Miracle-dz/UniFAR-FTUs huggingface dataset:
Strategies, facilitators and barriers to implementation of evidence-based practice in community nursing: a systematic mixed-studies review and qualitative synthesis

Abstract Aim To appraise and synthesize empirical literature on implementation of evidence within community nursing. To explore the use of implementation theory and identify the strategies required for, and the barriers and facilitators to, successful implementation within this context. Background There is an international consensus that evidence-based practice can improve outcomes for people using health and social care services. However, these practices are not always translated into care delivery. Community nursing is a relatively understudied area; little is known about how innovations in practice are implemented within this setting. Methods Systematic mixed-studies review, synthesizing quantitative and qualitative research. The electronic databases AMED, PsycINFO, Ovid Medline, CINAHL Plus, ASSIA, British Nursing Index and EMBASE were used. Two grey literature databases were also searched: OpenGrey and EThOS. English language, peer-reviewed papers published between January 2010 and July 2017 were considered. Criteria included implementation of an innovation and change to practice within adult community nursing. An approach called Critical Interpretive Synthesis was used to integrate the evidence from across the studies into a comprehensible theoretical framework. Results In total, 22 papers were reviewed. Few studies discussed the use of theory when planning, guiding and evaluating the implementation of the innovation (n=6). A number of implementation strategies, facilitators and barriers were identified across the included studies, highlighting the interplay of both service context and individual factors in successful implementation. Conclusion Implementation is an expanding area of research; yet is challenged by a lack of consistency in terminology and limited use of theory. Implementation within community nursing is a complex process, requiring both individual and organizational adoption, and managerial support. Successful adoption of evidence-based practice however, is only possible if community nurses themselves deem it useful and there is evidence that it could have a positive impact on the patient and/or their primary carer.
Berberine Could Ameliorate Cardiac Dysfunction via Interfering Myocardial Lipidomic Profiles in the Rat Model of Diabetic Cardiomyopathy

Background: Diabetic cardiomyopathy (DCM) is considered to be a distinct clinical entity independent of concomitant macro- and microvascular disorders, which is initiated partly by disturbances in energy substrates. This study was to observe the dynamic modulations of berberine in DCM rats and explore the changes of lipidomic profiles of myocardial tissue. Methods: Sprague-Dawley (SD) rats were fed high-sucrose and high-fat diet (HSHFD) for totally 22 weeks and intraperitoneally (i.p.) injected with 30 mg/kg of streptozotocin (STZ) at the fifth week to induce DCM. Seventy-two hours after STZ injection, the rats were orally given with berberine at 10, 30 mg/kg and metformin at 200 mg/kg, respectively. Dynamic changes of cardiac function, heart mass ratios and blood lipids were observed at f 4, 10, 16, and 22, respectively. Furthermore, lipid metabolites in myocardial tissue at week 16 were profiled by the ultra-high-performance liquid chromatography coupled to a quadruple time of flight mass spectrometer (UPLC/Q-TOF/MS) approach. Results: Berberine could protect against cardiac diastolic and systolic dysfunctions, as well as cardiac hypertrophy, and the most effective duration is with 16-week of administration. Meanwhile, 17 potential biomarkers of phosphatidylcholines (PCs), phosphatidylethanolamines (PEs) and sphingolipids (SMs) of DCM induced by HSFD/STZ were identified. The perturbations of lipidomic profiles could be partly reversed with berberine intervention, i.e., PC (16:0/20:4), PC (18:2/0:0), PC (18:0/18:2), PC (18:0/22:5), PC (20:4/0:0), PC (20:4/18:0), PC (20:4/18:1), PC (20:4/20:2), PE (18:2/0:0), and SM (d18:0/16:0). Conclusions: These results indicated a close relationship between PCs, PEs and SMs and cardiac damage mechanisms during development of DCM. The therapeutic effects of berberine on DCM are partly caused by interferences with PCs, PEs, and SMs metabolisms.
Did We Get It Right? Predicting Query Performance in e-Commerce Search

In this paper, we address the problem of evaluating whether results served by an e-commerce search engine for a query are good or not. This is a critical question in evaluating any e-commerce search engine. While this question is traditionally answered using simple metrics like query click-through rate (CTR), we observe that in e-commerce search, such metrics can be misleading. Upon inspection, we find cases where CTR is high but the results are poor and vice versa. Similar cases exist for other metrics like time to click which are often also used for evaluating search engines. We aim to learn the quality of the results served by the search engine based on users' interactions with the results. Although this problem has been studied in the web search context, this is the first study for e-commerce search, to the best of our knowledge. Despite certain commonalities with evaluating web search engines, there are several major differences such as underlying reasons for search failure, and availability of rich user interaction data with products (e.g. adding a product to the cart). We study large-scale user interaction logs from Flipkart's search engine, analyze behavioral patterns and build models to classify queries based on user behavior signals. We demonstrate the feasibility and efficacy of such models in accurately predicting query performance. Our classifier is able to achieve an average AUC of 0.75 on a held-out test set.
Identification and characterization of PDC1, a novel protein involved in the epigenetic cell degeneration Crippled Growth in Podospora anserina

The model fungus Podospora anserina exhibits Crippled Growth (CG), a cell degeneration process linked to the spreading of a prion‐like hereditary element. Previous work has shown that the PaMpk1 MAP kinase and the PaNox1 NADPH oxidase are key player in setting up CG. Here, we identified PDC1, a new gene that negatively regulates the PaMpk1 pathway, by identifying the gene mutated in the PDC2205 mutant. This mutant exhibits strong CG in conditions where the wild‐type does not. PDC1 encodes a small protein conserved in other Pezizomycotina. The protein contains four evolutionary‐conserved cysteines, a tryptophan and a histidine; all six amino‐acid are essential for function. PDC1 is located in the cytosol and is present in lower amounts in stationary hyphae in accordance with its role as a repressor. Epistasis analyses place PDC1 between PaMpk1 and PaNox1.
Bacterial Biomarkers of Marcellus Shale Activity in Pennsylvania

Unconventional oil and gas (UOG) extraction, also known as hydraulic fracturing, is becoming more prevalent with the increasing use and demand for natural gas; however, the full extent of its environmental impacts is still unknown. Here we measured physicochemical properties and bacterial community composition of sediment samples taken from twenty-eight streams within the Marcellus shale formation in northeastern Pennsylvania differentially impacted by hydraulic fracturing activities. Fourteen of the streams were classified as UOG+, and thirteen were classified as UOG- based on the presence of UOG extraction in their respective watersheds. One stream was located in a watershed that previously had UOG extraction activities but was recently abandoned. We utilized high-throughput sequencing of the 16S rRNA gene to infer differences in sediment aquatic bacterial community structure between UOG+ and UOG- streams, as well as correlate bacterial community structure to physicochemical water parameters. Although overall alpha and beta diversity differences were not observed, there were a plethora of significantly enriched operational taxonomic units (OTUs) within UOG+ and UOG- samples. Our biomarker analysis revealed many of the bacterial taxa enriched in UOG+ streams can live in saline conditions, such as Rubrobacteraceae. In addition, several bacterial taxa capable of hydrocarbon degradation were also enriched in UOG+ samples, including Oceanospirillaceae. Methanotrophic taxa, such as Methylococcales, were significantly enriched as well. Several taxa that were identified as enriched in these samples were enriched in samples taken from different streams in 2014; moreover, partial least squares discriminant analysis (PLS-DA) revealed clustering between streams from the different studies based on the presence of hydraulic fracturing along the second axis. This study revealed significant differences between bacterial assemblages within stream sediments of UOG+ and UOG- streams and identified several potential biomarkers for evaluating and monitoring the response of autochthonous bacterial communities to potential hydraulic fracturing impacts.
STATISTICAL ISSUES IN THE ANALYSIS OF MICROBIAL COMMUNITIES IN SOIL

Corn and soybean production dominates the agricultural systems of the mid-western United States. Studies have found that when a single crop species is grown continually, without the rotation of other crops, yield decline occurs. At present, this phenomenon, remains poorly understood, but there are possible links to microbial community dynamics in the associated rhizosphere soil. In this study, corn plants were grown in disturbed and undisturbed soils with a 24 year history of growth as a mono culture crop or two crops grown in annual rotation. Characteristic profiles of the microbial communities were obtained by denaturing gradient gel electrophoresis of polymerase chain reaction amplified 16S rDNA from soil extracted DNA. This problem is approached as the statistical analysis of high-dimensional multivariate binary data with an emphasis on modeling and variable selection. Conference on Applied Statistics in Agriculture Kansas State University New Prairie Press https://newprairiepress.org/agstatconference/2001/proceedings/11 130 Kansas State University
The effect of age and smoking on the hippocampus and memory in late middle age

From middle age the hippocampus atrophies at an accelerating rate. Factors that further this acceleration may hasten memory decline and the onset of memory disorder. We studied associations between smoking history, age, ApoE e4 genotype, vascular risk factors, hippocampal volume, and cognition in 67 middle‐aged subjects (mean age = 56 years) who were offspring of parents with dementia. Subjects underwent isotropic T1‐weighted 3 T MRI brain scanning with FreeSurfer volumetric data extraction for the hippocampus, a neuropsychological assessment battery, extensive medical data collection, and ApoE genotyping. ApoE e4, vascular risk variables, and alcohol history were unrelated to hippocampal volume. Hippocampal volume correlated negatively with age and positively with memory performance, but not with global cognition. Aging diminished hippocampal volume by 0.52% per year. Female subjects (only two males smoked) with a heavy smoking history (≥ 9.5 pack‐years; n = 11) exhibited hippocampal volumes that were 7.4% smaller than the volumes of females (n = 37) with a light or no smoking history. In our sample by late middle age, a history of moderate to heavy smoking is associated with hippocampal atrophy equivalent to 12 years of aging. Since only a small number of subjects within the sample have a smoking history, validation of this finding in larger samples is desirable.
A Network Structure to Explicitly Reduce Confusion Errors in Semantic Segmentation

Confusing classes that are ubiquitous in real world often degrade performance for many vision related applications like object detection, classification, and segmentation. The confusion errors are not only caused by similar visual patterns but also amplified by various factors during the training of our designed models, such as reduced feature resolution in the encoding process or imbalanced data distributions. A large amount of deep learning based network structures has been proposed in recent years to deal with these individual factors and improve network performance. However, to our knowledge, no existing work in semantic image segmentation is designed to tackle confusion errors explicitly. In this paper, we present a novel and general network structure that reduces confusion errors in more direct manner and apply the network for semantic segmentation. There are two major contributions in our network structure: 1) We ensemble subnets with heterogeneous output spaces based on the discriminative confusing groups. The training for each subnet can distinguish confusing classes within the group without affecting unrelated classes outside the group. 2) We propose an improved cross-entropy loss function that maximizes the probability assigned to the correct class and penalizes the probabilities assigned to the confusing classes at the same time. Our network structure is a general structure and can be easily adapted to any other networks to further reduce confusion errors. Without any changes in the feature encoder and post-processing steps, our experiments demonstrate consistent and significant improvements on different baseline models on Cityscapes and PASCAL VOC datasets (e.g., 3.05% over ResNet-101 and 1.30% over ResNet-38).
It takes two transducins to activate the cGMP-phosphodiesterase 6 in retinal rods

Among cyclic nucleotide phosphodiesterases (PDEs), PDE6 is unique in serving as an effector enzyme in G protein-coupled signal transduction. In retinal rods and cones, PDE6 is membrane-bound and activated to hydrolyse its substrate, cGMP, by binding of two active G protein α-subunits (Gα*). To investigate the activation mechanism of mammalian rod PDE6, we have collected functional and structural data, and analysed them by reaction–diffusion simulations. Gα* titration of membrane-bound PDE6 reveals a strong functional asymmetry of the enzyme with respect to the affinity of Gα* for its two binding sites on membrane-bound PDE6 and the enzymatic activity of the intermediary 1 : 1 Gα* · PDE6 complex. Employing cGMP and its 8-bromo analogue as substrates, we find that Gα* · PDE6 forms with high affinity but has virtually no cGMP hydrolytic activity. To fully activate PDE6, it takes a second copy of Gα* which binds with lower affinity, forming Gα* · PDE6 · Gα*. Reaction–diffusion simulations show that the functional asymmetry of membrane-bound PDE6 constitutes a coincidence switch and explains the lack of G protein-related noise in visual signal transduction. The high local concentration of Gα* generated by a light-activated rhodopsin molecule efficiently activates PDE6, whereas the low density of spontaneously activated Gα* fails to activate the effector enzyme.
Risk factors for noninvasive ventilation failure in patients with post-extubation acute respiratory failure after cardiac surgery.

Background The effect of noninvasive ventilation (NIV) in patients with acute respiratory failure (ARF) after cardiac surgery is controversial. This study identified the feasibility of NIV and assessed the risk factors of NIV failure in patients with ARF after cardiac surgery. Methods We retrospectively reviewed data from 112 patients with ARF requiring NIV and categorized them into the NIV failure and success groups. Patient data were extracted for further analysis, the primary outcomes were the need for endotracheal intubation and NIV-related in-hospital mortality. The risk factors for NIV failure in patients with post-extubation ARF was analyzed. Results The median time from extubation to NIV was 11 hours. No difference in the EuroSCORE existed between the two groups. NIV failed in 38.4% of the patients. The NIV failure group had a higher in-hospital mortality and stay at the longer intensive care unit (ICU). Most cases of NIV failure occurred within 1-48 hours of the treatment. The main causes of early NIV failure were a weak cough reflex and/or excessive secretions and hemodynamic instability. A Sequential Organ Failure Assessment (SOFA) score ≥10.5, vasoactive-inotropic score ≥6, and pneumonia were predictors of NIV failure, whereas a body mass index (BMI) ≥25.0 kg/m2 predicted NIV success. Conclusions NIV was effective in the study population. Multiple organ dysfunction, pneumonia, and significant inotropic drug support before NIV were associated with NIV failure, whereas a BMI ≥25 kg/m2 was a predictor of NIV success.
Adaptive evolution of distinct prey-specific toxin genes in rear-fanged snake venom

Venom proteins evolve rapidly, and as a trophic adaptation are excellent models for predator–prey evolutionary studies. The key to a deeper understanding of venom evolution is an integrated approach, combining prey assays with analysis of venom gene expression and venom phenotype. Here, we use such an approach to study venom evolution in the Amazon puffing snake, Spilotes sulphureus, a generalist feeder. We identify two novel three-finger toxins: sulditoxin and sulmotoxin 1. These new toxins are not only two of the most abundant venom proteins, but are also functionally intriguing, displaying distinct prey-specific toxicities. Sulditoxin is highly toxic towards lizard prey, but is non-toxic towards mammalian prey, even at greater than 22-fold higher dosage. By contrast, sulmotoxin 1 exhibits the reverse trend. Furthermore, evolutionary analysis and structural modelling show highest sequence variability in the central loop of these proteins, probably driving taxon-specific toxicity. This is, to our knowledge, the first case in which a bimodal and contrasting pattern of toxicity has been shown for proteins in the venom of a single snake in relation to diet. Our study is an example of how toxin gene neofunctionalization can result in a venom system dominated by one protein superfamily and still exhibit flexibility in prey capture efficacy.
High incidence of advanced colorectal neoplasia during endoscopic surveillance in serrated polyposis syndrome

Abstract Background Serrated polyposis syndrome (SPS) has been associated with an increased risk of colorectal cancer (CRC). Accordingly, intensive surveillance with annual colonoscopy is advised. The aim of this multicenter study was to describe the risk of advanced lesions in SPS patients undergoing surveillance, and to identify risk factors that could guide the prevention strategy. Methods From March 2013 to April 2015, 296 patients who fulfilled criteria I and/or III for SPS were retrospectively recruited at 18 centers. We selected patients in whom successful clearing colonoscopy had been performed and who underwent subsequent endoscopic surveillance. Advanced neoplasia was defined as CRC, advanced adenoma, or advanced serrated lesion that were ≥ 10 mm and/or with dysplasia. Cumulative incidence of advanced neoplasia was calculated and independent predictors of advanced neoplasia development were identified. Results In 152 SPS patients a total of 315 surveillance colonoscopies were performed (median 2, range 1 – 7). The 3-year cumulative incidence of CRC and advanced neoplasia were 3.1 % (95 % confidence interval [CI] 0 – 6.9) and 42.0 % (95 %CI 32.4 – 51.7), respectively. Fulfilling both I + III criteria and the presence of advanced serrated lesions at baseline colonoscopy were independent predictors of advanced neoplasia development (odds ratio [OR] 1.85, 95 %CI 1.03 – 3.33, P = 0.04 and OR 2.62, 95 %CI 1.18 – 5.81, P = 0.02, respectively). During follow-up, nine patients (5.9 %) were referred for surgery for invasive CRC (n = 4, 2.6 %) or because of polyp burden (n = 5, 3.3 %). After total colectomy, 17.9 % patients developed advanced neoplasia in the retained rectum. Conclusions Patients with SPS have a substantial risk of developing advanced neoplasia under endoscopic surveillance, whereas CRC incidence is low. Personalized endoscopic surveillance based on polyp burden and advanced serrated histology could help to optimize prevention in patients with SPS.
Model‐Based Prediction of Plasma Concentration and Enterohepatic Circulation of Total Bile Acids in Humans

Bile acids released postprandially can modify the rate and extent of lipophilic compounds’ absorption. This study aimed to predict the enterohepatic circulation (EHC) of total bile acids (TBAs) in response to caloric intake from their spillover in plasma. A model for TBA EHC was combined with a previously developed gastric emptying (GE) model. Longitudinal gallbladder volumes and TBA plasma concentration data from 30 subjects studied after ingestion of four different test drinks were supplemented with literature data. Postprandial gallbladder refilling periods were implemented to improve model predictions. The TBA hepatic extraction was reduced with the high‐fat drink. Basal and nutrient‐induced gallbladder emptying rates were altered by type 2 diabetes (T2D). The model was predictive of the central trend and the variability of gallbladder volume and TBA plasma concentration for all test drinks. Integration of this model within physiological pharmacokinetic modeling frameworks could improve the predictions for lipophilic compounds’ absorption considerably.
Real-time image-based instrument classification for laparoscopic surgery

During laparoscopic surgery, context-aware assistance systems aim to alleviate some of the difficulties the surgeon faces. To ensure that the right information is provided at the right time, the current phase of the intervention has to be known. Real-time locating and classification the surgical tools currently in use are key components of both an activity-based phase recognition and assistance generation. In this paper, we present an image-based approach that detects and classifies tools during laparoscopic interventions in real-time. First, potential instrument bounding boxes are detected using a pixel-wise random forest segmentation. Each of these bounding boxes is then classified using a cascade of random forest. For this, multiple features, such as histograms over hue and saturation, gradients and SURF feature, are extracted from each detected bounding box. We evaluated our approach on five different videos from two different types of procedures. We distinguished between the four most common classes of instruments (LigaSure, atraumatic grasper, aspirator, clip applier) and background. Our method succesfully located up to 86% of all instruments respectively. On manually provided bounding boxes, we achieve a instrument type recognition rate of up to 58% and on automatically detected bounding boxes up to 49%. To our knowledge, this is the first approach that allows an image-based classification of surgical tools in a laparoscopic setting in real-time.
Critical Observability for Automata and Petri Nets

Critical observability is a property of cyber-physical systems to detect whether the current state belongs to a set of critical states. In safety-critical applications, critical states model operations that may be unsafe or of a particular interest. De Santis et al. introduced critical observability for linear switching systems, and Pola et al. adapted it for discrete-event systems, focusing on algorithmic complexity. We study the computational complexity of deciding critical observability for systems modeled as (networks of) finite-state automata and Petri nets. We show that deciding critical observability is: 1) NL-complete for finite automata, i.e., it is efficiently verifiable on parallel computers; 2) PSPACE-complete for networks of finite automata, i.e., it is very unlikely solvable in polynomial time; and 3) undecidable for labeled Petri nets, but becoming decidable if the set of critical states (markings) is finite or cofinite, in which case the problem is as hard as the nonreachability problem for Petri nets.