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Hemolytic peptides are therapeutic peptides that damage red blood cells. However, therapeutic peptides used in medical treatment must exhibit low toxicity to red blood cells to achieve the desired therapeutic effect. Therefore, accurate prediction of the hemolytic activity of therapeutic peptides is essential for the development of peptide therapies. In this study, a multi-feature cross-fusion model, HemoFuse, for hemolytic peptide identification is proposed. The feature vectors of peptide sequences are transformed by word embedding technique and four hand-crafted feature extraction methods. We apply multi-head cross-attention mechanism to hemolytic peptide identification for the first time. It captures the interaction between word embedding features and hand-crafted features by calculating the attention of all positions in them, so that multiple features can be deeply fused. Moreover, we visualize the features obtained by this module to enhance its interpretability. On the comprehensive integrated dataset, HemoFuse achieves ideal results, with ACC, SP, SN, MCC, F1, AUC, and AP of 0.7575, 0.8814, 0.5793, 0.4909, 0.6620, 0.8387, and 0.7118, respectively. Compared with HemoDL proposed by Yang et al., it is 3.32%, 3.89%, 5.93%, 10.6%, 8.17%, 5.88%, and 2.72% higher. Other ablation experiments also prove that our model is reasonable and efficient. The codes and datasets are accessible at https://github.com/z11code/Hemo. [ABSTRACT FROM AUTHOR]

As a class of biologically active molecules with significant immunomodulatory and anti-inflammatory effects, anti-inflammatory peptides have important application value in the medical and biotechnology fields due to their unique biological functions. Research on the identification of anti-inflammatory peptides provides important theoretical foundations and practical value for a deeper understanding of the biological mechanisms of inflammation and immune regulation, as well as for the development of new drugs and biotechnological applications. Therefore, it is necessary to develop more advanced computational models for identifying anti-inflammatory peptides. In this study, we propose a deep learning model named DAC-AIPs based on variational autoencoder and contrastive learning for accurate identification of anti-inflammatory peptides. In the sequence encoding part, the incorporation of multi-hot encoding helps capture richer sequence information. The autoencoder, composed of convolutional layers and linear layers, can learn latent features and reconstruct features, with variational inference enhancing the representation capability of latent features. Additionally, the introduction of contrastive learning aims to improve the model's classification ability. Through cross-validation and independent dataset testing experiments, DAC-AIPs achieves superior performance compared to existing state-of-the-art models. In cross-validation, the classification accuracy of DAC-AIPs reached around 88%, which is 7% higher than previous models. Furthermore, various ablation experiments and interpretability experiments validate the effectiveness of DAC-AIPs. Finally, a user-friendly online predictor is designed to enhance the practicality of the model, and the server is freely accessible at http://dac-aips.online. [ABSTRACT FROM AUTHOR]

Tertiary lymphoid structures (TLSs) play a crucial role in determining prognosis and response to immunotherapy in several solid malignancies. Nevertheless, the effect of TLS-associated gene signature based on The Cancer Genome Atlas (TCGA) cohort in patients with breast cancer (BRCA) remains controversial. Based on TCGA-BRCA dataset (n = 866), 9-gene was identified to construct an TLS signature and further analyzed its prognostic value. Then, we explored the relationship of this TLS signature with molecular subtype, immune microenvironment, tumor mutational burden (TMB). High-TLS signature patients had a better overall survival (OS) than low-TLS signature patients, consistent with the results in the METABRIC cohort. Multivariate analysis revealed that TLS signature remained an independent prognostic indicator for OS. In addition, we established a nomogram with the integration of TLS signature and other independent variables to predict individual risk of death. The comprehensive results showed that patients with high TLS signature benefit from immunotherapy; the signature was also correlated with inhibition of cell proliferation pathways, low TP53 mutation rate, high infiltration of B cells, CD8 + T cells, CD4 + T cells, and M1 macrophages. Therefore, TLS signature is a promising biomarker to distinguish the prognosis and immune microenvironment in BRCA. [ABSTRACT FROM AUTHOR]

The integrity of cement–salt rock interfaces (CSI) in salt cavern gas storage wells may be damaged during circulating gas injection, production, and long-term gas pressure maintenance. In this study, permeability tests were conducted on cement–salt rock samples under cyclic loading confining pressure at different holding times. The cracks and micro-annuli at the interface were visualised using X-ray computed tomography. The bonding strength of the interface after the permeability test was measured in a triaxial environment. The results showed that with an increase in the number of cycles, the cumulative volumetric strain rate and permeability first decreased rapidly and then tended to stabilise, and the decreasing amplitude was positively correlated with the holding time. When the holding times were 10, 30, and 60 min, the permeability change ratios were 0.844, 0.762, and 0.638, respectively, after one cycle and 0.722, 0.444, and 0.420, respectively, after five cycles. The permeability of CSI is mainly influenced by the holding time. The mechanical properties of the CSI specimens with similar permeabilities showed differences at different holding times. Cyclic loading easily degrades the mechanical properties, which can be improved by extending the holding time. The CSI is the main channel for gas leakage, extending the holding time, and increasing the number of cycles can effectively improve the pore structure of CSI and enhance the cement sheath integrity. This study provides a reference for long-term safe operation of salt cavern gas storage wells. Highlights: Permeability tests and triaxial shear bond strength tests after permeability tests were performed on a cement–salt rock interface. The effects of cyclic loading and holding time on the permeability and mechanical properties of the cement–salt rock interface were investigated. Microcracks' distribution within the cement–salt rock interface under cyclic loads was characterised using computed tomography. [ABSTRACT FROM AUTHOR]

Highlights: A comprehensive summary of the representative promising applications of metal halide perovskite materials, including traditional optoelectronic devices (solar cells, light-emitting diodes, photodetectors, lasers), and cutting-edge technologies in terms of neuromorphic devices (artificial synapses and memristors) and pressure-induced emission. For each application, the fundamentals of the field, the current progress and the remaining challenges are provided, based on the up-to-date works. Nowadays, the soar of photovoltaic performance of perovskite solar cells has set off a fever in the study of metal halide perovskite materials. The excellent optoelectronic properties and defect tolerance feature allow metal halide perovskite to be employed in a wide variety of applications. This article provides a holistic review over the current progress and future prospects of metal halide perovskite materials in representative promising applications, including traditional optoelectronic devices (solar cells, light-emitting diodes, photodetectors, lasers), and cutting-edge technologies in terms of neuromorphic devices (artificial synapses and memristors) and pressure-induced emission. This review highlights the fundamentals, the current progress and the remaining challenges for each application, aiming to provide a comprehensive overview of the development status and a navigation of future research for metal halide perovskite materials and devices. [ABSTRACT FROM AUTHOR]

Using density functional theory, we explored the electronic and adsorption properties of pristine state and B-, N-, Al-, P-doped PdSe2 monolayers for NO2 sensing. It has been obtained that the adsorption energy of doped systems for NO2 is enhanced compared to the pristine PdSe2 monolayer. Among them, the Al- and P-doped PdSe2 monolayers are chemisorbed for NO2. The formation of covalent bonds is not conducive to desorption, and the recovery time is significantly large. In contrast, the B- and N-doped systems are physisorbed with NO2, and the recovery time of N-doped is shorter than that of the B-doped system. Therefore, the N-doped PdSe2 monolayer is an effective means to improve PdSe2 monolayer adsorption and sensing ability for NO2. [ABSTRACT FROM AUTHOR]

Hypertension (HT) is a general disease, and also one of the most ordinary and major causes of cardiovascular disease. Some diseases are caused by high blood pressure, including impairment of heart and kidney function, cerebral hemorrhage and myocardial infarction. Due to the limitations of laboratory methods, bioactive peptides for the treatment of HT need a long time to be identified. Therefore, it is of great immediate significance for the identification of anti-hypertensive peptides (AHTPs). With the prevalence of machine learning, it is suggested to use it as a supplementary method for AHTPs classification. Therefore, we develop a new model to identify AHTPs based on multiple features and deep learning. And the deep model is constructed by combining a convolutional neural network (CNN) and a gated recurrent unit (GRU). The unique convolution structure is used to reduce the feature dimension and running time. The data processed by CNN is input into the recurrent structure GRU, and important information is filtered out through the reset gate and update gate. Finally, the output layer adopts Sigmoid activation function. Firstly, we use Kmer, the deviation between the dipeptide frequency and the expected mean (DDE), encoding based on grouped weight (EBGW), enhanced grouped amino acid composition (EGAAC) and dipeptide binary profile and frequency (DBPF) to extract features. For Kmer, DDE, EBGW and EGAAC, it is widely used in the field of protein research. DBPF is a new feature representation method designed by us. It corresponds dipeptides to binary numbers, and finally obtains a binary coding file and a frequency file. Then these features are spliced together and input into our proposed model for prediction and analysis. After a tenfold cross-validation test, this model has a better competitive advantage than the previous methods, and the accuracy is 96.23% and 99.10%, respectively. From the results, compared with the previous methods, it has been greatly improved. It shows that the combination of convolution calculation and recurrent structure has a positive impact on the classification of AHTPs. The results show that this method is a feasible, efficient and competitive sequence analysis tool for AHTPs. Meanwhile, we design a friendly online prediction tool and it is freely accessible at http://ahtps.zhanglab.site/. [ABSTRACT FROM AUTHOR]

Al-doped zinc oxide (AZO) thin films are deposited on glass substrate by RF magnetron sputtering under reactive plasma with different contents of hydrogen (H2). Following optimized volume ratio of H2/(H2 + Ar) in working gas for large area uniformity, working pressure, and substrate temperature effects are investigated systematically to achieve AZO:H film with excellent optoelectronic properties. The crystallization, sheet resistance, and Hall mobility of AZO:H film are enhanced with reducing working pressure. AZO:H films perform average transmittance of 90 ± 2% and sheet resistance of 16.5 ± 2.0 Ω/sq regardless of the substrate temperature as the sputtering process is combined with H2-assited sputtering deposition. As the substrate temperature is 150 °C, the prepared film is with the lowest resistivity of 6.0 × 10− 4 Ω cm and large Hall mobility of ~ 37 cm2 V− 1 s− 1. With the optimization of AZO:H films, the efficiency of Cu2ZnSn(S,Se)4 device is improved by 38% compared to the conventional preparation process. This offers incredible flexibility of substrate temperature selection for the application of AZO:H thin film as window layer in solar cells and other optoelectronic devices. [ABSTRACT FROM AUTHOR]

Purpose: Forceful needle-nerve contact and high subepineural pressures and are recognised causes of nerve damage. Pressure and force measurements are necessary to inform the mechanisms of nerve injury, build virtual simulator environments and provide operator feedback during simulation training. However, the range of pressures and forces encountered at tissue layers during targeted needle insertion and fluid injection are not known. Methods: We built a needle that recorded in-line pressure during fluid injection and continuously measured force at the needle tip. Two anaesthetists were randomised to insert a 21 g block needle at 48 nerve sites on both sides of 3 soft embalmed Thiel cadavers. Our objective was to measure pressure and force during the course of targeted nerve injection at epimysium, in perineural tissue, on epineurium and during subepineural injection. At each interface, we infused a 0.5 ml bolus of embalming solution at a rate of 12 ml min−1 and recorded the pressure response. Force was measured continuously in the background throughout the procedure. Results: Pressure was greater at epineurium and within subepineurium than perineural tissue, geometric ratio (95% CI) 4.7 (3.0–7.3) kPa and 3.8 (2.5–5.7) kPa, respectively, both P < 0.0001. Force on nerve contact and on nerve penetration was greater than force in perineural tissue, geometric ratios (95% CI) 3.0 (1.9–4.7) N and 3.6 (2.2–7.5) N, respectively, both P < 0.0001. On nerve contact, 1 in 6 insertions were ≥ 5 N Conclusions: Despite valid infusion pressures, anaesthetists exerted excessive force on nerves. [ABSTRACT FROM AUTHOR]

Controllable anisotropic growth of perovskite nanocrystals (NCs) is challenging since it is difficult to separate the nucleation and growth processes. Here, a two-step nucleation strategy is proposed to control the binding interaction between surface ligands and NCs, resulting in facet-induced coordination competition. Oleic acid as surface activated ligand leads to the formation of defective lead bromine octahedron, and the binding interaction between 4-dodecylbenzenesulfonic acid and lead atoms promotes the formation of two kinds of binding interactions. Based on this strategy, the anisotropic growth of CsPbBr3 nanoplatelet (NPLs) with adjusted length from 11.4 to 24 nm, and the evolution of NPLs from stacked to tongue-shaped have been realized. Elemental line scan reveals the sulfur atoms mainly distribute at the edge of NPLs. Furthermore, binding energy calculation and experimental results illustrate the coordination competition of different binding interaction on specific facets induces the anisotropic growth of NPLs. Importantly, strong emission anisotropy of highly ordered NPLs with polarization ratio up to 0.58 is illustrated. This work not only deepens our understanding of the controllable synthesis of perovskite NCs, but also provides a reference for the regulation of light emitting diode and soler cells. [ABSTRACT FROM AUTHOR]

The IEEE 802.11ax standard defined a set of new specifications to improve spectrum efficiency, power efficiency, and reliability of future wireless local area networks (WLANs). Among these specifications, the uplink multi-user multiple-input, multiple-output (MU-MIMO) remedies the uplink shortcomings of existing WLANs and enables high-efficiency uplink transmissions. This paper considers the problem of adaptive modulation and coding (AMC) for uplink MU-single-input, multiple-output (MU-SIMO) in the upcoming WLANs. Our target is to select the appropriate modulation and coding scheme (MCS) for the existing users so as to maximize the throughput and at the same time guarantee that all users can satisfy a frame error constraint. We adopt a deep learning approach to tackle this problem. We propose to let the access point (AP) leverage the estimated channel state information (CSI) of all users along with the estimated noise standard deviation as the input features to a deep convolutional neural network (DCNN) that is trained to perform AMC. Simulation results reveal that the proposed DCNN for the uplink MU-SIMO AMC outperforms rival machine learning techniques such as fully-connected deep neural network (DNN), k-nearest neighbors (KNN), support vector machines (SVM), Gaussian Naive Bayes (GNB), and random forest (RF) in terms of the throughput and the frame error rate (FER). [ABSTRACT FROM AUTHOR]

Quantum geometric tensor, including a symmetric real part defined as quantum metric and an antisymmetric part defined as Berry curvature, is essential for understanding many phenomena. In this study, we investigated the photogalvanic effect of semiconductors with time-reversal-invariant and spatial inversion symmetries using the quantum kinetic equation. We concluded that the integral of the symmetric (antisymmetric) part of quantum geometric tensor on the equal energy surface in momentum space, satisfying the resonance condition, is related to the generation rate of carriers in semiconductors under linearly (circularly) polarized light. Under additional bias voltage, the dc photocurrent is proportional to the bias voltage. Our study provided an alternative interpretation for the photogalvanic effect in the view of quantum geometric tensor. Additionally, it classified the intrinsic difference between linearly and circularly polarized optical fields. [ABSTRACT FROM AUTHOR]

With the rapid evolution of microelectronics, the field of integrated circuits is facing unprecedented challenges. Traditional silicon-based transistors cannot maintain the advantages of high performance during the process of further ultra-scaling due to severe short-channel effects. Two-dimensional (2D) materials are potential channel materials that can replace silicon. Herein, 2D SiP is predicted to have a band gap of 1.49 eV with anisotropic electronic properties by means of first-principles calculations, which is suitable as a channel candidate of transistors. Hence, we investigate the ballistic transport properties of 2D SiP double-gate metal oxide semiconductor field-effect transistors (MOSFETs) by using ab initio quantum transport simulations. Despite anisotropic electronic properties of 2D SiP, the performances of monolayer SiP MOSFETs have weak directional dependence due to high valley degeneracy. The n-MOSFETs with 10-nm gate length can fulfill the high-performance requirements of the International Roadmap for Devices and Systems 2020 Edition (IRDS 2020). However, the p-MOSFETs cannot fulfill the demands of IRDS 2020 because of heavy hole effective masses. Considering the appropriate on-current of 1292 μA/μm for SiP n-MOSFETs, 2D SiP could be utilized as a potential channel material in the next-generation FETs. [ABSTRACT FROM AUTHOR]