Synthetic studies of natural compounds and their analogues

A83586C, a novel cyclodepsipeptide isolated from Streptomyces Kamatakensis, exhibits antitumour properties in vitro against a CCRF-CEM human T-cell Leukaemia line (IC50 = 0.0135 μg/mL). The strategy for synthesis of the peptide portion of A83586C can, in principle, be divided into three parts. First, the asymmetric synthesis of 3S- or 3R-piperazic acids via the Evans-Vederas alkylation procedure. Second, the formation of the linear hexapeptide Third, the intramolecular coupling to form the cyclic depsipeptide. The problem of N,O-acyl shifts during the cyclization of depsipeptides occurs when serine or threonine residues are present. It was therefore decided to form the lactone bond between (2S,3S)-3-hydroxyleucine and threonine in the last step. Three types of fragment condensations were employed in an attempt to synthesis the linear hexapeptide. The successful synthesis used a "1+2+3" condensation procedure. Synthesis of the linear hexapeptide was achieved by coupling specific amino acids using DCC or BOP-CI. 4-Methyl-2-E-pentenic acid was used inplace of (2S,3S)-3- hydroxyleucine in the coupling reaction. The Fmoc group was employed to protect the amino function of N-Me-(R)-alanine. The Z and Bn permanent protecting groups were employed to protect the amine of 3S- or 3R-piperazic acids and the hydroxyl group of N-hydroxylalanine. The carboxylic acid of threonine was protected by formation of the methyl ester. The 4-methyl-2-E-pentenic acid derivative could potentially be converted to the (2S,3S)-3-hydroxyleucine derivative by a Sharpless asymmetric epoxidation procedure from the linear pseudo-hexapeptide

An Improved Tax Scheme for Selfish Routing

We study the problem of routing traffic for independent selfish users in a congested network to minimize the total latency. The inefficiency of selfish routing motivates regulating the flow of the system to lower the total latency of the Nash Equilibrium by economic incentives or penalties. When applying tax to the routes, we follow the definition of [Christodoulou et al, Algorithmica, 2014] to define ePoA as the Nash total cost including tax in the taxed network over the optimal cost in the original network. We propose a simple tax scheme consisting of step functions imposed on the links. The tax scheme can be applied to routing games with parallel links, affine cost functions and single-commodity networks to lower the ePoA to at most 4/3 - epsilon, where epsilon only depends on the discrepancy between the links. We show that there exists a tax scheme in the two link case with an ePoA upperbound less than 1.192 which is almost tight. Moreover, we design another tax scheme that lowers ePoA down to 1.281 for routing games with groups of links such that links in the same group are similar to each other and groups are sufficiently different

Luteolin Suppresses Inflammatory Mediator Expression by Blocking the Akt/NFκB Pathway in Acute Lung Injury Induced by Lipopolysaccharide in Mice

Acute lung injury (ALI), instilled by lipopolysaccharide (LPS), is a severe illness with excessive mortality and has no specific treatment strategy. Luteolin is an anti-inflammatory flavonoid and widely distributed in the plants. Pretreatment with luteolin inhibited LPS-induced histological changes of ALI and lung tissue edema. In addition, LPS-induced inflammatory responses, including increased vascular permeability, tumor necrosis factor (TNF)-α and interleukin (IL)-6 production, and expression of inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2), were also reduced by luteolin in a concentration-dependent manner. Furthermore, luteolin suppressed activation of NFκB and its upstream molecular factor, Akt. These results suggest that the protection mechanism of luteolin is by inhibition of NFκB activation possibly via Akt

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Deploying large language models (LLMs) is challenging because they are memory inefficient and compute-intensive for practical applications. In reaction, researchers train smaller task-specific models by either finetuning with human labels or distilling using LLM-generated labels. However, finetuning and distillation require large amounts of training data to achieve comparable performance to LLMs. We introduce Distilling step-by-step, a new mechanism that (a) trains smaller models that outperform LLMs, and (b) achieves so by leveraging less training data needed by finetuning or distillation. Our method extracts LLM rationales as additional supervision for training small models within a multi-task framework. We present three findings across 4 NLP benchmarks: First, compared to both finetuning and distillation, our mechanism achieves better performance with much fewer labeled/unlabeled training examples. Second, compared to few-shot prompted LLMs, we achieve better performance using substantially smaller model sizes. Third, we reduce both the model size and the amount of data required to outperform LLMs; our finetuned 770M T5 model outperforms the few-shot prompted 540B PaLM model using only 80% of available data on a benchmark, whereas standard finetuning the same T5 model struggles to match even by using 100% of the dataset. We release the code at: https://github.com/google-research/distilling-step-by-step .Comment: Accepted to Findings of ACL 202

Outcome and prognostic factors in critically ill patients with systemic lupus erythematosus: a retrospective study

INTRODUCTION: Systemic lupus erythematosus (SLE) is an archetypal autoimmune disease, involving multiple organ systems with varying course and prognosis. However, there is a paucity of clinical data regarding prognostic factors in SLE patients admitted to the intensive care unit (ICU). METHODS: From January 1992 to December 2000, all patients admitted to the ICU with a diagnosis of SLE were included. Patients were excluded if the diagnosis of SLE was established at or after ICU admission. A multivariate logistic regression model was applied using Acute Physiology and Chronic Health Evaluation II scores and variables that were at least moderately associated (P < 0.2) with survival in the univariate analysis. RESULTS: A total of 51 patients meeting the criteria were included. The mortality rate was 47%. The most common cause of admission was pneumonia with acute respiratory distress syndrome. Multivariate logistic regression analysis showed that intracranial haemorrhage occurring while the patient was in the ICU (relative risk = 18.68), complicating gastrointestinal bleeding (relative risk = 6.97) and concurrent septic shock (relative risk = 77.06) were associated with greater risk of dying, whereas causes of ICU admission and Acute Physiology and Chronic Health Evaluation II score were not significantly associated with death. CONCLUSION: The mortality rate in critically ill SLE patients was high. Gastrointestinal bleeding, intracranial haemorrhage and septic shock were significant prognostic factors in SLE patients admitted to the ICU

Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection

Multi-label image classification is a fundamental but challenging task towards general visual understanding. Existing methods found the region-level cues (e.g., features from RoIs) can facilitate multi-label classification. Nevertheless, such methods usually require laborious object-level annotations (i.e., object labels and bounding boxes) for effective learning of the object-level visual features. In this paper, we propose a novel and efficient deep framework to boost multi-label classification by distilling knowledge from weakly-supervised detection task without bounding box annotations. Specifically, given the image-level annotations, (1) we first develop a weakly-supervised detection (WSD) model, and then (2) construct an end-to-end multi-label image classification framework augmented by a knowledge distillation module that guides the classification model by the WSD model according to the class-level predictions for the whole image and the object-level visual features for object RoIs. The WSD model is the teacher model and the classification model is the student model. After this cross-task knowledge distillation, the performance of the classification model is significantly improved and the efficiency is maintained since the WSD model can be safely discarded in the test phase. Extensive experiments on two large-scale datasets (MS-COCO and NUS-WIDE) show that our framework achieves superior performances over the state-of-the-art methods on both performance and efficiency.Comment: accepted by ACM Multimedia 2018, 9 pages, 4 figures, 5 table

Induction of Apoptosis by Luteolin Involving Akt Inactivation in Human 786-O Renal Cell Carcinoma Cells

There is a growing interest in the health-promoting effects of natural substances obtained from plants. Although luteolin has been identified as a potential therapeutic and preventive agent for cancer because of its potent cancer cell-killing activity, the molecular mechanisms have not been well elucidated. This study provides evidence of an alternative target for luteolin and sheds light on the mechanism of its physiological benefits. Treatment of 786-O renal cell carcinoma (RCC) cells (as well as A498 and ACHN) with luteolin caused cell apoptosis and death. This cytotoxicity was caused by the downregulation of Akt and resultant upregulation of apoptosis signal-regulating kinase-1 (Ask1), p38, and c-Jun N-terminal kinase (JNK) activities, probably via protein phosphatase 2A (PP2A) activation. In addition to being a concurrent substrate of caspases and event of cell death, heat shock protein-90 (HSP90) cleavage might also play a role in driving further cellular alterations and cell death, at least in part, involving an Akt-related mechanism. Due to the high expression of HSP90 and Akt-related molecules in RCC and other cancer cells, our findings suggest that PP2A activation might work in concert with HSP90 cleavage to inactivate Akt and lead to a vicious caspase-dependent apoptotic cycle in luteolin-treated 786-O cells

N-Acetyl Glucosamine Obtained from Chitin by Chitin Degrading Factors in Chitinbacter tainanesis

A novel chitin-degrading aerobe, Chitinibacter tainanensis, was isolated from a soil sample from southern Taiwan, and was proved to produce N-acetyl glucosamine (NAG). Chitin degrading factors (CDFs) were proposed to be the critical factors to degrade chitin in this work. When C. tainanensis was incubated with chitin, CDFs were induced and chitin was converted to NAG. CDFs were found to be located on the surface of C. tainanensis. N-Acetylglucosaminidase (NAGase) and endochitinase activities were found in the debris, and the activity of NAGase was much higher than that of endochitinase. The optimum pH of the enzymatic activity was about 7.0, while that of NAG production by the debris was 5.3. These results suggested that some factors in the debris, in addition to NAGase and endochitinase, were crucial for chitin degradation

Detection of the inferred interaction network in hepatocellular carcinoma from EHCO (Encyclopedia of Hepatocellular Carcinoma genes Online)

BACKGROUND: The significant advances in microarray and proteomics analyses have resulted in an exponential increase in potential new targets and have promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level. RESULTS: Here, we build an integrative platform, the Encyclopedia of Hepatocellular Carcinoma genes Online, dubbed EHCO , to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs. CONCLUSION: This architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment

Transcriptomic analyses of regenerating adult feathers in chicken

Transcriptome Expression Data. Table of mapped reads to Galgal4 transcripts for all 15 data sets. FPKM (Fragments per kilobase of exon per million fragments mapped): normalized transcript abundance values for each gene in the indicated tissues. (CSV 1314 kb