Typical Non–TiO2-Based Visible-Light Photocatalysts

Photocatalysis has received much attention as a potential solution to the worldwide energy shortage and for counteracting environmental degradation. However, the traditional photocatalyst, TiO2, cannot make use of visible light that accounts for 45% of solar spectrum because of a large bandgap (3.2 eV). Therefore, it is urgent to develop visible-light-driven photocatalysts. On the one hand, some modification technologies were explored to extend the light absorption of TiO2 to visible-light region, such as doping of metal and non-metal elements, dye sensitization, and so on. On the other hand, much effort has been directed toward the development of new visible-light photocatalysts. The good news is, some novel and efficient non-TiO2-based photocatalysts have been discovered, such as WO3, Ag3PO4, BiVO4, g-C3N4. In this chapter, these four typical visible light–driven semiconductor photocatalysts were highlighted. WO3 is a visible light–responsive photocatalyst that absorbs light up to ca. 480 nm. Besides that, WO3 has some advantages, such as low cost, harmlessness, and stability in acidic and oxidative conditions. Preparation of WO3 films with the deposition of noble metal is considered to be a promising approach for the photocatalytic applications. In addition, the characteristic morphology and improved photocatalytic performance of Ag3PO4-based and BiVO4-based have been raised up. New methods for fabrication Ag3PO4 with exposed high-energy facets and novel heterogeneous Ag3PO4 co-catalysts have been developed. Monoclinic BiVO4 is a promising photo-anode material for photocatalytic water splitting to produce hydrogen. Co-catalysts loaded on BiVO4 could improve the surface charge transfer efficiency. Furthermore, g-C3N4 is a promising visible-light photocatalyst due to its unique electronic structure. To date, g-C3N4-based photocatalysis has become a very hot research topic. The synthesis, bandgap engineering, and semiconductor composites of g-C3N4-based photocatalysts are highlighted

A Transferable Intersection Reconstruction Network for Traffic Speed Prediction

Traffic speed prediction is the key to many valuable applications, and it is also a challenging task because of its various influencing factors. Recent work attempts to obtain more information through various hybrid models, thereby improving the prediction accuracy. However, the spatial information acquisition schemes of these methods have two-level differentiation problems. Either the modeling is simple but contains little spatial information, or the modeling is complete but lacks flexibility. In order to introduce more spatial information on the basis of ensuring flexibility, this paper proposes IRNet (Transferable Intersection Reconstruction Network). First, this paper reconstructs the intersection into a virtual intersection with the same structure, which simplifies the topology of the road network. Then, the spatial information is subdivided into intersection information and sequence information of traffic flow direction, and spatiotemporal features are obtained through various models. Third, a self-attention mechanism is used to fuse spatiotemporal features for prediction. In the comparison experiment with the baseline, not only the prediction effect, but also the transfer performance has obvious advantages.Comment: 14 pages, 12 figure

Counterpart Fairness -- Addressing Systematic between-group Differences in Fairness Evaluation

When using machine learning (ML) to aid decision-making, it is critical to ensure that an algorithmic decision is fair, i.e., it does not discriminate against specific individuals/groups, particularly those from underprivileged populations. Existing group fairness methods require equal group-wise measures, which however fails to consider systematic between-group differences. The confounding factors, which are non-sensitive variables but manifest systematic differences, can significantly affect fairness evaluation. To mitigate this problem, we believe that a fairness measurement should be based on the comparison between counterparts (i.e., individuals who are similar to each other with respect to the task of interest) from different groups, whose group identities cannot be distinguished algorithmically by exploring confounding factors. We have developed a propensity-score-based method for identifying counterparts, which prevents fairness evaluation from comparing "oranges" with "apples". In addition, we propose a counterpart-based statistical fairness index, termed Counterpart-Fairness (CFair), to assess fairness of ML models. Empirical studies on the Medical Information Mart for Intensive Care (MIMIC)-IV database were conducted to validate the effectiveness of CFair. We publish our code at \url{https://github.com/zhengyjo/CFair}.Comment: 18 pages, 5 figures, 5 table

Population Genetic Diversity and Phylogenetic Characteristics for High-Altitude Adaptive Kham Tibetan Revealed by DNATyperTM 19 Amplification System

Tibetans residing in the high-altitude inhospitable environment have undergone significant natural selection of their genetic architecture. Recently, highly mutational autosomal short tandem repeats were widely used not only in the anthropology and population genetics to investigate the genetic structure and relationships, but also in the medical genetics to explore the pathogenesis of multiple genetic diseases and in the forensic science to identify individual and parentage relatedness. However, genetic variants and forensic efficiency of DNATyperTM 19 amplification system and genetic background of Kham Tibetan remain uncharacterized. Thus, we genotyped 19 forensic genetic markers in 11,402 Kham Tibetans to gain insight into the genetic diversity of Chinese high-altitude adaptive population. Highly discriminating and polymorphic forensic measures were observed, which indicated that this new-developed DNATyper 19 PCR amplification is suitable for routine forensic identification purposes and Chinese national DNA database establishment. Pairwise genetic distances among the comprehensive population comparisons suggested that this high-altitude adaptive Kham Tibetan has genetically closer relationships with lowlanders of Tibeto-Burman-speaking populations (Chengdu Tibetan, Liangshan Tibetan, and Liangshan Yi). Genetic substructure analyses via phylogenetic reconstruction, principal component analysis, and multidimensional scaling analysis in both nationwide and worldwide contexts suggested that the genetic proximity exists along the linguistic, ethnic, and continental geographical boundary. Further studies with whole-genome sequencing of modern or archaic Kham Tibetans would be useful in reconstructing the Tibetan population history

Inspiratory-Activated Airway Vagal Preganglionic Neurones Excited by Thyrotropin-Releasing Hormone via Multiple Mechanisms in Neonatal Rats

The airway vagal preganglionic neurons (AVPNs) providing projections to intrinsic tracheobronchial ganglia are considered to be crucial to modulation of airway resistance in physiological and pathological states. AVPNs classified into inspiratory-activated AVPNs (IA-AVPNs) and inspiratory-inhibited AVPNs (II-AVPNs) are regulated by thyrotropin-releasing hormone (TRH)-containing terminals. TRH causes a direct excitatory current and attenuates the phasic inspiratory glycinergic inputs in II-AVPNs, however, whether and how TRH influences IA-AVPNs remains unknown. In current study, TRH regulation of IA-AVPNs and its mechanisms involved were investigated. Using retrogradely fluorescent labeling method and electrophysiology techniques to identify IA-AVPNs in brainstem slices with rhythmic inspiratory hypoglossal bursts recorded by a suction electrode, the modulation of TRH was observed with patch-clamp technique. The findings demonstrate that under voltage clamp configuration, TRH (100 nM) caused a slow excitatory inward current, augmented the excitatory synaptic inputs, progressively suppressed the inhibitory synaptic inputs and elicited a distinctive electrical oscillatory pattern (OP). Such a current and an OP was independent of presynaptic inputs. Carbenoxolone (100 μM), a widely used gap junction inhibitor, fully suppressed the OP with persistence of TRH-induced excitatory slow inward current and augment of the excitatory synaptic inputs. Both tetrodotoxin (1 μM) and riluzole (20 μM) functioned to block the majority of the slow excitatory inward current and prevent the OP, respectively. Under current clamp recording, TRH caused a slowly developing depolarization and continuously progressive oscillatory firing pattern sensitive to TTX. TRH increased the firing frequency in response to injection of a square-wave current. The results suggest that TRH excited IA-AVPNs via the following multiple mechanisms: (1) TRH enhances the excitatory and depresses the inhibitory inputs; (2) TRH induces an excitatory postsynaptic slow inward current; (3) TRH evokes a distinctive OP mediated by gap junction

Synthesis and biological evaluation of chrysin derivatives containing α-lipoic acid for the treatment of inflammatory bowel disease

This study introduces newly discovered chrysin derivatives that show potential as candidate molecules for treating inflammatory bowel disease (IBD). Compound 4b, among the synthesized compounds, displayed significant inhibitory effects on monocyte adhesion to colon epithelium induced by TNF-α, with an IC50 value of 4.71 μM. Further mechanistic studies demonstrated that 4b inhibits the production of reactive oxygen species (ROS) and downregulates the expression of ICAM-1 and MCP-1, key molecules involved in monocyte-epithelial adhesion, as well as the transcriptional activity of NF-κB. In vivo experiments have shown that compound 4b exhibits a dose-dependent inhibition of 2, 4, 6-trinitrobenzenesulfonic acid (TNBS)-induced colitis in rats, thereby validating its effectiveness as a colitis inhibitor in animal models. These results indicate that 4b shows considerable promise as a therapeutic agent for managing IBD

Biocontrol potential and action mechanism of Bacillus amyloliquefaciens DB2 on Bipolaris sorokiniana

IntroductionBipolaris sorokiniana is the popular pathogenic fungi fungus which lead to common root rot and leaf spot on wheat. Generally, chemical fungicides are used to control diseases. However, the environmental pollution resulting from fungicides should not be ignored. It is important to study the mode of antagonistic action between biocontrol microbes and plant pathogens to design efficient biocontrol strategies.ResultsAn antagonistic bacterium DB2 was isolated and identified as Bacillus amyloliquefaciens. The inhibition rate of cell-free culture filtrate (CF, 20%, v/v) of DB2 against B. sorokiniana reached 92.67%. Light microscopy and scanning electron microscopy (SEM) showed that the CF significantly altered the mycelial morphology of B. sorokiniana and disrupted cellular integrity. Fluorescence microscopy showed that culture filtrate destroyed mycelial cell membrane integrity, decreased the mitochondrial transmembrane potential, induced reactive oxygen species (ROS) accumulation, and nuclear damage which caused cell death in B. sorokiniana. Moreover, the strain exhibited considerable production of protease and amylase, and showed a significant siderophore and indole-3-acetic acid (IAA) production. In the detached leaves and potted plants control assay, B. amyloliquefacien DB2 had remarkable inhibition activity against B. sorokiniana and the pot control efficacy was 75.22%. Furthermore, DB2 suspension had a significant promotion for wheat seedlings growth.ConclusionB. amyloliquefaciens DB2 can be taken as a potential biocontrol agent to inhibit B. sorokiniana on wheat and promote wheat growth

Computational Investigation of a Series of Small Molecules as Potential Compounds for Lysyl Hydroxylase-2 (LH2) Inhibition

The catalytic function of lysyl hydroxylase-2 (LH2), a member of the Fe(II)/αKG-dependent oxygenase superfamily, is to catalyze the hydroxylation of lysine to hydroxylysine in collagen, resulting in stable hydroxylysine aldehyde-derived collagen cross-links (HLCCs). Reports show that high amounts of LH2 lead to the accumulation of HLCCs, causing fibrosis and specific types of cancer metastasis. Some members of the Fe(II)/αKG-dependent family have also been reported to have intramolecular

Development and validation of a deep learning-based model to distinguish acetabular fractures on pelvic anteroposterior radiographs

Objective: To develop and test a deep learning (DL) model to distinguish acetabular fractures (AFs) on pelvic anteroposterior radiographs (PARs) and compare its performance to that of clinicians.Materials and methods: A total of 1,120 patients from a big level-I trauma center were enrolled and allocated at a 3:1 ratio for the DL model’s development and internal test. Another 86 patients from two independent hospitals were collected for external validation. A DL model for identifying AFs was constructed based on DenseNet. AFs were classified into types A, B, and C according to the three-column classification theory. Ten clinicians were recruited for AF detection. A potential misdiagnosed case (PMC) was defined based on clinicians’ detection results. The detection performance of the clinicians and DL model were evaluated and compared. The detection performance of different subtypes using DL was assessed using the area under the receiver operating characteristic curve (AUC).Results: The means of 10 clinicians’ sensitivity, specificity, and accuracy to identify AFs were 0.750/0.735, 0.909/0.909, and 0.829/0.822, in the internal test/external validation set, respectively. The sensitivity, specificity, and accuracy of the DL detection model were 0.926/0.872, 0.978/0.988, and 0.952/0.930, respectively. The DL model identified type A fractures with an AUC of 0.963 [95% confidence interval (CI): 0.927–0.985]/0.950 (95% CI: 0.867–0.989); type B fractures with an AUC of 0.991 (95% CI: 0.967–0.999)/0.989 (95% CI: 0.930–1.000); and type C fractures with an AUC of 1.000 (95% CI: 0.975–1.000)/1.000 (95% CI: 0.897–1.000) in the test/validation set. The DL model correctly recognized 56.5% (26/46) of PMCs.Conclusion: A DL model for distinguishing AFs on PARs is feasible. In this study, the DL model achieved a diagnostic performance comparable to or even superior to that of clinicians

LS-VCE Applied to Stochastic Modeling of GNSS Observation Noise and Process Noise

Stochastic models play a crucial role in global navigation satellite systems (GNSS) data processing. Many studies contribute to the stochastic modeling of GNSS observation noise, whereas few studies focus on the stochastic modeling of process noise. This paper proposes a method that is able to jointly estimate the variances of observation noise and process noise. The method is flexible since it is based on the least-squares variance component estimation (LS-VCE), enabling users to estimate the variance components that they are specifically interested in. We apply the proposed method to estimate the variances for the dual-frequency GNSS observation noise and for the process noise of the receiver code bias (RCB). We also investigate the impact of the stochastic model upon parameter estimation, ambiguity resolution, and positioning. The results show that the precision of GNSS observations differs in systems and frequencies. Among the dual-frequency GPS, Galileo, and BDS code observations, the precision of the BDS B3 observations is highest (better than 0.2 m). The precision of the BDS phase observations is better than two millimeters, which is also higher than that of the GPS and Galileo observations. For all three systems, the RCB process noise ranges from 0.5 millimeters to 1 millimeter, with a data sampling rate of 30 s. An improper stochastic model of the observation noise results in an unreliable ambiguity dilution of precision (ADOP) and position dilution of precision (PDOP), thus adversely affecting the assessment of the ambiguity resolution and positioning performance. An inappropriate stochastic model of RCB process noise disturbs the estimation of the receiver clock and the ionosphere delays and is thus harmful for timing and ionosphere retrieval applications