Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Tradeoff between Energy and Throughput in Wireless 5G Networks

Even though system energy and spectral efficiency are major issues in wireless network, reaching these objectives conjointly seems very difficult and requires the usage of tradeoffs. Moreover, depending on the context, the importance of either varies. In underloaded context, guaranteeing high Quality of Service (QoS) is easily achievable due to large surplus of available radio resources and focus should be put on energy rather than system throughput. On the contrary, in an overloaded context, the lack of available radio resources required that resources allocation algorithms focus on system capacity in order to preserve QoS. Since the major issue of the network is to satisfy users, in this specific case, energy consumption must become lesser important. Many specialized solutions have been proposed that focus either on energy saving or on throughput maximization. They provide high performances, respectively, on their specific network traffic load context, previously described, but are not optimized outside. Other solutions that proposed static tradeoffs provide average performances but can not be fully efficient in all scenarios. In this paper, we propose a Dynamic Tradeoff between energy and throughput efficiency that adapts the scheduler priorities to the network context and particularly to the traffic load. Considering the context, the scheduler is able to adjust its behavior in order to maintain high QoS while reducing as much energy as possible. Performance evaluation will show that the proposed solution succeeds to minimize energy consumption better than energy focused scheduler in underloaded context while being able to reach the same spectral efficiency as throughput oriented scheduler in highly loaded context.
Combination of NIR therapy and regulatory T cell modulation using layer-by-layer hybrid nanoparticles for effective cancer photoimmunotherapy

The efficacy of combined near-infrared (NIR) and immune therapies for inhibiting tumor growth and recurrence has gained increasing research attention. Regulatory T cells in the tumor microenvironment constitute a major obstacle in achieving robust CD8+ T cell antitumor immunotherapy. In the present study, we designed a photoimmunotherapy-based strategy involving a combination of photothermal and photodynamic therapies, followed by Treg cell suppression, for eliciting an immune response with IR-780- and imatinib-loaded layer-by-layer hybrid nanoparticles. Methods: The layer-by-layer hybrid nanoparticles were prepared through electrostatic interactions. Their photothermal effect, photodynamic effect as well as their effect on inhibiting Treg cells' suppressive function were investigated in vitro and in vivo. Their antitumor effect was evaluated using B16/BL6 and MC-38 tumor-bearing mice. Results: The layer-by-layer hybrid nanoparticles, which were pH-sensitive, enabled the release of IR-780 dye for NIR-induced photothermal and photodynamic effects, and the release of imatinib-loaded glucocorticoid-induced TNF receptor family-related protein/poly(lactic-co-glycolic acid) (GITR-PLGA) nanoparticles to initiate antitumor immunotherapy. The photothermal and photodynamic effects caused by IR-780 under NIR exposure resulted in direct tumor apoptosis/necrosis and the production of tumor-associated antigen, promoted dendritic cell maturation, and enhanced the presentation of tumor-associated antigen to T cells, while the imatinib-loaded GITR-PLGA cores reduced the suppressive function of Treg cells, and consequently activated effective CD8+ T cells towards tumors. Conclusion: With the significant photothermal, photodynamic and immunotherapies, the system successfully eradicated tumor growth, diminished tumor recurrence, and improved survival in vivo. The proposed nanoparticles provide a novel and versatile approach to boost antitumor photoimmunotherapy.
ST segment elevation myocardial infarction (STEMI) patients are more likely to achieve lipid-lowering treatment goals

Abstract Statin nonadherence or discontinuation is associated with increased cardiovascular events. Many factors related to the physicians or the patients are influential in this. We aimed to compare the compliance with statin therapy between the patients who first presented with ST-elevation myocardial infarction (STEMI), non-ST-elevation myocardial infarction (NSTEMI), and unstable angina pectoris (UA) based on the target achievement according to the current dyslipidemia guidelines. We retrospectively acquired all the information about demographic characteristics, in-hospital revascularization procedures, prescribed treatments, and index and up to 6-month follow-up laboratory results of the first acute coronary syndrome patients. Acute coronary syndrome patients were divided into 3 groups as STEMI, NSTEMI, and UA. The STEMI group consisted of 260 patients, NSTEMI group consisted of 560 patients, and UA group consisted of 206 patients. Seventy-six percent of patients underwent percutaneous coronary interventions, 18.3% were managed medically, and 5.7% were referred for coronary artery bypass grafting. There was a significant decrease in low-density lipoprotein-cholesterol (LDL-C) values with the statin treatment at the follow-up in all 3 groups (for all P < .001). In the STEMI group, the percentage of those achieving the target LDL-C level was significantly higher than those who did not achieve the target according to both The American College of Cardiology/American Heart Association (ACC/AHA) and European Society of Cardiology dyslipidemia guidelines. The LDL-C target achievement rates were also higher in the STEMI group than in the NSTEMI and UA groups. Our study concluded that statin treatment goals were more attained in STEMI patients than NSTEMI and UA. All physicians should encourage lifelong intensive statin treatment in UA and NSTEMI patients such as STEMI patients.
Reliability and Construct Validity of the Adapted Norwegian Version of the Early-Onset Scoliosis 24-item Questionnaire

Background: The Early-Onset Scoliosis 24-item Questionnaire (EOSQ-24) reflects issues important for patients with early-onset scoliosis (EOS) and their parents. The aim of this study was to translate the original EOSQ-24 into Norwegian and to evaluate the resulting questionnaire's reliability and construct validity. Methods: The EOSQ-24 was translated using a forward-backward translation method, followed by an expert review. One hundred parents of a heterogenic group of patients with EOS answered the EOSQ-24 and scored Numeric Rating Scales (NRSs) to evaluate the children's general health, pain, and physical function. Two weeks later, 55 parents (55%) answered the retest questionnaire. Data quality, internal consistency, and test-retest reliability were assessed, including the minimal detectable change. Construct validity was evaluated by predefined hypotheses and correlations with NRS scores. Results: There were considerable ceiling (19.0% to 63.0%) and floor effects (zero to 26.0%). The internal consistency was excellent (Cronbach &agr; = 0.95). The minimal detectable change for the EOSQ-24 total score was 15.2 and ranged from 21.6 to 33.0 for the subdomains scores. The EOSQ-24 showed discriminate capabilities among patients with different etiology, treatment status, and severity of deformity. High correlations were found between the EOSQ-24 total score and the NRS scores for general health (r = −0.66), pain (r = −0.63), and physical function (r = −0.78). Conclusion: The Norwegian version of the EOSQ-24 has acceptable reliability and validity for measuring quality of life and caregiver burden among EOS children. The EOSQ-24 total score is acceptable for evaluation of these patients over time. Level of Evidence: Level III, diagnostic study
Impact of a 6‐month family‐based weight management programme on child food and activity behaviours: short‐term and long‐term outcomes of the PEACH™ intervention

The PEACH™ randomized controlled trial measured changes to children's food and activity behaviours following participation in a weight management programme. We have previously reported a 10% reduction in body mass index z‐score at intervention end (6‐month post‐baseline) that was maintained to 24 months with no further intervention for the full study sample.
Extending Stan for Deep Probabilistic Programming

Deep probabilistic programming combines deep neural networks (for automatic hierarchical representation learning) with probabilistic models (for principled handling of uncertainty). Unfortunately, it is difficult to write deep probabilistic models, because existing programming frameworks lack concise, high-level, and clean ways to express them. To ease this task, we extend Stan, a popular high-level probabilistic programming language, to use deep neural networks written in PyTorch. Training deep probabilistic models works best with variational inference, so we also extend Stan for that. We implement these extensions by translating Stan programs to Pyro. Our translation clarifies the relationship between different families of probabilistic programming languages. Overall, our paper is a step towards making deep probabilistic programming easier.
Fighting Against XSS Attacks: A Usability Evaluation of OWASP ESAPI Output Encoding

Cross Site Scripting (XSS) is one of the most critical vulnerabilities exist in web applications. XSS can be prevented by encoding untrusted data that are loaded into browser content of web applications. Security Application Programming Interfaces (APIs) such as OWASP ESAPI provide output encoding functionalities for programmers to use to protect their applications from XSS attacks. However, XSS still being ranked as one of the most critical vulnerabilities in web applications suggests that programmers are not effectively using those APIs to encode untrusted data. Therefore, we conducted an experimental study with 10 programmers where they attempted to fix XSS vulnerabilities of a web application using the output encoding functionality of OWASP ESAPI. Results revealed 3 types of mistakes that programmers made which resulted in them failing to fix the application by removing XSS vulnerabilities. We also identified 16 usability issues of OWASP ESAPI. We identified that some of these usability issues as the reason for mistakes that programmers made. Based on these results, we provided suggestions on how the usability of output encoding APIs should be improved to give a better experience to programmers.
A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption

We study the problem of optimizing a function under a \emph{budgeted number of evaluations}. We only assume that the function is \emph{locally} smooth around one of its global optima. The difficulty of optimization is measured in terms of 1) the amount of \emph{noise} $b$ of the function evaluation and 2) the local smoothness, $d$, of the function. A smaller $d$ results in smaller optimization error. We come with a new, simple, and parameter-free approach. First, for all values of $b$ and $d$, this approach recovers at least the state-of-the-art regret guarantees. Second, our approach additionally obtains these results while being \textit{agnostic} to the values of both $b$ and $d$. This leads to the first algorithm that naturally adapts to an \textit{unknown} range of noise $b$ and leads to significant improvements in a moderate and low-noise regime. Third, our approach also obtains a remarkable improvement over the state-of-the-art SOO algorithm when the noise is very low which includes the case of optimization under deterministic feedback ($b=0$). There, under our minimal local smoothness assumption, this improvement is of exponential magnitude and holds for a class of functions that covers the vast majority of functions that practitioners optimize ($d=0$). We show that our algorithmic improvement is borne out in experiments as we empirically show faster convergence on common benchmarks.
Future strategies for remote damage control resuscitation after traumatic hemorrhage.

One of the key elements of remote damage control resuscitation (RDCR) is a hemostatic resuscitation strategy to prevent the triad of coagulopathy, acidosis and hypothermia. Transfusion of whole blood has been an effective solution for both military and civilian patients, and appears to be superior to individual component therapy. At first, whole blood seems to be the ideal solution for RDCR, but it has some clinical and logistic drawbacks that may limit its use in the prehospital, battlefield, austere, and remote settings, or during multiple casualty or massive transfusion scenarios. The ideal resuscitation product for RDCR after traumatic hemorrhagic shock would be light-weight, long lasting, easily stored in large quantities, free from refrigeration, carry no risk of blood-borne infection or ABO-incompatibility, and carry out the same physiological functions as blood. Lyophilized plasma appears to fulfil many of these criteria, and is in current clinical use. There have been promising preclinical investigations of haemoglobin-based oxygen carriers (HBOC) and lyophilized platelet preparations, and one HBOC is in clinical use. It seems feasible that in the near future there may be a total blood substitution strategy for RDCR that is priority based, aimed at repaying the oxygen debt, restoration of the endothelium, and mitigation of coagulopathy and inflammatory dysregulation. Further preclinical and clinical research activity may facilitate such a resuscitation strategy that is equivalent-or perhaps even superior-to whole blood, but without any of its drawbacks. TYPE OF ARTICLE Current opinion LEVEL OF EVIDENCE: Not applicable.
Technology Transfer Projects in the UK: An Analysis of University - Industry Collaboration

This paper examines how technology transfer has operated in university-company projects undertaken in small to medium sized enterprises (SMEs) via the UK Knowledge Transfer Partnership (KTP) scheme. The paper adopts a qualitative case study approach, focusing on three companies drawn from an initial review of fourteen technology transfer projects. This provides the foundation for the development of a model of 12 key factors that underpinned successful outcomes in these projects. The paper then reviews the full fourteen cases in terms of their impact on either process change, service improvement or product development. It draws upon both the post-project assessments of the funding body and the developed model, and concludes that using new technology to innovate in internal processes and services is likely to prove more successful than projects focusing on new product development. The model provides an analytical framework for this type of technology transfer project that will be of interest and value to academics and business practitioners looking to develop university-industry partnerships involving technology change and innovation.
Cross-Domain Recommendation in the Hotel Sector

Hotel recommendation suffers from a severe sparsity problem. Travelers only book hotels once or twice a year, and one booking dataset may not gather all the bookings done by one user. Cross-domain recommendation can be leveraged to face the sparsity problem by exploiting knowledge from a related domain where feedback can be easily collected. In this paper, we propose to leverage check-ins data from location based social networks to learn mobility patterns and use it for hotel recommendation, considering that the choice of destination is an important factor for hotel selection. We present our developed solution, where we map items and users from both domains based on a number of observations, learn preferences for regions and for hotels, and combine the results to perform the final recommendation. Experiments on a real booking dataset using a dataset of geolocated posts show the interest of using data from other domains to boost hotel recommendation.
Soluble gp130 prevents interleukin-6 and interleukin-11 cluster signaling but not intracellular autocrine responses

Autocrine responses to interleukin-6 family cytokines are insensitive to extracellular gp130 signaling receptor blockade. Inside IL-6 inflammation Members of the interleukin-6 (IL-6) family of proinflammatory cytokines, which includes IL-6 and IL-11, stimulate responses by binding to a complex of their cytokine-specific receptor and the gp130 signaling receptor. Both of these receptor subunits can be shed from the cell surface and regulate distinct mechanisms of gp130-mediated signaling. Lamertz et al. engineered a reductionist system to interrogate how cells respond to wild-type and synthetic IL-6 and IL-11 cytokines. The authors found that a soluble form of gp130 blocked responses to IL-6 and IL-11 that were presented in trans by a neighboring cell. In contrast, extracellular blockade of cell surface gp130 had no effect on the response to autocrine IL-6, suggesting that these responses could occur within the cell. Interleukin-6 (IL-6) is a proinflammatory cytokine of the IL-6 family, members of which signal through a complex of a cytokine-specific receptor and the signal-transducing subunit gp130. The interaction of IL-6 with the membrane-bound IL-6 receptor (IL-6R) and gp130 stimulates “classic signaling,” whereas the binding of IL-6 and a soluble version of the IL-6R to gp130 stimulates “trans-signaling. ” Alternatively, “cluster signaling” occurs when membrane-bound IL-6:IL-6R complexes on transmitter cells activate gp130 receptors on neighboring receiver cells. The soluble form of gp130 (sgp130) is a selective trans-signaling inhibitor, but it does not affect classic signaling. We demonstrated that the interaction of soluble gp130 with natural and synthetic membrane-bound IL-6:IL-6R complexes inhibited IL-6 cluster signaling. Similarly, IL-11 cluster signaling through the IL-11R to gp130 was also inhibited by soluble gp130. However, autocrine classic and trans-signaling was not inhibited by extracellular inhibitors such as sgp130 or gp130 antibodies. Together, our results suggest that autocrine IL-6 signaling may occur intracellularly.
Preclinical Models of Pediatric Brain Tumors—Forging Ahead

Approximately five out of 100,000 children from 0 to 19 years old are diagnosed with a brain tumor. These children are treated with medication designed for adults that are highly toxic to a developing brain. Those that survive are at high risk for a lifetime of limited physical, psychological, and cognitive abilities. Despite much effort, not one drug exists that was designed specifically for pediatric patients. Stagnant government funding and the lack of economic incentives for the pharmaceutical industry greatly limits preclinical research and the development of clinically applicable pediatric brain tumor models. As more data are collected, the recognition of disease sub-groups based on molecular heterogeneity increases the need for designing specific models suitable for predictive drug screening. To overcome these challenges, preclinical approaches will need continual enhancement. In this review, we examine the advantages and shortcomings of in vitro and in vivo preclinical pediatric brain tumor models and explore potential solutions based on past, present, and future strategies for improving their clinical relevancy.
Aberrant Default Mode Network Underlying the Cognitive Deficits in the Patients With Late-Onset Depression

Late-onset depression (LOD) is regarded as a risk factor or a prodrome of Alzheimer’s disease (AD). Moreover, LOD patients with cognitive deficits have the higher risk of subsequent AD. Thus, it is necessary to understand the neural underpinnings of cognitive deficits and its pathological implications in LOD. Consistent findings show that the default mode network (DMN) is an important and potentially useful brain network for the cognitive deficits in LOD patients. In recent years, genetics has been actively researched as a possible risk factor in the pathogenesis of LOD. So, in this review, we discuss the current research progress on the cognitive deficits and DMN in LOD through a combined view of brain network and genetics. We find that different structural and functional impairments of the DMN might be involved in the etiological mechanisms of different cognitive impairments in LOD patients.
Cost‐Effective Sampling and Analysis for Mycotoxins in a Cereal Batch

The presence of hazards (e.g., contaminants, pathogens) in food/feed, water, plants, or animals can lead to major economic losses related to human and animal health or the rejection of batches of food or feed. Monitoring these hazards is important but can lead to high costs. This study aimed to find the most cost‐effective sampling and analysis (S&A) plan in the cases of the mycotoxins deoxynivalenol (DON) in a wheat batch and aflatoxins (AFB1) in a maize batch. An optimization model was constructed, maximizing the number of correct decisions for accepting/rejecting a batch of cereals, with a budget as major constraint. The decision variables were the choice of the analytical method: instrumental method (e.g., liquid chromatography combined with mass‐spectrometry (LC‐MS/MS)), enzyme‐linked‐immuno‐assay (ELISA), or lateral flow devices (LFD), the number of incremental samples collected from the batch, and the number of aliquots analyzed. S&A plans using ELISA showed to be slightly more cost effective than S&A plans using the other two analytical methods. However, for DON in wheat, the difference between the optimal S&A plans using the three different analytical methods was minimal. For AFB1 in maize, the cost effectiveness of the S&A plan using instrumental methods or ELISA were comparable whereas the S&A plan considering onsite detection with LFDs was least cost effective. In case of nonofficial controls, which do not have to follow official regulations for sampling and analysis, onsite detection with ELISA for both AFB1 in maize and DON in wheat, or with LFDs for DON in wheat, could provide cost‐effective alternatives.
Predicting secondary school dropout among South African adolescents: A survival analysis approach

Education is one of the strongest predictors of health worldwide. In South Africa, school dropout is a crisis where by Grade 12, only 52% of the age appropriate population remain enrolled. Survival analysis was used to identify the risk of dropping out of secondary school for male and female adolescents and examine the influence of substance use and leisure experience predictors while controlling for demographic and known predictors using secondary, longitudinal data. Results indicated being male, not living with one’s mother, smoking cigarettes in the past month, and lower levels of leisure-related intrinsic motivation significantly predicted dropout. Results support comprehensive prevention programmes that target risk behaviour and leisure.