Transcriptional networks of transient cell states during human prefrontal cortex development

The human brain is divided into various anatomical regions that control and coordinate unique functions. The prefrontal cortex (PFC) is a large brain region that comprises a range of neuronal and non-neuronal cell types, sharing extensive interconnections with subcortical areas, and plays a critical role in cognition and memory. A timely appearance of distinct cell types through embryonic development is crucial for an anatomically perfect and functional brain. Direct tracing of cell fate development in the human brain is not possible, but single-cell transcriptome sequencing (scRNA-seq) datasets provide the opportunity to dissect cellular heterogeneity and its molecular regulators. Here, using scRNA-seq data of human PFC from fetal stages, we elucidate distinct transient cell states during PFC development and their underlying gene regulatory circuitry. We further identified that distinct intermediate cell states consist of specific gene regulatory modules essential to reach terminal fate using discrete developmental paths. Moreover, using in silico gene knock-out and over-expression analysis, we validated crucial gene regulatory components during the lineage specification of oligodendrocyte progenitor cells. Our study illustrates unique intermediate states and specific gene interaction networks that warrant further investigation for their functional contribution to typical brain development and discusses how this knowledge can be harvested for therapeutic intervention in challenging neurodevelopmental disorders

DNA Computing and Implementations

DNA Computing aims to harness the molecules at the Nano level for computational purpose. DNA Computing features high data density and massive storage capability therefore, its approach can be used to solve various combinatorial Problems like solving Non Deterministic Problems (i.e. NP- Complete and NP-Hard). This Molecular Level Computational involve input and output both in the molecule form. Since DNA has already been explored as an exquisite material and is a fundamental block for manufacturing large scale Nano mechanical devices. DNA Computing is an approach towards the Biomolecular Computation where the aim is not only to process the information but also to transfer it to other molecular structures for utilization. DNA Computing is slower when an individual DNA Computes in compare to silica based chips. Its Efficiently and throughput increases as the number of DNA increase. DNA provides the possibility of massive parallelism. Starting with the Introduction about the DNA Structure, followed up by DNA Computers, this paper will discuss some recent advancements and challenges of DNA Computing. We will also discuss the possible future scope and implementation as well how the Artificial Intelligence approach can be used with DNA Based Computers to achieve a better and efficient Machine Learning

Postpartum spontaneous bladder rupture

Spontaneous bladder rupture after normal vaginal delivery is a rare complication. Patients may present with abdominal distention, fever, haematuria, oliguria and deranged KFT (kidney function test). We are reporting two cases of primigravida with postpartum bladder rupture, one case was diagnosed at laparotomy and the other preoperatively. A patient who presents with retention of urine, haematuria ascites and deranged KFT after uneventful normal vaginal delivery, spontaneous bladder rupture should be suspected. Early diagnosis and management can decrease the morbidity. 

Cpx-dependent expression of YqjA requires cations at elevated pH

© FEMS 2017. All rights reserved. Under alkaline pH conditions, Escherichia coli must maintain a stable cytoplasmic pH of about 7.6 that is acidic relative to the environment. Bacteria employ various mechanisms to survive alkaline pH; however, membrane cation/H+ antiporters play a primary role by facilitating inward transport of protons. Escherichia coli YqjA belongs to the DedA/Tvp38 membrane protein family and, along with its paralog YghB, is required for growth at 42°C, proper cell division and antibiotic resistance. YqjA is required for viability at alkaline pH, requiring cations sodium or potassium to support growth under these conditions, suggesting it may be a transporter. We measured yqjA expression at different pHs and cation concentrations using a yqjA promoter-lacZ fusion. We found that yqjA promoter activity was highest at alkaline pH. Increased activity of the yqjA promoter required both the transcriptional regulator CpxR, in agreement with previous results, and sodium or potassium salts at alkaline pH. Extracellular cations are also required for activity of cpxP and degP promoters at alkaline pH, suggesting this is a general property of the Cpx regulon. To our knowledge, this is the first demonstration of cation-dependent expression of Cpx-regulated genes at alkaline pH

Fuzzy Closure Spaces vs. Fuzzy Rough Sets

AbstractThis paper investigates the relationship among fuzzy rough sets, fuzzy closure spaces and fuzzy topology. It is shown that there exists a bijective correspondence between the set of all fuzzy reflexive approximation spaces and the set of all quasi-discrete fuzzy closure spaces satisfying a certain extra condition. Similar correspondence is also obtained between the set of all fuzzy tolerance approximation spaces and the set of all symmetric quasi-discrete fuzzy closure spaces satisfying a certain extra condition

Green Carbon Footprint for Model Inference Serving via Exploiting Mixed-Quality Models and GPU Partitioning

This paper presents a solution to the challenge of mitigating carbon emissions from large-scale high performance computing (HPC) systems and datacenters that host machine learning (ML) inference services. ML inference is critical to modern technology products, but it is also a significant contributor to datacenter compute cycles and carbon emissions. We introduce Clover, a carbon-friendly ML inference service runtime system that balances performance, accuracy, and carbon emissions through mixed-quality models and GPU resource partitioning. Our experimental results demonstrate that Clover is effective in substantially reducing carbon emissions while maintaining high accuracy and meeting service level agreement (SLA) targets. Therefore, it is a promising solution toward achieving carbon neutrality in HPC systems and datacenters