Miracle-dz/UniFAR-FTUs huggingface dataset:
Rule-Based Healthcare-Associated Bloodstream Infection Classification and Surveillance System

Healthcare-associated infections (HAIs) are a major patient safety issue. These adverse events add to the burden of resource use, promote resistance to antibiotics, and contribute to patient deaths and disability. A rule-based HAI classification and surveillance system was developed for automatic integration, analysis, and interpretation of HAIs and related pathogens. Rule-based classification system was design and implement to facilitate healthcare-associated bloodstream infection (HABSI) surveillance. Electronic medical records from a 2200-bed teaching hospital in Taiwan were classified according to predefined criteria of HABSI. The detailed information in each HABSI was presented systematically to support infection control personnel decision. The accuracy of HABSI classification was 0.94, and the square of the sample correlation coefficient was 0.99.
Comparison Between Epidermal Growth Factor Receptor (EGFR) Gene Expression in Primary Non-small Cell Lung Cancer (NSCLC) and in Fine-Needle Aspirates from Distant Metastatic Sites

Purpose: Epidermal growth factor receptor (EGFR) gene copy number obtained by fluorescence in situ hybridization (FISH) has been recently found to predict treatment outcome in non-small cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. However, it is still unknown whether EGFR status differs in metastases compared with primary NSCLC. In all studies FISH have been performed on histologic material. The possibility to perform FISH analysis on cytologic material obtained by fine-needle aspiration from superficial and visceral metastases would allow us to know the real EGFR status avoiding invasive diagnostic procedures. Methods: EGFR gene copy number was analyzed by FISH on fine-needle aspirates obtained from 31 patients with metastatic NSCLC and the results were compared with those obtained on corresponding paraffin histologic sections from the primary tumor. Results: The feasibility of EGFR FISH on cytology was 90% (28 of 31 patients). EGFR FISH was positive in 61% (17 of 28 patients) of the metastases and in 36% (10 of 28 patients) of the primary tumors. Nine of the 28 cases (32%) were EGFR positive on both primary tumor and metastatic site and 10 (36%) were negative on both primary tumor and metastasis. Nine of the 28 cases (32%) showed discordance of primary tumor versus metastasis (McNemar test; p = 0.041). Conclusions: EGFR FISH can be reliably assessed on fine-needle aspirates obtained from NSCLC metastases. We found that EGFR gene copy number is discordant between primary NSCLC and the corresponding distant metastatic sites in a significant proportion of cases. These findings should be considered in future studies designed to elucidate the predictive role of EGFR FISH in NSCLC.
Enhanced trajectory-based action recognition using human pose

Action recognition using dense trajectories is a popular concept. However, many spatio-temporal characteristics of the trajectories are lost in the final video representation when using a single Bag-of-Words model. Also, there is a significant amount of extracted trajectory features that are actually irrelevant to the activity being analyzed, which can considerably degrade the recognition performance. In this paper, we propose a human-tailored trajectory extraction scheme, in which trajectories are clustered using information from the human pose. Two configurations are considered; first, when exact skeleton joint positions are provided, and second, when only an estimate thereof is available. In both cases, the proposed method is further strengthened by using the concept of local Bag-of-Words, where a specific codebook is generated for each skeleton joint group. This has the advantage of adding spatial human pose awareness in the video representation, effectively increasing its discriminative power. We experimentally compare the proposed method with the standard dense trajectories approach on two challenging datasets.
Transplantation of Human Corneal Endothelial Cells Cultured on Bio-Engineered Collagen Vitrigel in a Rabbit Model of Corneal Endothelial Dysfunction

ABSTRACT Purpose: To evaluate the effect of transplanting bioengineered corneal endothelial grafts in a rabbit model of corneal endothelial failure. Methods: Human corneal endothelial cells (HCECs) were seeded on a vitrigel carrier. After Descemet’s membrane was removed from the eyes of rabbits, transplantation was done with a vitrigel/HCEC graft or vitrigel alone without cells, or the eyes were left untreated. Slit lamp examinations and measurement of the central corneal thickness (CCT) were performed for 14 days postoperatively. Results: HCECs cultured on vitrigel were strongly positive for ZO-1 and Na+/K+ ATPase. On day 14, the cornea showed mild edema and the pupil margins were visible through the grafts in the vitrigel/HCEC graft group. HCECs completely covered the grafts on day 14. In contrast, there was severe corneal edema and the pupil margins were undetectable on day 14 after transplantation of the vitrigel carrier alone or no transplantation. Proliferation of host cells was not observed in these groups. On day 14, the mean CCT was significantly thinner in the vitrigel/HCEC graft group than in the other two groups (p = 0.0008). Conclusions: Transplantation of a vitrigel/HCEC graft was effective for reducing the corneal thickness and restoring corneal transparency, suggesting the usefulness of vitrigel as a carrier for corneal endothelial cells.
Hyaluronan and CD44 antagonize mitogen-dependent cyclin D1 expression in mesenchymal cells

High molecular weight (HMW) hyaluronan (HA) is widely distributed in the extracellular matrix, but its biological activities remain incompletely understood. We previously reported that HMW-HA binding to CD44 antagonizes mitogen-induced S-phase entry in vascular smooth muscle cells (SMCs; Cuff, C.A., D. Kothapalli, I. Azonobi, S. Chun, Y. Zhang, R. Belkin, C. Yeh, A. Secreto, R.K. Assoian, D.J. Rader, and E. Puré. 2001. J. Clin. Invest. 108:1031–1040); we now characterize the underlying molecular mechanism and document its relevance in vivo. HMW-HA inhibits the mitogen-dependent induction of cyclin D1 and down-regulation of p27kip1 in vascular SMCs. p27kip1 messenger RNA levels were unaffected by HMW-HA, but the expression of Skp2, the rate-limiting component of the SCF complex that degrades p27kip1, was reduced. Rescue experiments identified cyclin D1 as the primary target of HMW-HA. Similar results were observed in fibroblasts, and these antimitogenic effects were not detected in CD44-null cells. Analysis of arteries from wild-type and CD44-null mice showed that the effects of HMW-HA/CD44 on cyclin D1 and Skp2 gene expression are detected in vivo and are associated with altered SMC proliferation after vascular injury.
Discrete-time LPV H2 observer for vehicle model-based state observer

In this paper, we propose the novel scheme to design a vehicle model-based state observer. The observer problem for vehicle state is reformulated into the linear parameter varying (LPV) systems which depend on online accessible time-varying parameters. In vehicle state estimation problem using global positioning system, we consider the measured course angle as varying parameter. The observer gain scheduling is determined using H2 filter based on linear matrix inequality (LMI) approach. The state observer gain using a discrete-time H2 filter could minimize an error from a disturbance to a state. The proposed method was demonstrated via performance comparison with EKF which is most popular method through experimental test.
The effect of a behavioral activation treatment for substance use on post‐treatment abstinence: a randomized controlled trial

AIMS To compare outcomes for a behavioral activation group treatment for substance use [life enhancement treatment for substance use (LETS ACT)] versus a time and group size-matched control condition delivered in a residential treatment setting. DESIGN Single-site two-arm parallel-group randomized clinical trial with follow-up assessment at 3, 6 and 12 months post-treatment. SETTING Residential substance use treatment facility in the United States. PARTICIPANTS Participants were 263 adults [mean age 42.7 (11.8); 29.5% female; 95.4% African American; 73.2% court mandated] whose insurance dictated 30-day (65.9%) or 90-day (34.1%) treatment duration. INTERVENTION AND COMPARATOR LETS ACT (n = 142) is a treatment developed originally for depression and modified for substance use. It teaches participants to increase positively reinforcing value-driven activities in order to counter depression and relapse. The control group [supportive counseling (SC); n = 121] received time and group size-matched supportive counseling. Treatment was delivered in five or eight 1-hour sessions depending on patient length of stay. MEASUREMENTS Percentage abstinent at follow-up, percentage of substance use days among those reporting use, depressive symptoms [Beck Depression Inventory (BDI)] and adverse consequences of drug use [Short Inventory of Problems-Alcohol and Drug (SIP-AD)]. FINDINGS LETS ACT had significantly higher abstinence rates at 3 months [odds ratio (OR) = 2.2, 95% confidence interval (CI) = 1.3-3.7], 6 months (OR = 2.6, 95% CI= 1.3-5.0) and 12 months (OR = 2.9, 95% CI = 1.3-6.1) post-treatment compared with SC. LETS ACT participants reported significantly fewer adverse consequences from substance use at 12 months post-treatment [B = 4.50, standard error (SE) = 2.17, 95% CI = 0.22-8.78]. Treatment condition had no effect on percentage substance use days among those who resumed use or on change in depressive symptoms; the latter decreased over time only in those who remained abstinent after residential treatment irrespective of condition (B = 0.43, SE = 0.11, 95% confidence interval = 0.22-0.65). CONCLUSIONS A behavioral activation group treatment for substance use (LETS ACT) appears to increase the likelihood of abstinence and reduce adverse consequences from substance use up to 12 months post-treatment.
Generating Certification Evidence for Autonomous Unmanned Aircraft Using Model Checking and Simulation

The use of unmanned aircraft for civil applications is expected to increase over the next decade, particularly in so-called dull, dirty, and dangerous missions. Unmanned aircraft will undoubtedly require some form of autonomy to ensure safe operations for all airspace users. However, to be used for civil applications, unmanned aircraft must gain regulatory approval in a process known as “certification”. This paper presents a proof-of-concept approach to the generation of certification evidence for autonomous unmanned aircraft based on a combination of formal verification and flight simulation. In particular, a class of autonomous systems controlled by rational agents is examined, and we give examples of 23 different properties, based on the rules of the air and notions of airmanship, which can be used in the formal model checking of rational agents controlling autonomous unmanned aircraft. Our techniques can be based on either 1) implicit models of the aircraft’s physical environment specified in terms of.
Randomness in Deconvolutional Networks for Visual Representation

Toward a deeper understanding on the inner work of deep neural networks, we investigate CNN (convolutional neural network) using DCN (deconvolutional network) and randomization technique, and gain new insights for the intrinsic property of this network architecture. For the random representations of an untrained CNN, we train the corresponding DCN to reconstruct the input images. Compared with the image inversion on pre-trained CNN, our training converges faster and the yielding network exhibits higher quality for image reconstruction. It indicates there is rich information encoded in the random features; the pre-trained CNN may discard information irrelevant for classification and encode relevant features in a way favorable for classification but harder for reconstruction. We further explore the property of the overall random CNN-DCN architecture. Surprisingly, images can be inverted with satisfactory quality. Extensive empirical evidence as well as theoretical analysis are provided.
Volumetric reconstruction and interactive rendering of trees from photographs

Reconstructing and rendering trees is a challenging problem due to the geometric complexity involved, and the inherent difficulties of capture. In this paper we propose a volumetric approach to capture and render trees with relatively sparse foliage. Photographs of such trees typically have single pixels containing the blended projection of numerous leaves/branches and background. We show how we estimate opacity values on a recursive grid, based on alphamattes extracted from a small number of calibrated photographs of a tree. This data structure is then used to render billboards attached to the centers of the grid cells. Each billboard is assigned a set of view-dependent textures corresponding to each input view. These textures are generated by approximating coverage masks based on opacity and depth from the camera. Rendering is performed using a view-dependent texturing algorithm. The resulting volumetric tree structure has low polygon count, permitting interactive rendering of realistic 3D trees. We illustrate the implementation of our system on several different real trees, and show that we can insert the resulting model in virtual scenes.
Deep neural networks for anger detection from real life speech data

There has been a lot of previous work on deep neural networks for automatic speech recognition, however, little emphasis has been placed on an investigation of effective deep learning architectures for anger detection from speech. In this paper, inspired by the state-of-the-art deep learning algorithms, we propose a variant of Deep Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs), Convolution Neural Networks (CNNs) with 3 × 3 kernels, and LSTM RNNs combined with CNNs, in conjunction with log-mel filter bank features and brute forced low-level-descriptors from the standardised ComParE set for speech anger detection. We extensively evaluate the deep networks on a big real-life speech corpus of 26 970 utterances with utterance-level labels collected from a German voice portal, finding that our proposed neural networks significantly outperform traditional modelling algorithms for speech anger detection.
Learning process for reducing uncertainties on network parameters and design margins

In this paper, we propose to lower the network design margins by improving the estimation of the signal-tonoise ratio (SNR) given by a quality of transmission (QoT) estimator, for new optical demands in a brownfield phase, based on a mathematical model of the physics of propagation. During the greenfield phase and the network operation, we collect and correlate information on the QoT input parameters, issued from the established initial demands and available almost for free from the network elements: amplifiers output power and the SNR at the coherent receiver side. Since we have some uncertainties on these input parameters of the QoT model, we use a machine learning algorithm to reduce them, improving the accuracy of the SNR estimation. With this learning process and for a European backbone network (28 nodes, 41 links), we could reduce the QoT inaccuracy by several dBs for new demands whatever the amount of uncertainties of the initial parameters.
Compromised blood–brain barrier permeability: novel mechanism by which circulating angiotensin II signals to sympathoexcitatory centres during hypertension

Angiotensin II (AngII) is a pivotal peptide implicated in the regulation of blood pressure. In addition to its systemic vascular and renal effects, AngII acts centrally to modulate the activities of neuroendocrine and sympathetic neuronal networks, influencing in turn sympatho‐humoral outflows to the circulation. Moreover, a large body of evidence supports AngII signalling dysregulation as a key mechanism contributing to exacerbated sympathoexcitation during hypertension. Due to its hydrophilic actions, circulating AngII does not cross the blood–brain barrier (BBB), signalling to the brain via the circumventricular organs which lack a tight BBB. In this review, we present and discuss recent studies from our laboratory showing that elevated circulating levels of AngII during hypertension result in disruption of the BBB integrity, allowing access of circulating AngII to critical sympathoexcitatory brain centres such as the paraventricular nucleus of the hypothalamus and the rostral ventrolateral medulla. We propose the novel hypothesis that AngII‐driven BBB breakdown constitutes a complementary mechanism by which circulating AngII, working in tandem with the central renin–angiotensin system, further exacerbates sympatho‐humoral activation during hypertension. These results are discussed within the context of a growing body of evidence in the literature supporting AngII as a pro‐inflammatory signal, and brain microglia as key cell targets mediating central AngII actions during hypertension.
Low-Cycle Fatigue of TiNi Shape Memory Alloy and Formulation of Fatigue Life

The low-cycle fatigue of a TiNi shape memory alloy was investigated by the rotating-bending fatigue tests in air, in water and in silicone oil. (1) The influence of corrosion fatigue in water does not appear in the region of low-cycle fatigue. (2) The temperature rise measured through an infrared thermograph during the fatigue test in air is four times as large as that measured through a thermocouple. (3) The fatigue life at an elevated temperature in air coincides with the fatigue life at the same elevated temperature in water. (4) The shape memory processing temperature does not affect the fatigue life. (5) The fatigue equation is proposed to describe the fatigue life depending on strain amplitude, temperature and frequency. The fatigue life is estimated well by the proposed equation.
Studies on Crack Propagation of Carbon Fiber Reinforced Epoxy Resin Composite

Unidirectional carbon fibre reinforced resin matrix composite (CFRP) with different fibre volume fraction are stretched dynamically under static load in SEM, initiation and propagation mechanism of crack is in-situ observed, and tensile fracture of specimens is also observed. The results show that: Microscopic cracks are mainly originated from fracture of fibre, numerous fibre cracks transfixion each other in form of matrix or interface cracking, and cause failure of CFRP. Microscopic crack propagation path is related to the thickness of matrix layer between fibres. Propagation of single fibre crack at interface accord with description of microscopic crack deflection criterion for fibre reinforced composite, but the crack deflection criterion cannot descript microscopic crack propagation mechanism of unidirectional CFRP effectively, because distribution discreteness of fibre and its strength are not considered.
Occupational exposure to second hand smoke and respiratory and sensory symptoms: A cross-sectional survey of hospital workers in Egypt

ObjectiveExposure to Second Hand Smoke (SHS) has been associated with an increased risk of respiratory symptoms, upper and lower respiratory tract diseases and an increased risk of asthma and chronic obstructive pulmonary disease. The majority of cases of mortality and morbidity is attributable to exposure of adults to SHS and is related to cardiovascular diseases and lung cancer. In Egypt, comprehensive smoke-free laws exist, however, in many workplaces they are poorly enforced consequently exposing workers to the detrimental health hazards of SHS. We aimed at determination of workplace exposure to Second Hand Smoke (SHS) and its association with respiratory and sensory irritation symptoms in hospital workers in Port-said governorate in Egypt.Material and MethodsA cross-sectional face to face survey was conducted by the use of a standardised questionnaire among 415 adult hospital workers; representing 50% of all employees (81% response rate); recruited from 4 randomly selected general hospitals in Port-said governorate in Egypt.ResultsAll hospitals employees reported exposure to SHS — on average 1.5 (SD = 2.5) hours of exposure per day. After controlling for potential confounders, exposure to SHS at work was significantly associated with an increased risk of wheezes (OR = 1.14, p < 0.01), shortness of breath (OR = 1.17, p < 0.01), phlegm (OR = 1.23, p < 0.01), running and irritated nose (OR = 1.14, p < 0.01) as well as a sore, scratchy throat (OR = 1.23). ConclusionsThese findings point out that workplace exposure to SHS is evident in hospitals in Port-said governorate and that workers are adversely affected by exposure to it at work. This underlines the importance of rigorous enforcement of smoke-free policies to protect the workers’ health in Egypt.
RPU: a programmable ray processing unit for realtime ray tracing

Recursive ray tracing is a simple yet powerful and general approach for accurately computing global light transport and rendering high quality images. While recent algorithmic improvements and optimized parallel software implementations have increased ray tracing performance to realtime levels, no compact and programmable hardware solution has been available yet.This paper describes the architecture and a prototype implementation of a single chip, fully programmable Ray Processing Unit (RPU). It combines the flexibility of general purpose CPUs with the efficiency of current GPUs for data parallel computations. This design allows for realtime ray tracing of dynamic scenes with programmable material, geometry, and illumination shaders.Although, running at only 66 MHz the prototype FPGA implementation already renders images at up to 20 frames per second, which in many cases beats the performance of highly optimized software running on multi-GHz desktop CPUs. The performance and efficiency of the proposed architecture is analyzed using a variety of benchmark scenes.
Optimizing the Datapath for Key-value Middleware with NVMe SSDs over RDMA Interconnects

In-memory key-value store is a crucial building block of large-scale web architecture. Given the growth of the data volume and the need for low-latency responses, cost-effective storage expansion and fast large-message processing are the major challenges. In this paper, we explore the design of key-value middleware that takes advantage of modern NVMe SSDs and RDMA interconnects to achieve high performance without excessive DRAM deployment. We propose an all-in-userland approach to improve the data plane efficiency. Both NVMe and RDMA are interfaced directly from the user-space for effective data access and tailored data management. We present a low-latency storage extension framework based on NVMe and a new design of JVM-aware Memcache protocol based on RDMA. To further accelerate large-message transfer, we provide a hybrid communication protocol fusing Eager and Rendezvous schemas, and a united I/O staging approach to achieve maximum latency hiding through pipelining. As the benchmarking results indicate, with the non-negligible JVM overhead taken into account, our solution obtains comparable communication performance with the RDMA-Memcached released by the OSU. For SSD-involved operations, the latency decreases by up to 31% compared to the kernel-based I/O processing.