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Despite a substantial body of research, we lack fundamental understanding of the pathophysiology of COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) including pulmonary and cardiovascular outcomes, in part due to limitations of murine models. Most models use transgenic mice (K18) that express the human (h) angiotensin converting enzyme 2 (ACE2), ACE2 knock-in (KI) mice, or mouse-adapted strains of SARS-CoV-2. Further, many SARS-CoV-2 variants produce fatal neurologic disease in K18 mice and most murine studies focus only on acute disease in the first 14 days post inoculation (dpi). To better enable understanding of both acute (<14 dpi) and post-acute (>14 dpi) infection phases, we describe the development and characterization of a novel non-lethal KI mouse that expresses both the ACE2 and transmembrane serine protease 2 (TMPRSS2) genes (hACE2/hTMPRSS2). The human genes were engineered to replace the orthologous mouse gene loci but remain under control of their respective murine promoters, resulting in expression of ACE2 and TMPRSS2 instead of their murine counterparts. After intranasal inoculation with an omicron strain of SARS-CoV-2, hACE2/hTMPRSS2 KI mice transiently lost weight but recovered by 7 dpi. Infectious SARS-CoV-2 was detected in nasopharyngeal swabs 1-2 dpi and in lung tissues 2-6 dpi, peaking 4 dpi. These outcomes were similar to those in K18 mice that were inoculated in parallel. To determine the extent to which hACE2/hTMPRSS2 KI mice are suitable to model pulmonary and cardiovascular outcomes, physiological assessments measuring locomotion, behavior and reflexes, biomonitoring to measure cardiac activity and respiration, and micro computed tomography to assess lung function were conducted frequently to 6 months post inoculation. Male but not female SARS-CoV-2 inoculated hACE2/hTMPRSS2 KI mice showed a transient reduction in locomotion compared to control saline treated mice. No significant changes in respiration

Soilborne pathogens affect agricultural productivity and soil functions, yet their specific impacts of pathogen infections on the soil and plant root-associated microbiomes, the key determinants of plant health, remain unclear. Filling this knowledge gap is required to understanding microbial ecological responses, and to developing biological tools to manage and predict the prevalence and severity of soilborne diseases, which can improve soil health, and plant yield. We hypothesized that soilborne pathogens impact the diversity, function, and ecological interactions in soil microbiomes and form a pathobiome. To test the hypothess, we conducted field sampling in 35 cotton fields in Australia and collected a total of 560 soil samples, which included samples from the rhizosphere (the area in close contact with the roots) and bulk soils. We aimed to investigate how soil microbiomes and key soil properties respond to Verticillium wilt (VW) of cotton, which is caused by the soilborne pathogen Verticillium dahliae. We found that the presence of the pathogen altered the microbiome structure in both bulk and rhizosphere soils. Notably, healthy soils exhibited more complex microbial networks then diseased soils. Furthermore, a putative pathobiome consisting of various microbial taxa that could influence pathogen infection was identified. Specific pathobiome taxa, such as Gibellulopsis spp., displayed a positive association with V. dahliae. In contrast, known biocontrol agents (BCAs) such as Bacillus spp., Fusarium spp., and Talaromyces spp., were negatively correlated with pathogen abundance. After isolation, pathobiome BCA members demonstrated strong biocontrol activity against V. dahliae in vitro, indicating their potential for enhancing resistance to pathogen invasion in agricultural soils. Moreover, shotgun sequencing analysis revealed a gene encoding beta-glucosidase as a putative indicator of soil health. Structural Equation Modelling showed that VW disease incidence is

A growing rock engineering activity in cold regions is facing the threat of freeze-thaw (FT) weathering, especially in high mountains where the sunny-shady slope effects strongly control the difference in weathering behavior of rocks. In this paper, an investigation of the degradation of petrophysical characteristics of sandstone specimens subjected to FT cycle tests to simulate the sunny-shady slope effects is presented. To this aim, non-destructive and repeatable testing techniques including weight, ultrasonic waves, and nuclear magnetic resonance methods on standard specimens were performed. For the sunny slope specimens, accompanied by the enlargement of small pores, 100 FT cycles caused a significant decrease in P-wave velocity with an average of 23%, but a consistent rise of 0.18% in mass loss, 34% in porosity, 67% in pore geometrical mean radius, and a remarkable 14.5-fold increase in permeability. However, slight changes with some abnormal trends in physical parameters of the shady slope specimens were observed during FT cycling, which can be attributed to superficial granular disaggregation and pore throat obstruction. Thermal shocks enhance rock weathering on sunny slopes during FT cycles, while FT weathering on shady slopes is restricted to the small pores and the superficial cover. These two factors are primarily responsible for the differences in FT weathering intensity between sunny and shady slopes. The conclusions derived from the interpretation of the experimental results may provide theoretical guidance for the design of slope-failure prevention measures and the selection of transportation routes in cold mountainous regions.

The controlled vapor-phase synthesis of two-dimensional (2D) transition metal dichalcogenides (TMDs) is essential for functional applications. While chemical vapor deposition (CVD) techniques have been successful for transition metal sulfides, extending these methods to selenides and tellurides often faces challenges due to uncertain roles of hydrogen (H-2) in their synthesis. Using CVD growth of MoSe2 as an example, this study illustrates the role of a H-2-free environment during temperature ramping in suppressing the reduction of MoO3, which promotes effective vaporization and selenization of the Mo precursor to form MoSe2 monolayers with excellent crystal quality. As-synthesized MoSe2 monolayer-based field-effect transistors show excellent carrier mobility of up to 20.9 cm(2)/(Vs) with an on-off ratio of 7 x 10(7). This approach can be extended to other TMDs, such as WSe2, MoTe2, and MoSe2/WSe2 in-plane heterostructures. Our work provides a rational and facile approach to reproducibly synthesize high-quality TMD monolayers, facilitating their translation from laboratory to manufacturing.

Pre-existing fractures in rock engineering significantly affect the entire structural stability. To deepen the understanding of the fracture mechanism of fractured rocks under shear loading, a numerical study based on the distinct element method was conducted to investigate the shear behaviors of rock fractures. A discrete element model with fractures of Barton's ten standard profiles was established, and shear simulations under different normal stresses and joint roughness coefficient (JRC) were carried out. The simulation results show that the shear stress–displacement curve can be divided into three stages: elastic loading stage, inelastic stage and stress drop stage. The shear strength, internal friction angle and cohesion increase with the increase of normal stress and JRC. These macroscopic mechanical characteristics are consistent with the results of previous experimental studies. Most of the microcracks generated during the shearing process are tensile microcracks, which are first formed at the steep position of the fracture profile line, and the proportion of shear microcracks is less than 10%. In addition, the contact force between particles is mainly compressive stress, which is greater in magnitude and density than tensile stress. As the shear proceeds, the displacement of the particles gradually changes from non-uniform distribution to uniform distribution., QC 20240625

Lightweight boron carbon nitride foams which are a hybrid of graphene and hexagonal boron nitride (hBN), are produced using the nickel template method and remain freestanding after the etching of nickel without the stabilization of a polymer during etching. Chemical vapor deposition (CVD) precursors with varying carbon content were utilized, but only a 10% range of carbon atomic percentage variation is observed in the resulting boron carbon nitride foams due to the high carbon solubility of nickel. However, the cooling rate during the CVD process has a much more significant effect on the carbon content. Moreover, a 6.5-fold increase in resistivity is observed for our foams compared to a graphene foam with an overall trend of decreasing resistance with increasing carbon content. Furthermore, when the foam is infiltrated with epoxy to form a composite at 0.3% volumetric percentage concentration, there is a 27% increase in the thermal conductivity over hBN foam composites. These foams are suitable to use as a substitute for graphene foams when a lower electrical conductivity is desired and could potentially be used as a thermal interface material if a higher pore density nickel template is utilized.

The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI). However, replicating such advancements in open-source models has been challenging. This paper introduces InternLM2, an open-source LLM that outperforms its predecessors in comprehensive evaluations across 6 dimensions and 30 benchmarks, long-context modeling, and open-ended subjective evaluations through innovative pre-training and optimization techniques. The pre-training process of InternLM2 is meticulously detailed, highlighting the preparation of diverse data types including text, code, and long-context data. InternLM2 efficiently captures long-term dependencies, initially trained on 4k tokens before advancing to 32k tokens in pre-training and fine-tuning stages, exhibiting remarkable performance on the 200k ``Needle-in-a-Haystack" test. InternLM2 is further aligned using Supervised Fine-Tuning (SFT) and a novel Conditional Online Reinforcement Learning from Human Feedback (COOL RLHF) strategy that addresses conflicting human preferences and reward hacking. By releasing InternLM2 models in different training stages and model sizes, we provide the community with insights into the model's evolution.

DNA codes have many applications, such as in data storage, DNA computing, etc. Good DNA codes have large sizes and satisfy some certain constraints. In this paper, we present a new construction method for reversible DNA codes. We show that the DNA codes obtained using our construction method can satisfy some desired constraints and the lower bounds of the sizes of some DNA codes are better than the known results. We also give new lower bounds on the sizes of some DNA codes of lengths $80$, $96$ and $160$ for some fixed Hamming distance $d$.

The math abilities of large language models can represent their abstract reasoning ability. In this paper, we introduce and open-source our math reasoning LLMs InternLM-Math which is continue pre-trained from InternLM2. We unify chain-of-thought reasoning, reward modeling, formal reasoning, data augmentation, and code interpreter in a unified seq2seq format and supervise our model to be a versatile math reasoner, verifier, prover, and augmenter. These abilities can be used to develop the next math LLMs or self-iteration. InternLM-Math obtains open-sourced state-of-the-art performance under the setting of in-context learning, supervised fine-tuning, and code-assisted reasoning in various informal and formal benchmarks including GSM8K, MATH, Hungary math exam, MathBench-ZH, and MiniF2F. Our pre-trained model achieves 30.3 on the MiniF2F test set without fine-tuning. We further explore how to use LEAN to solve math problems and study its performance under the setting of multi-task learning which shows the possibility of using LEAN as a unified platform for solving and proving in math. Our models, codes, and data are released at \url{https://github.com/InternLM/InternLM-Math}.

Cyclic BCH codes and negacyclic BCH codes form important subclasses of cyclic codes and negacyclic codes, respectively, and can produce optimal linear codes in many cases. To the best of our knowledge, there are few results on the dual codes of cyclic and negacyclic BCH codes. In this paper, we study the dual codes of narrow-sense cyclic BCH codes of length $q^m+1$ over a finite field $\mathbb{F}_q$, where $q$ is an odd prime power, and the dual codes of narrow-sense negacyclic BCH codes of length $\frac{q^{m}+1}{2}$ over $\mathbb{F}_q$, where $q$ is an odd prime power satisfying $q\equiv 3~({\rm mod}~4)$. Some lower bounds on the minimum distances of the dual codes are established, which are very close to the true minimum distances of the dual codes in many cases. Sufficient and necessary conditions for the even-like subcodes of narrow-sense cyclic BCH codes of length $q^{m}+1$ being cyclic dually-BCH codes are given in terms of designed distances, where $q$ is odd and $m$ is odd or $m\equiv 2~({\rm mod~}4)$. The concept of negacyclic dually-BCH codes is proposed, and sufficient and necessary conditions in terms of designed distances are presented to ensure that narrow-sense negacyclic BCH codes of length $\frac{q^{m}+1}{2}$ are dually-BCH codes, where $q\equiv 3~({\rm mod}~4)$.

BCH codes form an important subclass of cyclic codes, and are widely used in compact discs, digital audio tapes and other data storage systems to improve data reliability. As far as we know, there are few results on $q$-ary BCH codes of length $n=\frac{q^{m}+1}{q+1}$. This is because it is harder to deal with BCH codes of such length. In this paper, we study $q$-ary BCH codes with lengths $n=\frac{q^{m}+1}{q+1}$ and $n=q^m+1$. These two classes of BCH codes are always LCD codes. For $n=\frac{q^{m}+1}{q+1}$, the dimensions of narrow-sense BCH codes of length $n$ with designed distance $\delta=\ell q^{\frac{m-1}{2}}+1$ are determined, where $q>2$ and $2\leq \ell \leq q-1$. Moreover, the largest coset leader is given for $m=3$ and the first two largest coset leaders are given for $q=2$. The parameters of BCH codes related to the first few largest coset leaders are investigated. Some binary BCH codes of length $n=\frac{2^m+1}{3}$ have optimal parameters. For ternary narrow-sense BCH codes of length $n=3^m+1$, a lower bound on the minimum distance of their dual codes is developed, which is good in some cases.

Recent advancements in large language models (LLMs) have showcased significant improvements in mathematics. However, traditional math benchmarks like GSM8k offer a unidimensional perspective, falling short in providing a holistic assessment of the LLMs' math capabilities. To address this gap, we introduce MathBench, a new benchmark that rigorously assesses the mathematical capabilities of large language models. MathBench spans a wide range of mathematical disciplines, offering a detailed evaluation of both theoretical understanding and practical problem-solving skills. The benchmark progresses through five distinct stages, from basic arithmetic to college mathematics, and is structured to evaluate models at various depths of knowledge. Each stage includes theoretical questions and application problems, allowing us to measure a model's mathematical proficiency and its ability to apply concepts in practical scenarios. MathBench aims to enhance the evaluation of LLMs' mathematical abilities, providing a nuanced view of their knowledge understanding levels and problem solving skills in a bilingual context. The project is released at https://github.com/open-compass/MathBench ., Comment: Project: https://github.com/open-compass/MathBench

For any simple-root constacyclic code $\mathcal{C}$ over a finite field $\mathbb{F}_q$, as far as we know, the group $\mathcal{G}$ generated by the multiplier, the constacyclic shift and the scalar multiplications is the largest subgroup of the automorphism group ${\rm Aut}(\mathcal{C})$ of $\mathcal{C}$. In this paper, by calculating the number of $\mathcal{G}$-orbits of $\mathcal{C}\backslash\{\bf 0\}$, we give an explicit upper bound on the number of non-zero weights of $\mathcal{C}$ and present a necessary and sufficient condition for $\mathcal{C}$ to meet the upper bound. Some examples in this paper show that our upper bound is tight and better than the upper bounds in [Zhang and Cao, FFA, 2024]. In particular, our main results provide a new method to construct few-weight constacyclic codes. Furthermore, for the constacyclic code $\mathcal{C}$ belonging to two special types, we obtain a smaller upper bound on the number of non-zero weights of $\mathcal{C}$ by substituting $\mathcal{G}$ with a larger subgroup of ${\rm Aut}(\mathcal{C})$. The results derived in this paper generalize the main results in [Chen, Fu and Liu, IEEE-TIT, 2024]}.

Bacterial genome size reflects bacterial evolutionary processes and metaboliclifestyles, with implications for microbial community assembly and ecosystemfunctions. However, to understand the extent of genome‐mediated microbialresponses to environmental selections, we require studies that observe genomesize distributions along environmental gradients representing differentconditions that soil bacteria normally encounter. In this study, we usedsurface soils collected from 237 sites across the globe and analyzed howenvironmental conditions (e.g., soil carbon and nutrients, aridity, pH, andtemperature) affect soil bacterial occurrences and genome size at thecommunity level using bacterial community profiling. We used a joint speciesdistribution model to quantify the effects of environments on speciesoccurrences and found that aridity was a major regulator of genome sizewith warmer and drier environments selecting bacteria with smaller genomes.Drought‐induced physiological constraints on bacterial growth (e.g., waterscarcity for cell metabolisms) may have led to these correlations. This findingsuggests that increasing cover by warmer and drier ecosystems may result inbacterial genome simplifications by a reduction of genome size.

Plant disease outbreaks pose significant risks to global food security and environmental sustainability worldwide, and result in the loss of primary productivity and biodiversity that negatively impact the environmental and socio-economic conditions of affected regions. Climate change further increases outbreak risks by altering pathogen evolution and host–pathogen interactions and facilitating the emergence of new pathogenic strains. Pathogen range can shift, increasing the spread of plant diseases in new areas. In this Review, we examine how plant disease pressures are likely to change under future climate scenarios and how these changes will relate to plant productivity in natural and agricultural ecosystems. We explore current and future impacts of climate change on pathogen biogeography, disease incidence and severity, and their effects on natural ecosystems, agriculture and food production. We propose that amendment of the current conceptual framework and incorporation of eco-evolutionary theories into research could improve our mechanistic understanding and prediction of pathogen spread in future climates, to mitigate the future risk of disease outbreaks. We highlight the need for a science–policy interface that works closely with relevant intergovernmental organizations to provide effective monitoring and management of plant disease under future climate scenarios, to ensure long-term food and nutrient security and sustainability of natural ecosystems.

Aims Soil-borne pathogens severely damage the yield and quality of crops worldwide. Plant and soil microbiomes (e.g. in the rhizosphere) intimately interact with the plant, the pathogen and influence outcomes of disease infection. Investigation of how these microbiomes respond to disease infection is critical to develop solutions to control diseases. Methods Here, we conducted a field experiment and collected healthy and crown rot disease infected (caused by Fusarium pseudograminearum, Fp) wheat plants. We investigated their microbiomes in different compartments, plant immune responses and interactions with the pathogen (Fp) aiming at advancing our knowledge on microbiome-mediated regulation of plant responses to pathogens. Results We found that Fp colonised wheat plants in significant loads, accounting for 11.3% and 60.7% of the fungal communities in the rhizosphere and root endosphere, respectively. However, Fp presented with a small fraction of the leaf microbiome, up to 1.2%. Furthermore, Fp-infection led to significant changes in the composition of the microbial communities in the rhizosphere and root endosphere while had little impact on leaves. We further found that wheat defence signalling pathways, wheat microbiomes and the pathogen intimately correlated with each other in structural equation modelling. As such, we also identified ecological clusters explained changes in the wheat defence signalling pathways. Lastly, microbial co-occurrence network complexity was higher in Fp-infected plants relative to healthy plants, suggesting that Fp-infection has potentially induced more microbial interactions in plants. Conclusions We provide novel evidence that soil-borne diseases significantly disrupt belowground plant microbiomes influencing the responses of plant immunity to pathogens.

Aims: Soil-borne pathogens severely damage the yield and quality of crops worldwide. Plant and soil microbiomes (e.g. in the rhizosphere) intimately interact with the plant, the pathogen and influence outcomes of disease infection. Investigation of how these microbiomes respond to disease infection is critical to develop solutions to control diseases. Methods: Here, we conducted a field experiment and collected healthy and crown rot disease infected (caused by Fusarium pseudograminearum, Fp) wheat plants. We investigated their microbiomes in different compartments, plant immune responses and interactions with the pathogen (Fp) aiming at advancing our knowledge on microbiome-mediated regulation of plant responses to pathogens. Results: We found that Fp colonised wheat plants significantly in terms of relative abundances, accounting for 11.3% and 60.7% of the fungal communities in the rhizosphere and roots, respectively. However, Fp presented with a small fraction of the leaf microbiome, up to 1.2%. Furthermore, Fp-infection led to significant changes in the composition of microbiomes in the rhizosphere and root while had little impact on leaves. We further found that wheat defence signalling pathways, microbiomes and the pathogen intimately correlated with each other in structural equation modelling. As such, we also identified ecological clusters explained changes in the wheat defence signalling pathways. Lastly, microbial co-occurrence network complexity was higher in Fp-infected plants relative to healthy plants, suggesting that Fp-infection may have led to changes in the wheat microbial community structure. Conclusions: We provide novel evidence that soil-borne diseases disrupt belowground plant microbiomes influencing the responses of plant immunity to pathogens. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

Prebiotics are compounds that selectively stimulate the growth and activity of beneficial microorganisms. The use of prebiotics is a well-established strategy for managing human gut health. This concept can also be extended to plants where plant rhizosphere microbiomes can improve the nutrient acquisition and disease resistance. However, we lack effective strategies for choosing metabolites to elicit the desired impacts on plant health. In this study, we target the rhizosphere of tomato (Solanum lycopersicum) suffering from wilt disease (caused by Ralstonia solanacearum) as source for potential prebiotic metabolites. We identify metabolites (ribose, lactic acid, xylose, mannose, maltose, gluconolactone, and ribitol) exclusively used by soil commensal bacteria (not positively correlated with R. solanacearum) but not efficiently used by the pathogen in vitro. Metabolites application in the soil with 1 µmol g−1 soil effectively protects tomato and other Solanaceae crops, pepper (Capsicum annuum) and eggplant (Solanum melongena), from pathogen invasion. After adding prebiotics, the rhizosphere soil microbiome exhibits enrichment of pathways related to carbon metabolism and autotoxin degradation, which were driven by commensal microbes. Collectively, we propose a novel pathway for mining metabolites from the rhizosphere soil and their use as prebiotics to help control soil-borne bacterial wilt diseases.

Plant disease outbreaks pose significant risks to global food security and environmental sustainability worldwide, and result in the loss of primary productivity and biodiversity that negatively impact the environmental and socio-economic conditions of affected regions. Climate change further increases outbreak risks by altering pathogen evolution and host–pathogen interactions and facilitating the emergence of new pathogenic strains. Pathogen range can shift, increasing the spread of plant diseases in new areas. In this Review, we examine how plant disease pressures are likely to change under future climate scenarios and how these changes will relate to plant productivity in natural and agricultural ecosystems. We explore current and future impacts of climate change on pathogen biogeography, disease incidence and severity, and their effects on natural ecosystems, agriculture and food production. We propose that amendment of the current conceptual framework and incorporation of eco-evolutionary theories into research could improve our mechanistic understanding and prediction of pathogen spread in future climates, to mitigate the future risk of disease outbreaks. We highlight the need for a science–policy interface that works closely with relevant intergovernmental organizations to provide effective monitoring and management of plant disease under future climate scenarios, to ensure long-term food and nutrient security and sustainability of natural ecosystems.

Clinical evidence of vascular dysfunction and hypercoagulability as well as pulmonary vascular damage and microthrombosis are frequently reported in severe cases of human coronavirus disease 2019 (COVID-19). Syrian golden hamsters recapitulate histopathologic pulmonary vascular lesions reported in patients with COVID-19. Herein, special staining techniques and transmission electron microscopy further define vascular pathologies in a Syrian golden hamster model of human COVID-19. The results show that regions of active pulmonary inflammation in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are characterized by ultrastructural evidence of endothelial damage with platelet marginalization and both perivascular and subendothelial macrophage infiltration. SARS-CoV-2 antigen/RNA was not detectable within affected blood vessels. Taken together, these findings suggest that the prominent microscopic vascular lesions in SARS-CoV-2-inoculated hamsters likely occur due to endothelial damage followed by platelet and macrophage infiltration.

The shear strength of rock fractures serves as a crucial control on the strength and deformation behavior of engineering rock masses. To reduce the uncertainties in the shear strength evaluation, a hybrid machine learning model (GS-SVR model) of the support vector regression (SVR) underpinned by the grid search optimization algorithm (GS) was proposed. It achieves the prediction of shear strength by generalization and deduction of a large amount of data on rock fracture parameters, which avoids the complex derivation of theoretical equations. For practical application, a dataset comprising more than 134 shear tests on various rocks was compiled to collect the relevant three-dimensional morphological and mechanical parameters for training and prediction. Three classical shear strength models and the original SVR model were introduced for further comparison. Finally, sensitivity analysis was carried out to explore the relative importance of input variables to the shear strength. The results showed that the GS-SVR model (correlation coefficient R2 = 0.984, root mean squared error RMSE=0.383) outperformed the original SVR model (R2 = 0.936, RMSE=0.568). Moreover, compared with three classical shear strength models, the prediction results of the GS-SVR model were also most consistent with the experimental results (with the lowest RMSE and the highest R2). This machine learning model enhanced by GS can be used as a reliable and accurate shear strength prediction tool to partially replace laboratory tests to save costs., QC 20230922

Climate warming and summer droughts alter soil microbial activity, affecting greenhouse gas (GHG) emissions in arctic and alpine regions. However, the long-term effects of warming, and implications for future microbial resilience, are poorly understood. Using one alpine and three arctic soils subjected to in situ long-term experimental warming, we simulated drought in laboratory incubations to test how microbial functional-gene abundance affects fluxes in three GHGs: carbon dioxide, methane, and nitrous oxide. We found that responses of functional gene abundances to drought and warming are strongly associated with vegetation type and soil carbon. Our sites ranged from a wet, forb dominated, soil carbon-rich systems to a drier, soil carbon-poor alpine site. Resilience of functional gene abundances, and in turn methane and carbon dioxide fluxes, was lower in the wetter, carbon-rich systems. However, we did not detect an effect of drought or warming on nitrous oxide fluxes. All gene-GHG relationships were modified by vegetation type, with stronger effects being observed in wetter, forb-rich soils. These results suggest that impacts of warming and drought on GHG emissions are linked to a complex set of microbial gene abundances and may be habitat-specific.

WSe2 has a high mobility of electrons and holes, which is an ideal choice as active channels of electronics in extensive fields. However, carrier-type tunability of WSe2 still has enormous challenges, which are essential to overcome for practical applications. In this work, the direct growth of n-doped few-layer WSe2 is realized via in situ defect engineering. The n-doping of WSe2 is attributed to Se vacancies induced by the H-2 flow purged in the cooling process. The electrical measurements based on field effect transistors demonstrate that the carrier type of WSe2 synthesized is successfully transferred from the conventional p-type to the rarely reported n-type. The electron carrier concentration is efficiently modulated by the concentration of H-2 during the cooling process. Furthermore, homomaterial inverters and self-powered photodetectors are fabricated based on the doping-type-tunable WSe2. This work reveals a significant way to realize the controllable carrier type of two-dimensional (2D) materials, exhibiting great potential in future 2D electronics engineering.

Dual-atom catalysts (DACs) have recently emerged as promising and high-activity catalysts for the oxygen reduction reaction (ORR), a key process in many electrochemical energy conversion devices. However, the ORR mechanism and kinetics on DACs has not yet been established. To address this problem, we employed grand canonical potential kinetics (GCP-K) with CANDLE solvation. The behavior of the free energy and grand canonical potential for ORR and hydrogen evolution reaction (HER) intermediates (OO*, OOH*, O*, OH*, and H*) and their corresponding transition states at constant charges were calculated and converted to free energy as a function of applied potential to predict current density as a function of applied potential for ORR (through the associative pathway) and the competitive HER on iron-nickel DAC (FeNiN6-DAC). We find a Tafel slope of 281 mV dec−1 for ORR, comparable with the experimental Tafel slope of 169 mV dec−1 at the current density of −1.7 mA cm−2. The change in concentration of ORR reaction intermediates on FeNiN6-DAC as a function of applied potentials indicates that the dominant intermediate is OH* at potentials >0.25 V vs. RHE. The charge transfer and spin density of Fe active sites reaches a maximum during each proton transfer step. The partial density of states of 3d orbitals on the Fe site indicates that the 3dx2-y2 orbital is near the Fermi level, while the position of the 3dz2 orbital and Fermi level reaches a minimum along the first reaction step of. These insights into the fundamental aspects of ORR on DACs provide guidance for the design of efficient catalysts.

The fierce competition amongst brands on online marketplaces makes the optimization of offerings within this context a significant challenge. To address this challenge, we draw upon network theory and model the degree of competition through consumers’ consideration sets. We use a large empirical dataset from one of the biggest online marketplaces to explore the dynamic relationship between network position and the degree of competition, and we depict the redistribution of market share of related offerings after adjusting their array. In doing so, we provide a theoretical reference on when and how brands should optimize their product offerings on online marketplaces. We further demonstrate that intra-brand cannibalization relations have a significantly greater impact on the degree of competition compared to inter-brand ones, while intra-brand cannibalization relations represent the main reason for fluctuations in the degree of competition. Hence, contrary to existing theoretical insights and practical intuitions, our findings demonstrate that brands should minimize the number and heterogeneity of their offerings within a market segment to increase their sales on online marketplaces.

Wafer-scale monolayer two-dimensional (2D) materials have been realized by epitaxial chemical vapor deposition (CVD) in recent years. To scale up the synthesis of 2D materials, a systematic analysis of how the growth dynamics depend on the growth parameters is essential to unravel its mechanisms. However, the studies of CVD-grown 2D materials mostly adopted the control variate method and considered each parameter as an independent variable, which is not comprehensive for 2D materials growth optimization. Herein, we synthesized a representative 2D material, monolayer hexagonal boron nitride (hBN), on single-crystalline Cu (111) by epitaxial chemical vapor deposition and varied the growth parameters to regulate the hBN domain sizes. Furthermore, we explored the correlation between two growth parameters and provided the growth windows for large flake sizes by the Gaussian process. This new analysis approach based on machine learning provides a more comprehensive understanding of the growth mechanism for 2D materials. © 2023 American Chemical Society.

The practical application of naturally abundant sodium (Na) metal anodes with high energy densities is hindered by large volume expansion and dendrite formation during battery operation. This work reports the synthesis of tin selenide nanoparticles uniformly grown on highly conductive, porous 3D graphene foam (SnSe@GF) as a stable host for Na metal anodes and the underlying conversion reactions as their energy storage mechanism. The SnSe@GF electrode prepared via hydrogel coating and phase transformation sustains remarkable reversibility after 1500 cycles in asymmetric cells and delivers extraordinary cyclic stability and low overpotentials for 2000 h at 1 mA cm−2 and 1 mAh cm−2 in symmetric cells. The conversion of crystalline SnSe into low-crystallinity Na15Sn4 and Na2Se dual nucleation sites after pre-sodiation is responsible for the outstanding performance according to the in-situ microscopy and density functional theory calculations. The conversion enables the in-situ formation of a unique interface that possesses high Na affinity featured by abundant active sites, contributing to uniform Na nucleation/plating and dendrite suppression, thus give rising to superior stability and electrochemical performance of the SnSe@GF electrode. The rational design of the current 3D architecture can shed new insights into the development of Na hosts for next-generation rechargeable batteries.

Hexagonal boron carbon nitride (hBCN) thin films with a wide range of carbon doping are synthesized in a chemical vapor deposition (CVD) process by varying 3 distinct precursors. Utilizing the optothermal method, the highest thermal conductivities of hBCN thin films is measured experimentally as 836 ± 312 W/m·K, for the hBCN with the highest carbon doping, surpassing the measured hexagonal boron nitride (hBN) conductivity of 462 ± 158 W/m·K. Moreover, we demonstrate that the electrical conductivity retains the insulative properties of hBN with lower carbon doping levels. Additionally, insights of how increasing amounts of carbon increase the conductivity by forming graphene like channels integrated into the hBN networks have been examined by molecular dynamics simulations. These simulations confirm a greater increase in high frequency phonon group velocities contribute to higher thermal conductivity, when aligning carbon rather than simply increasing the quantity of carbon. The aligned carbon channels are effective in overcoming the additional phonon scattering, that occurs especially in the LA mode, when adding carbon to the hBN network. By varying the carbon doping to maximize the thermal conductivity while minimizing the electrical conductivity, we have discovered an effective method of producing optimized hBCN thin films, which are ideal to be utilized in thermal interface material applications as a lower cost alternative to hBN. © 2023 Elsevier Ltd

Linear complementary dual (LCD) codes can provide an optimum linear coding solution for the two-user binary adder channel. LCD codes also can be used to against side-channel attacks and fault non-invasive attacks. Let $d_{LCD}(n, k)$ denote the maximum value of $d$ for which a binary $[n,k, d]$ LCD code exists. In \cite{BS21}, Bouyuklieva conjectured that $d_{LCD}(n+1, k)=d_{LCD}(n, k)$ or $d_{LCD}(n, k) + 1$ for any lenth $n$ and dimension $k \ge 2$. In this paper, we first prove Bouyuklieva's conjecture \cite{BS21} by constructing a binary $[n,k,d-1]$ LCD codes from a binary $[n+1,k,d]$ $LCD_{o,e}$ code, when $d \ge 3$ and $k \ge 2$. Then we provide a distance lower bound for binary LCD codes by expanded codes, and use this bound and some methods such as puncturing, shortening, expanding and extension, we construct some new binary LCD codes. Finally, we improve some previously known values of $d_{LCD}(n, k)$ of lengths $38 \le n \le 40$ and dimensions $9 \le k \le 15$. We also obtain some values of $d_{LCD}(n, k)$ with $41 \le n \le 50$ and $6 \le k \le n-6$., Comment: 28 pages

Electrocatalysis at the edge sites of transition-metal dichalcogenides has been well studied, particularly for the hydrogen evolution reaction (HER). Here, we explore instead the HER activity on the basal plane of MoTe2 by creating anion vacancies via computational predictions followed by experimental validation. Using the grand canonical potential kinetics method, we predict that overpotentials of 535 and 565 mV can achieve a current density of 10 mA cm−2 for 1T′-MoTe2 and 2H-MoTe2 containing 1.14×1014cm−2 and 3.45×1013cm−2 Te vacancies, respectively. This is in good agreement with experimental overpotentials of 561 and 634 mV for 1T′-MoTe2 and 2H-MoTe2 containing similar vacancies (1.28×1014cm−2 and 3.54×1013cm−2, respectively). Furthermore, we used Ar plasma treatment to increase the Te vacancy on the basal plane and found an optimal vacancy concentration of 3.18×1014cm−2 for 1T′-MoTe2 and 1.02×1014cm−2 for 2H-MoTe2. Increasing or decreasing the vacancy concentrations from this level further reduces HER performance. © 2022 Elsevier Inc.

Tin (II) selenide (SnSe) is an emerging 2D material with many intriguing properties, such as record-high thermoelectric figure of merit (ZT), purely in-plane ferroelectricity, and excellent nonlinear optical properties. To explore these functional properties and related applications, a crucial step is to develop controllable routes to synthesize large-area, ultrathin, and high-quality SnSe crystals. Physical vapor deposition (PVD) constitutes a reliable method to synthesize 2D SnSe, however, effects of various growth parameters have not yet been systematically investigated, and current PVD-synthesized flakes are often thick (>10 nm) with small lateral sizes (<10 µm). In this work, high-quality 2D SnSe crystals are synthesized via low-pressure PVD, which display in-plane ferroelectric domains observed by piezoresponse force microscopy and polarization-dependent reflection spectroscopy. Detailed studies regarding the roles of various parameters are further carried out, including substrate pre-annealing, growth duration, temperature, and pressure, which enable to rationally optimize the growth and obtain 2D SnSe crystals with lateral sizes up to ≈23.0 µm and thicknesses down to ≈2.0 nm (3–4 layers). This work paves the way for the controlled growth of large-area 2D SnSe, facilitating the future exploration of many interesting multiferroic properties and applications with atomic thickness. © 2023 The Authors. Advanced Electronic Materials published by Wiley-VCH GmbH.

The integration of various two-dimensional (2D) materials on wafers enables a more-than-Moore approach for enriching the functionalities of devices1–3. On the other hand, the additive growth of 2D materials to form heterostructures allows construction of materials with unconventional properties. Both may be achieved by materials transfer, but often suffer from mechanical damage or chemical contamination during the transfer. The direct growth of high-quality 2D materials generally requires high temperatures, hampering the additive growth or monolithic incorporation of different 2D materials. Here we report a general approach of growing crystalline 2D layers and their heterostructures at a temperature below 400 °C. Metal iodide (MI, where M = In, Cd, Cu, Co, Fe, Pb, Sn and Bi) layers are epitaxially grown on mica, MoS2 or WS2 at a low temperature, and the subsequent low-barrier-energy substitution of iodine with chalcogens enables the conversion to at least 17 different 2D crystalline metal chalcogenides. As an example, the 2D In2S3 grown on MoS2 at 280 °C exhibits high photoresponsivity comparable with that of the materials grown by conventional high-temperature vapour deposition (~700–1,000 °C). Multiple 2D materials have also been sequentially grown on the same wafer, showing a promising solution for the monolithic integration of different high-quality 2D materials. © 2023, The Author(s), under exclusive licence to Springer Nature Limited.

With the massive amount of digital data generated everyday, transactions of digital goods become a trend. One of the essential requirements for such transactions is fairness, which is defined as that both of the seller and the buyer get what they want, or neither. Current fair trade protocols generally involve a trusted third-party (TTP), which achieves fairness by heavily relying on the TTP's behaviors and the two parties' trust in the TTP. With the emergence of Blockchain, its decentralization and transparency make it a very good candidate to replace the TTP. In this work, we attempt to design a secure and fair protocol for digital goods transactions through smart contracts on Blockchain. To ensure security of the digital goods, we propose an advanced passive proxy re-encryption (PRE) scheme, which enables smart contracts to transfer the decryption right to a buyer after receiving his/her payment. Furthermore, based on smart contracts and the proposed passive PRE scheme, a fair trade protocol for digital goods transactions is proposed, whose fairness is guaranteed by the arbitration protocol. The proposed protocol supports Ciphertext publicity and repeatable sale, while involving less number of interactions. Comprehensive experiment results validate the feasibility and effectiveness of the proposed protocol.

Let C be an arbitrary simple-root cyclic code and let G be the subgroup of Aut(C) (the automorphism group of C) generated by the multiplier, the cyclic shift and the scalar multiplications. To the best of our knowledge, the subgroup G is the largest subgroup of Aut(C). In this paper, an explicit formula, in some cases an upper bound, for the number of orbits of G on C\{0} is established. An explicit upper bound on the number of non-zero weights of C is consequently derived and a necessary and sufficient condition for the code C meeting the bound is exhibited. Many examples are presented to show that our new upper bounds are tight and are strictly less than the upper bounds in [Chen and Zhang, IEEE-TIT, 2023]. In addition, for two special classes of cyclic codes, smaller upper bounds on the number of non-zero weights of such codes are obtained by replacing G with larger subgroups of the automorphism groups of these codes. As a byproduct, our main results suggest a new way to find few-weight cyclic codes.

Subspace codes have important applications in random network coding. It is interesting to construct subspace codes with both sizes, and the minimum distances are as large as possible. In particular, cyclic constant dimension subspaces codes have additional properties which can be used to make encoding and decoding more efficient. In this paper, we construct large cyclic constant dimension subspace codes with minimum distances $2k-2$ and $2k$. These codes are contained in $\mathcal{G}_q(n, k)$, where $\mathcal{G}_q(n, k)$ denotes the set of all $k$-dimensional subspaces of $\mathbb{F}_{q^n}$. Consequently, some results in \cite{FW}, \cite{NXG}, and \cite{ZT} are extended., Comment: This article was submitted to Designs, Codes and Cryptography on November 22nd, 2022

Recently, 3D object detection has attracted significant attention and achieved continuous improvement in real road scenarios. The environmental information is collected from a single sensor or multi-sensor fusion to detect interested objects. However, most of the current 3D object detection approaches focus on developing advanced network architectures to improve the detection precision of the object rather than considering the dynamic driving scenes, where data collected from sensors equipped in the vehicle contain various perturbation features. As a result, existing work cannot still tackle the perturbation issue. In order to solve this problem, we propose a group equivariant bird's eye view network (GeqBevNet) based on the group equivariant theory, which introduces the concept of group equivariant into the BEV fusion object detection network. The group equivariant network is embedded into the fused BEV feature map to facilitate the BEV-level rotational equivariant feature extraction, thus leading to lower average orientation error. In order to demonstrate the effectiveness of the GeqBevNet, the network is verified on the nuScenes validation dataset in which mAOE can be decreased to 0.325. Experimental results demonstrate that GeqBevNet can extract more rotational equivariant features in the 3D object detection of the actual road scene and improve the performance of object orientation prediction., Comment: 8 pages,3 figures

In this article we mainly study linear codes over $\mathbb{F}_{2^n}$ and their binary subfield codes. We construct linear codes over $\mathbb{F}_{2^n}$ whose defining sets are the certain subsets of $\mathbb{F}_{2^n}^m$ obtained from mathematical objects called simplicial complexes. We use a result in LFSR sequences to illustrate the relation of the weights of codewords in two special codes obtained from simplical complexes and then determin the parameters of these codes. We construct fiveinfinite families of distance optimal codes and give sufficient conditions for these codes to be minimal.

Function-correcting codes are a class of codes designed to protect the function evaluation of a message against errors whose key advantage is the reduced redundancy. In this paper, we extend function-correcting codes from binary symmetric channels to symbol-pair read channels. We introduce irregular-pair-distance codes and connect them with function-correcting symbol-pair codes. Using the connection, we derive general upper and lower bounds on the optimal redundancy of function-correcting symbol-pair codes. For ease of evaluation, we simplify these bounds and employ the simplified bounds to specific functions including pair-locally binary functions, pair weight functions and pair weight distribution functions., Comment: 17 pages, 2 figures, 2 tables

Interacting with human via high-quality multi-turn dialogues is a key feature of large language models (LLMs). However, human-based evaluation of such capability involves intensive manual labor. This report provides a preliminary evaluation of existing large language models for human-style multi-turn chatting, through an LLM-based approach. We start from real-world human dialogues and keep the very first utterances as the ChatSEED. Then we prompt LLMs to generate a full multi-turn dialogue (tens of utterances) based on the ChatSEED, utterance by utterance. Finally, we adopt state-of-the-art LLMs (GPT-4, \etc) as the judge to evaluate the generated dialogues. With different evaluation protocols, we come to substantially identical conclusions. We find that GPT-4 can generate human-style multi-turn dialogues with impressive quality, significantly outperforms its counterparts. It's difficult for a discriminator to distinguish between GPT-4 generated dialogues and human dialogues. In contrast, other LLMs struggle to generate multi-turn dialogues of satisfactory quality due to poor instruction-following capability, tendency to generate lengthy utterances, or limited general capability. All data and codes will be provided in https://github.com/open-compass/BotChat/ and we hope they can serve as a valuable resource for evaluating multi-turn chatting capabilities of LLMs.

To understand susceptibility of wild California sea lions and Northern elephant seals to influenza A virus (IAV), we developed an ex vivo respiratory explant model and used it to compare infection kinetics for multiple IAV subtypes. We first established the approach using explants from colonized rhesus macaques, a model for human IAV. Trachea, bronchi, and lungs from 11 California sea lions, 2 Northern elephant seals, and 10 rhesus macaques were inoculated within 24 h postmortem with 6 strains representing 4 IAV subtypes. Explants from the 3 species showed similar IAV infection kinetics, with peak viral titers 48 to 72 h post-inoculation that increased by 2 to 4 log10 PFU/explant relative to the inoculum. Immunohistochemistry localized IAV infection to apical epithelial cells. These results demonstrate that respiratory tissue explants from wild marine mammals support IAV infection. In the absence of the ability to perform experimental infections of marine mammals, this ex vivo culture of respiratory tissues mirrors the in vivo environment and serves as a tool to study IAV susceptibility, host range, and tissue tropism. IMPORTANCE Although influenza A virus can infect marine mammals, a dearth of marine mammal cell lines and ethical and logistical challenges prohibiting experimental infections of living marine mammals mean that little is known about IAV infection kinetics in these species. We circumvented these limitations by adapting a respiratory tract explant model first to establish the approach with rhesus macaques and then for use with explants from wild marine mammals euthanized for nonrespiratory medical conditions. We observed that multiple strains representing 4 IAV subtypes infected trachea, bronchi, and lungs of macaques and marine mammals with variable peak titers and kinetics. This ex vivo model can define infection dynamics for IAV in marine mammals. Further, use of explants from animals euthanized for other reasons reduces use of animals in research.

Grazing is a major driver of the composition of microbial communities, which play important roles in soil functioning. Mechanisms whereby grazing might regulate the spatial heterogeneity of microbial communities within ecological networks remain largely untested. We used network analysis to identify the impacts of increasing grazing intensity by livestock (cattle, sheep, goats), and native (kangaroos) and wild (rabbits) animals, on the spatial heterogeneity of the relative abundance of eight ecological clusters of co-occurring soil microbial taxa: four from Grasslands and four from Forests. Grazing effects on microbial spatial heterogeneity were strongly nuanced and depended on (1) plant community type, (2) herbivore type and (3) microbial identity. Microbial within-site spatial heterogeneity was greater in Grasslands than in Forests, and most effects of grazing on microbial spatial heterogeneity were in Forests, effecting three of the four Forest clusters, but only one Grassland cluster. The associations between grazing intensity and microbial heterogeneity were driven indirectly by changes in the spatial heterogeneity of litter cover and soil pH. For Grasslands, we also detected a direct effect of grazing intensity on the heterogeneity of particular microbial groups. Our results indicate that increased grazing intensity will advantage some microbial clusters but disadvantage others. Together, our study provides evidence that grazing intensity regulates the abundance and spatial heterogeneity of microbial communities within ecological networks. Knowing the potential effects of herbivores on different microbial clusters can help us predict the likely effects of grazing on soil function. This has important implications for future sustainable management and conservation policies.

We demonstrate the structure evolution of hexagonal boron nitride (hBN) flakes grown on molten Cu in atmospheric pressure chemical vapor deposition by regulating the flux of precursor. We found that under lower precursor flux, tuned by temperature that controls the sublimation rates, the hBN grains change from triangle to truncated triangle shape with additional B-terminated edges, which could be understood through kinetic Wulff construction, while under higher flux, they form circular shape following deposition-controlled growth and predicted by a phase field modeling. In addition to the monolayer morphology from a single nucleation, adlayer patterns with centered aggregation and diffusive features at high precursor flux are observed and simulated by a two-dimensional (2D) diffusion-reaction model, where the random diffusion and deposition are revealed to be the dominating kinetics. The nucleation density and growth velocity could also be modulated by the ammonia borane heating temperature, where 80 degrees C is found to be optimal for the largest hBN grain size. Our transmission electron microscopy study shows that a misalignment of coalescing grains occurs on such molten Cu substrate, deviated from those observed on molten Au. Our results provide a new tool for the shape and grain size control of 2D materials and the understanding of their growth kinetics for large scale production.

The poor air stability hinders the practical application of most 2D materials. P-type 2D ZnTe has drawn widespread attention, thanks to its wide direct bandgap and outstanding environmental stability. However, the controllable synthesis of ultrathin 2D ZnTe remains a huge challenge due to the intrinsic unlayered crystal structure. Here, p-type 2D ultrathin ZnTe flakes are controllably synthesized by using space-confined physical vapor deposition. In situ temperature-dependent Raman and electronical measurements show the thermal stability up to 420 K and outstanding air stability even under humidity of 95%. The ZnTe flakes-based photodetector demonstrates the broadband response varying from the visible to near infrared region, responsivity of 18.3 A W-1 and detectivity (D*) of 2.89 × 109 Jones under 405 nm illumination, implying promising potential application in electronics and optoelectronics worked in harsh environment. © 2022 Wiley-VCH GmbH

The origin(s) and mechanism(s) of fast radio bursts (FRBs), which are short radio pulses from cosmological distances, have remained a major puzzle since their discovery. We report a strong Quasi-Periodic Oscillation(QPO) of 40 Hz in the X-ray burst from the magnetar SGR J1935+2154 and associated with FRB 200428, significantly detected with the Hard X-ray Modulation Telescope (Insight-HXMT) and also hinted by the Konus-Wind data. QPOs from magnetar bursts have only been rarely detected; our 3.4 sigma (p-value is 2.9e-4) detection of the QPO reported here reveals the strongest QPO signal observed from magnetars (except in some very rare giant flares), making this X-ray burst unique among magnetar bursts. The two X-ray spikes coinciding with the two FRB pulses are also among the peaks of the QPO. Our results suggest that at least some FRBs are related to strong oscillation processes of neutron stars. We also show that we may overestimate the significance of the QPO signal and underestimate the errors of QPO parameters if QPO exists only in a fraction of the time series of a X-ray burst which we use to calculate the Leahy-normalized periodogram., Comment: 17 pages, 13 figures, accepted for publication by ApJ. arXiv admin note: text overlap with arXiv:1212.1011 by other authors. text overlap with arXiv:1212.1011 by other authors

The poor air stability hinders the practical application of most 2D materials. P-type 2D ZnTe has drawn widespread attention, thanks to its wide direct bandgap and outstanding environmental stability. However, the controllable synthesis of ultrathin 2D ZnTe remains a huge challenge due to the intrinsic unlayered crystal structure. Here, p-type 2D ultrathin ZnTe flakes are controllably synthesized by using space-confined physical vapor deposition. In situ temperature-dependent Raman and electronical measurements show the thermal stability up to 420 K and outstanding air stability even under humidity of 95%. The ZnTe flakes-based photodetector demonstrates the broadband response varying from the visible to near infrared region, responsivity of 18.3 A W-1 and detectivity (D*) of 2.89 × 109 Jones under 405 nm illumination, implying promising potential application in electronics and optoelectronics worked in harsh environment. © 2022 Wiley-VCH GmbH

We demonstrate the structure evolution of hexagonal boron nitride (hBN) flakes grown on molten Cu in atmospheric pressure chemical vapor deposition by regulating the flux of precursor. We found that under lower precursor flux, tuned by temperature that controls the sublimation rates, the hBN grains change from triangle to truncated triangle shape with additional B-terminated edges, which could be understood through kinetic Wulff construction, while under higher flux, they form circular shape following deposition-controlled growth and predicted by a phase field modeling. In addition to the monolayer morphology from a single nucleation, adlayer patterns with centered aggregation and diffusive features at high precursor flux are observed and simulated by a two-dimensional (2D) diffusion-reaction model, where the random diffusion and deposition are revealed to be the dominating kinetics. The nucleation density and growth velocity could also be modulated by the ammonia borane heating temperature, where 80 degrees C is found to be optimal for the largest hBN grain size. Our transmission electron microscopy study shows that a misalignment of coalescing grains occurs on such molten Cu substrate, deviated from those observed on molten Au. Our results provide a new tool for the shape and grain size control of 2D materials and the understanding of their growth kinetics for large scale production.

Machine learning (ML) integrated density functional theory (DFT) calculations have recently been used to accelerate the design and discovery of heterogeneous catalysts such as single atom catalysts (SACs) through the establishment of deep structure–activity relationships. This review provides recent progress in the ML-aided rational design of heterogeneous catalysts with the focus on SACs in terms of structure–activity relationships, feature importance analysis, high-throughput screening, stability, and metal–support interactions for electrochemistry. Support vector machine (SVM), random forest regression (RFR), and deep neural networks (DNN) along with atomic properties are mainly used for the design of SACs. The ML results have shown that the number of electrons in the d orbital, oxide formation enthalpy, ionization energy, Bader charge, d-band center, and enthalpy of vaporization are mainly the most important parameters for the defining of the structure–activity relationships for electrochemistry. However, the black-box nature of ML techniques occasionally makes a physical interpretation of descriptors, such as the Bader charge, d-band center, and enthalpy of vaporization, non-trivial. At the current stage, ML application is limited by the lack of a large and high-quality database. Future prospects for the development of a large database and a generalized ML algorithm for SAC design are discussed to give insights for further studies in this field.

Early in the SARS-CoV-2 pandemic, there was a high level of optimism based on observational studies and small controlled trials that treating hospitalized patients with convalescent plasma from COVID-19 survivors (CCP) would be an important immunotherapy. However, as more data from controlled trials became available, the results became disappointing, with at best moderate evidence of efficacy when CCP with high titers of neutralizing antibodies was used early in infection. To better understand the potential therapeutic efficacy of CCP, and to further validate SARS-CoV-2 infection of macaques as a reliable animal model for testing such strategies, we inoculated 12 adult rhesus macaques with SARS-CoV-2 by intratracheal and intranasal routes. One day later, 8 animals were infused with pooled human CCP with a high titer of neutralizing antibodies (RVPN NT50 value of 3,003), while 4 control animals received normal human plasma. Animals were monitored for 7 days. Animals treated with CCP had detectable but low levels of antiviral antibodies after infusion. In comparison to the control animals, CCP-treated animals had similar levels of viral RNA in upper and lower respiratory tract secretions, similar detection of viral RNA in lung tissues by in situ hybridization, but lower amounts of infectious virus in the lungs. CCP-treated animals had a moderate, but statistically significant reduction in interstitial pneumonia, as measured by comprehensive lung histology. Thus overall, therapeutic benefits of CCP were marginal and inferior to results obtained earlier with monoclonal antibodies in this animal model. By highlighting strengths and weaknesses, data of this study can help to further optimize nonhuman primate models to provide proof-of-concept of intervention strategies, and guide the future use of convalescent plasma against SARS-CoV-2 and potentially other newly emerging respiratory viruses.

We are aiming at studying the rheological behavior of ordinary and associating polymers. Ordinary polymers refer to polymers without stickers but under fast flows. Associating polymers are a group of polymers which are held together through reversible non covalent bonds, such as ionic interactions, metal-ligand, $\pi$-$\pi$ stacking, and hydrogen bonds. Both topics have attracted enormous attention either due to the non-universal features in fast flows of polymer melts or because of their unique abilities for working as self-healing, stimuli-sensitive, and shape-memory materials. Fast flows and large deformation rates typically are involved in polymer processing operations and no general constitutive equation can be written that describes these nonlinear rheological behavior exhibited by polymer flows. Therefore, molecular modeling of the rheology of polymers in fast flow was developed many years ago. The friction reduction due to coalignment of polymer chains has been confirmed both through molecular dynamics simulations and extensional flow experiments. In spite of the results achieved by the friction reduction model, the flow-induced reduction of the friction coefficient is still highly controversial. For instance, the Einstein relationship between $\zeta$ and the diffusion coefficient probably does not hold true away from equilibrium. This thesis partly focuses on enhancing our understanding of the mechanisms behind the nonlinear responses of the unentangled polymer melts under fast shear flow through a coarse-grained modeling approach. We take advantage of the rich set of data of Kremer-Grest melts in fast steady shear flows. First, we apply the data to the latest Watanabe's model. Then we reproduce them by suitable Brownian dynamics simulations, where the beads are endowed with isotropic and anisotropic friction separately. Likewise, molecular modeling is typically used to study the viscoelastic response of associating polymers as well. The most famous one is