A Universal, Genomewide GuideFinder for CRISPR/Cas9 Targeting in Microbial Genomes.

The CRISPR/Cas system has significant potential to facilitate gene editing in a variety of bacterial species. CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) represent modifications of the CRISPR/Cas9 system utilizing a catalytically inactive Cas9 protein for transcription repression and activation, respectively. While CRISPRi and CRISPRa have tremendous potential to systematically investigate gene function in bacteria, few programs are specifically tailored to identify guides in draft bacterial genomes genomewide. Furthermore, few programs offer open-source code with flexible design parameters for bacterial targeting. To address these limitations, we created GuideFinder, a customizable, user-friendly program that can design guides for any annotated bacterial genome. GuideFinder designs guides from NGG protospacer-adjacent motif (PAM) sites for any number of genes by the use of an annotated genome and FASTA file input by the user. Guides are filtered according to user-defined design parameters and removed if they contain any off-target matches. Iteration with lowered parameter thresholds allows the program to design guides for genes that did not produce guides with the more stringent parameters, one of several features unique to GuideFinder. GuideFinder can also identify paired guides for targeting multiplicity, whose validity we tested experimentally. GuideFinder has been tested on a variety of diverse bacterial genomes, finding guides for 95% of genes on average. Moreover, guides designed by the program are functionally useful-focusing on CRISPRi as a potential application-as demonstrated by essential gene knockdown in two staphylococcal species. Through the large-scale generation of guides, this open-access software will improve accessibility to CRISPR/Cas studies of a variety of bacterial species

Cluster-based Classification of Diabetic Nephropathy among Type 2

[[abstract]]The prevalence of type 2 diabetes is increasing at an alarming rate. Various complications are associated with type 2 diabetes, with diabetic nephropathy being the leading cause of renal failure among diabetics. Often, when patients are diagnosed with diabetic nephropathy, their renal functions have already been significantly damaged, speeding up the progression towards end stage renal disease. Therefore, a risk prediction tool may be beneficial for the implementation of early treatment and prevention. In the present study, we propose to develop a prediction model integrating clustering and classification approaches for the identification of diabetic nephropathy among type 2 diabetes patients. Clinical and genotyping data are obtained from 345 type 2 diabetic patients(160 with non-diabetic nephropathy and 185 with diabetic nephropathy). The performance of using clinical features alone for cluster-based classification is compared with that of utilizing a combination of clinical and genetic attributes. We find that the inclusion of genetic features yield better prediction results. Further refinement of the proposed approach has the potential to facilitate the accurate identification of diabetic nephropathy and the development of better treatment in a clinical setting.[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20140507~2014009[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Kyoto, Japa

Discovering Galaxy Features via Dataset Distillation

In many applications, Neural Nets (NNs) have classification performance on par or even exceeding human capacity. Moreover, it is likely that NNs leverage underlying features that might differ from those humans perceive to classify. Can we "reverse-engineer" pertinent features to enhance our scientific understanding? Here, we apply this idea to the notoriously difficult task of galaxy classification: NNs have reached high performance for this task, but what does a neural net (NN) "see" when it classifies galaxies? Are there morphological features that the human eye might overlook that could help with the task and provide new insights? Can we visualize tracers of early evolution, or additionally incorporated spectral data? We present a novel way to summarize and visualize galaxy morphology through the lens of neural networks, leveraging Dataset Distillation, a recent deep-learning methodology with the primary objective to distill knowledge from a large dataset and condense it into a compact synthetic dataset, such that a model trained on this synthetic dataset achieves performance comparable to a model trained on the full dataset. We curate a class-balanced, medium-size high-confidence version of the Galaxy Zoo 2 dataset, and proceed with dataset distillation from our accurate NN-classifier to create synthesized prototypical images of galaxy morphological features, demonstrating its effectiveness. Of independent interest, we introduce a self-adaptive version of the state-of-the-art Matching Trajectory algorithm to automate the distillation process, and show enhanced performance on computer vision benchmarks.Comment: Accepted to NeurIPS Workshop on Machine Learning and the Physical Sciences, 202

Designing real-time, continuous emotion annotation techniques for 360° VR videos

With the increasing availability of head-mounted displays (HMDs) that show immersive 360° VR content, it is important to understand to what extent these immersive experiences can evoke emotions. Typically to collect emotion ground truth labels, users rate videos through post-experience self-reports that are discrete in nature. However, post-stimuli self-reports are temporally imprecise, especially after watching 360° videos. In this work, we design six continuous emotion annotation techniques for the Oculus Rift HMD aimed at minimizing workload and distraction. Based on a co-design session with six experts, we contribute HaloLight and DotSize, two continuous annotation methods deemed unobtrusive and easy to understand. We discuss the next challenges for evaluating the usability of these techniques, and reliability of continuous annotations

Large-Scale CRISPRi and Transcriptomics of Staphylococcus epidermidis Identify Genetic Factors Implicated in Lifestyle Versatility.

Staphylococcus epidermidis is a ubiquitous human commensal skin bacterium that is also one of the most prevalent nosocomial pathogens. The genetic factors underlying this remarkable lifestyle plasticity are incompletely understood, mainly due to the difficulties of genetic manipulation, precluding high-throughput functional profiling of this species. To probe the versatility of S. epidermidis to survive across a diversity of environmental conditions, we developed a large-scale CRISPR interference (CRISPRi) screen complemented by transcriptional profiling (RNA sequencing) across 24 diverse conditions and piloted a droplet-based CRISPRi approach to enhance throughput and sensitivity. We identified putative essential genes, importantly revealing amino acid metabolism as crucial to survival across diverse environments, and demonstrated the importance of trace metal uptake for survival under multiple stress conditions. We identified pathways significantly enriched and repressed across our range of stress and nutrient-limited conditions, demonstrating the considerable plasticity of S. epidermidis in responding to environmental stressors. Additionally, we postulate a mechanism by which nitrogen metabolism is linked to lifestyle versatility in response to hyperosmotic challenges, such as those encountered on human skin. Finally, we examined the survival of S. epidermidis under acid stress and hypothesize a role for cell wall modification as a vital component of the survival response under acidic conditions. Taken together, this study integrates large-scale CRISPRi and transcriptomics data across multiple environments to provide insights into a keystone member of the human skin microbiome. Our results additionally provide a valuable benchmarking analysis for CRISPRi screens and are a rich resource for other staphylococcal researchers

Engineering a detect and destroy skin probiotic to combat methicillin-resistant Staphylococcus aureus.

The prevalence and virulence of pathogens such as methicillin-resistant Staphylococcus (S.) aureus (MRSA), which can cause recurrent skin infections, are of significant clinical concern. Prolonged antibiotic exposure to treat or decolonize S. aureus contributes to development of antibiotic resistance, as well as depletion of the microbiome, and its numerous beneficial functions. We hypothesized an engineered skin probiotic with the ability to selectively deliver antimicrobials only in the presence of the target organism could provide local bioremediation of pathogen colonization. We constructed a biosensing S. epidermidis capable of detecting the presence of S. aureus quorum sensing autoinducer peptide and producing lysostaphin in response. Here, we demonstrate in vitro activity of this biosensor and present and discuss challenges to deployment of this and other engineered topical skin probiotics

Enabling Geographic Research Across Disciplines: Building an Institutional Infrastructure for Geographic Analysis at Harvard University

Founded in 1818, the Harvard Map Collection (HMC) is the oldest map collection in America, holding 400,000 maps, more than 6,000 atlases and thousands of reference books. HMC has a strong commitment to digital resources and manages the Harvard Geospatial Library, a foundation for geospatial data service at Harvard. The Center for Geographic Analysis at Harvard University (CGA) was founded in 2006, independent of the library system, to serve the entire university. This article presents the history, organizational structure, and operational model of CGA and HMC, reviews achievements, lessons learned, suggests future improvements, and reviews GIS-related medical research at Harvard.Other Research Uni

Epigenetic Changes in Individuals with Arsenicosis

Inorganic arsenic (iAs) is an environmental toxicant currently poisoning millions of people worldwide, and chronically exposed individuals are susceptible to arsenicosis or arsenic poisoning. Using a state-of-the-art technique to map the methylomes of our study subjects, we identified a large interactome of hypermethylated genes that are enriched for their involvement in arsenic-associated diseases, such as cancer, heart disease, and diabetes. Notably, we have uncovered an arsenic-induced tumor suppressorome, a complex of 17 tumor suppressors known to be silenced in human cancers. This finding represents a pivotal clue in unraveling a possible epigenetic mode of arsenic-induced disease

Near-Earth plasma sheet boundary dynamics during substorm dipolarization.

We report on the large-scale evolution of dipolarization in the near-Earth plasma sheet during an intense (AL ~ -1000 nT) substorm on August 10, 2016, when multiple spacecraft at radial distances between 4 and 15 R E were present in the night-side magnetosphere. This global dipolarization consisted of multiple short-timescale (a couple of minutes) B z disturbances detected by spacecraft distributed over 9 MLT, consistent with the large-scale substorm current wedge observed by ground-based magnetometers. The four spacecraft of the Magnetospheric Multiscale were located in the southern hemisphere plasma sheet and observed fast flow disturbances associated with this dipolarization. The high-time-resolution measurements from MMS enable us to detect the rapid motion of the field structures and flow disturbances separately. A distinct pattern of the flow and field disturbance near the plasma boundaries was found. We suggest that a vortex motion created around the localized flows resulted in another field-aligned current system at the off-equatorial side of the BBF-associated R1/R2 systems, as was predicted by the MHD simulation of a localized reconnection jet. The observations by GOES and Geotail, which were located in the opposite hemisphere and local time, support this view. We demonstrate that the processes of both Earthward flow braking and of accumulated magnetic flux evolving tailward also control the dynamics in the boundary region of the near-Earth plasma sheet.Graphical AbstractMultispacecraft observations of dipolarization (left panel). Magnetic field component normal to the current sheet (BZ) observed in the night side magnetosphere are plotted from post-midnight to premidnight region: a GOES 13, b Van Allen Probe-A, c GOES 14, d GOES 15, e MMS3, g Geotail, h Cluster 1, together with f a combined product of energy spectra of electrons from MMS1 and MMS3 and i auroral electrojet indices. Spacecraft location in the GSM X-Y plane (upper right panel). Colorcoded By disturbances around the reconnection jets from the MHD simulation of the reconnection by Birn and Hesse (1996) (lower right panel). MMS and GOES 14-15 observed disturbances similar to those at the location indicated by arrows