Facile Construction of High-Performance Amorphous FePO₄/Carbon Nanomaterials as Cathodes of Lithium-Ion Batteries

A facile strategy to construct composites of amorphous FePO4 (a-FePO4) nanoparticles and carbon additives with high dispersion and tap density was developed in this work, in which the a-FePO4·2H2O nanoparticles were handled without drying until being mixed with carbon nanomaterials in water to assure high dispersion of a-FePO4·2H2O nanoparticles and carbon nanomaterials; the controlled sedimentation was exploited by rapid adjustment of the pH value via a micromixer to obtain the composites that are easy to manipulate; the composites were endowed with high tap density after simple ball-milling. Using this strategy, hybrid carbon additives were uniformly introduced into the a-FePO4 cathode to form a hierarchical 3D conductive network. Through proper distribution of these components to provide both long- and short-range electron pathways, the reversible discharge capacity could reach 175.6 mA h g–1 at 0.1 C and 139.1 mA h g–1 at 5 C. The composites of a-FePO4, carbon black, and carbon nanotubes (CNT) exhibited the distinct advantages of low cost and excellent rate capacity over the composites of a-FePO4 and CNT, indicating the importance of optimizing the hierarchical structure of cathode composites. The high effectiveness of this construction strategy to build a hierarchical conductive network is also promisingly used for the development of other functional nanocomposites

Cleavable Chiral Auxiliaries in 8π (8π, 6π) Electrocyclizations

Low to moderate diastereoselectivity was observed in the 8π electrocyclization of a series of chiral auxiliary-bearing tetraenic esters. In the 8-arylmenthyl series, diastereomeric products were separated by chromatography

Cleavable Chiral Auxiliaries in 8π (8π, 6π) Electrocyclizations

Low to moderate diastereoselectivity was observed in the 8π electrocyclization of a series of chiral auxiliary-bearing tetraenic esters. In the 8-arylmenthyl series, diastereomeric products were separated by chromatography

Graph Attention Network Model with Defined Applicability Domains for Screening PBT Chemicals

In silico models for screening environmentally persistent, bio-accumulative, and toxic (PBT) substances are necessary for sound management of chemicals. Due to the complex structure–activity landscapes (SALs) on the PBT attributes, previous models for screening PBT chemicals lack either applicability domain (AD) characterizations or interpretability, restricting their applications. Herein, graph attention networks (GATs), a novel neural network architecture, were introduced to construct models for screening PBT chemicals. Results show that the GAT model not only outperformed those in previous studies but also exhibited interpretability since it optimizes attention weight parameters (PAW) that indicate contributions of each atom to the PBT attributes. An AD characterization termed ADFP–AC, which considers both molecular fingerprint (FP) similarities and compounds at activity cliffs (ACs) of SALs, was proposed to describe the ADs, which further assured the performance of the GAT model. Eight previously unidentified classes of compounds were identified as PBT chemicals from the Inventory of Existing Chemical Substances in China. The GAT model together with the ADFP–AC characterization may serve as efficient tools for screening PBT chemicals, and the modeling methodology can be applied to other physicochemical, environmental, behavioral, and toxicological parameters of chemicals that are necessary for their risk assessment and management

DataSheet1_Characterizing the respiratory-induced mechanical stimulation at the maxillary sinus floor following sinus augmentation by computational fluid dynamics.PDF

Background: The relationship between maxillary sinus pneumatization and respiratory-induced fluid mechanics remains unclear. The purpose of this study was to simulate and measure the respiratory-induced mechanical stimulation at the sinus floor under different respiratory conditions and to investigate its potential effect on the elevated sinus following sinus-lifting procedures.Methods: The nasal airway together with the bilateral maxillary sinuses of the selected patient was segmented and digitally modeled from a computed tomographic image. The sinus floors of the models were elevated by simulated sinus augmentations using computer-aided design. The numerical simulations of sinus fluid motion under different respiratory conditions were performed using a computational fluid dynamics (CFD) algorithm. Sinus wall shear stress and static pressure on the pre-surgical and altered sinus floors were examined and quantitatively compared.Results: Streamlines with minimum airflow velocity were visualized in the sinus. The sinus floor pressure and the wall shear stress increased with the elevated inlet flow rate, but the magnitude of these mechanical stimulations remained at a negligible level. The surgical technique and elevated height had no significant influence on the wall pressure and the fluid mechanics.Conclusion: This study shows that respiratory-induced mechanical stimulation in the sinus floor is negligible before and after sinus augmentation.</p

Table_1_Effects of pre-partum dietary crude protein level on colostrum fat globule membrane proteins and the performance of Hu ewes and their offspring.XLSX

Dietary proteins play important roles in the growth and reproduction of sheep, and the ewe's demand for proteins increases dramatically during late pregnancy. This research aimed to investigate the effect of dietary crude protein (CP) levels during late pregnancy on colostrum fat globule membrane (MFGM) protein and the growth performance of Hu sheep and their offspring, and provide a reference for the protein intake of ewes during late pregnancy. A total of 108 multiparous Hu sheep (45.6 ± 1.18 kg) were selected for this study, then 60 pregnant ewes confirmed by B-scan ultrasonography were randomly divided into three treatments (20 ewes/treatment) and fed by total mixed ration pellet with CP levels at 9.00% (LP), 12.0% (MP), and 15.0% (HP) during late pregnancy, respectively. The weight and dry matter intake of ewes during late pregnancy were recorded to calculate the average daily gain (ADG) and feed conversion ratio (FCR). Twin lambs were weighed on days 0, 7, 14, 30, 60, and 180 after birth to calculate ADG. Meanwhile, the colostrum of ewes was collected within 12 h after delivery. The colostrum MFGM proteins were identified and quantified by the isobaric tag for relative and absolute quantification (iTRAQ) coupled with liquid chromatography-tandem mass spectrometry methods. In addition, biological functions of differentially expressed proteins (DEPs) were annotated by Gene Ontology annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. The results revealed that a 15.0% CP level had significant effects on the BW of lambs on days 0, 7, and 30 (P < 0.05). Notably, a total of 1,529 MFGM proteins were identified and 286 DEPs were found among three treatments. Functional analysis showed that DEPs were mainly involved in cell growth, differentiation, and tissue repair, and involved in metabolic pathways, such as the porphyrin and chlorophyll metabolism pathways. In this study, lambs in HP treatment had better growth performance; moreover, dietary 15.0% CP level also affected the colostrum MFGM proteins composition of Hu ewes. These observations can facilitate future studies on the feeding regimen of ewes during late pregnancy.</p

Applicability Domains Based on Molecular Graph Contrastive Learning Enable Graph Attention Network Models to Accurately Predict 15 Environmental End Points

In silico models for predicting physicochemical properties and environmental fate parameters are necessary for the sound management of chemicals. This study employed graph attention network (GAT) algorithms to construct such models on 15 end points. The results showed that the GAT models outperformed the previous state-of-the-art models, and their performance was not influenced by the presence or absence of compounds with certain structures. Molecular similarity density (ρs) was found to be a key metrics characterizing data set modelability, in addition to the proportion of compounds at activity cliffs. By introducing molecular graph (MG) contrastive learning, MG-based ρs and molecular inconsistency in activities (IA) were calculated and employed for characterizing the structure–activity landscape (SAL)-based applicability domain ADSAL{ρs, IA}. The GAT models coupled with ADSAL{ρs, IA} significantly improved the prediction coefficient of determination (R2) on all the end points by an average of 14.4% and enabled all the end points to have R2 > 0.9, which could hardly be achieved previously. The models were employed to screen persistent, mobile, and/or bioaccumulative chemicals from inventories consisting of about 106 chemicals. Given the current state-of-the-art model performance and coverage of the various environmental end points, the constructed models with ADSAL{ρs, IA} may serve as benchmarks for future efforts to improve modeling efficacy

Applicability Domains Based on Molecular Graph Contrastive Learning Enable Graph Attention Network Models to Accurately Predict 15 Environmental End Points

In silico models for predicting physicochemical properties and environmental fate parameters are necessary for the sound management of chemicals. This study employed graph attention network (GAT) algorithms to construct such models on 15 end points. The results showed that the GAT models outperformed the previous state-of-the-art models, and their performance was not influenced by the presence or absence of compounds with certain structures. Molecular similarity density (ρs) was found to be a key metrics characterizing data set modelability, in addition to the proportion of compounds at activity cliffs. By introducing molecular graph (MG) contrastive learning, MG-based ρs and molecular inconsistency in activities (IA) were calculated and employed for characterizing the structure–activity landscape (SAL)-based applicability domain ADSAL{ρs, IA}. The GAT models coupled with ADSAL{ρs, IA} significantly improved the prediction coefficient of determination (R2) on all the end points by an average of 14.4% and enabled all the end points to have R2 > 0.9, which could hardly be achieved previously. The models were employed to screen persistent, mobile, and/or bioaccumulative chemicals from inventories consisting of about 106 chemicals. Given the current state-of-the-art model performance and coverage of the various environmental end points, the constructed models with ADSAL{ρs, IA} may serve as benchmarks for future efforts to improve modeling efficacy

Ki67 protein was located at nucleus of the breast cancers.

<p>A(2+) and B(2+), Ki67 protein distributed as diffuse type; C(2+) and D(2+), Ki67 protein distributed as borderline type; E(2+), Ki67 protein expressed in the lymph node metastasis; F, negative control.</p