Toward the design of ultrahigh-entropy alloys via mining six million texts.

It has long been a norm that researchers extract knowledge from literature to design materials. However, the avalanche of publications makes the norm challenging to follow. Text mining (TM) is efficient in extracting information from corpora. Still, it cannot discover materials not present in the corpora, hindering its broader applications in exploring novel materials, such as high-entropy alloys (HEAs). Here we introduce a concept of "context similarity" for selecting chemical elements for HEAs, based on TM models that analyze the abstracts of 6.4 million papers. The method captures the similarity of chemical elements in the context used by scientists. It overcomes the limitations of TM and identifies the Cantor and Senkov HEAs. We demonstrate its screening capability for six- and seven-component lightweight HEAs by finding nearly 500 promising alloys out of 2.6 million candidates. The method thus brings an approach to the development of ultrahigh-entropy alloys and multicomponent materials.

Climate change did not alter the effects of Bt maize on soil Collembola in northeast China.

Bt maize is being increasingly cultivated worldwide as the effects of climate change are increasing globally. Bt maize IE09S034 and its near-isogenic non-Bt maize Zong 31 were used to investigate whether climate change alters the effects of Bt maize on soil Collembola. Warming and drought conditions were simulated using open-top chambers (OTC), and their effects on soil Collembola were evaluated. We found that the maize type had no significant effect on Collembola; however, the abundance and diversity of Collembola were significantly higher in the OTC than outside at the seedling stage; they were significantly lower in the OTC at the heading and mature stages. The interactions of the maize type with the OTC had no effect on these parameters. Therefore, Bt maize had no significant effect on soil Collembola, and the effects of climate warming and drought on soil Collembola depended on the ambient climatic conditions. When the temperature was low, collembolan abundance and diversity were promoted by warming; however, when the temperature was high and the humidity was low, collembolan abundance and diversity were inhibited by warming and drought. The climate changes simulated by the OTC did not alter the effects of Bt maize on soil Collembola.

Biochemical analyses demonstrate that Bt maize has no adverse effects on Eisenia fetida.

The potential effects of Bt (Bacillus thuringiensis) maize on non-target organisms should be evaluated before such maize is commercially planted. Earthworms play an indispensable role in the soil ecosystem; act as important bio-indicators of soil quality and environmental pollution. Therefore, earthworms are often used as the object to evaluate the non-target effect of Bt maize. To accelerate the commercialization of transgenic maize in China, a 90-day Eisenia fetida feeding experiment was conducted to evaluate the potential effects of Bt maize line, BT799-which was developed by China Agricultural University and contains the Cry1Ac gene-and its non-Bt conventional isoline-Zheng 58-on E. fetida. Our results showed that the Bt maize line had no significant effects on the growth, reproduction, or enzymatic activities of these earthworms. In summary, Bt maize had no toxic effects on E. fetida.

Construction of a Novel Degradation Model of Bacillus thuringiensis Protein in Soil and Its Application in Estimation of the Degradation Dynamics of Bt-Cry1Ah Protein.

Bacillus thuringiensis (Bt) protein expressed by genetically modified (GM) crops is released into the soil ecosystem, where it accumulates for a long time; therefore, degradation of Bt protein has gained increased attention for environmental risk assessments. A first-order kinetic model (Y = ae-b*X) is usually used to evaluate the degradation of Bt proteins, including Bt-Cry1Ab and Bt-Cry1Ac; this has some limitations regarding the precise fitting and explanation of the influence of various factors on Bt protein degradation in the later stage. Therefore, to amend these limitations, we report a new degradation model Y = Y0 + ae-b*X. The effects of soil temperature, water content, soil types, and soil sterilization on the degradation of Bt-Cry1Ah protein in soil were estimated in a 96d long laboratory study using a GM maize leaf-soil mixture. The results showed that the Bt-Cry1Ah protein degraded rapidly in the early stage and then slowly in the middle and late stages. Temperature was identified as the key factor affecting the degradation of Cry1Ah protein-a relatively higher temperature favored the degradation. The degradation rate of Cry1Ah protein was the fastest when the water content was 33 and 20% in the early and later stages, respectively. The soil types had a significant effect on the degradation of Cry1Ah protein. Moreover, soil sterilization slowed down the rate of protein degradation in both the early and later stages. In conclusion, the model Y = Y0 + ae-b*X established in this study provided a more robust model for exploring and simulating the degradation of Bt protein in soil growing GM crops and overcame the shortcomings of the Y = ae-b*X model. The findings of this study enriched the understanding of Bt protein degradation in soil ecosystems. They would be helpful for evaluating the environmental safety of GM crops.

Negligible Impact of Drought-Resistant Genetically Modified Maize on Arthropod Community Structure Observed in a 2-Year Field Investigation.

Dehydration-responsive element-binding (DREB) transcription factors regulate diverse processes during plant development. Here, a 2-year field study was conducted to assess the potential effects of DREB-genetically modified maize (GM1) on arthropod species and ecological communities. Arthropod abundance, diversity, and community composition in GM1 and its non-transformed counterpart maize variety, Chang 7-2, were compared using whole plant inspection, pitfall trap, and suction sampler methods. Based on Shannon-Wiener diversity, Simpson's diversity, Pielou's indexes, number of species, and total number of individuals, GM1 had a negligible effect on arthropod abundance and diversity. Redundancy analysis indicated that the composition of arthropod community was not associated with maize type in the three investigation methods, while it exhibited significant correlation with year and sampling time in whole plant inspection and suction sample methods, and distinctly correlated with sampling time in the pitfall trap method. Nonmetric multidimensional scaling analysis of variable factors in the three investigation methods showed that sampling time, rather than maize type or year, was closely related to the composition of arthropod community in the field. Our results provide direct evidence to support that DREB-GM maize had negligible effects on arthropods in the Jilin Province under natural conditions.

Language models for the prediction of SARS-CoV-2 inhibitors.

The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ∼9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets.

Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action.

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale.

The Role of Hydrophobic Nodes in the Dynamics of Class A ß-Lactamases.

Class A ß-lactamases are known for being able to rapidly gain broad spectrum catalytic efficiency against most ß-lactamase inhibitor combinations as a result of elusively minor point mutations. The evolution in class A ß-lactamases occurs through optimisation of their dynamic phenotypes at different timescales. At long-timescales, certain conformations are more catalytically permissive than others while at the short timescales, fine-grained optimisation of free energy barriers can improve efficiency in ligand processing by the active site. Free energy barriers, which define all coordinated movements, depend on the flexibility of the secondary structural elements. The most highly conserved residues in class A ß-lactamases are hydrophobic nodes that stabilize the core. To assess how the stable hydrophobic core is linked to the structural dynamics of the active site, we carried out adaptively sampled molecular dynamics (MD) simulations in four representative class A ß-lactamases (KPC-2, SME-1, TEM-1, and SHV-1). Using Markov State Models (MSM) and unsupervised deep learning, we show that the dynamics of the hydrophobic nodes is used as a metastable relay of kinetic information within the core and is coupled with the catalytically permissive conformation of the active site environment. Our results collectively demonstrate that the class A enzymes described here, share several important dynamic similarities and the hydrophobic nodes comprise of an informative set of dynamic variables in representative class A ß-lactamases.

Inter-Subunit Dynamics Controls Tunnel Formation During the Oxygenation Process in Hemocyanin Hexamers.

Hemocyanin from horseshoe crab in its active form is a homo-hexameric protein. It exists in open and closed conformations when transitioning between deoxygenated and oxygenated states. Here, we present a detailed dynamic atomistic investigation of the oxygenated and deoxygenated states of the hexameric hemocyanin using explicit solvent molecular dynamics simulations. We focus on the variation in solvent cavities and the formation of tunnels in the two conformational states. By employing principal component analysis and CVAE-based deep learning, we are able to differentiate between the dynamics of the deoxy- and oxygenated states of hemocyanin. Finally, our results identify the deoxygenated open conformation, which adopts a stable, closed conformation after the oxygenation process.

Dynamic Profiling of ß-Coronavirus 3CL Mpro Protease Ligand-Binding Sites.

ß-coronavirus (CoVs) alone has been responsible for three major global outbreaks in the 21st century. The current crisis has led to an urgent requirement to develop therapeutics. Even though a number of vaccines are available, alternative strategies targeting essential viral components are required as a backup against the emergence of lethal viral variants. One such target is the main protease (Mpro) that plays an indispensable role in viral replication. The availability of over 270 Mpro X-ray structures in complex with inhibitors provides unique insights into ligand-protein interactions. Herein, we provide a comprehensive comparison of all nonredundant ligand-binding sites available for SARS-CoV2, SARS-CoV, and MERS-CoV Mpro. Extensive adaptive sampling has been used to investigate structural conservation of ligand-binding sites using Markov state models (MSMs) and compare conformational dynamics employing convolutional variational auto-encoder-based deep learning. Our results indicate that not all ligand-binding sites are dynamically conserved despite high sequence and structural conservation across ß-CoV homologs. This highlights the complexity in targeting all three Mpro enzymes with a single pan inhibitor.

Use of Taxonomic and Trait-Based Approaches to Evaluate the Effect of Bt maize Expressing Cry1Ie Protein on Non-Target Collembola: A Case Study in Northeast China.

To evaluate the effect of Bt maize expressing Cry1Ie protein on non-target soil Collembola, a two-year field study was conducted in Northeast China. Bt maize line IE09S034 and its near isoline Zong 31 were selected as experimental crops; we investigated the collembolan community using both taxonomic and trait-based approaches, and elucidated the relationship between environmental variables and the collembolan community using redundancy analysis (RDA).The ANOVA results showed that maize variety neither had significant effect on the parameters based on taxonomic approach (abundance, species richness, Shannon-Wiener index, Pielou's evenness index), nor on the parameters based on trait-based approach (ocelli number, body length, pigmentation level, and furcula development) in either year. The results of RDA also showed that maize variety did not affect collembolan community significantly. These results suggest that two years cultivation of cry1Ie maize does not affect collembolan community in Northeast China.

Fast and stable deep-learning predictions of material properties for solid solution alloys.

We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The major idea is to make use of the correlations between different physical properties in alloy systems to improve the prediction accuracy of neural network (NN) models. We use multitasking NN models to simultaneously predict the total energy, charge density and magnetic moment. These physical properties mutually serve as constraints during the training of the multitasking NN, resulting in more reliable DL models because multiple physics properties are correctly learned by a single model. Two binary alloys, copper-gold (CuAu) and iron-platinum (FePt), were studied. Our results show that once the multitasking NN's are trained, they can estimate the material properties for a specific configuration hundreds of times faster than first-principles density functional theory calculations while retaining comparable accuracy. We used a simple measure based on the root-mean-squared errors to quantify the quality of the NN models, and found that the inclusion of charge density and magnetic moment as physical constraints leads to more stable models that exhibit improved accuracy and reduced uncertainty for the energy predictions.

Neural network-based order parameter for phase transitions and its applications in high-entropy alloys.

Phase transition is one of the most important phenomena in nature and plays a central role in materials design. All phase transitions are characterized by suitable order parameters, including the order-disorder phase transition. However, finding a representative order parameter for complex systems is non-trivial, such as for high-entropy alloys. Given the strength of dimensionality reduction of a variational autoencoder (VAE), we introduce a VAE-based order parameter. We propose that the Manhattan distance in the VAE latent space can serve as a generic order parameter for order-disorder phase transitions. The physical properties of our order parameter are quantitatively interpreted and demonstrated by multiple refractory high-entropy alloys. Using this order parameter, a generally applicable alloy design concept is proposed by mimicking the natural mixing process of elements. Our physically interpretable VAE-based order parameter provides a computational technique for understanding chemical ordering in alloys, which can facilitate the development of rational alloy design strategies.

Toxicological and biochemical analyses demonstrate no toxic effect of Bt maize on the Folsomia candida.

The potential effects of Bt (Bacillus thuringiensis) maize on non-target organisms must be conducted before the Bt maize is commercially planted. Folsomia candida is one of the non-target organisms of Bt maize, also as an important indicator of soil quality and environmental pollution. In this study, a 90-day F. candida feeding test were conducted to evaluate the potential effects of two Bt maize lines IE09S034 and BT799 and their non-Bt conventional isolines Zong 31 and Zheng 58. The results show that Bt maize lines had no significant effects on the survival rate, reproduction, adult body length, larval body length, and the activities of acetyl cholinesterase, catalase and superoxide dismutase on the F. candida. Namely, Bt maize had no toxic effects on the F. candida.

No impact of transgenic cry1Ie maize on the diversity, abundance and composition of soil fauna in a 2-year field trial.

Soil fauna play an essential role in the soil ecosystem, but they may be influenced by insecticidal Cry proteins derived from Bacillus thuringiensis (Bt) maize. In this study, a 2-year field trial was conducted to study the effects of transgenic cry1Ie maize, a type of Bt maize (Event IE09S034), on soil fauna, with the near-isogenic line non-Bt maize (Zong 31) as a control. The soil animals were collected with Macfadyen heat extractor and hand-sorting methods, respectively, and their diversity, abundance and community composition were calculated. Then, the effects of maize type, year, sampling time and soil environmental factors on the soil fauna were evaluated by repeated-measures ANOVA, redundancy analysis (RDA) and nonmetric multidimensional scaling (nMDS). Repeated-measures ANOVA showed that the diversity and abundance of the soil fauna were not affected by maize type, while they were significantly influenced by year and sampling time. Furthermore, for both the Macfadyen and hand-sorting methods, RDA indicated that soil fauna community composition was not correlated with maize type (Bt and non-Bt maize) but was significantly correlated with year, sampling time and root biomass. In addition, it was significantly related to soil pH according to the hand-sorting method. nMDS indicated that soil fauna community composition was significantly correlated with year and sampling time; however, it was not associated with maize type. In this study, we collected soil faunal samples according to the Macfadyen and hand-sorting methods and processed the obtained data with ANOVA, RDA, and nMDS in three ways, and our data indicate that transgenic cry1Ie maize (Event IE09S034) had no substantial influence on the diversity, abundance or community composition of the soil fauna.

Phylogenetically Novel Uncultured Microbial Cells Dominate Earth Microbiomes.

To describe a microbe's physiology, including its metabolism, environmental roles, and growth characteristics, it must be grown in a laboratory culture. Unfortunately, many phylogenetically novel groups have never been cultured, so their physiologies have only been inferred from genomics and environmental characteristics. Although the diversity, or number of different taxonomic groups, of uncultured clades has been studied well, their global abundances, or numbers of cells in any given environment, have not been assessed. We quantified the degree of similarity of 16S rRNA gene sequences from diverse environments in publicly available metagenome and metatranscriptome databases, which we show have far less of the culture bias present in primer-amplified 16S rRNA gene surveys, to those of their nearest cultured relatives. Whether normalized to scaffold read depths or not, the highest abundances of metagenomic 16S rRNA gene sequences belong to phylogenetically novel uncultured groups in seawater, freshwater, terrestrial subsurface, soil, hypersaline environments, marine sediment, hot springs, hydrothermal vents, nonhuman hosts, snow, and bioreactors (22% to 87% uncultured genera to classes and 0% to 64% uncultured phyla). The exceptions were human and human-associated environments, which were dominated by cultured genera (45% to 97%). We estimate that uncultured genera and phyla could comprise 7.3 × 1029 (81%) and 2.2 × 1029 (25%) of microbial cells, respectively. Uncultured phyla were overrepresented in metatranscriptomes relative to metagenomes (46% to 84% of sequences in a given environment), suggesting that they are viable. Therefore, uncultured microbes, often from deeply phylogenetically divergent groups, dominate nonhuman environments on Earth, and their undiscovered physiologies may matter for Earth systems. IMPORTANCE In the past few decades, it has become apparent that most of the microbial diversity on Earth has never been characterized in laboratory cultures. We show that these unknown microbes, sometimes called "microbial dark matter," are numerically dominant in all major environments on Earth, with the exception of the human body, where most of the microbes have been cultured. We also estimate that about one-quarter of the population of microbial cells on Earth belong to phyla with no cultured relatives, suggesting that these never-before-studied organisms may be important for ecosystem functions. Author Video: An author video summary of this article is available.

[Dynamic changes in vegetation NDVI from 1982 to 2012 and its responses to climate change and human activities in Xinjiang, China].

Vegetation plays an important role in regulating the terrestrial carbon balance and the climate system, and also overwhelmingly dominates the provisioning of ecosystem services. Therefore, it has significance to monitor the growth of vegetation. Based on AVHRR GIMMS NDVI and MODIS NDVI datasets, we analyzed the spatiotemporal patterns of change in NDVI and their linkage with climate change and human activity from 1982 to 2012 in the typical arid region, Xinjiang of northwestern China, at pixel and regional scales. At regional scale, although a statistically significant positive trend of growing season NDVI with a rate of 4.09 x 10⁻4· a⁻¹ was found during 1982-2012, there were two distinct periods with opposite trends in growing season NDVI before and after 1998, respectively. NDVI in growing season first significantly increased with a rate of 10 x 10⁻4· a⁻¹ from 1982 to 1998, and then decreased with a rate of -3 x 10⁻4· a⁻¹ from 1998 to 2012. The change in trend of NDVI from increase to decrease mainly occurred in summer, followed by autumn, and the reversal wasn't observed in spring. At pixel scale, the NDVI in farmland significantly increased; the NDVI changes in the growing season and all seasons showed polarization: Areas with significant change mostly increased in size as the NDVI record grown in length. The rate of increase in size of areas with significantly decreasing NDVI was larger than that with significantly increasing NDVI, which led to the NDVI increase obviously slowing down or stopping at regional scale. The vegetation growth in the study area was regulated by both climate change and human activity. Temperature was the most important driving factor in spring and autumn, whereas precipitation in summer. Extensive use of fertilizers and increased farmland irrigated area promoted the vegetation growth. However, the rapid increase in the proportion of cotton cultivation and use of drip irrigation might reduce spring NDVI in the part of farmlands, and the increase in stocking levels of livestock might lead to a decrease in NDVI in some grasslands.

Using hyperspectral indices to measure the effect of mine dust on the growth of three typical desert plants.

To examine the influence of coal dust from mining on vegetative growth, three typical plants from near an open-pit coalmine in an arid region were selected, and their spectral signals were determined. The present study was conducted near the Wucaiwan open-pit coalmine in the East Junggar Basin in Xinjiang. We extracted nineteen vegetation indices and examined their correlation with the dust flux. The objective was to determine which parameters that quantify vegetation damage could provide a basis for environmental monitoring in arid regions. The results indicate that when coal dust damages vegetation, both chlorophyll and moisture are reduced, and the amount of carotenoids increases with increasing coal dust. The pigment-specific normalized difference (PSNDb), structure-insensitive pigment index (SIPI) and plant water index (PWI) were the most sensitive indices, and sacsaoul was most sensitive to coal-dust pollution.