The repertoire of copy number alteration signatures in human cancer.

Copy number alterations (CNAs) are a predominant source of genetic alterations in human cancer and play an important role in cancer progression. However comprehensive understanding of the mutational processes and signatures of CNA is still lacking. Here we developed a mechanism-agnostic method to categorize CNA based on various fragment properties, which reflect the consequences of mutagenic processes and can be extracted from different types of data, including whole genome sequencing (WGS) and single nucleotide polymorphism (SNP) array. The 14 signatures of CNA have been extracted from 2778 pan-cancer analysis of whole genomes WGS samples, and further validated with 10 851 the cancer genome atlas SNP array dataset. Novel patterns of CNA have been revealed through this study. The activities of some CNA signatures consistently predict cancer patients' prognosis. This study provides a repertoire for understanding the signatures of CNA in cancer, with potential implications for cancer prognosis, evolution and etiology.

TLimmuno2: predicting MHC class II antigen immunogenicity through transfer learning.

Major histocompatibility complex (MHC) class II molecules play a pivotal role in antigen presentation and CD4+ T cell response. Accurate prediction of the immunogenicity of MHC class II-associated antigens is critical for vaccine design and cancer immunotherapies. However, current computational methods are limited by insufficient training data and algorithmic constraints, and the rules that govern which peptides are truly recognized by existing T cell receptors remain poorly understood. Here, we build a transfer learning-based, long short-term memory model named 'TLimmuno2' to predict whether epitope-MHC class II complex can elicit T cell response. Through leveraging binding affinity data, TLimmuno2 shows superior performance compared with existing models on independent validation datasets. TLimmuno2 can find real immunogenic neoantigen in real-world cancer immunotherapy data. The identification of significant MHC class II neoantigen-mediated immunoediting signal in the cancer genome atlas pan-cancer dataset further suggests the robustness of TLimmuno2 in identifying really immunogenic neoantigens that are undergoing negative selection during cancer evolution. Overall, TLimmuno2 is a powerful tool for the immunogenicity prediction of MHC class II presented epitopes and could promote the development of personalized immunotherapies.

Spontaneous stable rotation of flocking flexible active matter.

In this paper we present an n-node flexible active matter model to study the collective motion due to the flocking of individual achiral agents on a two-dimensional surface. By introducing a measure of the direction detectability of the agents to tune their body direction towards the food source, we find that a spontaneous stable cluster rotation emerges with increasing direction detectability. The spontaneous rotation is synchronized with the chirality produced by the alignment of their bodies under the impetus of the active force. A linear relationship between the normalized angular velocity and chirality is observed and the numerical simulation agrees well with the analytical derivation. The conclusions explain well the spontaneous stable rotation of clusters that exists in many flexible active matter systems, like worms or dogs, when they flock to the same single source.

Recent advances in multi-mechanism design of crack-resistant hydrogels.

For conventional hydrogels, the phenomenon of crack generation and propagation caused by high-stress concentration is ubiquitous. However, this phenomenon is unfavorable in many applications, such as wearable electronics, tissue engineering, and tunable adhesion. Fortunately, many hydrogels that can suppress crack growth during deformation and maintain the original mechanical properties during deformation, called crack-resistant hydrogels, have been published. Herein, the state-of-the-art of crack-resistant hydrogels is comprehensively reviewed. Starting from the principle of designing a crack-resistant hydrogel, we first survey the relevant crack-resistant strategies. The latest crack-resistant hydrogels are then categorized according to their crack-resistant mechanisms (including energy dissipation at the molecular level, multiscale structure, crack pinning, crack deflection, and sliding of chain), and their crack-resistant processes are described in detail. Furthermore, we summarize the current challenges and make an outlook for crack-resistant hydrogels, which might lead to substantial progress in the future design and development of these high-performance materials.

Seq2Neo: A Comprehensive Pipeline for Cancer Neoantigen Immunogenicity Prediction.

Neoantigens derived from somatic DNA alterations are ideal cancer-specific targets. In recent years, the combination therapy of PD-1/PD-L1 blockers and neoantigen vaccines has shown clinical efficacy in original PD-1/PD-L1 blocker non-responders. However, not all somatic DNA mutations result in immunogenicity among cancer cells and efficient tools to predict the immunogenicity of neoepitopes are still urgently needed. Here, we present the Seq2Neo pipeline, which provides a one-stop solution for neoepitope feature prediction using raw sequencing data. Neoantigens derived from different types of genome DNA alterations, including point mutations, insertion deletions and gene fusions, are all supported. Importantly, a convolutional neural network (CNN)-based model was trained to predict the immunogenicity of neoepitopes and this model showed an improved performance compared to the currently available tools in immunogenicity prediction using independent datasets. We anticipate that the Seq2Neo pipeline could become a useful tool in the prediction of neoantigen immunogenicity and cancer immunotherapy. Seq2Neo is open-source software under an academic free license (AFL) v3.0 and is freely available at Github.

Hidden Intermediate State and Second Pathway Determining Folding and Unfolding Dynamics of GB1 Protein at Low Forces.

Atomic force microscopy experiments found that GB1, a typical two-state model protein used for study of folding and unfolding dynamics, can sustain forces of more than 100 pN, but its response to low forces still remains unclear. Using ultrastable magnetic tweezers, we discovered that GB1 has an unexpected nonmonotonic force-dependent unfolding rate at 5-160 pN, from which a free energy landscape with two main barriers and a hidden intermediate state was constructed. A model combining two separate models by Dudko et al. with two pathways between the native state and this intermediate state is proposed to rebuild the unfolding dynamics over the full experimental force range. One candidate of this transient intermediate state is the theoretically proposed molten globule state with a loosely collapsed conformation, which might exist universally in the folding and unfolding processes of two-state proteins.

For: Pesticide biochemistry and physiology recG is involved with the resistance of Bt to UV.

As an ATP-dependent DNA helicase, RecG can repair DNA replication forks in many organisms. However, knowledge of recG in Bacillus thuringiensis (Bt) is limited. In our previous study, recG was found damaged in Bt LLP29-M19, which was more resistant to ultraviolet light (UV) after exposing Bt LLP29 to UV for 19 generations. To further understand the function of recG in the mechanism of Bt UV resistance, recG was knocked out and recovered with homologous recombination technology in Bt LLP29. Comparing the resistance of the different mutants to UVB, Bt ∆recG-LLP29 lacking recG was found more sensitive to UVB, hydroxyurea (HU) and H2O2 than LLP29 and the complementation strain. To compare the expression level of recG in the Bt strains under different UV treatments, Quantitative Real-time PCR (RT-qPCR) of recG was performed in the tested Bt strains, which showed that the expression level of recG in Bt ∆recG-LLP29 was substantially lower than that in the original strain and complementation strain. Interestingly, when exposed to UV for 20 min, RecG expression in both Bt LLP29 and Bt recG-R was the highest. The unwinding activity of recG in Bt LLP29 and the complementation strain were also found higher than that of the recG knockout strain, Bt ∆recG-LLP29. These results demonstrate that recG is involved with the resistance of Bt to UV. These findings not only enhance the understanding of the Bt UV resistance mechanism, but also provide an important theoretical basis for the application of Bt.

Function of Aedes aegypti galectin-6 in modulation of Cry11Aa toxicity.

Galectins are a family of ß-galactoside binding proteins, and insect galectins play a role in immune responses and may also affect Cry toxin activity. In this study, we aimed to further understand the function and molecular mechanism of Aedes aegypti galectin-6 in modulation of Cry11Aa toxicity. A. aegypti galectin-6 was cloned, and the recombinant galectin-6 was expressed and purified. Bioassays indicated that galectin-6 could reduce the toxicity of Cry11Aa, protecting A. aegypti larvae. To determine interactions among galectin-6, Cry11Aa and putative toxin receptors, Octet Red System, western blotting, far-western blotting and ELISA assays were performed. Octet Red System showed that galectin-6 bound to BBMVs of A. aegypti larvae with lower affinity than that of Cry11Aa. Western blotting and far-western blotting analyses demonstrated that galectin-6 bound to A. aegypti ALP1 and APN2 as well as to BBMVs, consistent with the results of ELISA and protein docking simulations. However, galectin-6 did not bind to Cadherin in far-western blotting or ELISA assay, though the protein docking simulations suggested their binding potential. These findings support the conclusion that galectin-6 may block Cry11Aa from binding to ALP1 and APN2 due to structural similarity, which might decrease the mosquitocidal toxicity of Cry11Aa.

Mechanical property of the helical configuration for a twisted intrinsically straight biopolymer.

We explore the effects of two typical torques on the mechanical property of the helical configuration for an intrinsically straight filament or biopolymer either in three-dimensional space or on a cylinder. One torque is parallel to the direction of a uniaxial applied force, and is coupled to the cross section of the filament. We obtain some algebraic equations for the helical configuration and find that the boundary conditions are crucial. In three-dimensional space, we show that the extension is always a monotonic function of the applied force. On the other hand, for a filament confined on a cylinder, the twisting rigidity and torque coupled to the cross section are irrelevant in forming a helix if the filament is isotropic and under free boundary condition. However, the twisting rigidity and the torque coupled to the cross section become crucial when the Euler angle at two ends of the filament are fixed. Particularly, the extension of a helix can subject to a first-order transition so that in such a condition a biopolymer can act as a switch or sensor in some biological processes. We also present several phase diagrams to provide the conditions to form a helix.

Directional transport of colloids inside a bath of self-propelling walkers.

We present a setup in which passive colloids inside a solvent are moved to the boundaries of the container. The directional transport is facilitated by self-propelling microparticles ("walkers") with an activity gradient, which reduces their propulsion in the vicinity of bounding walls. An attractive interaction leads to the adsorption of walkers onto the colloid-surfaces in regions of low walker activity. It is shown that the activity gradient generates a free energy gradient which in turn acts as a driving force on the passive colloids. We carry out molecular dynamics simulations and present approaches to a theoretical description of the involved processes. Although the simulation data are not reproduced on a fully quantitative level, their qualitative features are covered by the model. The effect described here may be applied to facilitate a directional transport of drugs or to eliminate pollutants.

Mixed brush made of 4-arm stars and linear chains: MD simulations.

We investigate the structural properties of binary polymer brushes, composed of functional 4-armed star polymers and chemically identical linear polymers of different molecular weights. The molecular dynamics simulations confirm recent self-consistent field studies, in which a considerable potential of these systems for the design of switchable surfaces has been claimed. The length of the linear chains serves as a control parameter, which, while passing over a critical value, induces a sharp transition of the molecular conformation. We investigate these transitions at different grafting densities and summarize our findings in a phase diagram. The temperature dependence of the brush structure is investigated in a non-selective solvent, and non-trivial variations of the surface composition are observed. The quantity of these latter effects would be insufficient to build switchable systems, and we argue that a minor quantity of solvent selectivity would suffice to enable the desired feature of an environment-responsive coating.

Polyelectrolyte brushes in external fields: molecular dynamics simulations and mean-field theory.

A mean-field model is developed to predict the layer-thickness of sparse and salt-free polyelectrolyte brushes, exposed to an external electric field, which attracts the polyelectrolytes to the substrate. In molecular dynamics simulations, it is shown that a fraction of polymers collapses entirely to screen the charge of the substrate. The remaining brushes are then treated as field-free brushes at reduced grafting density. The mean-field model may thus be applied to field-free brushes, both in their osmotic and their weak charge regimes. It yields simple, closed equations for the brush height and for the partition of counterions in- and outside the brushes, and accurately reproduces simulation data of the collapse of the brushes during the crossover between both charge regimes.

Counterion-mediated protein adsorption into polyelectrolyte brushes.

We present molecular dynamics simulations of the interaction of fullerene-like, inhomogeneously charged proteins with polyelectrolyte brushes. A motivation of this work is the experimental observation that proteins, carrying an integral charge, may enter like-charged polymer brushes. Simulations of varying charge distributions on the protein surfaces are performed to unravel the physical mechanism of the adsorption. Our results prove that an overall neutral protein can be strongly driven into polyelectrolyte brush whenever the protein features patches of positive and negative charge. The findings reported here give further evidence that the strong adsorption of proteins is also driven by entropic forces due to counterion release, since charged patches on the surface of the proteins can act as multivalent counterions of the oppositely charged polyelectrolyte chains. A corresponding number of mobile co- and counterions is released from the brush and the vicinity of the proteins so that the entropy of the total system increases.

Thin polymer-layer decorated, structure adjustable crystals of nanoparticles.

Flattened polymer chain decorated crystals of nanoparticles (NPs) are observed for polymer-NP mixtures confined between two parallel substrates. In order to minimize the entropy loss, polymer chains instead of NPs aggregate at the substrate surfaces when the number of NPs is high enough to have the conformation of chains significantly disturbed. Increasing NP concentration to be much higher than that of polymer chains leads to an ordered arrangement of NPs in the central region, which are sandwiched between two thin layers of polymer chains. A scaling model regarding polymer chains consisting of packed correlation blobs is provided to clarify the physics mechanism behind the formation of thin polymer layer and the crystallization of NPs. The order structure of the crystallized NPs is shown to be switchable through an adjustment of the bulk concentrations of polymer chains and NPs.

Depletion Strategies for Crystallized Layers of Two-Dimensional Nanosheets to Enhance Lithium-Ion Conductivity in Polymer Nanocomposites.

The assembly of long-range aligned structures of two-dimensional nanosheets (2DNSs) in polymer nanocomposites (PNCs) is in urgent need for the design of nanoelectronics and lightweight energy-storage materials of high conductivity for electricity or heat. These 2DNS are thin and exhibit thermal fluctuations, leading to an intricate interplay with polymers in which entropic effects can be exploited to facilitate a range of different assemblies. In molecular dynamics simulations of experimentally studied 2DNSs, we show that the layer-forming crystallization of 2DNSs is programmable by regulating the strengths and ranges of polymer-induced entropic depletion attractions between pairs of 2DNSs, as well as between single 2DNSs and a substrate surface, by exclusively tuning the temperature and size of the 2DNS. Enhancing the temperature supports the 2DNS-substrate depletion rather than crystallization of 2DNSs in the bulk, leading to crystallized layers of 2DNSs on the substrate surfaces. On the other hand, the interaction range of the 2DNS-2DNS depletion attraction extends further than the 2DNS-substrate attraction whenever the 2DNS size is well above the correlation length of the polymers, which results in a nonmonotonic dependence of the crystallization layer on the 2DNS size. It is demonstrated that the depletion-tuned crystallization layers of 2DNSs contribute to a conductive channel in which individual lithium ions (Li ions) migrate efficiently through the PNCs. This work provides statistical and dynamical insights into the balance between the 2DNS-2DNS and 2DNS-substrate depletion interactions in polymer-2DNS composites and highlights the possibilities to exploit depletion strategies in order to engineer crystallization processes of 2DNSs and thus to control electrical conductivity.

Numerical evidences for a free energy barrier in starlike polymer brushes.

The existence of a free energy barrier, which prohibits the upward motion of retracted molecules into the surface region of starlike polymer brushes, is analyzed through molecular dynamics simulations in good solvent. This barrier emerges at moderate and high grafting densities, as a result of a density-discontinuity at the branching points of the highly stretched starlike molecules. The vertical force profiles of brushes of varying densities are taken with the help of a probe-particle that is gradually moved into the brush, and the results are compared with the density profiles and their negative gradients which generate the local osmotic pressures. Chain expulsion simulations, supported by scaling theory, are conducted to understand the dynamics of individual molecules inside the brushes. We prove that the flip-rates between retracted and extended states, being of relevance for the generation of efficiently switchable, environment-responsive brush layers, are determined by the elastic tension of the stretched molecules.

Wrapping dynamics and critical conditions for active nonspherical nanoparticle uptake.

The cellular uptake of self-propelled nonspherical nanoparticles (NPs) or viruses by cell membrane is crucial in many biological processes, but its universal dynamics have yet to be elucidated. In this study, using the Onsager variational principle, we obtain a general wrapping equation for nonspherical self-propelled nanoparticles. Two analytical critical conditions are theoretically found, indicating a continuous full uptake for prolate particles and a snapthrough full uptake for oblate particles. They precisely capture the full uptake critical boundaries in the phase diagrams numerically constructed in terms of active force, aspect ratio, adhesion energy density, and membrane tension. It is found that enhancing activity (active force), reducing effective dynamic viscosity, increasing adhesion energy density, and decreasing membrane tension can significantly improve the wrapping efficiency of the self-propelled nonspherical nanoparticles. These results give a panoramic view of the uptake dynamics of active nonspherical nanoparticles, and may offer instructions for designing an effective active NP-based vehicle for controlled drug delivery.

Membrane buckling and the determination of Gaussian curvature modulus.

Biological membranes can exhibit various morphology due to the fluidity of the lipid molecules within the monolayers. The shape transformation of membranes has been well described by the classical Helfrich theory, which consists only a few phenomenological parameters, including the mean and the Gaussian curvature modulus. Though various methods have been proposed to measure the mean curvature modulus, determining the Gaussian curvature modulus remains difficult both in experiments and in simulations. In this paper we study the buckling process of a rectangular membrane and a circular membrane subject to compressive stresses and under different boundary conditions. We find that the buckling of a rectangular membrane takes place continuously, while the buckling of a circular membrane can be discontinuous depending on the boundary conditions. Furthermore, our results show that the stress-strain relationship of a buckled circular membrane can be used to determine the Gaussian curvature modulus effectively.

A Convolutional Neural Network Face Recognition Method Based on BiLSTM and Attention Mechanism.

Face recognition technology is a powerful means to capture biological facial features and match facial data in existing databases. With the advantages of noncontact and long-distance implementation, it is being used in more and more scenarios. Affected by factors such as light, posture, and background environment, the face images captured by the device are still insufficient in the recognition rate of existing face recognition models. We propose an AB-FR model, a convolutional neural network face recognition method based on BiLSTM and attention mechanism. By adding an attention mechanism to the CNN model structure, the information from different channels is integrated to enhance the robustness of the network, thereby enhancing the extraction of facial features. Then, the BiLSTM method is used to extract the timing characteristics of different angles or different time photos of the same person so that convolutional blocks can obtain more face detail information. Finally, we used the cross-entropy loss function to optimize the model and realize the correct face recognition. The experimental results show that the improved network model indicates better identification performance and stronger robustness on some public datasets (such as CASIA-FaceV5, LFW, MTFL, CNBC, and ORL). Besides, the accuracy rate is 99.35%, 96.46%, 97.04%, 97.19%, and 96.79%, respectively.

Accurate prediction of pan-cancer types using machine learning with minimal number of DNA methylation sites.

DNA methylation analysis has been applied to determine the primary site of cancer; however, robust and accurate prediction of cancer types with a minimum number of sites is still a significant scientific challenge. To build an accurate and robust cancer type prediction tool with a minimum number of DNA methylation sites, we internally benchmarked different DNA methylation site selection and ranking procedures, as well as different classification models. We used The Cancer Genome Atlas dataset (26 cancer types with 8296 samples) to train and test models and used an independent dataset (17 cancer types with 2738 samples) for model validation. A deep neural network model using a combined feature selection procedure (named MethyDeep) can predict 26 cancer types using 30 methylation sites with superior performance compared with the known methods for both primary and metastatic cancers in independent validation datasets. In conclusion, MethyDeep is an accurate and robust cancer type predictor with the minimum number of DNA methylation sites; it could help the cost-effective clarification of cancer of unknown primary patients and the liquid biopsy-based early screening of cancers.