The combination of a blood cell analyzer and artificial microscopy to detect white blood cells is used in hospitals. Blood cell analyzers not only have large throughput, but they also cannot detect cell morphology; although artificial microscopy has high accuracy, it is inefficient and prone to missed detections. In view of the above problems, a method based on Fourier ptychographic microscopy (FPM) and deep learning to detect peripheral blood leukocytes is proposed in this paper. Firstly, high-resolution and wide-field microscopic images of human peripheral blood cells are obtained using the FPM system, and the cell image data are enhanced with DCGANs  to construct datasets for performance evaluation. Then, an improved DETR (detection transformer) algorithm is proposed to improve the detection accuracy of small white blood cell targets; that is, the residual module Conv Block in the feature extraction part of the DETR network is improved to reduce the problem of information loss caused by downsampling. Finally, CIOU (complete intersection over union) is introduced as the bounding box loss function, which avoids the problem that GIOU  is difficult to optimize when the two boxes are far away and the convergence speed is faster. The experimental results show that the mAP of the improved DETR algorithm in the detection of human peripheral white blood cells is 0.936. In addition, this algorithm is compared with other convolutional neural networks in terms of average accuracy, parameters, and number of inference frames per second, which verifies the feasibility of this method in microscopic medical image detection. Medical imaging has become an important part of clinical diagnosis and biomedical research, which can help doctors to study, monitor, and diagnose diseases. Peripheral blood usually refers to the human body in addition to bone marrow outside of the blood, which mainly contains platelets, red blood cells, and white blood cells . A routiAt present, the detection and identification of WBCs mainly rely on the use of blood cell analyzers and artificial microscopy . ArtificWith the rapid development of image processing technology, the ability to detect WBCs is becoming better and faster. To realize WBC detection, the first step is to collect cell images . In ordeHere, the introduction of Fourier ptychographic microscopy (FPM) , as a soRecently, deep learning has significantly improved the level of object detection. According to the different structures of the model, object detection can be divided into two types based on a convolutional neural network (CNN) and a transformer. Among them, the CNN’s success in image recognition mainly lies in its powerful capabilities of bias, activation function, and filling encoded using a convolutional layer . RecentlIn short, an accurate and efficient calculation method is needed to support peripheral blood leukocyte detection, especially to achieve complete end-to-end automation. In this paper, improved DETR peripheral blood leukocyte detection is combined with the advantages of FPM to realize the detection of peripheral blood leukocytes. The experimental results verify the effectiveness of the algorithm in detecting white blood cells and can assist doctors in diagnosing diseases clinically.In this paper, the research background, motivation, and purpose are introduced in The traditional optical microscope consists mainly of a camera, objective lens, and lens barrel, with the performance primarily dependent on the objective lens. Generally, the microscope’s performance is evaluated based on two factors: resolution and field of view. Resolution is determined by the numerical aperture of the objective, while the field of view is determined by the aperture of each lens in the objective. From the perspective of a microscopic imaging system, spatial resolution and imaging field of view are a pair of irreconcilable contradictions. In order to observe the tiny details of the sample, it is necessary to increase the numerical aperture of the microscope objective and improve its resolution. However, this imaging field of view will become smaller after such an imaging operation. When observing an object through a low-power lens, the whole picture of the measured object can be observed, but the details are not visible. The high-power lens can clearly see the details of the measured object, but the entire object cannot be viewed simultaneously.In 2013, Professor Zheng Guoan and his team from the California Institute of Technology proposed a novel computational microscopy method with a wide field of view and high resolution, which is called FPM . This meThe introduction of FPM can solve the problems that exist in the current acquisition of white blood cell images. It can significantly improve the image resolution of the traditional microscopic system and reduce the complexity of the experimental structure by simply changing the light source structure. FPM has a simple light source structure and equipment price, and it can obtain white blood cell images with high resolution and a wide field of view. By utilizing its advantages and combining them with the excellent performance of deep learning networks in target detection tasks, the detection of white blood cells in peripheral blood is achieved.Artificial microscopy not only consumes a significant amount of manpower and material resources, but the test results are also easily affected by human factors. The blood cell analyzer is unable to observe the morphology of white blood cells, and there are still some shortcomings auxiliary in medical diagnosis. With the rapid advancement of digital information and deep learning in the field of computer vision, it has become a major development that uses computer graphics to assist doctors in detecting the quantity and morphology of white blood cells.The study of red blood cell counting methods began internationally as early as 1852. In 1855, a counting plate specifically dedicated to counting blood cells was introduced . At presCuevas et al. proposed an automatic algorithm for detecting white blood cells embedded in complex images. The algorithm utilizes the DE  algorithm to optimize the encoded candidate ellipse set, allowing it to adapt to the white blood cells present in the edge mapping of the smear image . Kasim eAlthough the above methods can achieve the detection of white blood cells, these methods can only be detected after the completion of cell color space conversion or cell segmentation. Furthermore, the detection effect is easily influenced by the results of image processing. Additionally, this method requires a significant amount of operations, and the process is cumbersome, resulting in low efficiency of cell detection. Therefore, it is unlikely to be applicable to the clinical diagnosis workflow.The target detection algorithm of traditional convolutional neural network. Namdev et al. proposed a new neural network classifier for bone marrow leukocyte classification. They introduced the FGS  algorithm into the weight update algorithm . Huang eAt present, the target detection algorithms based on deep learning are basically divided into two categories. The first category consists of single-stage target detection algorithms like YOLO ,38,39 anThe object-detection method based on the transformer network structure has gained significant attention and research due to the continuous development of deep learning. The transformer utilizes the attention mechanism to acquire image features and adjusts the weight parameters through the dot operation to reduce the learning bias of the model. Therefore, the transformer has a more powerful generalization ability compared to the CNN. Sun et al. introduced a blood cell image recognition method that builds upon the improved vision transformer . They inIn order to effectively address the limitations of existing research, this paper improves detection efficiency from two aspects. Firstly, it utilizes the FPM system to collect cell images. Secondly, it enhances the target detection model DETR. This method has several advantages for the research object, which is the detection of peripheral blood leukocytes. The introduction of FPM solves the issue of incompatibility between the field of view and resolution in leukocyte image acquisition. It enables the acquisition of white blood cell images with a wide field of view and high resolution. This method does not require any color conversion or binary segmentation, and the entire process is fully automated, fast, and accurate. The DETR algorithm does not rely on artificially designed anchor frames. This reduces the requirement for prior knowledge of peripheral blood leukocyte samples and improves the model’s ability to generalize.In the ResNet50 network structure, the convolution of the Conv Block residual structure has been improved, and the average pooling operation is used at the residual edge. This helps reduce the problem of losing small target feature information during downsampling and effectively reduces overfitting, thereby improving target detection performance;The GIOU loss function has been replaced with CIOU to address the difficulty of optimizing GIOU when the two boxes are far apart and to improve convergence speed. Compared to GIOU, CIOU more accurately considers the position and size offset of the bounding box, especially for small targets and targets with irregular shapes. CIOU demonstrates better robustness and accuracy.The main improvement strategies are as follows:The improved DETR algorithm converges more quickly and enhances DETR’s ability to detect small targets. Data preprocessing is completed based on the characteristics of white blood cell images, and comparative experiments are conducted using different backbone networks. Finally, the performance is compared with that of other algorithms. The results show that the improved method has enhanced the detection accuracy compared to the original algorithm and also exhibits better detection performance and generalization ability compared to other algorithms.In this paper, the FPM system is utilized for collecting high-resolution and wide-field white blood cell images. The collected images are then preprocessed. The improved DETR algorithm is employed for detecting and identifying white blood cells in peripheral blood. To address the problem, a practical neural network classifier is established using the PyTorch framework. The specific process is illustrated in The datasets are created using FPM technology, and the programmable LED array illumination module is controlled to replace the light source of the experimental platform. MATLAB software is used to control the LED and provide illumination at different angles. The DMK33UX264 camera is used as an image-acquisition device to capture a large number of low-resolution images that contain white blood cells, which are then saved. The collected RGB three-channel low-resolution images are reconstructed using the FPM algorithm to obtain high-resolution cell images. The peripheral blood cell images are preprocessed to create the basic datasets. The sample datasets are screened to eliminate empty samples and samples without white blood cells. The deep convolution generation adversarial network is used to enhance and label the basic datasets, and the enhanced data is divided proportionally Into a training set and a test set.The data of peripheral blood leukocytes was collected using FPM. The datasets were obtained through data processing, and then the peripheral blood leukocytes were detected using the improved DETR network. The DETR network structure, shown in Fourier ptychographic microscopy is a novel computational imaging technique that is based on synthetic aperture. This method overcomes the physical limitations and improves the performance of the optical system. It enables coherent imaging with a wide field of view and high resolution. The imaging method mainly utilizes a programmable controlled LED array as the light source to illuminate the samples from various angles. This translation of the sample frequency spectrum in the Fourier domain so that the numerical aperture at the original fixed position obtains a spectrum beyond the numerical aperture of the objective lens. Consequently, the system can collect components that contain high-frequency information about the sample. Sub-aperture overlap is then performed in the frequency domain to calculate the convergence solution of the high-resolution complex amplitude. This method, which replaces spatial mechanical scanning with frequency spectral scanning, not only surpasses the spatial bandwidth product of the objective lens numerical aperture but also enhances the imaging resolution.Fourier ptychographic microscopy only requires minor modifications to the traditional microscope. The illumination module of the microscope is replaced with a programmable LED array light source, and a charge-coupled device camera is added. Fourier ptychographic microscopy imaging technology mainly consists of two processes: imaging and reconstruction, as shown in The LED array light source is used for illuminating the sample. It is assumed that the distance between the light source array and the sample is sufficiently far, the light emitting unit on the light source array is small enough, and the emitted light wave is equivalent to a plane wave. When a LED lamp is turned on to illuminate the sample, the wave vector of the incident light is expressed as:Among them,  represents the mathematical model of the Fourier stack microscopic imaging system.Since the peripheral blood cell samples need to be stained, this special production method will result in the target and background in the collected blood cell images not being clearly distinguishable, and the method will cause the blood cells to overlap and distribute densely. These factors can easily interfere with cell detection. Conventional data enhancement methods, such as image cropping, rotation, translation, scaling, contrast change, and noise addition, only increase the number of images and do not significantly enhance the generalization ability of the network model.DCGAN  combinesDCGAN is a direct extension of GAN, which utilizes convolution and convolution transpose layers in the discriminator and generator, respectively. In other words, generator G employs the deconvolution reconstruction technique to recreate the original image during data generation. The discriminator D utilizes convolution technology to identify image features and subsequently make a judgment. The generator receives random noise, which is transformed into a 4 × 4 × 1024 feature map through a fully connected layer. It then passes through four deconvolution layers to produce an image with dimensions of 64 × 64 × 3. The discriminator uses the convolutional layer to downsample the image generated by the generator, resulting in a one-dimensional vector. The network model is depicted in In the field of target detection, the detected objects exhibit a range of sizes. If simple convolution is employed for image feature extraction, as the number of network layers increases, the number of features associated with small-sized or inconspicuous objects in the deep-level network may be significantly reduced. Consequently, the semantic information is not rich enough, which in turn affects the overall detection accuracy.The backbone component of the DETR algorithm extracts image features using the ResNet50 residual network. In the ResNet50 network structure, the conv block residual structure utilizes a 1 × 1 convolution kernel and uses an operation with a step size of 2 to achieve feature downsampling. However, this downsampling process using a 1 × 1 convolution kernel leads to information loss. Only certain regions can retain feature information, while features in other regions are unable to participate in the convolution calculation, resulting in the loss of most feature information. For white blood cells in peripheral blood cell images, the lack of abundant feature information makes it difficult for the model to extract relevant information related to the target. As a result, the recognition accuracy of the detection model is reduced.In order to address the issue of downsampling in the 1 × 1 convolution kernel within the conv block residual structure’s backbone branch, a downsampling process is applied using a 3 × 3 convolution kernel at a stride of 2. Among them, the 1 × 1 convolution has a stride of 1 for feature extraction, while the 3 × 3 convolution kernel is downsampling to minimize feature information loss during the downsampling process. At the residual boundary of the conv block residual structure, an average pooling operation is employed with a stride 2 and a 3 × 3 convolution kernel. Then, a 1 × 1 convolution kernel is added with a stride of 1 for image feature extraction. This ensures that the feature extraction is retained within the average pooling layer and allows for compression of the extracted image features. Since the pooling layer is not controlled by parameters, the potential for overfitting can be effectively reduced, resulting in improved target detection accuracy. The residual structure is illustrated in The loss of the target detection network includes category loss and border regression loss. The border regression loss function of the DETR network is a combination of the GIOU loss function and the Smooth-L1 loss function. Compared to IOU, GIOU processes the nonoverlapping area of the target, which can fully reflect how the target overlaps and compensates for the deficiency of the IOU boundary loss function in quantifying the real box and the prediction box when they do not intersect. However, when the real target box completely surrounds the prediction box, the relative position relationship cannot be distinguished. At the beginning of training, GIOU needs to enlarge the prediction box and intersect the annotation box, and then begin to reduce the detection result until it coincides with the annotation box. Therefore, it requires an increased number of iterations to converge. CIOU can solve the above problems: The penalty mechanism of CIOU is based on the ratio of the distance between the center point and the diagonal distance. This approach avoids the issue of GIOU being difficult to optimize when the two frames are far apart, resulting in faster convergence speed. Additionally, CIOU can be optimized even when the real target box completely surrounds the prediction box. It takes into account the aspect ratio of both the prediction box and the real target box. The CIOU loss function is as follows:Among them, Among them, The key to detecting peripheral blood cells is understanding how to use the FPM system to collect low-resolution images. In the FPM system, the blood smear sample is placed in the central position of the stage. The brightness is adjusted by lighting the LED array light source, and the focus is adjusted to obtain the clearest image. To integrate the integration of the Fourier stack microscopic imaging system, the upper computer program and reconstruction algorithm of the LED array light source are integrated using MATLAB software to complete the image acquisition and reconstruction. The running program controls the LED to light up for low-resolution image acquisition and saves the acquired image to the corresponding folder for the reconstruction algorithm. Each LED illuminates the sample with three color light sources  to obtain a color image. Therefore, a total of 13 × 13 × 3 (507) low-resolution images under three colors, red, green, and blue, will be collected. After applying the spectrum reconstruction algorithm, the high-resolution grayscale images for each of the RGB colors are reconstructed, respectively. Finally, the reconstruction results are synthesized to create high-resolution color images. The process is illustrated in The training datasets required to train the neural network model are very large, and the required sample size must be large enough. The bigger the number of samples, the better the effect of the trained model and the stronger the generalization ability of the model. The Fourier ptychographic microscopic imaging system needs to collect 507 low-resolution images each time and then reconstruct them in order to obtain a color high-resolution cell image, which makes the process very time-consuming. Because the peripheral blood cell image is taken under the microscope, this uniqueness results in obvious differences in the target characteristics of the image data compared to the commonly used target detection datasets VOC  and COCO (common objects in context).As depicted in To enhance the quality and quantity of peripheral blood white blood cell image datasets, the experimental objective is to employ the DCGAN network for generating a specific number of white blood cell images. These images will aid the target detection network in identifying white blood cells. To avoid potential issues with overfitting due to excessive generated data, the collected white blood cell datasets are expanded in a 1:1 ratio. This experiment is designed to have 300 training iterations, and images with distinct contour features can be generated by the 170th iteration. The corresponding iterative effect is depicted in In this experiment, the effectiveness of the DETR network model and the generalization ability of the generated data to the network model are verified. The datasets used are the data collected by the Fourier laminated microscopic imaging system and the data preprocessed by the DCGAN network. Both datasets are input into the DETR network for comparison, as shown in Because the blood cell image has not been preprocessed, there are errors between the prediction frame and the calibration frame due to cell overlap, adhesion, and complex background, resulting in a large fluctuation. After preprocessing the data, the frame loss function rapidly converges and steadily decreases, leading to more accurate detection of white blood cells. It can be observed from In the process of this experiment, considering that the DETR algorithm first extracts the network through the CNN feature and then inputs the feature map and position coding into the encoder and decoder together. Therefore, the different feature extraction network models were used for comparison. As shown in Different feature extraction networks are selected and deepened from ResNet18 to ResNet101. The experimental results are shown in After adding different improvement strategies to the DETR algorithm, the average accuracy of training, parameters, and the number of frames per second (FPS) are shown in To demonstrate the effectiveness of the proposed improved DETR algorithm, it is compared with the mainstream network model in the field of computer vision in terms of performance. This comparison aims to showcase the improvement of the method proposed in this paper for white blood cell detection, as well as the advantages and disadvantages of the improved method compared with to models. The Faster R-CNN algorithm, which has higher detection accuracy, was selected to compare its detection effect with the improved DETR algorithm. The images were inputted into the architecture using different training models to obtain the detection and recognition results, as shown in In this paper, peripheral blood leukocyte detection based on an improved DETR algorithm is proposed. The FPM can compensate for the limitations of traditional microscope resolution and field of view. It only requires a simple transformation of the traditional microscope to synthesize a high-resolution, wide-field white blood cell image in a low-resolution image obtained without mechanical scanning.The DCGAN network preprocesses the peripheral blood cell data, improving the quality of the cell image dataset and facilitating detection. The experimental results demonstrate that the mAP value reaches 0.746 after training and testing using the DCGAN network for data enhancement.In the ResNet50 backbone network, the residual structure of the backbone branch has been modified, and the average pooling operation is adopted to retain the feature information of the small cell target. CIOU addresses the issue of GIOU being difficult to optimize when the two boxes are far away and the convergence speed is faster. The final mAP value has increased by 14.2 percentage points. The ablation experiment has confirmed the effectiveness of the improved DETR residual structure and loss function in the model. Additionally, when compared to the existing target detection networks, the algorithm also surpasses the classical CNN detection algorithm in terms of parameters, detection accuracy, and FPS. It achieves high-precision detection of peripheral white blood cells.The model introduces the excellent DETR in machine vision into the field of medical images. The improved DETR demonstrates superior detection performance for small targets, thus confirming its viability in microscopic medical image detection. Considering the accuracy and detection performance of the proposed method, it can be concluded that it has the potential to simplify the artificial blood cell recognition process. This method offers assurance for future biomedical research, including cell counting and classification. It is a useful attempt to introduce it into the field of medical images.Although we have achieved excellent performance in experimental comparisons with other detection models, there is a minor issue in the cell image where a few white blood cells are not fully exposed at the edge. This leads to missed detections, but it does not impact the overall results. In order to meet the high standards of medicine, we are working on improving our network structure to achieve flawless detection results. In the future, clinical data for specific diseases (such as leukemia) will be sought, and more blood cell datasets will be collected for verification to expand the applicability of the model.


Experimental nutrient additions are a fundamental approach to investigating plankton ecology. Possibilities range from whole-lake fertilization to flask assays encompassing a trade-off between closeness to the “real world” and feasibility and replication. Here we describe an enclosure type that minimizes the manipulation of planktonic communities during the enclosure filling. The enclosure  consists of a narrow translucent cylinder that can comprise the entire photic zone  and holds a sediment trap at the bottom for recovering the sinking material. The enclosures are inexpensive and straightforward to build. Thus, many can be used in an experiment, favoring the diversity of treatments and the number of replicates. They also are lightweight with easy transport and use in lakes that cannot be reached by road. The enclosures are fundamentally aimed at investigating the short-term response of the planktonic community, integrated across the photic zone, to pulse perturbations using before and after comparisons and multiple replication and treatments. The pros and cons of the enclosure design are evaluated based on experience gained in Lake Redon, a high mountain ultraoligotrophic deep lake in the Pyrenees. Planktonic ecosystems commonly show episodes of higher productivity embedded within prolonged periods of reduced activity. This pattern is particularly discernible in oligotrophic ecosystems where phytoplankton growth increases during periods of water column mixing when nutrients are loaded into the photic zone from the deep layers . Experim3), mesocosms (1–1000 m3) and macrocosms (>1000 m3). Microcosms provide more experimental control  and lower cost, and macrocosms are closer to reality, capture better some biological complexity , and allow recurrent sampling (i.e. time series). In any case, the ability to deal with high trophic levels and approximate hydrodynamics found in nature is always limited  of relatively small size (50–1000 L) during days to weeks (f divers —the systThe experience gained during the “Episodic Nutrient Enrichment Experiment (ENEX)” in Lake Redon, an ultraoligotrophic deep high-mountain lake in the Pyrenees . The priEcological field experiments are inevitably subject to trade-offs between what is desirable and what can be afforded. Whatever the size of the experiment, replication is a critical issue. The lower the cost, the higher the potential number of replicates and treatments and, thus, more robust results  and two polyvinyl chloride (PVC) tubes, attached one at each extreme of the bag . The PVCThe deployment of the enclosure consists of the following steps. (i) The enclosure is assembled: the polythene bag is folded around the upper tube, and this tube is held together with the sediment trap by a rope lacing using an escape knot  according to the Scor-Working-Group-85 , a few weeks were a reasonable approach . Shorter4+, NO2−, NO3−; dissolved organic carbon (DOC), particulate C (PC), particulate N (PN), particulate P (PP), chlorophyll a (Chla) and other pigments by liquid chromatography, protists abundance, and biovolume, and the prokaryote abundance estimated as 4′,6-diamidino-2-fenilindol stain (DAPI) counts. The same biogeochemical variables were measured in the sediment traps, and diatom valves and chrysophyte cysts were assessed. The critical point is to maintain top-to-bottom integrated water samples, as the heterogeneity within the enclosure may be vertically substantial. The method’s strength is comparing treatments rather than a time series of changes within the enclosure. The initial conditions must be assessed with a similar integrated sample of the lake water column at the beginning of the experiment. Comparing the initial and final conditions provides information about rates as the system is basically closed to dissolved and particulate matter exchanges , NHThe enclosure’s filling method is quite friendly for the organisms as they are not submitted to any particular stress , and the vertical heterogeneity in the photic zone is fully captured during the regular and slow-pace filling. There is an initial community representative of the photic zone in each enclosure. Enclosure and incubation systems that require pumping or weighted integration of samples in containers may introduce initial biases  compared with the self-filling system used here  growth. However, it was not the case in the ENEX experiment, and the changes in composition were also coherent between replicates .The enclosures are intended for before/after experiments. We may wonder if what is found at the end corresponds to the primary response to the treatment or, perhaps, the experiment has been too long, and we are sampling a decaying phase of the community response. The comparison between the material accumulated in the sediment traps and the material remaining in the water column is highly informative. In the ENEX experiment, the amount of particulate organic matter in the water column of the enclosures was two orders of magnitude higher than in the sediment traps, thus indicating that most of the initial response had not been sedimented.Chrysophytes are the dominant group in the phytoplankton of Lake Redon  to studyFragilaria nanana, a small and thin life form, in higher P treatments of the ENEX experiment. In the initial community, another planktonic diatom, Cyclotella pseudostilligera, was relatively abundant; however, this species apparently did not proliferate within the enclosures in any treatment according to the specimens accounted for in the water column of the enclosures. However, we found that the patterns of the two species in the sediment traps were quite similar  is too low . In the The light environment is also greatly modified by the enclosure walls. The relative irradiance gradient across the photic zone may be maintained, but the light quality is markedly modified because of the absorption properties of the bag material. In the ENEX experiment, the polythene walls drastically reduced the high UV radiation levels existing in high mountain lakes . Indeed,Pseudokephyrion inflatum, and the ciliate, Askenasia acrostomia. Most species grew in many treatments , <50 individuals in 62% of the enclosures, <100 in 90% and <300 individuals in all of them. The differences were mainly due to Cyclops, in which juvenile and adults are mainly predators of Daphnia in this lake  . Large zhis lake . In addi3) and probably longer experiments  in the inner part of the treatments with the highest P-addition. These algae are common in the littoral epilithon of the lake. The growth and influence of bacterial biofilms are more difficult to be evaluated by direct inspection unless they are huge.One of the particular features of the enclosures described is the high aspect ratio . This feature introduces the risk of becoming an experiment on benthic colonization if the duration is too long or some treatments stimulate rapid wall colonization. Adjusting the experiment to short periods minimizes the undesired effects of periphyton growth on the enclosure surfaces  can be used for the upper tube to minimize light reduction. The diameter can also be wider: 20–30 cm appears manageable for transport and deployment from small boats. Polythene bag rolls are still commercially available with these diameters. The upper tube should accommodate the new dimensions, but the sediment trap can be kept as currently designed, just placing an appropriate funnel connecting the bag and trap. Changing the bag material can also be helpful for experiments on light/dark expositions or investigating irradiance effects. It can also be considered to sample the internal gradient that develops within the long bags rather than integrate it. Another option could be modifying the bottom tub for benthic-pelagic coupling experiments (The enclosure type described is an addition to the experimental toolbox for plankton investigation. The enclosures are particularly suitable for studying the immediate response of the planktonic community to pulse perturbations of nutrients, or other dissolved substances, using before and after comparisons. The design offers the possibility of obtaining an integrated assessment across the entire or most of the photic zone, facilitating multiple replicates and treatments. Although the enclosures were initially thought for experiments in remote oligotrophic systems, the design can be easily tailored and expanded for applications to a large variety of water masses, maintaining its essential differential features: low perturbation of the planktonic organisms during filling and possibilities for high replication.enclosures_fbac074Click here for additional data file.


Wearable devices are often used to diagnose arrhythmia, but the electrocardiogram (ECG) monitoring process generates a large amount of data, which will affect the detection speed and accuracy. In order to solve this problem, many studies have applied deep compressed sensing (DCS) technology to ECG monitoring, which can under-sampling and reconstruct ECG signals, greatly optimizing the diagnosis process, but the reconstruction process is complex and expensive. In this paper, we propose an improved classification scheme for deep compressed sensing models. The framework is comprised of four modules: pre-processing; compression; and classification. Firstly, the normalized ECG signals are compressed adaptively in the three convolutional layers, and then the compressed data is directly put into the classification network to obtain the results of four kinds of ECG signals. We conducted our experiments on the MIT-BIH Arrhythmia Database and Ali Cloud Tianchi ECG signal Database to validate the robustness of our model, adopting Accuracy, Precision, Sensitivity and F1-score as the evaluation metrics. When the compression ratio (CR) is 0.2, our model has 98.16% accuracy, 98.28% average accuracy, 98.09% Sensitivity and 98.06% F1-score, all of which are better than other models. The World Health Organization’s 2022 report, Invisible numbers: The true extent of noncommunicable diseases and what to do about them, shows that deaths from non-communicable diseases account for three quarters of all deaths globally. Cardiovascular diseases account for 44% of all noncommunicable disease deaths and are the deadliest disease worldwide, accounting for one-third of all deaths. And 86% of cardiovascular diseases can be prevented and delayed by prevention, treatment and elimination of risk factors .Intelligent health monitoring technology brought by the development of artificial intelligence can detect serious abnormalities without direct contact with patients. The technology can simultaneously monitor heart rate (HR), blood oxygen saturation SpO2) and body temperature, and give warnings when abnormal  and body.The ECG As a kind of long time frequency signal, the ECG signal generated in the acquisition process is often accompanied by noise artifacts, which will affect the accuracy of detection to some extent. In order to eliminate these effects, most studies on ECG signal detection classification will perform waveform transformation on ECG signals during preprocessing, such as fast Fourier transform (FFT), short-time Fourier transform (STFT), continuous wavelet transform (CWT), etc. –12. In aAfter a simple pre-processing and cutting of the ECG signal, the classification process can begin. Due to the specificity of wearable devices, it is required that the model for classification occupies as little memory as possible. Traditional classifiers such as Support vector machine (SVM) and k-nearest neighbor (KNN) have been relatively saturated with classification results –28. WithThe organization of the research work is as follows: Section 2 presents a review related to ECG signal classification with the main contributions of this paper. Section 3 presents the material and the proposed methodology. Section 4 shows the results and analysis of the study. The research is summarized in the conclusion.The Compressed sensing (CS) model proposed by David L. Donoho in 2007 has greatly improved the signal processing technique by breaking the original Nyquist-Shannon sampling law and eliminating the need to guarantee that the sampling frequency must be greater than twice the maximum frequency . Once thIn wearable devices, the compression and classification of ECG signals is processed in two main modes.The first one is compressed on the wearable device, then the compressed data is transmitted to the remote end for reconstruction and classification, and finally the detection results are transmitted to the device user. Hua et al. used a binary measurement matrix for compression, which can greatly reduce the energy consumption of sensor nodes, the BSBL algorithm with better recovery performance was used in the reconstruction process, and Deep Neural Networks (DNN) was used in the classification process, which had 94% accuracy when the data was less compressed. However, the classification accuracy is not ideal at high compression ratios and only classifies the ECG signals into abnormal and normal categories . Zheng eThe second method is to classify compressed data directly on the wearable device. This processing method can significantly reduce the amount of data to be processed, reducing energy consumption and extending battery life while also speeding up recognition. Braun et al. skipped the costly reconstruction process in compression perception and compressed the MNIST database using a random orthogonal observation matrix and classified the compressed data. The error rate of the measured data at a compression rate of 0.4 differs from the error rate of the uncompressed data by only 0.22%, demonstrating the feasibility of compressed domain classification . Hua et In addition to the above two common models, Yildirim et al. proposed a nonlinear compression structure based on a self-encoder (CAE), which can perform compression as well as decompression operations on ECG signals, and designed a Long short-term memory (LSTM) model to identify the compressed ECG signals, which can not only increase the classification efficiency, but also recover the compressed data when necessary .In the existing literature, the compression and classification of ECG signals are usually carried out in two steps.In the compression stage, the method based on compressed sensing is to use measurement matrix to compress signals. However, for long-term ECG signals, the design of measurement matrix is very difficult. It is difficult to find an appropriate measurement matrix to compress a series of ECG signals, and the compression time is long, which is not suitable for wearable devices requiring rapid recognition. Many studies have proposed using SVD, PCA, LEE and other methods to compress signals. These compression methods have good applicability, but SVD cannot accurately control the compression ratio of ECG signals, and PCA and LEE cannot retain the features of original signals well under the extremely low compression ratio.In the classification stage, although simple classifiers such as SVM, KNN and MLP can quickly recognize ECG signals, the accuracy of classification results is low, and they are not suitable for wearable devices that need to accurately identify heart rate abnormalities. With the development of deep learning, many classification networks based on CNN and LSTM have shown good results in the recognition of ECG signals. However, due to the small volume of compressed ECG signals, many classification networks of larger magnitude will consume a lot of time in the work, which is not good for rapid recognition.To sum up, a lightweight network capable of fast compression and accurate recognition should be equipped with the actual application environment of heart rate monitoring combined with wearable devices.The purpose of this study is to develop a model for compressing and classifying ECG signals in wearable devices with the smallest possible storage space and high classification accuracy. The main studies are distributed in the processes of ECG signal pre-processing, heartbeat segment localization cutting, compression and classification. Our contributions are listed below:We propose a convolutional neural network-based compression method that replaces the traditional compression method and quickly and efficiently compresses the ECG signal while preserving its signal characteristics and greatly reducing the time required for classification.We propose a lightweight classification network structure based on deep learning, which has faster training speed and can effectively classify the compressed ECG signals.We conducted experiments on two different ECG signal databases to demonstrate the robustness of our model. The experimental results show that the compression method and classification method proposed in this paper have higher classification accuracy than those of the same type.This article does not include any studies involving humans or animals.We propose a classification framework for compressed ECG signals, implementing an end-to-end ECG signal classification task that requires the input of the original ECG signal to derive its corresponding classification result, and the whole framework is shown in We chose the MIT-BIH arrhythmia database provided by the Massachusetts Institute of Technology . The datIn order to retain the original features of the ECG signal as much as possible and to enhance the generalization ability of the classification model, we did not filter the signal.To ensure better processing of the data by the classification model, the ECG signal is segmented by heartbeat beats before the data is fed into the model. A complete heartbeat beat is defined as the beginning of a P-wave to the end of a U-wave. As shown in All 48 ECG signals in the MIT-BIH arrhythmia database were segmented, resulting in a total of 109,451 heartbeat beats. The classification is based on the five ECG signals recommended by the AAMI standard, which are divided into five categories: Normal beat (N), Premature or ectopic supraventricular beat (S), Premature ventricular contraction (V), Fusion of ventricular and normal beat (F), and Unclassifiable beat (Q). The number of each of these categories is shown in Compressing ECG data can eliminate a large amount of redundant data and retain only the critical data that can be used for diagnosis. We define the compression ratio (CR) as:We used a neural network instead of the traditional compression method, and the compression process To improve the accuracy of recognition of compressed data, we propose a classification network model for compressed ECG signals, as shown in ijW, the value of each position of the corresponding convolution region is ijx, the output value of each convolution region after convolution is y, and b is the offset. The one-dimensional convolution is calculated as follows:1D-CNN is mainly used for feature extraction of time-series data. The input shape of the network is , where n means there are n consecutive time-series data and m is the feature of each time-series data, which is taken as 1. The convolution kernel of 1D convolution needs to specify only one dimension, and the depth of the convolution kernel is automatically consistent according to the input layer depth. The essence of convolution is the multiplication and addition operation. Assume that the weight of each position of the convolution kernel is The Concatenate layer allows the fusion of multiple features, mainly for the merging of the number of channels, increasing the features described by the data without increasing the information under each feature. The Concatenate layer is able to fuse multiple convolutional features for ECG data with high compression ratios, increasing the number of features for different categories of ECG signals as much as possible, which facilitates the subsequent classification process.We performed network training and testing on Keras, a deep learning framework backed by Tensorflow, on a computer with an Intel(R) Xeon(R) Silver 4112 CPU @ 2.60GHz and an NVIDIA Quadro RTX5000 graphics card.There are two major limitations in this study that could be addressed in future research. The first is the limitation of samples. In most ECG signal data sets, the number of abnormal ECG data is usually small. If these data are trained directly, the classifier will not be able to effectively identify abnormal ECG data. Although this study balanced the data set and ensured the training of the model, the ECG signal data of class F was still small, which affected the classification results to a certain extent. The method of balancing data sets will be further studied in the future. The second is the limitation of the method. The compressed domain classification method can improve the speed of ECG signal recognition. However, under normal circumstances, it is not guaranteed that CR is proportional to the classification accuracy. Only when the difference of CR is large, for example, CR = 0.5 is compared with 0.3, the accuracy rate of CR = 0.5 is higher. Moreover, due to the limitation of the length of ECG signal, the ECG signal compressed below CR = 0.05 cannot be effectively recognized.The number of training rounds was 50 per round, and the training and test sets were randomly divided 0.8:0.2 using the train_test_split in Keras, with the random_state value set to 0, and the batch size is 64, the weights are updated using the Adam optimization function, and the initial learning rate is set to 0.001. In order to speed up the training and improve the model performance, an adaptive mode in which the learning rate is automatically modified with the training process is set in the network. icy is a symbolic function that takes 1 if the true category of sample i is equal to c and 0 otherwise. icp denotes the predicted probability that the observed sample i belongs to category c. We have chosen the cross-entropy loss function as our loss function, which is suitable for multiclassification work, with the following equation:In order to accurately evaluate the performance of the model, the evaluation criteria chosen in this paper are accuracy, precision, sensitivity and f1-score. Each parameter is defined as follows:We discard the traditional compression method and use a combination of three convolutional layers with a maximum pooling layer to compress the signal. In order to verify the effectiveness and advancement of the proposed compression method, we compared it with several commonly used compression algorithms while keeping the classification model unchanged. To verify that our proposed network can effectively classify compressed ECG signals, we compared several lightweight networks that are also used for ECG signal classification. To verify the robustness of the classification model, we also chose the database provided by AliCloud Tianchi for heartbeat signal classification prediction, which provides 100,000 heartbeat data, each with a length of 205 data volume, divided into a total of four categories, corresponding to the four labels 0, 1, 2, and 3 . We randFirstly, different compression methods are compared, and the classification method proposed in this paper is chosen to keep the same, and the experimental results obtained by classification under different compression ratios are shown in Then the compression method proposed in this paper is chosen to keep the same, and the different classification methods are compared. To verify the validity of the proposed classification model, two ablation experiments were performed on the MIT-BIH ECG signal database. The first set was to delete the parallel convolution structure and directly use three convolution blocks for classification. The second group is to add LSTM or BILSTM layer after three convolution blocks. not only reduces the storage space but also increases the classification speed and ensures the classification accuracy. The experimental results show that both the compression and classification methods included in our proposed model outperform other traditional methods and are well suited to the task of classification of compressed signals.We propose a classification model for identifying compressed ECG signals. In order to save the energy consumption of wearable devices, we compress the lengthy ECG signals before classification, which


Computational modeling has emerged as a critical tool in investigating the complex molecular processes involved in biological systems and diseases. In this study, we apply Boolean modeling to uncover the molecular mechanisms underlying Parkinson’s disease (PD), one of the most prevalent neurodegenerative disorders. Our approach is based on the PD-map, a comprehensive molecular interaction diagram that captures the key mechanisms involved in the initiation and progression of PD. Using Boolean modeling, we aim to gain a deeper understanding of the disease dynamics, identify potential drug targets, and simulate the response to treatments. Our analysis demonstrates the effectiveness of this approach in uncovering the intricacies of PD. Our results confirm existing knowledge about the disease and provide valuable insights into the underlying mechanisms, ultimately suggesting potential targets for therapeutic intervention. Moreover, our approach allows us to parametrize the models based on omics data for further disease stratification. Our study highlights the value of computational modeling in advancing our understanding of complex biological systems and diseases, emphasizing the importance of continued research in this field. Furthermore, our findings have potential implications for the development of novel therapies for PD, which is a pressing public health concern. Overall, this study represents a significant step forward in the application of computational modeling to the investigation of neurodegenerative diseases, and underscores the power of interdisciplinary approaches in tackling challenging biomedical problems. The interpretation of omics data is crucial to develop meaningful hypotheses to understand complex disease mechanisms . To inteBy utilizing logical models, disease maps can be encoded in a computable form, and the dynamics of diseases can be analyzed in a more straightforward manner. These logical models can be further integrated with experimental data to create more comprehensive and accurate representations of disease dynamics. This approach has the potential to facilitate the discovery of novel drug targets and the development of personalized medicine.Therefore, further research is needed to better understand the molecular mechanisms underlying diseases and advance personalized medicine. By leveraging community-driven initiatives such as disease maps and computational modeling approaches, researchers can gain a deeper understanding of complex diseases, and ultimately, develop more effective treatments.Logical modeling in systems biology is a mathematical approach that uses Boolean algebra to represent the interactions between components in a biological system . LogicalBoolean models are the most commonly used type of logical modeling in systems biology . BooleanIn this study, we utilized Boolean modeling to represent the complex molecular mechanisms underlying Parkinson’s disease (PD). By using the Parkinson’s disease map The PD map is translated into BMs in an automated fashion using CaSQ tool . The comThe diagrams analyzed in this paper were obtained from the MINERVA Platform  see the. This plRule 1: In a reaction, where a receptor and a ligand form complex, the receptor reactant is removed.Rule 2: In a reaction, where two proteins form a complex, the reactants are removed and modifiers are linked directly to the product.Rule 3: Inactive forms of a biomolecule in a single reaction are removed (do not participate in other interactions).Rule 4: In a single transport reaction a reactant is removed and the reactions are linked to the product (in case product and reactant are the same).The translation process involves converting the Process Description notation into the Activity Flow notation see . BiomoleThe structural and functional correctness of BM was evaluated by analyzing the interactions between the biomolecules. To this end the topological features of the BM were analyzed as a network. To do so, Simple Interaction Format (SIF) was created as a simple way to represent pairwise interactions among biomolecules in a network graph. To transform an SBML model into SIF format, the model is first converted into a graph representation with nodes representing biomolecules and edges representing interactions between biomolecules. Relevant information such as the source and target nodes of each edge is then extracted from the graph representation and mapped to the SIF format. The resulting SIF representation is a list of pairwise interactions between nodes, which can be useful for evaluating the structural correctness of biological models. Established tools Betweenness centrality measures the importance of a node as an intermediary between other nodes, by quantifying how frequently a node lies on the shortest path between pairs of other nodes in the network. Stress centrality measures the importance of a node in connecting other nodes by quantifying the number of shortest paths that pass through the node. .Betweenness centrality:V is the set of nodes in the network, σ is the total number of shortest paths from node s to node t, and σ is the number of those shortest paths that pass through node v.where Stress centrality:V is the set of nodes in the network, and gst(v) is the number of shortest paths from node s to node t that pass through node v. Molecular targets for perturbation were prioritised based on their centrality and sensitivity measures. First, we computed betweenness centrality and stress centrality scores for all nodes in the network and identified the top-ranked nodes with high centrality values. Next, we simulated perturbations (Knockouts and overexpressions) to the network and measured the similarity and identity based distances between the original and perturbed states to assess the sensitivity of each node to the perturbations. By combining the centrality and sensitivity scores, we prioritized the molecular targets for perturbation that were highly central and sensitive.where The updating schemes of Boolean functions (BFs) were evaluated and compared, including synchronous updating, asynchronous updating  was also be used to update BFs in a probabilistic manner by assigning probabilities to BFs and updating biomolecules based on these probabilities . A PBM cXt is the state of the network at time t, xt is a vector of binary values representing the state of each node in the network at time t, pai is the set of parent nodes of node i in the network, and θi is a vector of probabilities representing the conditional probability distribution of node i given its parent nodes. The equation states that the probability of the network being in state xt at time t, given its previous states and the network structure, is the product of the conditional probabilities of each node in the network.where Synchronous updating scheme was simulated using BoolNet tool in which all the biomolecules in the network updated simultaneously, based on the states of their regulators . AsynchrThe behavior of a BM under different update schemes was visualized through state transition graphs see . The staAttractors are states in a state transition graph that have no outgoing edges and can be stable or complex. The basin of attraction is the set of states that lead to an attractor and can provide insight into potential biological scenarios . During The SAT-based technique can be formulated mathematically as follows:x1, x2, …, xn be the Boolean variables that represent the state of the network, where xi is either 0 or 1. Let f1, f2, …, fm be the Boolean functions that represent the transitions between states, where each function is a logical constraint that depends on the values of the Boolean variables. The goal of the SAT-based technique is to find a set of values for the Boolean variables that satisfy the logical constraints and correspond to an attractor in the network. This can be represented as a SAT problem as follows:Let x1, x2, …, xn such that Find The SAT problem can be efficiently solved using SAT solvers to determine the attractors in the network.f(x) is the original problem, n is the number of subproblems, and gi(xi) is the solution to each subproblem. The subproblems are usually defined so that they can be solved independently and then combined to obtain a solution to the original problem. The decomposition method is an approximation of the Boolean system and may not work well in all cases, depending on the network simulated.Another approach, the decomposition method, aimed to optimize speed and reduce complexity by breaking down the model into strongly connected components (SCCs). The method can be represented mathematically as:In addition, a heuristic search and an asynchronous search were conducted. The heuristic search involved selecting a subset of possible states as initial conditions and performing synchronous transitions until an attractor was reached. The asynchronous search used random asynchronous transitions to identify steady states and complex attractors from the chosen initial conditions. To evaluate cyclic attractors, we performed multiple simulation runs starting from different random initial states until all cyclic attractors were identified. We identified all the attractors present in the system during each simulation run and considered them as potential steady states or complex attractors that the system could reach under certain initial conditions. By performing multiple simulation runs and identifying all the attractors present in the system, we were able to gain a more comprehensive understanding of the system’s behavior and the potential biological scenarios it could represent.A perturbation analysis was conducted to evaluate the effect on the topological robustness, dynamic resilience, and attractors reached by the models . SpecialAunpert) and the perturbed attractor (Apert) can be defined as:The evaluation was performed by performing sensitivity analysis on a selected set of models, examining each biomolecule. Sensitivity analysis is a technique that assesses how changes in a model or system’s inputs affect its output, in this case, the two attractors reached by the model (unperturbed and perturbed). To quantify the difference between the two attractors, similarity-based distance and identity-based distance were used. Similarity-based distance assessed the similarity between the two attractors by taking into account the common and unique states in both. The similarity-based distance between the unperturbed attractor . The interactions between the variables are defined by Boolean rules, which indicate the impact of one variable’s state on another. The Monte Carlo algorithm randomly samples the possible states of the system at each time step, based on the current state of the system, the probabilities of each state, and the interactions between the variables. By simulating the system over multiple time steps, pyMaBoSS estimates the probability of each state at each time point .We investigated the dynamic behavior of molecular mechanisms in Parkinson’s disease (PD) and their response to changes. To achieve this goal, a variety of BM formats - including SBML-qual - were created and analyzed using a range of tools. The validity of the BMs was evaluated by examining their structural and dynamic properties. The results of this assessment demonstrated that the models displayed consistent structural and dynamic properties, suggesting their use in study of the dynamics of PD.To construct the Boolean models of PD mechanisms, we first selected a group of PD map diagrams for downstream modeling and verification. We carefully chose diagrams that were directly relevant to PD-related phenotypes, as outlined in To generate the SBML-qual models necessary for our modeling work, we used CaSQ. This software allowed us to convert the CellDesigner SBML diagrams into a SBML-qual model that facilitated the interoperability between modeling tools. With these Boolean models, we analysed the PD mechanisms to explore the underlying biology of the disease and gain insights into potential therapeutic targets.To verify the accuracy and reliability of BMs, both structural and dynamic aspects of the model must be examined. First, we evaluated the interactions and relationships between the model’s components, to examine how they reflect the underlying biological system. For dynamic verification, we analysed the model’s behavior over time and compared it with the known biological behaviour represented in the literature.We aimed to identify molecules that can serve as potential targets and intervention points in pathways. To achieve this, we conducted a structural and sensitivity analysis. We selected the molecules with the highest betweenness centralities of each respective model (top quantile). These molecules were proposed to play a role in maintaining the stability of the system as they are located at the intersection of multiple paths. The table below  shows thThe second step of our analysis aimed to evaluate the sensitivity of the identified high central molecules to perturbations, specifically gene knockouts. We investigated whether the levels of the molecules are reliable measures to estimate the effect of perturbation and to identify potential targets for intervention.We found that most of the high central molecules showed less sensitivity against knockouts, indicating their robustness in maintaining the system’s stability. This observation suggests that targeting these molecules may not have a significant impact on the overall system stability. However, this does not necessarily mean that they are not potential targets for intervention, as their perturbation may have downstream effects that affect other pathways or processes see .To further investigate the reliability of the identified molecules as potential targets for intervention, we identified the common molecules that have significant centralities and/or sensitivities and also have reported biological importance see , 4. ThesIn the Wnt Pi3kAKT pathway, we identified molecules such as ROCK2, EIF4E, IGFR1, IGF1, and INS that can compensate for the absence of PDPK1, RPS6KB1 phosphorylated . The preIn this study, we aimed to identify stable states, known as attractors, within the analyzed pathways using Boolean modeling. To ensure the biological relevance of the identified attractors, a thorough literature search was conducted. The search aimed to identify experimental evidence for the molecular states and interactions observed in the identified attractors. The findings of this literature search were summarized in Our results indicated that the attractors produced by decomposition-based approaches (in methods) were not biologically relevant for all pathways see ; Table 3However, the attractors produced by Heuristic and SAT solver algorithms were found to be viable for all pathways except for the Wnt/PI3K pathway see . TherefoWe validated the model outputs by comparing them to the literature. We first selected targets for the pathways and perturbed them according to scenarios suggested by literature. We then simulated the changes in the corresponding outputs. This was achieved through: i) running a simulation with modeling tools, including probablistic Boolean modelling tools and the CellCollective platform The TCA cycle, a metabolic pathway occurring in the mitochondria of cells, is disrupted in Parkinson’s disease (PD), potentially contributing to its development . This paThe validity and precision of the TCA cycle BM are established through a comparison with literature and fluxomics data. To run the simulation, the knockouts of isocitrate dehydrogenase, alpha-ketoglutarate dehydrogenase, and the pyruvate dehydrogenase complex are selected as the initial state parameters based on prior research . Using tAs shown in the fluxomics data, the validated results based on literature indicate that the simulation of increased activation of acetyl CoA, NADH, and PDKs leads to an increase in phosphorylation reaction, subsequently reducing the activity of the pyruvate dehydrogenase complex (PDC). This finding supports the hypothesis that PDC deficiency, a potential target for therapeutic intervention in age-related diseases, arises from the activation of the phosphorylation reaction involving these substances . The defThe cellular metabolites undergo fluctuations during the cell cycle, adapting to changes within the cell. The impact of alpha KGDH, GTP, and GDP on ATP levels are modeled in the simulation. The simulated ATP activity levels match the measured concentrations in synchronized HeLa cells every 3 h following release from growth arrestOne of the pathways validated in this study was the dopamine transcription pathway, known to be disrupted in Parkinson’s disease (PD) .NR4A2 were selected as the key elements in this validation, as they are proteins that play crucial roles in the development and maintenance of neurotransmitters and various cellular processes .• The production of brain-derived neurotrophic factor (BDNF), crucial for the survival and growth of neurons, was impacted by the NR4A2 knockout. This result aligns with previous research showing that NR4A2 is involved in the stimulation of BDNF production in response to the neurotransmitter N-methyl-D-aspartate (NMDA) .• The NR4A2 knockout was also observed to affect other molecules such as GCH1, TH, DDC, SLC18A2, SLC6A3, and DRD2, which play significant roles in the development and maintenance of neurotransmitters through various targets, as previously reported in In the simulation, the effects of perturbations to NR4A2 and SIRT1 on dopamine release and mitochondrial biosynthesis were observed as activity levels using CellCollective. These results, confirmed through a comprehensive attractor analysis (available in the Gitlab repository), demonstrate the following behavior in line with literature :• The prThe Wnt-PI3K/AKT pathway was evaluated in this study as it has been reported to be impacted by mutations in Parkinson’s disease (PD) . The key• The simulation results showed that overexpression of the LRRK2 G2019S mutant increased autophagy .• This increase was found to be mediated by the inhibition of mTORC1/2, which are proteins that regulate autophagy . These f• Activating both 4EBP2 and TFEB was found to increase autophagy activity more than activating them separately . Specifi• In addition, the combination of inhibiting the proteins RPS6KB1 and PHLPP and activating TFEB significantly decreased neuronal death and the active state of autophagy . This co• Both activating the Wnt protein and inhibiting the protein GSK3B were found to increase autophagy . The comThe simulation scenario involved perturbing these biomolecules and observing the effects on autophagy and neuron survival as activity levels using CellCollective. These results were confirmed through an exhaustive attractor search analysis. The results showed the following behaviors that are in line with the published literature , 5:• TheThe FOXO3 activity pathway was also evaluated in this study due to its dysregulation in Parkinson’s disease (PD) . The key• The simulation results showed that FOXO3 activation increased autophagy in mitochondria .• FOXO3 activation also activated different biomolecules involved in RNA-mediated mechanisms, including BECN1, GABARAPL1, MAP1LC3A, BNIP3, ATG12, and MUL1, which are known to be important regulators of autophagy .The simulation scenario involved perturbing FOXO3 and observing the effects on autophagy and RNA-mediated biomolecules as activity levels using CellCollective. These results were confirmed through an exhaustive attractor search analysis. The results showed the following behaviors that are in line with published literature :• The siIn order to validate the relevance of biomolecules for studying PD mechanisms, four pathways were analyzed, including one metabolic and three signaling pathways. The simulated behavior of these pathways was compared to the expected behavior based on published literature . While tSeveral factors could contribute to these discrepancies, such as differences in experimental conditions or protocols, the use of different LRRK2 models or cellular systems, the complexity of the autophagy pathway, and limited understanding of the exact mechanisms by which LRRK2 regulates the autophagy pathway. To address these discrepancies, corrective measures were taken, including modifying the Boolean function to better represent the interactions between LRRK2, ARFGAP1, and autophagy . These rIn this study, Boolean modeling was used to examine the complexity of PD by simulating the dynamic interactions between various biomolecules. These models were used to test hypotheses about the role of certain biomolecules in PD progression, by simulating the effects of these biomolecules and comparing the results to experimental or observational data. Additionally, the models were used to investigate the impact of multiple perturbations on the disease and to identify patterns that may not be evident from experimental or observational data alone.The Boolean models were automatically constructed using the CaSQ tool . One of The transformation of the diagrams into Simple Interaction Format (SIF) using the CaSQ tool was also beneficial, as it allowed for the use of various tools to analyze the diagrams. By using different tools to analyze the diagrams, researchers can gain a more comprehensive view of the disease, leading to an improved understanding of its underlying mechanisms.The reduction of Process Description notation into Activity Flow notation and inference of logical functions from interactions, in combination with the use of SBML-qual format, resulted in the creation of models that are both accurate and computationally efficient. This makes the generated models suitable for studying large and complex systems, and allows for a deeper understanding of the system and more accurate predictions about its behavior. Therefore, the constructed Boolean models have the potential to be expanded to study larger and more complex systems.It’s worth mentioning that using specific knowledge resources, such as PD map, has the advantage of providing a high quality of disease-specific knowledge. This is because the diagrams are focused on a specific disease and are created and reviewed by experts in the field. This expertise and specificity make the generated models more reliable.The construction of models in compliance with systems biology standards enhances their compatibility with various tools and programs, leading to improved reproducibility and easier pipeline development. By adhering to these standards, researchers can ensure that their models are easily comprehended and integrated with other models and data sources in a consistent and dependable manner. Inadequate modeling details can lead to erroneous predictions. Thus, it is crucial to prioritize model quality during construction to minimize false positives during simulation. Verification is a critical aspect of the modeling process, as it confirms the reliability of the model and its ability to make accurate predictions and inform decision-making.This study evaluated the BMs by demonstrating their ability to replicate experimentally validated studies from literature. The results, as shown in The TCA cycle model from the PD map was used to validate the Boolean modeling approach. The TCA cycle is a widely studied pathway with ample experimental data on enzyme activity and metabolite levels, making it possible to compare BM predictions with experimental data and assess the accuracy and reliability of the model. Additionally, dysregulation of the TCA cycle is linked to oxidative stress, inflammation, and cell death, which are all hallmarks of Parkinson’s disease .in silico increased the phosphorylation reaction, reducing the activity of the pyruvate dehydrogenase complex (PDC), which led to decreased levels of succinic semialdehyde and succinic acid , and ATP levels, directly impacting mitochondrial function .• The simulation of the effect of alpha KGDH and GTP, GDP on ATP levels was validated using a probabilistic BM based on temporal-fluxomics data, which describes the oscillations of metabolites in the TCA. The simulated activity levels matched the measured concentrations in synchronized HeLa cells at various time points post-release from growth arrest .The structural and functional roles of key molecules in the TCA cycle BM were analyzed. This involved examining the involved enzymes and cofactors, as well as the reactions they catalyze and the intermediates they produce. The regulatory mechanisms controlling the activity of these molecules were also considered to predict their impact on the TCA cycle . The effThe TCA cycle BM used in this study was capable of reproducing molecular activity that affects ATP levels and mitochondrial function. These results indicate that the TCA BM is a trustworthy tool for depicting the general picture of energy metabolism and can shed light on the mechanisms behind the oscillations of cellular metabolites. Nevertheless, this evaluation has some limitations, such as the possibility of missing relevant data in the literature search or not accurately reflecting all complex interactions within cells. Additionally, the context of evaluating the TCA cycle may differ based on the organism or tissue being studied. Despite these limitations, the primary goal of validating the TCA cycle BM is to ensure its accuracy in reflecting the underlying biological processes.in silico methods, the knockout of the NR4A2 gene correctly predicts the decrease in BDNF production :The FOXO3 activity pathway has been identified as a critical mediator of mitochondrial homeostasis . The BM Translational medicine aims to bridge the gap between basic research and clinical practice by applying scientific findings to medical practice. One way to achieve this goal is through the use of modeling and simulation techniques. These techniques use existing knowledge from bench experiments and disease-relevant omics datasets to develop new hypotheses about the disease and propose improved therapies and diagnostics. The Boolean modeling approach, which is based on systems biology and systems medicine, is particularly useful because it allows for iterative improvements in understanding through a continuous cycle of data-driven modeling and model-driven experimentation. The Boolean modeling approach has the potential to generate therapy-related hypotheses by designing perturbation experiments to compare model attractors to the disease signature. This allows the identification of the basins of attraction that could alleviate the pathological signature of the disease and suggest the best combinations of targets to achieve a healthy state.This study offers valuable insights into the potential use of a standardized SBML-qual format for Parkinson’s disease pathway models. Through the use of the CaSQ translation tool, the authors were able to generate models that can inform future research efforts. Moreover, the study highlights the importance of accurate and relevant data input, appropriate assumptions and algorithms, and further integration with omics data for improving the accuracy and relevance of modeling and simulation approaches in understanding disease mechanisms and proposing targeted therapies. We also emphasize the need for disease-specific datasets to accurately parametrize models and better understand individual dysregulations in different disease subtypes. This personalized approach has the potential to lead to the development of more effective treatments tailored to the unique characteristics of each disease subtype.While the study acknowledges certain limitations, such as the high-level representation of observable disease effects and variations in disease mechanism dynamics between patients, it highlights the potential for continued improvement in modeling and simulation techniques to advance translational medicine and improve disease diagnosis and therapy. By addressing these limitations and incorporating more precise and individualized data, modeling and simulation approaches can provide a powerful tool for advancing our understanding of disease progression and the development of targeted therapeutic approaches.Boolean modeling presents a promising opportunity to understand the complexity of the disease and propose potential therapeutic strategies. By modeling the dynamic interactions between various biomolecules, researchers can gain insight into the underlying mechanisms of the disease, test hypotheses about the role of particular biomolecules, explore the impact of multiple perturbations, and identify patterns that may not be apparent from experimental or observational data alone. However, to leverage the full potential of Boolean models, researchers must address several challenges, such as improving the accuracy and predictive power of these models. To address these challenges, researchers can apply model refinement, data integration, and parameter optimization techniques. Additionally, model stratification is a promising future direction for the field, which has the potential to increase the accuracy of the models and allow for more precise treatments.To enhance the accuracy of Boolean models, it is crucial to improve their interoperability, annotations, and reproducibility. Standardized annotations provide more information about the model, making it easier to understand and use. Improved interoperability can help integrate different models and datasets, leading to more accurate predictions. Reproducibility can validate the results of the model and increase confidence in its predictive power.By addressing these challenges and enhancing these parameters, we can improve the accuracy and reliability of Boolean models, ultimately leading to better predictions and insights into the underlying mechanisms of complex diseases such as Parkinson’s disease. As such, Boolean modeling is a promising tool for future research in this field, and its continued development will enable the discovery of novel therapeutic strategies and advance our understanding of complex diseases.


Ureterocele is a rare congenital anomaly often believed to be caused due to incomplete dissolution of the Chwalla membrane. In this pathology, the distal end of the ureter is invaginated in the bladder and is dilated. We present a case of an 81-year-old male from rural India who came with complaints of hematuria and was diagnosed with ureterocele. Ureterocele is considered a congenital anomaly characterized by abnormal intravesical dilatation of the distal end of the ureter . It can An 81-year-old male came to the urology outpatient department complaining of right-sided flank pain radiating to his back for ten months, which increased in severity so that the patient could not sleep at night. He also complained of on-and-off fever, burning micturition, hematuria, incomplete voiding after micturition, and incomplete clearance of the urinary bladder after micturating. There was no history of any systemic illness.The patient has been advised ultrasonography of the abdomen and pelvis, which revealed a small calculus in the upper pole of the right kidney with dilatation of the pelvicalyceal system and visualized proximal ureter, multiple calcifications in the distal ureter and the urinary bladder. The patient was further advised to undergo a computed tomography urography scan of the kidney-ureter-bladder, which revealed a non-obstructive calculus in the upper pole of the right kidney, intravesical ureterocele with multiple calculi within and in the distal ureter. There was associated upstream dilatation of the right ureter and pelvicalyceal system, as shown in Figure The patient underwent cystolitholapaxy the next day for calculi removal, and the intravesical ureterocele was reconstructed. The patient was discharged and advised to follow up with ultrasonography six months later and with micturating cystourethrography 12 months later to check for vesicoureteric reflux.Ureterocele is a congenital anomaly referring to abnormal dilatation of the distal end of the ureter. The pathogenesis of the development of ureterocele is poorly understood; however, many mechanisms have been proposed, with incomplete dissolution of the Chwalla membrane being the most common .These can be classified based on location as simple/orthotopic/intra-vesical when the location is at the bladder trigone or ectopic/extra-vesical when it is present in the posterior urethra or the neck of the bladder. Based on the collecting system, a ureterocele can be classified as a single-system ureterocele having a functioning kidney with a single ureter; or a duplex system having ureteric and renal duplication ,4.Ureterocele is managed surgically by transurethral incision of the ureterocele, where the ureterocele is removed via a urinary bladder approach . AnotherUnilateral cases of ureterocele account for approximately 80% and may prolapse into the bladder neck, obstructing the complete renal tract and bladder outlet obstruction . Due to In our case, the patient complained of incomplete voiding of the urinary bladder, which acted as a medium for the growth of multiple calculi within the ureterocele.Uric acid calculus is the most common calculus in adults due to urine retention. Calcium phosphate, calcium oxalate, cystine, ammonium urate, and calcium-ammonium-phosphate  are some other elements leading to calculus formation. Patients prone to urinary infections and chronic bacteriuria tend to develop calcium phosphate and struvite calculus .Treatment of the calculus includes medical or surgical treatment. Medical management facilitates in the passage of ureteric calculus and stone fragments from shock wave lithotripsy . MedicalNephrolithiasis or obstructive urolithiasis increases the risk for the development of chronic kidney disease, and hence dietary modification and fluid management should be encouraged among the population with the help of the government and through local programs . AnotherImaging modalities like ultrasound, anterograde pyelography, intravenous urography, computed tomography, and magnetic resonance imaging are used to make the diagnosis of the diseases of the urinary system and its anomalies .Ureterocele is a congenital anomaly and should be diagnosed antenatally. The mainstay of treatment is by surgical approach. The mother must take routine antenatal scans to check for any anomalies. Post-surgical complications are reported in cases of operated ureterocele; therefore, follow-up is very important in such patients.