418 research outputs found
Identification of human blood messenger ribonucleic acids through non-polymerase chain reaction based multiplexing.
The goal of this research project was to develop a method for the identification of human blood that was simple and fast to use, yet sensitive and specific enough for forensic casework. Based on these criteria, the ideal assay would be based on messenger ribonucleic acid (mRNA) multiplexing specific for blood identification. According to the central dogma of molecular biology and gene expression, it can be theorized that the identity of human specific biological material can be obtained based only on the mRNA expressed by genes of a particular tissue. Once isolated, the tissue specific mRNA can be detected using the affinity of deoxyribonucleic acid (DNA) probes which contain the complementary sequence for annealing. The newly annealed double stranded nucleic acid will then serve as the target for detection via fluorescent molecular labeling, which will allow the human specific biological material targets to be detectable under ultraviolet light. Development of such a method would provide the field with a more rapid and accurate assay for analysis of forensic serology sample
On the existence and structures of almost axisymmetric solutions to 3-D Navier-Stokes equations
In this paper, we consider 3-D Navier-Stokes equations with almost
axisymmetric initial data, which means that by writing in the cylindrical coordinates, then
and are small in some sense (recall axisymmetric means these three
quantities vanish). Then with additional smallness assumption on ,
we prove the global existence of a unique strong solution , and this
solution keeps close to some axisymmetric vector field. We also establish some
refined estimates for the integral average in variable for .
Moreover, as and here depend on , it is
natural to expand them into Fourier series in variable. And we shall
consider one special form of , with some small parameter to
measure its swirl part and oscillating part. We study the asymptotic expansion
of the corresponding solution, and the influences between different profiles in
the asymptotic expansion. In particular, we give some special symmetric
structures that will persist for all time. These phenomena reflect some
features of the nonlinear terms in Navier-Stokes equations
Hypothalamic Vitamin D Improves Glucose Homeostasis and Reduces Weight
Despite clear associations between vitamin D deficiency and obesity and/or type 2 diabetes, a causal relationship is not established. Vitamin D receptors (VDRs) are found within multiple tissues, including the brain. Given the importance of the brain in controlling both glucose levels and body weight, we hypothesized that activation of central VDR links vitamin D to the regulation of glucose and energy homeostasis. Indeed, we found that small doses of active vitamin D, 1α,25-dihydroxyvitamin D3 (1,25D3) (calcitriol), into the third ventricle of the brain improved glucose tolerance and markedly increased hepatic insulin sensitivity, an effect that is dependent upon VDR within the paraventricular nucleus of the hypothalamus. In addition, chronic central administration of 1,25D3 dramatically decreased body weight by lowering food intake in obese rodents. Our data indicate that 1,25D3-mediated changes in food intake occur through action within the arcuate nucleus. We found that VDR colocalized with and activated key appetite-regulating neurons in the arcuate, namely proopiomelanocortin neurons. Together, these findings define a novel pathway for vitamin D regulation of metabolism with unique and divergent roles for central nervous system VDR signaling. Specifically, our data suggest that vitamin D regulates glucose homeostasis via the paraventricular nuclei and energy homeostasis via the arcuate nuclei
RefBERT: A Two-Stage Pre-trained Framework for Automatic Rename Refactoring
Refactoring is an indispensable practice of improving the quality and
maintainability of source code in software evolution. Rename refactoring is the
most frequently performed refactoring that suggests a new name for an
identifier to enhance readability when the identifier is poorly named. However,
most existing works only identify renaming activities between two versions of
source code, while few works express concern about how to suggest a new name.
In this paper, we study automatic rename refactoring on variable names, which
is considered more challenging than other rename refactoring activities. We
first point out the connections between rename refactoring and various
prevalent learning paradigms and the difference between rename refactoring and
general text generation in natural language processing. Based on our
observations, we propose RefBERT, a two-stage pre-trained framework for rename
refactoring on variable names. RefBERT first predicts the number of sub-tokens
in the new name and then generates sub-tokens accordingly. Several techniques,
including constrained masked language modeling, contrastive learning, and the
bag-of-tokens loss, are incorporated into RefBERT to tailor it for automatic
rename refactoring on variable names. Through extensive experiments on our
constructed refactoring datasets, we show that the generated variable names of
RefBERT are more accurate and meaningful than those produced by the existing
method
EALink: An Efficient and Accurate Pre-trained Framework for Issue-Commit Link Recovery
Issue-commit links, as a type of software traceability links, play a vital
role in various software development and maintenance tasks. However, they are
typically deficient, as developers often forget or fail to create tags when
making commits. Existing studies have deployed deep learning techniques,
including pretrained models, to improve automatic issue-commit link
recovery.Despite their promising performance, we argue that previous approaches
have four main problems, hindering them from recovering links in large software
projects. To overcome these problems, we propose an efficient and accurate
pre-trained framework called EALink for issue-commit link recovery. EALink
requires much fewer model parameters than existing pre-trained methods,
bringing efficient training and recovery. Moreover, we design various
techniques to improve the recovery accuracy of EALink. We construct a
large-scale dataset and conduct extensive experiments to demonstrate the power
of EALink. Results show that EALink outperforms the state-of-the-art methods by
a large margin (15.23%-408.65%) on various evaluation metrics. Meanwhile, its
training and inference overhead is orders of magnitude lower than existing
methods.Comment: 13 pages, 6 figures, published to AS
Activation of Serotonin 2C Receptors in Dopamine Neurons Inhibits Binge-like Eating in Mice
Acknowledgments and Disclosures This work was supported by the National Institutes of Health (Grant Nos. R01DK093587 and R01DK101379 [to YX], R01DK092605 to [QT], R01DK078056 [to MM]), the Klarman Family Foundation (to YX), the Naman Family Fund for Basic Research (to YX), Curtis Hankamer Basic Research Fund (to YX), American Diabetes Association (Grant Nos. 7-13-JF-61 [to QW] and 1-15-BS-184 [to QT]), American Heart Association postdoctoral fellowship (to PX), Wellcome Trust (Grant No. WT098012 [to LKH]), and Biotechnology and Biological Sciences Research Council (Grant No. BB/K001418/1 [to LKH]). The anxiety tests (e.g., open-field test, light-dark test, elevated plus maze test) were performed in the Mouse Neurobehavior Core, Baylor College of Medicine, which was supported by National Institutes of Health Grant No. P30HD024064. PX and YH were involved in experimental design and most of the procedures, data acquisition and analyses, and writing the manuscript. XC assisted in the electrophysiological recordings; LV-T assisted in the histology study; XY, KS, CW, YY, AH, LZ, and GS assisted in surgical procedures and production of study mice. MGM, QW, QT, and LKH were involved in study design and writing the manuscript. YX is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The authors report no biomedical financial interests or potential conflicts of interest.Peer reviewedPublisher PD
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