Miracle-dz/UniFAR-FTUs huggingface dataset:
Underweight predicts extubation failure after planned extubation in intensive care units

Background Body weight is associated with different physiological changes and the association between weight and mortality in critical care setting had been discussed before. In this study, we investigated the linkage between underweight and post-extubation failure in mechanical ventilated patients in critical setting. Methods This is a retrospective cohort study including patients who were admitted to medical or surgical intensive care units (ICU) between June 2016 and July 2018 and had received endotracheal intubation for more than 72 hours. Those who passed spontaneous breathing trial and underwent a planned extubation were enrolled. Extubation failure was defined as those who required reintubation within the first 72 hours for any reasons. The probability of extubation failure was calculated. Demographic and clinical characteristics were recorded. Multivariate logistic regression models were then used to determine the potential risk factors associated with extubation failure. Results Overall, 268 patients met the inclusion criteria and were enrolled in our study for analysis. The median age of included patients was 67 years (interquartile range, 55–80 years) with 65.3% being male; 63.1% of the patients were included from medical ICU. The proportion of extubation failure in our cohort was 7.1% (19/268; 95% confidence interval [CI], 4.3–10.9%). Overall, underweight patients had the highest risk of extubation failure (8/50), as compared with normoweight (9/135) and overweight patients (2/83). In the multivariate analysis, being underweight (adjust OR [aOR], 3.80, compared to normoweight; 95% CI, 1.23–11.7) and lower maximal inspiratory airway pressure (aOR per one cmH2O decrease, 1.05; 95% CI 1.00–1.09) remained significantly associated with extubation failure. Conclusion In our study, being underweight and lower maximal inspiratory airway pressure was associated with post-extubation respiratory failure after a planned extubation.
Multivariate analysis of curvature estimators

ABSTRACTPrincipal curvature is one of the defining features of surfaces studied in differential geometry. While well-defined and easy to evaluate for smooth surfaces, it cannot be evaluated exactly if the surface is represented by a polygon mesh, unless some special conditions apply. Nevertheless, estimating the curvature of a surface mesh is a crucial step in common mesh processing algorithms, such as mesh segmentation, mesh smoothing, remeshing and others. While a wealth of approaches for estimating the curvature has been proposed in the literature, aiming at the best possible precision of the estimation, an objective study identifying the strengths and weaknesses of the different methods was usually lacking. We present results of a comprehensive study focused on different aspects of curvature estimation. We extend some of the estimators in order to match properties of others and thus provide comparable results. The results of the study indicate that currently there is no one universally optimal method .
Rethink reporting of evaluation results in AI

Aggregate metrics and lack of access to results limit understanding Artificial intelligence (AI) systems have begun to be deployed in high-stakes contexts, including autonomous driving and medical diagnosis. In contexts such as these, the consequences of system failures can be devastating. It is therefore vital that researchers and policy-makers have a full understanding of the capabilities and weaknesses of AI systems so that they can make informed decisions about where these systems are safe to use and how they might be improved. Unfortunately, current approaches to AI evaluation make it exceedingly difficult to build such an understanding, for two key reasons. First, aggregate metrics make it hard to predict how a system will perform in a particular situation. Second, the instance-by-instance evaluation results that could be used to unpack these aggregate metrics are rarely made available (1). Here, we propose a path forward in which results are presented in more nuanced ways and instance-by-instance evaluation results are made publicly available.
Privacy-Preserving and Outsourced Computation Framework for Power Usage Control in Smart Grids

The evolution of intelligent systems to effectively manage our energy services and mitigate climate change have made the adoption of smart grids crucial for a sustainable environment and economic growth. Although smart grid technology offers numerous benefits, such as improved reliability and efficient transmission of electricity, security threats remain a top concern for consumers and utility companies. In general, the confidentiality of the power consumption data collected by the utility companies is an important privacy concern for households. In this paper, we mainly focus on the power outage control problem in smart grids. When the total power usage from a given neighborhood exceeds a pre-defined threshold t, some or all of the households need to reduce their energy consumption to avoid power outage. This problem is referred to as the threshold-based power usage control (TPUC). In particular, we focus on solving the TPUC problem in a privacy-preserving manner. We emphasize that the existing secure TPUC algorithms are either insecure or inaccurate. To address this issue, in this paper, we propose a novel privacy-preserving and outsourced computation framework to address the TPUC problem based on Shamir's secret sharing scheme. We show that our solution always produces correct results. Additionally, we demonstrate that the proposed solution achieves higher privacy and incurs significantly lower costs when compared with existing protocols.
Safety and Tolerability of Cariprazine for the Adjunctive Treatment of Major Depressive Disorder: A Pooled Analysis of Phase 2B and 3 Clinical Trials

Abstract Background Cariprazine has been shown to be efficacious in placebo-controlled clinical trials. In this pooled analysis, the safety of cariprazine in patients with major depressive disorder (MDD) with inadequate response to antidepressants was evaluated using data from placebo-controlled studies of up to 8 weeks’ duration and a long-term open-label safety study. Methods The safety, tolerability, and efficacy of cariprazine as an adjunctive treatment for patients with MDD with inadequate response to antidepressant alone was assessed in five placebo-controlled studies (two 6-week fixed-dose studies [NCT03738215; NCT03739203] and three 8-week flexible-dose studies [NCT00854100; NCT01715805; NCT01469377]) and one 26-week open-label flexible-dose study (NCT01838876). Fixed doses of cariprazine 1.5 and 3 mg/d and flexible doses of 0.1-4.5 mg/d were evaluated. Safety assessments included adverse event (AE) reporting, clinical laboratory tests, weight and other vital signs, and suicide evaluation with Columbia-Suicide Severity Rating Scale (C-SSRS). Pooled analyses of the incidence of safety endpoints overall and within each treatment arm were performed using the most frequent (modal) daily dose taken by patients during the study. Results A total of 2,222 MDD patients with an ongoing antidepressant received treatment with cariprazine, representing 370 patient-years of exposure in placebo-controlled and open-label studies. In the placebo-controlled studies, 1,969 patients were randomized to cariprazine (dose range, 0.1–4.5 mg/d) and 1,108 patients were randomized to placebo. Overall, treatment-emergent AEs occurred in 61% of cariprazine- and 48% of placebo-treated patients; discontinuation due to an AE occurred with 6% of cariprazine- and 2% of placebo-treated patients. The 2 AEs that occurred in at least 5% of cariprazine-treated patients and at a rate at least twice the rate in placebo-treated patients were akathisia (cariprazine=11%; placebo=2%) and restlessness (cariprazine=6%; placebo=2%). Changes in metabolic parameters, including shifts in fasting glucose and lipid parameters, were similar in cariprazine- and placebo-treated patients. In the long-term safety study, mean weight change was 1.6 kg over 6 months. In the placebo-controlled and long-term studies, other safety endpoints including laboratory and C-SSRS assessments of suicidality were generally consistent with the safety profile of cariprazine in approved indications of bipolar disorder and schizophrenia. Conclusion Cariprazine is generally safe and well-tolerated in MDD patients with inadequate response to antidepressant monotherapy. Safety analysis of 2,222 cariprazine-treated patients with MDD revealed no new safety signals, and the data is consistent with the currently approved prescribing information. Funding AbbVie
Polarization effect on four-wave mixing efficiency in a single-mode fiber

Four-wave mixing in a single-mode fiber is studied for general polarization states in input lights. A theory is developed for fiber four-wave mixing where the polarization states of input light are various and they change randomly when propagating through a fiber line. The results show that intensity beating between the propagating lights plays an important role in four-wave mixing efficiency in a fiber. Experimental results support the theoretical treatment. >
Investigating the Early-to-Late Mesolithic Transition in Northeastern Italy: A Multifaceted Regional Perspective

Abstract This article focuses on the Mesolithic record of northeastern Italy, one of the key European regions for studying the last prehistoric hunter-gatherer groups. Most specifically, it aims to compare the rich Early and Late Mesolithic evidence, trying to shed some light on the shift between these two periods. Such a topic is approached at a regional scale, that is to say, by comparing the overall record and trying to identify similarities and divergences concerning different aspects of past lifeways such as settlement strategies, technology, exploitation of faunal resources, ornamental traditions and burial rituals. Overall, by providing an updated regional synthesis, the presented data highlight aspects of continuity and discontinuity between these two periods and contribute significantly to the debate concerning the modalities in which this transition took place in Southern Europe.
Weak convergence of conditioned processes on a countable state space

We consider the problem of conditioning a continuous-time Markov chain (on a countably infinite state space) not to hit an absorbing barrier before time T; and the weak convergence of this conditional process as T → ∞. We prove a characterization of convergence in terms of the distribution of the process at some arbitrary positive time, t, introduce a decay parameter for the time to absorption, give an example where weak convergence fails, and give sufficient conditions for weak convergence in terms of the existence of a quasi-stationary limit, and a recurrence property of the original process.
Lossless compression of medical images by prediction and classification

A lossless image compression algorithm based on a prediction and classification scheme is presented. The algorithm decomposes an image into four subimages by subsampling pixels at even and odd (both row and column) locations. Because the four subimages have strong correlations to one another, one of them is used as a reference in predicting the other three, and the resulting differences between the predicted subimages and the original subimages are encoded. Even though these differences are decorrelated and tend to be random, there is still a relatively large correlation left between the estimated differences and the subimage used in prediction. For example, the estimated differences tend to be large in a high-detailed region, where pixel values change rapidly, whereas the differences are small in a low-detailed region, where pixel values change smoothly and slowly. This redundancy is exploited by dividing the estimated differences into subsets based on a slope change in the subimage used in prediction. The performance of the proposed algorithm using two different predictors, linear interpolation and third-order polynomial interpolation, is compared with that of the hierarchical interpolation (HINT) scheme and Fourier transform interpolation by measuring the first-order entropies of the estimated differences. With a third-order polynomial interpolation and division into two subsets, an average entropy of 3.1 bits/pixel is achieved for the three predicted difference subimages of the 12 bits/pixel x-ray computed tomography images. It is about 0.86 bits/pixel lower in the first-order entropy than the HINT for the three subimages.
Analytical solutions of coupled functionally graded conical shells of revolution

Abstract In this article, axisymmetric deformations of coupled functionally graded conical shells are studied. The analytical solution is presented by using the complex hypergeometric and Legendre polynomial series. The presented results agree closely with reference results for isotropic conical shells of revolution. The symbolic package Mathematica commands is added to the article to help readers search for particular solutions. The detailed solutions to two problems are discussed, i.e. the shells subjected to axisymmetric pressure or to edge loadings. The influence of material property effects is characterized by a multiplier characterizing an unsymmetric shell wall construction (stiffness coupling). The results can be easily adopted in design procedures.
Exchange bias, training effect, and bimodal distribution of blocking temperatures in electrodeposited core-shell nanotubes

M.P.P. and C.T. S. are thankful to FCT for doctoral and postdoctoral Grants No. SFRH/BD/43440/2008 and No. SFRH/BPD/82010/2011, respectively. J.V. acknowledges financial support through FSE/POPH. M.V. thanks the Spanish Ministry of Economia y Competitividad, MEC, under Project No. MAT2010-20798-C05-01. J.P.A. also thanks the Fundacao Gulbenkian for its financial support within the “Programa Gulbenkian de Estimulo a Investigacao Cientifica. ” The authors acknowledge funding from FCT through the Associated Laboratory: IN.
A predictive coding model of the N400

The N400 event-related component has been widely used to investigate the neural mechanisms underlying real-time language comprehension. However, despite decades of research, there is still no unifying theory that can explain both its temporal dynamics and functional properties. In this work, we show that predictive coding – a biologically plausible algorithm for approximating Bayesian inference – offers a promising framework for characterizing the N400. Using an implemented predictive coding computational model, we demonstrate how the N400 can be formalized as the lexico-semantic prediction error produced as the brain infers meaning from linguistic form of incoming words. We show that the magnitude of lexico-semantic prediction error mirrors the functional sensitivity of the N400 to various lexical variables, priming, contextual effects, as well as their higher-order interactions. We further show that the dynamics of the predictive coding algorithm provide a natural explanation for the temporal dynamics of the N400, and a biologically plausible link to neural activity. Together, these findings directly situate the N400 within the broader context of predictive coding research, and suggest that the brain may use the same computational mechanism for inference across linguistic and non-linguistic domains.
Solar wind interaction with planetary atmospheres

Several quite dissimilar models have been proposed for the direct interaction of the solar wind with planetary ionospheres, based, respectively, on the assumption of (1) restricted flow into the ionosphere, (2) an interface between the solar wind and the ionospheric plasma, or (3) a magnetic field barrier (‘induced magnetopause’). All three require the existence of a standing bow shock in the solar wind. We review these models, discuss their individual limitations, and consider what observational data would distinguish among them.
Wide-field calcium imaging of cortical activation and functional connectivity in externally- and internally-driven locomotion

The neural dynamics underlying self-initiated versus sensory driven movements is central to understanding volitional action. Upstream motor cortices are associated with the generation of internally-driven movements over externally-driven. Here we directly compare cortical dynamics during internally- versus externally-driven locomotion using wide-field Ca2+ imaging. We find that secondary motor cortex (M2) plays a larger role in internally-driven spontaneous locomotion transitions, with increased M2 functional connectivity during starting and stopping than in the externally-driven, motorized treadmill locomotion. This is not the case in steady-state walk. In addition, motorized treadmill and spontaneous locomotion are characterized by markedly different patterns of cortical activation and functional connectivity at the different behavior periods. Furthermore, the patterns of fluorescence activation and connectivity are uncorrelated. These experiments reveal widespread and striking differences in the cortical control of internally- and externally-driven locomotion, with M2 playing a major role in the preparation and execution of the self-initiated state.
An Intrinsically Interpretable Entity Matching System

Explainable classification systems generate predictions along with a weight for each term in the input record measuring its contribution to the prediction. In the entity matching (EM) scenario, inputs are pairs of entity descriptions and the resulting explanations can be difficult to understand for the users. They can be very long and assign different impacts to similar terms located in different descriptions. To address these issues, we introduce the concept of decision units, i.e., basic information units formed either by pairs of (similar) terms, each one belonging to a different entity description, or unique terms, existing in one of the descriptions only. Decision units form a new feature space, able to represent, in a compact and meaningful way, pairs of entity descriptions. An explainable model trained on such features generates effective explanations customized for EM datasets. In this paper, we propose this idea via a three-component architecture template, which consists of a decision unit generator, a decision unit scorer, and an explainable matcher. Then, we introduce WYM (Why do You Match? ), an implementation of the architecture oriented to textual EM databases. The experiments show that our approach has accuracy comparable to other state-of-the-art Deep Learning based EM models, but, differently from them, its predictions are highly interpretable.
Fast and Efficient Update Handling for Graph H2TAP

Offloading graph analytics to GPU yields significant performance speedups. In heterogeneous hybrid transactional/analytical graph processing (graph H 2 TAP), where each graph workload type is executed on the most suitable processor, transactions are executed on a CPU-based main graph and analytics are executed on a GPU-optimized graph replica. The problem that arises, as a result, is that updates by transactions on the main graph have to be particularly handled with respect to the graph replica. In this paper, we present a fast and efficient approach to this update handling problem, based on a delta store optimized for graphs. The delta store is a differential graph store that captures the transactional updates, which are later propagated to the graph replica so that analytical queries are executed on the most recently committed version of the graph in accordance with freshness requirements. Our approach ensures consistency be-tween the main graph and the replica. Our evaluation shows the performance advantage of our approach over existing HTAP approaches.
Acute inflammatory reaction during anti-angiogenesis therapy combined with immunotherapy as a possible indicator of the therapeutic effect: Three case reports and literature review

The posterior line treatment of unresectable advanced or metastatic gastrointestinal (GI) tumors has always been a challenging point. In particular, for patients with microsatellite stable (MSS)/mismatch repair proficient (pMMR) 0GI tumors, the difficulty of treatment is exacerbated due to their insensitivity to immune drugs. Accordingly, finding a new comprehensive therapy to improve the treatment effect is urgent. In this study, we report the treatment histories of three patients with MSS/pMMR GI tumors who achieved satisfactory effects by using a comprehensive treatment regimen of apatinib combined with camrelizumab and TAS-102 after the failure of first- or second-line regimens. The specific contents of the treatment plan were as follows: apatinib (500 mg/d) was administered orally for 10 days, followed by camrelizumab (200 mg, ivgtt, day 1, 14 days/cycle) and TAS-102 (20 mg, oral, days 1–21, 28 days/cycle). Apatinib (500 mg/d) was maintained during treatment. Subsequently, we discuss the possible mechanism of this combination and review the relevant literature, and introduce clinical trials on anti-angiogenesis therapy combined with immunotherapy.
Personalised therapeutic approaches to glioblastoma: A systematic review

Introduction Glioblastoma is the most common and malignant primary brain tumour with median survival of 14.6 months. Personalised medicine aims to improve survival by targeting individualised patient characteristics. However, a major limitation has been application of targeted therapies in a non-personalised manner without biomarker enrichment. This has risked therapies being discounted without fair and rigorous evaluation. The objective was therefore to synthesise the current evidence on survival efficacy of personalised therapies in glioblastoma. Methods Studies reporting a survival outcome in human adults with supratentorial glioblastoma were eligible. PRISMA guidelines were followed. MEDLINE, Embase, Scopus, Web of Science and the Cochrane Library were searched to 5th May 2022. Clinicaltrials.gov was searched to 25th May 2022. Reference lists were hand-searched. Duplicate title/abstract screening, data extraction and risk of bias assessments were conducted. A quantitative synthesis is presented. Results A total of 102 trials were included: 16 were randomised and 41 studied newly diagnosed patients. Of 5,527 included patients, 59.4% were male and mean age was 53.7 years. More than 20 types of personalised therapy were included: targeted molecular therapies were the most studied (33.3%, 34/102), followed by autologous dendritic cell vaccines (32.4%, 33/102) and autologous tumour vaccines (10.8%, 11/102). There was no consistent evidence for survival efficacy of any personalised therapy. Conclusion Personalised glioblastoma therapies remain of unproven survival benefit. Evidence is inconsistent with high risk of bias. Nonetheless, encouraging results in some trials provide reason for optimism. Future focus should address target-enriched trials, combination therapies, longitudinal biomarker monitoring and standardised reporting.