Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of biofilm forming on mortality in patients with candidemia: a study derived from real world data

Abstract Background: Evaluation of the role on patient mortality exerted by biofilm forming (BF) Candida strains, by using predictive clinical data. Methods: Eighty-nine strains isolated from Candida bloodstream infection, occurring in two Italian University Hospitals, were employed in this study. A random forest (RF) model was built with a procedure of iterative selection of the risk factors potentially able to predict the probability of death. The similarity between patient conditions and Bayesian clustering was calculated in order to evaluate the role of predictors in the stratification of the death risk. Results: Three different groups of patients with different probability of death were obtained with a RF approach: Group 1 (mortality in 33.3% of cases), Group 2 (death in 50% of cases), and Group 3 (mortality in 76.9% of cases). The comparison between these three groups showed that BF correlated well with increased mortality in patients, admitted for medical diagnosis, with high APACHE II score and treated with azoles. Early treatment within 24 h between candidemia diagnosis and the beginning of antifungal therapy was associated with the lowest of BF rate and mortality. Conclusions: BF by Candida spp. seems to be clinically associated with increased mortality especially in medical patients with higher Apache II score or treated with azoles.
A Systematic Review of Individual and Contextual Factors Affecting ART Initiation, Adherence, and Retention for HIV-Infected Pregnant and Postpartum Women

Background Despite progress reducing maternal mortality, HIV-related maternal deaths remain high, accounting, for example, for up to 24 percent of all pregnancy-related deaths in sub-Saharan Africa. Antiretroviral therapy (ART) is effective in improving outcomes among HIV-infected pregnant and postpartum women, yet rates of initiation, adherence, and retention remain low. This systematic literature review synthesized evidence about individual and contextual factors affecting ART use among HIV-infected pregnant and postpartum women. Methods Searches were conducted for studies addressing the population (HIV-infected pregnant and postpartum women), intervention (ART), and outcomes of interest (initiation, adherence, and retention). Quantitative and qualitative studies published in English since January 2008 were included. Individual and contextual enablers and barriers to ART use were extracted and organized thematically within a framework of individual, interpersonal, community, and structural categories. Results Thirty-four studies were included in the review. Individual-level factors included both those within and outside a woman’s awareness and control (e.g., commitment to child’s health or age). Individual-level barriers included poor understanding of HIV, ART, and prevention of mother-to-child transmission, and difficulty managing practical demands of ART. At an interpersonal level, disclosure to a spouse and spousal involvement in treatment were associated with improved initiation, adherence, and retention. Fear of negative consequences was a barrier to disclosure. At a community level, stigma was a major barrier. Key structural barriers and enablers were related to health system use and engagement, including access to services and health worker attitudes. Conclusions To be successful, programs seeking to expand access to and continued use of ART by integrating maternal health and HIV services must identify and address the relevant barriers and enablers in their own context that are described in this review. Further research on this population, including those who drop out of or never access health services, is needed to inform effective implementation.
Finding Top-k Answers in Keyword Search over Relational Databases Using Tuple Units

Existing studies on keyword search over relational databases usually find Steiner trees composed of connected database tuples as answers. They on-the-fly identify Steiner trees by discovering rich structural relationships between database tuples, and neglect the fact that such structural relationships can be precomputed and indexed. Recently, tuple units are proposed to improve search efficiency by indexing structural relationships, and existing methods identify a single tuple unit to answer keyword queries. However, in many cases, multiple tuple units should be integrated to answer a keyword query. Thus, these methods will involve false negatives. To address this problem, in this paper, we study how to integrate multiple related tuple units to effectively answer keyword queries. To achieve a high performance, we devise two novel indexes, single-keyword-based structure-aware index and keyword-pair-based structure-aware index, and incorporate structural relationships between different tuple units into the indexes. We use the indexes to efficiently identify the answers of integrated tuple units. We develop new ranking techniques and algorithms to progressively find the top-k answers. We have implemented our method in real database systems, and the experimental results show that our approach achieves high search efficiency and result quality, and outperforms state-of-the-art methods significantly.
ANALYSIS OF FIELD-SCALE SALINIZATION: A COMPARISON OF DIFFERENT MODELING APPROACHES

Soil and groundwater salinization are major problems for irrigated agriculture in many arid and semiarid areas of the world. Studies addressing such problems require accurate estimation of salt loadings from irrigated areas through the vadose zone to underlying groundwater. We studied Cl - transport in the vadose zone at 45 locations in a field in the San Joaquin Valley, California, through a combination of soil sampling at six depths (0–1.8 m) and numerical modeling using a coupled water flow and solute transport code (Unsatchem). Our purpose was to assess water and salt loadings from the heterogeneous field to groundwater over a two-year period, and to test applicability of the code to the data. Soil sampling in November, 1995, defined the initial water content and the Cl concentration, and the soil hydraulic properties. Four more sampling periods, ending in November 1997, provided data for evaluating model performance. Cl - distributions in 1997 exhibited a variety of shapes including monotonically increasing or decreasing distributions versus depth, and profiles with maxima, minima or and sigmoidal shapes. The standard modeling approach, based on the Richards equation and the convection-dispersion equation, predicted more Cl leaching than was observed in the field. Somewhat improved predictions were obtained when the potential transpiration rate was increased by a factor of 1.5. Better leaching predictions were also obtained when the model included separate mobile and immobile water fractions, mostly by improving the profile shapes. Our study shows the importance of accurate descriptions of the lower boundary conditions, spatial variability in the water infiltration rate, and estimation of soil surface evaporation and transpiration rates.
Changes in Anxiety and Depression Are Mediated by Changes in Pain Severity in Patients Undergoing Lower-Extremity Total Joint Arthroplasty

Background Depression and anxiety are common comorbidities in chronic pain including osteoarthritis patients undergoing total joint arthroplasty (TJA). What is not clear is whether psychiatric comorbidity precedes the manifestation of painful states or represents a reaction to living with chronic pain and associated functional impairment. The objective of this research was to explore whether decreases in depressive and anxiety symptoms after lower-extremity TJA could be due to postsurgical reductions in pain. Methods We conducted a secondary analysis of data from 1448 TJA patients enrolled in the Analgesics Outcome Study. Patients completed measures of pain intensity, functional status, and depressive and anxiety symptoms preoperatively and at 3 and 6 months postoperatively. Data were analyzed using a structural equation modeling approach. Results We found that improvement in pain and physical function from baseline to 6 months postoperatively was associated with improvement in depression and anxiety symptoms. We also found that a change in overall body pain at 3 months after surgery significantly mediated changes in both the depression and anxiety scores at 6 months after surgery even when controlling for age, sex, baseline body pain, education, opioid use, and type of surgery. Conclusions Presurgical affective symptoms not only have an effect on change in postsurgical pain, whereby lower preoperative scores on depression and anxiety were associated with lower postsurgical pain, but also postsurgical decreases in pain were associated with lower levels of depression and anxiety after surgery. Taking these points into consideration may prove useful in working toward better outcomes for TJA.
Physical Activity Modulates Common Neuroplasticity Substrates in Major Depressive and Bipolar Disorder

Mood disorders (MDs) are chronic, recurrent mental diseases that affect millions of individuals worldwide. Although the biogenic amine model has provided some clinical utility, a need remains to better understand the interrelated mechanisms that contribute to neuroplasticity deficits in MDs and the means by which various therapeutics mitigate them. Of those therapeutics being investigated, physical activity (PA) has shown clear and consistent promise. Accordingly, the aims of this review are to (1) explicate key modulators, processes, and interactions that impinge upon multiple susceptibility points to effectuate neuroplasticity deficits in MDs; (2) explore the putative mechanisms by which PA mitigates these features; (3) review protocols used to induce the positive effects of PA in MDs; and (4) highlight implications for clinicians and researchers.
Emerging Role of Extracellular Vesicles in Bone Remodeling

Extracellular vesicles (EVs), as nanometer-scale particles, include exosomes, microvesicles, and apoptotic bodies. EVs are released by most cell types, such as bone marrow stem cells, osteoblasts, osteoclasts, and immune cells. In bone-remodeling microenvironments, EVs deliver specific proteins (e.g., tenascin C and Sema4D), microRNAs (e.g., miR-214-3p, miR-183-5p, and miR-196a), and other growth factors (e.g., bone morphogenetic protein 1 to 7 and transforming growth factor β1) to osteoblasts and regulate bone formation. In addition, EVs can deliver cytokines, such as RANK (receptor activator of nuclear factor κB) and RANKL (RANK ligand), and microRNAs, such as miR-218 and miR-148a, to modulate osteoclast differentiation during bone resorption. EVs also transfer bioactive molecules and have targeted therapies in bone-related diseases. Moreover, bioactive molecules in EVs are biomarkers in bone-related diseases. We highlight the emerging role of EVs in bone remodeling during physiologic and pathologic conditions and summarize the role of EVs in tooth development and regeneration. At the end of this review, we discuss the challenges of EV application in the treatment of bone diseases.
Electrical impedance tomography as ventilation monitoring in ICU patients

Electrical impedance tomography (EIT), as a monitoring tool of regional lung ventilation, is radiation-free imaging with high temporal resolution. The most important purpose of EIT is to visualize the distribution of tidal volume in different lung regions especially between dependent (dorsal in supine patients) and non-dependent (ventral in supine patients) regions. Many clinical studies evaluated the applicability of PulmoVista® 500 (Dräger Medical GmbH, Lübeck, Germany) and similar EIT devices in estimating optimal PEEP after recruitment maneuvers (RM) in lung healthy patients and acute respiratory distress syndrome (ARDS), ventilation distribution in cystic fibrosis, COPB, pneumonia and respiratory diseases syndrome in
Revisiting Antipsychotic-induced Akathisia: Current Issues and Prospective Challenges

Background: Akathisia continues to be a significant challenge in current neurological and psychiatric practice. Prompt and accurate detection is often difficult and there is a lack of consensus concerning the neurobiological basis of akathisia. No definitive treatment has been established for akathisia despite numerous preclinical and clinical studies. Method: We reviewed antipsychotic-induced akathisia including its clinical presentation, proposed underlying pathophysiology, current and under investigation therapeutic strategies. Conclusion: Despite the initial promise that second generation antipsychotics would be devoid of akathisia effects, this has not been confirmed. Currently, there are limited therapeutic options for the clinical practice and the evidence supporting the most widely used treatments (beta blockers, anticholinergic drugs) is still absent or inconsistent.
Diurnal Oscillations of Soybean Circadian Clock and Drought Responsive Genes

Rhythms produced by the endogenous circadian clock play a critical role in allowing plants to respond and adapt to the environment. While there is a well-established regulatory link between the circadian clock and responses to abiotic stress in model plants, little is known of the circadian system in crop species like soybean. This study examines how drought impacts diurnal oscillation of both drought responsive and circadian clock genes in soybean. Drought stress induced marked changes in gene expression of several circadian clock-like components, such as LCL1-, GmELF4- and PRR-like genes, which had reduced expression in stressed plants. The same conditions produced a phase advance of expression for the GmTOC1-like, GmLUX-like and GmPRR7-like genes. Similarly, the rhythmic expression pattern of the soybean drought-responsive genes DREB-, bZIP-, GOLS-, RAB18- and Remorin-like changed significantly after plant exposure to drought. In silico analysis of promoter regions of these genes revealed the presence of cis-elements associated both with stress and circadian clock regulation. Furthermore, some soybean genes with upstream ABRE elements were responsive to abscisic acid treatment. Our results indicate that some connection between the drought response and the circadian clock may exist in soybean since (i) drought stress affects gene expression of circadian clock components and (ii) several stress responsive genes display diurnal oscillation in soybeans.
Influence of Reel Lay on Residual Stress and Ultimate Bearing Capacity of Pipe

Reel lay is a fast and cost-effective way to install subsea pipelines. During reel lay, pipe’s repeated plastic bending produces residual stress, which has influence on pipe’s ultimate bearing capacity. First, the deformation of pipe in reel lay is analyzed, and the cyclic bending stages are simplified for convenience of theoretical research. Based on the finite element method (FEM), the Ramberg-Osgood model is adopted to describe material’s mechanical property with kinematic hardening rule, and five bending stages are simulated. Further, the influence of material parameters and geometry parameters on pipe’s residual stress is studied. Finally, the effect of residual stress on pipe’s external pressure bearing capacity and tensile capacity is analyzed. Some important conclusions can be drawn: (1) the influence of diameter-thick ratio on residual stress is small, and material parameters’ effect on the residual stress is large; (2) the influence of residual stress on pipe’s external pressure bearing capacity is small, but its influence on tensile capacity is large.
Ieee Transactions on Wireless Communications, Accepted for Publication 1 Optimal Scheduling for Quality of Monitoring in Wireless Rechargeable Sensor Networks

—Wireless Rechargeable Sensor Network (WRSN) is an emerging technology to address the energy constraint in sensor networks. The protocol design in WRSN is extremely challenging due to the complicated interactions between rechargeable sensor nodes and readers, capable of mobility and functioning as energy distributors and data collectors. In this paper, we for the first time investigate the optimal scheduling problem in WRSN for stochastic event capture, i.e., how to jointly mobilize the readers for energy distribution and schedule sensor nodes for optimal quality of monitoring (QoM). We analyze the QoM for three application scenarios: i) the reader travels at a fixed speed to recharge sensor nodes and sensor nodes consume the collected energy in an aggressive way, ii) the reader stops to recharge sensor nodes for a predefined time during its periodic traveling and sensor nodes deplete energy aggressively, iii) the reader stops to recharge sensor nodes but sensor nodes can adopt optimal duty cycle scheduling for maximal QoM. We provide analytical results for achieving the optimal QoM under arbitrary parameter settings. Extensive simulation results are offered to demonstrate the correctness and effectiveness of our results.
AUTOMATIC CLASSIFICATION OF CENTRAL ITALY LAND COVER : COMPARATIVE ANALYSIS OF ALGORITHMS

The specific objective of this paper was to provide a comparative analysis of three automatic classification algorithms: Quinlan’s C4.5 and two robust probabilistic classifiers like Support Vector Machine (SVM) and AdaBoost (a short for “adaptive boosting”). This work is part of a wider project whose general objective is to develop a methodology for the automatic classification, based on CORINE land-cover (CLC) classes, of high resolution multispectral IKONOS images. The dataset used for the comparison is an area of approximately 150 km comprising both urban and rural environments. Input data are basically constituted by multispectral (red, green, blue and infrared bands), 4m ground-resolution images. In some classifications they are integrated by the NormalizedDifference-Vegetation-Index (NVDI), derived from the red and infrared bands, a Digital Terrain Model (DTM) of the area and pixel by pixel gradient values, derived by the DTM. All the above algorithms had to perform full data classification into four classes: vegetation, water bodies, bare soil, and artificial cover. The output is constituted by an image with each pixel assigned to one of the above classes or, with the exception of C4.5, let unclassified (somehow a better solution than a classification error). In addition, a confusion matrix for control data is produced to evaluate the accuracy of each algorithm, by computing the percentage of correctly classified pixels with respect to the total number of pixels, the user’s and producer’s accuracy indexes and the Cohen’s coefficient to evaluate global accuracy. Even if an optimal distribution of the samples in the training set has a great influence, results demonstrate the suitability of supervised classifiers for high resolution land cover classification. In particular, all the proposed approaches work fine, so that we are now exploring the use of more classes, that is at the second level of the CORINE legend. * Corresponding author.
Side-chain Charge Effects and Conductance Determinants in the Pore of ClC-0 Chloride Channels

The charge on the side chain of the internal pore residue lysine 519 (K519) of the Torpedo ClC-0 chloride (Cl−) channel affects channel conductance. Experiments that replace wild-type (WT) lysine with neutral or negatively charged residues or that modify the K519C mutant with various methane thiosulfonate (MTS) reagents show that the conductance of the channel decreases when the charge at position 519 is made more negative. This charge effect on the channel conductance diminishes in the presence of a high intracellular Cl− concentration ([Cl−]i). However, the application of high concentrations of nonpermeant ions, such as glutamate or sulfate (SO4 2−), does not change the conductance, suggesting that the electrostatic effects created by the charge at position 519 are unlikely due to a surface charge mechanism. Another pore residue, glutamate 127 (E127), plays an even more critical role in controlling channel conductance. This negatively charged residue, based on the structures of the homologous bacterial ClC channels, lies 4–5 Å from K519. Altering the charge of this residue can influence the apparent Cl− affinity as well as the saturated pore conductance in the conductance-Cl− activity curve. Amino acid residues at the selectivity filter also control the pore conductance but mutating these residues mainly affects the maximal pore conductance. These results suggest at least two different conductance determinants in the pore of ClC-0, consistent with the most recent crystal structure of the bacterial ClC channel solved to 2.5 Å, in which multiple Cl−-binding sites were identified in the pore. Thus, we suggest that the occupancy of the internal Cl−-binding site is directly controlled by the charged residues located at the inner pore mouth. On the other hand, the Cl−-binding site at the selectivity filter controls the exit rate of Cl− and therefore determines the maximal channel conductance.
Comparing the staples in latent factor models for recommender systems

Since the Netflix Prize competition, latent factor models (LFMs) have become the comparison "staples" for many of the recent recommender methods. The performance improvement of LFMs over baseline approaches, however, hovers at only low percentage numbers. Therefore, it is time for a better understanding of their real power beyond the overall RMSE (root-mean-square error), which as it happens, lies at a very compressed range, without providing too much chance for deeper insight. This paper provides a detailed experimental study regarding the performance of classical staple LFMs on a classical dataset, Movielens 1M1, that sheds light on a much more pronounced excellence of LFMs for particular categories of users and items, for RMSE and other measures. In particular, LFMs exhibit surprising and excellent advantages when handling several difficult user and item categories. By comparing the distributions of the test and predicted ratings, we show that the performance of LFMs is influenced by the rating distribution. We then propose a method to estimate the performance of LFMs for a given rating dataset. Also, we provide a very simple, open-source, library that implements staple LFMs achieving a similar performance as some very recent (2013) developments in LFMs, and at the same time being more transparent than some other libraries in wide use.