Mapping the temporal and spatial dynamics of the human endometrium in vivo and in vitro

The endometrium, the mucosal lining of the uterus, undergoes dynamic changes throughout the menstrual cycle in response to ovarian hormones. We have generated dense single-cell and spatial reference maps of the human uterus and three-dimensional endometrial organoid cultures. We dissect the signaling pathways that determine cell fate of the epithelial lineages in the lumenal and glandular microenvironments. Our benchmark of the endometrial organoids reveals the pathways and cell states regulating differentiation of the secretory and ciliated lineages both in vivo and in vitro. In vitro downregulation of WNT or NOTCH pathways increases the differentiation efficiency along the secretory and ciliated lineages, respectively. We utilize our cellular maps to deconvolute bulk data from endometrial cancers and endometriotic lesions, illuminating the cell types dominating in each of these disorders. These mechanistic insights provide a platform for future development of treatments for common conditions including endometriosis and endometrial carcinoma.

Quantum Proof Encryption Technology

Nowadays we use text passwords to encrypt a file. This research paper proposes to use multimedia files like images videos, audio files and even applications as the password key to encrypt sensitive information. This algorithm can encrypt bulk data as well as single data sets. Keywords: steganography, multimedia file as key, Quantum computer, cryptography, Quantum computer proof encryption.

DTN Trustworthiness for Permafrost Telemetry IoT Network

The SHETLAND-NET research project aims to build an Internet of Things (IoT) telemetry service in Antarctica to automatize the data collection of permafrost research studies on interconnecting remote wireless sensor networks (WSNs) through near vertical incidence skywave (NVIS) long fat networks (LFN). The proposed architecture presents some properties from challenging networks that require the use of delay tolerant networking (DTN) opportunistic techniques that send the collected data during the night as a bulk data transfer whenever a link comes available. This process might result in network congestion and packet loss. This is a complex architecture that demands a thorough assessment of the solution’s viability and an analysis of the transport protocols in order to find the option which best suits the use case to achieve superior trustworthiness in network congestion situations. A heterogeneous layer-based model is used to measure and improve the trustworthiness of the service. The scenario and different transport protocols are modeled to be compared, and the system’s trustworthiness is assessed through simulations.

Airpart: Interpretable statistical models for analyzing allelic imbalance in single-cell datasets

Motivation: Allelic expression analysis aids in detection of cis-regulatory mechanisms of genetic variation which produce allelic imbalance (AI) in heterozygotes. Measuring AI in bulk data lacking time or spatial resolution has the limitation that cell-type-specific (CTS), spatial-, or time-dependent AI signals may be dampened or not detected. Results: We introduce a statistical method airpart for identifying differential CTS AI from single-cell RNA-sequencing (scRNA-seq) data, or other spatially- or time-resolved datasets. airpart outputs discrete partitions of data, pointing to groups of genes and cells under common mechanisms of cis-genetic regulation. In order to account for low counts in single-cell data, our method uses a Generalized Fused Lasso with Binomial likelihood for partitioning groups of cells by AI signal, and a hierarchical Bayesian model for AI statistical inference. In simulation, airpart accurately detected partitions of cell types by their AI and had lower RMSE of allelic ratio estimates than existing methods. In real data, airpart identified differential AI patterns across cell states and could be used to define trends of AI signal over spatial or time axes. Availability: The airpart package is available as a R/Bioconductor package at https://bioconductor.org/packages/airpart.

A NAVEGAÇÃO DE CABOTAGEM NO BRASIL: SUA HISTÓRIA, PERSPECTIVAS ATUAIS E FUTURAS.

This study makes a historical retrospective on the Brazilian Coastal navigation pointing all relevant moments, good or not. This text will also cover the main improvements which must be done to achieve a solid coastal navigation. A visible grown in activity brings the role of coastal navigation. Every year it becomes more important because coastal navigation is responsible for the flow of significant products like liquid and gaseous bulk. Data are brought in order to elucidate the evolution of the activity and the future perspectives of the sector. Finally, some projection are made, result of research, in order to clarify the trend of Brazilian Coastal navigation

Automation of Large Parametric Flow Assurance Analyses in the Cloud

The objective of this study was to determine the highest flowrate through a client's existing flowline without top-of-line condensation rates exceeding a critical value of 0.25 g/m2.s. Automation of the workflow allowed a large combination of operating conditions to be analysed within a shorter timeframe than a traditional flow assurance analysis process. A multiparameter case matrix was developed to analyse the full range of process and environmental variables. A proprietary multiphase flow assurance software in the cloud was used to develop a reference case model. Then a software script was developed to read in the reference case model's code and produce input files for 1,080 cases. All cases were run within 30 minutes in the cloud. Another software script then extracted key data from the 1,080 output files into a single Excel spreadsheet to enable data visualisation and identification of a simple and effective flow rate criterion to limit condensation rates. Automation of the workflow allowed all combinations of variables to be analysed within a shorter timeframe compared to the traditional flow assurance analysis process, which usually analyses a somewhat limited number of suspected worst-case scenarios selected based on engineering judgement. The bulk data resulting from the automated workflow enabled a single integrity limit criterion to be applied with a high level of confidence, namely the fluid temperature measured at a subsea corrosion probe. This simplified integrity limit allows the operators to easily maximise production for any combination of process and environmental conditions, whilst maintaining confidence that they are not exceeding the critical condensation rate.

Machine-Learned Recognition of Network Traffic for Optimization through Protocol Selection

We introduce optimization through protocol selection (OPS) as a technique to improve bulk-data transfer on shared wide-area networks (WANs). Instead of just fine-tuning the parameters of a network protocol, our empirical results show that the selection of the protocol itself can result in up to four times higher throughput in some key cases. However, OPS for the foreground traffic (e.g., TCP CUBIC, TCP BBR, UDT) depends on knowledge about the network protocols used by the background traffic (i.e., other users). Therefore, we build and empirically evaluate several machine-learned (ML) classifiers, trained on local round-trip time (RTT) time-series data gathered using active probing, to recognize the mix of network protocols in the background with an accuracy of up to 0.96.

Transcriptional landscape of human microglia reveals robust gene expression signatures that implicates age, sex and APOE-related immunometabolic pathway perturbations

Microglia have fundamental roles in health and disease, however effects of age, sex and genetic factors on human microglia have not been fully explored. We applied bulk and single cell approaches to comprehensively characterize human microglia transcriptomes and their associations with age, sex and APOE. We identified a novel microglial signature, characterized its expression in bulk data from 1,306 brain samples across 6 regions and in single cell microglia transcriptome. We discovered microglial co-expression network modules associated with age, sex and APOE-ε4 that are enriched for lipid and carbohydrate metabolism genes. Integrated analyses of modules with single cell transcriptomes revealed significant overlap between age-associated module genes and both pro-inflammatory and disease-associated microglial clusters. These modules and clusters harbor known neurodegenerative disease genes including APOE, PLCG2 and BIN1. These data represent a well-characterized human microglial transcriptome resource; and highlight age, sex and APOE-related microglial immunometabolism perturbations with potential relevance in neurodegeneration.