Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcriptional profiling of spinal cord injury‐induced central neuropathic pain

Central neuropathic pain (CNP) is an important problem following spinal cord injury (SCI), because it severely affects the quality of life of SCI patients. As in the patient population, the majority of rats develop significant allodynia (CNP rats) after moderate SCI. However, about 10% of SCI rats do not develop allodynia, or develop significantly less allodynia than CNP rats (non‐CNP rats). To identify transcriptional changes underlying CNP development after SCI, we used Affymetrix DNA microarrays and RNAs extracted from the spinal cords of CNP and non‐CNP rats. DNA microarry analysis showed significantly increased expression of a number of genes associated with inflammation and astrocytic activation in the spinal cords of rats that developed CNP. For example, mRNA levels of glial fibrilary acidic protein (GFAP) and Aquaporin 4 (AQP4) significantly increased in CNP rats. We also found that GFAP, S100β and AQP4 protein elevation persisted for at least 9 months throughout contused spinal cords, consistent with the chronic nature of CNP. Thus, we hypothesize that CNP development results, in part, from dysfunctional, chronically “over‐activated” astrocytes. Although, it has been shown that activated astrocytes are associated with peripheral neuropathic pain, this has not previously been demonstrated in CNP after SCI.
EM-Based Maximum-Likelihood Channel Estimation in Multicarrier Systems With Phase Distortion

In this paper, we address channel-impulse response (CIR) estimation in multicarrier systems with phase distortion, namely, phase noise (PHN) and carrier frequency offset (CFO). The estimation problem also considers the joint estimation of the channel noise variance, CFO, and PHN bandwidth. We develop a general state-space model for multicarrier systems, separating the complex signals into their real and imaginary parts. This provides a valid framework for any modulation scheme (proper or improper). We use the expectation-maximization (EM) algorithm to solve the maximum-likelihood (ML) estimation problem. Our approach exploits the linear and Gaussian structure associated with the transmitted signal. Due to the nonlinear nature of the PHN, sequential Monte Carlo (MC) techniques are considered. Our analysis includes general expressions under different training scenarios. We show, via simulation, the impact of PHN bandwidth estimation on overall parameter estimation, and we study the impact of different training levels. In addition, we consider the accuracy of the parameter estimates, providing expressions for the Fisher information matrix (FIM) and focusing on the estimation accuracy of the PHN bandwidth.
Bacterial Vaginosis Diagnosed by Analysis of First-Void-Urine Specimens

ABSTRACT Bacterial vaginosis (BV) is traditionally diagnosed using vaginal samples. The aim of this study was to investigate whether BV can be diagnosed from first-void urine (FVU). Self-collected vaginal smears, vaginal swabs, and FVU were obtained from 176 women. BV was diagnosed by Nugent's criteria. The FVU and vaginal swabs were analyzed by quantitative PCRs (qPCRs) for selected vaginal bacteria (Atopobium vaginae, Prevotella spp., Gardnerella vaginalis, bacterial vaginosis-associated bacterium 2, Eggerthella-like bacterium, “Leptotrichia amnionii,” Megasphaera type 1), and all had an area under the receiver operating characteristic (ROC) curve of >85%, suggesting good prediction of BV according to the Nugent score. All seven bacteria in FVU were significantly associated with BV in univariate analysis. An accurate diagnosis of BV from urine was obtained in this population by a combination of qPCRs for Megasphaera type 1 and Prevotella spp. The same two bacteria remained significantly associated with BV in a multivariate model after adjusting for the other five species. There was no statistically significant difference between the sensitivities and specificities of BV diagnosis by molecular methods performed on swabs and FVU samples. A linear regression analysis showed good agreement between bacterial loads from swabs and FVU, but Prevotella spp. could be detected in high numbers in a few FVU samples without being present in swabs. This method will allow diagnosis of BV in studies where only urine has been collected and where detection of BV is considered relevant.
Genetic Relatedness of Clostridium difficile Isolates from Various Origins Determined by Triple-Locus Sequence Analysis Based on Toxin Regulatory Genes tcdC, tcdR, and cdtR

ABSTRACT A triple-locus nucleotide sequence analysis based on toxin regulatory genes tcdC, tcdR and cdtR was initiated to assess the sequence variability of these genes among Clostridium difficile isolates and to study the genetic relatedness between isolates. A preliminary investigation of the variability of the tcdC gene was done with 57 clinical and veterinary isolates. Twenty-three isolates representing nine main clusters were selected for tcdC, tcdR, and cdtR analysis. The numbers of alleles found for tcdC, tcdR and cdtR were nine, six, and five, respectively. All strains possessed the cdtR gene except toxin A-negative toxin B-positive variants. All but one binary toxin CDT-positive isolate harbored a deletion (>1 bp) in the tcdC gene. The combined analyses of the three genes allowed us to distinguish five lineages correlated with the different types of deletion in tcdC, i.e., 18 bp (associated or not with a deletion at position 117), 36 bp, 39 bp, and 54 bp, and with the wild-type tcdC (no deletion). The tcdR and tcdC genes, though located within the same pathogenicity locus, were found to have evolved separately. Coevolution of the three genes was noted only with strains harboring a 39-bp or a 54-bp deletion in tcdC that formed two homogeneous, separate divergent clusters. Our study supported the existence of the known clones (PCR ribotype 027 isolates and toxin A-negative toxin B-positive C. difficile variants) and evidence for clonality of isolates with a 39-bp deletion (toxinotype V, PCR ribotype 078) that are frequently isolated worldwide from human infections and from food animals.
Risk and prognosis of bacteremia and fungemia among first-time kidney transplant recipients: a population-based cohort study

Abstract Background: Bacterial infections are common complications in kidney transplant recipients (KTRs). Little is known about incidence rates of bacteremia and fungemia (BAF) in KTRs. Methods: In this population-based cohort study, we used medical and administrative registries to identify episodes of BAF among KTRs in the Central Denmark and North Denmark Regions during 1995–2010. KTRs were followed from the date of their first transplantation to the earliest of BAF, graft loss, death, emigration or 31 December 2010. We calculated incidence rates of first BAF episode overall and stratified by time from transplantation. Potential risk factors were assessed using Cox regression analysis. The Kaplan-Meier analysis was used to estimate 30- and 90-day mortality. Results: Among 612 KTRs, we identified 138 first episodes of bacteremia during 2397 person-years of follow-up (PYFU). The overall incidence rate (IR) was 5.8 BAF episodes per 100 PYFU (95% confidence interval [CI]: 4.9–6.8). The incidence rate declined from 84.0 per 100 PYFU (95% CI: 61.6–114.5) during post-transplant day 0–30 to 2.3 per 100 PYFU (95% CI: 1.7–3.0) from post-transplant day 365 and onwards. Hospital-onset BAF comprised 39% of the episodes of BAF. The most frequently isolated microorganisms were Escherichia coli and Klebsiella species causing 49 (35.5%) and 29 (21.0%) episodes of BAF, respectively. The 30-day mortality was 2.1% (95% CI: 0.7–6.6). Conclusions: While the risk of BAF in KTRs was high, thirty-day mortality was low. After the first post-transplant year, the IR of bacteremia was substantially lower than in the immediate post-transplant period.
Neutralizing Anti-Influenza Virus Monoclonal Antibodies: Therapeutics and Tools for Discovery

The human antibody response to influenza virus infection plays a protective role against re-infection, yet little molecular detail is available regarding how human antibodies, when characterized at the monoclonal level, neutralize this important human pathogen. Recent studies, using a diverse array of strategies, have isolated and characterized human anti-virus neutralizing antibodies and shed light not only on the specificity and origin of these antibodies but on their potential for therapeutic use against influenza virus infection.
Autophagy: A Potential Therapeutic Target for Reversing Sepsis-Induced Immunosuppression

Sepsis remains the leading cause of mortality in intensive care units and an intractable condition due to uncontrolled inflammation together with immune suppression. Dysfunction of immune cells is considered as a major cause for poor outcome of septic patients but with little specific treatments. Currently, autophagy that is recognized as an important self-protective mechanism for cellular survival exhibits great potential for maintaining immune homeostasis and alleviating multiple organ failure, which further improves survival of septic animals. The protective effect of autophagy on immune cells covers both innate and adaptive immune responses and refers to various cellular receptors and intracellular signaling. Multiple drugs and measures are reportedly beneficial for septic challenge by inducing autophagy process. Therefore, autophagy might be an effective target for reversing immunosuppression compromised by sepsis.
Validation of a questionnaire for assessing physical work load.

OBJECTIVES Reliable, valid, and compatible methods are required for exploring the complex interactive effects of psychosocial and physical stressors on complaints and disorders. An instrument for assessing physical work load that integrates information from a biomechanical model of lumbar load is presented and validated. METHODS Four hundred and fifty-five people working in nursing homes for elderly people in Germany filled out the developed questionnaire 3 times within 1 year. Test-retest reliability was calculated, and validity was checked several times. Relationships with other, theoretically related and unrelated variables were examined. RESULTS The test-retest reliability of the questionnaire measures was about 0.65. The convergent and discriminant validity was satisfactory, and the questionnaire was able to separate professional subgroups with different physical work loads. The Spearman rank-order correlations between physical load and musculoskeletal complaints were about 0.30. CONCLUSIONS The method developed in this study is a reliable and valid instrument for assessing physical work load. The integration of statistical methods from psychological testing and theory in the development of methods exploring the effects of physical work load is advocated.
Investment Decisions in Distribution Networks Under Uncertainty With Distributed Generation—Part II: Implementation and Results

This second part of a two-part paper presents the implementation and simulations of a risk-based optimization approach for distribution expansion planning with distributed generation (DG), as described in Part I. The proposed approach is first applied to a test system in order to test convergence of the optimization algorithm and then comparing its numerical results with the ones from the exhaustive method. Later, the expansion planning of a typical Latin American distribution network is assessed and analyzed. For this, two expansion optimizations are performed: one, taking into account traditional expansion alternatives (without DG) and, the other, considering DG as flexible expansion options. Finally, the computational effort is analyzed. Results show that the greatest contribution of DG lies in the flexibility it gives to expansion planning, mainly by deferring network reinforcements.
A randomised comparison between 6 months of bolus fluorouracil/leucovorin and 12 weeks of protracted venous infusion fluorouracil as adjuvant treatment in colorectal cancer.

BACKGROUND We performed a multicentre randomised trial to compare the efficacy and toxicity of 12 weeks of protracted venous infusion (PVI) 5-fluorouracil (5-FU) against the standard bolus monthly regimen of 5-FU/leucovorin (LV) given for 6 months as adjuvant treatment in colorectal cancer (CRC). PATIENTS AND METHODS Patients with curatively resected stage II and III CRC were randomly assigned to 5-FU/LV [5-FU 425 mg/m(2) intravenously (i.v.) and LV 20 mg/m(2) i.v. bolus days 1-5 every 28 days for 6 months] or to PVI 5-FU (300 mg/m(2)/day for 12 weeks). RESULTS Between 1993 and 2003, 801 eligible patients were randomised to 5-FU/LV (n=404) or PVI 5-FU (n=397). With a median follow-up of 5.3 years, 231 relapses and 220 deaths have been observed. Five-year relapse-free survival (RFS) was 66.7% [95% confidence interval (CI) 61.6% to 71.3%] and 73.3% (95% CI 68.4% to 77.6%) with bolus 5-FU/LV and PVI 5-FU, respectively [hazard ratio (HR) 0.8; 95% CI 0.62-1.04; P=0.10]. Five-year overall survival (OS) was 71.5% (95% CI 66.4% to 75.9%) and 75.7% (95% CI 70.8% to 79.9%) with bolus 5-FU/LV and PVI 5-FU, respectively (HR 0.79; 95% CI 0.61-1.03; P=0.083). There was a significant survival advantage for patients starting adjuvant chemotherapy within 8 weeks (P=0.044). Significantly less diarrhoea, stomatitis, nausea and vomiting, alopecia, lethargy, and neutropenia (all with P <0.0001) were seen with PVI 5-FU. CONCLUSIONS There was no OS difference between the two arms, although PVI 5-FU was associated with a trend towards better RFS and OS compared with bolus 5-FU/LV, as well as significantly less toxicity. Based on our results, the probability of 12 weeks of PVI 5-FU being inferior to 6 months of bolus 5-FU/LV is extremely low (P <0.005), and therefore shorter duration of adjuvant treatment should be explored further.
miR-9 is an essential oncogenic microRNA specifically overexpressed in mixed lineage leukemia–rearranged leukemia

MicroRNAs (miRNAs), small noncoding RNAs that regulate target gene mRNAs, are known to contribute to pathogenesis of cancers. Acute myeloid leukemia (AML) is a group of heterogeneous hematopoietic malignancies with various chromosomal and/or molecular abnormalities. AML with chromosomal translocations involving the mixed lineage leukemia (MLL) gene are usually associated with poor survival. In the present study, through a large-scale, genomewide miRNA expression assay, we show that microRNA-9 (miR-9) is the most specifically up-regulated miRNA in MLL-rearranged AML compared with both normal control and non–MLL-rearranged AML. We demonstrate that miR-9 is a direct target of MLL fusion proteins and can be significantly up-regulated in expression by the latter in human and mouse hematopoietic stem/progenitor cells. Depletion of endogenous miR-9 expression by an appropriate antagomiR can significantly inhibit cell growth/viability and promote apoptosis in human MLL-rearranged AML cells, and the opposite is true when expression of miR-9 is forced. Blocking endogenous miR-9 function by anti-miRNA sponge can significantly inhibit, whereas forced expression of miR-9 can significantly promote, MLL fusion–induced immortalization/transformation of normal mouse bone marrow progenitor cells in vitro. Furthermore, forced expression of miR-9 can significantly promote MLL fusion–mediated leukemogenesis in vivo. In addition, a group of putative target genes of miR-9 exhibited a significant inverse correlation of expression with miR-9 in a series of leukemia sample sets, suggesting that they are potential targets of miR-9 in MLL-rearranged AML. Collectively, our data demonstrate that miR-9 is a critical oncomiR in MLL-rearranged AML and can serve as a potential therapeutic target to treat this dismal disease.
CONSTANT TEMPERATURE AND TIME PERIOD EFFECTS ON ANOPHELES GAMBIAE EGG HATCHING

ABSTRACT Anopheles gambiae Giles sensu stricto (Diptera: Culicidae) egg development and its relation to environmental parameters is an understudied aspect of vector biology. Although several studies have illustrated the dramatic effects of temperature on egg development, egg hatching dynamics remain unclear. The objective of this study was to expose An. gambiae eggs to various temperatures for different lengths of time and determine the impact on egg development and hatching count. Batches of mosquito eggs (N = 30 eggs/replicate) were incubated under moist conditions at temperatures of 12, 22, 27, 33, and 42°C for intervals of 1, 3, 7, and 10 days. After that, they were flooded with distilled water at 27°C, and hatching counts were observed for up to 7 days. Mosquito eggs held at 22 and 27°C had the highest overall mean hatching count. During early incubation periods, eggs held at 33°C had hatching counts comparable to 22 and 27°C, but counts decreased drastically during later incubation periods. Temperatures of 12 and 42°C reduced mosquito egg viability, because few eggs hatched in these temperature regimes. Other experiments revealed that during early embryonic development, temperature had a major effect on the developing embryo, while later in embryonic development it had no dramatic effect. Microscopic observation of the An. gambiae embryo showed that extreme low and high temperatures affected the normal development of the embryo. A regression model was developed to describe the effect of incubation temperature and incubation period on egg hatching counts, which demonstrated that the optimum temperature for egg hatching ranges from 24 to 30°C, irrespective of incubation period. The interaction between temperature and time period may have implications for dry-season survival and climate-based models of malaria risk.
A Comprehensive Analysis of Inter-Relationships among Overall Service Quality, Trust, Satisfaction and Learner’s Loyalty within the Supportive Vocational Education and Training

It is seen that education institutes use many techniques to increase the quality of the services being provided. However, as competition in the education sector has been rising dramatically, it is hard to differentiate the services offered. In this perspective, it is becoming hard to attract and keep loyal learners in education institutes. So, lifelong learning and continuing professional development are the two tools that enhance the level of loyalty to the education institutes. In this study, an attempt has been made to understand the factors that define learner’s loyalty in the Vocational Education Institutes. In addition, the relation of trust to the Institute, satisfaction, and learner’s loyalty variables to the service quality will be researched. In this sense, 184 questionnaires have been conducted to students who have attended the Public Oversight Board Education Program offered by Istanbul Commerce University’s Lifelong Learning Center. Data has been analyzed by Structural Equation Modelling. According to the results of the study, education institutes who deal solely with the issue of trust, satisfaction, and learner’s loyalty can have the potential to keep their learners on hand on a life-long basis thereby earning their loyalty to the fullest extent.
The Many-Faced Program of Epithelial–Mesenchymal Transition: A System Biology-Based View

System biology uses a range of experimental and statistical methods to dissect complex processes that results from alterations in biological models. Given the complexity of the epithelial–mesenchymal transition (EMT) program, system biology represents a promising approach to understanding its fine molecular regulation by the interpretation of high-throughput datasets. Herein, we review recent contributions of system biology applied to the field of EMT physiology and illustrate the importance of these approaches to model biological networks that are perturbed during the transition. Together, these results allowed the definition of an EMT signature across different tumor types, the identification of dysregulated processes and new modules of regulation, making possible to reveal the EMT molecular visage underneath.
Evaluating the impact and use of Transparent Reporting of Evaluations with Non-randomised Designs (TREND) reporting guidelines

Introduction Accurate and full reporting of evaluation of interventions in health research is needed for evidence synthesis and informed decision-making. Evidence suggests that biases and incomplete reporting affect the assessment of study validity and the ability to include this data in secondary research. The Transparent Reporting of Evaluations with Non-randomised Designs (TREND) reporting guideline was developed to improve the transparency and accuracy of the reporting of behavioural and public health evaluations with non-randomised designs. Evaluations of reporting guidelines have shown that they can be effective in improving reporting completeness. Although TREND occupies a niche within reporting guidelines, and despite it being 8 years since publication, no study yet has assessed its impact on reporting completeness or investigated what factors affect its use by authors and journal editors. This protocol describes two studies that aim to redress this. Methods and analysis Study 1 will use an observational design to examine the uptake and use of TREND by authors, and by journals in their instructions to authors. A comparison of reporting completeness and study quality of papers that do and do not use TREND to inform reporting will be made. Study 2 will use a cross-sectional survey to investigate what factors inhibit or facilitate authors’ and journal editors’ use of TREND. Semistructured interviews will also be conducted with a subset of authors and editors to explore findings from study 1 and the surveys in greater depth. Ethics and dissemination These studies will generate evidence of how implementation and dissemination of the TREND guideline has affected reporting completeness in studies with experimental, non-randomised designs within behavioural and public health research. The project has received ethics approval from the Research Ethics Committee of the Peninsula College of Medicine and Dentistry, Universities of Exeter and Plymouth.
Land-Use Classification via Extreme Learning Classifier Based on Deep Convolutional Features

One of the challenging issues in high-resolution remote sensing images is classifying land-use scenes with high quality and accuracy. An effective feature extractor and classifier can boost classification accuracy in scene classification. This letter proposes a deep-learning-based classification method, which combines convolutional neural networks (CNNs) and extreme learning machine (ELM) to improve classification performance. A pretrained CNN is initially used to learn deep and robust features. However, the generalization ability is finite and suboptimal, because the traditional CNN adopts fully connected layers as classifier. We use an ELM classifier with the CNN-learned features instead of the fully connected layers of CNN to obtain excellent results. The effectiveness of the proposed method is tested on the UC-Merced data set that has 2100 remotely sensed land-use-scene images with 21 categories. Experimental results show that the proposed CNN-ELM classification method achieves satisfactory results.