Pore structure, barrier layer topography and matrix alumina structure of porous anodic alumina film

Different anodic voltages and methods were adopted to produce porous anodic alumina films (PAAF) in an aqueous solution of oxalic acid. Carbon tube growth by chemical vapor deposition (CVD) in the films was used to copy the internal pore structure and was recorded by transmission electron microscopy (TEM) photos. Atomic force microscope (AFM) was employed to obtain the topography of the barrier layer of the corresponding films. When the anodic voltage was 40 V and the two-step method adopted, the barrier layer of the film had domains with highly ordered hexagonal cell distribution, and the corresponding pores were straight. When the anodic voltage increased to 60 V, the barrier layer showed random cell distribution with an obvious difference in cell size and form, and the corresponding pores exhibited multi-branch features. When the anodic voltage increased further to 110 V, the barrier layer also showed a random cell distribution. Additionally, smaller protrusions connected to bigger cells were found, which can be correlated to the formation of branches with smaller diameters. Most of the branches of carbon tubes grown in the film anodized at 110 V have a saw-tooth like feature. X-Ray diffraction analysis shows that all the anodic films are amorphous, regardless of the anodic voltage. However, unoxidized aluminum particles in the film anodized at 110 V was observed by TEM

Electrochemical hydrogenation of mixed-phase TiO₂ nanotube arrays enables remarkably enhanced photoelectrochemical water splitting performance

We first report that photoelectrochemical (PEC) performance of electrochemically hydrogenated TiO2 nanotube arrays (TNTAs) as high-efficiency photoanodes for solar water splitting could be well tuned by designing and adjusting the phase structure and composition of TNTAs. Among various TNTAs annealed at different temperature ranging from 300 to 700 °C, well-crystallized single anatase (A) phase TNTAs-400 photoanode shows the best photoresponse properties and PEC performance due to the favorable crystallinity, grain size and tubular structures. After electrochemical hydrogenation (EH), anatase-rutile (A-R) mixed phase EH-TNTAs-600 photoanode exhibits the highest photoactivity and PEC performance for solar water splitting. Under simulated solar illumination, EH-TNTAs-600 achieves the best photoconversion efficiency of up to 1.52% and maximum H2 generation rate of 40.4 µmol h−1 cm−2, outstripping other EH-TNTAs photoanodes. Systematic studies reveal that the signigicantly enhanced PEC performance for A-R mixed phaes EH-TNTAs-600 photoanode could be attributed to the synergy of A-R mixed phases and intentionally introduced Ti3+ (oxygen vacancies) which enhances the photoactivity over both UV and visible-light regions, and boosts both charge separation and transfer efficiencies. These findings provide new insight and guidelines for the construction of highly efficient TiO2-based devices for the application of solar water splitting.This work was supported by the National Natural Science Foundation of China (51402078, 21702041, and 11674354), the National Basic Research Program of China (2014CB660815), and the Fundamental Research Funds for the Central Universities (JZ2016HGTB0711, JZ2016HGTB0719, and JZ2017HGPA0167)

Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning

Multi-class cell segmentation in high-resolution Giga-pixel whole slide images (WSI) is critical for various clinical applications. Training such an AI model typically requires labor-intensive pixel-wise manual annotation from experienced domain experts (e.g., pathologists). Moreover, such annotation is error-prone when differentiating fine-grained cell types (e.g., podocyte and mesangial cells) via the naked human eye. In this study, we assess the feasibility of democratizing pathological AI deployment by only using lay annotators (annotators without medical domain knowledge). The contribution of this paper is threefold: (1) We proposed a molecular-empowered learning scheme for multi-class cell segmentation using partial labels from lay annotators; (2) The proposed method integrated Giga-pixel level molecular-morphology cross-modality registration, molecular-informed annotation, and molecular-oriented segmentation model, so as to achieve significantly superior performance via 3 lay annotators as compared with 2 experienced pathologists; (3) A deep corrective learning (learning with imperfect label) method is proposed to further improve the segmentation performance using partially annotated noisy data. From the experimental results, our learning method achieved F1 = 0.8496 using molecular-informed annotations from lay annotators, which is better than conventional morphology-based annotations (F1 = 0.7051) from experienced pathologists. Our method democratizes the development of a pathological segmentation deep model to the lay annotator level, which consequently scales up the learning process similar to a non-medical computer vision task. The official implementation and cell annotations are publicly available at https://github.com/hrlblab/MolecularEL

Microbiome and metabolome analyses reveal significant alterations of gut microbiota and bile acid metabolism in ETEC-challenged weaned piglets by dietary berberine supplementation

This study mainly investigated the effects of berberine (BBR) on the bile acid metabolism in gut-liver axis and the microbial community in large intestine of weaned piglets challenged with enterotoxigenic Escherichia coli (ETEC) by microbiome and metabolome analyses. Sixty-four piglets were randomly assigned to four groups including Control group, BBR group, ETEC group, and BBR + ETEC group. Dietary BBR supplementation upregulated the colonic mRNA expression of Occludin, Claudin-5, trefoil factor 3 (TFF3), and interleukin (IL)-10, and downregulated colonic IL-1β and IL-8 mRNA expression in piglets challenged with ETEC K88 (p < 0.05). The hepatic non-targeted metabolome results showed that dietary BBR supplementation enriched the metabolic pathways of primary bile acid biosynthesis, tricarboxylic acid cycle, and taurine metabolism. The hepatic targeted metabolome analyses showed that BBR treatment increased the hepatic concentrations of taurocholic acid (TCA) and taurochenodeoxycholic acid (TDCA), but decreased the hepatic cholic acid (CA) concentration (p < 0.05). Further intestinal targeted metabolome analyses indicated that the deoxycholic acid (DCA), hyocholic acid (HCA), 7-ketodeoxycholic acid (7-KDCA), and the unconjugated bile acid concentrations in ileal mucosa was decreased by dietary BBR treatment (p < 0.05). Additionally, BBR treatment significantly upregulated the hepatic holesterol 7 α-hydroxylase (CYP7A1) and sterol 27-hydroxylase (CYP27A1) mRNA expression, and upregulated the ileal mRNA expression of farnesoid X receptor (FXR) and apical sodium-dependent bile acid transporter (ASBT) as well as the colonic mRNA expression of FXR, fibroblast growth factor19 (FGF19), takeda G protein-coupled receptor 5 (TGR5) and organic solute transporters beta (OST-β) in piglets (p < 0.05). Moreover, the microbiome analysis showed that BBR significantly altered the composition and diversity of colonic and cecal microbiota community, with the abundances of Firmicutes (phylum), and Lactobacillus and Megasphaera (genus) significantly increased in the large intestine of piglets (p < 0.05). Spearman correlation analysis showed that the relative abundances of Megasphaera (genus) were positively correlated with Claudin-5, Occludin, TFF3, and hepatic TCDCA concentration, but negatively correlated with hepatic CA and glycocholic acid (GCA) concentration (p < 0.05). Moreover, the relative abundances of Firmicute (phylum) and Lactobacillus (genus) were positively correlated with hepatic TCDCA concentration (p < 0.05). Collectively, dietary BBR supplementation could regulate the gut microbiota and bile acid metabolism through modulation of gut-liver axis, and attenuate the decreased intestinal tight junction expression caused by ETEC, which might help maintain intestinal homeostasis in weaned piglets

Observation of room-temperature ferroelectricity in elemental Te nanowires

Ferroelectrics are essential in low-dimensional memory devices for multi-bit storage and high-density integration. A polar structure is a necessary premise for ferroelectricity, mainly existing in compounds. However, it is usually rare in elemental materials, causing a lack of spontaneous electric polarization. Here, we report an unexpected room-temperature ferroelectricity in few-chain Te nanowires. Out-of-plane ferroelectric loops and domain reversal are observed by piezoresponse force microscopy. Through density functional theory, we attribute the ferroelectricity to the ion-displacement created by the interlayer interaction between lone pair electrons. Ferroelectric polarization can induce a strong field effect on the transport along the Te chain, supporting a self-gated field-effect transistor. It enables a nonvolatile memory with high in-plane mobility, zero supply voltage, multilevel resistive states, and a high on/off ratio. Our work provides new opportunities for elemental ferroelectrics with polar structures and paves a way towards applications such as low-power dissipation electronics and computing-in-memory devices

OpenCL-accelerated first-principles calculations of all-electron quantum perturbations on HPC resources

We have proposed, for the first time, an OpenCL implementation for the all-electron density-functional perturbation theory (DFPT) calculations in FHI-aims, which can effectively compute all its time-consuming simulation stages, i.e., the real-space integration of the response density, the Poisson solver for the calculation of the electrostatic potential, and the response Hamiltonian matrix, by utilizing various heterogeneous accelerators. Furthermore, to fully exploit the massively parallel computing capabilities, we have performed a series of general-purpose graphics processing unit (GPGPU)-targeted optimizations that significantly improved the execution efficiency by reducing register requirements, branch divergence, and memory transactions. Evaluations on the Sugon supercomputer have shown that notable speedups can be achieved across various materials

Feasibility of Universal Anomaly Detection without Knowing the Abnormality in Medical Images

Many anomaly detection approaches, especially deep learning methods, have been recently developed to identify abnormal image morphology by only employing normal images during training. Unfortunately, many prior anomaly detection methods were optimized for a specific "known" abnormality (e.g., brain tumor, bone fraction, cell types). Moreover, even though only the normal images were used in the training process, the abnormal images were often employed during the validation process (e.g., epoch selection, hyper-parameter tuning), which might leak the supposed ``unknown" abnormality unintentionally. In this study, we investigated these two essential aspects regarding universal anomaly detection in medical images by (1) comparing various anomaly detection methods across four medical datasets, (2) investigating the inevitable but often neglected issues on how to unbiasedly select the optimal anomaly detection model during the validation phase using only normal images, and (3) proposing a simple decision-level ensemble method to leverage the advantage of different kinds of anomaly detection without knowing the abnormality. The results of our experiments indicate that none of the evaluated methods consistently achieved the best performance across all datasets. Our proposed method enhanced the robustness of performance in general (average AUC 0.956)

Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

The segment anything model (SAM) was released as a foundation model for image segmentation. The promptable segmentation model was trained by over 1 billion masks on 11M licensed and privacy-respecting images. The model supports zero-shot image segmentation with various segmentation prompts (e.g., points, boxes, masks). It makes the SAM attractive for medical image analysis, especially for digital pathology where the training data are rare. In this study, we evaluate the zero-shot segmentation performance of SAM model on representative segmentation tasks on whole slide imaging (WSI), including (1) tumor segmentation, (2) non-tumor tissue segmentation, (3) cell nuclei segmentation. Core Results: The results suggest that the zero-shot SAM model achieves remarkable segmentation performance for large connected objects. However, it does not consistently achieve satisfying performance for dense instance object segmentation, even with 20 prompts (clicks/boxes) on each image. We also summarized the identified limitations for digital pathology: (1) image resolution, (2) multiple scales, (3) prompt selection, and (4) model fine-tuning. In the future, the few-shot fine-tuning with images from downstream pathological segmentation tasks might help the model to achieve better performance in dense object segmentation

mGenomeSubtractor: a web-based tool for parallel in silico subtractive hybridization analysis of multiple bacterial genomes

mGenomeSubtractor performs an mpiBLAST-based comparison of reference bacterial genomes against multiple user-selected genomes for investigation of strain variable accessory regions. With parallel computing architecture, mGenomeSubtractor is able to run rapid BLAST searches of the segmented reference genome against multiple subject genomes at the DNA or amino acid level within a minute. In addition to comparison of protein coding sequences, the highly flexible sliding window-based genome fragmentation approach offered can be used to identify short unique sequences within or between genes. mGenomeSubtractor provides powerful schematic outputs for exploration of identified core and accessory regions, including searches against databases of mobile genetic elements, virulence factors or bacterial essential genes, examination of G+C content and binucleotide distribution bias, and integrated primer design tools. mGenomeSubtractor also allows for the ready definition of species-specific gene pools based on available genomes. Pan-genomic arrays can be easily developed using the efficient oligonucleotide design tool. This simple high-throughput in silico ‘subtractive hybridization’ analytical tool will support the rapidly escalating number of comparative bacterial genomics studies aimed at defining genomic biomarkers of evolutionary lineage, phenotype, pathotype, environmental adaptation and/or disease-association of diverse bacterial species. mGenomeSubtractor is freely available to all users without any login requirement at: http://bioinfo-mml.sjtu.edu.cn/mGS/