Miracle-dz/UniFAR-FTUs huggingface dataset:
Failure to Rescue Deteriorating Patients: A Systematic Review of Root Causes and Improvement Strategies

Objectives “Failure to rescue” (FTR) is the failure to prevent a death resulting from a complication of medical care or from a complication of underlying illness or surgery. There is a growing body of evidence that identifies causes and interventions that may improve institutional FTR rates. Why do patients “fail to rescue” after complications in hospital? What clinically relevant interventions have been shown to improve organizational fail to rescue rates? Can successful rescue methods be classified into a simple strategy? Methods A systematic review was performed and the following electronic databases searched between January 1, 2006, to February 12, 2018: MEDLINE, PsycINFO, Cochrane Library, CINAHL, and BNI databases. All studies that explored an intervention to improve failure to rescue in the adult population were considered. Results The search returned 1486 articles. Eight hundred forty-two abstracts were reviewed leaving 52 articles for full assessment. Articles were classified into 3 strategic arms (recognize, relay, and react) incorporating 6 areas of intervention with specific recommendations. Conclusions Complications occur consistently within healthcare organizations. They represent a huge burden on patients, clinicians, and healthcare systems. Organizations vary in their ability to manage such events. Failure to rescue is a measure of institutional competence in this context. We propose “The 3 Rs of Failure to Rescue” of recognize, relay, and react and hope that this serves as a valuable framework for understanding the phases where failure of patient salvage may occur. Future efforts at mitigating the differences in outcome from complication management between units may benefit from incorporating this proposed framework into institutional quality improvement.
Synthesis of Silica-Coated Cs4PbBr6 and Cs4Pb(Br0.4I0.6)6 Quantum Dots With Long Lifetime and Enhancement in Quantum Efficiency for WLEDs Applications: Lightings With High CRI and Displays With Wide Color Gamut

To focus on developing white light-emitting diodes (WLEDs) for lightings with high color rendering index (CRI), low correlated color temperature (CCT), and the displays with wide color gamut, inorganic perovskite quantum dots (QDs) such as CsPbX3 (X = C1, Br, I) were the promising candidate owing to the excellent optoelectronic properties such as high quantum efficiency, narrow emission wavelengths, and tunable emission spectrum. Nevertheless, the CsPbBr3QDs in the form of powders or films had a poor air stability and severe decline of quantum efficiency. Therefore, in this article, a new idea was proposed that 0-D green–red perovskite QDs powders such as Cs4PbBr6 and Cs4Pb(Br0.4I0.6)6 with improved quantum efficiency and long lifetime were first developed by silica-coated method and crystal phase transition in low-temperature synthesis. The quantum efficiency in green Cs4PbBr6 powders could be significantly enhanced from 31.41% to 45.87% and red Cs4Pb(Br0.4I0.6)6 powders was 22.79%. Moreover, the as-prepared perovskite QD powders and commercial YAG phosphors combined with blue chips were applied to high-quality WLEDs for lightings and displays. More importantly, the as-fabricated wide Commission Internationale de l’Eclairage (CIE) color gamut WLEDs for displays possessed 115% National Television System Committee (NTSC) coverage rate and luminous efficiency of 51 lm/W under 20-mA driving current. On the other hand, the constructed WLEDs for high-power lightings would generate a warm white light with a luminous efficiency of 38 lm/W, extremely high CRI of 92.8, and low CCT of 3828 K under 350 mA. Hence, the proposed green–red perovskite QD powders had outstanding potential applications in WLEDs.
Multi-User Hybrid Beamforming System Based on Deep Neural Network in Millimeter-Wave Communication

Millimeter-wave (mmWave) communication with a large bandwidth can result in a significantly improved data rate in wireless communications. To overcome high path-loss in the mmWave frequency band, beamforming technology is necessary. Especially, there has been widespread interest in development of hybrid beamforming (HB) technologies, in view of reducing cost and power consumption in massive multiple input multiple output (MIMO) systems. Some of existing researches on HB algorithms assumed perfect channel state information (CSI) and the others used beam training process in case of assuming imperfect CSI. When beam training process is used, enough beam training has to be conducted to achieve sufficient system performance in massive MIMO systems, which results in significant training overhead. Thus, it is necessary to reduce beam training complexity. Compared to state-of-the-art technology, we propose a multi-user HB system using codebooks based on a deep neural network (DNN) in this paper. In our proposed scheme, beam codewords for the base station (BS) and all users can be inferred using limited beam training in cases when the channel state information (CSI) is unknown. In order to apply the proposed scheme to situations where the CSI is unknown, reference radio frequency (RF) beamformers were introduced. Also, the proposed DNN structure is designed considering introduced reference RF beamformers. By using the proposed DNN with reference RF beamformers, the proposed system can inferred optimal beam codewords with limited beam training. Results obtained from simulations indicate that the proposed scheme can achieve almost the same performance as a conventional scheme with less beam training complexity. We also show that the performances achieved by the proposed scheme are gradually increased as training epoch is increased, eventually converging to a steady-state value.
Decoding Facial Emotion from Activity in The Human Visual Cortex using Functional Magnetic Resonance Imaging

At present, the neuroimaging research on visual cortex mainly focuses on decoding semantics of visual stimuli. However, it is still not clear how visual cortex can represent the emotional information in images. At the same time, the huge number of visual voxels and the subtle emotion factors bring great difficulties to accurate emotion decoding. In this paper, by using functional magnetic resonance imaging, we explore the methods of extract facial emotion information from the activity of human visual cortex through multi-voxel analysis based machine learning. Firstly, primary visual areas and facial recognition-related brain areas such as IOG, pSTS, mFus and pFus are extracted from the expanded cerebral cortex as regions of interest (ROI), and the responses of all cortical vertices to five kinds of facial emotional image stimuli are estimated by general linear model (GLM). Secondly, to solve the problem that the number of voxels in ROI is too large to be decoded further, a Principal Component-Max Relevance (PC-MR) method is proposed to extract voxel features and optimize voxel selection, and the voxel feature dimension is reduced to facilitate decoder recognition and decoding. Lastly, a parameter learning optimal SVM classifier is proposed for decoding. The experimental results show that the selected ROI contains the factor information of facial emotion, and can decode it through the voxel signal characteristics of these regions.
Multiple-GPU-Based Simulation of Ka-Band Helix Traveling Wave Tube

The finite-difference time-domain (FDTD) algorithm and the particle-in-cell (PIC) method-based simulation are classic approaches to design and optimize the helix traveling wave tubes (TWTs). In this article, a multiple-graphics processing unit (GPU)-based 3-D-FDTD-PIC parallel program is developed to complete the full-wave simulation of a Ka-band helix TWT in the time domain. The specific parallel simulation scheme is given. The code based on compute unified device architecture (CUDA) and message passing interface (MPI) can run on a scalable heterogeneous cluster consisting of multiple CPUs and GPUs, substantially improving the simulation speed and shortening the development period of TWTs. In this article, the specific parameters of the experimental TWT are given. The simulation results are found basically consistent with the measured values and theoretical analysis, verifying the correctness of this code. Moreover, this program can realistically restore the physical processes occurring in the tube in a short period of time, which means it can be applied as an efficient simulation tool for further research of TWTs or even other microwave power devices based on beam–wave interaction.
Aggregation Measure Factor-Based Workflow Application Scheduling in Heterogeneous Environments

With the development of heterogeneous distributed computing environment, workflow application scheduling has become an important and challenging problem while Quality of Service (QoS) guarantees are ensured for science workflows. In this paper, we first introduce an aggregation measure factor to balance the execution time and cost of workflow applications. Then, we propose an aggregation measure factor-based scheduling algorithm (AFSA) for workflow applications in a heterogeneous distributed environment. The proposed algorithm through allocating the sub-budget and sub-deadline for each task to choose available processors takes into account of the budget and deadline aggregation to select the processor for the science workflows. Furthermore, we introduce both a planning success rate and normalized deadline (ND) as performance metrics to evaluate workflow application scheduling algorithms. Furthermore, we use both a randomly generated data set and a real-world workflow data set in our experiments for the performance evaluation. Moreover, our experimental results demonstrate that the proposed AFSA has a higher balance factor and an almost equal or higher planning success rate under different workflow application structures compared to the existing algorithms, BHEFT, HBCS, WMFCO, and DBCS.
LinkLab: A Scalable and Heterogeneous Testbed for Remotely Developing and Experimenting IoT Applications

Developing and experimenting IoT applications are still difficult and time-consuming, mainly due to their heterogeneous hardware and diverse software. Testbeds are useful for developers to test the performance of IoT applications before deploying them to the fields. In this paper, we present LinkLab (http://linklab.emnets.org), a scalable IoT testbed for heterogeneous devices. Compared with existing IoT testbeds, LinkLab not only supports running experiments but also supports remote development via a web-based IDE and remote compiling. Therefore, LinkLab is able to improve the efficiency of the developing-experimenting-optimizing loop for IoT developers. By using a distributed architecture, LinkLab is a scalable, multi-site, and multi-user IoT testbed, with fine-grained access control and a flexible naming mechanism. Further, an incremental online compilation and a multi-user cache techniques are used to speed up the cross compilation process. LinkLab is implemented with more than 150 IoT devices and evaluated by case studies, micro-benchmarks, and real experiments. Results show that LinkLab achieves convenient application development, efficient online compilation, and robust device management under various evaluation settings.
Tropomyosin receptor kinase inhibitors in the management of sarcomas.

PURPOSE OF REVIEW Genetic aberrations resulting in tropomyosin receptor kinase (TRK) fusion proteins can drive oncogenesis and are postulated to occur in up to 1% of solid tumours. However, TRK fusions in adult sarcomas are rare and there is a significant challenge in identifying patients with sarcomas harbouring TRK fusions in the clinical setting. Despite a recent European Society of Medical Oncology consensus article regarding screening of tumours for TRK fusions, economical and practical limitations present a barrier to widespread screening of sarcomas. RECENT FINDINGS Larotrectinib and entrectinib are pan-TRK inhibitors which have both received FDA approval for the management of solid tumours harbouring NTRK fusions. Initial results of a number of clinical trials have demonstrated promising efficacy and safety data, including dramatic and durable responses in patients with sarcomas. As such, TRK inhibitors represent a promising treatment option in a small cohort of adult sarcoma patients, where currently treatment options are limited. The emergence of acquired resistance is a concern associated with TRK inhibitor therapy and a number of second-generation agents targeting TRK kinase mutations driving acquired resistance have entered early-phase clinical trials. SUMMARY With the growing appreciation of the implications of TRK fusions, this review will summarize the emerging clinical trial data of TRK inhibitors in sarcomas. Although in their infancy, clinical trial results are encouraging, and as further results and analysis are released, we will have a greater understanding of their impact on clinical practice and the management of patients with sarcomas.
Determination of Optimum Segmentation Schemes for Pattern Recognition-Based Myoelectric Control: A Multi-Dataset Investigation

Pattern recognition (PR) algorithms have shown promising results for upper limb myoelectric control (MEC). Several studies have explored the efficacy of different pre and post processing techniques in implementing PR-based MECs. This paper explores the effect of segmentation type (disjoint and overlap) and segment size on the performance of PR-based MEC, for multiple datasets recorded with different recording devices. Two PR-based methods; linear discriminant analysis (LDA) and support vector machine (SVM) are used to classify hand gestures. Optimum values of segment size, step size and segmentation type were considered as performance measure for a robust MEC. Statistical analysis showed that optimum values of segment size for disjoint segmentation are between 250ms and 300ms for both LDA and SVM. For overlap segmentation, best results have been observed in the range of 250ms-300ms for LDA and 275ms-300ms for SVM. For both classifiers the step size of 20% achieved highest mean classification accuracy (MCA) on all datasets for overlap segmentation. Overall, there is no significant difference in MCA of disjoint and overlap segmentation for LDA (P-value = 0.15) but differ significantly in the case of SVM (P-value < 0.05). For disjoint segmentation, MCA of LDA is 88.68% and for SVM, it is 77.83%. Statistical analysis showed that LDA outperformed SVM for disjoint segmentation (P-value<0.05). For overlap segmentation, MCA of LDA is 89.86% and for SVM, it is 89.16%, showing that statistically, there is no significant difference between MCA of both classifiers for overlap segmentation (P-value = 0.45). The indicated values of segment size and overlap size can be used to achieve better performance results, without increasing delay time, for a robust PR-based MEC system.
Automatic eNodeB state management in LTE networks using Semi-Supervised Learning with Adversarial Autoencoder

It is crucial to identify the cause immeditely when a failure occurs at the base station called eNodeB in LTE networks. However, a huge amount of log data generated from the eNodeB prevents the human operator to quickly identify the cause of failure. In order to improve the network operation efficiency, machine learning technique is used to analyze Key Performance Indicator (KPI) data generated from eNodeB and classify the operational status of the eNodeB. However an issue classification with supervised learning requires a large amount of labeled dataset, which takes costly human-labor and time to annotate raw performance metric data. To address this issue, we propose a method that employs Adversarial Autoencoder (AAE), which is a semi-supervised learning method. We evaluate the proposed method using eNodeB log data collected from a service provider LTE network. We confirm that our approach achieves on average 94% accuracy and yields high accuracy even for the class with a small amount of labeled data.
Production of Textile Fibers from Zein and a Soy Protein‐Zein Blend

ABSTRACT Zein fibers were successfully prepared by a wet-spinning technique from a zein suspension formulated with 15% zein, 60% water, 22% 0.4N sodium hydroxide, and 3% urea by weight. Tenacities were measured with an Instron machine, and flexibility was determined by noting the smallest diameter rod around which a fiber could be looped without breaking. After spinning, the tenacity of the zein fibers was improved by modification with several agents: acetic anhydride for acetylation, glutaraldehyde, and dialcohols for cross-linking and physical stretching. The tenacity and flexibility of the fibers were measured at 11, 65, and 100% rh. Untreated fibers had tenacities of 3.41, 2.65, and 0.17 g/tex at 11, 65, and 100% rh, respectively. A combination of chemical treatments (20% glutaraldehyde and 95% acetic anhydride) and 115% stretching increased tenacities to 6.89. 6.56, and 1.17 g/tex at 11, 65, and 100% rh, respectively. Control zein fibers had flexibilities of 5 and 2.5 mm at 11 and 65% rh, respectivel.
Learning Amyloid Pathology Progression from Longitudinal PIB-PET Images in Preclinical Alzheimer's Disease

Amyloid accumulation is acknowledged to be a primary pathological event in Alzheimer's disease (AD). The literature suggests that propagation of amyloid occurs along neural pathways as a function of the disease process (prion-like transmission), but the pattern of spread in the preclinical stages of AD is still poorly understood. Previous studies have used diffusion processes to capture amyloid pathology propagation using various strategies and shown how future time-points can be predicted at the group level using a population-level structural connectivity template. But connectivity could be different between distinct subjects, and the current literature is unable to provide estimates of individual-level pathology propagation. We use a trainable network diffusion model that infers the propagation dynamics of amyloid pathology, conditioned on an individual-level connectivity network. We analyze longitudinal amyloid pathology burden in 16 gray matter (GM) regions known to be affected by AD, measured using Pittsburgh Compound B (PiB) positron emission tomography at 3 different time points for each subject. Experiments show that our model outperforms inference based on group-level trends for predicting future time points data (using individual-level connectivity networks). For group-level analysis, we find parameter differences (via permutation testing) between the models for APOE positive and APOE negative subjects.
Role of non-raft cholesterol in lymphocytic choriomeningitis virus infection via α-dystroglycan

Dystroglycan (DG) is an extracellular matrix receptor necessary for the development of metazoans from flies to humans and is also an entry route for various pathogens. Lymphocytic choriomeningitis virus (LCMV), a member of the family Arenaviridae, infects by binding to α-DG. Here, the role of cholesterol lipid rafts in infection by LCMV via α-DG was investigated. The cholesterol-sequestering drugs methyl-β-cyclodextrin (MβCD), filipin and nystatin inhibited the infectivity of LCMV selectively, but did not affect infection by vesicular stomatitis virus. Cholesterol loading after depletion with MβCD restored infectivity to control levels. DG was not found in lipid rafts identified with the raft marker ganglioside GM1. Treatment with MβCD, however, enhanced the solubility of DG. This may reflect the association of DG with cholesterol outside lipid rafts and suggests that association of DG with non-raft cholesterol is critical for infection by LCMV through α-DG.
Sombreamento de plantas de Catharanthus roseus (L.) G. Don 'Pacifica White' por malhas coloridas: desenvolvimento vegetativo

As malhas coloridas tem sido utilizadas para manipular o desenvolvimento vegetativo, melhorando a utilizacao da radiacao solar por plantas ornamentais. Objetivou-se, neste trabalho estudar o efeito da reducao de 50% da radiacao fotossinteticamente ativa sobre o crescimento vegetativo de plantas de Catharanthus roseus (L.) G. Don, por meio de malhas azul e vermelha e malha preta, em comparacao com plantas crescidas na ausencia de sombreamento (pleno sol). As plantas foram obtidas a partir de sementes e tratadas por 180 dias. Foram avaliados o ganho de biomassa e a distribuicao de materia seca nas plantas, o conteudo de pigmentos foliares (clorofilas e carotenoides) e de nitrogenio foliar. A malha vermelha provocou um aumento de materia seca total e de area foliar das plantas em comparacao com as malhas azul, preta e ao tratamento a pleno sol, porem, exceto em relacao a esse tratamento, a malha vermelha causou menor conteudo de nitrogenio e pigmentos foliares. A maior relacao raiz/parte aerea e relacao clorofila a/b, menores razoes de area foliar e de massa foliar das plantas crescidas a pleno sol em relacao as plantas sombreadas indicam um efeito mais proeminente da irradiância mais alta do que da alteracao do espectro de luz. O sombreamento altera significativamente a distribuicao de materia seca e o uso de malhas de diferentes cores modifica o conteudo de pigmentos fotossinteticos dessa especie.
Call combinations in wild chimpanzees

Summary By combining different call types, such as barks with screams, individuals have the potential to vastly increase the range of information that can be decoded by listeners. Few animal studies, however, have examined the information content of call combinations compared with the information conveyed by each call singularly. We examined several aspects of call combinations in the repertoire of wild chimpanzees in the Tai Forest, Ivory Coast, including the types of combinations, the contexts and possible functions and compared these with the use of single calls. Almost half of all vocalisations produced by the Tai chimpanzees occurred in combination with other vocalisations or with drumming. A total of 88 different types of combinations were used. Single calls and call combinations were each produced in specific contexts. The contexts in which six of ten combinations were produced differed from the contexts of at least one of the component calls. The contexts in which the combinations were produced varied from the component calls in five different ways, three having potentially novel functions. Two of the three may have an additive function, such that two pieces of information can be conveyed simultaneously, increasing message complexity. This analysis clearly shows that call combinations are an important part of chimpanzee communication. The implications with regard to the evolution of human language are discussed. Testing of the information conveyed by single calls and of call combinations, using playback experiments, is advocated.