Background: The purpose of our study is to develop a quite precise mathematical model which describes epidemics spread in a country with non-uniform population density. This model gives explanation of quite long duration of the peak of a respiratory infection such as the coronavirus disease 2019 (COVID-19).

Methods: The theory of kinetic equations and fractal analysis are used in our mathematical model. According to our model, COVID-19 spreading takes the form of several spatio-temporal waves developing almost independently and simultaneously in areas with different population density. The intensity of each wave is described by a power-law dependence. The parameters of the dependence are determined by real statistical data at the initial stage of the disease spread.

Results: The results of the model simulation were verified using statistical data for the Republic of Belarus. Based on the developed model, a forecast calculation was made at the end of May, 2020. It was shown that the epidemiological situation in the Republic of Belarus is well described by three waves, which spread respectively in large cities with the highest population density (the first wave), in medium-sized cities with a population of 50−200 thousands people (the second wave), in small towns and rural areas (the third wave). It was shown that a new wave inside a subpopulation with a lower density was born 20−25 days after the appearance of the previous wave. Comparison with actual data several months later showed that the accuracy of forecasting the total number of cases for a period of 3 months for total population in the proposed approach was approximately 3%.

Conclusions: The high accuracy mathematical model is proposed. It describes the development of a respiratory epidemic in a country non-uniform population density without quarantine. The model is useful for predicting the development of possible epidemics in the future. An accurate forecast allows to correctly allocating available resources to effectively withstand the epidemic.

Background: Due to the limited availability and high cost of the reverse transcription-polymerase chain reaction (RT- PCR) test, many studies have proposed machine learning techniques for detecting COVID-19 from medical imaging. The purpose of this study is to systematically review, assess and synthesize research articles that have used different machine learning techniques to detect and diagnose COVID-19 from chest X-ray and CT scan images.

Methods: A structured literature search was conducted in the relevant bibliographic databases to ensure that the survey solely centered on reproducible and high-quality research. We selected papers based on our inclusion criteria.

Results: In this survey, we reviewed 98 articles that fulfilled our inclusion criteria. We have surveyed a complete pipeline of chest imaging analysis techniques related to COVID-19, including data collection, pre-processing, feature extraction, classification, and visualization. We have considered CT scans and X-rays as both are widely used to describe the latest developments in medical imaging to detect COVID-19.

Conclusions: This survey provides researchers with valuable insights into different machine learning techniques and their performance in the detection and diagnosis of COVID-19 from chest imaging. At the end, the challenges and limitations in detecting COVID-19 using machine learning techniques and the future direction of research are discussed.

Background: Coronavirus disease (COVID-19) is a contagious infection caused by severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) and it has infected and killed millions of people across the globe.

Objective: In the absence or inadequate provision of therapeutic treatments of COVID-19 and the limited convenience of diagnostic techniques, there is a necessity for some alternate spontaneous screening systems that can easily be used by the physicians to rapidly recognize and isolate the infected patients to circumvent onward surge. A chest X-ray (CXR) image can effortlessly be used as a substitute modality to diagnose the COVID-19.

Method: In this study, we present an automatic COVID-19 diagnostic and severity prediction system (COVIDX) that uses deep feature maps of CXR images along with classical machine learning algorithms to identify COVID-19 and forecast its severity. The proposed system uses a three-phase classification approach (healthy vs unhealthy, COVID-19 vs pneumonia, and COVID-19 severity) using different conventional supervised classification algorithms.

Results: We evaluated COVIDX through 10-fold cross-validation, by using an external validation dataset, and also in a real setting by involving an experienced radiologist. In all the adopted evaluation settings, COVIDX showed strong generalization power and outperforms all the prevailing state-of-the-art methods designed for this purpose.

Conclusions: Our proposed method (COVIDX), with vivid performance in COVID-19 diagnosis and its severity prediction, can be used as an aiding tool for clinical physicians and radiologists in the diagnosis and follow-up studies of COVID-19 infected patients.

Availability: We made COVIDX easily accessible through a cloud-based webserver and python code available at the site of google and the website of Github.

Background: Inference of population structure is crucial for studies of human evolutionary history and genome-wide association studies. While several genomic regions have been reported to distort population structure analysis of European populations, no systematic analysis has been performed on non-European continental groups and with the latest human genome assembly.

Methods: Using the 1000 Genomes Project high coverage whole-genome sequencing data from four major continental groups (Europe, East Asia, South Asia, and Africa), we developed a statistical framework and systematically detected genomic regions with unusual contributions to the inference of population structure for each of the continental groups.

Results: We identified and characterized 27 unusual genomic regions mapped to GRCh38, including 13 regions around centromeres, 2 with chromosomal inversions, 8 under natural selection, and 4 with unknown causes. Excluding these regions would result in a more interpretable population structure inferred by principal components analysis and ADMIXTURE analysis.

Conclusions: Unusual genomic patterns in certain regions can distort the inference of population structure. Our compiled list of these unusual regions will be useful for many population-genetic studies, including those from non-European populations. Availability: The code to reproduce our results is available at the website of Github (/dwuab/UnRegFinder).

Background: Image-based automatic diagnosis of field diseases can help increase crop yields and is of great importance. However, crop lesion regions tend to be scattered and of varying sizes, this along with substantial intra-class variation and small inter-class variation makes segmentation difficult.

Methods: We propose a novel end-to-end system that only requires weak supervision of image-level labels for lesion region segmentation. First, a two-branch network is designed for joint disease classification and seed region generation. The generated seed regions are then used as input to the next segmentation stage where we design to use an encoder-decoder network. Different from previous works that use an encoder in the segmentation network, the encoder-decoder network is critical for our system to successfully segment images with small and scattered regions, which is the major challenge in image-based diagnosis of field diseases. We further propose a novel weakly supervised training strategy for the encoder-decoder semantic segmentation network, making use of the extracted seed regions.

Results: Experimental results show that our system achieves better lesion region segmentation results than state of the arts. In addition to crop images, our method is also applicable to general scattered object segmentation. We demonstrate this by extending our framework to work on the PASCAL VOC dataset, which achieves comparable performance with the state-of-the-art DSRG (deep seeded region growing) method.

Conclusion: Our method not only outperforms state-of-the-art semantic segmentation methods by a large margin for the lesion segmentation task, but also shows its capability to perform well on more general tasks.

Background: The use of image analysis to understand the structure of chromosome and chromatin is critical to the study of genetic evolution and diversification. Furthermore, a detailed chromosome map and the structure of chromatin in the nucleus may contribute to the plant breeding and the study of fundamental biology and genetics in organisms.

Results: In plants with a fully annotated genome project, such as the Leguminosae species, the integration of genetic information, including DNA sequence data, a linkage map, and the cytological quantitative chromosome map could further improve their genetic value. The numerical parameters of chromocenters in 3D can provide useful genetic information for phylogenetic studies of plant diversity and heterochromatic markers whose epigenetic changes may explain the developmental and environmental changes in the plant genome. Extended DNA fibers combined with fluorescence in situ hybridization revealed the highest spatial resolution of the obtained genome structure. Moreover, image analysis at the nano-scale level using a helium ion microscope revealed the surface structure of chromatin, which consists of chromatin fibers compacted into plant chromosomes.

Conclusions: The studies described in this review sought to measure and evaluate chromosome and chromatin using the image analysis method, which may reduce measurement time and improve resolution. Further, we discussed the development of an effective image analysis to evaluate the structure of chromosome and chromatin. An effective application study of cell biology and the genetics of plants using image analysis methods is expected to be a major propeller in the development of new applications.

Background: The analysis of COVID-19 infection data through the eye of Physics-inspired Artificial Intelligence leads to a clearer understanding of the infection dynamics and assists in predicting future evolution. The spreading of the pandemic during the first half of 2020 was curtailed to a larger or lesser extent through social distancing measures imposed by most countries. In the context of the standard Susceptible-Infected-Recovered (SIR) model, changes in social distancing enter through time-dependent infection rates.

Methods: In this work we use machine learning and the infection dynamical equations of SIR to extract from the infection data the degree of social distancing and, through it, assess the effectiveness of the imposed measures.

Results: Quantitative machine learning analysis is applied to eight countries with infection data from the first viral wave. We find as two extremes Greece and USA where the measures were successful and unsuccessful, respectively, in limiting spreading. This physics-based neural network approach is employed to the second wave of the infection, and by training the network with the new data, we extract the time-dependent infection rate and make short-term predictions with a week-long or even longer horizon. This algorithmic approach is applied to all eight countries with good short-term results. The data for Greece is analyzed in more detail from August to December 2020.

Conclusions: The model captures the essential spreading dynamics and gives useful projections for the spreading, both in the short-term but also for a more intermediate horizon, based on specific social distancing measures that are extracted directly from the data.

Background: Superpixel segmentation is a powerful preprocessing tool to reduce the complexity of image processing. Traditionally, size uniformity is one of the significant features of superpixels. However, in medical images, in which subjects scale varies greatly and background areas are often flat, size uniformity rarely conforms to the varying content. To obtain the fewest superpixels with retaining important details, the size of superpixel should be chosen carefully.

Methods: We propose a scale-adaptive superpixel algorithm relaxing the size-uniformity criterion for medical images, especially pathological images. A new path-based distance measure and superpixel region growing schema allow our algorithm to generate superpixels with different scales according to the complexity of image content, that is smaller (larger) superpixels in color-riching areas (flat areas).

Results: The proposed superpixel algorithm can generate superpixels with boundary adherence, insensitive to noise, and with extremely big sizes and extremely small sizes on one image. The number of superpixels is much smaller than size-uniformly superpixel algorithms while retaining more details of images.

Conclusion: With the proposed algorithm, the choice of superpixel size is automatic, which frees the user from the predicament of setting suitable superpixel size for a given application. The results on the nuclear dataset show that the proposed superpixel algorithm superior to the respective state-of-the-art algorithms on both quantitative and quantitative comparisons.

Background: Fear of negative evaluation (FNE), referring to negative expectation and feelings toward other people’s social evaluation, is closely associated with social anxiety that plays an important role in our social life. Exploring the neural markers of FNE may be of theoretical and practical significance to psychiatry research (e.g., studies on social anxiety).

Methods: To search for potentially relevant biomarkers of FNE in human brain, the current study applied multivariate relevance vector regression, a machine-learning and data-driven approach, on brain morphological features (e.g., cortical thickness) derived from structural imaging data; further, we used these features as indexes to predict self-reported FNE score in each participant.

Results: Our results confirm the predictive power of multiple brain regions, including those engaged in negative emotional experience (e.g., amygdala, insula), regulation and inhibition of emotional feeling (e.g., frontal gyrus, anterior cingulate gyrus), and encoding and retrieval of emotional memory (e.g., posterior cingulate cortex, parahippocampal gyrus).

Conclusions: The current findings suggest that anxiety represents a complicated construct that engages multiple brain systems, from primitive subcortical mechanisms to sophisticated cortical processes.

Background: There is an urgent demand of drug or therapy to control the COVID-19. Until July 22, 2021 the worldwide total number of cases reported is more than 192 million and the total number of deaths reported is more than 4.12 million. Several countries have given emergency permission for use of repurposed drugs for the treatment of COVID-19 patients. This report presents a computational analysis on repurposing drugs—tenofovir, bepotastine, epirubicin, epoprostenol, tirazavirin, aprepitant and valrubicin, which can be potential inhibitors of the COVID-19.

Method: Density functional theory (DFT) technique is applied for computation of these repurposed drug. For geometry optimization, functional B3LYP/6-311G (d, p) is selected within DFT framework.

Results: DFT based descriptors—highest occupied molecular orbital (HOMO)-lowest unoccupied molecular orbital (LUMO) gap, molecular hardness, softness, electronegativity, electrophilicity index, nucleophilicity index and dipole moment of these species are computed. IR and Raman activities are also analysed and studied. The result shows that the HOMO-LUMO gap of these species varies from 1.061 eV to 5.327 eV. Compound aprepitant with a HOMO-LUMO gap of 1.419 eV shows the maximum intensity of IR (786.176 km mol^‒1) and Raman spectra (15036.702 a.u.).

Conclusion: Some potential inhibitors of COVID-19 are studied by using DFT technique. This study shows that epirubicin is the most reactive compound whereas tenofovir is found to be the most stable. Further analysis and clinical trials of these compounds will provide more insight.

Background: Pooled CRISPR screen is a promising tool in drug targets or essential genes identification with the utilization of three different systems including CRISPR knockout (CRISPRko), CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa). Aside from continuous improvements in technology, more and more bioinformatics methods have been developed to analyze the data obtained by CRISPR screens which facilitate better understanding of physiological effects.

Results: Here, we provide an overview on the application of CRISPR screens and bioinformatics approaches to analyzing different types of CRISPR screen data. We also discuss mechanisms and underlying challenges for the analysis of dropout screens, sorting-based screens and single-cell screens.

Conclusion: Different analysis approaches should be chosen based on the design of screens. This review will help community to better design novel algorithms and provide suggestions for wet-lab researchers to choose from different analysis methods.

Background: Images of anatomical regions and neuron type distribution, as well as their related literature are valuable assets for neuroscience research. They are vital evidence and vehicles in discovering new phenomena and knowledge refinement through image and text big data. The knowledge acquired from image data generally echoes with the literature accumulated over the years. The knowledge within the literature can provide a comprehensive context for a deeper understanding of the image data. However, it is quite a challenge to manually identify the related literature and summarize the neuroscience knowledge in the large-scale corpus. Thus, neuroscientists are in dire need of an automated method to extract neuroscience knowledge from large-scale literature.

Methods: A proposed deep learning model named BioBERT-CRF extracts brain region entities from the WhiteText dataset. This model takes advantage of BioBERT and CRF to predict entity labels while training.

Results: The proposed deep learning model demonstrated comparable performance against or even outperforms the previous models on the WhiteText dataset. The BioBERT-CRF model has achieved the best average precision, recall, and F1 score of 81.3%, 84.0%, and 82.6%, respectively. We used the BioBERT-CRF model to predict brain region entities in a large-scale PubMed abstract dataset and used a rule-based method to normalize all brain region entities to three neuroscience dictionaries.

Conclusions: Our work shows that the BioBERT-CRF model can be well-suited for brain region entity extraction. The rankings of different brain region entities by their appearance in the large-scale corpus indicate the anatomical regions that researchers are most concerned about.

Background: Modern machine learning-based models have not been harnessed to their total capacity for disease trend predictions prior to the COVID-19 pandemic. This work is the first use of the conditional RNN model in predicting disease trends that we know of during development that complemented classical epidemiological approaches.

Methods: We developed the long short-term memory networks with quantile output (condLSTM-Q) model for making quantile predictions on COVID-19 death tolls.

Results: We verified that the condLSTM-Q was accurately predicting fine-scale, county-level daily deaths with a two-week window. The model’s performance was robust and comparable to, if not slightly better than well-known, publicly available models. This provides unique opportunities for investigating trends within the states and interactions between counties along state borders. In addition, by analyzing the importance of the categorical data, one could learn which features are risk factors that affect the death trend and provide handles for officials to ameliorate the risks.

Conclusion: The condLSTM-Q model performed robustly, provided fine-scale, county-level predictions of daily deaths with a two-week window. Given the scalability and generalizability of neural network models, this model could incorporate additional data sources with ease and could be further developed to generate other valuable predictions such as new cases or hospitalizations intuitively.

Background: As one of the leading causes of global disability, major depressive disorder (MDD) places a noticeable burden on individuals and society. Despite the great expectation on finding accurate biomarkers and effective treatment targets of MDD, studies in applying functional magnetic resonance imaging (fMRI) are still faced with challenges, including the representational ambiguity, small sample size, low statistical power, relatively high false positive rates, etc. Thus, reviewing studies with solid methodology may help achieve a consensus on the pathology of MDD.

Methods: In this systematic review, we screened fMRI studies on MDD through strict criteria to focus on reliable studies with sufficient sample size, adequate control of head motion, and a proper multiple comparison control strategy.

Results: We found consistent evidence regarding the dysfunction within and among the default mode network (DMN), the frontoparietal network (FPN), and other brain regions. However, controversy remains, probably due to the heterogeneity of participants and data processing strategies.

Conclusion: Future studies are recommended to apply a comprehensive set of neuro-behavioral measurements, consider the heterogeneity of MDD patients and other potentially confounding factors, apply surface-based neuroscientific network fMRI approaches, and advance research transparency and open science by applying state-of-the-art pipelines along with open data sharing.

Background: MicroRNAs (miRNAs) play an essential role in various biological processes and signaling pathways through the regulation of gene expression and genome stability. Recent data indicated that the next-generation sequencing (NGS)-based high-throughput quantification of miRNAs from biofluids provided exciting possibilities for discovering biomarkers of various diseases and might help promote the development of the early diagnosis of cancer. However, the complex process of library construction for sequencing always introduces bias, which may twist the actual expression levels of miRNAs and reach misleading conclusions.

Results: We discussed the deviation issue in each step during constructing miRNA sequencing libraries and suggested many strategies to generate high-quality data by avoiding or minimizing bias. For example, improvement of adapter design (a blocking element away from the ligation end, a randomized fragment adjacent to the ligation junction and UMI) and optimization of ligation conditions (a high concentration of PEG 8000, reasonable incubation temperature and time, and the selection of ligase) in adapter ligation, high-quality input RNA samples, removal of adapter dimer (solid phase reverse immobilization (SPRI) magnetic bead, locked nucleic acid (LNA) oligonucleotide, and Phi29 DNA polymerase), PCR (linear amplification, touch-down PCR), and product purification are essential factors for achieving high-quality sequencing data. Moreover, we described several protocols that exhibit significant advantages using combinatorial optimization and commercially available low-input miRNA library preparation kits.

Conclusions: Overall, our work provides the basis for unbiased high-throughput quantification of miRNAs. These data will help achieve optimal design involving miRNA profiling and provide reliable guidance for clinical diagnosis and treatment by significantly increasing the credibility of potential biomarkers.

Background: Physiological signal-based research has been a hot topic in affective computing. Previous works mainly focus on some strong, short-lived emotions (e.g., joy, anger), while the attention, which is a weak and long-lasting emotion, receives less attraction. In this paper, we present a study of attention recognition based on electrocardiogram (ECG) signals, which contain a wealth of information related to emotions.

Methods: The ECG dataset is derived from 10 subjects and specialized for attention detection. To relieve the impact of noise of baseline wondering and power-line interference, we apply wavelet threshold denoising as preprocessing and extract rich features by pan-tompkins and wavelet decomposition algorithms. To improve the generalized ability, we tested the performance of a variety of combinations of different feature selection algorithms and classifiers.

Results: Experiments show that the combination of generic algorithm and random forest achieve the highest correct classification rate (CCR) of 86.3%.

Conclusion: This study indicates the feasibility and bright future of ECG-based attention research.

Background: Mathematical models are essential to predict the likely outcome of an epidemic. Various models have been proposed in the literature for disease spreads. Some are individual based models and others are compartmental models. In this study, discrete mathematical models are developed for the spread of the coronavirus disease 2019 (COVID-19).

Methods: The proposed models take into account the known special characteristics of this disease such as the latency and incubation periods, and the different social and infectiousness conditions of infected people. In particular, they include a novel approach that considers the social structure, the fraction of detected cases over the real total infected cases, the influx of undetected infected people from outside the borders, as well as contact-tracing and quarantine period for travelers. The first model is a simplified model and the second is a complete model.

Results: From a numerical point of view, the particular case of Lebanon has been studied and its reported data have been used to estimate the complete discrete model parameters using optimization techniques. Moreover, a parameter analysis and several prediction scenarios are presented in order to better understand the role of the parameters.

Conclusions: Understanding the role of the parameters involved in the models help policy makers in deciding the appropriate mitigation measures. Also, the proposed approach paves the way for models that take into account societal factors and complex human behavior without an extensive process of data collection.

Background: In this paper, we conduct an analysis of the COVID-19 data in the United States in 2020 via functional data analysis methods. Through this research, we investigate the effectiveness of the practice of public health measures, and assess the correlation between infections and deaths caused by the COVID-19. Additionally, we look into the relationship between COVID-19 spread and geographical locations, and propose a forecasting method to predict the total number of confirmed cases nationwide.

Methods: The functional data analysis methods include functional principal analysis methods, functional canonical correlation analysis methods, an expectation-maximization (EM) based clustering algorithm and a functional time series model used for forecasting.

Results: It is evident that the practice of public health measures helps to reduce the growth rate of the epidemic outbreak over the nation. We have observed a high canonical correlation between confirmed and death cases. States that are geographically close to the hot spots are likely to be clustered together, and population density appears to be a critical factor affecting the cluster structure. The proposed functional time series model gives more reliable and accurate predictions of the total number of confirmed cases than standard time series methods.

Conclusions: The results obtained by applying the functional data analysis methods provide new insights into the COVID-19 data in the United States. With our results and recommendations, the health professionals can make better decisions to reduce the spread of the epidemic, and mitigate its negative effects to the national public health.

Background: Single-cell RNA sequencing (scRNA-seq) technology is now becoming a widely applied method of transcriptome exploration that helps to reveal cell-type composition as well as cell-state heterogeneity for specific biological processes. Distinct sequencing platforms and processing pipelines may contribute to various results even for the same sequencing samples. Therefore, benchmarking sequencing platforms and processing pipelines was considered as a necessary step to interpret scRNA-seq data. However, recent comparing efforts were constrained in sequencing platforms or analyzing pipelines. There is still a lack of knowledge of analyzing pipelines matched with specific sequencing platforms in aspects of sensitivity, precision, and so on.

Methods: We downloaded public scRNA-seq data that was generated by two distinct sequencers, NovaSeq 6000 and MGISEQ 2000. Then data was processed through the Drop-seq-tools, UMI-tools and Cell Ranger pipeline respectively. We calculated multiple measurements based on the expression profiles of the six platform-pipeline combinations.

Results: We found that all three pipelines had comparable performance, the Cell Ranger pipeline achieved the best performance in precision while UMI-tools prevailed in terms of sensitivity and marker calling.

Conclusions: Our work provided an insight into the selection of scRNA-seq data processing tools for two sequencing platforms as well as a framework to evaluate platform-pipeline combinations.

Background: Computer simulation studies complement in vitro experiments and provide avenue to understand allosteric regulation in the absence of other molecular viewing techniques. Molecular dynamics captures internal motion within the protein and enables tracing the communication path between a catalytic site and a distal allosteric site. In this article, we have identified the communication pathway between the viral protein genome linked (VPg) binding region and catalytic active site in nuclear inclusion protein-a protease (NIa-Pro).

Methods: Molecular dynamics followed by in silico analyses have been used to map the allosteric pathway.

Results: This study delineates the residue interaction network involved in allosteric regulation of NIa-Pro activity by VPg. Simulation studies indicate that point mutations in the VPg interaction interface of NIa-Pro lead to disruption in these networks and change the orientation of catalytic residues. His142Ala and His167Ala mutations do not show a substantial change in the overall protease structure, but rather in the residue interaction network and catalytic site geometry.

Conclusion: Our mutagenic study delineates the allosteric pathway and facilitates the understanding of the modulation of NIa-Pro activity on a molecular level in the absence of the structure of its complex with the known regulator VPg. Additionally, our in silico analysis explains the molecular concepts and highlights the dynamics behind the previously reported wet lab study findings.

Background: Cows actions are important factors of cows health and their well-being. By monitoring the individual cows actions, we prevent cows diseases and realize modern precision cows rearing. However, traditional cows actions monitoring is usually conducted through video recording or direct visual observation, which is time-consuming and laborious, and often lead to misjudgement due to the subjective consciousness or negligence.

Methods: This paper proposes a method of cows actions recognition based on tracked trajectories to automatically recognize and evaluate the actions of cows. First, we construct a dataset including 60 videos to describe the popular actions existing in the daily life of cows, providing the basic data for designing our actions recognition method. Second, eight famous trackers are used to track and obtain temporal and spatial information of targets. Third, after studying and analysing the tracked trajectories of different actions about cows, a rigorous and effective constraint method is designed to realize actions recognition by us.

Results: Many experiments demonstrate that our method of actions recognition performs favourably in detecting the actions of cows, and the proposed dataset basically satisfies the actions evaluation for farmers.

Conclusion: The proposed tracking guided actions recognition provides a feasible way to maintain and promote cows health and welfare.

Background: China is a multi-ethnic country. It is of great significance for the skull identification to realize the skull ethnic classification through computers, which can promote the development of forensic anthropology and accelerate the exploration of national development.

Methods: In this paper, the 3D skull model is transformed into 2D auxiliary image including curvature, depth and elevation information, and then the deep learning method of the 2D auxiliary image is used for ethnic classification. We construct a convolution neural network structure inspired by VGGNet16 which has achieved excellent performance on image classification. In order to optimize the network, Adam algorithm is adopted to avoid falling into local minimum, and to ensure the stability of the algorithm with regularization terms.

Results: Experiments on 400 skull models have been conducted for ethnic classification by our method. We set different learning rates to compare the performance of the model, the highest accuracy of ethnic classification is 98.75%, which have better performance than other five classical neural network structures.

Conclusions: Deep learning based on skull auxiliary image for skull ethnic classification is an automatic and effective method with great application significance.

Background: N⁶-methyl adenosine (m⁶A) modifications of mRNA and long non-coding RNA (lncRNAs) are known to play a significant role in regulation of gene expression and organismal development. Besides writer and eraser proteins of this dynamic modification, the YT521-B homology (YTH) domain can recognize the modification involved in numerous cellular processes. The function of proteins containing YTH domain and its binding mode with N⁶-Methyladenosine RNA has attracted considerable attention. However, the structural and dynamic characteristics of the YTH domain in complex with m⁶A RNA is still unknown.

Method: This work presents results of accelerated molecular dynamics (aMD) simulations at the timescale of microseconds. Principal component analysis (PCA), molecular mechanics generalized Born surface area (MM/GBSA) calculations, contact analysis and contact-based principal component analysis (conPCA) provide new insights into structure and dynamics of the YTH-RNA complex.

Results: The aMD simulations indicate that the recognition loop has a larger movement away from the binding pocket in the YTH-A3 RNA than that in the YTH-m⁶A3 RNA. In aMD trajectories of the apo YTH, there is a significant close-open transition of the recognition loop, that is to say, the apo YTH can take both the closed and open structure. We have found that the YTH domain binds more favorably to the methylated RNA than the non-methylated RNA. The per-residue free energy decomposition and conPCA suggest that hydrophobic residues including W380, L383-V385, W431-P434, M437, and M441-L442, may play important roles in favorable binding of the m⁶A RNA to the YTH domain, which is also supported by aMD simulations of a double mutated system (L383A/M437A).

Conclusion: The results are in good agreement with higher structural stability of the YTH-m⁶A RNA than that of the YTH-A3 RNA. The addition of a methylation group on A3 can enhance its binding to the hydrophobic pocket in the YTH domain. Our simulations support a ‘conformational selection’ mechanism between the YTH-RNA binding. This work may aid in our understanding of the structural and dynamic characteristics of the YTH protein in complex with the methylated RNA.

Background: As one of the representative protein materials, protein nanocages (PNCs) are self-assembled supramolecular structures with multiple advantages, such as good monodispersity, biocompatibility, structural addressability, and facile production. Precise quantitative functionalization is essential to the construction of PNCs with designed purposes.

Results: With three modifiable interfaces, the interior surface, outer surface, and interfaces between building blocks, PNCs can serve as an ideal platform for precise multi-functionalization studies and applications. This review summarizes the currently available methods for precise quantitative functionalization of PNCs and highlights the significance of precise quantitative control in fabricating PNC-based materials or devices. These methods can be categorized into three groups, genetic, chemical, and combined modification.

Conclusion: This review would be constructive for those who work with biosynthetic PNCs in diverse fields.

Background: Synthetic microbial communities, with different strains brought together by balancing their nutrition and promoting their interactions, demonstrate great advantages for exploring complex performance of communities and for further biotechnology applications. The potential of such microbial communities has not been explored, due to our limited knowledge of the extremely complex microbial interactions that are involved in designing and controlling effective and stable communities.

Results: Genome-scale metabolic models (GEM) have been demonstrated as an effective tool for predicting and guiding the investigation and design of microbial communities, since they can explicitly and efficiently predict the phenotype of organisms from their genotypic data and can be used to explore the molecular mechanisms of microbe-habitats and microbe-microbe interactions. In this work, we reviewed two main categories of GEM-based approaches and three uses related to design of synthetic microbial communities: predicting multi-species interactions, exploring environmental impacts on microbial phenotypes, and optimizing community-level performance.

Conclusions: Although at the infancy stage, GEM-based approaches exhibit an increasing scope of applications in designing synthetic microbial communities. Compared to other methods, especially the use of laboratory cultures, GEM-based approaches can greatly decrease the trial-and-error cost of various procedures for designing synthetic communities and improving their functionality, such as identifying community members, determining media composition, evaluating microbial interaction potential or selecting the best community configuration. Future efforts should be made to overcome the limitations of the approaches, ranging from quality control of GEM reconstructions to community-level modeling algorithms, so that more applications of GEMs in studying phenotypes of microbial communities can be expected.