Concern localization using information retrieval: An empirical study on Linux kernel

10.1109/WCRE.2011.72Proceedings - Working Conference on Reverse Engineering, WCRE92-9

Transplantation of a Peripheral Nerve with Neural Stem Cells Plus Lithium Chloride Injection Promote the Recovery of Rat Spinal Cord Injury

Transplantation of neural stem cells (NSCs) holds great potential for the treatment of spinal cord injury (SCI). However, transplanted NSCs poorly survive in the SCI environment. We injected NSCs into tibial nerve and transplanted tibial nerve into a hemisected spinal cord and investigated the effects of lithium chloride (LiCl) on the survival of spinal neurons, axonal regeneration, and functional recovery. Our results show that most of the transplanted NSCs expressed glial fibrillary acidic protein, while there was no obvious expression of nestin, neuronal nuclei, or acetyltransferase found in NSCs. LiCl treatment produced less macrosialin (ED1) expression and axonal degeneration in tibial nerve after NSC injection. Our results also show that a regimen of LiCl treatment promoted NSC differentiation into NF200-positive neurons with neurite extension into the host spinal cord. The combination of tibial nerve transplantation with NSCs and LiCl injection resulted in more host motoneurons surviving in the spinal cord, more regenerated axons in tibial nerve, less glial scar area, and decreased ED1 expression. We conclude that lithium may have therapeutic potential in cell replacement strategies for central nervous system injury due to its ability to promote survival and neuronal generation of grafted NSCs and reduced host immune reaction

Plasma microRNAs as potential biomarkers for non-small-cell lung cancer

Non-small-cell lung cancer (NSCLC) is the leading cause of cancer-related death. Developing minimally invasive techniques that can diagnose NSCLC, particularly at an early stage, may improve its outcome. Using microarray platforms, we previously identified 12 microRNAs (miRNAs) the aberrant expressions of which in primary lung tumors are associated with early-stage NSCLC. Here, we extend our previous research by investigating whether the miRNAs could be used as potential plasma biomarkers for NSCLC. We initially validated expressions of the miRNAs in paired lung tumor tissues and plasma specimens from 28 stage I NSCLC patients by real-time quantitative reverse transcription PCR, and then evaluated diagnostic value of the plasma miRNAs in a cohort of 58 NSCLC patients and 29 healthy individuals. The altered miRNA expressions were reproducibly confirmed in the tumor tissues. The miRNAs were stably present and reliably measurable in plasma. Of the 12 miRNAs, five displayed significant concordance of the expression levels in plasma and the corresponding tumor tissues (all r>0.850, all P<0.05). A logistic regression model with the best prediction was defined on the basis of the four genes (miRNA-21, -126, -210, and 486-5p), yielding 86.22% sensitivity and 96.55% specificity in distinguishing NSCLC patients from the healthy controls. Furthermore, the panel of miRNAs produced 73.33% sensitivity and 96.55% specificity in identifying stage I NSCLC patients. In addition, the genes have higher sensitivity (91.67%) in diagnosis of lung adenocarcinomas compared with squamous cell carcinomas (82.35%) (P<0.05). Altered expressions of the miRNAs in plasma would provide potential blood-based biomarkers for NSCLC

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Sterigmatocystin-induced DNA damage triggers G2 arrest via an ATM/p53-related pathway in human gastric epithelium GES-1 cells in vitro.

Sterigmatocystin (ST), which is commonly detected in food and feed commodities, is a mutagenic and carcinogenic mycotoxin that has been recognized as a possible human carcinogen. Our previous study showed that ST can induce G2 phase arrest in GES-1 cells in vitro and that the MAPK and PI3K signaling pathways are involved in the ST-induced G2 arrest. It is now widely accepted that DNA damage plays a critical role in the regulation of cell cycle arrest and apoptosis. In response to DNA damage, a complex signaling network is activated in eukaryotic cells to trigger cell cycle arrest and facilitate DNA repair. To further explore the molecular mechanism through which ST induces G2 arrest, the current study was designed to precisely dissect the role of DNA damage and the DNA damage sensor ataxia telangiectasia-mutated (ATM)/p53-dependent pathway in the ST-induced G2 arrest in GES-1 cells. Using the comet assay, we determined that ST induces DNA damage, as evidenced by the formation of DNA comet tails, in GES-1 cells. We also found that ST induces the activation of ATM and its downstream molecules, Chk2 and p53, in GES-1 cells. The ATM pharmacological inhibitor caffeine was found to effectively inhibit the activation of the ATM-dependent pathways and to rescue the ST-induced G2 arrest in GES-1 cells, which indicating its ATM-dependent characteristic. Moreover, the silencing of the p53 expression with siRNA effectively attenuated the ST-induced G2 arrest in GES-1 cells. We also found that ST induces apoptosis in GES-1 cells. Thus, our results show that the ST-induced DNA damage activates the ATM/53-dependent signaling pathway, which contributes to the induction of G2 arrest in GES-1 cells

Effect of Salt Solution Environment on the Aging of Styrene−Butadiene−Styrene (SBS)-Modified Asphalt

The aim of this paper is to investigate the aging mechanism of asphalt in the sea salt erosion environment from a rheological point of view. In order to simulate the real pavement aging process in the sea salt erosion environment, base asphalt and Styrene−Butadiene−Styrene (SBS)-modified asphalt were selected for salt environment aging tests. The asphalt samples were aged via a thin film oven test (TFOT) and a pressure aging vessel (PAV) test. Then, thermo-oxidizing conditions were created after the samples were immersed in salt solution, mixed with four different concentrations of sodium chloride (NaCl) and sodium sulphate (Na2SO4), to investigate the aging state of asphalt. Temperature scan (TS), frequency scan (FS), and multiple stress creep and recovery (MSCR) tests performed using a Dynamic Shear Rheometer (DSR) were used to investigate the effects on the rheological properties of aged asphalt in a salt environment. The results showed that both base asphalt and SBS-modified asphalt were aged to different degrees under mixed salt solutions. The two asphalt samples aged in a salt environment showed increased hardness. SBS-modified asphalt exhibited higher aging resistance compared with base asphalt in the sea salt environment. However, due to the degradation of the SBS modifier and the aging of base asphalt, the properties of the SBS-modified asphalt showed more obvious complexity with changes in salt solution concentrations

NIRvP as a remote sensing proxy for measuring gross primary production across different biomes and climate zones: Performance and limitations

The product of near-infrared radiation reflected by vegetation (NIRv) and PAR (NIRvP) is a promising proxy for the remote estimation of gross primary production (GPP). However, the efficiency of NIRvP in estimating the GPP and its limitations across multiple biomes and climate zones remain unclear. In this study, we aimed to evaluate the performance and limitations of NIRvP in estimating the GPP in comparison to absorbed photosynthetically active radiation (APAR), solar-induced chlorophyll fluorescence (SIF), and the MOD17A2H GPP product. Overall, the correlation between NIRvP and eddy covariance (EC) GPP was stronger than that of APAR, SIF, and MOD17A2H GPP across most biomes with usually similar seasonal variations in radiation, air temperature (TA), and precipitation. The near-​infrared (NIR) reflectance (ρNIR) and light use efficiency (LUE) exhibited a covarying relationship under these environmental conditions, which suggested that the ρNIR contributed positively to the NIRvP-GPP relationship under such climatic conditions. However, the performance of NIRvP was poor in some biomes and climate zones, which exhibited different variations in the seasonal patterns of radiation, TA, and precipitation. The resulting inconsistencies between ρNIR and LUE implied that the ρNIR contributed negatively to the NIRvP-GPP relationship in these regions. Altogether, the findings demonstrated that the NIRvP-GPP relationship was robust but attained a moderate overall relationship across ecosystems (R2 < 0.50) in the majority of biomes and climate zones. In addition, this study also elucidated the limitations of NIRvP as a GPP proxy in certain climate zones, which was attributed to the synergistic contributions of APAR and ρNIR in the NIRvP-GPP relationship