Miracle-dz/UniFAR-FTUs huggingface dataset:
Corticosteroids for the prevention of bronchopulmonary dysplasia in preterm infants: a network meta-analysis

Objective To determine the comparative efficacy and safety of corticosteroids in the prevention of bronchopulmonary dysplasia (BPD) in preterm infants. Study design We systematically searched PubMed, EMBASE and the Cochrane Library. Two reviewers independently selected randomised controlled trials (RCTs) of postnatal corticosteroids in preterm infants. A Bayesian network meta-analysis and subgroup analyses were performed. Results We included 47 RCTs with 6747 participants. The use of dexamethasone at either high dose or low dose decreased the risk of BPD (OR 0.29, 95% credible interval (CrI) 0.14 to 0.52; OR 0.58, 95% CrI 0.39 to 0.76, respectively). High-dose dexamethasone was more effective than hydrocortisone, beclomethasone and low-dose dexamethasone. Early and long-term dexamethasone at either high dose or low dose decreased the risk of BPD (OR 0.11, 95% CrI 0.02 to 0.4; OR 0.37, 95% CrI 0.16 to 0.67, respectively). There were no statistically significant differences in the risk of cerebral palsy (CP) between different corticosteroids. However, high-dose and long-term dexamethasone ranked lower than placebo and other regimens in terms of CP. Subgroup analyses indicated budesonide was associated with a decreased risk of BPD in extremely preterm and extremely low birthweight infants (OR 0.60, 95% CrI 0.36 to 0.93). Conclusions Dexamethasone can reduce the risk of BPD in preterm infants. Of the different dexamethasone regimens, aggressive initiation seems beneficial, while a combination of high-dose and long-term use should be avoided because of the possible adverse neurodevelopmental outcome. Dexamethasone and inhaled corticosteroids need to be further evaluated in large-scale RCTs with long-term follow-ups.
Characterization of a transient TCF/LEF-responsive progenitor population in the embryonic mouse retina.

PURPOSE High mobility group (HMG) transcription factors of the T-cell-specific transcription factor/lymphoid enhancer binding factor (TCF/LEF) family are a class of intrinsic regulators that are dynamically expressed in the embryonic mouse retina. Activation of TCF/LEFs is a hallmark of the Wnt/beta-catenin pathway; however, the requirement for Wnt/beta-catenin and noncanonical Wnt signaling during mammalian retinal development remains unclear. The goal of the study was to characterize more fully a TCF/LEF-responsive retinal progenitor population in the mouse embryo and to correlate this with Wnt/beta-catenin signaling. METHODS TCF/LEF activation was analyzed in the TOPgal (TCF optimal promoter) reporter mouse at embryonic ages and compared to Axin2 mRNA expression, an endogenous readout of Wnt/beta-catenin signaling. Reporter expression was also examined in embryos with a retina-specific deletion of the beta-catenin gene (Ctnnb1), using Six3-Cre transgenic mice. Finally, the extent to which TOPgal cells coexpress cell cycle proteins, basic helix-loop-helix (bHLH) transcription factors, and other retinal cell markers was tested by double immunohistochemistry. RESULTS TOPgal reporter activation occurred transiently in a subpopulation of embryonic retinal progenitor cells. Axin2 was not expressed in the central retina, and TOPgal reporter expression persisted in the absence of beta-catenin. Although a proportion of TOPgal-labeled cells were proliferative, most coexpressed the cyclin-dependent kinase inhibitor p27/Kip1. CONCLUSIONS TOPgal cells give rise to the four earliest cell types: ganglion, amacrine, horizontal, and photoreceptor. TCF/LEF activation in the central retina does not correlate with Wnt/beta-catenin signaling, pointing to an alternate role for this transcription factor family during retinal development.
Applications of machine learning in induction cooking

In this paper we present a new line of work combining digital signal processing and machine learning algorithms to identify and classify cooking recipients used in domestic induction heating. First, a recipient `signature' is obtained from processing current and voltage waveforms, which includes impedance harmonics and the power factor. Then, the non-linear fitting capabilities of artificial neural networks and other machine learning algorithms allow processing the pot signature. We show two applications of this technique: recipient size estimation and identification of every cooking recipient of a specific user (for instance, for assigning a specific cooking profile to each vessel). Finally, we implement our procedure onto a low-cost electronic circuit such as those included in commercial induction home appliances. This new approach is of interest for developing new applications in the context of automatic cooking.
Understanding the Credit Multiplier: The Working Capital Channel

We provide novel evidence that frictions in the financing of working capital can limit firms’ production capacity, leading to the amplification and propagation of liquidity problems over time. We propose a new approach to identify this firm credit multiplier that compares how a same firm responds to permanent shocks differently when these shocks are initiated in the period in which they are predicted to be most profitable (their “main quarter”). Our analysis implements this test with oil price shocks and provides extensive evidence supporting our identification strategy. Our results suggest that the financing of working capital can be an important channel for understanding the real effects and determinants of short-term funding problems faced by firms.
Landfills, complexity and biogas risk assessment

The purpose of this paper is to introduce the context of complexity in landfills as a conceptual framework for biogas risk assessment procedures. The paper suggests that there is an endogenous non-predictability to landfills’ accidents and sudden events, which is created by the iterative process of building a landfill and the emerging complexity that characterizes it. Following the view of complexity a conceptual framework for a new biogas risk assessment model is proposed.
Directed Forgetting in Post-Traumatic-Stress-Disorder: A Study of Refugee Immigrants in Germany

People with post-traumatic stress disorder (PTSD) often suffer from memory disturbances. In particular, previous studies suggest that PTSD patients perform atypically on tests of directed forgetting, which may be mediated by an altered emotional appraisal of the presented material. Also, a special role of dissociative symptoms in traumatized individuals’ memory performance has been suggested. Here, we investigate these issues in traumatized immigrants in Germany. In an item-method directed forgetting task, pictures were presented individually, each followed by an instruction to either remember or forget it. Later, recognition memory was tested for all pictures, regardless of initial instruction. Overall, the PTSD group’s discrimination accuracy was lower than the control group’s, as PTSD participants produced fewer hits and more false alarms, but the groups did not differ in directed forgetting itself. Moreover, the more negatively participants evaluated the stimuli, the less they were able to discriminate old from new items. Participants with higher dissociation scores were particularly poor at recognizing to-be-forgotten items. Results confirm PTSD patients’ general discrimination deficits, but provide no evidence for a distinct directed forgetting pattern in PTSD. Furthermore, data indicate that, in general, more negatively perceived items are discriminated with less accuracy than more positively appraised ones. Results are discussed in the larger context of emotion and stress-related modulations of episodic memory, with particular focus on the role of dissociative symptoms.
THE EFFECT OF Pt LOADING ON ELECTROCHEMICAL PERFORMANCE OF CNT-BASED ELECTROCATALYST

Pt loading on CNT surface as electrocatalyst support affects electrochemical performance of electrocatalyst and proton exchange membrane fuel cell stability. In some recent study, Pt-loaded electrocatalyst was a synthesis using poliol method, undergone by controling weight ratio of Pt percursor and CNT in order to find out the importance of appropriate Pt loading and configuration on electrochemical performance. According to the analysis, Pt-loaded electrocatalyst 23.4 and 36.3% show good material properties and electrochemical performance. Electrocatalyst 23.4% Pt loading was selected for preparing low Pt-loaded electrocatalyst layer by screen printing techniques. Nafion ionomer on electrocatalytic film plays an important role to determine the morphological electrocatalyst surface and performance in changing hydrophobicity and electrochemical properties. Nafion ionomer consequently causes reduction on electrochemical properties from 338.6 to 31.6 m2/g whereas the highest electrochemical surface area (ECSA) is achieved by Pt loading 23.4% in average particle size 3.7 nm, corresponding to Pt activity on hydrogen desorption.
An Investigation of the Mechanical Implications of Sacroplasty Using Finite Element Models Based on Tomographic Image Data

ii Acknowledgements . iv Table of
Towards a framework for online modeling and optimization of airflow and temperature distribution in server rooms of data centers

Thermal process control plays a crucial role in energy management in large data centers. This paper proposes a conceptual view of the framework for integrated modelling and optimization for controlling airflow and temperature of server rooms. The main spirit is to continuously evolve the model to adapt to varied situations so as to support optimal decisions of control actions in a dynamic environment inside data centers. Various online learning and optimization methods are discussed as interesting techniques and tools that are recommended to be utilized in the framework.
Prevalence of Hazardous Occupational Noise Exposure, Hearing Loss, and Hearing Protection Usage Among a Representative Sample of Working Canadians

Objective: The aim of this study was to estimate the prevalence of hearing loss (HL), self-reported occupational noise exposure, and hearing protection usage among Canadians. Methods: In-person household interviews were conducted with 3666 participants, aged 16 to 79 years (1811 males) with 94% completing audiometry and distortion-product otoacoustic emission (DPOAE) evaluations. Occupational noise exposure was defined as hazardous when communicating with coworkers at an arm's length distance required speaking in a raised voice. Results: An estimated 42% of respondents reported hazardous occupational noise exposure; 10 years or more was associated with HL regardless of age, sex or education. Absent DPOAEs, tinnitus, and the Wilson audiometric notch were significantly more prevalent in hazardous workplace noise-exposed workers than in nonexposed. When mandatory, 80% reported wearing hearing protection. Conclusions: These findings are consistent with other industrialized countries, underscoring the need for ongoing awareness of noise-induced occupational HL.
A Collaborative Framework for Non-Linear Integer Arithmetic Reasoning in Alt-Ergo

In this paper, we describe a collaborative framework for reasoning modulo simple properties of non-linear integer arithmetic. This framework relies on the AC(X) combination method and on interval calculus. The first component is used to handle equalities of linear integer arithmetic and associativity and commutativity properties of non-linear multiplication. The interval calculus component is used - in addition to standard linear operations over inequalities - to refine bounds of non-linear terms and to inform the SAT solver about judicious case-splits on bounded intervals. The framework has been implemented in the Alt-Ergo theorem prover. We show its effectiveness on a set of formulas generated from deductive program verification.
Fungal attachment and penetration

Many plant pathogenic fungi infect their hosts without using wounds or natural openings, such as stomata. They can penetrate the plant epider! Jjlis directly, although its structure is composed in such a way as to deter microorganisms Ouniper, 1991). The outermost layer of the epidermis, the cuticle, is a mixture of the polyester cutin and wa. ,'{ (Holloway, 1993; see Chapter 2) anchored to the wall layers below which consist mainly of carbohydrates and proteins (Carpita and Gibeaut, 1993). The way fungi manage to infect the epidermis has been a matter of interest since the earliest studies On plant diseases. DeBary (1884) and Chester (1946) were the first to review the literature on fungal penetration. More recently, Martin (1964) and, some years later, Van den Ende (1974) concentrated on the degradation of the cuticle and its role in disease resistance. Kolattukudy (1985) was the first to summarize the exciting new facts on the molecular qualities of the enzyme cutinase and the mechanism of cutin degradation. In most recent years, reviews have concentrated on the different tasks of cutinase isozymes (Kbller, 1991; KblIer et al., 1995) and their importance as a pathogenicity factor during the early stages of infection (Schafer, 1994). Since cutin is only one hurdle in the obstacle race of the fungus to gain access to the epidermal protoplast, more general reviews may also be of interest to the reader. Elad and Evensen (1995) discuss the mode of infection by Bot"rytis and other reviews deal with the different types of fungal infection structures produced by biotrophic or necrotrophic pathogens (Hardham, 1992; Mendgen and Deising, 1993; Mendgen et al., 1996). This chapter concentrates on recent results explaining the factors that contribute to the initiation of the penetration process. In addition, observations demonstrating modifications of the plant cell wall during fungal penetration are included .
Co-expression Patterns between ATN1 and ATXN2 Coincide with Brain Regions Affected in Huntington’s Disease

Cytosine-adenine-guanine (CAG) repeat expansions in the coding regions of nine polyglutamine (polyQ) genes (HTT, ATXN1, ATXN2, ATXN3, CACNA1A, ATXN7, ATN1, AR, and TBP) are the cause of several neurodegenerative diseases including Huntington’s disease (HD), six different spinocerebellar ataxias (SCAs), dentatorubral-pallidoluysian atrophy, and spinobulbar muscular atrophy. The expanded CAG repeat length in the causative gene is negatively related to the age-at-onset (AAO) of clinical symptoms. In addition to the expanded CAG repeat length in the causative gene, the normal CAG repeats in the other polyQ genes can affect the AAO, suggesting functional interactions between the polyQ genes. However, there is no detailed assessment of the relationships among polyQ genes in pathologically relevant brain regions. We used gene co-expression analysis to study the functional relationships among polyQ genes in different brain regions using the Allen Human Brain Atlas (AHBA), a spatial map of gene expression in the healthy brain. We constructed co-expression networks for seven anatomical brain structures, as well as a region showing a specific pattern of atrophy in HD patients detected by magnetic resonance imaging (MRI) of the brain. In this HD-associated region, we found that ATN1 and ATXN2 were co-expressed and shared co-expression partners which were enriched for DNA repair genes. We observed a similar co-expression pattern in the frontal lobe, parietal lobe, and striatum in which this relation was most pronounced. Given that the co-expression patterns for these anatomical structures were similar to those for the HD-associated region, our results suggest that their disruption is likely involved in HD pathology. Moreover, ATN1 and ATXN2 also shared many co-expressed genes with HTT, the causative gene of HD, across the brain. Although this triangular relationship among these three polyQ genes may also be dysregulated in other polyQ diseases, stronger co-expression patterns between ATN1 and ATXN2 observed in the HD-associated region, especially in the striatum, may be more specific to HD.
Deep Learning for Decoding of Linear Codes - A Syndrome-Based Approach

We present a novel framework for applying deep neural networks (DNN) to soft decoding of linear codes at arbitrary block lengths. Unlike other approaches, our framework allows unconstrained DNN design, enabling the free application of powerful designs that were developed in other contexts. Our method is robust to overfitting that inhibits many competing methods, which follows from the exponentially large number of codewords required for their training. We achieve this by transforming the channel output before feeding it to the network, extracting only the syndrome of the hard decisions and the channel output reliabilities. We prove analytically that this approach does not involve any intrinsic performance penalty, and guarantees the generalization of performance obtained during training. Our best results are obtained using a recurrent neural network (RNN) architecture combined with simple preprocessing by permutation. We provide simulation results that demonstrate performance that sometimes approaches that of the ordered statistics decoding (OSD) algorithm.
Non-Gaussian Resistance Fluctuations in Gold-Nanoparticle-Based Gas Sensors: An Appraisal of Different Evaluation Techniques

Volatile organic compounds, such as formaldehyde, can be used as biomarkers in human exhaled breath in order to non-invasively detect various diseases, and the same compounds are of much interest also in the context of environmental monitoring and protection. Here, we report on a recently-developed gas sensor, based on surface-functionalized gold nanoparticles, which is able to generate voltage noise with a distinctly non-Gaussian component upon exposure to formaldehyde with concentrations on the ppm level, whereas this component is absent, or at least much weaker, when the sensor is exposed to ethanol or to pure air. We survey four different statistical methods to elucidate a non-Gaussian component and assess their pros and cons with regard to efficient gas detection. Specifically, the non-Gaussian component was clearly exposed in analysis using level-crossing parameters, which require nothing but a modest computational effort and simple electronic circuitry, and analogous results could be reached through the bispectrum function, albeit with more intense computation. Useful information could be obtained also via the Lévy-stable distribution and, possibly, the second spectrum.
Theoretical insight into diagnosing observation error correlations using observation‐minus‐background and observation‐minus‐analysis statistics

To improve the quantity and impact of observations used in data assimilation, it is necessary to take into account the full, potentially correlated, observation error statistics. A number of methods for estimating correlated observation errors exist, but a popular method is a diagnostic that makes use of statistical averages of observation‐minus‐background and observation‐minus‐analysis residuals. The accuracy of the results it yields is unknown as the diagnostic is sensitive to the difference between the exact background and exact observation error covariances and those that are chosen for use within the assimilation. It has often been stated in the literature that the results using this diagnostic are only valid when the background and observation error correlation length‐scales are well separated. Here we develop new theory relating to the diagnostic. For observations on a 1D periodic domain we are able to the show the effect of changes in the assumed error statistics used in the assimilation on the estimated observation error covariance matrix. We also provide bounds for the estimated observation error variance and eigenvalues of the estimated observation error correlation matrix. We demonstrate that it is still possible to obtain useful results from the diagnostic when the background and observation error length‐scales are similar. In general, our results suggest that when correlated observation errors are treated as uncorrelated in the assimilation, the diagnostic will underestimate the correlation length‐scale. We support our theoretical results with simple illustrative examples. These results have potential use for interpreting the derived covariances estimated using an operational system.
A Novel Concurrent Pictorial Choice Model of Mood-Induced Relapse in Hazardous Drinkers

This study tested whether a novel concurrent pictorial choice procedure, inspired by animal self-administration models, is sensitive to the motivational effect of negative mood induction on alcohol-seeking in hazardous drinkers. Forty-eight hazardous drinkers (scoring ≥7 on the Alcohol Use Disorders Inventory) recruited from the community completed measures of alcohol dependence, depression, and drinking coping motives. Baseline alcohol-seeking was measured by percent choice to enlarge alcohol- versus food-related thumbnail images in two alternative forced-choice trials. Negative and positive mood was then induced in succession by means of self-referential affective statements and music, and percent alcohol choice was measured after each induction in the same way as baseline. Baseline alcohol choice correlated with alcohol dependence severity, r = . 42, p = . 003, drinking coping motives (in two questionnaires, r = .33, p = .02 and r = .46, p = .001), and depression symptoms, r = . 31, p = . 03. Alcohol choice was increased by negative mood over baseline (p < .001, &eegr;p2 = .280), and matched baseline following positive mood (p = .54, &eegr;p2 = .008). The negative mood-induced increase in alcohol choice was not related to gender, alcohol dependence, drinking to cope, or depression symptoms (ps ≥ .37). The concurrent pictorial choice measure is a sensitive index of the relative value of alcohol, and provides an accessible experimental model to study negative mood-induced relapse mechanisms in hazardous drinkers.
Random Access Performance of Distributed Sensors Attacked by Unknown Jammers

In this paper, we model and investigate the random access (RA) performance of sensor nodes (SN) in a wireless sensor network (WSN). In the WSN, a central head sensor (HS) collects the information from distributed SNs, and jammers disturb the information transmission primarily by generating interference. In this paper, two jamming attacks are considered: power and code jamming. Power jammers (if they are friendly jammers) generate noises and, as a result, degrade the quality of the signal from SNs. Power jamming is equally harmful to all the SNs that are accessing HS and simply induces denial of service (DoS) without any need to hack HS or SNs. On the other hand, code jammers mimic legitimate SNs by sending fake signals and thus need to know certain system parameters that are used by the legitimate SNs. As a result of code jamming, HS falsely allocates radio resources to SNs. The code jamming hence increases the failure probability in sending the information messages, as well as misleads the usage of radio resources. In this paper, we present the probabilities of successful preamble transmission with power ramping according to the jammer types and provide the resulting throughput and delay of information transmission by SNs, respectively. The effect of two jamming attacks on the RA performances is compared with numerical investigation. The results show that, compared to RA without jammers, power and code jamming degrade the throughput by up to 30.3% and 40.5%, respectively, while the delay performance by up to 40.1% and 65.6%, respectively.