Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of pre-exercise carbohydrate availability on fat oxidation and energy expenditure after a high-intensity exercise

The aim of this study was to test the hypothesis that reduced pre-exercise carbohydrate (CHO) availability potentiates fat oxidation after an exhaustive high-intensity exercise bout. Eight physically active men underwent a high-intensity exercise (∼95% V̇O2max) until exhaustion under low or high pre-exercise CHO availability. The protocol to manipulate pre-exercise CHO availability consisted of a 90-min cycling bout at ∼70% V̇O2max + 6 × 1-min at 125% V̇O2max with 1-min rest, followed by 48 h under a low- (10% CHO, low-CHO availability) or high-CHO diet (80% CHO, high-CHO availability). Time to exhaustion was shorter and energy expenditure (EE) lower during the high-intensity exercise in low- compared to high-CHO availability (8.6±0.8 and 11.4±1.6 min, and 499±209 and 677±343 kJ, respectively, P<0.05). Post-exercise EE was similar between low- and high-CHO availability (425±147 and 348±54 kJ, respectively, P>0.05), but post-exercise fat oxidation was significantly higher (P<0.05) in low- (7,830±1,864 mg) than in high-CHO availability (6,264±1,763 mg). The total EE (i.e., exercise EE plus post-exercise EE) was similar between low- and high-CHO availability (924±264 and 1,026±340 kJ, respectively, P>0.05). These results suggest that a single bout of high-intensity exercise performed under low-CHO availability increased post-exercise fat oxidation, and even with shorter exercise duration, both post-exercise EE and total EE were not impaired.
Learning Efficient Deep Feature Representations via Transgenerational Genetic Transmission of Environmental Information During Evolutionary Synthesis of Deep Neural Networks

The computational complexity of deep neural networks for extracting deep features is a significant barrier to widespread adoption, particularly for use in embedded devices. One strategy to addressing the complexity issue is the evolutionary deep intelligence framework, which has been demonstrated to enable the synthesis of highly efficient deep neural networks that retain modeling performance. Here, we introduce the notion of trans-generational genetic transmission into the evolutionary deep intelligence framework, where the intra-generational environmental traumatic stresses are imposed to synapses during training to favor the synthesis of more efficient deep neural networks over successive generations. Results demonstrate the efficacy of the proposed framework for synthesizing networks with significant decreases in synapses (e.g., for SVHN dataset, a 230-fold increase in architectural efficiency) while maintaining modeling accuracy and a significantly more efficient feature representation.
The application of deep learning in communication signal modulation recognition

Automated Modulation Classification (AMC) has been applied in various emerging areas such as cognitive radio (CR). We also notice that Deep Learning (DL) is a powerful classification tool that has gained great popularity in various field. This article focuses on DL and aims at using it to solve communications problems. We propose a new data conversion algorithm in order to gain a better classification accuracy of communication signal modulation. This paper will show that our new method will bring significant improvement in signal modulation classification accuracy. Besides, AlexNet and GoogLeNet, two well-known DL network models, ResNet and VGG, will be utilized in this task to compare with each other.
A national surveillance project on chronic kidney disease management in Canadian primary care: a study protocol

Introduction Effective chronic disease care is dependent on well-organised quality improvement (QI) strategies that monitor processes of care and outcomes for optimal care delivery. Although healthcare is provincially/territorially structured in Canada, there are national networks such as the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) as important facilitators for national QI-based studies to improve chronic disease care. The goal of our study is to improve the understanding of how patients with chronic kidney disease (CKD) are managed in primary care and the variation across practices and provinces and territories to drive improvements in care delivery. Methods and analysis The CPCSSN database contains anonymised health information from the electronic medical records for patients of participating primary care practices (PCPs) across Canada (n=1200). The dataset includes information on patient sociodemographics, medications, laboratory results and comorbidities. Leveraging validated algorithms, case definitions and guidelines will help define CKD and the related processes of care, and these enable us to: (1) determine prevalent CKD burden; (2) ascertain the current practice pattern on risk identification and management of CKD and (3) study variation in care indicators (eg, achievement of blood pressure and proteinuria targets) and referral pattern for specialist kidney care. The process of care outcomes will be stratified across patients’ demographics as well as provider and regional (provincial/territorial) characteristics. The prevalence of CKD stages 3–5 will be presented as age–sex standardised prevalence estimates stratified by province and as weighted averages for population rates with 95% CIs using census data. For each PCP, age–sex standardised prevalence will be calculated and compared with expected standardised prevalence estimates. The process-based outcomes will be defined using established methods. Ethics and dissemination The CPCSSN is committed to high ethical standards when dealing with individual data collected, and this work is reviewed and approved by the Network Scientific Committee. The results will be published in peer-reviewed journals and presented at relevant national and international scientific meetings.
Quantum physics in one dimension

1. Peculiarities of d=1 2. Bosonization 3. Luttinger liquids 4. Refinements 5. Microscopic methods 6. Spin 1/2 chains 7. Interacting fermions on a lattice 8. Coupled fermionic chains 9. Disordered systems 10. Boundaries and isolated impurities 11. Significant others A. Basics of many body B. Not so important fine technical points C. Correlation functions D. Bosonization directory E. Sine-Gordon F. Numerical solution
Structured databases on the web: observations and implications

The Web has been rapidly "deepened" by the prevalence of databases online. With the potentially unlimited information hidden behind their query interfaces, this "deep Web" of searchable databses is clearly an important frontier for data access. This paper surveys this relatively unexplored frontier, measuring characteristics pertinent to both exploring and integrating structured Web sources. On one hand, our "macro" study surveys the deep Web at large, in April 2004, adopting the random IP-sampling approach, with one million samples. (How large is the deep Web? How is it covered by current directory services? ) On the other hand, our "micro" study surveys source-specific characteristics over 441 sources in eight representative domains, in December 2002. (How "hidden" are deep-Web sources? How do search engines cover their data? How complex and expressive are query forms? ) We report our observations and publish the resulting datasets to the research community. We conclude with several implications (of our own) which, while necessarily subjective, might help shape research directions and solutions.
USING A DYNAMIC ARTIFICIAL NEURAL NETWORK FOR FORECASTING THE VOLATILITY OF A FINANCIAL TIME SERIES

The ability to obtain accurate volatility forecasts is an important issue for the financial analyst. In this paper, we use the DAN2 model, a multilayer perceptron and an ARCH model to predict the monthly conditional variance of stock prices. The results show that DAN2 model is more accurate for predicting in-sample and out-of-sample variance that the other considered models for the used dataset. Thus, the value of this neural network as a predictive tool is demonstrated.
Factor Demand Linkages, Technology Shocks, and the Business Cycle

Abstract This paper argues that factor demand linkages can be important for the transmission of both sectoral and aggregate shocks. We show this using a panel of highly disaggregated manufacturing sectors together with sectoral structural VARs. When sectoral interactions are explicitly accounted for, a contemporaneous technology shock to all manufacturing sectors implies a positive response in both output and hours at the aggregate level. Otherwise there is a negative correlation, as in much of the existing literature. Furthermore, we find that technology shocks are important drivers of the business cycle.
International Workshop on Embeddings and Semantics

The main objective of the International Workshop on Embeddings and Semantics (IWES) is to bring together researchers interested in the use of continuous space embeddings for modelling language, semantics and meaning. Recent advances on neural networks and related machine learning applications have proven the use of continuous spaces to be useful for natural language applications in both the monolingual and cross-language settings. The topics of the workshop include, but are not restricted to: • latent semantics for natural language processing • properties and applications of continuous space representations of language • use of embeddings for multimodal semantics • deep learning for language modelling • cross-language information retrieval and information extraction The workshop had contained a keynote speech with title “From conceptual to referential properties with distributional semantics” by Gemma Boleda and 7 paper presentations. The selected papers cover a wide range of technical contributions to embeddings and semantics. Some of them are showing the benefits of embeddings for applications such as definition extraction, machine translation, medical vocabulary expansion and topic models for short-text. Some others present modelling aspects of the embeddings like generative modelling, multimodal embeddings and word-order sensitive vector space models. The workshop had also contained panel discussion on the current state and future directions on embeddings and
Investigation of the dynamics of coupled cantilever arrays on a micro and macro scale with applications to AFM

Since the invention of atomic force microscopy (AFM) researchers have been trying to increase imaging speed. One method is to bring multiple cantilever probes together in close proximity to form an array. By using each probe independently, multiple points on a sample can be imaged simultaneously. AFM arrays have been developed and produced by the Rangelow research group under the PRONANO project at Technische Universitat Ilmenau [1]. These arrays are fabricated from multilayer silicon beams and have bimetallic heater actuators and piezo-resistive sensors incorporated into each probe, allowing for individual actuation and sensing (Figure 1). Due to the close proximity of the cantilevers, the system response exhibits coupling phenomena (mechanical, electrical, thermal and fluidic). The way this coupling affects the dynamics of each beam and the system as a whole is not fully understood.
Large femoral bone loss after hip revision using the uncemented proximally porous-coated Bi-Metric prosthesis

Background and purpose Periprosthetic bone loss after uncemented femoral hip revision is a matter of concern. We have used a proximally porous- and hydroxyapatite-coated prosthesis (Bi-Metric) in revision since 1989 and now we report the bone changes. This prosthesis is intended to distribute the forces more evenly and to avoid proximal femoral unloading. Methods 22 patients were unilaterally reoperated because of aseptic loosening. Only patients with a healthy contralateral hip were included. Mean age at revision was 69 (55–80) years. Bone defects were graded by Gustilo-Pasternak and Endo-Klinik classifications. Clinical assessment was performed with Harris hip score. We used radiographs and dual-energy X-ray absorptiometry to evaluate migration, femoral remodeling, and bone mineral density after 72 (30–158) months. Results The mean Harris hip score was 74 (30–100) points at follow-up. Mild thigh discomfort was present in 1 patient and moderate thigh pain in 3 patients. There was no loosening or subsidence. Osteolysis seen at revision had diminished in 19 of the 22 hips at follow-up. We noted a large reduction in bone mineral density. It was most pronounced in Gruen regions 1, 2, 6, and 7. Interpretation Revision with this stem is a reliable procedure; however, we noted a large degree of proximal bone loss that could lead to later mechanical complications or fractures.
A FULLY INTEGRATED MULTIPLEXED IMMUNOASSAY ON A DISC

A fully automated lab-on-a-disc for simultaneous detection of multiple protein biomarkers in raw samples such as whole blood or whole saliva is developed. Based on the bead-based sandwich-type Enzyme-linked immunosorbent assay (ELISA), here a novel centrifugal microfluidic layout was designed and tested to accomplish the simultaneous detection of C-reactive protein (hsCRP), cardiac troponin I (cTnI), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) for the diagnosis of cardiovascular disease. Three reaction chambers are initially interconnected for the common processes such as sample inje ction, incubation, and washing and isolated on-demand for the independent processes such as substrate incubation and final detection. The assay performances such as the limit of detection and the dynamic range were as good as the conventional ELISA despite the significant scale-down in assay conditions .
Hardware-Assisted Rootkits: Abusing Performance Counters on the ARM and x86 Architectures

In this paper, a novel hardware-assisted rootkit is introduced, which leverages the performance monitoring unit (PMU) of a CPU. By configuring hardware performance counters to count specific architectural events, this research effort proves it is possible to transparently trap system calls and other interrupts driven entirely by the PMU. This offers an attacker the opportunity to redirect control flow to malicious code without requiring modifications to a kernel image. The approach is demonstrated as a kernel-mode rootkit on both the ARM and Intel x86-64 architectures that is capable of intercepting system calls while evading current kernel patch protection implementations such as PatchGuard. A proof-of-concept Android rootkit is developed targeting ARM (Krait) chipsets found in millions of smartphones worldwide, and a similar Windows rootkit is developed for the Intel x86-64 architecture. The prototype PMU-assisted rootkit adds minimal overhead to Android, and less than 10% overhead to Windows OS. Further analysis into performance counters also reveals that the PMU can be used to trap returns from secure world on ARM as well as returns from System Management Mode on x86-64.
Compact solid-state CMOS single-photon detector array for in vivo NIR fluorescence lifetime oncology measurements

In near infrared fluorescence-guided surgical oncology, it is challenging to distinguish healthy from cancerous tissue. One promising research avenue consists in the analysis of the exogenous fluorophores’ lifetime, which are however in the (sub-)nanosecond range. We have integrated a single-photon pixel array, based on standard CMOS SPADs (single-photon avalanche diodes), in a compact, time-gated measurement system, named FluoCam. In vivo measurements were carried out with indocyanine green (ICG)-modified derivatives targeting the αvβ3 integrin, initially on a genetically engineered mouse model of melanoma injected with ICG conjugated with tetrameric cyclic pentapeptide (ICG−E[c(RGD f K)4]), then on mice carrying tumour xenografts of U87-MG (a human primary glioblastoma cell line) injected with monomeric ICG−c(RGD f K). Measurements on tumor, muscle and tail locations allowed us to demonstrate the feasibility of in vivo lifetime measurements with the FluoCam, to determine the characteristic lifetimes (around 500 ps) and subtle lifetime differences between bound and unbound ICG-modified fluorophores (10% level), as well as to estimate the available photon fluxes under realistic conditions.
Classification of genome data using Random Forest Algorithm : Review

Random Forest is a popular machine learning tool for classification of large datasets. The Dataset classified with Random Forest Algorithm (RF) are correlated and the interaction between the features leads to the study of genome interaction. The review is about RF with respect to its variable selection property which reduces the large datasets into relevant samples and predicting the accuracy for the selected variable. The variables are selected among the huge datasets and then its error rate are calculated with prediction accuracy methods, when these two properties are applied then the classification of huge data becomes easy. Various variable selection and accuracy prediction methods are discussed in this review.
A Two-Step Process of Nitrous Oxide before Carbon Dioxide for Humanely Euthanizing Piglets: On-Farm Trials

Simple Summary The current approved method of using carbon dioxide (CO2) to euthanize newborn piglets is raising animal welfare concerns on whether the method is truly humane. A new form of euthanasia that is humane, practical, and socially acceptable is needed. Nitrous oxide (N2O), also known as laughing gas, has been shown to induce narcosis in piglets. We used a novel two-step system of exposing compromised piglets for six minutes to N2O followed by carbon dioxide and compared it to using CO2 alone. After exposure to nitrous oxide, all piglets lost posture, a sign of the onset of loss of consciousness, before being exposed to CO2 when they showed behavioral distress. On-farm use of a two-step method reduced the amount of time the piglets were exposed to CO2 but did not reduce the amount of distressful behaviors. Therefore, the results do not support the hypothesis that using N2O in a two-step system is more humane than CO2 alone. Abstract Current methods of euthanizing piglets are raising animal welfare concerns. Our experiment used a novel two-step euthanasia method, using nitrous oxide (N2O) for six minutes and then carbon dioxide (CO2) on compromised 0- to 7-day-old piglets. A commercial euthanasia chamber was modified to deliver two euthanasia treatments: the two-step method using N2O then CO2 (N2O treatment) or only CO2 (CO2 treatment). In Experiment 1, 18 piglets were individually euthanized. In Experiment 2, 18 groups of four to six piglets were euthanized. In the N2O treatment, piglets lost posture, indicating the onset of losing consciousness, before going into CO2 where they showed heavy breathing and open-mouth breathing; whereas piglets in the CO2 treatment did not lose posture until after exhibiting these behaviors (p ≤ 0.004). However, piglets in the N2O treatment took longer to lose posture compared to the CO2 treatment (p < 0.001). Piglets in the N2O treatment displayed more behavioral signs of stress and aversion: squeals/minute (p = 0.004), escape attempts per pig (p = 0.021), and righting responses per pig (p = 0.084) in a group setting. In these regards, it cannot be concluded that euthanizing piglets for 6 min with N2O and then CO2 is more humane than euthanizing with CO2 alone.
Language Evolution and Robotics: Issues on Symbol Grounding and Language Acquisition

One of the key aspects that distinguishes humans from other species is that humans use a complex communication system that is among other things symbolic, learnt, compositional and recursive, whereas all other species’ communication systems typically lack these properties. It is often thought that this unique human feature is the key to understanding the nature (and nurture! ) of human cognition. In order to understand the foundations of this distinction between humans and other species, scientists study the origins and evolution of language.
Bevacizumab may improve quality of life, but not overall survival in glioblastoma: an epidemiological study

Background The vascular endothelial growth factor antibody bevacizumab (Avastin®), received approval for the treatment of recurrent glioblastoma in many countries including the USA and Switzerland, but not the European Union, in 2009. Here, we explored the hypothesis that the approval of bevacizumab improved outcome with glioblastoma on a population level. Patients and methods The prognostic significance of epidemiological, molecular genetic, and clinical data including treatment for glioblastoma patients diagnosed from 2010 to 2014 in the Canton of Zurich, Switzerland, was retrospectively analyzed using log-rank test and Cox proportional hazards models. Data were compared with data for the years 2005-2009. Results In total, 310 glioblastoma patients were identified in the years 2010-2014. Median overall survival was 13.5 months for patients with known isocitrate dehydrogenase (IDH) wild-type (wt) (IDH1R132H-non-mutant) tumors (N = 248), compared with 11.3 months for IDH wt patients (P = 0.761) before (2005-2009). In the IDH wt cohort, bevacizumab use at any time increased from 19% in 2005-2009 to 49% in 2010-2014. Multivariate analysis did not identify bevacizumab exposure at any time to be associated with survival. Yet, upon the second-line treatment, baseline doses of corticosteroids were reduced by more than half in 83% of patients on bevacizumab compared with 48% of the patients treated with bevacizumab-free regimens (P = 0.007). Conclusion This epidemiological study of a small, but clinically well-annotated patient cohort fails to support the assumption that the strong increase of bevacizumab use since 2010 improved survival in glioblastoma although clinical benefit associated with decreased steroid use may have been achieved.
Investigation of the seasonal microbiome of Anopheles coluzzii in Mali

The poorly understood mechanisms of the seasonal maintenance of Anopheles spp. mosquitoes through the dry season in Africa remain a critical gap in our knowledge of Plasmodium disease transmission. While it is thought that adult mosquitoes remain in a dormant state throughout this seven-month dry season, the nature of this state remains unknown and has largely not been recapitulated in laboratory settings. To elucidate possible life history traits allowing for this phenotype, the spatiotemporal change in the microbiome of mosquitoes in the dry and wet seasons in Mali was analyzed by sequencing the 16S ribosome bacterial region in whole bodies of adult mosquitoes collected from two locations with varying water availability. These locations were a village near the Niger River with year-round water sources (N’Gabakoro, “Riparian”), and an area closer to the Sahara with highly seasonal breeding sites (Thierola Area, “Sahelian”). The 16S bacterial data consisted of 2057 unique sequence variants in 426 genera across 184 families. With these, we found several compositional differences that were seasonally and spatially linked. Counter to our initial hypothesis, there was a more pronounced seasonal difference in the bacterial microbiome in the Riparian than Sahelian area. These major seasonal shifts were in Ralstonia, Sphingorhabdus, and Duganella spp. bacteria that are usually soil and water-associated, indicating that these changes may be from bacteria acquired in the larval environment, rather than during adulthood. In the Sahelian dry season mosquitoes, there was a unique intracellular bacteria, Anaplasma, which likely was acquired through non-human blood feeding. Coupled with this finding, cytochrome B analysis showed a greater heterogeneity in host choice of An. coluzzii independent of season in the Thierola area compared to N’Gabakoro (77.5% vs. 94.6% human-origin blood meal, respectively), which may indicate a relaxation of anthropophily in some locations. This study highlights the diversity present in the bacterial composition of individual mosquitoes, characterizes the spatial and seasonal differences in this composition, and indicates some possible qualitative biomarkers in areas of intense seasonal change.