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.

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.

Allosterically inhibited PFKL via prostaglandin E2 withholds glucose metabolism and ovarian cancer invasiveness.

Metastasis is the leading cause of high ovarian-cancer-related mortality worldwide. Three major processes constitute the whole metastatic cascade: invasion, intravasation, and extravasation. Tumor cells often reprogram their metabolism to gain advantages in proliferation and survival. However, whether and how those metabolic alterations contribute to the invasiveness of tumor cells has yet to be fully understood. Here we performed a genome-wide CRISPR-Cas9 screening to identify genes participating in tumor cell dissemination and revealed that PTGES3 acts as an invasion suppressor in ovarian cancer. Mechanistically, PTGES3 binds to phosphofructokinase, liver type (PFKL) and generates a local source of prostaglandin E2 (PGE2) to allosterically inhibit the enzymatic activity of PFKL. Repressed PFKL leads to downgraded glycolysis and the subsequent TCA cycle for glucose metabolism. However, ovarian cancer suppresses the expression of PTGES3 and disrupts the PTGES3-PGE2-PFKL inhibitory axis, leading to hyperactivation of glucose oxidation, eventually facilitating ovarian cancer cell motility and invasiveness.

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.

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.

Copy number alteration features in pan-cancer homologous recombination deficiency prediction and biology.

Homologous recombination deficiency (HRD) renders cancer cells vulnerable to unrepaired double-strand breaks and is an important therapeutic target as exemplified by the clinical efficacy of poly ADP-ribose polymerase (PARP) inhibitors as well as the platinum chemotherapy drugs applied to HRD patients. However, it remains a challenge to predict HRD status precisely and economically. Copy number alteration (CNA), as a pervasive trait of human cancers, can be extracted from a variety of data sources, including whole genome sequencing (WGS), SNP array, and panel sequencing, and thus can be easily applied clinically. Here we systematically evaluate the predictive performance of various CNA features and signatures in HRD prediction and build a gradient boosting machine model (HRDCNA) for pan-cancer HRD prediction based on these CNA features. CNA features BP10MB[1] (The number of breakpoints per 10MB of DNA is 1) and SS[ > 7 & <=8] (The log10-based size of segments is greater than 7 and less than or equal to 8) are identified as the most important features in HRD prediction. HRDCNA suggests the biallelic inactivation of BRCA1, BRCA2, PALB2, RAD51C, RAD51D, and BARD1 as the major genetic basis for human HRD, and may also be applied to effectively validate the pathogenicity of BRCA1/2 variants of uncertain significance (VUS). Together, this study provides a robust tool for cost-effective HRD prediction and also demonstrates the applicability of CNA features and signatures in cancer precision medicine.

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.

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.

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.

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.

Force-induced wrapping phase transition in activated cellular uptake.

Intracellular pathogens, including all viruses and many bacteria, enter a host cell through either passive endocytosis or active self-propulsion. Though the cellular uptake of passive particles via endocytic process has been studied extensively, little work has been done on the active entry of self-propelled pathogens, such as Listeria monocytogenes. Here, we present a theoretical model to investigate the adhesive wrapping of a self-propelled particle by a plasma membrane, and find a type of first-order wrapping transition from a small partial wrapping state to a large partial wrapping state triggered by the active force. The phase diagram displays more complex behaviors compared with the passive wrapping mediated merely by adhesion. We also find that a tubular protrusion can be formed if the active force exceeds a force barrier. These results may provide a useful guidance to the study of activity-driven cellular entry of active particles into cells.

Addressing the Enzyme-independent tumor-promoting function of NAMPT via PROTAC-mediated degradation.

Aberrant overexpression of nicotinamide phosphoribosyltransferase (NAMPT) has been reported in a variety of tumor cells and is a poor prognosis factor for patient survival. It plays an important role in tumor cell proliferation, acting concurrently as an nicotinamide adenine dinucleotide (NAD+) synthase and, unexpectedly, as an extracellular signaling molecule for several tumor-promoting pathways. Although previous efforts to modulate NAMPT activity were limited to enzymatic inhibitors with low success in clinical studies, protein degradation offers the possibility to simultaneously disrupt NAMPT's enzyme activity and ligand capabilities. Here we report the development of two highly selective proteolysis-targeting chimeras (PROTACs) that promote NAMPT degradation in a cereblon-dependent manner. Both PROTAC degraders outperform a clinical candidate, FK866, in killing effect on hematological tumor cells. These results emphasize the importance and feasibility of applying PROTACs as a superior strategy for targeting proteins with multiple tumor-promoting functions like NAMPT, which is not easily achieved by conventional enzymatic inhibitors.

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.

Screening confinement of entanglements: Role of a self-propelling end inducing ballistic chain reptation.

Synthetic and natural nanomaterials with self-propelling mechanisms continue to be explored to boost chain mobility beyond normal reptation in the crowded environments of entangled chains. Here we employ scaling theory and numerical simulations to demonstrate that activating one chain end of a singular or isolated chain boosts entanglement-constrained chain reptation from the one-dimensional diffusive mobility as described by the de Gennes-Edwards-Doi model to ballistic motion along the entanglement tube contour. The active chain is effectively screened from the constraint of entanglements on length scales exceeding the tube size.

Oncogenic EFNA4 Amplification Promotes Lung Adenocarcinoma Lymph Node Metastasis.

Lymph nodes metastases are common in patients with lung cancer. Additionally, those patients are often at a higher risk for death from lung tumor than those with tumor-free lymph nodes. Somatic DNA alterations are key drivers of cancer, and copy number alterations (CNAs) are major types of DNA alteration that promote lung cancer progression. Here, we performed genome-wide DNA copy number analysis, and identified a novel lung-cancer-metastasis-related gene, EFNA4. The EFNA4 genome locus was significantly amplified, and EFNA4 mRNA expression was significantly up-regulated in lung cancer compared with normal lung tissue, and also in lung cancer with lymph node metastases compared with lung cancer without metastasis. EFNA4 encodes Ephrin A4, which is the ligand for Eph receptors. The function of EFNA4 in human lung cancer remains largely unknown. Through cell line experiments we showed that EFNA4 overexpression contributes to lung tumor cells growth, migration and adhesion. Conversely, EFNA4 knockdown or knockout led to the growth suppression of cells and tumor xenografts in mice. Lung cancer patients with EFNA4 overexpression have poor prognosis. Together, by elucidating a new layer of the role of EFNA4 in tumor proliferation and migration, our study demonstrates a better understanding of the function of the significantly amplified and overexpressed gene EFNA4 in lung tumor metastasis, and suggests EFNA4 as a potential target in metastatic lung cancer therapy.

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.

Droplet dynamics driven by electrowetting.

Even though electrowetting-on-dielectric (EWOD) is a useful strategy in a wide array of biological and engineering processes with numerous droplet-manipulation applications, there is still a lack of complete theoretical interpretation on the dynamics of electrowetting. In this paper we present an effective theoretical model and use the Onsager variational principle to successfully derive general dynamic shape equations for electrowetting droplets in both the overdamped and underdamped regimes. It is found that the spreading and retraction dynamics of a droplet on EWOD substrates can be fairly well captured by our model, which agrees with previous experimental results very well in the overdamped regime. We also confirm that the transient dynamics of EW can be characterized by a timescale independent of liquid viscosity, droplet size, and applied voltage. Our model provides a complete fundamental explanation of EW-driven spreading dynamics, which is important for a wide range of applications, from self-cleaning to novel optical and digital microfluidic devices.

Quantification of Neoantigen-Mediated Immunoediting in Cancer Evolution.

Immunoediting includes three temporally distinct stages, termed elimination, equilibrium, and escape, and has been proposed to explain the interactions between cancer cells and the immune system during the evolution of cancer. However, the status of immunoediting in cancer remains unclear, and the existence of neoantigen depletion in untreated cancer has been debated. Here we developed a distribution pattern-based method for quantifying neoantigen-mediated negative selection in cancer evolution. The method can provide a robust and reliable quantification for immunoediting signal in individual patients with cancer. Moreover, this method demonstrated the prevalence of immunoediting in the immunotherapy-naive cancer genome. The elimination and escape stages of immunoediting can be quantified separately, where tumor types with strong immunoediting-elimination exhibit a weak immunoediting-escape signal, and vice versa. The quantified immunoediting-elimination signal was predictive of clinical response to cancer immunotherapy. Collectively, immunoediting quantification provides an evolutionary perspective for evaluating the antigenicity of neoantigens and reveals a potential biomarker for precision immunotherapy in cancer. SIGNIFICANCE: Quantification of neoantigen-mediated negative selection in cancer progression reveals distinct features of cancer immunoediting and can serve as a potential biomarker to predict immunotherapy response.

Extrachromosomal DNA formation enables tumor immune escape potentially through regulating antigen presentation gene expression.

Extrachromosomal DNA (ecDNA) is a type of circular and tumor specific genetic element. EcDNA has been reported to display open chromatin structure, facilitate oncogene amplification and genetic material unequal segregation, and is associated with poor cancer patients' prognosis. The ability of immune evasion is a typical feature for cancer progression, however the tumor intrinsic factors that determine immune evasion remain poorly understood. Here we show that the presence of ecDNA is associated with markers of tumor immune evasion, and obtaining ecDNA could be one of the mechanisms employed by tumor cells to escape immune surveillance. Tumors with ecDNA usually have comparable TMB and neoantigen load, however they have lower immune cell infiltration and lower cytotoxic T cell activity. The microenvironment of tumors with ecDNA shows increased immune-depleted, decreased immune-enriched fibrotic types. Both MHC class I and class II antigen presentation genes' expression are decreased in tumors with ecDNA, and this could be the underlying mechanism for ecDNA associated immune evasion. This study provides evidence that ecDNA formation is an immune escape mechanism for cancer cells.

Free-Standing, Flexible Carbon@MXene Films with Cross-Linked Mesoporous Structures toward Supercapacitors and Pressure Sensors.

The preparation of multifunctional materials with low cost and simple synthesis processes is still challenging. Herein, by employing various sizes (50-500 nm) of polystyrene (PS) spheres as templates, different free-standing carbon@MXene films with three-dimensional (3D) mesoporous structures were fabricated through a simple multistep route. The microstructure, composition, mechanical property, conductivity, electrochemical activity, and sensing characteristics of these carbon@MXene films were investigated in detail. The intercalation of the PS spheres can effectively reduce the self-accumulation of MXene nanosheets and construct 3D cross-linked mesoporous structures, therefore broadening the ion transport channels and exposing more active sites of carbon@MXene films. When applied in a symmetrical supercapacitor, the optimized carbon@MXene electrode has a satisfactory specific capacitance of 447.67 F g-1 at a current density of 1 A g-1. Moreover, the 3D mesoporous structures of carbon@MXene films can significantly improve the sensitivity of the resultant pressure sensors with excellent stability (10,000 cycles). Thus, such mesoporous carbon@MXene films prepared by a facile yet robust route will be a versatile material for many applications.