Poly[(μ5-5-carboxylatotetrahydrofuran-2,3,4-tricarboxylic acid)sodium]

The search for the novel metal-organic frameworks (MOFs) materials using tetra­hydro­furan-2,3,4,5-tetra­carboxylic acid (THFTCA) as a versatile multi-carboxyl ligand, lead to the synthesis and the structure determination of the title compound, [Na(H3THFTCA)] or [Na(C8H7O9)]n, which was obtained by a solution reaction at room temperature. The ligand is mono-deprotonated, coordinating five sodium ions through one furan oxygen atom and six carboxyl oxygen atoms. The sodium ion exhibits a distorted penta­gonal-bipyramidal NaO7 geometry consisting of seven O atoms derived from five surrounding ligands. Two adjacent pentagonal bipyramids share an O—O edge, forming a dinuclear sodium cluster. Finally, these clusters are effectively linked by the carboxyl groups of THFTCA ligands, forming a firm metal organic framework and O—H⋯O hydrogen bonds contribute to the crystal packing

2′-Eth­oxy-1,3,3-trimethyl­spiro­[indoline-2,3′-3H-naphtho­[2,1-b][1,4]oxazine]

In the title compound, C24H24N2O2, the five-membered ring of the indoline ring system adopts an envelope conformation with the spiro C atom at the flap. The dihedral angle between the benzene ring of the indoline ring system and the naphthalene ring system is 71.70 (7)°. In the crystal structure, pair of weak C—H⋯O hydrogen bonds link the mol­ecules into centrosymmetric dimers

Propheter: Prophetic Teacher Guided Long-Tailed Distribution Learning

The problem of deep long-tailed learning, a prevalent challenge in the realm of generic visual recognition, persists in a multitude of real-world applications. To tackle the heavily-skewed dataset issue in long-tailed classification, prior efforts have sought to augment existing deep models with the elaborate class-balancing strategies, such as class rebalancing, data augmentation, and module improvement. Despite the encouraging performance, the limited class knowledge of the tailed classes in the training dataset still bottlenecks the performance of the existing deep models. In this paper, we propose an innovative long-tailed learning paradigm that breaks the bottleneck by guiding the learning of deep networks with external prior knowledge. This is specifically achieved by devising an elaborated ``prophetic'' teacher, termed as ``Propheter'', that aims to learn the potential class distributions. The target long-tailed prediction model is then optimized under the instruction of the well-trained ``Propheter'', such that the distributions of different classes are as distinguishable as possible from each other. Experiments on eight long-tailed benchmarks across three architectures demonstrate that the proposed prophetic paradigm acts as a promising solution to the challenge of limited class knowledge in long-tailed datasets. Our code and model can be found in the supplementary material

Direct conversion of astrocytes into neuronal cells by drug cocktail

Direct conversion of astrocytes into neuronal cells by drug cocktail Cell Research advance online publication 2 October 2015; doi:10.1038/cr.2015.120 Dear Editor, Neurological disorder is one of the greatest threats to public health according to the World Health Organization. Because neurons have little or no regenerative capacity, conventional therapies for neurological disorders yielded poor outcomes. While the introduction of exogenous neural stem cells or neurons holds promise, many challenges still need to be tackled, including cell resource, delivery strategy, cell integration and cell maturation. Reprogramming of fibroblasts into induced pluripotent stem cells or directly into desirable neuronal cells by transcription factors (TFs) or small molecules can solve some problems, but other issues remain to be addressed, including safety, conversion efficiency and epigenetic memory [1, 2]. Astrocytes are considered to be the ideal starting candidate cell type for generating new neurons, due to their proximity in lineage distance to neurons and ability to proliferate after brain damage. Many studies have already revealed that astrocytes of the central nervous system can be reprogrammed into induced neuronal cells by virus-mediated overexpression of specific TFs in vitro and in vivo [3-6]. However, application of this virus-mediated direct conversion is still limited due to concerns on clinical safety. We have previously reported direct conversion of somatic cells into neural progenitor cells (NPCs) in vitro by cocktail of small molecules under hypoxia [7]. Here we set out to explore whether astrocytes can be induced into neuronal cells by the chemical cocktail in vitro

Chloridobis(1,10-phenanthroline)zinc(II) tetra­chlorido(1,10-phenan­throline)bis­muthate(III) monohydrate

In the crystal structure of the title monohydrate salt, [ZnCl(C12H8N2)2][BiCl4(C12H8N2)]·H2O, the ionic components are linked into three-dimensional supra­molecular channels by five pairs of C—H⋯Cl hydrogen bonds and π–π stacking inter­actions with an inter­planar distance of 3.643 (2) Å. The solvent water mol­ecules are lodged in the channels

Polysaccharides from Enteromorpha prolifera Improve Glucose Metabolism in Diabetic Rats

This study investigated the effects of polysaccharides from Enteromorpha prolifera (PEP) on glucose metabolism in a rat model of diabetes mellitus (DM). PEP (0, 150, 300, and 600 mg/kg) was administered intragastrically to rats for four weeks. After treatment, fasting blood glucose (FBG) and insulin (INS) levels were measured, and the insulin sensitivity index (ISI) was calculated. The morphopathological changes in the pancreas were observed. Serum samples were collected to measure the oxidant-antioxidant status. The mRNA expression levels of glucokinase (GCK) and insulin receptor (InsR) in liver tissue and glucose transporter type 4 (GLUT-4) and adiponectin (APN) in adipose tissue were determined. Compared with the model group, the FBG and INS levels were lower, the ISI was higher, and the number of islet β-cells was significantly increased in all the PEP groups. In the medium- and high-dose PEP groups, MDA levels decreased, and the enzymatic activities of SOD and GSH-Px increased. The mRNA expression of InsR and GCK increased in all the PEP groups; APN mRNA expression increased in the high-dose PEP group, and GLUT-4 mRNA expression increased in adipose tissue. These findings suggest that PEP is a potential therapeutic agent that can be utilized to treat DM

LdsConv : learned depthwise separable convolutions by group pruning

Standard convolutional filters usually capture unnecessary overlap of features resulting in a waste of computational cost. In this paper, we aim to solve this problem by proposing a novel Learned Depthwise Separable Convolution (LdsConv) operation that is smart but has a strong capacity for learning. It integrates the pruning technique into the design of convolutional filters, formulated as a generic convolutional unit that can be used as a direct replacement of convolutions without any adjustments of the architecture. To show the effectiveness of the proposed method, experiments are carried out using the state-of-the-art convolutional neural networks (CNNs), including ResNet, DenseNet, SE-ResNet and MobileNet, respectively. The results show that by simply replacing the original convolution with LdsConv in these CNNs, it can achieve a significantly improved accuracy while reducing computational cost. For the case of ResNet50, the FLOPs can be reduced by 40.9%, meanwhile the accuracy on the associated ImageNet increases

The diagnostic significance of the ZNF gene family in pancreatic cancer: a bioinformatics and experimental study

Background: Pancreatic adenocarcinoma (PAAD) is among the most devastating of all cancers with a poor survival rate. Therefore, we established a zinc finger (ZNF) protein-based prognostic prediction model for PAAD patients.Methods: The RNA–seq data for PAAD were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Differentially expressed ZNF protein genes (DE-ZNFs) in PAAD and normal control tissues were screened using the “lemma” package in R. An optimal risk model and an independent prognostic value were established by univariate and multivariate Cox regression analyses. Survival analyses were performed to assess the prognostic ability of the model.Results: We constructed a ZNF family genes-related risk score model that is based on the 10 DE-ZNFs (ZNF185, PRKCI, RTP4, SERTAD2, DEF8, ZMAT1, SP110, U2AF1L4, CXXC1, and RMND5B). The risk score was found to be a significant independent prognostic factor for PAAD patients. Seven significantly differentially expressed immune cells were identified between the high- and low-risk patients. Then, based on the prognostic genes, we constructed a ceRNA regulatory network that includes 5 prognostic genes, 7 miRNAs and 35 lncRNAs. Expression analysis showed ZNF185, PRKCI and RTP4 were significantly upregulated, while ZMAT1 and CXXC1 were significantly downregulated in the PAAD samples in all TCGA - PAAD, GSE28735 and GSE15471 datasets. Moreover, the upregulation of RTP4, SERTAD2, and SP110 were verified by the cell experiments.Conclusion: We established and validated a novel, Zinc finger protein family - related prognostic risk model for patients with PAAD, that has the potential to inform patient management