Spatial distribution of tuberculosis and its socioeconomic influencing factors in mainland China 2013-2016.

BACKGROUND: To assess the spatial epidemic characteristics of TB and identify the key areas for disease prevention and control. OBJECTIVE: To explore the spatial distribution and socioeconomic influencing factors of TB in mainland China from 2013 to 2016. METHODS: Spatial autocorrelation was used to explore the spatial distribution characteristics of TB at the quantitative level. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were conducted to explore the association between factors and TB incidence from both global and local perspectives. RESULTS: There was a significant positive spatial autocorrelation of TB at the provincial level (P < 0.05): hot spots were mainly located in the west of Xinjiang and Tibet, and cold spots in the eastern coastal areas. Four latent factors on the socioeconomic dimension, involving the proportion of illiterate people aged 15 and over, per capita disposable income in rural areas, the number of health technicians per 1000 population and the urban population density, were associated with TB incidence. The GWR model showed that the effect of the same factor on TB incidence varied with geographical location. CONCLUSIONS: Spatial clustering of TB incidence in mainland China still exists. The differences of socioeconomic factors in different locations can be confirmed by GWR model. Targeted preventive and control measures or policies will be conducive in effectively reducing the incidence of TB, especially in hot spots.

CONTEXTE: Evaluer les caractéristiques spatiales épidémiques de la tuberculose (TB) et identifier les domaines clés pour la prévention et le contrôle de la maladie. OBJECTIF: Explorer la distribution spatiale et les facteurs d'influence socioéconomiques de la TB en Chine continentale de 2013 à 2016. MÉTHODES: Une autocorrélation spatiale a été utilisée pour explorer les caractéristiques de distribution spatiale de la TB à une échelle quantitative. Les moindres carrés ordinaires (OLS) et des modèles de régression géographiquement pondérés (GWR) ont été utilisés pour explorer l'association entre les facteurs et l'incidence de la TB, tant du point de vue global que local. RÉSULTATS: Il y avait une autocorrélation spatiale positive significative au niveau provincial (P <0,05): les points chauds étaient principalement situés à l'ouest du Xinjiang et du Tibet et les points froids dans les zones côtières est. Quatre facteurs latents de dimension socioéconomique, à savoir la proportion d'analphabètes âgés de 15 ans et plus, le revenu disponible par habitant en zone rurale, le nombre de techniciens de la santé pour 1000 habitants et la densité de la population urbaine ont été associés à l'incidence de la TB. Le modèle GWR a montré que l'effet du même facteur sur l'incidence de la TB variait avec la localisation géographique. CONCLUSIONS: Le regroupement spatial de l'incidence de la TB en Chine continentale existe toujours. Les différences de facteurs socioéconomiques dans différents endroits peuvent être confirmées par le modèle GWR. Des mesures ou politiques de prévention et de contrôle ciblées permettront de réduire efficacement l'incidence de la TB, en particulier dans les zones les plus touchées.

Robust Fuzzy Neural Network With an Adaptive Inference Engine.

Fuzzy neural networks (FNNs) have been very successful at handling uncertainty in data using fuzzy mappings and if-then rules. However, they suffer from generalization and dimensionality issues. Although deep neural networks (DNNs) represent a step toward processing high-dimensional data, their capacity to address data uncertainty is limited. Furthermore, deep learning algorithms designed to improve robustness are either time consuming or yield unsatisfactory performance. In this article, we propose a robust fuzzy neural network (RFNN) to overcome these problems. The network contains an adaptive inference engine that is capable of handling samples with high-level uncertainty and high dimensions. Unlike traditional FNNs that use a fuzzy AND operation to calculate the firing strength for each rule, our inference engine is able to learn the firing strength adaptively. It also further processes the uncertainty in membership function values. Taking advantage of the learning ability of neural networks, the acquired fuzzy sets can be learned from training inputs automatically to cover the input space well. Furthermore, the consequent layer uses neural network structures to enhance the reasoning ability of the fuzzy rules when dealing with complex inputs. Experiments on a range of datasets show that RFNN delivers state-of-the-art accuracy even at very high levels of uncertainty. Our code is available online. https://github.com/leijiezhang/RFNN.

Milk-derived extracellular vesicles functionalized with anti-tumour necrosis factor-α nanobody and anti-microbial peptide alleviate ulcerative colitis in mice.

Ulcerative colitis (UC) manifests clinically with chronic intestinal inflammation and microflora dysbiosis. Although biologics can effectively control inflammation, efficient delivery to the colon and colon epithelial cells remains challenging. Milk-derived extracellular vesicles (EV) show promise as an oral delivery tool, however, the ability to load biologics into EV presents challenges to therapeutic applications. Here, we demonstrate that fusing cell-penetrating peptide (TAT) to green fluorescent protein (GFP) enabled biologics loading into EV and protected against degradation in the gastrointestinal environment in vitro and in vivo after oral delivery. Oral administration of EV loaded with anti-tumour necrosis factor-α (TNF-α) nanobody (VHHm3F) (EVVHH) via TAT significantly reduced tissue TNF-α levels and alleviated pathologies in mice with acute UC, compared to VHH alone. In mice with chronic UC, simultaneously introducing VHH and an antimicrobial peptide LL37 into EV (EVLV), then administering orally improved intestinal barrier, inflammation and microbiota balance, resulted in relief of UC-induced depression and anxiety. Collectively, we demonstrated that oral delivery of EVLV effectively alleviated UC in mice and TAT efficiently loaded biologics into EV to confer protection from degradation in the gastrointestinal tract. This therapeutic strategy is promising for UC and is a simple and generalizable approach towards drug-loaded orally-administrable EV treatment for other diseases.

Nitrogen-doped Fe7S8 as highly efficient electrocatalysts for the hydrogen evolution reaction.

The high unoccupied d band energy of FeS2 basically results in weak orbital coupling with water molecules, consequently leading to sluggish water dissociation kinetics. Herein, we demonstrate that the N-induced doping effect and phase transition engineering (FeS2 to N-Fe7S8) can downshift the unoccupied d orbitals and strengthen the interfacial orbital coupling to boost the water dissociation kinetics. The fabricated N-Fe7S8/carbon cloth (CC) displays superb hydrogen evolution reaction performance with a low overpotential (89 mV at 10 mA cm-2) and small Tafel slope (105 mV dec-1) under alkaline conditions. It is revealed that the electronic structure of Fe is modulated by N doping and phase transition. The downshifted d band energy can strengthen water adsorption and reduce the energy barrier of water dissociation. Our work provides a new strategy to modify metal sulfide electrocatalysts for electrochemical energy conversion.

Classification of inattentional blindness using brain dynamics of ERPs.

Situational awareness (SA) is vital for understanding our surroundings. Multiple variables, including inattentive blindness (IB), contribute to the deterioration of SA, which may have detrimental effects on individuals' cognitive performance. IB occurs due to attentional limitations, ignoring critical information and resulting in a loss of SA and a decline in general performance, particularly in complicated situations requiring substantial cognitive resources. To the best of our knowledge, however, past research has not fully uncovered the neurological characteristics of IB nor classified these characteristics in life-alike virtual situations. Therefore, the purpose of this study is to determine whether ERP dynamics in the brain may be utilised as a neural feature to predict the occurrence of IB using machine learning (ML) algorithms. In a virtual reality simulation of an IB experiment, 30 participants' behaviour and Electroencephalography (EEG) measurements were obtained. Participants were given a target detection task in the IB experiment without knowing the unattended shapes displayed on the background building. The targets were presented in three different sensory modalities (auditory, visual, and visual-auditory). On the post-experiment questionnaire, participants who claimed not to have noticed the unattended shapes were assigned to the IB group. Subsequently, the Aware group was formed from individuals who reported seeing the unattended shapes. Using EEGNet to classify IB and Aware groups demonstrated a high classification performance. According to the research, ERP brain dynamics are associated with the awareness of unattended shapes and have the potential to serve as a reliable indication for predicting the visual consciousness of unexpected objects.(p/)(p)Clinical relevance- This research offers a potential brain marker for the mixed-reality and BCI systems that will be used in the future to identify cognitive deterioration, maintain attentional capacity, and prevent disasters.

Functional imaging and targeted drug delivery in mice and patient tumors with a cell nucleolus-localizing and tumor-targeting peptide.

Tumor-targeting peptides have profound clinical implications in early detection and delineation of microscopic lesions for surgical resection, and also delivery of therapeutics with reduced systemic toxicity. Here, we demonstrate that a peptide (RS), evolved from a previously reported hepatocellular carcinoma (HCC)-targeting peptide P47, enables improved HCC micrometastasis discrimination and delineation from noncancerous tissues in murine orthotopic mice and patient biopsies, with up to 21-fold contrast. Importantly, RS targets non-small cell lung (NSCLC) and colon cancers in mice and patient biopsies, with higher selectivity for highly proliferative tumor nodules. Moreover, RS localizes to cell nucleoli of HCC, NSCLC, breast, colon and cervical cancer cells and induces nucleolar stress when conjugated with chemotherapeutic Oxaliplatin (OXA) (RS-OXA), demonstrating both cellular and subcellular targeting. RS-delivered OXA elicits significant tumor retardation in orthotopic HCC mice with markedly reduced systemic toxicity compared to OXA alone. Injection of fluorescence-labeled RS enables dynamic visualization of tumor growth in RS-OXA-treated subcutaneous HCC mice. Our study demonstrates that RS targets a spectrum of tumors and localizes to cell nucleolus, thus enabling functional imaging and targeted delivery of OXA in HCC mice, and consequently provides a versatile tool for tumor imaging and targeted therapeutics.

In2O3 Nanocrystals for CO2 Fixation: Atomic-Level Insight into the Role of Grain Boundaries.

N-functionalization of amines with CO2 and H2 is one of the most important processes to make use of CO2. Although noble metal-based catalysts with remarkable performance have been widely used in this process, developing efficient non-noble-metal-based catalysts remains a grand challenge. Herein, we report In2O3 nanocrystals with high density of grain boundaries (HGB-In2O3), which show excellent activity toward methylation of amines. Impressively, HGB-In2O3 achieved the optimal yield of 82.7% for N,N-dimethylaniline with a mass activity of 21.2 mmol·g-1h-1 in methylation of N-methylaniline, comparable to noble-metal-based catalysts. As a bonus, HGB-In2O3 held noticeable stability, remarkable selectivity, and comprehensive applicability. Further mechanistic studies revealed that the presence of high density of grain boundaries not only facilitated the adsorption and activation of CO2 to generate CH3OH as the intermediate but also enhanced the activation of N-H bond in amines, contributing to the attractive activity of HGB-In2O3 toward methylation of amines.

Anchoring Pt Single Atoms on Te Nanowires for Plasmon-Enhanced Dehydrogenation of Formic Acid at Room Temperature.

Formic acid (HCOOH), as a promising hydrogen carrier, is renewable, safe, and nontoxic. However, the catalytic dehydrogenation of HCOOH is typically conducted at elevated temperature. Here, HCOOH decomposition is successfully achieved for hydrogen production on the developed Pt single atoms modified Te nanowires with the Pt mass loading of 1.1% (1.1%Pt/Te) at room temperature via a plasmon-enhanced catalytic process. Impressively, 1.1%Pt/Te delivers 100% selectivity for hydrogen and the highest turnover frequency number of 3070 h-1 at 25 °C, which is significantly higher than that of Pt single atoms and Pt nanoclusters coloaded Te nanowires, Pt nanocrystals decorated Te nanowires, and commercial Pt/C. A plasmonic hot-electron driven mechanism rather than photothermal effect domains the enhancement of catalytic activity for 1.1%Pt/Te under light. The transformation of HCOO* to CO2 δ -* on Pt atoms is proved to be the rate-determining step by further mechanistic studies. 1.1%Pt/Te exhibits tremendous catalytic activity toward the decomposition of HCOOH owing to its plasmonic hot-electron driven mechanism, which efficiently stimulates the rate-determining step. In addition, hot electrons generated by the Te atoms nearby Pt single atoms are regarded to directly inject into the reactants adsorbed and activated on Pt single atoms.