Modulation of Hydrogen Desorption Capability of Ruthenium Nanoparticles via Electronic Metal-Support Interactions for Enhanced Hydrogen Production in Alkaline Seawater.

The development of efficient and stable electrocatalysts for the hydrogen evolution reaction (HER) is essential for the realization of effective hydrogen production via seawater electrolysis. Herein, the study has developed a simple method that combines electrospinning with subsequent thermal shock technology to effectively disperse ruthenium nanoparticles onto highly conductive titanium carbide nanofibers (Ru@TiC). The electronic metal-support interactions (EMSI) resulted from charge redistribution at the interface between the Ru nanoparticles and the TiC support can optimize hydrogen desorption kinetics of Ru sites and induce the hydrogen spillover phenomenon, thereby improving hydrogen evolution.

A Memristor-Based Neural Network Circuit With Retrospective Revaluation Effect and Application in Intelligent Household Robots.

The traditional association theory maintains that associations between cues can change only in trials where the cue is actually presented. However, the retrospective revaluation (RR) studies the phenomenon that responses to a cue can change even when the cue is not actually presented. A hardware memristor-based neural network circuit with an RR effect is proposed in this article.

Joint effect of modifiable risk factors on Parkinson's disease: a large-scale longitudinal study.

Introduction: Previous researches have often underestimated the diversity and combined effects of risk factors for Parkinson's disease (PD). This study aimed to identify how multiple modifiable risk factors collectively impact PD.

Methods: The study included 452,492 participants from the UK Biobank, utilizing genetic data and 255 phenotypic variables.

A systematic review and meta-analysis of the effects of combined aripiprazole on glycolipid metabolism in schizophrenia.

Background: Current research on aripiprazole adjunct therapy suggests potential benefits in improving psychiatric symptoms and metabolic disorders in patients with schizophrenia. However, the evidence remains limited due to the scarcity of research and a lack of detailed analysis on glucose and lipid metabolism indicators. This study aims to systematically review and analyze randomized controlled trials (RCTs) to evaluate the effects of aripiprazole combination therapy on both psychiatric symptoms and glycolipid metabolism.

The sp Hybridization of Tin Single Atoms for Dendrite-Free Sodium Metal Batteries.

Restricting the growth of sodium (Na) dendrites at the atomic level is the premise to enable both the stability and safety of sodium metal batteries (SMBs). Here, the universal synthesis of the fourth main group element (Sn, Ge, Pb) as single metal atoms anchored on graphene (Sn, Ge, Pb SAs/G) with sp hybridization for dendrite-free sodium metal anode is reported. The in situ real-time observation of Na growth on Sn SAs/G uncoils a kinetically uniform planar deposition at the atomic level for substantially suppressing the dendrite growth.

Molecular characterization of the sea lamprey retina illuminates the evolutionary origin of retinal cell types.

The lamprey, a primitive jawless vertebrate whose ancestors diverged from all other vertebrates over 500 million years ago, offers a unique window into the ancient formation of the retina. Using single-cell RNA-sequencing, we characterize retinal cell types in the lamprey and compare them to those in mouse, chicken, and zebrafish. We find six cell classes and 74 distinct cell types, many shared with other vertebrate species.

A memristor-based circuit design of avoidance learning with time delay and its application.

Currently, the research in memristor-based associative memory neural networks pays more attention to positive stimuli and lays less attention to negative stimuli. Negative stimuli are superior to positive stimuli in some ways, but lack the associated circuit implementation. In this paper, a memristor-based circuit design of avoidance learning with time delay is designed.

Dynamic analysis of FN-HR neural network coupled of bistable memristor and encryption application based on Fibonacci Q-Matrix.

In this paper, a cosine hyperbolic memristor model is proposed with bistable asymmetric hysteresis loops. A neural network of coupled hyperbolic memristor is constructed by using the Fitzhugh-Nagumo model and the Hindmarsh-Rose model. The coupled neural network with a large number of equilibrium points is obtained by numerical analysis.

Breast Tumor Diagnosis Based on Molecular Learning Vector Quantization Neural Networks.

DNA nanotechnology plays a crucial role in precise cancer medicine. Currently, molecular logic circuits are applied to detect tumor-specific biomarkers and control the release of therapeutic drugs. However, these systems lack self-learning capabilities for intelligent diagnostics in biological samples, and their data processing capabilities are limited.

Construction and analysis of protein-protein interaction network for esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is a prevalent malignant tumor of the digestive tract. Clinical findings reveal that the five-year survival rate for mid-to late-stage ESCC patients is merely around 20 %, whereas those diagnosed at an early stage can achieve up to a 95 % survival rate. Consequently, early detection is paramount to improving ESCC patient survival.

State Observer Synchronization of Three-dimensional Chaotic Oscillatory Systems Based on DNA Strand Displacement.

Currently, DNA strand displacement (DSD) as the theoretical basis of DNA chemical reaction networks (CRNs) has promoted the development of chaotic synchronization technique. This paper introduces the synchronization technology of two isomorphic three-dimensional chaotic systems based on DNA strand displacement under state observer. By studying the theoretical knowledge of DNA molecules, multiple DSD reactions are used to construct three-dimensional chaotic system.

Neural Network Circuits for Bionic Associative Memory and Temporal Order Memory Based on DNA Strand Displacement.

Pavlovian associative memory plays an important role in our daily life and work. The realization of Pavlovian associative memory at the deoxyribonucleic acid (DNA) molecular level will promote the development of biological computing and broaden the application scenarios of neural networks. In this article, bionic associative memory and temporal order memory circuits are constructed by DNA strand displacement (DSD) reactions.

Bacterial and fungal communities regulated directly and indirectly by tobacco-rape rotation promote tobacco production.

Tobacco continuous cropping is prevalent in intensive tobacco agriculture but often leads to microbial community imbalance, soil nutrient deficiency, and decreased crop productivity. While the tobacco-rape rotation has demonstrated significant benefits in increasing tobacco yield. Microorganisms play a crucial role in soil nutrient cycling and crop productivity.

Design and Implementation of Pavlovian Associative Memory Based on DNA Neurons.

In the field of biocomputing and neural networks, deoxyribonucleic acid (DNA) strand displacement (DSD) technology performs well in computation, programming, and information processing. In this article, the multiplication gate, addition gate, and threshold gate based on DSD are used to cascade into a single DNA neuron. Multiple DNA neurons can be cascaded to form different neural networks.

Diallyl Trisulfide Acts as a Soil Disinfestation Against the through Inducing the Burst of Reactive Oxygen Species.

Soil-borne diseases represent an impediment to the sustainable development of agriculture. A soil-borne disease caused by severely impacts species, and soil disinfestation has proven to be an effective management approach. Here, diallyl trisulfide (DATS), derived from garlic, exhibited pronounced inhibitory effects on the growth of tests and contributed to the alleviation of soil-borne diseases in the field.

Operant Conditioning Neuromorphic Circuit With Addictiveness and Time Memory for Automatic Learning.

Most operant conditioning circuits predominantly focus on simple feedback process, few studies consider the intricacies of feedback outcomes and the uncertainty of feedback time. This paper proposes a neuromorphic circuit based on operant conditioning with addictiveness and time memory for automatic learning. The circuit is mainly composed of hunger output module, neuron module, excitement output module, memristor-based decision module, and memory and feedback generation module.

NNT-AS1 in CAFs-derived exosomes promotes progression and glucose metabolism through miR-889-3p/HIF-1α in pancreatic adenocarcinoma.

It is metabolic and signaling crosstalk between stromal cells and tumors in the tumor microenvironment, which influences several aspects of tumor formation and drug resistance, including metabolic reprogramming. Despite considerable findings linking lncRNAs in HIF-1-related regulatory networks to cancer cell, little emphasis has been given to the role in communication between cancer-associated fibroblasts (CAFs) and tumor cells. Previously, we observed that NNT-AS1 was substantially expressed in CAFs cells and CAFs exosomes, and subsequently investigated the influence of CAFs exosomal NNT-AS1 on glucose metabolism, proliferation, and metastasis of pancreatic ductal adenocarcinoma (PDAC) cells.

Atomic-Level Tailoring of the Electronic Metal-Support Interaction Between Pt-CoO Interfaces for High Hydrogen Evolution Performance.

Atomic-level modulation of the metal-oxide interface is considered an effective approach to optimize the electronic structure and catalytic activity of metal catalysts but remains highly challenging. Here, we employ the atomic layer deposition (ALD) technique together with a heteroatom doping strategy to effectively tailor the electronic metal-support interaction (EMSI) at the metal-oxide interface on the atomic level, thereby achieving high hydrogen evolution performance and Pt utilization. Theoretical calculations reveal that the doping of N atoms in CoO significantly adjusts the EMSI between Pt-CoO interfaces and, consequently, alters the d-band center of Pt and optimizes the adsorption/desorption of reaction intermediates.

Causal relationship between gut microbiota and polycystic ovary syndrome: a literature review and Mendelian randomization study.

Introduction: Numerous studies have suggested an association between gut microbiota and polycystic ovarian syndrome (PCOS). However, the causal relationship between these two factors remains unclear.

Methods: A review of observational studies was conducted to compare changes in gut microbiota between PCOS patients and controls.

Molecular circuit for exponentiation based on the domain coding strategy.

DNA strand displacement (DSD) is an efficient technology for constructing molecular circuits. However, system computing speed and the scale of logical gate circuits remain a huge challenge. In this paper, a new method of coding DNA domains is proposed to carry out logic computation.

Diagnostic and prognostic risk factors analysis for distant metastasis in melanoma: a population-based study.

Background: We aimed to develop tools that could predict the occurrence of distant metastases in melanoma and its prognosis based on clinical and pathological characteristics.

Materials And Methods: We obtained data from the Surveillance, Epidemiology, and End Results (SEER) database of melanoma patients diagnosed between 2010 and 2019. Logistic analyses were performed to identify independent risk factors associated with distant metastasis.

Memristor-Based Neural Network Circuit of Associative Memory With Overshadowing and Emotion Congruent Effect.

Most memristor-based neural network circuits consider only a single pattern of overshadowing or emotion, but the relationship between overshadowing and emotion is ignored. In this article, a memristor-based neural network circuit of associative memory with overshadowing and emotion congruent effect is designed, and overshadowing under multiple emotions is taken into account. The designed circuit mainly consists of an emotion module, a memory module, an inhibition module, and a feedback module.