Detecting dynamic domains and local fluctuations in complex molecular systems via timelapse neighbors shuffling.

It is known that the behavior of many complex systems is controlled by local dynamic rearrangements or fluctuations occurring within them. Complex molecular systems, composed of many molecules interacting with each other in a Brownian storm, make no exception. Despite the rise of machine learning and of sophisticated structural descriptors, detecting local fluctuations and collective transitions in complex dynamic ensembles remains often difficult. Here, we show a machine learning framework based on a descriptor which we name Local Environments and Neighbors Shuffling (LENS), that allows identifying dynamic domains and detecting local fluctuations in a variety of systems in an abstract and efficient way. By tracking how much the microscopic surrounding of each molecular unit changes over time in terms of neighbor individuals, LENS allows characterizing the global (macroscopic) dynamics of molecular systems in phase transition, phases-coexistence, as well as intrinsically characterized by local fluctuations (e.g., defects). Statistical analysis of the LENS time series data extracted from molecular dynamics trajectories of, for example, liquid-like, solid-like, or dynamically diverse complex molecular systems allows tracking in an efficient way the presence of different dynamic domains and of local fluctuations emerging within them. The approach is found robust, versatile, and applicable independently of the features of the system and simply provided that a trajectory containing information on the relative motion of the interacting units is available. We envisage that "such a LENS" will constitute a precious basis for exploring the dynamic complexity of a variety of systems and, given its abstract definition, not necessarily of molecular ones.

Stimulus of Work Function on Electron Transfer Process of Intermetallic Nickel-Antimonide Toward Bifunctional Electrocatalyst for Overall Water Splitting.

Engineering the intermetallic nanostructures as an effective bifunctional electrocatalyst for hydrogen and oxygen evolution reactions (HER and OER) is of great interest in green hydrogen production. However, a few non-noble metals act as bifunctional electrocatalysts, exhibiting terrific HER and OER processes reported to date. Herein the intermetallic nickel-antimonide (Ni─Sb) dendritic nanostructure via cost-effective electro-co-deposition method is designed and their bifunctional electrocatalytic property toward HER and OER is unrevealed. The designed Ni─Sb delivers a superior bifunctional activity in 1 m KOH electrolyte, with a shallow overpotential of ≈119 mV at -10 mA for HER and ≈200 mV at 50 mA for OER. The mechanism behind the excellent bifunctional property of Ni─Sb is discussed via "interfacial descriptor" with the aid of Kelvin probe force microscopy (KPFM). This study reveals the rate of electrocatalytic reaction depends on the energy required for electron and proton transfer from the catalyst's surface. It is noteworthy that the assembled Ni─Sb-90 electrolyzer requires only a minuscule cell voltage of ≈1.46 V for water splitting, which is far superior to the art of commercial catalysts.

High-Valence W6+ Ions Boost Cr2+ Activity in CrWO4 for Ideal Water Oxidation.

Electrocatalytic activity of multi-valence metal oxides for oxygen evolution reaction (OER) arises from various interactions among the constituent metal elements. Although the high-valence metal ions attract recent attentions due to the interactions with their neighboring 3d transition metal catalytic center, atomic-scale explanations for the catalytic efficiencies are still lacking. Here, by employing density functional theory predictions and experimental verifications, unprecedented electronic isolation of the catalytic 3d center (M2+) induced by the surrounding high-valence ions such as W6+ is discovered in multivalent oxides MWO4 (M = Ti, V, Cr, Mn, Fe, Co, Ni, Cu, and Zn). Due to W6+'s extremely high oxidation state with the minimum electron occupations (d0), the surrounding W6+ blocks electron transfer toward the catalytic M2+ ions and completely isolates the ions electronically. Now, the isolated M2+ ions solely perform OER without any assistant electron flow from the adjacent metal ions, and thus the original strong binding energies of Cr with OER intermediates are effectively moderated. Through exploiting "electron isolators" such as W6+ surrounding the catalytic ion, exploring can be done beyond the conventional materials such as Ni- or Co-oxides into new candidate groups such as Cr and Mn on the left side of the periodic table for ideal OER.

Influence of inframalleolar modifier P0/P1 on wound healing in bypass surgery vs endovascular therapy in patients with chronic limb-threatening ischemia.

OBJECTIVE: This study aimed to compare the influence of inframalleolar (IM) P0/P1 on wound healing in bypass surgery vs endovascular therapy (EVT) in patients with chronic limb-threatening ischemia (CLTI). METHODS: We retrospectively analyzed the multicenter data of patients who underwent infra-inguinal revascularization for CLTI between 2015 and 2022. IM P represents target artery crossing into foot, with intact pedal arch (P0) and absent or severely diseased pedal arch (P1). The endpoints were wound healing, limb salvage (LS), and postoperative complications. RESULTS: We analyzed 66 and 189 propensity score-matched pairs in the IM P0 and IM P1 cohorts, respectively. In the IM P0 cohort, the 1-year wound healing rates were 94.5% and 85.7% in the bypass surgery and EVT groups, respectively (P = .092), whereas those in the IM P1 cohort were 86.2% and 66.2% in the bypass surgery and EVT groups, respectively (P < .001). In the IM P0 cohort, the 2-year LS rates were 96.7% and 94.1% in the bypass surgery and EVT groups, respectively (P = .625), and those in the IM P1 cohort were 91.8% and 81.5% in the bypass surgery and EVT groups, respectively (P = .004). No significant differences were observed between the bypass surgery and EVT in terms of postoperative complication rates in either the IM P0 or P1 cohorts. CONCLUSIONS: Bypass surgery facilitated better wound healing and LS than EVT in patients with IM P1. Conversely, no differences in wound healing or LS were observed between groups in patients with IM P0. Bypass surgery should be considered a better revascularization strategy than EVT in patients with tissue loss and IM P1 disease.

A Data-driven Approach for Enhanced CO2 Capture with Ruthenium Complexes.

To attain carbon neutrality, significant efforts have been made to capture and use CO2. The homogeneous hydrogenation of CO2 catalyzed by transition metal complexes, particularly ruthenium complexes, has demonstrated significant advantages and is regarded as a viable approach for practical application. Insertion of CO2 into the Ru-H bond, producing the Ru-formate product, is the key step in the hydrogenation of CO2. In order to parameterize the catalytic activities in the CO2 insertion into the Ru-H bond, the concept of simplified mechanism-based approach with data-driven practice (SMADP) has been introduced in this paper. The results showed that the hydricity of the Ru-H complex (ΔGH-) might serve as a single active descriptor in the process of CO2 insertion, and that a novel Ru complex in CO2 catalysis may not be easily obtained by mere modification of the auxiliary ligand at the ruthenium metal site.

Design of Single-Atom Catalysts on C5N2 for Nitrogen Fixation at Ambient Conditions: A First-Principles Study.

Single atom catalysts (SACs) exhibit the flexible coordination structure of the active site and high utilization of active atoms, making them promising candidates for nitrogen reduction reaction (NRR) under ambient conditions. By the aid of first-principles calculations based on DFT, we have systematically explored the NRR catalytic behavior of thirteen 4d- and 5d-transition metal atoms anchored on 2D porous graphite carbon nitride C 5 ${_5 }$ N 2 ${_2 }$ . With high selectivity and outstanding activity, Zr, Nb, Mo, Ta, W and Re-doped C 5 ${_5 }$ N 2 ${_2 }$ are identified as potential nominees for NRR. Particularly, Mo@C 5 ${_5 }$ N 2 ${_2 }$ possesses an impressive low limiting potential of -0.39 V (corresponding to a very low temperature and atmospheric pressure), featuring the potential determining step involving *N-N transitions to *N-NH via the distal path. The catalytic performance of TM@C 5 ${_5 }$ N 2 ${_2 }$ can be well characterized by the adsorption strength of intermediate *N 2 ${_2 }$ H. Moreover, there exists a volcanic relationship between the catalytic property U L ${_{\rm{L}} }$ and the structure descriptor Ψ ${{{\Psi }}}$ , which validates the robustness and universality of Ψ ${{{\Psi }}}$ , combined with our previous study. This work sheds light on the design of SACs with eminent NRR performance.

Regulating Excess Electrons in Reducible Metal Oxides for Enhanced Oxygen Evolution Reaction Activity: A Mini-Review.

Identifying a universal activity descriptor for metal oxides, akin to the d-band center for transition metals, remains a significant challenge in catalyst design, largely due to the intricate electronic structures of metal oxides. This review highlights a major advancement in formulating the number of excess electrons (NEE) as an activity descriptor for oxygen evolution reaction (OER) on reducible metal oxide surfaces. We elaborate on the quantitative relationship between NEE and the adsorption properties of OER intermediates, and unveil the decisive role of the octet rule on the OER performance of these oxides. This insight provides a robust theoretical basis for designing effective OER catalysts. Moreover, we discuss critical experimental evidence supporting this theory and summarize recent advances in employing NEE as a guiding principle for developing highly efficient OER catalysts experimentally.

A General Model of Impact Sensitivity for Nitrogen-Rich Energetic Materials: A Combined Incremental Theory and Genetic Function Approximation Study.

In this paper, we report the first example of impact sensitivity prediction based on the genetic function approximation (GFA) as a regression method. The prediction is applicable for a wide variety of chemical families, which include nitro compounds, peroxides, nitrogen-rich salts, heterocycles, etc. Within this work, we have obtained 7 empirical models (with 27-32 basis functions), which all provide 0.80≤R2≤0.83 and 7.2 J≤RMSE≤7.8 J (for 450 training set compounds) and 0.64≤R2≤0.70 and 11.2 J≤RMSE≤12.4 J (for 170 test set compounds). The models were developed using Friedman Lack-of-Fit as a scoring function, which allows avoiding an overfitting. All the models have simple descriptors as basis functions and include linear splines. Furthermore, the applied descriptors do not require expensive calculation procedures, namely, non-empirical quantum-chemical calculations, complex iterative procedures, real space electron density analysis, etc. Most descriptors are based on structural and topological analysis and a part of them require very cheap semi-empirical PM6 calculations. The prediction takes a few minutes as an average, and most of the time is for the structure preparation and manual calculation of the descriptor "Increment", which is based on our recent incremental theory.

Inhibitor design for TMPRSS2: insights from computational analysis of its backbone hydrogen bonds using a simple descriptor.

Transmembrane protease serine 2 (TMPRSS2) is an important drug target due to its role in the infection mechanism of coronaviruses including SARS-CoV-2. Current understanding regarding the molecular mechanisms of known inhibitors and insights required for inhibitor design are limited. This study investigates the effect of inhibitor binding on the intramolecular backbone hydrogen bonds (BHBs) of TMPRSS2 using the concept of hydrogen bond wrapping, which is the phenomenon of stabilization of a hydrogen bond in a solvent environment as a result of being surrounded by non-polar groups. A molecular descriptor which quantifies the extent of wrapping around BHBs is introduced for this. First, virtual screening for TMPRSS2 inhibitors is performed by molecular docking using the program DOCK 6 with a Generalized Born surface area (GBSA) scoring function. The docking results are then analyzed using this descriptor and its relationship to the solvent-accessible surface area term ΔGsa of the GBSA score is demonstrated with machine learning regression and principal component analysis. The effect of binding of the inhibitors camostat, nafamostat, and 4-guanidinobenzoic acid (GBA) on the wrapping of important BHBs in TMPRSS2 is also studied using molecular dynamics. For BHBs with a large increase in wrapping groups due to these inhibitors, the radial distribution function of water revealed that certain residues involved in these BHBs, like Gln438, Asp440, and Ser441, undergo preferential desolvation. The findings offer valuable insights into the mechanisms of these inhibitors and may prove useful in the design of new inhibitors.

Elucidating Key Characteristics of PFAS Binding to Human Peroxisome Proliferator-Activated Receptor Alpha: An Explainable Machine Learning Approach.

Per- and polyfluoroalkyl substances (PFAS) are widely employed anthropogenic fluorinated chemicals known to disrupt hepatic lipid metabolism by binding to human peroxisome proliferator-activated receptor alpha (PPARα). Therefore, screening for PFAS that bind to PPARα is of critical importance. Machine learning approaches are promising techniques for rapid screening of PFAS. However, traditional machine learning approaches lack interpretability, posing challenges in investigating the relationship between molecular descriptors and PPARα binding. In this study, we aimed to develop a novel, explainable machine learning approach to rapidly screen for PFAS that bind to PPARα. We calculated the PPARα-PFAS binding score and 206 molecular descriptors for PFAS. Through systematic and objective selection of important molecular descriptors, we developed a machine learning model with good predictive performance using only three descriptors. The molecular size (b_single) and electrostatic properties (BCUT_PEOE_3 and PEOE_VSA_PPOS) are important for PPARα-PFAS binding. Alternative PFAS are considered safer than their legacy predecessors. However, we found that alternative PFAS with many carbon atoms and ether groups exhibited a higher affinity for PPARα. Therefore, confirming the toxicity of these alternative PFAS compounds with such characteristics through biological experiments is important.

Prospective external validation of IOTA methods for classifying adnexal masses and retrospective assessment of two-step strategy using benign descriptors and ADNEX: a Portuguese multicenter study.

OBJECTIVE: This study aimed to externally and prospectively validate the International Ovarian Tumor Analysis (IOTA) Simple Rules (SRs), Logistic Regression model 2 (LR2) and Assessment of Different NEoplasias in the adneXa (ADNEX) in a Portuguese population, comparing them with operator subjective assessment (SA), Risk-of-Malignancy Index (RMI), as well as with each other. This study also aimed to retrospectively validate IOTA two-step strategy, using modified benign descriptors (MBDs) followed by the application of ADNEX in cases where MBDs were not applicable (MBDs + ADNEX). METHODS: In this multicenter diagnostic accuracy study, conducted between January 2016 and December 2021, three tertiary referral centers prospectively included consecutive patients with ultrasound diagnosis of at least one adnexal tumor who underwent surgery. All ultrasound assessments were performed by level II or III sonologists with IOTA certification. Patient clinical data and serum cancer antigen (CA125) levels were collected from the hospital databases. Each adnexal mass was classified as benign or malignant using SA, RMI, IOTA SRs, LR2 and ADNEX (with and without CA125). The reference standard was histopathological diagnosis. In the second phase, all adnexal tumors were retrospectively classified using the two-step strategy (MBDs + ADNEX). The sensitivity, specificity, positive (PPV) and negative predictive value (NPV), positive (LR+) and negative likelihood ratio (LR-) as well as overall accuracy were determined for SA, RMI, IOTA SRs, LR2, ADNEX and two-step strategy (MBDs + ADNEX). Receiver-operator characteristic curves were constructed and corresponding areas under the curve (AUC) determined for RMI, LR2 and ADNEX and two-step strategy (MBDs + ADNEX). The ADNEX calibration plots were constructed and estimated by LOESS smoother. RESULTS: Of the 571 included patients, 428 had benign disease, 42 borderline ovarian tumors, 93 primary invasive adnexal cancers and 8 metastatic tumors in adnexa (malignancy prevalence: 25.0%). The operator SA had an overall sensitivity of 97.9% and a specificity of 83.6% for distinguishing between benign and malignant lesions. RMI showed high specificity (95.6%) but very low sensitivity (58.7%), with an AUC of 0.913. The IOTA SRs were applicable in 80.0% of patients, with a sensitivity of 94.8% and a specificity of 98.6%. LR2 revealed a sensitivity of 84.6%, a specificity of 86.9% and an AUC of 0.939, at the malignancy risk cut-off of 10%. At the same cut-off, ADNEX with and without CA125 had a sensitivity of 95.8% and 98.6%, respectively, and a specificity of 82.5% and 79.7%, respectively. The AUC of ADNEX with vs. without CA125 was 0.962 vs. 0.960. The ADNEX model provided heterogeneous results in distinguishing between benign and different subtypes of malignancy, with the highest AUC (0.991) for discriminating benign masses from primary adnexal cancer stage II-IV, and the lowest AUC (0.696) for distinguishing primary adnexal cancer stage I and metastatic lesion in adnexa. The ADNEX calibration plots suggested an underestimation of the predicted risk in relation with the observed proportion of malignancies. The MBDs were applicable in 26.3% of cases (150/571 tumors, none of which were malignant). Similar to the ADNEX model applied in all patients, the two-step strategy using ADNEX in the second step only, with and without CA125, had an AUC of 0.964 and 0.961, respectively. CONCLUSIONS: Our results showed a good to excellent performance of the IOTA methods in the studied Portuguese population, outperforming RMI. ADNEX was superior in accuracy, but interpretation of its ability to distinguish malignant subtypes was fundamentally limited not only by sample size but also by large differences in the prevalence of tumor subtypes. The IOTA MBDs have been shown to be reliable in identifying benign disease. The two-step strategy based on the application of MBDs, followed by the ADNEX model if MBDs are not applicable, has proven to be suitable for daily practice circumventing the need to use electronic support in all patients. This article is protected by copyright. All rights reserved.

The use of core descriptors from the ENiGMA code study in recent literature: a systematic review.

AIM: The heterogeneity in data quality presented in studies regarding Crohn's anal fistula (CAF) limit extrapolation into clinical practice. The ENiGMA collaborators established a core descriptor set to standardize reporting of CAF. The aim of this work was to quantify the use of these descriptors in recent literature. METHOD: We completed a systematic review of PubMed and the Cochrane Library, extracting publications from the past 10 years specific to the clinical interventions and outcomes of CAF, and reported in line with PRISMA guidance. Each article was assessed for inclusion of ENiGMA descriptors. The median number of descriptors per publication was evaluated along with the overall frequency of each individual descriptor. Use of ENiGMA descriptors was compared between medical and procedural publications. RESULTS: Ninety publications were included. The median number of descriptors was 15 of 37; 16 descriptors were used in over half of the publications while 17 were used in fewer than a third. Descriptors were more frequently used in procedural (n = 16) than medical publications (n = 14) (p = 0.031). In procedural publications, eight descriptors were more frequently used including Faecal incontinence, Number of previous fistula interventions, Presence and severity of anorectal stenosis and Current proctitis. Medical publications were more likely to include Previous response to biological therapy and Duration and type of current course of biological therapy. CONCLUSION: With many descriptors being used infrequently and variations between medical and procedural literature, the colorectal community should assess the need for all 37 descriptors.

Unveiling critical structural features for effective HDAC8 inhibition: a comprehensive study using quantitative read-across structure-activity relationship (q-RASAR) and pharmacophore modeling.

Histone deacetylases constitute a group of enzymes that participate in several biological processes. Notably, inhibiting HDAC8 has become a therapeutic strategy for various diseases. The current inhibitors for HDAC8 lack selectivity and target multiple HDACs. Consequently, there is a growing recognition of the need for selective HDAC8 inhibitors to enhance the effectiveness of therapeutic interventions. In our current study, we have utilized a multi-faceted approach, including Quantitative Structure-Activity Relationship (QSAR) combined with Quantitative Read-Across Structure-Activity Relationship (q-RASAR) modeling, pharmacophore mapping, molecular docking, and molecular dynamics (MD) simulations. The developed q-RASAR model has a high statistical significance and predictive ability (Q2F1:0.778, Q2F2:0.775). The contributions of important descriptors are discussed in detail to gain insight into the crucial structural features in HDAC8 inhibition. The best pharmacophore hypothesis exhibits a high regression coefficient (0.969) and a low root mean square deviation (0.944), highlighting the importance of correctly orienting hydrogen bond acceptor (HBA), ring aromatic (RA), and zinc-binding group (ZBG) features in designing potent HDAC8 inhibitors. To confirm the results of q-RASAR and pharmacophore mapping, molecular docking analysis of the five potent compounds (44, 54, 82, 102, and 118) was performed to gain further insights into these structural features crucial for interaction with the HDAC8 enzyme. Lastly, MD simulation studies of the most active compound (54, mapped correctly with the pharmacophore hypothesis) and the least active compound (34, mapped poorly with the pharmacophore hypothesis) were carried out to validate the observations of the studies above. This study not only refines our understanding of essential structural features for HDAC8 inhibition but also provides a robust framework for the rational design of novel selective HDAC8 inhibitors which may offer insights to medicinal chemists and researchers engaged in the development of HDAC8-targeted therapeutics.

Utility of human cytochrome P450 inhibition data in the assessment of drug-induced liver injury.

Drug-induced liver injury (DILI) is a major cause of drug development discontinuation and drug withdrawal from the market, but there are no golden standard methods for DILI risk evaluation. Since we had found the association between DILI and CYP1A1 or CYP1B1 inhibition, we further evaluated the utility of cytochrome P450 (P450) inhibition assay data for DILI risk evaluation using decision tree analysis.The inhibitory activity of drugs with DILI concern (DILI drugs) and no DILI concern (no-DILI drugs) against 10 human P450s was assessed using recombinant enzymes and luminescent substrates. The drugs were also subjected to cytotoxicity assays and high-content analysis using HepG2 cells. Molecular descriptors were calculated by alvaDesc.Decision tree analysis was performed with the data obtained as variables with or without P450-inhibitory activity to discriminate between DILI drugs and no-DILI drugs. The accuracy was significantly higher when P450-inhibitory activity was included. After the decision tree discrimination, the drugs were further discriminated with the P450-inhibitory activity. The results demonstrated that many false-positive and false-negative drugs were correctly discriminated by using the P450 inhibition data.These results suggest that P450 inhibition assay data are useful for DILI risk evaluation.

The relevance and accuracy of an AI algorithm-based descriptor on 23 facial attributes in a diverse female US population.

BACKGROUND: The response of AI in situations that mimic real life scenarios is poorly explored in populations of high diversity. OBJECTIVE: To assess the accuracy and validate the relevance of an automated, algorithm-based analysis geared toward facial attributes devoted to the adornment routines of women. METHODS: In a cross-sectional study, two diversified groups presenting similar distributions such as age, ancestry, skin phototype, and geographical location was created from the selfie images of 1041 female in a US population. 521 images were analyzed as part of a new training dataset aimed to improve the original algorithm and 520 were aimed to validate the performance of the AI. From a total 23 facial attributes (16 continuous and 7 categorical), all images were analyzed by 24 make-up experts and by the automated descriptor tool. RESULTS: For all facial attributes, the new and the original automated tool both surpassed the grading of the experts on a diverse population of women. For the 16 continuous attributes, the gradings obtained by the new system strongly correlated with the assessment made by make-up experts (r ≥ 0.80; p < 0.0001) and supported by a low error rate. For the seven categorical attributes, the overall accuracy of the AI-facial descriptor was improved via enrichment of the training dataset. However, some weaker performance in spotting specific facial attributes were noted. CONCLUSION: In conclusion, the AI-automatic facial descriptor tool was deemed accurate for analysis of facial attributes for diverse women although some skin complexion, eye color, and hair features required some further finetuning.

Robust Point Cloud Registration for Aircraft Engine Pipeline Systems.

Aircraft engine systems are composed of numerous pipelines. It is crucial to regularly inspect these pipelines to detect any damages or failures that could potentially lead to serious accidents. The inspection process typically involves capturing complete 3D point clouds of the pipelines using 3D scanning techniques from multiple viewpoints. To obtain a complete and accurate representation of the aircraft pipeline system, it is necessary to register and align the individual point clouds acquired from different views. However, the structures of aircraft pipelines often appear similar from different viewpoints, and the scanning process is prone to occlusions, resulting in incomplete point cloud data. The occlusions pose a challenge for existing registration methods, as they can lead to missing or wrong correspondences. To this end, we present a novel registration framework specifically designed for aircraft pipeline scenes. The proposed framework consists of two main steps. First, we extract the point feature structure of the pipeline axis by leveraging the cylindrical characteristics observed between adjacent blocks. Then, we design a new 3D descriptor called PL-PPFs (Point Line-Point Pair Features), which combines information from both the pipeline features and the engine assembly line features within the aircraft pipeline point cloud. By incorporating these relevant features, our descriptor enables accurate identification of the structure of the engine's piping system. Experimental results demonstrate the effectiveness of our approach on aircraft engine pipeline point cloud data.

Place Recognition through Multiple LiDAR Scans Based on the Hidden Markov Model.

Autonomous driving systems for unmanned ground vehicles (UGV) operating in enclosed environments strongly rely on LiDAR localization with a prior map. Precise initial pose estimation is critical during system startup or when tracking is lost, ensuring safe UGV operation. Existing LiDAR-based place recognition methods often suffer from reduced accuracy due to only matching descriptors from individual LiDAR keyframes. This paper proposes a multi-frame descriptor-matching approach based on the hidden Markov model (HMM) to address this issue. This method enhances the place recognition accuracy and robustness by leveraging information from multiple frames. Experimental results from the KITTI dataset demonstrate that the proposed method significantly enhances the place recognition performance compared with the scan context-based single-frame descriptor-matching approach, with an average performance improvement of 5.8% and with a maximum improvement of 15.3%.

Bridging the gap: Multi-omics profiling of brain tissue in Alzheimer's disease and older controls in multi-ethnic populations.

INTRODUCTION: Multi-omics studies in Alzheimer's disease (AD) revealed many potential disease pathways and therapeutic targets. Despite their promise of precision medicine, these studies lacked Black Americans (BA) and Latin Americans (LA), who are disproportionately affected by AD. METHODS: To bridge this gap, Accelerating Medicines Partnership in Alzheimer's Disease (AMP-AD) expanded brain multi-omics profiling to multi-ethnic donors. RESULTS: We generated multi-omics data and curated and harmonized phenotypic data from BA (n = 306), LA (n = 326), or BA and LA (n = 4) brain donors plus non-Hispanic White (n = 252) and other (n = 20) ethnic groups, to establish a foundational dataset enriched for BA and LA participants. This study describes the data available to the research community, including transcriptome from three brain regions, whole genome sequence, and proteome measures. DISCUSSION: The inclusion of traditionally underrepresented groups in multi-omics studies is essential to discovering the full spectrum of precision medicine targets that will be pertinent to all populations affected with AD. HIGHLIGHTS: Accelerating Medicines Partnership in Alzheimer's Disease Diversity Initiative led brain tissue profiling in multi-ethnic populations. Brain multi-omics data is generated from Black American, Latin American, and non-Hispanic White donors. RNA, whole genome sequencing and tandem mass tag proteomicsis completed and shared. Multiple brain regions including caudate, temporal and dorsolateral prefrontal cortex were profiled.

Catalytic Activity of 2-Imino-1,10-phenthrolyl Fe/Co Complexes via Linear Machine Learning.

In anticipation of the correlations between catalyst structures and their properties, the catalytic activities of 2-imino-1,10-phenanthrolyl iron and cobalt metal complexes are quantitatively investigated via linear machine learning (ML) algorithms. Comparatively, the Ridge Regression (RR) model has captured more robust predictive performance compared with other linear algorithms, with a correlation coefficient value of R2= 0.952 and a cross-validation value of Q2= 0.871. It shows that different algorithms select distinct types of descriptors, depending on the importance of descriptors. Through the interpretation of the RR model, the catalytic activity is potentially related to the steric effect of substituents and negative charged groups. This study refines descriptor selection for accurate modeling, providing insights into the variation principle of catalytic activity.

Intrinsic Activity Identification of Noble Metal Single-Sites for Electrocatalytic Chlorine Evolution.

Single-atom catalysts with maximal atom-utilization have emerged as promising alternatives for chlorine evolution reaction (CER) toward valuable Cl2 production. However, understanding their intrinsic CER activity have so far been plagued due to the lack of well-defined atomic structure controlling. Herein, we prepare and identify a series of atomically dispersed noble metals (e.g., Pt, Ir, Ru) in nitrogen-doped nanocarbons (M1-N-C) with an identical M-N4 moiety, which allows objective activity evaluation. Electrochemical experiments, operando Raman spectroscopy, and quasi-in situ electron paramagnetic resonance spectroscopy analyses collectively reveal that all the three M1-N-C proceed the CER via a direct Cl-mediated Vomer-Heyrovský mechanism with reactivity following the trend of Pt1-N-C > Ir1-N-C > Ru1-N-C. Density functional theory (DFT) calculations reveal that this activity trend is governed by the binding strength of Cl*-Cl intermediate (ΔGCl*-Cl) on M-N4 sites (Pt < Ir < Ru) featuring distinct d-band centers, providing a reliable thermodynamic descriptor for rational design of single metal sites toward Cl2 electrosynthesis.