Hollowayjackson3169
4% test error under 600M FLOPs with 8 GPUs in 18 hours. As for semantic segmentation task, DSO-NAS also achieve competitive result compared with manually designed architectures on PASCAL VOC dataset.
Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a small cohort due to the data-variability and data-inefficiency issues. This work presents a deep transfer learning approach to overcome these issues and enable transferring knowledge from a large dataset to a small cohort for automatic sleep staging.
We start from a generic end-to-end deep learning framework for sequence-to-sequence sleep staging and derive two networks as the means for transfer learning. The networks are first trained in the source domain (i.e. the large database). The pretrained networks are then finetuned in the target domain (i.e. the small cohort) to complete knowledge transfer. We employ the Montreal Archive of Sleep Studies (MASS) database consisting of 200 subjects as the source domain and study deep transfer learning on three different target domains the Sleep Cassette subset and the Sleep Telemetry subset of the Sleep-EDF Expanded database, and the Surrey-cEEGrid database. The target domains are purposely adopted to cover different degrees of data mismatch to the source domains.
Our experimental results show significant performance improvement on automatic sleep staging on the target domains achieved with the proposed deep transfer learning approach.
These results suggest the efficacy of the proposed approach in addressing the above-mentioned data-variability and data-inefficiency issues.
As a consequence, it would enable one to improve the quality of automatic sleep staging models when the amount of data is relatively small.
As a consequence, it would enable one to improve the quality of automatic sleep staging models when the amount of data is relatively small.
A miniaturized accelerometer can be incorporated in temporary pacemaker leads which are routinely attached to the epicardium during cardiac surgery and provide continuous monitoring of cardiac motion during and following surgery. We tested if such a sensor could be used to assess volume status, which is essential in hemodynamically unstable patients.
An accelerometer was attached to the epicardium of 9 pigs and recordings performed during baseline, fluid loading, and phlebotomy in a closed chest condition. Alterations in left ventricular (LV) preload alter myocardial tension which affects the frequency of myocardial acceleration associated with the first heart sound ( f
). The accuracy of f
as an estimate of preload was evaluated using sonomicrometry measured end-diastolic volume ( EDV
). Standard clinical estimates of global end-diastolic volume using pulse index continuous cardiac output (PiCCO) measurements ( GEDV
) and pulmonary artery occlusion pressure (PAOP) were obtained for comparison. The diagnostic accuracy of identifying fluid responsiveness was analyzed for f
, stroke volume variation ( SVV
), pulse pressure variation ( PPV
), GEDV
, and PAOP.
Changes in f f
correlated well to changes in EDV
(r^2=0.81, 95%CI [0.68, 0.89]), as did GEDV
(r^2=0.59, 95%CI [0.36, 0.76]) and PAOP (r^2=0.36, 95%CI [0.01, 0.73]). The diagnostic accuracy [95%CI] in identifying fluid responsiveness was 0.79 [0.66, 0.94] for f
, 0.72 [0.57, 0.86] for [Formula see text], and 0.63 (0.44, 0.82) for PAOP.
An epicardially placed accelerometer can assess changes in preload in real-time.
This novel method can facilitate continuous monitoring of the volemic status in open-heart surgery patients and help guiding fluid resuscitation.
This novel method can facilitate continuous monitoring of the volemic status in open-heart surgery patients and help guiding fluid resuscitation.Efforts and focus regarding the COVID-19 pandemic have largely been centered around physical health. However, mental health is equally as critical-as such, psychological impacts can resonate adversely during and following the pandemic. click here examined a sample of 729 Bangladeshi people and aimed to assess the psychological impact of the pandemic. Through this replication analysis, the results supported the validation and reliability of the COVID-19 Worry Scale on a Bangladeshi population. The validation of another COVID-19 mental health measure can help determine who is mentally affected by the pandemic and the extent of COVID-19's psychological impact.Superresolution microscopy is becoming increasingly widespread in biological labs. While it holds enormous potential for biological discovery, it is a complex imaging technique that requires thorough optimization of various experimental parameters to yield data of the highest quality. Unfortunately, it remains challenging even for seasoned users to judge from the acquired images alone whether their superresolution microscopy pipeline is performing at its optimum, or if the image quality could be improved. Here, we describe how superresolution microscopists can objectively characterize their imaging pipeline using suitable reference standards, which are stereotypic so that the same structure can be imaged everywhere, every time, on every microscope. Quantitative analysis of reference standard images helps characterizing the performance of one's own microscopes over time, allows objective benchmarking of newly developed microscopy and labeling techniques, and finally increases comparability of superresolution microscopy data between labs.Crohn's disease (CD) is a debilitating gastrointestinal (GI) disorder that can impact the entirety of the GI tract. While substantial progress has been made in the medical management of CD, it remains incurable, frequently relapses, and is a significant financial and medical burden. #link# The pathophysiology of CD is not well understood, but it is thought to arise in genetically susceptible individuals upon an environmental insult. Further elucidation of the disease etiology promises to expose additional therapeutic avenues, with the hope of reducing the burden of CD. One approach to understanding disease pathophysiology is to identify clinically relevant molecular disease subsets by using transcriptomics. In this report, we use hierarchical clustering of the ileal transcriptomes of 34 patients and identify two CD subsets. Clinically, these clusters differed in the age of the patients at CD diagnosis, suggesting that age of onset affects disease pathophysiology. The clusters were segregated by three major gene ontology categories developmental processes, ion homeostasis, and the immune response. Of the genes constituting the immune system category, expression of extracellular matrix-associated genes, COL4A1, S100A9, ADAMTS2, SERPINE1, and FCN1, exhibits the strongest correlation with an individual's age at CD diagnosis. Together these findings demonstrate that transcriptional profiling is a powerful approach to subclassify CD patients.Little is known about gene regulation by fasting in human adipose tissue. Accordingly, the objective of this study was to investigate the effects of fasting on adipose tissue gene expression in humans. To that end, subcutaneous adipose tissue biopsies were collected from 11 volunteers 2 and 26 h after consumption of a standardized meal. For comparison, epididymal adipose tissue was collected from C57Bl/6J mice in the ab libitum-fed state and after a 16 h fast. The timing of sampling adipose tissue roughly corresponds with the near depletion of liver glycogen. Transcriptome analysis was carried out using Affymetrix microarrays. We found that, 1) fasting downregulated numerous metabolic pathways in human adipose tissue, including triglyceride and fatty acid synthesis, glycolysis and glycogen synthesis, TCA cycle, oxidative phosphorylation, mitochondrial translation, and insulin signaling; 2) fasting downregulated genes involved in proteasomal degradation in human adipose tissue; 3) fasting had much less pronounced effects on the adipose tissue transcriptome in humans than mice; 4) although major overlap in fasting-induced gene regulation was observed between human and mouse adipose tissue, many genes were differentially regulated in the two species, including genes involved in insulin signaling (PRKAG2, PFKFB3), PPAR signaling (PPARG, ACSL1, HMGCS2, SLC22A5, ACOT1), glycogen metabolism (PCK1, PYGB), and lipid droplets (PLIN1, PNPLA2, CIDEA, CIDEC). In conclusion, although numerous genes and pathways are regulated similarly by fasting in human and mouse adipose tissue, many genes show very distinct responses to fasting in humans and mice. Our data provide a useful resource to study adipose tissue function during fasting.Much of our understanding of the regulatory mechanisms governing the cell cycle in mammals has relied heavily on methods that measure the aggregate state of a population of cells. While instrumental in shaping our current understanding of cell proliferation, these approaches mask the genetic signatures of rare subpopulations such as quiescent (G0) and very slowly dividing (SD) cells. Results described in this study and those of others using single-cell analysis reveal that even in clonally derived immortalized cancer cells, ∼1-5% of cells can exhibit G0 and SD phenotypes. Therefore to enable the study of these rare cell phenotypes we established an integrated molecular, computational, and imaging approach to track, isolate, and genetically perturb single cells as they proliferate. A genetically encoded cell-cycle reporter (K67p-FUCCI) was used to track single cells as they traversed the cell cycle. A set of R-scripts were written to quantify K67p-FUCCI over time. To enable the further study G0 and SD phenotypes, we retrofitted a live cell imaging system with a micromanipulator to enable single-cell targeting for functional validation studies. Single-cell analysis revealed HT1080 and MCF7 cells had a doubling time of ∼24 and ∼48 h, respectively, with high duration variability in G1 and G2 phases. Direct single-cell microinjection of mRNA encoding (GFP) achieves detectable GFP fluorescence within ∼5 h in both cell types. link2 These findings coupled with the possibility of targeting several hundreds of single cells improves throughput and sensitivity over conventional methods to study rare cell subpopulations.Long noncoding RNAs (lncRNAs) are intracellular transcripts longer than 200 nucleotides and lack protein-coding information. link3 A subclass of lncRNA known as long intergenic noncoding RNAs (lincRNAs) are transcribed from genomic regions that share no overlap with annotated protein-coding genes. Increasing evidence has shown that some annotated lincRNA transcripts do in fact contain open reading frames (ORFs) encoding functional short peptides in the cell. Few robust methods for lincRNA-encoded peptide identification have been reported, and the tissue-specific expression of these peptides has been largely unexplored. Here we propose an integrative workflow for lincRNA-encoded peptide discovery and test it on the mouse kidney inner medulla (IM). In brief, low molecular weight protein fractions were enriched from homogenate of IMs and trypsinized into shorter peptides, which were sequenced by high resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS). To curate a hypothetical lincRNA-encoded peptide database for peptide-spectrum matching following LC-MS/MS, we performed RNA-Seq on IMs, computationally removed reads overlapping with annotated protein-coding genes, and remapped the remaining reads to a database of mouse noncoding transcripts to infer lincRNA expression.
