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Despite recent advances of data acquisition and algorithms development, machine learning (ML) faces tremendous challenges to being adopted in practical catalyst design, largely due to its limited generalizability and poor explainability. Herein, we develop a theory-infused neural network (TinNet) approach that integrates deep learning algorithms with the well-established d -band theory of chemisorption for reactivity prediction of transition-metal surfaces. With simple adsorbates (e.g., *OH, *O, and *N) at active site ensembles as representative descriptor species, we demonstrate that the TinNet is on par with purely data-driven ML methods in prediction performance while being inherently interpretable. Incorporation of scientific knowledge of physical interactions into learning from data sheds further light on the nature of chemical bonding and opens up new avenues for ML discovery of novel motifs with desired catalytic properties. Machine learning faces challenges in catalyst design due to its black-box nature. Here, the authors develop a theory-infused neural network approach that integrates deep learning algorithms with the well-established d -band theory of chemisorption for reactivity prediction of transition-metal surfaces.

The electrochemical ammonia oxidation to dinitrogen as a means for energy and environmental applications is a key technology toward the realization of a sustainable nitrogen cycle. The state-of-the-art metal catalysts including Pt and its bimetallics with Ir show promising activity, albeit suffering from high overpotentials for appreciable current densities and the soaring price of precious metals. Herein, the immense design space of ternary Pt alloy nanostructures is explored by graph neural networks trained on ab initio data for concurrently predicting site reactivity, surface stability, and catalyst synthesizability descriptors. Among a few Ir-free candidates that emerge from the active learning workflow, Pt 3 Ru-M (M: Fe, Co, or Ni) alloys were successfully synthesized and experimentally verified to be more active toward ammonia oxidation than Pt, Pt 3 Ir, and Pt 3 Ru. More importantly, feature attribution analyses using the machine-learned representation of site motifs provide fundamental insights into chemical bonding at metal surfaces and shed light on design strategies for high-performance catalytic systems beyond the d -band center metric of binding sites. Electrochemical conversion of ammonia to nitrogen has important energy and environmental applications but is hindered by lack of efficient electrocatalysts. Here the authors use quantum chemistry and machine learning to gain insights into the reaction mechanism and accelerate the design of highly active Ir-free trimetallic catalysts.

Antimicrobial peptides are important components of the host innate defense mechanism against invading pathogens, especially for drug-resistant bacteria. In addition to bactericidal activity, the 25 residue peptide TP4 isolated from Nile tilapia also stimulates cell proliferation and regulates the innate immune system in mice. In this report, TP4 hyperpolarized and depolarized the membrane potential of Pseudomonas aeruginosa at sub-lethal and lethal concentrations. It also inhibited and eradicated biofilm formation. The in vitro binding of TP4 to bacterial outer membrane target protein, OprI, was markedly enhanced by a membrane-like surfactant sarkosyl and lipopolysaccharide, which converted TP4 into an α-helix. The solution structure of TP4 in dodecylphosphocholine was solved by NMR analyses. It contained a typical α-helix at residues Phe10-Arg22 and a distorted helical segment at Ile6-Phe10, as well as a hydrophobic core at the N-terminus and a cationic patch at the C-terminus. Residues Ile16, Leu19 and Ile20 in the hydrophobic face of the main helix were critical for the integrity of amphipathic structure, other hydrophobic residues played important roles in hemolytic and bactericidal activities. A model for the assembly of helical TP4 embedded in sarkosyl vesicle is proposed. This study may provide valuable insight for engineering AMPs to have potent bactericidal activity but low hemolytic activity.

The expression of testicular genes following acute heat stress has been reported in layer-type roosters, but few similar studies have been conducted on broilers. This study investigated the effect of acute heat stress on the gene expression in the testes of a broiler-type strain of Taiwan country chickens. Roosters were subjected to acute heat stress (38°C) for 4 h, and then exposed to 25°C, with testes collected 0, 2, and 6 h after the cessation of heat stress, using non-heat-stressed roosters as controls (n = 3 roosters per group). The body temperature and respiratory rate increased significantly (p<0.05) during the heat stress. The numbers of apoptotic cells increased 2 h after the acute heat stress (79 ± 7 vs. 322 ± 192, control vs. heat stress; p<0.05), which was earlier than the time of increase in layer-type roosters. Based on a chicken 44 K oligo microarray, 163 genes were found to be expressed significantly different in the testes of the heat-stressed chickens from those of the controls, including genes involved in the response to stimulus, protein metabolism, signal transduction, cell adhesion, transcription, and apoptosis. The mRNA expressions of upregulated genes, including HSP25, HSP90AA1, HSPA2, and LPAR2, and of downregulated genes, including CDH5, CTNNA3, EHF, CIRBP, SLA, and NTF3, were confirmed through quantitative real-time polymerase chain reaction (qRT-PCR). Moreover, numerous transcripts in the testes exhibited distinct expressions between the heat-stressed broiler-type and layer-type chickens. We concluded that the transcriptional responses of testes to acute heat stress may differ between the broiler-type and layer-type roosters. Whether the differential expression patterns associate with the heat-tolerance in the strains require a further exploration.

Antimicrobial peptides (AMPs) are important components of the host innate defense mechanism against invading microorganisms. Although AMPs are known to act on bacterial membranes and increase membrane permeability, the action mechanism of most AMPs still remains unclear. In this report, we found that the TP4 peptides from Nile tilapia anchored on E. coli cells and enabled them permeable to SYTOX Green in few minutes after TP4 addition. TP4 peptides existed in small dots either on live or glutaraldehyde-fixed cells. TP4 peptides were driven into oligomers either in soluble or insoluble form by a membrane-mimicking anionic surfactant, sarkosyl, depending on the concentrations employed. The binding forces among TP4 components were mediated through hydrophobic interaction. The soluble oligomers were negatively charged on surface, while the insoluble oligomers could be fused with each other or piled on existing particles to form larger particles with diameters 0.1 to 20 μm by hydrophobic interactions. Interestingly, the morphology and solubility of TP4 particles changed with the concentration of exogenous sarkosyl or trifluoroethanol. The TP4 peptides were assembled into oligomers on or in bacterial membrane. This study provides direct evidence and a model for the oligomerization and insertion of AMPs into bacterial membrane before entering into cytosol.

Sugar-sweetened beverages (SSB) are previously reported to jeopardize maternal fetal health, most well-known for gestational diabetes, childhood obesity, and cognitive impairment. Although warnings and diet recommendations urge pregnant women to consume less SSB, there is no noticeable difference in their behavior. How and why reproductive women change their SSB consumption patterns were not investigated previously. Our study aims to investigate beverage consumption patterns and how these patterns change in pregnancy in the context of substance use disorder (SUD). We invited all pregnant women visiting the clinic to answer a structured 20-min questionnaire every trimester during the regular antennal visit. At the end of the study, 337 pregnant women aged over 20 participated. A total of 301 responses entered for final analysis, with a response rate of 89.3%. Our finding showed those with high DSM-5-TR scores reduced SSB intake after becoming pregnant, while those with mild or low DSM-5-TR scores increased SSB intake after becoming pregnant. The top 3 factors related to their SSB consumption were “use despite of known health hazard (n = 133)”, “increased desire to drink (n = 88)”, and “excessive time spent on seeking SSB (n = 85)”. The least reported factors were in the domains of social impairment (ranging from n = 3 to n = 26), pharmacologic effects (i.e., tolerance (n = 24) and withdrawal (n = 70). When participants reduced SSB consumption after becoming pregnant, their choice of beverages largely shifted to sugarless beverage but not much plain water. The result provided new insights in deciphering pregnant women’s psychomotor factors for SSB intake, which served as useful references for making clinical or even public health recommendations.

The association among sugar sweetened beverages (SSB) consumption, addiction and depression in adults, children and adolescents is widely reported. Dieting patterns during pregnancy is complicated by maternal fetal concerns. Specifically, restrained use of SSB might be potentially a source of perinatal distress. The current study modified diagnostic criteria for Substance Use Disorder (SUD) in Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), into SSB-specific questions to assess SSB use tendency. Edinburgh Postpartum Depression Scores (EPDS) is used to assess maternal distress during pregnancy. One hundred and ninety-six consecutive pregnant women receiving antenatal care at Kaohsiung Medical University Hospital were invited to participate in this study. In the first trimester, 46.6% of women had none or 1 DSM-5 symptom, 27.0% had 2–3 symptoms, while 26.4% had ≥ 4 symptoms. The mean numbers of DSM-5 symptoms in each trimester were found to be 2.5 ± 2.25, 2.6 ± 2.45, 2.4 ± 2.43 for the first, second and third trimester, respectively, p  = 0.750. While EPDS score showed no difference among DSM-5 symptoms 0–1, 2–3 and ≥ 4 groups in the first trimester (8.1 ± 4.59, 8.4 ± 5.00, 8.8 ± 4.82, p  = 0.343), women with ≥ 4 DSM-symptoms was found significantly higher EPDS scores than those with < 4 DSM-symptoms in the second (7.2 ± 4.81, 7.7 ± 4.98, 8.8 ± 4.33, p  = 0.030) and third trimester (6.8 ± 5.00, 7.2 ± 4.63, 8.7 ± 5.24, p  = 0.019). The relationship remained significant after adjusting for covariates including actual SSB amount consumed (adjusted β = 0.25 with 95% confident interval (CI) 0.04–0.45 and 0.21 with 95% CI 0.04–0.38 for the second and third trimesters, respectively). Overall, the study is the first to characterize the positive relationship between SSB use tendency and antenatal distress in pregnancy, independent of actual SSB amount consumed. The observational nature of the study design precludes inferences of its underlying socio-psychomotor mechanisms, although restrained SSB use in pregnancy is suspected to contribute. The novel employment of modified SSB-specific DSM-5 scores and EPDS in this setting is feasible and further validation is promising. With better understanding and awareness, pregnant women with increased SSB use tendency should be properly counseled with special attention to their mental state.

De novo thrombotic microangiopathy (TMA) is a rare and challenging condition in kidney transplant recipients, with limited research on its incidence and impact on graft survival. This study conducted a systematic review and meta-analysis of 28 cohorts/single-arm studies and 46 case series/reports from database inception to June 2022. In meta-analysis, among 14,410 kidney allograft recipients, de novo TMA occurred in 3.20% [95% confidence interval (CI): 1.93–4.77], with systemic and renal-limited TMA rates of 1.38% (95% CI: 06.5–2.39) and 2.80% (95% CI: 1.27–4.91), respectively. The overall graft loss rate of de novo TMA was 33.79% (95% CI: 26.14–41.88) in meta-analysis. This study provides valuable insights into the incidence and graft outcomes of de novo TMA in kidney transplant recipients.

Background Zoledronic acid (ZA), a nitrogen-containing bisphosphonate, inhibits osteoclastogenesis. Emerging evidence suggests that ZA has anti-tumor and anti-metastatic properties for breast cancer cells. In a mouse model of ZA-related osteonecrosis of the jaw, ZA administration was found to suppress regulatory T-cells (Tregs) function. Our previous reports also demonstrated ZA acted as an immune modulator to block Tregs. Manipulation of Tregs represents a new strategy for cancer treatment. However, the relationship among ZA, Tregs, and cancer cells remains unclear. In this study, we investigated the effects of ZA on the interaction of breast cancer cells and Tregs. Methods The anti-tumor effect of ZA on triple negative breast cancer cell lines were validated by XTT, wound healing and apoptosis analysis. A flow cytometry-based assay was used to analyze the immunosuppressive effect of Tregs treated with media conditioned by breast cancer cells, and a transwell assay was used to evaluate the chemotactic migration of Tregs. Differential gene expression profile on MDA-MB-231 treated with ZA (25 μM) was analyzed by. microarrays to describe the molecular basis of actions of ZA for possible direct anti-tumor effects. Enzyme-linked immunosorbent assays and quantitative real-time PCR were used to investigate the effect of ZA on the expression of cytokines/factors by breast cancer cells. Results ZA was found to inhibit the proliferation and migration of breast cancer cells. Media conditioned by the MDA-MB-231 cells promoted the expansion, chemotactic migration, and immunosuppressive activity of Tregs, and these effects were attenuated in a dose-dependent manner by ZA treatment, and the attenuation was due to reduced expression of selected breast cancer cell factors (CCL2, CCL5, and IDO). Conclusions ZA can significantly affect the interaction between breast cancer cells and Tregs. Our findings indicate that ZA is a potential therapeutic agent that can be used to reduce cancer aggressiveness by abolishing the supportive role of Tregs.

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/