Miracle-dz/UniFAR-FTUs huggingface dataset:
Academic Medical Centers and High-Need, High-Cost Patients: A Call to Action

Health care delivery systems, including academic medical centers (AMCs), are increasingly focused on improving care for vulnerable, high-need, high-cost patients, in part because value-based payment models offer the promise of financial returns, or the avoidance of losses, for doing so. AMCs and other providers that have participated in Medicare and Medicaid demonstrations and value-based payment programs have important insights to offer about the features of successful and promising programs for high-need, high-cost patients. As more AMCs embrace value-based payment, they may have greater flexibility to provide services that address the medical and nonmedical needs of clinically complex patients and thereby reduce avoidable health care utilization. AMCs have many opportunities to create high-performing health systems, establish operational evidence for how to transform delivery systems, and train the next generation of providers to better address the care of high-need, high-cost individuals.
Performance Analysis for Interconnected Virtual Reality With Two-Stage Cooperative Transmission Scheme in 3D UDNs

Virtual reality (VR) is regarded as a profoundly disruptive technology in the future because of its ability to create an immersive experience for people. Instead of being wired by cable, interconnected VR devices are connected over wireless, which brings big challenges for the wireless network to cope with the high data rate and low latency requirements of interconnected VR. In this paper, the critical performance of VR is analyzed by using the tool of stochastic geometry in a 3-D urban scenario with densely deployed low-power nodes which form a so called ultra-dense network. To mitigate the severe inter-cell interference for these densely deployed small base stations, a two-stage cooperative transmission scheme is proposed, where the coordination range is determined and further divided into two sub-regions with different interference coordination methods employed. The expressions of the average VR data rate and the latency probability based on the proposed scheme are derived. Numerical results are further conducted to evaluate the advantage of the proposed scheme and verify the accuracy of the theoretical analysis.
Deficiency of NOD1 Improves the β-Adrenergic Modulation of Ca2+ Handling in a Mouse Model of Heart Failure

Heart failure (HF) is a complex syndrome characterized by cardiac dysfunction, Ca2+ mishandling, and chronic activation of the innate immune system. Reduced cardiac output in HF leads to compensatory mechanisms via activation of the adrenergic nervous system. In turn, chronic adrenergic overstimulation induces pro-arrhythmic events, increasing the rate of sudden death in failing patients. Nucleotide-binding oligomerization domain-containing protein 1 (NOD1) is an innate immune modulator that plays a key role in HF progression. NOD1 deficiency in mice prevents Ca2+ mishandling in HF under basal conditions, but its role during β-adrenergic stimulation remains unknown. Here, we evaluated whether NOD1 regulates the β-adrenergic modulation of Ca2+ signaling in HF. Ca2+ dynamics were examined before and after isoproterenol perfusion in cardiomyocytes isolated from healthy and from post-myocardial infarction (PMI) wild-type (WT) and Nod1-/- mice. Isoproterenol administration induced similar effects on intracellular [Ca2+]i transients, cell contraction, and sarcoplasmic reticulum (SR)-Ca2+ load in healthy WT and Nod1-/- cells. However, compared with WT-PMI cells, isoproterenol exposure induced a significant increase in the [Ca2+]i transients and cell contraction parameters in Nod1-/--PMI cells, which mainly due to an increase in SR-Ca2+ load. NOD1 deficiency also prevented the increase in diastolic Ca2+ leak (Ca2+ waves) induced by isoproterenol in PMI cells. mRNA levels of β1 and β2 adrenergic receptors were significantly higher in Nod1-/--PMI hearts vs WT-PMI hearts. Healthy cardiomyocytes pre-treated with the selective agonist of NOD1, iE-DAP, and perfused with isoproterenol showed diminished [Ca2+]i transients amplitude, cell contraction, and SR-Ca2+ load compared with vehicle-treated cells. iE-DAP-treated cells also presented increased diastolic Ca2+ leak under β-adrenergic stimulation. The selectivity of iE-DAP on Ca2+ handling was validated by pre-treatment with the inactive analog of NOD1, iE-Lys. Overall, our data establish that NOD1 deficiency improves the β-adrenergic modulation of Ca2+ handling in failing hearts.
Only Bayes should learn a manifold (on the estimation of differential geometric structure from data)

We investigate learning of the differential geometric structure of a data manifold embedded in a high-dimensional Euclidean space. We first analyze kernel-based algorithms and show that under the usual regularizations, non-probabilistic methods cannot recover the differential geometric structure, but instead find mostly linear manifolds or spaces equipped with teleports. To properly learn the differential geometric structure, non-probabilistic methods must apply regularizations that enforce large gradients, which go against common wisdom. We repeat the analysis for probabilistic methods and find that under reasonable priors, the geometric structure can be recovered. Fully exploiting the recovered structure, however, requires the development of stochastic extensions to classic Riemannian geometry. We take early steps in that regard. Finally, we partly extend the analysis to modern models based on neural networks, thereby highlighting geometric and probabilistic shortcomings of current deep generative models.
A numerical analysis of the slot film-cooling effectiveness

Cooling gas turbine blades is crucial cooling techniques that allows higher gas turbine inlet temperature. A higher gas turbine inlet temperature allows boosting gas turbine efficiency which reduces fuel consumption. One of the main cooling techniques of the turbine blades is film cooling where a relatively low temperature air is used to form a blanket of cool air around the blade to shield it from high temperature gases. Many complex interrelated geometry and flow parameters affect the effectiveness of the film cooling. The complex interrelations between these parameters are considered the main challenge in properly understanding the effect of these parameters on film cooling. Numerical techniques are viable analysis techniques that are used to better understand film cooling techniques. In this study, a simplified 2D film cooling jet blown from slot jet is investigated for blowing ratio for a wide range between 0.5 and 2. The performance of the film cooling is reported using adiabatic film effectiveness.
Sustainability of community-based women’s groups: reflections from a participatory intervention for newborn and maternal health in Nepal

Abstract Participatory community-based women’s group interventions have been successful in improving maternal and newborn survival. In rural Makwanpur, Nepal, exposure to these Participatory Learning and Action groups resulted in a thirty-percent reduction in neonatal mortality rate and significantly fewer maternal deaths. It is often theorised that participatory approaches are more likely to be sustained than top-down approaches, but this is rarely evaluated after the withdrawal of external support. We sought to understand how participatory learning and action (PLA) groups in Makwanpur fared after the supporting non-governmental organisation withdrew their support as well as factors affecting their sustainability. We used mixed methods, conducting a cross-sectional survey of 239 groups, thirty focus group discussions with group members and thirty key informant interviews within twelve–seventeen months after support was withdrawn. Eighty percent of groups were still active which suggests that PLA groups have a high chance of being sustained over time. Groups were more likely to be sustained if the group had local importance and members continued to acquire new knowledge. However, the participatory nature of the group and local embeddedness were not enough to sustain all groups. They also needed leadership capacity, a unifying activity such as a fund, and a strong belief in the value of their meeting to sustain. These key factors should be considered when seeking to enable sustainability of participatory interventions.
Gray Bananas and a Red Letter A — From Synesthetic Sensation to Memory Colors

Grapheme–color synesthesia is a condition in which objectively achromatic graphemes induce concurrent color experiences. While it was long thought that the colors emerge during perception, there is growing support for the view that colors are integral to synesthetes’ cognitive representations of graphemes. In this work, we review evidence for two opposing theories positing either a perceptual or cognitive origin of concurrent colors: the cross-activation theory and the conceptual-mediation model. The review covers results on inducer and concurrent color processing as well as findings concerning the brain structure and grapheme–color mappings in synesthetes and trained mappings in nonsynesthetes. The results support different aspects of both theories. Finally, we discuss how research on memory colors could provide a new perspective in the debate about the level of processing at which the synesthetic colors occur.
Impact of Indoor Physical Environment on Learning Efficiency in Different Types of Tasks: A 3 × 4 × 3 Full Factorial Design Analysis

Indoor physical environments appear to influence learning efficiency nowadays. For improvement in learning efficiency, environmental scenarios need to be designed when occupants engage in different learning tasks. However, how learning efficiency is affected by indoor physical environment based on task types are still not well understood. The present study aims to explore the impacts of three physical environmental factors (i.e., temperature, noise, and illuminance) on learning efficiency according to different types of tasks, including perception, memory, problem-solving, and attention-oriented tasks. A 3 × 4 × 3 full factorial design experiment was employed in a university classroom with 10 subjects recruited. Environmental scenarios were generated based on different levels of temperature (17 °C, 22 °C, and 27 °C), noise (40 dB(A), 50 dB(A), 60 dB(A), and 70 dB(A)) and illuminance (60 lx, 300 lx, and 2200 lx). Accuracy rate (AC), reaction time (RT), and the final performance indicator (PI) were used to quantify learning efficiency. The results showed ambient temperature, noise, and illuminance exerted significant main effect on learning efficiency based on four task types. Significant concurrent effects of the three factors on final learning efficiency was found in all tasks except problem-solving-oriented task. The optimal environmental scenarios for top learning efficiency were further identified under different environmental interactions. The highest learning efficiency came in thermoneutral, relatively quiet, and bright conditions in perception-oriented task. Subjects performed best under warm, relatively quiet, and moderately light exposure when recalling images in the memory-oriented task. Learning efficiency peaked to maxima in thermoneutral, fairly quiet, and moderately light environment in problem-solving process while in cool, fairly quiet and bright environment with regard to attention-oriented task. The study provides guidance for building users to conduct effective environmental intervention with simultaneous controls of ambient temperature, noise, and illuminance. It contributes to creating the most suitable indoor physical environment for improving occupants learning efficiency according to different task types. The findings could further supplement the present indoor environment-related standards or norms with providing empirical reference on environmental interactions.
A Software-Defined Surveillance System With Energy Harvesting: Design and Performance Optimization

Even though energy harvesting is a promising technology for energy-efficient surveillance systems, energy harvesting levels are highly dynamic depending on the time and location. Thus, the deployment of nonenergy-harvesting sensor nodes (NHSs) and sophisticated sleep scheduling of sensor nodes are necessary for performance guaranteed surveillance systems. In this paper, we present a software-defined surveillance system (SDSS) in which a centralized controller determines the sleep schedules of energy harvesting and NHSs on the basis of the collected information such as the spatial distribution of targets and the energy levels of sensor nodes. To derive the optimal sleep schedules minimizing the number of active sensor nodes while providing sufficient surveillance performance, a constraint Markov decision process problem is formulated and the optimal policy on sleep scheduling is obtained by linear programming. The evaluation results demonstrate that the SDSS with the optimal policy can reduce energy consumption by employing fewer active sensor nodes while providing the required level of target monitoring probability.
Hypoxia Induces Changes in AMP-Activated Protein Kinase Activity and Energy Metabolism in Muscle Tissue of the Oriental River Prawn Macrobrachium nipponense

Hypoxia has important effects on biological activity in crustaceans, and modulation of energy metabolism is a crucial aspect of crustaceans’ ability to respond to hypoxia. The adenosine 5′-monophosphate (AMP)-activated protein kinase (AMPK) enzyme is very important in cellular energy homeostasis; however, little information is known about the role of AMPK in the response of prawns to acute hypoxia. In the present study, three subunits of AMPK were cloned from the oriental river prawn, Macrobrachium nipponense. The full-length cDNAs of the α, β, and γ AMPK subunits were 1,837, 3,174, and 3,773 bp long, with open reading frames of 529, 289, and 961 amino acids, respectively. Primary amino acid sequence alignment of these three subunits revealed conserved similarity between the functional domains of the M. nipponense AMPK protein with AMPK proteins of other animals. The expression of the three AMPK subunits was higher in muscle tissue than in other tissues. Furthermore, the mRNA expression of AMPKα, AMPKβ, and AMPKγ were significantly up-regulated in M. nipponense muscle tissue after acute hypoxia. Probing with a phospho-AMPKα antibody revealed that AMPK is phosphorylated following hypoxia; this phosphorylation event was found to be essential for AMPK activation. Levels of glucose and lactic acid in hemolymph and muscle tissue were significantly changed over the course of hypoxia and recovery, indicating dynamic changes in energy metabolism in response to hypoxic stress. The activation of AMPK by hypoxic stress in M. nipponense was compared to levels of muscular AMP, ADP, and ATP, as determined by HPLC; it was found that activation of AMPK may not completely correlate with AMP:ATP ratios in prawns under hypoxic conditions. These findings confirm that the α, β, and γ subunits of the prawn AMPK protein are regulated at the transcriptional and protein levels during hypoxic stress to facilitate maintenance of energy homeostasis.
Revisiting the modeling of user association patterns in a university wireless network

This paper presents an analysis of a large trace of user associations in a university wireless network, which includes around one thousand access points over five campuses. The trace is obtained from RADIUS authentication logs and its merit is in its recency, scale and duration. We propose a methodology for extracting association statistics from these logs, and look at visiting time distributions and processes of user arrivals to access points. We find that a large fraction of the network — around half of all access points — experiences time-varying Poisson arrival process, and association distributions can be modeled by two-stage hyper-exponential distributions at most of the access point. While network associations in campus wireless networks have been extensively studied in the literature, our study reveals changing patterns in user arrival processes and association durations, which seem to be characteristic for networks of predominantly mobile users, and allows the use of tractable network occupancy models.
1-Bit compressive sensing encryption in multicarrier systems

Compressive sensing (CS) encryption has attracted increasing attention as a lightweight encryption technique for the Internet of things (IoT) and wireless sensor networks (WSNs). Since devices and sensors may have limited hardware, it might be needed to consider quantization for CS encryption. In this paper, we consider a 1-bit CS encryption using artificial noise and a retransmission strategy with interleaving to mitigate degradation of detection performance at a legitimate receiver caused by the quantization errors. Through the analysis and simulation results, we demonstrate that the retransmission strategy can improve the detection performance at a legitimate receiver, while it slightly improves decision performance to estimate a measurement matrix at an eavesdropper. In addition, the proposed 1-bit CS encryption has a better performance than the existing CS encryption in terms of security, while mitigating the performance degradation caused by quantization errors of detection probability at a legitimate receiver.
Joint Link Adaptation and Scheduling for 5G Ultra-Reliable Low-Latency Communications

This paper presents solutions for efficient multiplexing of ultra-reliable low-latency communications (URLLC) and enhanced mobile broadband (eMBB) traffic on a shared channel. This scenario presents multiple challenges in terms of radio resource scheduling, link adaptation, and inter-cell interference, which are identified and addressed throughout this paper. We propose a joint link adaptation and resource allocation policy that dynamically adjusts the block error probability of URLLC small payload transmissions in accordance with the instantaneous experienced load per cell. Extensive system-level simulations of the downlink performance showpromising gains of this technique, reducing the URLLC latency from 1.3 to 1 ms at the 99.999% percentile, with less than 10% degradation of the eMBB throughput performance as compared with conventional scheduling policies. Moreover, an exhaustive sensitivity analysis is conducted to determine the URLLC and eMBB performance under different offered loads, URLLC payload sizes, and link adaptation and scheduling strategies. The presented results give valuable insights on the maximum URLLC offered traffic load that can be tolerated while still satisfying the URLLC requirements, as well as what conditions are more appropriate for dynamic multiplexing of URLLC and eMBB traffic in the upcoming 5G systems.
KRAS, NRAS and BRAF mutations detected by next generation sequencing, and differential clinical outcome in metastatic colorectal cancer (MCRC) patients treated with first line FIr-B/FOx adding bevacizumab (BEV) to triplet chemotherapy

Background First line triplet chemotherapy/BEV significantly improved clinical outcome of MCRC. KRAS/NRAS/BRAF mutations were evaluated by next generation sequencing (NGS) in MCRC patients treated with first line FIr-B/FOx. Methods KRAS exons 2-4 (KRAS2-4), NRAS2-4, BRAF15 were evaluated in 67 tumours by ION Torrent platform. Mutation detection criteria: >500×sequence coverage (cov); >1% mutant allelic fraction (AF). Clinical outcomes were compared by log-rank. Results In 63 samples, KRAS2-4/NRAS2-4/BRAF15 wild-type (wt) were 14 (22.2%), mutant (mut) 49 (77.8%): KRAS2-4 42 (66.7%); NRAS2-4 11 (16.4%); BRAF15 5 (7.5%). Sixty mutations were detected, range 1-3 mut: 43 (71.7%) >1000×cov/>5% AF; 9 (15%) >500×cov/>5% AF; 8 (13.3%) >1000×cov/<5% AF. Mut distribution in KRAS2-4/NRAS2-4/BRAF15: 40 (63.5%) >1000×cov/>5% AF, 8 (12.7%) >500×cov/>5% AF, 1 (1.6%) >1000×cov/<5% AF; BRAF15 1 (1.5%) >500×cov/>5% AF, 4 (6%) >1000×cov/<5% AF. Prevalence of ≥2 mut samples: KRAS2-4/NRAS2-4/BRAF15 8 (12.7%); KRAS2-4 7 (11.1%); NRAS2-4 5 (7.5%). BRAF15 mutant were all ≥2 mut (7.5%), atypical and associated to KRAS and/or NRAS mut: c.1405 G>A; c.1406 G>C; c.1756 G>A, 2 samples; c.1796 C>T. At 21 months (m) follow-up, clinical outcome wt compared to mut was not significantly different: in KRAS2-4/NRAS2-4/BRAF15, progression-free survival (PFS) 18/12 m, overall survival (OS) 28/22 m; 1/≥2 mutations, PFS 14/11, OS 37/22. PFS was trendy worse in RAS/BRAF wt vs ≥2 mut genes (P 0.059). Conclusions Most MCRC harboured KRAS2-4/NRAS2-4/BRAF15 mutations by NGS, often multiple and affecting few tumoral clones; 22% were triple wt. Clinical outcome is not significantly affected by KRAS2-4/NRAS2-4/BRAF15 genotype, trendy different in triple wt, compared with KRAS2-4/NRAS2-4/BRAF15 ≥2 mut.
Gunshot recognition using low level features in the time domain

This paper explores the possibility of using scarcely used time-domain features for the task of gunshot recognition. A set of 11 features derived from temporal characteristics (waveform) of signals is calculated from a mixed dataset of gunshots and non-gunshots. The features leverage the impulsive nature of gunshots and their dissimilarity to other, especially more stationary signals. The paper includes a description of feature extraction, distribution of features and their recognition performance on a selected audio dataset. A subset achieves promising results in comparison with more frequently used spectral-domain features. This makes them a valuable addition to other frequently used features, especially for tasks of impulsive sound recognition.
Overfitting or perfect fitting? Risk bounds for classification and regression rules that interpolate

Many modern machine learning models are trained to achieve zero or near-zero training error in order to obtain near-optimal (but non-zero) test error. This phenomenon of strong generalization performance for "overfitted" / interpolated classifiers appears to be ubiquitous in high-dimensional data, having been observed in deep networks, kernel machines, boosting and random forests. Their performance is consistently robust even when the data contain large amounts of label noise. Very little theory is available to explain these observations. The vast majority of theoretical analyses of generalization allows for interpolation only when there is little or no label noise. This paper takes a step toward a theoretical foundation for interpolated classifiers by analyzing local interpolating schemes, including geometric simplicial interpolation algorithm and singularly weighted $k$-nearest neighbor schemes. Consistency or near-consistency is proved for these schemes in classification and regression problems. Moreover, the nearest neighbor schemes exhibit optimal rates under some standard statistical assumptions. Finally, this paper suggests a way to explain the phenomenon of adversarial examples, which are seemingly ubiquitous in modern machine learning, and also discusses some connections to kernel machines and random forests in the interpolated regime.