Enumerasi Total Populasi Mikroba Tanah Gambut Di Teluk Meranti Kabupaten Riau

Teluk Meranti is one of the peatland area in Riau province. Most of these lands have beenchanged into palm oil plantation, timber plantation, agricultural area and settlement. Theaim of this research was to analyze the impact of land use changes on soil physical-chemical characteristics and microbial cell number. Soil samples were taken from eightdifferent locations, namely primary forest as control, secondary forest, rubber plantation(15 monthsyears old), rubber forest (40-60 years old), palm oil plantation (7-8 years old),acacia plantation (2-3 years old), corn field, and cassava field. Microbial cell number wasdetermined by spread plate method, employing appropriate media for the growth ofbacteria, fungi and actinomycetes. The results showed that the soil humidity, soiltemperature, percentage of soil dry weight, water content, soil bulk density and pH rangedfrom 29,63-55,88%, 27-31,5 o C, 14,9-35,5%, 64,9-85,1%, 0,16-0,39 g/cm 3 and 3,63-4,00,respectively. The copiotrophic bacterial cell number ranged from 0,6x10 5 -1,8x10 5 CFU/gsoil where the highest population was at the palm oil plantation,whereas the oligotrophicbacterial cell number ranged from 0,5x10 5 -1,4x10 5 CFU/g soil where the highest populationwas at the palm oil plantation. The population of fungi ranged from 0,4x10 5 -1,0x10 5 CFU/gsoil where the highest population was at the corn field. The population of actinomycetesranged from 0,4x10 5 -10,7x10 5 CFU/g soil where the highest population was at the palm oilplantation. Land use changes caused microbial cell number increased. The results indicatedthat land use changes influenced the microbial cell numbers

Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification

Dynamic functional connectivity (dFC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) help us understand fundamental dynamic characteristics of human brains, thereby providing an efficient solution for automated identification of brain diseases, such as Alzheimer's disease (AD) and its prodromal stage. Existing studies have applied deep learning methods to dFC network analysis and achieved good performance compared with traditional machine learning methods. However, they seldom take advantage of sequential information conveyed in dFC networks that could be informative to improve the diagnosis performance. In this paper, we propose a convolutional recurrent neural network (CRNN) for automated brain disease classification with rs-fMRI data. Specifically, we first construct dFC networks from rs-fMRI data using a sliding window strategy. Then, we employ three convolutional layers and long short-term memory (LSTM) layer to extract high-level features of dFC networks and also preserve the sequential information of extracted features, followed by three fully connected layers for brain disease classification. Experimental results on 174 subjects with 563 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of our proposed method in binary and multi-category classification tasks

METTL14-mediated Epitranscriptome Modification of Mn1 Mrna Promote Tumorigenicity and All-trans-retinoic Acid Resistance in Osteosarcoma

BACKGROUND: Osteosarcoma (OS) is the most common primary malignant bone tumor in adolescents. The molecular mechanism behind OS progression and metastasis remains poorly understood, which limits the effectiveness of current therapies. RNA N METHODS: Liquid chromatography-tandem mass spectrometry (LC-MS/MS), dot blotting, and colorimetric ELISA were used to detect m FINDINGS: We observed the abundance of m INTERPRETATION: Our study revealed that METTL14 contributes to OS progression and ATRA resistance as an m FUNDING: This work was supported by the National Natural Science Foundation of China (Grants 81972510 and 81772864)

بررسی حیطه‌های موجود در فرم‌های ارزشیابی از دیدگاه دانشجویان در دانشگاه علوم پزشکی زنجان در سال تحصیلی 86- 87

زمینه و هدف: ارزشیابی استادان متداول‌ترین روش جهت سنجش کیفیت آموزش می‌باشد. دانشجویان بیش از دست‌اندرکاران در جریان روند آموزش قرار‌دارند بنابراین با نظرخواهی از آنان دیدگاه کاملی برای مسئولین در مورد نقاط قوت و ضعف استادان به‌دست می‌آید. هدف از این پژوهش بررسی حیطه‌های موجود در فرم‌های ارزشیابی از دیدگاه دانشجویان در دانشکده‌های پزشکی، پیراپزشکی و پرستاری و مامایی می‌باشد. روش بررسی: این تحقیق به صورت توصیفی انجام گرفت. 1683 برگ ارزشیابی دانشجویان از استادان هیأت علمی (73 نفر) مربوط به دانشکده‌های پزشکی، پیراپزشکی و پرستاری- مامایی بررسی شد. پرسش‌نامه‌ی دانشجویان پزشکی حاوی 15 سؤوال و دانشجویان پیراپزشکی و پرستاری مامایی 21 سؤوال بود که بر اساس مقیاس لیکرات از حیطه‌های مختلف مقرراتی، علمی و آموزشی، نظارتی و نگرشی تشکیل شده بود. نمرات سؤوالات از نمره‌ی 100 محاسبه شد، نمرات بالاتر بیانگر عملکرد مطلوب‌تراستادان می‌باشد. تجزیه و تحلیل داده‌ها به‌صورت آمار توصیفی با نرم‌افزار SPSS انجام شد. یافته‌ها: نتایج نشان داد مقایسه در سطوح کلی بین دانشکده‌ها، دانشکده‌ی پیراپزشکی با میانگین کل و انحراف معیار 61/3 ±50/85 نسبت به سایر دانشکده‌ها برتری دارد. دانشکده‌ی پیراپزشکی در حیطه‌ی مقرراتی با میانگین و انحراف معیار 89/3±01/91، دانشکده‌ی پزشکی در حیطه‌ی نگرشی با میانگین و انحراف معیار 45/5±48/90 و دانشکده‌ی پرستاری مامایی در حیطه‌ی مقرراتی با میانگین و انحراف معیار 25/4±34/88 بیشترین امتیاز را داشتند. نتیجه‌نهایی نشان می‌دهد، حیطه‌ی علمی و آموزشی نسبت به سایر حیطه‌ها در سطح پایین‌تر می‌باشد. نتایج حیطه‌ها (علمی و آموزشی، نظارتی و نگرشی) بین دانشکده‌ها معنی‌دار می‌باشد (0001/0=P). نتیجه‌گیری: به نظر می‌رسد با برنامه‌ریزی جهت برگزاری کارگاه‌های آموزشی، روش تدریس و تحقیق جهت ارتقای آموزش استادان، اعطا‌ی فرصت مطالعاتی و تشویق انجام کارهای تحقیقاتی و پژوهشی گام مؤثری جهت ارتقای سطح علمی و بالاخره عملکرد بالای استادان خواهد بود

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent. Keywords: Protein function prediction, Disease gene prioritizationpublishedVersio

An Expanded Evaluation of Protein Function Prediction Methods Shows an Improvement In Accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent