Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of neutrophil-lymphocyte ratio on risk reclassification of patients with advanced renal cell cancer to guide risk-directed therapy

Abstract Background: An elevated neutrophil-lymphocyte ratio (NLR) is associated with poor prognosis in advanced renal cell carcinoma (RCC). We examined whether the addition of NLR improves the risk reclassification of advanced RCC using current prognostic tools from the Memorial Sloan Kettering Cancer Center (MSKCC) and International Metastatic Renal Cell Carcinoma Database Consortium (IMDC). Methods: Using randomised data from the COMPARZ trial of first-line pazopanib vs. sunitinib in advanced RCC, we constructed multivariable models containing MSKCC and IMDC predictor variables with and without NLR. We evaluated model discrimination using the concordance index (C-index). We computed net reclassification improvement to quantify patient reclassification into low/intermediate/poor risk groups with the addition of NLR. Results: Of 1102 patients, NLR ≥ 5 (16%) was associated with shorter survival adjusting for MSKCC variables (adjusted HR 1.89, p < .001). Adding NLR to MSKCC variables increased the C-index by 0.01. Among patients who died before 24 months (N = 415), adding NLR reclassified 8% and 2% to a higher and lower risk category, respectively. Among those alive at 24 months (N = 636), adding NLR reclassified 4% and 1% to a higher and lower risk category, respectively. This finding translates to a net benefit of eight additional patients who die within 24 months correctly identified as poor risk per 1000 patients tested. We obtained similar results when evaluating NLR with IMDC variables. Conclusions: NLR does not substantially improve risk reclassification over pre-existing prognostic tools. MSKCC and IMDC classifications remain the standard for guiding risk-directed therapy and trial stratification of patients with advanced RCC.
An Insight Into the Involvement of Mothers of Low Socioeconomic Status in Scottish Primary School Health Education Activities

School-based health activities that involve parents are more likely to be effective for child health and well-being than activities without a parent component. However, such school-based interventions tend to recruit the most motivated parents, and limited evidence exists surrounding the involvement of hard-to-reach parents with low socioeconomic status (SES). Mothers remain responsible for the majority of family care; therefore, this study investigated mothers with low SES to establish the reasons and barriers to their involvement in school-based health activities and to propose strategies to increase their involvement in those activities. Interviews were conducted with mothers with low SES, who were typically not involved in school-based health activities (n = 16). An inductive–deductive approach to hierarchical analysis revealed that there are several barriers resulting in mothers being less involved, particularly due to issues surrounding the schools’ Parent Councils and the exclusivity of school-based events. Efforts made by the school to promote health activities and involve parents in such activities were revealed, alongside recommendations to improve on these practices. The findings offer multiple ways in which future school-based health interventions can recruit and involve mothers with low SES.
A Novel Magnetic Molecular Imprinted Polymer for Selective Extraction of Zearalenone from Cereal Flours before Liquid Chromatography-Tandem Mass Spectrometry Determination

Zearalenone (ZEN) is a nonsteroidal estrogenic mycotoxin produced by various Fusarium species and commonly occurring in corn and other cereals. Even though its acute toxicity is low, still the estrogenic activity of ZEN and metabolites is a matter of concern. In this work, a new magnetic molecularly imprinted polymer (mMIP) for the selective extraction of ZEN from cereal flours is presented. The mMIP was synthesized previously using quercetin as dummy template, and here we wanted to test its applicability to complex food samples. Analyte determination was carried out by high-performance liquid chromatography coupled to tandem mass spectrometry. The selectivity of the mMIP and the main validation method parameters were assessed. In particular, even in samples as complex as cereals, matrix effect was negligible. Although the mMIP showed cross-selectivity towards both ZEN-related and quercetin-related compounds, nonetheless ZEN recovery was > 95% for the two lower spiking levels, and the quantification limit was 0.14 ng g−1, i.e., ca. 500 times lower than the maximum limit fixed for most cereals by European law. Therefore, the material, also in comparison with a commercial sorbent, appears suitable for the application in food analysis, also to isolate ZEN at trace levels.
Convergence analysis of an inexact inertial Krasnoselskii-Mann algorithm with applications

The classical Krasnoselskii-Mann iteration is broadly used for approximating fixed points of nonexpansive operators. To accelerate the convergence of the Krasnoselskii-Mann iteration, the inertial methods were received much attention in recent years. In this paper, we propose an inexact inertial Krasnoselskii-Mann algorithm. In comparison with the original inertial Krasnoselskii-Mann algorithm, our algorithm allows error for updating the iterative sequence, which makes it more flexible and useful in practice. We establish weak convergence results for the proposed algorithm under different conditions on parameters and error terms. Furthermore, we provide a nonasymptotic convergence rate for the proposed algorithm. As applications, we propose and study inexact inertial proximal point algorithm and inexact inertial forward-backward splitting algorithm for solving monotone inclusion problems and the corresponding convex minimization problems.
A shrunken world – micropsia after a right occipito-parietal ischemic stroke

ABSTRACT Micropsia is a rare condition in which patients perceive the outside world smaller in size than it actually is. We examined a patient who, after a right occipito-parietal stroke, subjectively reported perceiving everything at seventy percent of the actual size. Using experimental tasks, we confirmed the extent of his micropsia at 70%. Visual half-field tests showed an impaired perception of shape, location and motion in the left visual field. As his micropsia concerns the complete visual field, we suggest that it is caused by a higher-order compensation process in order to reconcile the conflicting information from the two hemifields.
Compressive Sampling for Array Cameras

While design of high performance lenses and image sensors has long been the focus of camera development, the size, weight and power of image data processing components is currently the primary barrier to radical improvements in camera resolution. Here we show that Deep-Learning- Aided Compressive Sampling (DLACS) can reduce operating power on camera-head electronics by 20x. Traditional compressive sampling has to date been primarily applied in the physical sensor layer, we show here that with aid from deep learning algorithms, compressive sampling offers unique power management advantages in digital layer compression.
A Summarization System for Scientific Documents

We present a novel system providing summaries for Computer Science publications. Through a qualitative user study, we identified the most valuable scenarios for discovery, exploration and understanding of scientific documents. Based on these findings, we built a system that retrieves and summarizes scientific documents for a given information need, either in form of a free-text query or by choosing categorized values such as scientific tasks, datasets and more. Our system ingested 270,000 papers, and its summarization module aims to generate concise yet detailed summaries. We validated our approach with human experts.
Effects of Urinary Kallidinogenase on NIHSS score, mRS score, and fasting glucose levels in acute ischemic stroke patients with abnormal glucose metabolism

Abstract Urinary kallidinogenase may assist recovery acute ischemic stroke. This study evaluated the effect of urinary kallidinogenase on National Institute of Health Stroke Scale (NIHSS) score, modified Rankin scale (mRS) score, and fasting glucose levels in patients with acute ischemic stroke (AIS) combined with diabetes mellitus and impaired fasting glucose. Patients with AIS and abnormal glucose metabolism were enrolled in this prospective cohort study and divided into 2 groups. The human urinary kallidinogenase (HUK) group were treated with urinary kallidinogenase and standard treatment; the control group received standard treatment. NIHSS scores, mRS scores, and fasting blood glucose were evaluated and compared. A total of 113 patients were included: 58 in the HUK group and 55 in the control group. NIHSS scores decreased with treatment in both groups (time effect P < .05), but were lower in the HUK group (main effect P = .026). The mRS score decreased in both groups from 10 until 90 days after treatment (time effect P < .05); the 2 groups were similar (main effect, P = .130). Blood glucose levels decreased in both groups 10 days after treatment (time effect, P < .05), but there was no significant treatment effect (main effect, P = .635). Multivariate analysis showed blood uric acid >420 μmol/L (odds ratio [OR]: 0.053, 95% confidence interval [CI]: 0.008–0.350; P = .002) and application of HUK (OR: 0.217, 95% CI: 0.049–0.954; P = .043) were associated with 90% NIHSS recovery. Baseline NIHSS score was independently associated with poor curative effect. Urinary kallidinogenase with conventional therapy significantly improved NIHSS scores in patients with AIS. Urinary kallidinogenase also showed a trend toward lower fasting blood glucose levels, although the level did not reach significance.
ActivFORMS: A Model-Based Approach to Engineer Self-Adaptive Systems

Handling change is an increasingly important challenge for software engineers. Our focus is on changes caused by uncertainties in the operating conditions of a system, such as changes in the availability of resources in a highly dynamic environment. To deal with such uncertainties, an external feedback loop system can be added to the system that collects additional data during operation to resolve the uncertainties and adapt the system to achieve particular quality requirements (i.e., adaptation goals); this approach is commonly referred to as self-adaptation. To ensure that the system complies with the adaptation goals, recent research suggests the use of formal techniques at runtime. Existing approaches have three shortcomings that limit their practical applicability: (i) they ignore correctness of the behavior of the feedback loop, (ii) they apply exhaustive verification at runtime to select adaptation options to realize the adaptation goals, which is very resource demanding, and (iii) they provide limited or no support for changing adaptation goals at runtime. To tackle these shortcomings, we present ActivFORMS (Active FORmal Models for Self-adaptation). ActivFORMS: (i) provides guarantees for the correct behavior of the feedback loop with respect to a set of correctness properties at design time and preserves the guarantees at runtime by directly executing the verified models of the feedback loop, (ii) guides the adaptation of the system by selecting adaptation options that realize the adaptation goals in an efficient manner using statistical model checking at runtime, and (iii) offers basic support for changing adaptation goals and updating verified models of the feedback loop on-the-fly to meet the new goals. To validate ActivFORMS, we present a tool-supported instance of the approach that we apply to an IoT application for building security monitoring deployed in Leuven.
Deep Learning and MARS: A Connection

We consider least squares regression estimates using deep neural networks. We show that these estimates satisfy an oracle inequality, which implies that (up to a logarithmic factor) the error of these estimates is at least as small as the optimal possible error bound which one would expect for MARS in case that this procedure would work in the optimal way. As a result we show that our neural networks are able to achieve a dimensionality reduction in case that the regression function locally has low dimensionality. This assumption seems to be realistic in real-world applications, since selected high-dimensional data are often confined to locally-low-dimensional distributions. In our simulation study we provide numerical experiments to support our theoretical results and to compare our estimate with other conventional nonparametric regression estimates, especially with MARS. The use of our estimates is illustrated through a real data analysis.
Influence of obesity on in-hospital and postoperative outcomes of hepatic resection for malignancy: a 10-year retrospective analysis from the US National Inpatient Sample

Objectives The influence of obesity on the outcomes of curative liver resection for malignancies remains controversial. We aimed to compare the in-hospital outcomes of liver resection for malignancy between obese and non-obese patients. Design This was a population-based, retrospective, observational study using data from the Nationwide Inpatient Sample (NIS), the largest all-payer US inpatient care database. Setting Hospitalisations of adults ≥18 years old with diagnoses of primary hepatobiliary malignancy or secondary malignant neoplasms of liver in the USA were identified from the NIS database between 2005 and 2014. Participants Data of 18 398 patients ≥18 years old and underwent liver resection without pancreatic resection in the NIS were extracted. All included subjects had primary hepatobiliary malignancy or secondary malignant neoplasms of the liver. Patients were divided into obese and non-obese groups. These groups were compared with respect to postoperative complications, length of hospital stay and hospital cost according to surgical extent and approach. Interventions Patients were undergoing lobectomy of liver or partial hepatectomy. Primary and secondary outcome measures The primary endpoints of this study were postoperative complications, length of hospital stay and hospital cost. Results After adjustment, obese patients were significantly more likely to experience postoperative complications than were non-obese patients (adjusted OR 1.25, 95% CI 1.10 to 1.42), regardless of whether lobectomy or partial hepatectomy was performed. Furthermore, obesity was significantly associated with increased risk of postoperative complications in patients who underwent open liver resection, but not laparoscopic resection. No significant difference was observed in length of hospital stay or total hospital costs between obese and non-obese patients. Conclusions After adjustment for preoperative comorbidities and other potential confounders, obesity is significantly associated with greater risk of complications in patients undergoing open liver resection for malignancy, but not laparoscopic resection.
Contextual Factors Influencing Access to Teaching Computational Thinking

ABSTRACT This study attempts to determine whether teachers' access to computational thinking (CT) and CT technologies varies by rurality (rural versus urban) of the school county and grade level taught (primary versus secondary). A total of 81 teachers from West Virginia, Georgia, and Oklahoma participated in this survey study. Overall, teachers found CT and CT tools useful and relevant to their teaching and had access to an essential level of technology in schools supporting computational thinking. However, teachers' CT skills and their teaching of CT were limited and varied depending on rurality and grade level. For instance, primary school teachers in rural settings reported significantly lower levels of CT skills than those in urban settings. Findings and implications are discussed.
Variationally Inferred Sampling Through a Refined Bound for Probabilistic Programs

A framework to boost efficiency of Bayesian inference in probabilistic programs is introduced by embedding a sampler inside a variational posterior approximation, which we call the refined variational approximation. Its strength lies both in ease of implementation and in automatically tuning the sampler parameters to speed up mixing time. Several strategies to approximate the \emph{evidence lower bound} (ELBO) computation are introduced, including a rewriting of the ELBO objective. A specialization towards state-space models is proposed. Experimental evidence of its efficient performance is shown by solving an influence diagram in a high-dimensional space using a conditional variational autoencoder (cVAE) as a deep Bayes classifier; an unconditional VAE on density estimation tasks; and state-space models for time-series data.
Augmenting a historic house: Villa Ciani 3D

This paper summarizes the main results of a qualitative study of Villa Ciani 3D, an augmented reality project at the historic house Villa Ciani in Lugano, Switzerland. At the moment the building is an empty shell, with a few canvases but without its original furniture. The project, developed in collaboration with the local historic archive and the historic house, allowed visitors to admire images from the archive projected onto glass while being guided on a tour by an actor playing the role of one of the two Ciani brothers, the original owners of the house. Interviews with visitors were collected after the visit and analyzed using grounded theory as the main method of study. On the one hand, the paper highlights the positive impact of the augmented storytelling when visitors explore empty spaces while, on the other, it outlines the limits of using an interaction model that does not allow visitors to interact with each other. The paper includes a more general analysis of the use of augmented reality in historic houses.
PolyJuS: a Squeak/Smalltalk-based polyglot notebook system for the GraalVM

Jupyter notebooks are used by data scientists to publish their research in an executable format. These notebooks are usually limited to a single programming language. Current polyglot notebooks extend this concept by allowing multiple languages per notebook, but this comes at the cost of having to externalize and to import data across languages. Our approach for polyglot notebooks is able to provide a more direct programming experience by executing notebooks on top of a polyglot execution environment, allowing each code cell to directly access foreign data structures and to call foreign functions and methods. We implemented this approach using GraalSqueak, a Squeak/Smalltalk implementation for the GraalVM. To prototype the programming experience and experiment with further polyglot tool support, we build a Squeak/Smalltalk-based notebook UI that is compatible with the Jupyter notebook file format. We evaluate PolyJuS by demonstrating an example polyglot notebook and discuss advantages and limitations of our approach.
PSYCHOLOGICAL EVIDENCE IN COURT ON STATEMENT CREDIBILITY , PSYCHOLOGICAL INJURY AND MALINGERING : THE GLOBAL EVALUATION SYSTEM ( GES )

quent materialization in judicial sentences, which constitute the cornerstone of the Judicial System (Sallmann and Willis, 1984), rest on the two basic dimensions formulated by the Information Integration Models: reliability and validity (Ostrom, Werner & Saks, 1978). Briefly, a judgement is an assessment of the evidence in one dimension. Judgements are based on a set of beliefs about the evidence (e.g., inferences about the accused’s motives, or capacities) that are relevant to the assessment dimension, so that each belief has a weight that affects assessment of the evidence for the judgement dimension. This weight is known as the scalar value of the belief. But not all beliefs contribute in equal measure to the assessment of the evidence: this contribution derives from the estimation of the reliability and validity of the belief. Reliability in the courtroom is basically defined by the credibility of the witnesses; validity is determined by the relevance of the evidence to the judgement to be made. However, credibility of witnesses contributes the highest scalar value in the verdict reached by both juries (Arce, Fariña & Real, 2000) and judges (Arce, Fariña, Novo & Seijo, 2001), and is the key to reaching conflicting verdicts (e.g., attribution of high credibility to a victim’s testimony is a highly robust predictor of guilt, while lack of credibility for a victim’s testimony reliably predicts a not-guilty verdict). Estimation of the credibility of a testimony would be the appreciation of the accuracy or correctness inspired in the assessor by the witness or a part of his/her statement, which induces the assessor to believe that events occurred as stated (Mira, 1989). Two models have been formulated for the estimation of credibility: the subjective or social model and the objective or scientific model (Vrij, 2000). The social model is understood as the recourse to subjective indicators –not scientifically validated– of credibility, while the objective model rests on the assumption of empirical criteria of credibility. In everyday judicial practice, both experts, that is, judges and magistrates (Piñeiro, 2005), and lay persons in law (juries) (Arce, Fariña & Seijo, 2003) use subjective criteria for estimating the credibility of testimonies, which are ineffective for the correct classification of their accuracy. Therefore, courts find it necessary to employ qualified agents for assessing the credibility of witnesses in an empirical manner by means of productive and scientifically validated techniques. In relation to S e c c i ó n M o n o g r á f i c a
Mechanism of Thiazide Diuretic Arterial Pressure Reduction: The Search Continues

Thiazide diuretic (TZD)-mediated chronic reduction of arterial pressure is thought to occur through decreased total peripheral vascular resistance. Further, the decreased peripheral vascular resistance is accomplished through TZD activation of an extrarenal target, resulting in inhibition of vascular constriction. However, despite greater than five decades of investigation, little progress has been made into the identification of the TZD extrarenal target. Proposed mechanisms range from direct inhibition of constrictor and activation of relaxant signaling pathways in the vascular smooth muscle to indirect inhibition through decreased neurogenic and hormonal regulatory pathways. Surprisingly, particularly in view of this lack of progress, comprehensive reviews of the subject are absent. Moreover, even though it is well recognized that 1) several types of hypertension are insensitive to TZD reduction of arterial pressure and, further, TZD fail to reduce arterial pressure in normotensive subjects and animals, and 2) different mechanisms underlie acute and chronic TZD, findings derived from these models and parameters remain largely undifferentiated. This review 1) comprehensively describes findings associated with TZD reduction of arterial pressure; 2) differentiates between observations in TZD-sensitive and TZD-insensitive hypertension, normotensive subjects/animals, and acute and chronic effects of TZD; 3) critically evaluates proposed TZD extrarenal targets; 4) proposes guiding parameters for relevant investigations into extrarenal TZD target identification; and 5) proposes a working model for TZD chronic reduction of arterial pressure through vascular dilation.