (motesanib) is a multikinase inhibitor which significantly inhibits VEGF-induced vascular permeability and angiogenesis and induces regression in tumor xenografts . The RosTo sum up, our research constructed a robust 4 genes of signature prognostic biomarker in UM which could be regard as an independent prognostic model in clinical application. The patients with the high risk score could benefit more chemotherapy. However, the discoveries are obtained from the bioinformatics analysis and needed to validate in further basic and clinical studies.


The coronavirus disease 2019 (COVID-19) pandemic and the resulting containment measures, such as “lockdown” and “social distancing”, have had important consequences on people’s mental and physical health.We aimed to study the effect of social isolation and subsequent re- exposure and eventual changes in general and ED-specific psychopathology in people with Eating Disorders (EDs).Three-hundred twelve Italian people with EDs  were asked to fill-in an online survey to explore several dimensions such as: anxiety, depression, panic, insomnia, suicide ideation, stress, post-traumatic stress and obsessive-compulsive symptoms. Differences in ED specific and general symptoms among the 3 investigated time periods  were assessed with a one-way ANOVA with repeated measures. Subsequently, ED diagnosis was introduced as covariate in the analysis in order to investigate the possible contribution on psychopathological changes.ED core symptoms increased during the lockdown but most of them returned to pre-COVID19 levels at re-opening. The severity of general psychopathology also increased during the lockdown and persisted high in the following phase, except for depression and suicide ideation. None of this symptoms was affected by ED diagnosis, participants’age and illness duration.People with EDs showed worsening of both general and specific psychopathology; moreover, changes in general psychopathology persisted in the re-opening period suggesting a higher stress vulnerability in this kind of patients.No significant relationships.COVID19 and Eating Disorders


To apply EEG-based brain-machine interfaces during rehabilitation, separating various tasks during motor imagery (MI) and assimilating MI into motor execution (ME) are needed. Previous studies were focusing on classifying different MI tasks based on complex algorithms. In this paper, we implement intelligent, straightforward, comprehensible, time-efficient, and channel-reduced methods to classify ME versus MI and left- versus right-hand MI. EEG of 30 healthy participants undertaking motional tasks is recorded to investigate two classification tasks. For the first task, we first propose a “follow-up” pattern based on the beta rebound. This method achieves an average classification accuracy of 59.77% ± 11.95% and can be up to 89.47% for finger-crossing. Aside from time-domain information, we map EEG signals to feature space using extraction methods including statistics, wavelet coefficients, average power, sample entropy, and common spatial patterns. To evaluate their practicability, we adopt a support vector machine as an intelligent classifier model and sparse logistic regression as a feature selection technique and achieve 79.51% accuracy. Similar approaches are taken for the second classification reaching 75.22% accuracy. The classifiers we propose show high accuracy and intelligence. The achieved results make our approach highly suitable to be applied to the rehabilitation of paralyzed limbs. Motor imagery (MI) is a popular method developed to help patients undergoing post-stroke rehabilitation to learn or improve specific motor functions . It is aOn the other hand, neurophysiology has revealed that EEG signals experience suppression or enhancement during both MI and ME in the mu and beta frequency bands, which is known as event-related desynchronization (ERD) or event-related synchronization (ERS). Various EEG-based MI BMIs have been developed to detect this phenomenon. The authors of  have conTo assess the restoration objectively, we investigated the difference between ME and MI, intending to assimilate MI into ME in EEG signals with neurofeedback. It is possible to retrain the brain toward becoming more capable of movement, which improves recovery. While the lateral classification (left versus right hand) has achieved high accuracy in upper and lower limbs, few studies have investigated the difference between ME and MI ,21. FocuWe built a dataset underlining both ME and MI involving delicate motors to address the above-described limitations. This dataset aimed to explore the feasibility of differentiating between ME versus MI and left- versus right-hand MI by optimizing the feature extraction and classification methods. We put forward a stable and straightforward detector of ME and MI based on beta rebound called “follow-up pattern”. We also proposed corresponding methods to address the limitations mentioned before: (1) we reproduced motions that require the engagement of both hands, investigating their application to ME and MI classification; (2) we optimized the number and location of EEG channels to achieve high accuracy with a few channels of EEG-based MI BMI, also proposing a stable and straightforward detector of ME and MI based on beta rebound; (3) we adopted various approaches for extracting features and trained classifiers to validate their utility; and (4) we recognized useful features that improved classification performance with feature-selection techniques. The prepared dataset and analysis methods we proposed can be combined with noninvasive brain stimulation (NIBS) techniques to induce plasticity during post-stroke rehabilitation .The paper is summarized as follows: details of the experiments and the methods of feature selection are described in In our research, 30 healthy individuals  volunteered. All participants provided written informed consent in accordance with the Declaration of Helsinki before the experiment, which was approved by the ethical committee of Westlake University, Hangzhou, China . All participants received CNY 100 as an inconvenience allowance. Participants were required to make movements based on auditory stimuli, undertaking the following actions: finger tapping, holding a pen, opening a pen, crossing fingers, and moving the arm, as shown in Brain Products actiCHamp Plus  and actiCAP slim (active EEG electrodes) provided by Brain Products GmbH, Munich, Germany, as shown in The EEG system examined in this study was the Before each experiment, a disinfectant wipe was applied to the electrodes. When finished, electrodes and caps were carefully cleaned from gels. These practices can effectively prevent crosstalk between channels induced by resting gels and enhance connectivity by removing dust and particles within the system.EEG signals were recorded with Ag/AgCl electrodes in a 32-channel cap arranged under a 10–20 international standard system . The central frontal electrode (Fz) served as a reference to a common ground, and the impedance was controlled to be lower than 10 kΩ. The EEG data were recorded with a sampling rate of 1000 Hz. The montage used in our experiment is shown in Preprocessing included the following procedures: removal of bad channels (channels that coupled noise or had irregular power spectra) or segments, re-referencing to a common average , filter from 1 to 60 Hz and a 50 Hz notch filter , independent component analysis (ICA), epoch extraction, and baseline correction. In the two sets (ME and MI) of preprocessed EEG data, a total of 812 epochs were generated. According to , the priR is the average power in the reference interval [The definition of the ratio ERD/ERS can be formulated as:interval . The valERD/ERS values ranging from 13 to 40 Hz were computed to observe beta rebound. ERD/ERS values were considered significant with 95% confidence by adopting a bootstrap t-test.Sample entropy (SampEn) evaluates the complexity and regularity of time-series data, measuring the unpredictability of fluctuations in physiological signals . Let xT Similar tolerance SampEn is then defined as the negative logarithm of Here, we computed the SampEn of the Cz, C3, and C4 channels from 10 participants, with a series of vector lengths The common spatial pattern (CSP) is an advanced algorithm based on principal component analysis (PCA), and it has been successfully applied to brain–computer interfaces . CSP filStatistical feature vectors include standard deviation of raw signals and the mean of the absolute values of both the first and second differences of the raw and standardized signal.We applied Daubechies mother wavelets of order 4 (db4) to analyze the raw EEG data, and the detailed coefficients at level 3 were used to extract features. The related feature vectors were wavelet root mean square (py (ENT) .Average power within a specific frequency band was estimated by the average power spectrum density (PSD). The average band power is defined as the power ratio in a specific frequency band to total power. We applied the Welch approach to estimate the PSD with a Hamming window. We performed a PSD estimation on two rhythms, alpha (8–12 Hz) and beta (13–40 Hz).Details of the feature vectors applied to the classification of ME versus MI, and the classification of laterality in MI, are listed in fitcsvm was applied to train and cross-validates SVM models for our classification tasks.With statistical learning, a support vector machine (SVM) can tackle problems involving small training sets and nonlinear relationships in classification tasks . SVM is For neuroimaging data, where the training set is small while the feature dimensionality is large, logistic regression is not applicable. In sparse logistic regression (SLR), every weight parameter has its own adjustable variance referred to as relevance parameters, controlling the possible range of the corresponding weight parameters. The weight parameters are estimated as the marginal posterior mean, which can be estimated by variational Bayesian approximation (SLR-VAR) or Laplace approximation (SLR-LAP). The L1-norm-SLR with a Laplace approximation (L1-SLR-LAP) and the component-wise implementation (L1-SLR-COMP) were also investigated in this study .Beta rebound is a stable phenomenon that occurs several seconds after ME or MI. As shown in We computed the difference in the ERD/ERS values between ME and MI by subtracting the signals of each motion recorded from each subject. The results of the subtracted signals during M4 , open a Based on the findings mentioned above, we can conclude how to identify ME and MI with beta rebound at channel Cz in the time-domain: compared with ME, beta rebounds of MI have smaller amplitude, appear later after stimulus, and last longer.We used feature vectors in We adopted features in We adopted different derivatives of SLR to select features and to calculate weights. The number of features left and the corresponding accuracies are shown in Previous studies of MI classification tasks generate interesting classification accuracies, based on different datasets, models and techniques. We applied a single neuroimaging modality, EEG, in the present study. EEG has a high temporal resolution and can produce good performance in BMI . Other mMost reported EEG-based BMI systems can be categorized into one of three paradigms: motor imagery (MI), event-related potential (ERP), and steady-state visually evoked potential (SSVEP). We adopted MI, although successful cases of other paradigms have been proposed, such as P300 ERP , SSVEP , spatialThe “follow-up” pattern we proposed is based on beta rebound. The mid-centrally located beta rebounds reveal electrophysiological correlates of synchronized “resetting” from overlapping brain networks. The occurrence of beta rebound depends heavily on the types of MI. Our study found that motors with more fingers involved can lead to better results. It can probably be explained by the superposition effect of MI, i.e., the neural activities triggered by hand MI can be interpreted as the summation of the activities invoked by simple finger MIs, which is validated in . The varIt is common practice to extract features based on statistical properties, wavelet coefficients, and average power . In thisDespite its popularity, SVM is not the only classifier model that can succeed in MI-EEG classification tasks. To evaluate their performance in EEG-based MI BMI, a comparative analysis of five classifiers, SVM, k-NN, naïve Bayes, decision tree, and logistic regression, was conducted in  and it cInstead of feeding large quantities of feature vectors directly into classifiers, a three-feature selection method SLR was applied to lower the dimensionality of features in this work, with the intention of improving classification accuracy. Gu et al. applied a similar method in foot MI classification tasks, with the most remarkable accuracy of 75.33% achieved by SLR-variational approximation (SLR-VAR) . Rejer eIt is important to note that only a small portion of channels was used in subsequent analysis—to be specific, a single channel (Cz) in ME versus MI classification and three channels  in left- versus right-hand MI classification. These channels have been proven to induce better classification results ,27. In pMost classifiers in EEG-based BMI studies are trained in a subject-specific manner, which can decode intention from a specific patient based on his own signal features ,57. ThisDespite the group-level classification, our accuracy is still comparable. A possible merit lies in our carefully selected features for training SVM. In classification between ME and MI, we selected power-based features manually, which improved the accuracy minutely but reduced the feature dimension greatly. In classification between left- and right-hand MI, we adopted SLR to abrogate redundant feature vectors, so the corresponding accuracy increased by 13.22%. Admittedly, we did not reach perfect accuracy, but this appears reasonable given that untrained subjects can be unaffected by BMI protocols. The term “BMI illiteracy” was coined for this non-negligible portion of users, which is estimated at 15% to 30% . The BMIOur research explored the feasibility of EEG for evaluating post-stroke recovery. Previous work cross-validates the efficacy of EEG signals with other assessments, such as motor functions and activities of daily living (ADL), Fugl–Meyer assessment (FMA) scores, and the modified Ashworth scale (MAS). Based upon this fact and our results, we further propose a prospective for EEG assessment, wherein therapists record EEG signals from patients during rehabilitative MI tasks, then label them with classifiers trained by group level training sets and provide real-time feedback to make patients aware of the similarity between their neural activities and the correct ones.Admittedly, the current study embodies some limitations. The experimental procedure can be modified, allowing subjects to repeat MI within specific time slots. Such modification will not only facilitate analysis but also induce more detectable signals. Moreover, our analysis was based on sensor-level techniques, while the volume conduction effect calls for source-level analysis, which would map EEG signals to cortical areas. The “follow-up” pattern we generalized still improves classification accuracy, so more detailed characteristics can be drawn from it.EEG-based MI BMI has great potential in evaluating post-stroke rehabilitation. However, present assessments suffer from low efficiency and lack of objectiveness, and few related studies underline the difference between ME and MI. In this work, we proposed a dataset and corresponding analysis methods to classify both ME versus MI and left- versus right-hand MI tasks, which can induce plasticity during restoration. This study put forward a stable and straightforward detector of ME and MI based on beta rebound, investigated extracted feature vectors, and applied SVM with SLR to classification. The conclusions are summarized as follows:“Follow-up” pattern based on the beta rebound is a stable indicator of ME and MI. Compared with ME, the beta rebounds of MI have smaller amplitude, appear later after stimulus, and last longer. The phenomenon is most significant at channel Cz. Such characteristics defined the “follow-up” pattern. Its occurrence is 59.77% ± 11.95% among all subjects, and motors with more fingers involved can generate better results (finger-crossing: 89.47%).The ME versus MI classification accuracy is 79.51% with power-based features and SVM. We extracted 13 features with statistic, wavelet-based, and power-based methods. SVM generated a classification accuracy of 78.57% with these feature vectors. After examining the support vectors, features fed into SVM were pruned back to four power-based ones, while the accuracy increased.The left- versus right-hand MI classification accuracy is 75.22% with SVM and L1-SLR-LAP. We extracted 59 features with statistic, wavelet-based, power-based, SampEn, and CSP methods. We compared the performance of different derivatives of SLR and found out that L1-SLR-LAP win over others with 42 feature vectors left. We concluded that power-based features and SampEn displayed distinctive weights in the remaining vectors.Therefore, this work demonstrates an innovative approach that can be used for evaluating the rehabilitation results of MI BMI with neurofeedback. In future work, we will focus on the back-end design of the system and explore the addition of NIBS as an adjunct therapy. BMI+NIBS interventions could inform patients and therapists about real-time MI performance and enhance rehabilitation with additional clinical gains.


BackgroundImplementation of coronavirus disease 2019 (COVID-19) pandemic control measures requires the engagement and participation of the public in a synchronized manner. Language may be a barrier to captivating public interest in a concerted manner. The relative volume of English and non-English COVID-19-related web searches estimate public interest among English and Non-English “searchers,” respectively. Asynchrony between English and non-English search interest may suggest language-related lapses in public engagement. Addressing these lapses may improve public health communications. In this study, we aimed to describe the distribution and temporal trends in the evolution of English and non-English online search interest for COVID-19 and to identify lags between English and non-English search interest.MethodologySearch interest data  was queried for the keywords “coronavirus,” “covid 19,” and their non-English equivalents between January 1, 2019, and September 30, 2020, for each country (n = 230). Daily total, English, and non-English search interest were recorded. Search Interest variables were described at global, regional, and country levels. The cross-correlation function was used to identify lags between English and non-English search interest at global, regional, and country levels.ResultsGlobally, 9.69% of total searches relating to COVID-19 utilized non-English keywords. Among included regions, 64.7% (11/17) had significant non-English interest. Central Asia had the highest proportion of non-English interest , followed by Eastern Europe (56.17%), Eastern Asia, Western Asia, and Northern Africa . Among included countries, 33.5% (77/230) had significant non-English interest. Cross-correlation function identified significant lags between English and non-English Interest in six regions  and 24 countries (median lag [IQR]: -1 [4.25] days).ConclusionsNon-English keywords contribute substantially to searches relating to COVID-19 in certain countries and regions. Numerous locations exhibit significant lags between English and non-English search interest, suggesting language-related discrepancies in the interest for COVID-19. Further research is required to address the root cause of these lags. The coronavirus disease 2019 (COVID-19) pandemic has resulted in unprecedented morbidity and mortality, as well as unparalleled economic, political, and social losses worldwide. Public health bodies have implemented a plethora of interventions to manage the pandemic [Google Trends and Baidu Index are search engine analytics tools . They prThus far, language utilization for COVID-19-related searches has not been explored. An understanding of the distribution of and temporal trends in language utilization may allow optimization of public health communications to the target audience. Furthermore, identifying asynchrony based on search language choice may suggest language-related inefficiencies in communication.Here, we aim to describe the distribution and temporal trends in the evolution of English and non-English online search interest for COVID-19 at global, regional, and country levels. Furthermore, we hope to identify temporal trends and lags between English and non-English search interest in various countries and regions. Thereby, this research may inform public health officials on regions with inefficient health communications based on differences in language utilization.Preparation of datasetsAn outline of the acquisition and processing of data is presented in Figure List of countries and regionsCountries listed by the United Nations Statistics Division (UNSD) were considered for inclusion in this study (n = 250) . Twenty Compilation of keyword listEnglish KeywordsTwo English keywords relating to COVID-19 were selected. The first, “covid 19,” is the official name for the 2019 coronavirus disease . The secNon-English KeywordsA reference list of languages spoken in various countries was acquired from data published by the Central Intelligence Agency (United States) . Google Google Trends and Baidu Index dataThe measure of search interest provided by Google Trends is the RSV. Google describes how RSVs are calculated as follows:Google Trends normalizes search data to make comparisons between terms easier. Search results are normalized to the time and location of a query by the following process: Each data point is divided by the total searches of the geography and time range it represents to compare relative popularity. Otherwise, places with the most search volume would always be ranked highest. The resulting numbers are then scaled on a range of 0 to 100 based on a topic’s proportion to all searches on all topics. Different regions that show the same search interest for a term don't always have the same total search volumes .Although Google elaborates on how RSVs are calculated, they do not disclose the absolute search volumes from which RSVs are derived.Unlike Google Trends, Baidu Index provides time series data of absolute search volumes for keywords queried. For the sake of comparability with Google Trends data, these values were converted into RSVs through the same normalization process used by Google Trends.Querying search interest dataSearch data were queried by specifying the following parameters: (1) Location: each country included in the study; (2) Keywords: country-specific keyword list, as defined above; (3) Time range: study period, i.e., January 1, 2020, to September 30, 2020. Google Trends data were queried using the Pytrends API for Python for all countries except the People’s Republic of China (PRC) . Baidu IData processingCountry-Level DataDaily total interest, English interest, non-English interest, and non-English percentage  were calculated for each country, on each day, as described in Table Regional and Global DataRegional and global data were derived from the country-level data. For regional data, the median value of interest variables among countries in each region was calculated for each day. For global data, the median values among all included countries were calculated. Table English-Only and Multi-Language Searching CountriesA country or region was considered to have significant non-English interest if the proportion of non-English searches (non-English percentage) on any day during the study period was ≥5%. Countries with significant non-English interest were defined as multi-language searching countries, while the rest were defined as English-only searching countries.For each region, median values of total interest among English-only and multi-language searching countries within the region were calculated separately. Global total interest among English-only and multi-language searching countries was calculated similarly. Table Statistical analysisDescriptive statistical analyses were performed on Python v3.8.3 using the Jupyter Notebook v6.03.3 ,15 develTime-series analyses were performed on R Studio v1.2.1335 . To assess the lags between two time series, cross-correlation function (CCF) analysis and ARIMA modeling were used . The x tDescriptive statisticsCountry-level summary statistics are presented in Appendices. In Table The regional and global lag between total interest in English-only and multi-language searching countriesOf the 230 countries included in this study, 77 33.48%) utilized both English and non-English keywords (multi-language searching countries), while 153 (65.22%) utilized exclusively English keywords (English-only countries). Cross-correlation of total interest between English-only and multi-language searching countries suggested no significant global or regional lags  of regions with significant non-English interest  of countries with significant non-English interest Table . SpecifiThe widespread use of search engines, such as Google and Baidu, to query pandemic-related information using local language keywords has provided new opportunities for health surveillance. Using Google Trends and Baidu Index search analytics data, we identified countries and regions with language-related lags in search interest for the first time. Further research is required to identify the reason for asynchrony in search interest based on the keyword language used. Addressing these issues on a case-by-case basis may improve health communications and result in better implementation of pandemic control policies.In 2021, Google Search accounted for approximately 92% of the global search engine market share . While GPrevious studies have investigated epidemiological applications of Google Trends and Baidu Index data with regard to the COVID-19 pandemic. Ciaffi et al. demonstrated that symptom searches for keywords such as “fever” and “cough” was associated with intensive care unit (ICU) admissions and deaths related to COVID-19. The premise of this hypothesis was that as patients experienced symptoms, the frequency of Google searches for those symptoms would increase . Using aGlobal distribution of search languageHerein, the worldwide distribution of English and non-English language keyword utilization for COVID-19-related web searches on Google (worldwide) and Baidu (China) search engines is described Figure -4. WhileOur results suggest that non-English search keywords are often used, accounting for 9.69% of total searches relating to COVID-19 globally. Most countries with significant non-English keyword utilization were concentrated around geographically contiguous regions, including Central Asia, Eastern Europe, Eastern Asia, Western Asia, and Northern Africa, among other regions. This data may be utilized to tailor the language of global health communications to local search language preferences.Temporal trends in search language useThe temporal changes in search interest in various regions and countries are depicted in Figure Interestingly, Northern Africa, Southeastern Asia, Southern Asia, and Western Asia showed a high percentage of non-English language searches early in the pandemic, peaking between January and February 2020, followed by a precipitous decline. Referring to individual country-level plots for search interest in each respective region Figures -11, it iSpeculatively, early in the pandemic, there may have been a sudden increase in the demand for information without knowledge of the most appropriate keywords to use. Once the public was educated on appropriate English keywords by the media, government agencies, and other sources, their search habits might have changed. The standardization of nomenclature for the 2019 coronavirus disease by the WHO on February 11, 2020, might have also contributed to the change in search language preferences. These trends bear important implications for future global health emergencies. The early definition of standard terminology in multiple languages is essential to direct the sudden increase in the demand for information to appropriate resources.Interpretation of cross-correlation coefficientsThe CCF analyzes the similarity between a pair of time series when one time series is displaced against the other. One drawback of CCF is that real-world data may suffer from autocorrelation resulting in spurious cross-correlations. This is tackled by removing the autocorrelated component from the input series using a process called pre-whitening. Details are described in the statistics section and are elaborated on in authoritative textbooks . ConsideLags Between Total Interest of English-Only and Multi-Language Searching CountriesReassuringly, no lags in total interest were found between English-only and multi-language searching countries within any region or globally Table .Regional, Global, and Country-Level Lags Between English and Non-English Search InterestIt is concerning that the interest of English and non-English searching subpopulations within several countries and regions were not contemporaneous , their viewership, and their impact may vary, thus resulting in the asynchronous public interest.Intrinsic subpopulation characteristics: The baseline characteristics of individuals searching with English and non-English keywords may vary. Differences in education, socio-economic strata, access to the internet, or other factors might result in a delayed reaction to public health communications, even if communications are delivered in appropriate languages and in a timely manner.Further research is required to identify the reason for language-related lags and develop interventions to remedy these issues. Neglecting lapses in communication may hamper pandemic control measures and put vulnerable subpopulations at a greater risk.LimitationsThis study has certain limitations that merit consideration. First, although Google (92%) and Baidu (1.3%) represent most online searches worldwide, the exclusion of data from other search engines may lead to bias . Second,Non-English keywords contribute substantially to searches relating to COVID-19 in certain countries and regions. Numerous locations exhibit significant lags between English and non-English search interest, suggesting language-related discrepancies in the interest for COVID-19. Further research is required to address the root cause of these lags.


Organ fibrosis represents a vital health threat that substantially contributes to yearly mortality rates. While a considerable amount of research has been conducted on fibrosis, these reports have only focused on specific organs as affected within distinct disorders. Accordingly, results from such studies have been unable to provide a comprehensive understanding of the pathological processes involved. Here, we describe the development of FibROAD, an open-access database that integrates evidence from fibrosis-associated disorders as obtained from both the literature and multi-omics data. This resource will greatly assist both researchers and clinicians in the comprehension and treatment of this condition. FibROAD currently involves an assembly of 232 strong evidence-based fibrosis-related genes (FRGs) as garnered from 909 PubMed publications and contains lists of multi-omics data from > 4000 samples including RNA-seq, single-cell RNA-seq, miRNA-seq, ChIP-seq, ATAC-seq MeDIP-seq and MBD-seq as obtained from 17 different organs in 5 species. Results from integrative analyses as obtained using FibROAD have demonstrated that FRGs can be indicators for a wide range of organ fibrosis and reveal potential pro-fibrotic candidate genes for fibrosis research. In conclusion, FibROAD serves as a convenient platform where researchers can acquire integrated evidence and a more comprehensive understanding of fibrosis-related disorders.Database URL  https://www.fibroad.org Fibrosis is a term used to describe the process of connective tissue deposition in response to organ injury and aging, and represents a condition that has attracted considerable attention and interest of late. As part of routine physiological processes, fibrosis can occur in almost all organs  but is pWith the advent and development of high-throughput omics techniques, it is now possible to obtain a more comprehensive evaluation regarding processes involved with fibrosis. As achieved with the use of the application of single-cell transcriptomics, Xie  describeCurrently, only a limited number of databases are available that can provide investigative information regarding fibrosis. These databases are concentrated on either unique types of diseases, such as the Cystic Fibrosis Database, Fibromine  and PulmFibrosis-Related Omnibus for Archives and Datasets, https://www.fibroad.org). This resource enables researchers to browse and analyze a wide range of multi-omics data associated with fibrosis, based on a manually curated, evidence-based fibrosis-related gene set. The current version of FibROAD consists of a set of 232 Fibrosis-Related Genes (FRGs) carefully filtered as based on experimental evidence from PubMed publications and lists the omics data from researches on 4351 samples involving RNA-seq, single-cell RNA-seq, miRNA-seq, ChIP-seq, ATAC-seq MeDIP-seq and MBD-seq. With FibROAD, researchers are provided with a ‘roadmap’ enabling them to evaluate fibrosis from a more global perspective. We believe this database would be extensively used and significantly promote new avenues for future research in fibrosis.In an attempt to bridge this gap, we developed FibROAD  and their literature sources (PubMed IDs). For gene descriptions, FibROAD introduces each FRG with its basic information and aliases collected from both NCBI-Gene and UNIPROT databases. In addition, FibROAD also provides information on FRGs related protein–protein interactions, transcription factor regulations and drug–target interactions, as obtained from STRING, RNAInter  and DrugHomo sapiens, Mus musculus, Rattus norvegicus, Sus scrofa, Acomys cahirinus). Among these projects a total of 17 different sources of fibrotic organs are included, with lung (75), liver (58), skin (22), kidney (22) and heart (18) being the top five, and others  present in smaller numbers. Detailed dataset statistics are shown in For the Omics component , FibROADIn order to better facilitate the retrieval of relative information in FibROAD, we constructed an open-access website that offers an opportunity for users to search, browse and download the data. On the home page , the useFibROAD also offers a user-friendly portal to search gene expression profiles within the entire database to enable a more individualized use of the database . Users cOther important functional components of FibROAD such as Download, Submit and the Help page can be accessed through the navigation bar of the website .In an attempt to interpret the molecular essence of fibrosis, we provide a manually curated collection of 232 FRGs with inclusion criteria as described above, along with associated experimental evidence as resulting from 909 representative PubMed publications. According to the experimental evidence, FRGs can be divided into three functional groups, namely pro-fibrotic (91 genes), anti-fibrotic (41 genes) and both  . It shouTo further describe functions of these FRGs, we next performed an enrichment analysis for both Gene Ontology Biological Process (GO-BP) and KEGG Pathway with use of the entire list of FRGs . The topNext, we assessed the average expressions (in terms of FPKM) of FRGs in several RNA-seq datasets collected in FibROAD to determine if the FRGs list proposed would effectively describe the features of fibrosis . When suP-value thresholds to 0.5 and 0.05, respectively, a considerable number of genes were found to be differentially expressed (DEGs) in multiple datasets, with the top 15 extensively expressed collagenous and non-collagenous genes illustrated in To go further into the molecular features of fibrosis and their associations with FRGs, we integrated a series of multi-omics data as collected in FibROAD from different organ sources, including lung, liver, kidney, heart and skin. As fibroblast is the key cellular component associated with fibrotic processes , we firsFibrosis, a condition resulting from extracellular matrix accumulation in response to organ injury, represents a major causative factor for a number of diseases, such as idiopathic pulmonary fibrosis , cystic The aim of our database is to facilitate researchers in the field of fibrosis by providing an integration of high-quality omics data of the field, as well as curating a highly confidential gene set to characterize the essential process of organ fibrosis. We provided a convenient platform and tool for investigators to relate evidences from different aspects of fibrosis researches. With the current version of FibROAD, users could explore fibrotic information such as gene expressions, epigenomic regulations, protein interactions and drug targets from 232 FRGs as garnered from PubMed publications, and datasets for a total number of 4351 samples from multi-omics studies as obtained from 17 different organs in 5 different species, and hopefully reveal important regulation network and hub features underlying biological and pathological mechanism of fibrosis.Although there have been some attempts to characterize the processes of fibrosis as achieved by summarizing a set of specific gene entities, either with use of text mining  or from With the advent of new research directed toward fibrosis, in particular with that employing new techniques such as mass-spectrometry and post-transcriptional modification assays, substantial increases in valuable information regarding fibrosis will be revealed. Therefore, our FibROAD program will not only be critical in integrating this information but also will be continuously managed and upgraded to provide a long-term and current database of this information. As a result, we believe this database will be extensively used and significantly promote new avenues for future research in the area of fibrosis.FRGs were collected from PubMed literature published prior to July, 2021, as retrieved using the terms ‘fibrosis’ or ‘fibrotic’ in the searches of Titles/Abstracts. The search results were initially filtered by limiting the literature type to ‘experimental research’ and ‘review with experimental references’. Then, evidence strength of the experiments in the literature was further evaluated, to isolate studies with strong evidence . Any reported genes that satisfied the above criteria in at least two independent studies were considered as fibrosis-related genes and thus curated into the Fibrosis-Related Genes (FRGs) component of FibROAD. Together with the gene symbols, their representative references (2–8 studies) along with pro-/anti-fibrotic function in each reference and level of evidence were also collected.High-throughput omic data were collected from GEO, SRA and EBI-ENA databases, published prior to February 2021 as achieved by searching the keywords ‘fibrosis’ or ‘fibrotic’. A manual screening of the retrieved results was performed to ascertain whether the subject and experimental design focused on fibrosis. Among the filtered omic approaches, RNA-seq, single-cell RNA-seq, miRNA-seq, ATAC-seq, ChIP-seq and Methylation-seq (MBD-seq and MeDIP-seq) were curated for further data processing.P-value  of <0.05 and fold change of >2 or <0.5.We used Hisat2 (v2.0.52) to build the index of the reference genome for different organisms and align the paired-end clean reads with the reference genome . Then, SZ-scored residuals of the model were used as normalized expression values. We defined the top 2000 most variable genes as based on their average expression and dispersion as being highly variable genes (HVG). Dimensionality of the data was reduced by performing a principal component analysis (PCA) on the HVG. To identify cell subpopulations, clustering was performed on PCA scores using significant PCs assigned by a randomization approach as proposed by Chung and Storey  or mouse (mm10) genome and assigned to individual cells of origin according to cell-specific barcodes, using the Cell Ranger pipeline (10X Genomics). To ensure that PCR amplified transcripts were counted only once, only single UMIs were counted for gene expression levels . In thisd Storey , 33. To d Storey . Cells wd Storey . To dispd Storey .http://broadinstitute.github.io/picard/). Accessible regions and peaks of each sample were identified using MACS2  were considered as the initial start of the differentially methylated regions.After removing adaptors using Trimmomatic , reads wng MACS2 , with thng MACS2  was usedng MACS2 . For theng MACS2 . The P-vc window . Windowshttp://www.sanger.ac.uk/software/Rfam) (http://www.ncbi.nlm.nih.gov/genbank/). The noncoding RNAs annotated as tRNAs, rRNAs, small nuclear RNAs (snRNAs) and small nucleolar RNAs (snoRNAs) were excluded from the analysis. Known miRNAs were identified by alignment against the miRBase (v.22.1) database (http://www.mirbase.org/) with Bowtie (v1.1.1), and their expression levels were quantified with miRDeep2    and GenBmiRDeep2 . Differepackage) .P-values  or Wilcoxon Rank Sum test P-values were <0.05. Figures were plotted with use of either the ggplot2 library of R (version 3.6.3) or matplotlib package of Python (version 3.8.8) programs.All statistical calculations were performed with use of R (version 3.6.3) or Python (version 3.8.8) programs. Statistical significance was considered when adjusted baac015_SuppClick here for additional data file.


Problem: Similarity measures are widely used as an approved method for spectral discrimination or identification with their applications in different areas of scientific research. Even though a range of works have been presented, only a few showed slightly promising results for human tissue, and these were mostly focused on pathological and non-pathological tissue classification. Methods: In this work, several spectral similarity measures on hyperspectral (HS) images of in vivo human tissue were evaluated for tissue discrimination purposes. Moreover, we introduced two new hybrid spectral measures, called SID-JM-TAN(SAM) and SID-JM-TAN(SCA). We analyzed spectral signatures obtained from 13 different human tissue types and two different materials , collected from HS images of 100 patients during surgeries. Results: The quantitative results showed the reliable performance of the different similarity measures and the proposed hybrid measures for tissue discrimination purposes. The latter produced higher discrimination values, up to 6.7 times more than the classical spectral similarity measures. Moreover, an application of the similarity measures was presented to support the annotations of the HS images. We showed that the automatic checking of tissue-annotated thyroid and colon tissues was successful in 73% and 60% of the total spectra, respectively. The hybrid measures showed the highest performance. Furthermore, the automatic labeling of wrongly annotated tissues was similar for all measures, with an accuracy of up to 90%. Conclusion: In future work, the proposed spectral similarity measures will be integrated with tools to support physicians in annotations and tissue labeling of HS images. Medical hyperspectral imaging (HSI) is an emerging technique in non-invasive and contactless image acquisition that supplies spatial and spectral information collected from biological tissue . The 3-dFurther, to help interpret generated HSI data that are sufficiently complex, artificial intelligence approaches have been used . MachineThe present study investigated spectral similarity measures that effectively employ the spectral signature information for biological tissue discrimination. Precisely, object discrimination accuracy, precision and validity rely on similarity measures and the correctly defined target signature. Earlier, researchers used spectral similarity measures for spectral signature detection in the geographical, geological, and agriculture fields. Spectral measures have been used for the identification of crops , mineralSpectral similarity measures were also evaluated in medicine in a limited number of studies. Martin et al. used SAM for detecting altered mucosa of the human larynx . Chen etThis study aimed to analyze which similarity measures are best-suited for the discrimination of spectral data of human tissue. Several deterministic and probabilistic similarity measures were evaluated. Further, we developed two optimized hybrid spectral similarity measures as a combination of known similarity measures that are deterministic and probabilistic. We evaluated the performance of the proposed methods in two case studies, the discrimination of thyroid and colon structures from surrounding tissues and surgical instruments. Moreover, we tested the new spectral similarity measures to automatically check the identity of tissues manually labeled by users and to correct wrong annotations by comparing their spectral signatures with other available tissue signatures stored in a database. This preliminary study aimed to demonstrate the potential use of spectral similarity measures to assist surgeons and researchers in tissue annotation.® Tissue system . It is a push broom scanner HSI camera with a spectral resolution of 5 nm. The HS data cube has a dimension of 640 × 480 × 100 pixels . The camera was placed at 50 cm from the in vivo tissue intraoperatively.The HSI data used in this paper were collected from the University of Leipzig Medical Center. This study was approved by the ethics committee of the University Hospital of Leipzig, Germany (393/16-ek). Images between 500 nm to 1000 nm were recorded by using the TIVITAData on a total of 100 patients were used. Nine patients were recorded during thyroidectomies/parathyroidectomies, and 91 during gastrointestinal surgeries. In vivo tissues and surgical material visible in the HSI data were labeled by an experienced surgeon. The annotations in the thyroid/parathyroid dataset were thyroid, parathyroid, muscle, nerve, skin, instruments, and gauze. A total of 147,799 spectral signatures were collected .The second gastrointestinal dataset included 138,962 spectral signatures of the small intestine, colon, fat, gallbladder, esophagus, pancreas, spleen, and stomach . The sumMeasuring the similarity between data vectors was performed in HSI applications. In this part, six classical discriminatory spectral measures were studied. These methods were suitable to evaluate the tissue’s discriminatory ability. Apart from this, a newly optimized spectral measure method was presented.K labeled spectral signatures. Let us assume that is and js are spectral signatures of two pixel vectors, where is = T and js = T. L is the number of wavelengths or spectral bands.In the following, Δ = L wavelengths. Its values are included in the range 0 to π/2 radians. The smaller angle shows higher spectral similarity.The SAM measure, a supervised classification method , was intBased on divergence calculation, the SID, which is a stochastic method, computes the distance between the probability distribution produced by two spectral signatures . The smaHere, vergence , which isin and tan are the usual sine and tangent trigonometric functions. The tan version showed superior results in comparison to the sin version.SAM computes angle and gives the correlation between two spectra. SID calculates divergence in terms of distance. Du et al. presented a hybrid method  by combiThe second variant is noted as SID-TAN(SAM) in the following.The SCA is based on the Pearson correlation coefficient. It is similar to the SAM method. SCA eliminates the negative correlation and enhances the better shading effect in comparison to SAM ,37,38. TThe Pearson correlation coefficient is defined asThe values of the Pearson correlation coefficient are included in the range −1 and +1.Kumar et al. proposed two SID-SCA mixed measure methods , which aThe second variant is noted SID-TAN(SCA) in the following.The Bhattacharya distance or coefficient BC measures how similar two probability distributions are. It is widely used to calculate the separability between two different spectra. It is calculated as follows: In the literature, the JM distance is an enhancement over the BC and scales the distance between 0 and 2 .(9) JM=tan function calculates the perpendicular distance between the spectra t and s. The JM distance is used for separability criteria and is optimal for classification tasks [Adep et al. showed that the combination SID-TAN(SCA) has a higher ability than the SID-SIN(SCA) to identify the target spectra for mineral classification . The reaon tasks . The hybIn this work, a new hybrid measure based on the tangent function was developed by combining a deterministic method, i.e., SCA and SAM, and two stochastic methods, i.e., SID and JM. Combination of these measures increases the chance of detecting the similarity and dissimilarity between spectra.Both measures are noted SID-JM-TAN(SCA) and SID-JM-TAN(SAM) in the following.The spectral similarity measures are given with different units. Therefore, Naresh Kumar et al. introduced three statistical methods in order to be able to compare and evaluate them . They art is any specific target spectrum that is compared with the spectra m is any spectral similarity measure previously defined. The RSDPW is defined as follows:Let us assume that The high values of RSDPW  method . We const previously annotated by medical experts is checked. Secondly, a new label is automatically suggested for the annotated target spectrum t, identified as incorrect. Both steps compare the target spectrum with reference spectra whose labels, e.g., tissues A, B and C, are known, using the spectral similarity measures. In order to speed up the process, the target spectrum is not compared with all reference spectra, but with the centers of clusters of spectra similarly labeled, obtained using the K-means clustering with two clusters.An application to automatically support tissue annotations performed by users and using the spectral similarity measures is presented. Firstly, the label of a target spectrum tissueA, m(r) were calculated as the mean values of the spectral similarity measures m between the cluster centers r and all other spectra labeled tissue A within the cluster. Afterwards, these thresholds TtissueA, m(r) were used to check if a target spectrum t labeled tissue A was correct. If the spectral similarity values between t and all other cluster centers labeled tissue A were lower than the thresholds TtissueA, m(r), the label tissue A of the target spectrum t was assumed to be correct. The spectra labeled thyroid and colon were checked. A LOPOCV on both datasets separately was conducted to evaluate this approach.In the first step, a threshold-based check was performed. The thresholds Tt was compared with three clusters with labels tissue A, tissue B and tissue C. So, three spectral similarity values were calculated. The label of the cluster with the smallest value was chosen as correct. The predicted tissue label was compared with the annotation of t performed manually by the physician, which was assumed to be correct. This was conducted for each biological tissue in both databases and similarity measures. For the thyroid/parathyroid dataset, a LOPOCV was performed. For the gastrointestinal dataset, a k-fold cross validation using k= 10 was performed. The data were split according to patients, whereby the same patient would not appear in two different folds. The folds had the same percentage of samples for each class.The second steps suggested a tissue label for the target spectrum whose label was identified as incorrect. The suggested label corresponded to the cluster with the smallest spectral similarity measure value. For example, the target spectrum The performance of both steps was estimated using the accuracy. It was the proportion of true positives and true negatives in all evaluated predictions.The spectral similarity measures SAM, SID, SID-TAN(SAM), SID-TAN(SCA), SID-JM-TAN(SAM) and SID-JM-TAN(SCA) were evaluated statistically using the two different datasets described in In the first study, the thyroid tissue was selected as a reference and compared with other human tissues, i.e., muscle, nerve, skin, parathyroid, surgical instruments and gauze. The results of the computation of the spectral similarity measures are represented in SID-TAN(SCA) and SID-JM-TAN(SCA) showed the lowest similarity values for thyroid versus parathyroid and thyroid versus skin in comparison to the other similarity measures. The similarity value measured by SAM between thyroid versus thyroid and thyroid versus parathyroid was very close; indeed, a remarkable difference was visible while comparing skin, parathyroid, muscle, and nerve. On the other hand, the averaged similarity value calculated by SID-JM-TAN(SCA) between thyroid versus thyroid was 1.1 times smaller than that between thyroid versus parathyroid. For dissimilar spectra, SID-JM-TAN(SCA) produced the most significant value compared to the other five measures. The SID-JM-TAN(SCA) method measured up to 6.7 times higher values for dissimilar spectra. Contrarily, SAM measured only up to 1.5 times higher values for dissimilar spectra. So, based on the smallest produced value, SID-JM-TAN(SCA) and SID-JM-TAN(SAM) measures are both highly recommended. Most of the methods can analyze the similarity found between two spectral signatures.SID-JM-TAN(SCA)(thyroid-parathyroid-nerve) = 3.66, RSDPWSID-JM-TAN(SCA) (thyroid-parathyroid-muscle) = 6.12, and RSDPWSID-JM-TAN(SCA) (thyroid- parathyroid-skin) = 1.21 implies that the signatures of nerve, muscle, and skin are dissimilar. The results indicate that the SID-JM-TAN(SAM) and SID-JM-TAN(SCA) measures are suitable for tissue discrimination.In the second case study, the colon tissue was used as a reference and compared to the tissue of other gastrointestinal organs.SID-JM-TAN(SCA)  = 18, RSDPWSID-JM-TAN (SCA)  = 4 and RSDPWSID-JM-TAN (SCA)  = 1.1 suggested that the signature of the spleen was quite different from that of small intestine relative to gallbladder and esophagus. RSDPW  showed all values between 1 and 2, representing the similar nature of small intestine and stomach tissue. Regarding the evaluation of the measures, RSDPWSID-JM-TAN (SCA) showed relatively higher values in comparison to SAM. This evidence also suggests that the combination of stochastic and deterministic measures is more powerful than the individual measures.The relative spectral discriminatory entropy RSDE measured by SID-JM-TAN(SCA) was lower when compared to other measures D. A smalWe evaluated the approach to automatically support the annotations of tissues. The first step used thresholds on the spectral similarity measures to check the tissue labels. For the thyroid/parathyroid dataset, both hybrid measures showed the best results. The SID-JM-TAN(SCA) correctly identified thyroid tissue in three out of seven patients. In failure cases, thyroid was detected as parathyroid or skin. For the gastrointestinal dataset, the hybrid measure SID-JM-TAN(SAM) showed the best results. It showed that in four out of eight patients, the colon was correctly annotated. Only the hybrid measure SID-JM-TAN(SCA) showed similar results with less accuracy. In the end, the similarity measures could correctly check the annotations of the spectra in 73% of the total spectra labeled thyroid and in 60% of the total spectra labeled colon. The hybrid similarity measures showed higher performance.In the second step, the tissue label corresponding to the reference spectra cluster with the smallest spectral similarity measure value was suggested for the target spectrum identified as incorrectly annotated in the previous step. The thyroid/parathyroid dataset was evaluated in a binary classification, where only thyroid and parathyroid tissues were labeled. The accuracies were similar for all spectral similarity measures and equal to 76%. The accuracies obtained on the gastrointestinal dataset were similar for all similarity measures for all tissues, too. An accuracy of 78% for esophagus, 64% for intestine, 69% for fat, 66% for pancreas, 77% for colon, 53% for stomach, 85% for gallbladder, and 90% for spleen was obtained. The results showed that automatic tissue labeling using spectral similarity measures is feasible with acceptable accuracy.SID-JM-TAN (SCA)= 1.58 for colon) showed better applicability than standard similarity measures such as SAM (RSDESAM= 2.64 for colon).In this work, we developed two new hybrid spectral similarity measures to discriminate tissues. They combine SID, JM, and tan of SAM and SCA, respectively. We applied them on HS images with thyroid/parathyroid and gastrointestinal tissues in a spectral range from 540 nm to 995 nm. Our developed hybrid measures SID-JM-TAN(SAM) and SID-JM-TAN(SCA) demonstrated their potential to identify the tissues. They produced higher discrimination values, by up to 6.7 fold, than the other classical spectral similarity measures, to discriminate dissimilar tissues. The RSDE values of the hybrid measures , SID-TAN(SCA), SID-JM-TAN(SAM), and SID-JM-TAN(SCA), was performed.Our newly improved hybrid measures, SID-JM-TAN(SAM) and SID-JM-TAN(SCA), were shown to be the most suitable measures that can be used for tissue characterization.We showed that spectral similarity measures have the potential to automatically support tissue annotation tasks. It is worth noting that our proposed hybrid spectral similarity measures delivered good accuracy.


Combined with other microRNAs like hsa-miR-195, their expression correlates with recurrence-free survival. Most studies support the applicability of liquid biopsy for assessing temporal tumor heterogeneity (i.e. tumor progression) in adrenocortical cancer. In this mini-review, the available findings of liquid biopsy for assessing tumor heterogeneity in adrenocortical cancer are presented.Almost 10 years have passed since the first attempts of liquid biopsy aimed at the characterisation of tumor cells present in the bloodstream from a regular sample of peripheral blood were performed. Liquid biopsy has been used to characterise tumor heterogeneity in various types of solid tumors including adrenocortical carcinoma. The development of molecular biology, genetics, and methodological advances such as digital PCR and next-generation sequencing allowed us to use besides circulating tumor cells a variety of circulating cell-free nucleic acids, DNAs, RNAs and microRNAs secreted by tumors into blood and other body fluids as specific molecular markers. These markers are used for diagnosis, to check tumor development, selecting efficient therapies, therapy monitoring and even possess prognostic power. In adrenocortical carcinoma, there are some studies reporting analysis of circulating tumor cells, circulating cell free DNA and microRNAs for assessing tumor heterogeneity. Among microRNAs,  Tumor heterogeneity is the attribute of malignant tumors which describes that different tumor cells can show different genetic or phenotypic properties. This can present for example as differing morphological properties, gene expression profiles, metabolic discrepancies and higher proliferative and metastatic potential . This phGenomic instability plays a major role in tumor heterogeneity. It ranges in magnitude from single nucleotide variations to whole genome doubling, and can be caused by exposures to DNA altering agents, dysfunction of DNA repair and replication mechanisms . MicrosaAccording to the cancer stem cell model, only a small subset of tumor cells is capable of proliferation and the difference in tumor cells is based on which stem cell they originated from. The clonal selection hypothesis  states tTumor microenvironment (TME) can provide an additional source of intra-tumor heterogeneity. TME is a playground where non-cancerous cells can develop interactions with tumor cells, including tumor stroma, proliferating tumor cells, immune cells and blood vessels. These interactions create a complex signaling network influencing the fate of individual cells , 11. TheThe spectrum of heterogeneity can be described in two forms, spatial and temporal. Spatial heterogeneity means that different genetic and phenotypic properties are found within or in different metastases originating from the tumor. Temporal heterogeneity is the dynamic variation of the same tumor over time. Both can be observed at the same time, and both contribute to the diminishing effectiveness of treatments and relapses .Understanding tumor heterogeneity is pivotal for future treatments of malignancies, as it is the driving force behind metastasis, relapse, and treatment resistance. Heterogeneity can be measured by molecular biology methods, including genetic sequencing. The invention of high throughput methods made this task significantly easier .To examine the heterogeneity of a metastatic neoplasm, multiple samples should be taken from multiple tumors and for an optimal result at multiple intervals in time. This process would require the patient to undergo multiple biopsies, which is difficult and cumbersome, moreover repeated traditional biopsies might contribute to further metastasizing by physically dispersing tumor cells. A new option is liquid biopsy, which is the process of sampling liquid biological matter with tumor-specific molecular pieces of information.In this review article, we present data on the potential use of liquid biopsy in adrenocortical cancer for the assessment of tumor heterogeneity. The available few studies target temporal heterogeneity (tumor progression).Adrenocortical tumors (ACT) are common, and their prevalence rises with age . The majImaging is pivotal for the follow-up of adrenocortical cancer, but blood-borne tumor markers of progression would be needed, as well.Liquid biopsy (LB) at its introduction in 2013, referred to the analysis of circulating tumor cells (CTC) in the blood of cancer patients . With thLB offers a non-invasive possibility for longitudinal monitoring of genetic and phenotypic changes in heterogeneous cancer . CirculaCTCs are rather rare: 0.1–10 per mL whole blood. Cell stabilization is important and enrichment of the CTCs is necessary for further applications. CTCs can be characterised based on physical and morphological traits  and cell surface markers , cytokeratins) . CTC assRegarding ACT and CTCs, only two studies involving small cohorts have been published, to date. In the first study , 14 ACC In a more recent monocentric study from the same resarch group performed on a small cohort of patients affected with ACC CTCs were raised as potential markers of prognosis and overall survival . 68% of The revolutionary discovery of cell free DNAs in circulation and its correlation with different type of cancers was published decades ago . From thn = 6) and searched for characteristic tumor specific mutations [Two preliminary studies examined the potential biomarker role of ctDNA in ACC, to date. Creemers et al. investigated ctDNAs in ACC patients (utations . Tumor-sn = 2). In the background of this phenomenon, it could be hypothesized that only the most agressive ACCs secreted enough ctDNA that could be detected. A correlation with disease progression was found, thus a potential for assessing ACC temporal heterogeneity could be raised. However, in some patients even with high tumor burden, no ctDNA was detected.In Garinet’s study, 11 ACC patients were evaluated . CtDNA mMSH2 deletion that was identified in the ACC tissue as a pathogenic driver mutation. In this particular case, the somatic MSH2 deletion could not be found during follow-up, and the patient was considered tumor free [McCabe et al. reported an ACC case where ctDNA analysis was used to monitor tumor recurrence aimed at the detection of a somatic mor free , thus thIn conlusion, despite some promising results, the application of ctDNA in the clinical setting as a potential biomarker in ACC is not clearly established. It might be useful in a subset of patients, but further studies on larger cohorts are needed to prove its potential utility.MicroRNAs  are the endogenous mediators of RNA interference . MicroRNhsa-miR-483-5p is the most consequently observed overexpressed microRNA in ACC [Few circulating blood-borne microRNAs have been found to be differentially expressed between benign and malignant ACT. Circulating A in ACC , 65.hsa-miR-483-5p and underexpressed hsa-miR-195 in ACC should be highlighted [The diagnostic applicability of circulating miRNAs in different studies is rather variable. Overexpressed hlighted , 67.hsa-miR-483 and hsa-miR-483-5p in plasma samples of patients with ACT should be highlighted [Hsa-miR-483-5p was significantly overexpressed in advanced stages of ACC (stages III-IV) compared to early stages (stages I-II). However, there was no significant association between overall survival and hsa-miR-483-5p. Circulating hsa-miR-483-5p could differentiate recurring and non-recurring ACC. High circulating hsa-miR-483-5p expression was associated with significantly shorter recurrence-free and overall survival [The study by Salvianti et al. using a novel real-time PCR approach aimed at the absolute quantification of circulating hlighted . Hsa-miRsurvival .hsa-miR-483-5p [In contrast with ctDNA and CTC, circulating miRNA might be detectable in most ACC blood samples, as exemplified by the absolute quantification of R-483-5p . Howeverhsa-miR-483-5p [hsa-miR-210 were affected by anti-cancer treatments [Circulating miRNA might also exploited for the monitoring of of anticancer therapy, but there are only published findings made in animal models available to date , 71. In R-483-5p  and the hsa-miR-483-5p as a marker for treatment efficacy monitoring is demonstrated in Fig. The potential applicability of circulating The investigation of markers of tumor heterogeneity in adrenocortical cancer are in the preliminary phase. Few studies are available including some promising approaches. Circulating tumor cells and some circulating microRNAs appear to be the most promising modalities, but further studies on larger cohorts with uniform methodologies will be needed to assess the applicability of these techniques in the clinical setting.


A major challenge in stance detection is the large  and diverse set of stance topics. Collecting data for such a set is unrealistic due to both the expense of annotation and the continuous creation of new real-world topics . Furthermore, stancetaking occurs in a wide range of languages and genres . While zero-shot stance detection in English, where evaluation is on topics not seen during training, has received increasing attention, we argue that this attention should be expanded to multilingual and multi-genre settings. We discuss two paradigms for English zero-shot stance detection evaluation, as well as recent work in this area. We then discuss recent work on multilingual and multi-genre stance detection, which has focused primarily on non-zero-shot settings. We argue that this work should be expanded to multilingual and multi-genre zero-shot stance detection and propose best practices to systematize and stimulate future work in this direction. While domain adaptation techniques are well-suited for work in these settings, we argue that increased care should be taken to improve model explainability and to conduct robust evaluations, considering not only empirical generalization ability but also the understanding of complex language and inferences. Furthermore, as more people around the world turn to online platforms for news or sharing opinions , stance models must be able to generalize to new topics regardless of language or genre. However, due to both the expense of annotation and the continuous creation of new topics , it is unrealistic to collect data for all possible topics, languages, and genres. Therefore, ZSSDTopic). Although this is similar to cross-topic stance detectionTopic , and (ii) few-topic . We survey existing work on these two datasets and argue that incorporating external knowledge  and training topic-invariant features are the most promising directions for further exploration.Studies in ZSSD typically do zero-shot learning over topics , genres (ZSSDGenre), and label sets (ZSSDLabelSet).Despite recent advances in English ZSSDe Plank, , such asmulti-domain stance  and improving explainability.Although recent work on Topic in English by describing the most common datasets (Section 2.1), as well as existing work on these datasets (Section 2.2) and its strengths and weaknesses (Section 2.3).We provide an overview of ZSSDTopic: VAST (Section 2.1.1)—a dataset for many-topic stance covering a broad range of topics , education , and public health   see . AlthougTopic uses a combination of techniques: (1) learning latent topics in order to generalize (Section 2.2.1), (2) leveraging pragmatic information (Section 2.2.2), (3) learning topic-invariant features (Section 2.2.3), and (4) incorporating external knowledge (Section 2.2.4). We discuss each of these in turn.Existing work on ZSSDTopic, especially many-topic, the large and diverse set of topics can be challenging for models. In order to exploit this variation, in our prior work  are most important for conveying the stance on a topic can help models identify general stance patterns and so generalize to new topics. Model attention, whether unguided  and domain-invariant features are a common component of many domain adaptation techniques  similar to positive examples and different from negative examples. In stance detection, contrastive learning is applied to inputs with different topics to encourage topic-invariant feature representations. Often, stance labels  are used to choose positive examples  and few-topic settings (on Sem16). We note that we include cross-topic stance models in our results in order to present a full picture of progress. However, in our prior work . Since zero-shot topics make up 79% of the test topics  are more adaptable and better suited to such scenarios.We next survey existing work on multi-domain stance detection (Section 3.1) and then discuss important considerations for future studies on zero-shot multi-domain stance detection (Section 3.2).not intended for zero-shot scenarios, since the same languages and topics appear in both the training and evaluation sets. For example, multiple datasets have been created for stance detection on the topic of Catalonian independence with the Spanish and Catalan languages in both training and evaluation  . However, the zero-shot subsets of these datasets are still quite small. The NewsEye test subsets per language range from ~250 to ~1k instances. Similarly, in xstance there are only 1456 instances in the zero-shot language , of which only 283 also have zero-shot topics, compared to ~11k test instances with zero-shot topics in seen languages (German and French).Additionally, there are a small number of datasets with subsets for ZSSDmulti-dataset learning . The experiments are similarly not ZSSDGenre because multiple datasets from a single genre are used . Finally, these experiments are not ZSSDTopic because, although, many datasets contain a unique single topic  topic overlap does exist  and may be exacerbated by pre-training on English stance datasets .In fact, vagueness surrounding the notion of ZSSD makes the results of prior work difficult to evaluate clearly. In particular, the multilingual zero-shot experiments of Hardalov et al.  cannot bGenre because multiple other datasets from the same genre as the test dataset are used for training  Furthermore, these experiments are also not ZSSDTopic because multiple datasets share topics . For example, in ZSSDLanguage, at least one language that is different from the training and test sets should be designated for development. This ensures the test set remains zero-shot while providing an approximation of zero-shot performance for tuning.In order to support future work on ZSSD and address the evaluation problems of prior work, we propose best practices for ZSSD training and evaluation. First, in order to conduct such experiments it is important to explicitly designated and overlap controlled for within these aspects . This is especially important in multi-dataset learning, since multiple datasets may share the value of a domain aspect . Controlling for overlap ensures that experiments accurately measure ZSSD.Secondly, domain aspects intended for zero-shot evaluation should be A of zero-shot domain aspects being studied  all combinations of the aspects should be evaluated. That is, experiments should be conducted on ZSSDα for every non-empty α⊆A, in order to explicitly distinguish improvement in particular types of zero-shot transfer. For example, if A = {Topic, Language}, then we should evaluate not only on ZSSDTopic, Language, but also ZSSDTopic and ZSSD Language.Finally, for the set We hope that these propositions will support and systematize research in ZSSD.robustness and explainability. Although these are not unique to ZSSD, they are important considerations for ZSSD due to the sensitive nature of many stance topics .Regardless of the setting, ZSSD presents a number of ongoing challenges: evaluating for F1  and potentially spurious signals in the data . Only two systems report results for this set , explainability can help to validate that model predictions are based on reasonable and true facts or inferences. This will in turn increase both human confidence in the models and real-world viability.Explainability is especially important in ZSSD, where predictions on zero-shot instances should be grounded in valid human reasoning. Although, we  and review existing methods and data for each. We argue that incorporating external knowledge and domain-invariant features are the most promising techniques. Additionally, we argue that ZSSD should be expanded beyond English and we analyze work in multi-lingual and multi-genre stance (including non-ZSSD). Although multi-dataset learning is promising, there is still significant room for improvement, particularly in zero-shot settings. To stimulate further work and systematize evaluation, we propose a set of best practices for ZSSD. Finally, we argue that robustness and explainability should be considered both in the construction and evaluation of ZSSD models.In this work, we present two paradigms for ZSSDhttps://github.com/emilyallaway/zero-shot-stance/tree/master/data/VAST; https://github.com/MalavikaSrikanth16/adversarial-learning-for-stance/blob/main/src/data/twitter_data_naacl.zip.Publicly available datasets were analyzed in this study. This data can be found at: EA: writing, analysis of the datasets, methods, and discussion. KM: advising and editing. All authors contributed to the article and approved the submitted version.