Miracle-dz/UniFAR-FTUs huggingface dataset:
Divergent Mitochondrial Antioxidant Activities and Lung Alveolar Architecture in the Lungs of Rats and Mice at High Altitude

Compared with mice, adult rats living at 3,600 m above sea level (SL—La Paz, Bolivia) have high hematocrit, signs of pulmonary hypertension, and low lung volume with reduced alveolar surface area. This phenotype is associated with chronic mountain sickness in humans living at high altitude (HA). We tested the hypothesis that this phenotype is associated with impaired gas exchange and oxidative stress in the lungs. We used rats and mice (3 months old) living at HA (La Paz) and SL (Quebec City, Canada) to measure arterial oxygen saturation under graded levels of hypoxia (by pulse oximetry), the alveolar surface area in lung slices and the activity of pro- (NADPH and xanthine oxidases—NOX and XO) and anti- (superoxide dismutase, and glutathione peroxidase—SOD and GPx) oxidant enzymes in cytosolic and mitochondrial lung protein extracts. HA rats have a lower arterial oxygen saturation and reduced alveolar surface area compared to HA mice and SL rats. Enzymatic activities (NOX, XO, SOD, and GPx) in the cytosol were similar between HA and SL animals, but SOD and GPx activities in the mitochondria were 2–3 times higher in HA vs. SL rats, and only marginally higher in HA mice vs. SL mice. Furthermore, the maximum activity of cytochrome oxidase-c (COX) measured in mitochondrial lung extracts was also 2 times higher in HA rats compared with SL rats, while there was only a small increase in HA mice vs. SL mice. Interestingly, compared with SL controls, alterations in lung morphology are not observed for young rats at HA (15 days after birth), and enzymatic activities are only slightly altered. These results suggest that rats living at HA have a gradual reduction of their alveolar surface area beyond the postnatal period. We can speculate that the elevation of SOD, GPx, and COX activities in the lung mitochondria are not sufficient to compensate for oxidative stress, leading to damage of the lung tissue in rats.
Data and Hardware Efficient Design for Convolutional Neural Network

Hardware design of deep convolutional neural networks (CNNs) faces challenges of high computational complexity and data bandwidth as well as huge divergence in different CNN network layers, in which the throughput of the convolutional layer would be bounded by available hardware resource, and throughput of the fully connected layer would be bounded by available data bandwidth. Thus, a highly flexible and efficient design is desired to meet these needs. This paper presents an end-to-end CNN accelerator that maximizes hardware utilization with run-time configurations of different kernel sizes. It also minimizes data bandwidth with the output first strategy to improve the data reuse of the convolutional layers by up to $300\times \sim 600\times $ compared with the non-reused case. The whole CNN implementation of the target network is generated optimally for both hardware and data efficiency under design resource constraints, which can be run-time reconfigured by the layer optimized parameters to achieve real-time and end-to-end CNN acceleration. An implementation example for AlexNet consumes a 1.783 M gate count for 216 MACs and a 142.64 kb internal buffer with TSMC 40 nm process, and achieves 99.7 and 61.6 f/s under 454 MHz clock frequency for the convolutional layers and the whole AlexNet, respectively.
Preliminary results of proton radiotherapy for pediatric rhabdomyosarcoma: a multi‐institutional study in Japan

To evaluate preliminary results of proton radiotherapy (PRT) for pediatric patients with rhabdomyosarcoma (RMS). From 1987 to 2014, PRT was conducted as initial radiotherapy in 55 patients (35 males, 20 females, median age 5 years, range 0–19) with RMS at four institutes in Japan. Thirty‐one, 18, and six patients had embryonal, alveolar, and other RMS, respectively. One, 11, 37, and six patients were in IRSG groups I, II, III, and IV, respectively, and the COG risk group was low, intermediate, and high for nine, 39, and seven patients, respectively. The irradiation dose was 36–60 GyE (median: 50.4 GyE). The median follow‐up period was 24.5 months (range: 1.5–320.3). The 1‐ and 2‐year overall survival rates were 91.9% (95% CI: 84.3–99.5%) and 84.8% (95% CI 75.2–94.3%), respectively, and these rates were 100% and 100%, 97.1% and 90.1%, and 57.1% and 42.9% for COG low‐, intermediate‐, and high‐risk groups, respectively. There were 153 adverse events of Grade ≥3, including 141 hematologic toxicities in 48 patients (87%) and 12 radiation‐induced toxicities in nine patients (16%). Proton‐specific toxicity was not observed. PRT has the same treatment effect as photon radiotherapy with tolerable acute radiation‐induced toxicity.
Medicare Bundled Payments for Post-Acute Care: Characteristics and Baseline Performance of Participating Skilled Nursing Facilities

Medicare bundled payment models have focused on post-acute care as a key component of improving the efficiency and quality of health care. This study investigated the characteristics and baseline performance of skilled nursing facilities (SNFs) that participated in Medicare Bundled Payments for Care Improvement Initiative Model 3. As of July 2016, 657 SNFs participated in 7,932 episodes in risk-bearing phase. Our retrospective analyses found that larger facilities, higher occupancy rate, chain affiliation, better five-star overall rating, and higher market competition for SNF care were associated with increased likelihood of enrolling in clinical episodes in Model 3, whereas not-for-profit ownership, higher adjusted staffing levels, higher percentage of Medicaid residents, and rural location were associated with reduced likelihood of participation in Bundled Payments for Care Improvement. Policy makers should consider approaches to encourage participation of post-acute care providers in this voluntary program and evaluate its impact on patient selection, cost of care, and health outcomes.
On Extraction of Rules from Deep Learner: The Deeper, the Better?

In recent years, multilayer perceptron (MLP) has been successfully used for solving various problems in different fields. However, it is difficult to interpret the reasoning process of an MLP, and therefore in most cases the MLP is used as a black box. In our previous study, we tried to extract rules from a learned shallow MLP based on the hidden neuron outputs. In this study, we investigate the possibility of extracting simpler and better rules from a deep MLP. It is believed that hidden layers closer to the output layer can learn more abstract concepts. It is natural to expect that simpler and better rules can be extracted from higher layers. Experimental results on several public datasets reveal that this is true because the decision trees designed based on hidden layers closer to the output layer are actually smaller. That is, it is possible to extract more understandable knowledge from a deep MLP, even if the MLP as a whole is difficult to understand. In addition, based on the complexity of the extracted knowledge, it is also possible to determine the number of layers needed for solving a given problem.
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning

A lot of the recent success in natural language processing (NLP) has been driven by distributed vector representations of words trained on large amounts of text in an unsupervised manner. These representations are typically used as general purpose features for words across a range of NLP problems. However, extending this success to learning representations of sequences of words, such as sentences, remains an open problem. Recent work has explored unsupervised as well as supervised learning techniques with different training objectives to learn general purpose fixed-length sentence representations. In this work, we present a simple, effective multi-task learning framework for sentence representations that combines the inductive biases of diverse training objectives in a single model. We train this model on several data sources with multiple training objectives on over 100 million sentences. Extensive experiments demonstrate that sharing a single recurrent sentence encoder across weakly related tasks leads to consistent improvements over previous methods. We present substantial improvements in the context of transfer learning and low-resource settings using our learned general-purpose representations.
Production of Thermal Insulator from Water Hyacinth Fiber and Natural Rubber Latex

Aquatic weeds as raw materials for thermal insulator are considered as great advantages of the utilization of the weeds to conserve energy and environment as well as economic benefits. In this work, an environmental-friendly thermal insulator made from water hyacinth fiber (aquatic weed) with natural rubber latex as binder was developed. The objective is to monitor and compare the physical properties of different compositions between water hyacinth fiber and natural rubber latex as well as to compare with commercial insulators as fiberglass and rockwool. The results showed that the insulator with a density of 465-646 kg/m 3 had the thermal conductivity values ranging from 0.0246-0.0305 W/m K, which was close to that of the commercial ones. Thus, this friendly insulation material is a good candidate for insulation in building. However, moisture content and water absorption capacity of the material must be taken into account due its higher moisture content and water absorption than that of testing standard, TISI 876-2532.
The Physiological Bases of Hidden Noise-Induced Hearing Loss: Protocol for a Functional Neuroimaging Study

Background Rodent studies indicate that noise exposure can cause permanent damage to synapses between inner hair cells and high-threshold auditory nerve fibers, without permanently altering threshold sensitivity. These demonstrations of what is commonly known as hidden hearing loss have been confirmed in several rodent species, but the implications for human hearing are unclear. Objective Our Medical Research Council–funded program aims to address this unanswered question, by investigating functional consequences of the damage to the human peripheral and central auditory nervous system that results from cumulative lifetime noise exposure. Behavioral and neuroimaging techniques are being used in a series of parallel studies aimed at detecting hidden hearing loss in humans. The planned neuroimaging study aims to (1) identify central auditory biomarkers associated with hidden hearing loss; (2) investigate whether there are any additive contributions from tinnitus or diminished sound tolerance, which are often comorbid with hearing problems; and (3) explore the relation between subcortical functional magnetic resonance imaging (fMRI) measures and the auditory brainstem response (ABR). Methods Individuals aged 25 to 40 years with pure tone hearing thresholds ≤20 dB hearing level over the range 500 Hz to 8 kHz and no contraindications for MRI or signs of ear disease will be recruited into the study. Lifetime noise exposure will be estimated using an in-depth structured interview. Auditory responses throughout the central auditory system will be recorded using ABR and fMRI. Analyses will focus predominantly on correlations between lifetime noise exposure and auditory response characteristics. Results This paper reports the study protocol. The funding was awarded in July 2013. Enrollment for the study described in this protocol commenced in February 2017 and was completed in December 2017. Results are expected in 2018. Conclusions This challenging and comprehensive study will have the potential to impact diagnostic procedures for hidden hearing loss, enabling early identification of noise-induced auditory damage via the detection of changes in central auditory processing. Consequently, this will generate the opportunity to give personalized advice regarding provision of ear defense and monitoring of further damage, thus reducing the incidence of noise-induced hearing loss.
Investigation of Multimodal Electret-Based MEMS Energy Harvester With Impact-Induced Nonlinearity

This paper presents an electret-based MEMS energy harvester synergizing the advantages of multi-modal structure and impact mechanism for broad operating bandwidth. The device with a volume of 295 mm3 comprises an electret-based primary subsystem for power generation and an electrode-free auxiliary subsystem for frequency tuning. The tiny auxiliary subsystem helps to induce close resonances with comparable outputs at low excitations, as well as introduces impact-based nonlinearity to drive the first resonant peak upward and further approach the second one at elevated excitations. The experimental results demonstrate that at an excitation of 12.8 m/ $\text{s}^{2}$ , the 3-dB bandwidth of the first peak is increased from 20.4 to 60.4 Hz and a low frequency ratio of 1.15 between the two peaks is achieved. The two degree-of-freedom resonant structure with impact-based nonlinearity is systematically investigated through an equivalent circuit representation. An electrical equivalent circuit model of the proposed device with impact mechanism is derived. The circuit simulation confirms the nonlinear behavior of the system, and reveals the mechanism of peak shifting and bandwidth enhancing dynamics. [2017-0207]
Microbiome-Mediated Upregulation of MicroRNA-146a in Sporadic Alzheimer’s Disease

The first indication of a potential mechanistic link between the pathobiology of the human gastrointestinal (GI)-tract microbiome and its contribution to the pathogenetic mechanisms of sporadic Alzheimer’s disease (AD) came a scant 4 years ago (1). Ongoing research continues to strengthen the hypothesis that neurotoxic microbial-derived components of the GI tract microbiome can cross aging GI tract and blood–brain barriers and contribute to progressive proinflammatory neurodegeneration, as exemplified by the AD-process. Of central interest in these recent investigations are the pathological roles played by human GI tract resident Gram-negative anaerobic bacteria and neurotropic viruses—two prominent divisions of GI tract microbiome-derived microbiota—which harbor considerable pathogenic potential. It is noteworthy that the first two well-studied microbiota—the GI tract abundant Gram-negative bacteria Bacteroides fragilis and the neurotropic herpes simplex virus-1 both share a final common pathway of NF-κB (p50/p65) activation and microRNA-146a induction with ensuing pathogenic stimulation of innate-immune and neuroinflammatory pathways. These appear to strongly contribute to the inflammation-mediated amyloidogenic neuropathology of AD. This communication: (i) will review recent research contributions that have expanded our understanding of the nature of the translocation of microbiome-derived neurotoxins-across biophysiological barriers; (ii) will assess multiple-recent investigations of the induction of the proinflammatory pathogenic microRNA-146a by these two prominent classes of human microbiota; and (iii) will discuss the role of molecular neurobiology and mechanistic contribution of polymicrobial infections to AD-type neuropathological change.
An RBF-PSO based approach for early detection of DDoS attacks in SDN

The architecture of SDN helps in early detection and mitigation of DDoS attacks, which is a challenge in the traditional network. With appropriate identification patterns and detection scheme, the SDN controller detects DDoS attacks in infant stages and suppresses their impact on the entire network. This paper proposes a feature set for classification of DDoS attacks from normal traffic. Further, a system model for detection of DDoS attacks implemented with SDN controller is proposed. The detection module in system model is based on RBF network having PSO optimized learning that is further compared with several schemes. The effectiveness of the system model has been verified by its real-time emulation with mininet emulator. The proposed system model is efficient in classifying heavy traffic load in the network from that of DDoS attacks and is capable of handling the DDoS attacks in their early stages itself.
AGWallP — Automatic guided wall painting system

This paper presents the development of prototype of Automatic Wall Painting Machine guided by wall dimensions and paint colour being sent from user through a camera embedded Raspberry Pi 2 module. The Raspberry Pi Module captures the wall image and calculates the dimensions using Image Processing tools and remotely forwards the dimensions to the base painter system using bluetooth transmission. The Mechanical structure consists of a 2-D plotter with the painting joint attached to a DC operated DCV (Directional Control Valve) spray paint that moves from a point to another line by line. The plotter runs with two Nema 17 stepper motors and contains a STM32 as the slave controller, receiving the dimensions via bluetooth module connected to it. The overall system is run by a dual shaft Nema 23 Stepper motor.
Pairwise Preference Over Mixed-Type Item-Sets Based Bayesian Personalized Ranking for Collaborative Filtering

Nowadays, providing high quality recommendation services to users is an essential component in online Web applications, including shopping, making friends, healthcare, etc. In some recent works, the recommendation problem of one-class collaborative filtering has been proposed and been receiving more attention. This approach can make use of users' behaviors with the "one-class" feedbacks form coming from different services to improve the recommendation performance, e.g., "purchasing" in shopping services, "watching" in video websites, and "making appointment" in online health community. Previous works solve the issue via proposing the assumptions of pointwise preference on one item, pairwise preference on items or item-sets based on relative score over two item sets, in which one previous work named Bayesian personalized ranking (BPR) was empirically found performing much better by means of leveraging such one-class data well. Nevertheless, such pairwise preference assumptions with regard to items or item-sets may always invalid. In this paper, we present an improved assumption of pairwise preferences over mixed-type item-sets by defining the preference on two item sets with different type instead of on a simple item set. One is an item set with the same-type feedback relationship; the other is a mixed-type item set. With this improved assumption, we then develop a novel algorithm, MT-BPR (Bayesian Personalized Ranking over Mixed-Type Item-sets). The empirical results based on two data sets, which are collected from a healthcare website and mobile e-commerce application in real world, demonstrate that MT-BPR outperforms than several previous methods in ranking predictions.
INFLUENCE OF CONCRETE TENSILE SOFTENING ON THE PERFORMANCE OF FRP STRENGTHENED RC BEAMS: EXPERIMENTS

Two main techniques have been studied for the strengthening of reinforced concrete (RC) beams using Fiber Reinforced Polymer (FRP) materials: pre-cured or lay-up laminates applied to the external surfaces of the beams, and bars of round or rectangular cross section embedded in the concrete cover (near surface mounted, NSM). According to the ACI 440 design guide, in order to avoid delamination, the designer should limit the strain in the FRP laminate according to a "bond factor" km that is dependent of FRP stiffness and amount. In other words, it seems that delamination is not affected by the quality of the concrete substrate or of the cementitious repair mortar often used to replace the existing degraded concrete cover. In this paper, the experimental results of 110 scaled RC beams are presented in order to determine the safety of this ACI 440 approach. Samples are divided into two main categories: RC beams strengthened with high modulus carbon FRP laminates and with high modulus carbon FRP bars. Each category is divided into 19 groups with different concrete strengths ranging from 20 to 60 MPa. To simulate the real field situation, the reconstruction of the concrete cover was achieved with high strength concrete mortars on 50% of the samples. Comparisons between the obtained experimental results are presented in order to understand the failure mechanisms and adhesion performance. Both techniques provide relevant load increment from 130 up to 430%. It appears also that "pseudo-ductile concrete," such as steel fiber reinforced concrete, performs much better than standard concrete.
Radionuclide-fluorescence Reporter Gene Imaging to Track Tumor Progression in Rodent Tumor Models

Metastasis is responsible for most cancer deaths. Despite extensive research, the mechanistic understanding of the complex processes governing metastasis remains incomplete. In vivo models are paramount for metastasis research, but require refinement. Tracking spontaneous metastasis by non-invasive in vivo imaging is now possible, but remains challenging as it requires long-time observation and high sensitivity. We describe a longitudinal combined radionuclide and fluorescence whole-body in vivo imaging approach for tracking tumor progression and spontaneous metastasis. This reporter gene methodology employs the sodium iodide symporter (NIS) fused to a fluorescent protein (FP). Cancer cells are engineered to stably express NIS-FP followed by selection based on fluorescence-activated cell sorting. Corresponding tumor models are established in mice. NIS-FP expressing cancer cells are tracked non-invasively in vivo at the whole-body level by positron emission tomography (PET) using the NIS radiotracer [18F]BF4-. PET is currently the most sensitive in vivo imaging technology available at this scale and enables reliable and absolute quantification. Current methods either rely on large cohorts of animals that are euthanized for metastasis assessment at varying time points, or rely on barely quantifiable 2D imaging. The advantages of the described method are: (i) highly sensitive non-invasive in vivo 3D PET imaging and quantification, (ii) automated PET tracer production, (iii) a significant reduction in required animal numbers due to repeat imaging options, (iv) the acquisition of paired data from subsequent imaging sessions providing better statistical data, and (v) the intrinsic option for ex vivo confirmation of cancer cells in tissues by fluorescence microscopy or cytometry. In this protocol, we describe all steps required for routine NIS-FP-afforded non-invasive in vivo cancer cell tracking using PET/CT and ex vivo confirmation of in vivo results. This protocol has applications beyond cancer research whenever in vivo localization, expansion and long-time monitoring of a cell population is of interest.
Characterizing graphs of maximum principal ratio

The principal ratio of a connected graph, denoted $\gamma(G)$, is the ratio of the maximum and minimum entries of its first eigenvector. Cioab\u{a} and Gregory conjectured that the graph on $n$ vertices maximizing $\gamma(G)$ is a kite graph: a complete graph with a pendant path. In this paper we prove their conjecture.