云南抚仙湖沉水植物分布及群落结构特征

2005年6～7月对抚仙湖沉水植物进行了调查。共采集到沉水植物12种。沉水植物在抚仙湖沿岸浅水区均有不同程度的分布,主要分布在北岸、南岸、河口以及湖湾。分布区内平均水深4．27m,平均透明度2．96m。优势种为黑藻、穗状狐尾藻、篦齿眼子菜、金鱼藻和苦草,其优势度分别为59．08％、54．47％、54．26％、48．71％和48．30％,占群落总优势度的65．19％。根据优势种及组成特征,可将沉水植物群落分为11个类型。全湖沉水植物分布区面积318．8hm^2,资源量19502．79t,平均生物量6118g/m^2。2005年与1980年前后和2003年的调查结果相比,抚仙湖沉水植物、生物量和分布范围显著增加。     

Current applications of deep-learning in neuro-oncological MRI

PurposeMagnetic Resonance Imaging (MRI) provides an essential contribution in the screening, detection, diagnosis, staging, treatment and follow-up in patients with a neurological neoplasm. Deep learning (DL), a subdomain of artificial intelligence has the potential to enhance the characterization, processing and interpretation of MRI images. The aim of this review paper is to give an overview of the current state-of-art usage of DL in MRI for neuro-oncology.MethodsWe reviewed the Pubmed database by applying a specific search strategy including the combination of MRI, DL, neuro-oncology and its corresponding search terminologies, by focussing on Medical Subject Headings (Mesh) or title/abstract appearance. The original research papers were classified based on its application, into three categories: technological innovation, diagnosis and follow-up.ResultsForty-one publications were eligible for review, all were published after the year 2016. The majority (N = 22) was assigned to technological innovation, twelve had a focus on diagnosis and seven were related to patient follow-up. Applications ranged from improving the acquisition, synthetic CT generation, auto-segmentation, tumor classification, outcome prediction and response assessment. The majority of publications made use of standard (T1w, cT1w, T2w and FLAIR imaging), with only a few exceptions using more advanced MRI technologies. The majority of studies used a variation on convolution neural network (CNN) architectures.ConclusionDeep learning in MRI for neuro-oncology is a novel field of research; it has potential in a broad range of applications. Remaining challenges include the accessibility of large imaging datasets, the applicability across institutes/vendors and the validation and implementation of these technologies in clinical practise.     

Homeoviscous theory under pressure: II. The molecular order of membranes from deep-sea fish

The molecular order of brain and liver membranes isolated from deep sea and continental shelf fish species have been estimated and compared using the fluorescence polarization technique in order to determine whether life in a high pressure habitat is associated with an adjustment of membrane order. Fish were trawled at depths between 200 m and 4000 m, liver and brain membranes were fractionated, and fluorescence polarization was measured at 4°C and ambient pressure. Polarization of the brain myelin fraction provided a statistically significant regression with depth of capture ($\text{P<0.001}$) with a slope of ?0.004 km^?1. This change in polarization with depth was sufficient to offset approximately half of the pressure-induced increase in polarization and thus represents the first structural evidence of homeoviscous adaptation to pressure. Polarization of the brain synaptic and liver mitochondrial fraction was not significantly related to depth. This may be due, at least in part, to a high individual variability of polarization compared to laboratory-acclimated freshwater fish.     

Large-scale pharmacogenomic studies and drug response prediction for personalized cancer medicine

The response rate of most anti-cancer drugs is limited because of the high heterogeneity of cancer and the complex mechanism of drug action. Personalized treatment that stratifies patients into subgroups using molecular biomarkers is promising to improve clinical benefit. With the accumulation of preclinical models and advances in computational approaches of drug response prediction, pharmacogenomics has made great success over the last 20 years and is increasingly used in the clinical practice of personalized cancer medicine. In this article, we first summarize FDA-approved pharmacogenomic biomarkers and large-scale pharmacogenomic studies of preclinical cancer models such as patient-derived cell lines, organoids, and xenografts. Furthermore, we comprehensively review the recent developments of computational methods in drug response prediction, covering network, machine learning, and deep learning technologies and strategies to evaluate immunotherapy response. In the end, we discuss challenges and propose possible solutions for further improvement.     

A Hybrid Deep Learning Model for Predicting Molecular Subtypes of Human Breast Cancer Using Multimodal Data

BackgroundThe prediction of breast cancer subtypes plays a key role in the diagnosis and prognosis of breast cancer. In recent years, deep learning (DL) has shown good performance in the intelligent prediction of breast cancer subtypes. However, most of the traditional DL models use single modality data, which can just extract a few features, so it cannot establish a stable relationship between patient characteristics and breast cancer subtypes.DatasetWe used the TCGA-BRCA dataset as a sample set for molecular subtype prediction of breast cancer. It is a public dataset that can be obtained through the following link: https://portal.gdc.cancer.gov/projects/TCGA-BRCAMethodsIn this paper, a Hybrid DL model based on the multimodal data is proposed. We combine the patient's gene modality data with image modality data to construct a multimodal fusion framework. According to the different forms and states, we set up feature extraction networks respectively, and then we fuse the output of the two feature networks based on the idea of weighted linear aggregation. Finally, the fused features are used to predict breast cancer subtypes. In particular, we use the principal component analysis to reduce the dimensionality of high-dimensional data of gene modality and filter the data of image modality. Besides, we also improve the traditional feature extraction network to make it show better performance.ResultsThe results show that compared with the traditional DL model, the Hybrid DL model proposed in this paper is more accurate and efficient in predicting breast cancer subtypes. Our model achieved a prediction accuracy of 88.07% in 10 times of 10-fold cross-validation. We did a separate AUC test for each subtype, and the average AUC value obtained was 0.9427. In terms of subtype prediction accuracy, our model is about 7.45% higher than the previous average.     

Phylogeny and physiology of candidate phylum BRC1 inferred from the first complete metagenome-assembled genome obtained from deep subsurface aquifer

Candidate bacterial phylum BRC1 has been identified in a broad range of mostly organic-rich oxic and anoxic environments through molecular analysis of microbial communities. None of the members of BRC1 have been cultivated and only a few draft genome sequences have been obtained from metagenomes or as a result of single-cell sequencing. We have reconstructed complete genome of BRC1 bacterium, BY40, from metagenome of the microbial community of a deep subsurface thermal aquifer in the Tomsk Region of the Western Siberia, Russia, and used it for metabolic reconstruction and comparison with existing genomic data. Analysis of 3.3 Mb genome of BY40 bacterium revealed numerous glycoside hydrolases that could enable utilization of carbohydrates, including enzymes of chitin-degradation pathway. The bacterium lacks flagellar machinery but the twitching motility is encoded. The reconstructed central metabolism revealed pathways enabling the fermentation of organic substrates, as well as their complete oxidation through aerobic and anaerobic respiration. Phylogenetic analysis using BY40 genome supported the phylum level classification of BRC1 lineage. Based on phylogenetic and genomic analyses, the novel bacterium is proposed to be classified as Candidatus Sumerlaea chitinivorans, within a candidate phylum Sumerlaeota.     

Influence of segmental and extra-segmental conditioning stimuli on cortical potentials evoked by painful electrical stimulation

This study evaluated the effects of two different types of segmental/extra-segmental conditioning stimuli (tonic muscle pain and non-painful vibration) on the subjective experience (perceived pain intensity) and on the cortical evoked potentials to standardized test stimuli (cutaneous electrical stimuli). Twelve subjects participated in two separate sessions to investigate the effects of tonic muscle pain or cutaneous vibration on experimental test stimuli. The experimental protocol contained a baseline registration (test stimuli only), a registration with the test stimuli in combination with the conditioning stimuli, followed by a registration with the test stimuli only. In addition, the effects of the conditioning stimuli were examined at two anatomically separated locations (segmental and extra-segmental). Compared with the test stimulus alone, the perceived pain intensity and peak-to-peak amplitudes of the evoked potentials were unchanged in the presence of non-painful conditioning stimuli at either location. In contrast, a significant decrease of the perceived pain intensity and peak-to-peak amplitudes was found in the presence of painful conditioning stimuli at the extra-segmental sites. Moreover, the topographic maps of the 32-channel recordings suggested that the distribution of the scalp evoked potentials was almost symmetrical around the vertex Cz in the baseline registration. The evoked potentials were generally decreased during hypertonic saline infusion at the extra-segmental sites, but the distribution of the topographic maps did not appear to change. Vibration has previously been shown to inhibit pain, but in the present study the perceived intensity of phasic painful electrical stimuli was unchanged. The reduced perceived pain intensity and the smaller peak-to-peak amplitude of the evoked potential in the presence of extra-segmental conditioning pain are in accordance with the concept of diffuse noxious inhibitory control.     

外科深部真菌感染的病原学分析

目的分析外科住院患者合并真菌感染状况。方法分离的真菌用API 20C AUX真菌鉴定条,用ATB FUNGUS-2进行药敏试验。结果真菌的种类分布中第1位是白色念珠菌占53%,第2位是热带念珠菌占20%,第3位是光滑念珠菌占14%,第4位是近平滑念珠菌占4%,第5位是其余念珠菌占2%;真菌在外科各区分布中,外科ICU占42%,移植外科占25%;真菌在各类标本分布中,痰占43%,尿液占13%,粪便占10%,引流液占9%,血液占7%;真菌对药物5-氟胞嘧啶、两性霉素B、氟康唑、伊曲康唑的药物敏感性分别是93%、98.9%、90.8%、59.8%。结论外科真菌感染集中在重症病区(ICU),分离的致病真菌主要是白色念珠菌、热带念珠菌、光滑念珠菌,真菌对两性霉素B敏感性最高。     

基于深度学习与多层次信息融合的药物靶标亲和力预测*

药物研发是非常重要但也十分耗费人力物力的过程。利用计算机辅助预测药物与蛋白质亲和力的方法可以极大地加快药物研发过程。药物靶标亲和力预测的关键在于对药物和蛋白质进行准确详细地信息表征。提出一种基于深度学习与多层次信息融合的药物靶标亲和力的预测模型,试图通过综合药物与蛋白质的多层次信息,来获得更好的预测表现。首先将药物表述成分子图和扩展连接指纹两种形式,分别利用图卷积神经网络模块和全连接层进行学习;其次将蛋白质序列和蛋白质K-mer特征分别输入卷积神经网络模块和全连接层来学习蛋白质潜在特征;随后将4个通道学习到的特征进行融合,再利用全连接层进行预测。在两个基准药物靶标亲和力数据集上验证了所提方法的有效性,并与其他已有模型作对比研究。结果说明提出的模型相比基准模型能得到更好的预测性能,表明提出的综合药物与蛋白质多层次信息的药物靶标亲和力预测策略是有效的。