A fuzzy fine-tuned model for COVID-19 diagnosis.

The COVID-19 disease pandemic spread rapidly worldwide and caused extensive human death and financial losses. Therefore, finding accurate, accessible, and inexpensive methods for diagnosing the disease has challenged researchers. To automate the process of diagnosing COVID-19 disease through images, several strategies based on deep learning, such as transfer learning and ensemble learning, have been presented. However, these techniques cannot deal with noises and their propagation in different layers. In addition, many of the datasets already being used are imbalanced, and most techniques have used binary classification, COVID-19, from normal cases. To address these issues, we use the blind/referenceless image spatial quality evaluator to filter out inappropriate data in the dataset. In order to increase the volume and diversity of the data, we merge two datasets. This combination of two datasets allows multi-class classification between the three states of normal, COVID-19, and types of pneumonia, including bacterial and viral types. A weighted multi-class cross-entropy is used to reduce the effect of data imbalance. In addition, a fuzzy fine-tuned Xception model is applied to reduce the noise propagation in different layers. Quantitative analysis shows that our proposed model achieves 96.60% accuracy on the merged test set, which is more accurate than previously mentioned state-of-the-art methods.

Accelerating Cell-Free Biosensors by Entropy-Driven Assembly of Transcription Templates.

Due to its high efficiency and selectivity, cell-free biosynthesis has found broad utility in the fields of bioproduction, environment monitoring, and disease diagnostics. However, the practical application is limited by its low productivity. Here, we introduce the entropy-driven assembly of transcription templates as dynamic amplifying modules to accelerate the cell-free transcription process. The catalytic DNA circuit with high sensitivity and enzyme-free format contributes to the production of large amounts of transcription templates, drastically accelerating the as-designed cell-free transcription system without interference from multiple enzymes. The proposed approach was successfully applied to the ultrasensitive detection of SARS-CoV-2, improving the sensitivity by 3 orders of magnitude. Thanks to the high programmability and diverse light-up RNA pairs, the method can be adapted to multiplexing detection, successfully demonstrated by the analysis of two different sites of the SARS-CoV-2 gene in parallel. Further, the flexibility of the entropy-driven circuit enables a dynamic responding range by tuning the circuit layers, which is beneficial for responding to targets with different concentration ranges. The strategy was also applied to the analysis of clinical samples, providing an alternative for sensitively detecting the current SARS-CoV-2 RNA that quickly mutates.

Towards a free energy-based elastic network model and its application to the SARS-COV2 binding to ACE2.

Classical normal mode analysis (cNMA) is a standard method for studying the equilibrium vibrations of macromolecules. A major limitation of cNMA is that it requires a cumbersome step of energy minimization that also alters the input structure significantly. Variants of normal mode analysis (NMA) exist that perform NMA directly on PDB structures without energy minimization, while maintaining most of the accuracy of cNMA. Spring-based NMA (sbNMA) is such a model. sbNMA uses an all-atom force field as cNMA does, which includes bonded terms such as bond stretching, bond angle bending, torsional, improper, and non-bonded terms such as van der Waals interactions. Electrostatics was not included in sbNMA because it introduced negative spring constants. In this work, we present a way to incorporate most of the electrostatic contributions in normal mode computations, which marks another significant step toward a free-energy-based elastic network model (ENM) for NMA. The vast majority of ENMs are entropy models. One significance of having a free energy-based model for NMA is that it allows one to study the contributions of both entropy and enthalpy. As an application, we apply this model to study the binding stability between SARS-COV2 and angiotensin converting enzyme 2 (or ACE2). Our results show that the stability at the binding interface is contributed nearly equally by hydrophobic interactions and hydrogen bonds.

Entropic contribution of ACE2 glycans to RBD binding.

The spike protein of the SARS-CoV-2 virus (the causative agent of COVID-19) recognizes the host cell by binding to the peptidase domain (PD) of the extracellular receptor angiotensin-converting enzyme 2 (ACE2). A variety of carbohydrates could be attached to the six asparagines in the PD, resulting in a heterogeneous population of ACE2 glycoforms. Experiments have shown that the binding affinity of glycosylated and deglycosylated ACE2 to the virus is virtually identical. In most cases, the reduction in glycan size correlates with stronger binding, which suggests that volume exclusion, and hence entropic forces, determine the binding affinity. Here, we quantitatively test the entropy-based hypothesis by developing a lattice model for the complex between ACE2 and the SARS-CoV-2 spike protein receptor-binding domain (RBD). Glycans are treated as branched polymers with only volume exclusion, which we justify using all-atom molecular dynamics simulations in explicit water. We show that the experimentally measured changes in the ACE2-RBD dissociation constants for a variety of engineered ACE2 glycoforms are in reasonable agreement with our theory, thus supporting our hypothesis. However, a quantitative recovery of all the experimental data could require weak attractive interactions.

Entropy-driven assisted T7 RNA polymerase amplification-activated CRISPR/Cas13a activity for SARS-CoV-2 detection in human pharyngeal swabs and environment by an electrochemiluminescence biosensor.

In this study, we introduce an electrochemiluminescence (ECL) sensing platform based on the "Entropy-driven triggered T7 amplification-CRISPR/Cas13a system" (EDT-Cas). This platform combines a programmable entropy-driven cycling strategy, T7 RNA polymerase, and the CRISPR/Cas13a system to amplify the determination of the SARS-CoV-2 RdRp gene. The Ti3C2Tx-compliant ECL signaling molecule offers unique benefits when used with the ECL sensing platform to increase the assay sensitivity and the electrode surface modifiability. To obtain the T7 promoter, the SARS-CoV-2 RdRp gene may first initiate an entropy-driven cyclic amplification response. Then, after recognizing the T7 promoter sequence on the newly created dsDNA, T7 RNA polymerase starts transcription, resulting in the production of many single-stranded RNAs (ssRNAs), which in turn trigger the action of CRISPR/Cas13a. Finally, Cas13a/crRNA identifies the transcribed ssRNA. When it cleaves the ssRNA, many DNA reporter probes carrying -U-U- are cleaved on the electrode surface, increasing the ECL signal and allowing for the rapid and highly sensitive detection of SARS-CoV-2. With a detection limit of 7.39 aM, our method enables us to locate the SARS-CoV-2 RdRp gene in clinical samples. The detection method also demonstrates excellent repeatability and stability. The SARS-CoV-2 RdRp gene was discovered using the "Entropy-driven triggered T7 amplification-CRISPR/Cas13a system" (EDT-Cas). The developed ECL test had excellent recoveries in pharyngeal swabs and environmental samples. It is anticipated to offer an early clinical diagnosis of SARS-CoV-2 and further control the spread of the pandemic.

Knowledge grounded medical dialogue generation using augmented graphs.

Smart healthcare systems that make use of abundant health data can improve access to healthcare services, reduce medical costs and provide consistently high-quality patient care. Medical dialogue systems that generate medically appropriate and human-like conversations have been developed using various pre-trained language models and a large-scale medical knowledge base based on Unified Medical Language System (UMLS). However, most of the knowledge-grounded dialogue models only use local structure in the observed triples, which suffer from knowledge graph incompleteness and hence cannot incorporate any information from dialogue history while creating entity embeddings. As a result, the performance of such models decreases significantly. To address this problem, we propose a general method to embed the triples in each graph into large-scalable models and thereby generate clinically correct responses based on the conversation history using the recently recently released MedDialog(EN) dataset. Given a set of triples, we first mask the head entities from the triples overlapping with the patient's utterance and then compute the cross-entropy loss against the triples' respective tail entities while predicting the masked entity. This process results in a representation of the medical concepts from a graph capable of learning contextual information from dialogues, which ultimately aids in leading to the gold response. We also fine-tune the proposed Masked Entity Dialogue (MED) model on smaller corpora which contain dialogues focusing only on the Covid-19 disease named as the Covid Dataset. In addition, since UMLS and other existing medical graphs lack data-specific medical information, we re-curate and perform plausible augmentation of knowledge graphs using our newly created Medical Entity Prediction (MEP) model. Empirical results on the MedDialog(EN) and Covid Dataset demonstrate that our proposed model outperforms the state-of-the-art methods in terms of both automatic and human evaluation metrics.

Entropy as a Measure of Auditory Environment Diversity: An Ecological Momentary Assessment (EMA) Approach.

OBJECTIVES: To determine the validity and usefulness of entropy computed using ecological momentary assessment (EMA) data as a measure of auditory environment diversity. DESIGN: We conducted two secondary analyses on existing EMA datasets. The first determined the construct validity of auditory environment entropy by examining the effect of COVID-19 on entropy. To demonstrate entropy's usefulness, the second examined if entropy could predict the benefit of hearing aid (HA) noise reduction features. RESULTS: Consistent with the known effect of COVID-19 on social lifestyle, COVID-19 significantly reduced auditory environment diversity, supporting entropy's construct validity. HA users with higher entropy reported poorer outcomes and perceived more benefit from HA features, supporting the feasibility of using entropy to predict communication performance and feature benefit. CONCLUSIONS: Entropy derived from EMA data is a valid and useful auditory environment diversity measure. This measure could allow researchers to better understand the communication needs of people with hearing loss.

Densely attention mechanism based network for COVID-19 detection in chest X-rays.

Automatic COVID-19 detection using chest X-ray (CXR) can play a vital part in large-scale screening and epidemic control. However, the radiographic features of CXR have different composite appearances, for instance, diffuse reticular-nodular opacities and widespread ground-glass opacities. This makes the automatic recognition of COVID-19 using CXR imaging a challenging task. To overcome this issue, we propose a densely attention mechanism-based network (DAM-Net) for COVID-19 detection in CXR. DAM-Net adaptively extracts spatial features of COVID-19 from the infected regions with various appearances and scales. Our proposed DAM-Net is composed of dense layers, channel attention layers, adaptive downsampling layer, and label smoothing regularization loss function. Dense layers extract the spatial features and the channel attention approach adaptively builds up the weights of major feature channels and suppresses the redundant feature representations. We use the cross-entropy loss function based on label smoothing to limit the effect of interclass similarity upon feature representations. The network is trained and tested on the largest publicly available dataset, i.e., COVIDx, consisting of 17,342 CXRs. Experimental results demonstrate that the proposed approach obtains state-of-the-art results for COVID-19 classification with an accuracy of 97.22%, a sensitivity of 96.87%, a specificity of 99.12%, and a precision of 95.54%.

Entropic Overcompensation of the N501Y Mutation on SARS-CoV-2 S Binding to ACE2.

Recent experimental work has shown that the N501Y mutation in the SARS-CoV-2 S glycoprotein's receptor binding domain (RBD) increases binding affinity to the angiotensin-converting enzyme 2 (ACE2), primarily by overcompensating for a less favorable enthalpy of binding by greatly reducing the entropic penalty for complex formation, but the basis for this entropic overcompensation is not clear [Prévost et al. J. Biol. Chem.2021, 297, 101151]. We use all-atom molecular dynamics simulations and free-energy calculations to qualitatively assess the impact of the N501Y mutation on the enthalpy and entropy of binding of RBD to ACE2. Our calculations correctly predict that N501Y causes a less favorable enthalpy of binding to ACE2 relative to the original strain. Furthermore, we show that this is overcompensated for by a more entropically favorable increase in large-scale quaternary flexibility and intraprotein root mean square fluctuations of residue positions upon binding in both RBD and ACE2. The enhanced quaternary flexibility stems from N501Y's ability to remodel the inter-residue interactions between the two proteins away from interactions central to the epitope and toward more peripheral interactions. These findings suggest that an important factor in determining protein-protein binding affinity is the degree to which fluctuations are distributed throughout the complex and that residue mutations that may seem to result in weaker interactions than their wild-type counterparts may yet result in increased binding affinity thanks to their ability to suppress unfavorable entropy changes upon binding.

Risk Assessment of Import Cold Chain Logistics Based on Entropy Weight Matter Element Extension Model: A Case Study of Shanghai, China.

The development of world trade and fresh-keeping technology has led to the rapid development of international cold chain logistics. However, the novel coronavirus epidemic continues to spread around the world at the present stage, which challenges disease transmission control and safety supervision of international cold chain logistics. Constructing an Import Cold Chain Logistics Safety Supervision System (ICCL-SSS) is helpful for detecting and controlling disease import risk. This paper constructs an evaluation index system of ICCL safety that comprehensively considers the potential risk factors of three ICCL processes: the logistics process in port, the customs clearance process, and the logistics process from port to door. The risk level of ICCL-SSS is evaluated by combining the Extension Decision-making Model and the Entropy Weight Method. The case study of Shanghai, China, the world's largest city of ICCL, shows that the overall risk level of ICCL-SSS in Shanghai is at a moderate level. However, the processes of loading and unloading, inspection and quarantine, disinfection and sterilization, and cargo storage are at high risk specifically. The construction and risk assessment of ICCL-SSS can provide theoretical support and practical guidance for improving the safety supervision ability of ICCL regulation in the post-epidemic era, and helps the local government to scientifically formulate ICCL safety administration policies and accelerate the development of world cold chain trade.

Evaluation Study on the Use of Non-Contact Prevention and Protection Products in the Context of COVID-19: A Comprehensive Evaluation Method from AHP and Entropy Weight Method.

In the post-epidemic era, there is an endless supply of epidemic prevention products that cover a wide range of public areas. The introduction of such products has eased the tense pattern of virus proliferation in the context of the epidemic, and effectively demonstrated the initiatives implemented by the Chinese people in response to the outbreak. This paper therefore begins with the study of contactless epidemic prevention products, which appear in a form that meets the needs of contemporary society and offers a new mode of living to it. It enriches the measures for epidemic prevention and control. By obtaining satisfaction ratings from the user community, the performance of such products can be understood in time to provide a substantial basis for the subsequent upgrading and optimization or transformation of such products. This study uses the KJ method and questionnaires to construct an index system for contactless epidemic prevention products, grasp users' needs for epidemic prevention products in real time, classify and identify such products, and select such products as epidemic prevention smart security gates, medical delivery robots, infrared handheld thermometers, thermographic body temperature screening, contactless inductive lift buttons, and contactless medical vending machines. The questionnaire was designed with four dimensions: safety, intelligence, aesthetics and economy. A sample size of 262 was collected through the distribution of questionnaires. We used AHP and entropy weighting methods for the comprehensive evaluation; AHP basically tells us how satisfied most users are with this type of product. The use of the entropy weighting method can achieve objectivity in the weighting process. Combining the two approaches helps to improve the scientific nature of the weighting of the evaluation indexes for contactless and epidemic-proof products. It is clear from the AHP analysis that, firstly, there are differences in the perceptions of the performance of this type of product between different age groups. Secondly, the user group rated the perceived performance of the product presented as high (Bn>0.200), which users can subjectively and directly perceive. Next, the perceived future sustainable economic development of this product category is low (Bn≤0.200), and users place low importance on its economic aspects as an objective additional condition. The entropy method of analysis shows that, under reasonable government control of the market for intelligent products, the safety, intelligence and aesthetic effects of these products are significant (Cm≤0.100); further, the economic presentation of these products has yet to be optimized and upgraded (Cm>0.100).

Identifying causality drivers and deriving governing equations of nonlinear complex systems.

Identifying and describing the dynamics of complex systems is a central challenge in various areas of science, such as physics, finance, or climatology. While machine learning algorithms are increasingly overtaking traditional approaches, their inner workings and, thus, the drivers of causality remain elusive. In this paper, we analyze the causal structure of chaotic systems using Fourier transform surrogates and three different inference techniques: While we confirm that Granger causality is exclusively able to detect linear causality, transfer entropy and convergent cross-mapping indicate that causality is determined to a significant extent by nonlinear properties. For the Lorenz and Halvorsen systems, we find that their contribution is independent of the strength of the nonlinear coupling. Furthermore, we show that a simple rationale and calibration algorithm are sufficient to extract the governing equations directly from the causal structure of the data. Finally, we illustrate the applicability of the framework to real-world dynamical systems using financial data before and after the COVID-19 outbreak. It turns out that the pandemic triggered a fundamental rupture in the world economy, which is reflected in the causal structure and the resulting equations.

A new buffering theory of social support and psychological stress.

A dynamical model linking stress, social support, and health has been recently proposed and numerically analyzed from a classical point of view of integer-order calculus. Although interesting observations have been obtained in this way, the present work conducts a fractional-order analysis of that model. Under a periodic forcing of an environmental stress variable, the perceived stress has been analyzed through bifurcation diagrams and two well-known metrics of entropy and complexity, such as spectral entropy and C0 complexity. The results obtained by numerical simulations have shown novel insights into how stress evolves with frequency and amplitude of the perturbation, as well as with initial conditions for the system variables. More precisely, it has been observed that stress can alternate between chaos, periodic oscillations, and stable behaviors as the fractional order varies. Moreover, the perturbation frequency has revealed a narrow interval for the chaotic oscillations, while its amplitude may present different values indicating a low sensitivity regarding chaos generation. Also, the perceived stress has been noted to be highly sensitive to initial conditions for the symptoms of stress-related ill-health and for the social support received from family and friends. This work opens new directions of research whereby fractional calculus might offer more insight into psychology, life sciences, mental disorders, and stress-free well-being.

Application of the Two-Dimensional Entropy Measures in the Infrared Thermography-Based Detection of Rider: Horse Bodyweight Ratio in Horseback Riding.

As obesity is a serious problem in the human population, overloading of the horse's thoracolumbar region often affects sport and school horses. The advances in using infrared thermography (IRT) to assess the horse's back overload will shortly integrate the IRT-based rider-horse fit into everyday equine practice. This study aimed to evaluate the applicability of entropy measures to select the most informative measures and color components, and the accuracy of rider:horse bodyweight ratio detection. Twelve horses were ridden by each of the six riders assigned to the light, moderate, and heavy groups. Thermal images were taken pre- and post-exercise. For each thermal image, two-dimensional sample (SampEn), fuzzy (FuzzEn), permutation (PermEn), dispersion (DispEn), and distribution (DistEn) entropies were measured in the withers and the thoracic spine areas. Among 40 returned measures, 30 entropy measures were exercise-dependent, whereas 8 entropy measures were bodyweight ratio-dependent. Moreover, three entropy measures demonstrated similarities to entropy-related gray level co-occurrence matrix (GLCM) texture features, confirming the higher irregularity and complexity of thermal image texture when horses worked under heavy riders. An application of DispEn to red color components enables identification of the light and heavy rider groups with higher accuracy than the previously used entropy-related GLCM texture features.

Evaluation and Obstacle Analysis of Emergency Response Capability in China.

Emergency response capability evaluation is an essential means to strengthen emergency response capacity-building and improve the level of government administration. Based on the whole life cycle of emergency management, the emergency capability evaluation index system is constructed from four aspects: prevention and emergency preparedness, monitoring and early warning, emergency response and rescue, and recovery and reconstruction. Firstly, the entropy method is applied to measure the emergency response capability level of 31 Chinese provinces from 2011 to 2020. Second, the Theil index and ESDA (Exploratory Spatial Data Analysis) are applied in exploring the regional differences and spatial-temporal distribution characteristics of China's emergency response capacity. Finally, the obstacle degree model is used to explore the obstacle factors and obstacle degrees that affect the emergency response capability. The results show that: (1) The average value of China's emergency response capacity is 0.277, with a steady growth trend and a gradient distribution of "high in the east, low in the west, and average in center and northeast" in the four major regions. (2) From the perspective of spatial distribution characteristics, the unbalanced regional development leads to the obvious aggregation effect of "high-efficiency aggregation and low-efficiency aggregation", and the interaction of the "centripetal effect" and "centrifugal effect" finally forms the spatial clustering result of emergency response capability level in China. (3) Examining the source of regional differences, inter-regional differences are the decisive factor affecting the overall differences in emergency response capability, and the inter-regional differences show a reciprocating fluctuation of narrowing-widening-narrowing from 2011 to 2020. (4) Main obstacles restricting the improvement of China's emergency response capabilities are "the business volume of postal and telecommunication services per capita", "the daily disposal capacity of city sewage" and "the general public budget revenue by region". The extent of the obstacles' impacts in 2020 are 12.19%, 7.48%, and 7.08%, respectively. Based on the evaluation results, the following countermeasures are proposed: to realize the balance of each stage of emergency management during the holistic process; to strengthen emergency coordination and balanced regional development; and to implement precise measures to make up for the shortcomings of emergency response capabilities.

Dynamic Evaluation of Transformation Ability for Emergency Scientific Research Achievements Based on an Improved Minimum Distance-Maximum Entropy Combination Weighting Method: A Case Study of COVID-19 Epidemic Data.

In the process of responding to major public health emergencies, the transformation of emergency scientific research results often faces many unfavourable factors such as limited resources, tight time, changes in needs, and lack of results. It is necessary to evaluate and analyze the ability to transform emergency scientific research results under public health emergencies, so as to rationally allocate emergency scientific research resources between subjects and regions, improve the efficiency of emergency results transformation, enhance emergency scientific research capabilities, and efficiently support incident prevention, control, and treatment. Starting from the patent level, this paper constructs an indicator system to evaluate the transformation ability of emergency scientific research results under major public health emergencies. It improves the minimum distance-maximum entropy combination weighting method to realize the static evaluation of transformation ability for emergency scientific research results from the perspective of patents, then constructs the dynamic evaluation model of transformation ability for emergency scientific research results in public health emergencies from the perspective of patents, and carries out the dynamic evaluation of the emergency scientific research achievements transformation ability of different subjects and different regions. We also improve the ER index, measure the static polarization effect of the transformation ability for regional emergency scientific research results, and consider the time factor to construct a dynamic polarization effect measurement model for the transformation ability of emergency scientific research achievement. Furthermore, this paper improves the measurement model of contribution degree to the polarization effect, and analyzes the contribution degree to polarization of the transformation ability for regional emergency scientific research results.

A regularization-driven Mean Teacher model based on semi-supervised learning for medical image segmentation.

Objective.A semi-supervised learning method is an essential tool for applying medical image segmentation. However, the existing semi-supervised learning methods rely heavily on the limited labeled data. The generalization performance of image segmentation is improved to reduce the need for the number of labeled samples and the difficulty of parameter tuning by extending the consistency regularization.Approach.We propose a new regularization-driven Mean Teacher model based on semi-supervised learning for medical image segmentation in this work. We introduce a regularization-driven strategy with virtual adversarial training to improve segmentation performance and the robustness of the Mean Teacher model. We optimize the unsupervised loss function and the regularization term with an entropy minimum to smooth the decision boundary.Main results.We extensively evaluate the proposed method on the International Skin Imaging Cooperation 2017(ISIC2017) and COVID-19 CT segmentation datasets. Our proposed approach gains more accurate results on challenging 2D images for semi-supervised medical image segmentation. Compared with the state-of-the-art methods, the proposed approach has significantly improved and is superior to other semi-supervised segmentation methods.Significance.The proposed approach can be extended to other medical segmentation tasks and can reduce the burden of physicians to some extent.

Correlation between the binding affinity and the conformational entropy of nanobody SARS-CoV-2 spike protein complexes.

Camelid single-domain antibodies, also known as nanobodies, can be readily isolated from naïve libraries for specific targets but often bind too weakly to their targets to be immediately useful. Laboratory-based genetic engineering methods to enhance their affinity, termed maturation, can deliver useful reagents for different areas of biology and potentially medicine. Using the receptor binding domain (RBD) of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein and a naïve library, we generated closely related nanobodies with micromolar to nanomolar binding affinities. By analyzing the structure-activity relationship using X-ray crystallography, cryoelectron microscopy, and biophysical methods, we observed that higher conformational entropy losses in the formation of the spike protein-nanobody complex are associated with tighter binding. To investigate this, we generated structural ensembles of the different complexes from electron microscopy maps and correlated the conformational fluctuations with binding affinity. This insight guided the engineering of a nanobody with improved affinity for the spike protein.

Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation.

This paper focuses on the study of Coronavirus Disease 2019 (COVID-19) X-ray image segmentation technology. We present a new multilevel image segmentation method based on the swarm intelligence algorithm (SIA) to enhance the image segmentation of COVID-19 X-rays. This paper first introduces an improved ant colony optimization algorithm, and later details the directional crossover (DX) and directional mutation (DM) strategy, XMACO. The DX strategy improves the quality of the population search, which enhances the convergence speed of the algorithm. The DM strategy increases the diversity of the population to jump out of the local optima (LO). Furthermore, we design the image segmentation model (MIS-XMACO) by incorporating two-dimensional (2D) histograms, 2D Kapur's entropy, and a nonlocal mean strategy, and we apply this model to COVID-19 X-ray image segmentation. Benchmark function experiments based on the IEEE CEC2014 and IEEE CEC2017 function sets demonstrate that XMACO has a faster convergence speed and higher convergence accuracy than competing models, and it can avoid falling into LO. Other SIAs and image segmentation models were used to ensure the validity of the experiments. The proposed MIS-XMACO model shows more stable and superior segmentation results than other models at different threshold levels by analyzing the experimental results.

Hazardous Shortcuts in Standard Binding Free Energy Calculations.

Calculating the standard binding free energies of protein-protein and protein-ligand complexes from atomistic molecular dynamics simulations in explicit solvent is a problem of central importance in computational biophysics. A rigorous strategy for carrying out such calculations is the so-called "geometrical route". In this method, two molecular objects are progressively separated from one another in the presence of orientational and conformational restraints serving to control the change in configurational entropy that accompanies the dissociation process, thereby allowing the computations to converge within simulations of affordable length. Although the geometrical route provides a rigorous theoretical framework, a tantalizing computational shortcut consists of simply leaving out such orientational and conformational degrees of freedom during the separation process. Here the accuracy and convergence of the two approaches are critically compared in the case of two protein-ligand complexes (Abl kinase-SH3:p41 and MDM2-p53:NVP-CGM097) and three protein-protein complexes (pig insulin dimer, SARS-CoV-2 spike RBD:ACE2, and CheA kinase-P2:CheY). The results of the simulations that strictly follow the geometrical route match the experimental standard binding free energies within chemical accuracy. In contrast, simulations bereft of geometrical restraints converge more poorly, yielding inconsistent results that are at variance with the experimental measurements. Furthermore, the orientational and positional time correlation functions of the protein in the unrestrained simulations decay over several microseconds, a time scale that is far longer than the typical simulation times of the geometrical route, which explains why those simulations fail to sample the relevant degrees of freedom during the separation process of the complexes.