Probing the electronic properties of the electrified silicon/water interface by combining simulations and experiments

Silicon (Si) is broadly used in electrochemical and photoelectrochemical devices, where the capacitive and Faradaic reactions at the Si/water interfaces are critical for signal transduction or noise generation. However, probing the electrified Si/water interface at the microscopic level remains a challenging task. Here we focus on hydrogenated Si surfaces in contact with water, relevant to transient electronics and photoelectrochemical modulation of biological cells and tissues. We show that by carrying out first-principles molecular dynamics simulations of the Si(100)/water interface in the presence of an electric field we can realistically correlate the computed flat-band potential and tunneling current images at the interface with experimentally measured capacitive and Faradaic currents. Specifically, we validate our simulations in the presence of bias by performing pulsed chronoamperometry measurements on Si wafers in solution. Consistent with prior experiments, our measurements and simulations indicate the presence of voltage-dependent capacitive currents at the interface. We also find that Faradaic currents are weakly dependent on the applied bias, which we relate to surface defects present in newly prepared samples

Semantic Complete Scene Forecasting from a 4D Dynamic Point Cloud Sequence

We study a new problem of semantic complete scene forecasting (SCSF) in this work. Given a 4D dynamic point cloud sequence, our goal is to forecast the complete scene corresponding to the future next frame along with its semantic labels. To tackle this challenging problem, we properly model the synergetic relationship between future forecasting and semantic scene completion through a novel network named SCSFNet. SCSFNet leverages a hybrid geometric representation for high-resolution complete scene forecasting. To leverage multi-frame observation as well as the understanding of scene dynamics to ease the completion task, SCSFNet introduces an attention-based skip connection scheme. To ease the need to model occlusion variations and to better focus on the occluded part, SCSFNet utilizes auxiliary visibility grids to guide the forecasting task. To evaluate the effectiveness of SCSFNet, we conduct experiments on various benchmarks including two large-scale indoor benchmarks we contributed and the outdoor SemanticKITTI benchmark. Extensive experiments show SCSFNet outperforms baseline methods on multiple metrics by a large margin, and also prove the synergy between future forecasting and semantic scene completion.Comment: AAAI 2024, see https://scsfnet.github.io

Efficient Maximum Fair Clique Search over Large Networks

Mining cohesive subgraphs in attributed graphs is an essential problem in the domain of graph data analysis. The integration of fairness considerations significantly fuels interest in models and algorithms for mining fairness-aware cohesive subgraphs. Notably, the relative fair clique emerges as a robust model, ensuring not only comprehensive attribute coverage but also greater flexibility in distributing attribute vertices. Motivated by the strength of this model, we for the first time pioneer an investigation into the identification of the maximum relative fair clique in large-scale graphs. We introduce a novel concept of colorful support, which serves as the foundation for two innovative graph reduction techniques. These techniques effectively narrow the graph's size by iteratively removing edges that do not belong to relative fair cliques. Furthermore, a series of upper bounds of the maximum relative fair clique size is proposed by incorporating consideration of vertex attributes and colors. The pruning techniques derived from these upper bounds can significantly trim unnecessary search space during the branch-and-bound procedure. Adding to this, we present a heuristic algorithm with a linear time complexity, employing both a degree-based greedy strategy and a colored degree-based greedy strategy to identify a larger relative fair clique. This heuristic algorithm can serve a dual purpose by aiding in branch pruning, thereby enhancing overall search efficiency. Extensive experiments conducted on six real-life datasets demonstrate the efficiency, scalability, and effectiveness of our algorithms

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License

First-Principles Studies of Water at Semiconductor Interfaces

Water plays a pivotal role in a wide array of physical and chemical processes. For example, in several batteries, photoelectrochemical cells, and bioelectronic devices, solid-water interfaces are present and critically influence the devices' properties. Water also constitutes a major portion of the Earth's crust and mantle, participating in several geological processes under high-pressure, high-temperature conditions, including metasomatism, carbon transport, and continental crust evolution. To understand the electronic characteristics and structural modifications of water molecules at interfaces or in extreme environments, computational modeling at the atomistic scale is an essential tool. In this thesis, we employed first-principles simulations to study semiconductor-water interfaces and water under extreme conditions. We focused on hydrogenated silicon (Si) surfaces interfaced with water, given silicon's widespread use in electronic devices. Furthermore, we investigated water under pressures and conditions relevant to the Earth's interior (11 GPa and 1000 K). In our interfacial studies, we explored the electronic structure of the hydrogenated Si(100) and water interfaces by performing first-principles molecular dynamics simulations in the presence of an electric field. We correlated the computed flat-band potential and tunneling current images at the interface with experimentally measured capacitive and Faradaic currents. Consistent with chronoamperometry measurements, our simulations indicate that the capacitive currents at the interface are voltage-dependent, while the Faradaic currents are weakly dependent on the applied voltage but are related to surface defects. Next, we investigated the dynamic and vibrational properties of water at the electrified interface. We analyzed the H-bond structures and orientation of water molecules, and we related the structural properties of interfacial water molecules to the OH stretching mode in Raman spectra. The calculated spectra reveal a combined effect of the surface and the electric field on the Raman features observed at the interface. The presence of the surface leads to low-coordinated hydrogen bonding configurations and, hence, a blue-shift of the O-H stretching band relative to that of bulk water. The electric field regulates the orientation of interfacial water molecules, resulting in a stable H-bond network that gives rise to specific Raman peaks in the low-frequency region of the spectrum. Our computational studies provided comprehensive insights into the electronic and dynamic properties of Si-based electrochemical or photoelectrochemical devices. In our study of water in extreme conditions, we carried out calculations of photoelectron spectra of water and a simple solution of NaCl under high pressure and high temperature. We combined first-principles and deep-potential molecular dynamics with dielectric-dependent hybrid functionals. We found notable changes in the spectra relative to ambient conditions; in particular, we observed anion energy levels closer to the valence band maximum of the liquid than those observed at ambient conditions, indicating that as pressure and temperature are increased, the defect levels of chloride and hydroxide ions in water may eventually lie below the valence band maximum of water. We also elucidated the electronic states associated with proton transfer events at high pressure by calculating the projected density of states. Our results represent an important first step in predicting the electronic properties of solutions in supercritical conditions

Semantic Complete Scene Forecasting from a 4D Dynamic Point Cloud Sequence

We study a new problem of semantic complete scene forecasting (SCSF) in this work. Given a 4D dynamic point cloud sequence, our goal is to forecast the complete scene corresponding to the future next frame along with its semantic labels. To tackle this challenging problem, we properly model the synergetic relationship between future forecasting and semantic scene completion through a novel network named SCSFNet. SCSFNet leverages a hybrid geometric representation for high-resolution complete scene forecasting. To leverage multi-frame observation as well as the understanding of scene dynamics to ease the completion task, SCSFNet introduces an attention-based skip connection scheme. To ease the need to model occlusion variations and to better focus on the occluded part, SCSFNet utilizes auxiliary visibility grids to guide the forecasting task. To evaluate the effectiveness of SCSFNet, we conduct experiments on various benchmarks including two large-scale indoor benchmarks we contributed and the outdoor SemanticKITTI benchmark. Extensive experiments show SCSFNet outperforms baseline methods on multiple metrics by a large margin, and also prove the synergy between future forecasting and semantic scene completion.The project page with code is available at scsfnet.github.io

Inhibition of cGAS–STING pathway alleviates neuroinflammation-induced retinal ganglion cell death after ischemia/reperfusion injury

Abstract Acute glaucoma is a vision-threatening disease characterized by a sudden elevation in intraocular pressure (IOP), followed by retinal ganglion cell (RGC) death. Cytosolic double-stranded DNA (dsDNA)—a damage-associated molecular pattern (DAMP) that triggers inflammation and immune responses—has been implicated in the pathogenesis of IOP-induced RGC death, but the underlying mechanism is not entirely clear. In this study, we investigated the effect of the inflammatory cascade on dsDNA recognition and examined the neuroprotective effect of the cyclic GMP-AMP (cGAMP) synthase (cGAS) antagonist A151 on a retinal ischemia/reperfusion (RIR) mouse model. Our findings reveal a novel mechanism of microglia-induced neuroinflammation-mediated RGC death associated with glaucomatous vision loss. We found that RIR injury facilitated the release of dsDNA, which initiated inflammatory responses by activating cGAS–stimulator of interferon genes (STING) pathway. Correspondingly, elevated expressions of cGAS and STING were found in retinal samples from human glaucoma donors. Furthermore, we found that deletion or inhibition of cGAS or STING in microglia transfected with poly(dA:dT) specifically decreased microglia activation and inflammation response. We also observed that A151 treatment promoted poly(dA:dT)--stimulated changes in polarization from the M1 to the M2 phenotype in microglia. Subsequently, A151 administered to mice effectively inhibited the cGAS–STING pathway, absent in melanoma 2 (AIM2) inflammasome and pyroptosis-related molecules. Furthermore, A151 administration significantly reduced neuroinflammation, ameliorated RGC death and RGC-related reductions in visual function. These findings provide a unique perspective on glaucomatous neuropathogenesis and suggest cGAS as an underlying target of retinal inflammation to provide a potential therapeutic for acute glaucoma

A Novel Nomogram Combined the Aggregate Index of Systemic Inflammation and PIRADS Score to Predict the Risk of Clinically Significant Prostate Cancer

Background. This study is aimed at constructing a nomogram to predict the risk of clinically significant prostate cancer (csPCa) based on the aggregate index of systemic inflammation (AISI) and prostate imaging-reporting and data system version (PIRADS) score. Methods. Clinical data on patients who had undergone initial prostate biopsy from January 2019 to December 2021 were collected. Patients were randomized in a 7 : 3 ratio to the training cohort and the validation cohort. Potential risk factors for csPCa were identified by univariable and multivariate logistic regression. Nomogram was conducted with these independent risk factors, and calibration curves, the receiver operating characteristic (ROC), and decision curve analysis (DCA) were employed to assess the nomogram’s ability for prediction. Results. A total of 1219 patients were enrolled in this study. Multivariate logistic regression identified that age, AISI, total prostatic specific-antigen (tPSA), free to total PSA (f/tPSA), prostate volume (PV), and PIRADS score were potential risk predictors of csPCa, and the nomogram was developed based on these factors. The area under the curve (AUC) of the training cohort and validation cohort was 0.884 (95% CI: 0.862-0.906) and 0.899 (95% CI: 0.867-0.931). The calibration curves showed that the apparent curves were closer to the ideal curves. The DCA results revealed that the nomogram model seemed to have clinical application value per DCA. Conclusion. The nomogram model can efficiently predict the risk of csPCa and may assist clinicians in determining if a prostate biopsy is necessary

13C-Stable isotope resolved metabolomics uncovers dynamic biochemical landscape of gut microbiome-host organ communications in mice

Abstract Background Gut microbiome metabolites are important modulators of host health and disease. However, the overall metabolic potential of the gut microbiome and interactions with the host organs have been underexplored. Results Using stable isotope resolved metabolomics (SIRM) in mice orally gavaged with 13C-inulin (a tracer), we first observed dynamic enrichment of 13C-metabolites in cecum contents in the amino acids and short-chain fatty acid metabolism pathways. 13C labeled metabolites were subsequently profiled comparatively in plasma, liver, brain, and skeletal muscle collected at 6, 12, and 24 h after the tracer administration. Organ-specific and time-dependent 13C metabolite enrichments were observed. Carbons from the gut microbiome were preferably incorporated into choline metabolism and the glutamine-glutamate/GABA cycle in the liver and brain, respectively. A sex difference in 13C-lactate enrichment was observed in skeletal muscle, which highlights the sex effect on the interplay between gut microbiome and host organs. Choline was identified as an interorgan metabolite derived from the gut microbiome and fed the lipogenesis of phosphatidylcholine and lysophosphatidylcholine in host organs. In vitro and in silico studies revealed the de novo synthesis of choline in the human gut microbiome via the ethanolamine pathway, and Enterococcus faecalis was identified as a major choline synthesis species. These results revealed a previously underappreciated role for gut microorganisms in choline biosynthesis. Conclusions Multicompartmental SIRM analyses provided new insights into the current understanding of dynamic interorgan metabolite transport between the gut microbiome and host at the whole-body level in mice. Moreover, this study singled out microbiota-derived metabolites that are potentially involved in the gut-liver, gut-brain, and gut-skeletal muscle axes. Video Abstrac

Synergistic collaboration between AMPs and non-direct antimicrobial cationic peptides

Non-direct antimicrobial cationic peptides (NDACPs) are components of the animal innate immune system. But their functions and association with antimicrobial peptides (AMPs) are incompletely understood. Here, we reveal a synergistic interaction between the AMP AW1 and the NDACP AW2, which are co-expressed in the frog Amolops wuyiensis. AW2 enhances the antibacterial activity of AW1 both in vitro and in vivo, while mitigating the development of bacterial resistance and eradicating biofilms. AW1 and AW2 synergistically damage bacterial membranes, facilitating cellular uptake and interaction of AW2 with the intracellular target bacterial genomic DNA. Simultaneously, they trigger the generation of ROS in bacteria, contributing to cell death upon reaching a threshold level. Moreover, we demonstrate that this synergistic antibacterial effect between AMPs and NDACPs is prevalent across diverse animal species. These findings unveil a robust and previously unknown correlation between AMPs and NDACPs as a widespread antibacterial immune defense strategy in animals. Antimicrobial peptides and non-direct antimicrobial cationic peptides are secreted in response to invasive pathogens. Here, Ye et al show that there is a synergistic interaction between these two types of expressed peptides from the amphibian frog Amolops wuyiensis