Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of long noncoding RNAs HOX transcript antisense intergenic RNA single‐nucleotide polymorphisms on breast cancer, cervical cancer, and ovarian cancer susceptibility: A meta‐analysis

Recent studies have shown that long noncoding RNAs (lncRNA) HOX transcript antisense intergenic RNA (HOTAIR) polymorphisms are associated with cancer susceptibility. The greatest threat to women’s health among a variety of cancers is breast cancer (BC), cervical cancer (CC), and ovarian cancer (OC), and the incidence of it is increasing. We performed a meta‐analysis to clarify the relationship between lncRNA HOTAIR expression and BC, CC, and OC susceptibility. We thoroughly searched PubMed, Embase, and the Cochrane Library to obtain the relevant literature. We extracted data from case groups and control groups for each single‐nucleotide polymorphism (SNP) (rs4759314, rs920778, rs189663, rs12826786, rs7958904, and rs874945) and compared the relationship between alleles, codominance models, dominant and invisible models and BC, CC, and OC susceptibility. Our study included 11 studies with a total of 5322 patients. There was a significant association between the rs4759314 polymorphism of HOTAIR and susceptibility to BC, CC, and OC (codominant model: AG/AA odds ratio [OR] = 1.13 [95% confidence intervals [CI], 1.00‐1.29], GG/AA OR = 1.54 [95% CI, 1.06‐2.23]; dominant model: GG + AG/AA OR = 1.16 [95% CI, 1.02‐1.32]; and recessive model: GG/AA + AG OR = 1.51 [95% CI, 1.05‐2.19]). The association between the expression of rs920778 and BC, CC, and OC susceptibility was not clear (alleles T/C: OR = 1.28 [95% CI, 0.87‐1.89]; in codominant model: CT/CC OR = 1.10, [95% CI, 0.71‐1.71], TT/CC OR = 1.29 [95% CI, 0.59‐2.80]; dominant model: TC + TT/CC OR = 1.16, [95% CI, 0.73‐1.86]; and recessive model: TT/TC + CC OR = 1.43, [95% CI, 0.83‐2.47]). HOTAIR polymorphism rs1899663 was associated with BC, CC, and OC susceptibility to a certain extent, (alleles T/G OR = 0.90 [95% CI, 0.69‐1.16]; in the codominant model: GT/GG OR = 0.81 [95% CI, 0.50‐1.30], TT/GG OR = 1.04 [95% CI, 0.63‐1.72]; dominant model: GT + TT/GG OR = 0.82 [95% CI, 0.52‐1.29]; and recessive model: TT/GT + GG OR = 1.21 [95% CI, 0.76‐1.94]). The rs12826786, rs7958904, and rs874945 polymorphisms were associated with a certain degree of BC, CC, and OC susceptibility, but they were not statistically significant. HOTAIR rs4759314 increased susceptibility to BC, CC, and OC in some patients; rs029778 and rs1899663 also increased susceptibility to some extent. SNPs rs12826786, rs7958904, and rs874945 did not correlate with an effect on patient susceptibility to BC, CC, and OC.
Can Education Resolve Nursing Shortage in China

The paper aims to understand how the Chinese nursing education and recruitment policy impacts nurses to leave nursing practice. There is a lack of feasible strategies to maintain a sustainable effective nursing workforce with an increasing trend of nurses' leaving clinical care. In its efforts to resolve the nursing shortage, the Chinese government currently emphasizes expanding nursing education while controlling staffing ratios. This qualitative research design was based on the framework of symbolic interactionism. The in-depth interviews with 19 nurses who have left the nursing practice were theoretically sampled from one provincial capital city in China. The core category "Mismatched Expectations: Individual vs. Organization" emerged from five subcategories, which illustrates how the mismatched individual and organizational expectations of nursing were created through nurses educational and employment processes within the Chinese health care system. When the individual and organizational expectations of nursing are highly mismatched, there is a risk that highly educated nurses will leave nursing practice. Students should be enabled to have realistic expectations of nursing, and new graduates need sufficient supports to adapt to the health care environment without losing their vision of nursing for future.
The promises and pitfalls of Stochastic Gradient Langevin Dynamics

Stochastic Gradient Langevin Dynamics (SGLD) has emerged as a key MCMC algorithm for Bayesian learning from large scale datasets. While SGLD with decreasing step sizes converges weakly to the posterior distribution, the algorithm is often used with a constant step size in practice and has demonstrated spectacular successes in machine learning tasks. The current practice is to set the step size inversely proportional to N where N is the number of training samples. As N becomes large, we show that the SGLD algorithm has an invariant probability measure which significantly departs from the target posterior and behaves like as Stochastic Gradient Descent (SGD). This difference is inherently due to the high variance of the stochastic gradients. Several strategies have been suggested to reduce this effect; among them, SGLD Fixed Point (SGLDFP) uses carefully designed control variates to reduce the variance of the stochastic gradients. We show that SGLDFP gives approximate samples from the posterior distribution, with an accuracy comparable to the Langevin Monte Carlo (LMC) algorithm for a computational cost sublinear in the number of data points. We provide a detailed analysis of the Wasserstein distances between LMC, SGLD, SGLDFP and SGD and explicit expressions of the means and covariance matrices of their invariant distributions. Our findings are supported by limited numerical experiments.
Effect of Different Storage Temperatures on the Efficacy of the Bivalent Foot and Mouth Disease Oil Vaccine

The storage stability of locally produced double oil emulsion adjuvant bivalent Foot and mouth disease (FMD) vaccine prepared from type O1/Aga/ EGY/93 strain and A/EGY/1/2006 had been determined depending on its shelf life in different storage temperatures during the registration of this vaccine by the Central Laboratory for Evaluation of Veterinary Biologics, Abbasia, Cairo. Samples of this vaccine were kept at 4°C for period of 27 months; at 25°C for 5 weeks and at 37°C for 3 weeks. The potency of these vaccine samples was evaluated in guinea pigs as laboratory animal's model. The obtained results confirmed that the vaccine keep its potency beyond the normal conservation period at 4°C for two years with 100% protection against challenge with FMDV O1/Aga/EGY/93 and at 25°C for 3 weeks and at 37°C for 1 week, showing 80% protection when storage of the vaccine at 25°C for 4 weeks; at 37°C for 2 weeks. On challenge with A/EGY/1/2006 the vaccine gave 100% protection when storage at 4°C for 21 months; at 25°C for 2 weeks and at 37°C for 1 week. Otherwise it gave 80% protection when storage at 4°C for 24 months; at 25°C for 3 weeks and at 37°C for 2 weeks then became invalid after 27 months at 4°C; after 4 weeks at 25°C and for 3 weeks at 37°C. So it could be concluded that 4°C is the best temperature of choice for storage of the oil inactivated bivalent FMD vaccine.
Template-Based Step Detection with Inertial Measurement Units

This article presents a method for step detection from accelerometer and gyrometer signals recorded with Inertial Measurement Units (IMUs). The principle of our step detection algorithm is to recognize the start and end times of the steps in the signal thanks to a predefined library of templates. The algorithm is tested on a database of 1020 recordings, composed of healthy subjects and patients with various neurological or orthopedic troubles. Simulations on more than 40,000 steps show that the template-based method achieves remarkable results with a 98% recall and a 98% precision. The method adapts well to pathological subjects and can be used in a medical context for robust step estimation and gait characterization.
Expectancies influence attention to neutral but not necessarily to threatening stimuli: An fMRI study.

Recent behavioral observations suggest an influence of prior expectancies on attention to neutral targets, whereas the detection of threatening targets remains comparably immune to these expectancies. The origin of this asymmetry, however, remains unclear. Here, therefore, we investigated its neural basis by using fMRI. Specifically, we tested whether, in accordance with the idea of a resetting attentional system during phylogenetic threat detection, neural responses for threatening compared with neutral targets would remain largely unaffected by prior expectancies. Alternatively, neural responses could reflect equally strong expectancy influences on both types of targets, with the respective patterns differing, thereby producing the asymmetric effect observed in behavior. Predictive cues in our study evoked specific behavioral and neural expectancy states and effectively modulated response latencies to detect neutral (bird) targets in a 3 × 3 visual search matrix: When threat-related (spider) rather than neutral targets were expected, bird detection was considerably slowed, and the neural response to expected birds differed from that to unexpected birds. Conversely, and in line with the hypothesis of a resetting attentional system for phylogenetic threat, expectancy cues had no impact on RTs or neural responses for spider targets-either in spider phobic participants or in non-spider-fearful control participants. Our data support the idea of bottom-up enhancement of threat-related information through processing pathways unaffected by top-down modulatory influences such as expectancy. These pathways may subserve rapid and comparably automatic responding to threat stimuli by safeguarding independence from more controlled and explicit expectancies, consequently promoting adaptive behavior and survival. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Lipid-Modified Azurin of Neisseria gonorrhoeae Is Not Surface Exposed and Does Not Interact With the Nitrite Reductase AniA

Lipid-modified cupredoxin azurin (Laz) is involved in electron transport in Neisseria and proposed to act as an electron donor to the surface-displayed nitrite reductase AniA. We identified Laz in Neisseria gonorrhoeae cell envelopes and naturally elaborated membrane vesicles in proteomic investigations focused on discovering new vaccine and therapeutic targets for this increasingly difficult to treat pathogen. Its surface exposure in N. meningitidis suggested Laz could be a vaccine candidate for N. gonorrhoeae. Here we characterized the localization, expression, and role of Laz within the gonococcal cell envelope and challenged the hypothesis that Laz and AniA interact. While we demonstrate that Laz indeed shows some good features of a vaccine antigen, such as stable expression, high conservation, and ability to elicit antibodies that cross-react with a diverse panel of Neisseria, it is not a surface-displayed lipoprotein in the gonococcus. This discovery eliminates Laz as a gonorrhea vaccine candidate, further highlighting the necessity of examining homologous protein localization between closely related species. Absence of Laz slightly altered cell envelope integrity but was not associated with growth defects in vitro, including during anoxia, implicating the presence of other electron pathways to AniA. To further dissect the implied AniA-Laz interaction, we utilized biolayer interferometry and optimized and executed chemical cross-linking coupled with immunoblotting to covalently link interacting protein partners in living gonococci. This method, applied for the first time in N. gonorrhoeae research to interrogate protein complexes, was validated by the appearance of the trimer form of AniA, as well as by increased formation of the β-barrel assembly machinery complex, in the presence of cross-linker. We conclude that Laz is not an electron donor to AniA based on their distinct subcellular localization, discordant expression during infection of the female mouse lower genital tract, and lack of interaction in vivo and in vitro.
Assessing four Neural Networks on Handwritten Digit Recognition Dataset (MNIST)

Although the image recognition has been a research topic for many years, many researchers still have a keen interest in it. In some papers, however, there is a tendency to compare models only on one or two datasets, either because of time restraints or because the model is tailored to a specific task. Accordingly, it is hard to understand how well a certain model generalizes across image recognition field. In this paper, we compare four neural networks on MNIST dataset with different division. Among of them, three are Convolutional Neural Networks (CNN), Deep Residual Network (ResNet) and Dense Convolutional Network (DenseNet) respectively, and the other is our improvement on CNN baseline through introducing Capsule Network (CapsNet) to image recognition area. We show that the previous models despite do a quite good job in this area, our retrofitting can be applied to get a better performance. The result obtained by CapsNet is an accuracy rate of 99.75\%, and it is the best result published so far. Another inspiring result is that CapsNet only needs a small amount of data to get the excellent performance. Finally, we will apply CapsNet's ability to generalize in other image recognition field in the future.
Patterns of discrepancies in different objects of awareness in mild and moderate Alzheimer’s disease

Abstract Objectives: Awareness is considered a heterogeneous and non-linear phenomenon in dementia. We aim to investigate patterns of change of different domains of awareness (awareness of cognitive functioning and health condition, activities of daily living, emotional state, social functioning, and relationships) in people with mild and moderate Alzheimer’s disease (AD) and aspects related to each domain. Method: Cross-sectional assessment of dyads of people with AD (PwAD) and caregivers (n = 128; CDR1 = 74, CDR2 = 54). PwAD completed assessments about quality of life, cognition and their awareness of disease. Caregivers provided information about PwAD and received quality of life and burden of care assessments. Results: Mild AD group showed a mildly impaired awareness (n = 40; 54.05%), while moderate AD group, showed higher presence of moderately impaired awareness (n = 22; 40.74%). There was a significant difference between groups in awareness of cognitive functioning and health condition (p < 0.004), functional activity impairments (p < 0.001) and total score of awareness (p < 0.01). Conversely, awareness of emotional state (p = 0.22) and of social functioning and relationship (p = 0.44) presented no significant difference between groups. Unawareness of functional activity impairments showed higher discrepancy scores between PwAD and caregivers in both groups. Conclusions: Significant differences were found only in patterns of discrepancies in awareness of cognitive functioning and health condition, of ADL and socio-emotional functioning. Different factors are related to different domains in mild and moderate group, reinforcing the heterogeneity of awareness in dementia. ADL deficits have an important role in awareness phenomenon, independent of the severity of disease.
Genetic Polymorphisms of TYMS, MTHFR, ATIC, MTR, and MTRR Are Related to the Outcome of Methotrexate Therapy for Rheumatoid Arthritis in a Chinese Population

Objective: Analysis of the relationship between single nucleotide polymorphisms (SNPs) and outcomes of methotrexate (MTX) therapy for rheumatoid arthritis (RA) in China. Materials and Methods: TYMS 28 bp VNTR (rs34743033), MTHFR [677C>T (rs1801133) and 1298A>C (rs1801131)], ATIC 347C>G (rs2372536), MTR A2756G (rs1805087), and MTRR 66A>G (rs1801394) enzyme proteins may be related to the outcomes of MTX therapy, according to our previous meta-analysis. A total of 162 patients with RA were included in our study. SNPs were evaluated using polymerase chain reaction (PCR). Disease Activity Score 28 (DAS28) was used to evaluate the clinical response, and adverse drug reactions (ADRs) were collected after physical examinations of the patients. Results: The MTHFR 677C>T gene showed a relationship with the ADRs of MTX in the Recessive model [TT vs. (CC+CT)] (p = 0.04, OR = 2.20, 95% CI: 1.01, 4.77). In the Codominant model [CT vs. (CC+TT)], the MTHFR 677C>T gene also showed a trend of association with ADRs (p = 0.08, OR = 0.52, 95% CI: 0.25, 1.08). No significant difference was found between TYMS, MTHFR, ATIC, MTR, and MTRR gene polymorphisms and the RA response or ADRs related to MTX in our study. Conclusion: Our results showed that the MTHFR [677C>T (rs1801133)] TT genotype is associated with ADRs to MTX in Chinese RA patients. Other SNPs, including TYMS 28bp VNTR (rs34743033), MTHFR [677C>T (rs1801133) and 1298A>C (rs1801131)], ATIC 347C>G (rs2372536), MTR A2756G (rs1805087), and MTRR 66A>G (rs1801394) gene polymorphisms, were not associated with MTX treatment outcomes. Further studies are required to validate these findings.
The direct lighting computation in global illumination methods

Creating realistic images is a computationally expensive process, but it is very important for applications such as interior design, product design, education, virtual reality, and movie special effects. To generate realistic images, state-of-art rendering techniques are employed to simulate global illumination, which accounts for the interreflection of light among objects. In this document, we formalize the global illumination problem into a eight-dimensional integral and discuss various methods that can accelerate the process of approximating this integral. We focus on the direct lighting computation, which accounts for the light reaching the viewer from the emitting sources after exactly one reflection, Monte Carlo sampling methods, and light source simplification. Results include a new sample generation method, a framework for the prediction of the total number of samples used in a solution, and a generalized Monte Carlo approach for computing the direct lighting from an environment which for the first time makes ray tracing feasible for highly complex environments.
Drought or/and Heat-Stress Effects on Seed Filling in Food Crops: Impacts on Functional Biochemistry, Seed Yields, and Nutritional Quality

Drought (water deficits) and heat (high temperatures) stress are the prime abiotic constraints, under the current and climate change scenario in future. Any further increase in the occurrence, and extremity of these stresses, either individually or in combination, would severely reduce the crop productivity and food security, globally. Although, they obstruct productivity at all crop growth stages, the extent of damage at reproductive phase of crop growth, mainly the seed filling phase, is critical and causes considerable yield losses. Drought and heat stress substantially affect the seed yields by reducing seed size and number, eventually affecting the commercial trait ‘100 seed weight’ and seed quality. Seed filling is influenced by various metabolic processes occurring in the leaves, especially production and translocation of photoassimilates, importing precursors for biosynthesis of seed reserves, minerals and other functional constituents. These processes are highly sensitive to drought and heat, due to involvement of array of diverse enzymes and transporters, located in the leaves and seeds. We highlight here the findings in various food crops showing how their seed composition is drastically impacted at various cellular levels due to drought and heat stresses, applied separately, or in combination. The combined stresses are extremely detrimental for seed yield and its quality, and thus need more attention. Understanding the precise target sites regulating seed filling events in leaves and seeds, and how they are affected by abiotic stresses, is imperative to enhance the seed quality. It is vital to know the physiological, biochemical and genetic mechanisms, which govern the various seed filling events under stress environments, to devise strategies to improve stress tolerance. Converging modern advances in physiology, biochemistry and biotechnology, especially the “omics” technologies might provide a strong impetus to research on this aspect. Such application, along with effective agronomic management system would pave the way in developing crop genotypes/varieties with improved productivity under drought and/or heat stresses.
A seven-Gene Signature assay improves prognostic risk stratification of perioperative chemotherapy treated gastroesophageal cancer patients from the MAGIC trial

Abstract Background Following neoadjuvant chemotherapy for operable gastroesophageal cancer, lymph node metastasis is the only validated prognostic variable; however, within lymph node groups there is still heterogeneity with risk of relapse. We hypothesized that gene profiles from neoadjuvant chemotherapy treated resection specimens from gastroesophageal cancer patients can be used to define prognostic risk groups to identify patients at risk for relapse. Patients and methods The Medical Research Council Adjuvant Gastric Infusional Chemotherapy (MAGIC) trial (n = 202 with high quality RNA) samples treated with perioperative chemotherapy were profiled for a custom gastric cancer gene panel using the NanoString platform. Genes associated with overall survival (OS) were identified using penalized and standard Cox regression, followed by generation of risk scores and development of a NanoString biomarker assay to stratify patients into risk groups associated with OS. An independent dataset served as a validation cohort. Results Regression and clustering analysis of MAGIC patients defined a seven-Gene Signature and two risk groups with different OS [hazard ratio (HR) 5.1; P < 0.0001]. The median OS of high- and low-risk groups were 10.2 [95% confidence interval (CI) of 6.5 and 13.2 months] and 80.9 months (CI: 43.0 months and not assessable), respectively. Risk groups were independently prognostic of lymph node metastasis by multivariate analysis (HR 3.6 in node positive group, P = 0.02; HR 3.6 in high-risk group, P = 0.0002), and not prognostic in surgery only patients (n = 118; log rank P = 0.2). A validation cohort independently confirmed these findings. Conclusions These results suggest that gene-based risk groups can independently predict prognosis in gastroesophageal cancer patients treated with neoadjuvant chemotherapy. This signature and associated assay may help risk stratify these patients for post-surgery chemotherapy in future perioperative chemotherapy-based clinical trials.
Trends in the Causes of Death among Kidney Transplant Recipients in the United States (1996–2014)

Background: Death with graft function remains an important cause of graft loss among kidney transplant recipients (KTRs). Little is known about the trend of specific causes of death in KTRs in recent years. Methods: We analyzed United States Renal Data System data (1996–2014) to determine 1- and 10-year all-cause and cause-specific mortality in adult KTRs who died with a functioning allograft. We also studied 1- and 10-year trends in the various causes of mortality. Results: Of 210,327 KTRs who received their first kidney transplant from 1996 to 2014, 3.2% died within 1 year after transplant. Cardiovascular deaths constituted the majority (24.7%), followed by infectious (15.2%) and malignant (2.9%) causes; 40.1% of deaths had no reported cause. Using 1996 as the referent year, all-cause as well as cardiovascular mortality declined, whereas mortality due to malignancy did not. For analyses of 10-year mortality, we studied 94,384 patients who received a first kidney transplant from 1996 to 2005. Of those, 22.1% died over 10 years and the causative patterns of their causes of death were similar to those associated with 1-year mortality. Conclusions: Despite the downtrend in mortality over the last 2 decades, a significant percentage of KTRs die in 10-years with a functioning graft, and cardiovascular mortality remains the leading cause of death. These data also highlight the need for diligent collection of mortality data in KTRs.