Miracle-dz/UniFAR-FTUs huggingface dataset:
Antigenic and genomic analyses of influenza A(H1N1) viruses from different regions of the world, February 1978 to March 1980

Antigenic analysis of influenza A(H1N1) virus isolates by hemagglutination inhibition reactions with postinfection ferret sera and mouse monoclonal antibodies showed that, although the predominant virus in 1978 to 1979 had an A/Brazil/11/78-like hemagglutinin, several new antigenic variants could be detected which differed from those identified in 1977 to 1978. Most of the variants showed very minor changes and did not achieve epidemiological significance. Genome analysis by ribonucleic acid-ribonucleic acid hybridization, and supported by oligonucleotide mapping, indicated that nonrecombinant H1N1 viruses cocirculated in the world with recombinant H1N1 viruses, identified in 1978 to 1979. The results of our antigenic analysis and genomic analysis, in conjunction with genomic analysis by other authors, showed that recombinants with A/USSR/90/77-like and A/Brazil/11/78-like hemagglutinin had different genome compositions, indicating that H3N2 virus recombined separately with these different antigenic variants. Prevalence of the nonrecombinant and recombinant H1N1 viruses varied between regions of the world, and influenza A(H1N1) virus with a genotype almost identical to that of nonrecombinant A/Brazil/11/78 virus reappeared as a cause of local outbreaks in the United States in 1980 despite the predominance of recombinant H1N1 strains in the preceding winter.
Beyond Interruptibility

Many of today's mobile products and services engage their users proactively via push notifications. However, such notifications are not always delivered at the right moment, therefore not meeting products' and users' expectations. To address this challenge, we aim at developing an intelligent mobile system that automatically infers moments in which users are open to engage with suggested content. To inform the development of such a system, we carried out a field study with 337 mobile phone users. For 4 weeks, participants ran a study application on their primary phones. They were tasked to frequently report their current mood via a notification-administered experience-sampling questionnaire. In this study, however, we analyze whether they voluntarily engaged with content that we offered at the bottom of that questionnaire. In addition, the study app logged a wide range of data related to their phone use. Based on 120 Million phone-use events and 78,930 questionnaire notifications, we build a machine-learning model that before delivering a notification predicts whether a participant will click on the notification and subsequently engage with the offered content. When compared to a naïve baseline, which emulates current non-intelligent engagement strategies, our model achieves 66.6% higher success rate in its predictions. If the model also considers the user's past behavior, predictions improve 5-fold over the baseline. Based on these findings, we discuss the implications for building an intelligent service that identifies opportune moments for proactive user engagement, while, at the same time, reduces the number of undesirable interruptions.
Correlated neural variability in persistent state networks

Neural activity that persists long after stimulus presentation is a biological correlate of short-term memory. Variability in spiking activity causes persistent states to drift over time, ultimately degrading memory. Models of short-term memory often assume that the input fluctuations to neural populations are independent across cells, a feature that attenuates population-level variability and stabilizes persistent activity. However, this assumption is at odds with experimental recordings from pairs of cortical neurons showing that both the input currents and output spike trains are correlated. It remains unclear how correlated variability affects the stability of persistent activity and the performance of cognitive tasks that it supports. We consider the stochastic long-timescale attractor dynamics of pairs of mutually inhibitory populations of spiking neurons. In these networks, persistent activity was less variable when correlated variability was globally distributed across both populations compared with the case when correlations were locally distributed only within each population. Using a reduced firing rate model with a continuum of persistent states, we show that, when input fluctuations are correlated across both populations, they drive firing rate fluctuations orthogonal to the persistent state attractor, thereby causing minimal stochastic drift. Using these insights, we establish that distributing correlated fluctuations globally as opposed to locally improves network's performance on a two-interval, delayed response discrimination task. Our work shows that the correlation structure of input fluctuations to a network is an important factor when determining long-timescale, persistent population spiking activity.
Aquaporin Expression in Normal and Pathological Skeletal Muscles: A Brief Review with Focus on AQP4

Freeze-fracture electron microscopy enabled us to observe the molecular architecture of the biological membranes. We were studying the myofiber plasma membranes of health and disease by using this technique and were interested in the special assembly called orthogonal arrays (OAs). OAs were present in normal myofiber plasma membranes and were especially numerous in fast twitch type 2 myofibers; while OAs were lost from sarcolemmal plasma membranes of severely affected muscles with dystrophinopathy and dysferlinopathy but not with caveolinopathy. In the mid nineties of the last century, the OAs turned out to be a water channel named aquaporin 4 (AQP4). Since this discovery, several groups of investigators have been studying AQP4 expression in diseased muscles. This review summarizes the papers which describe the expression of OAs, AQP4, and other AQPs at the sarcolemma of healthy and diseased muscle and discusses the possible role of AQPs, especially that of AQP4, in normal and pathological skeletal muscles.
Going Public

The following accounts of what some resourceful music teachers have been able to accomplish at the local level represent a small sample of the efforts taking place across the country. In each case, teachers maximized limited resources of time, energy, and money through an organized, unified approach that brought music education to the attention of the public. The payoffs of "going public" proved well worth the effort.
Interviewing and Representation in Qualitative Research

CONTENTS Introduction Chapter 1 The interview in the project context Chapter 2 Language as Method. As model, As World Chapter 3 Doing the Inter-view Chapter 4 Interpreting, Understanding, Explaining Chapter 5 Positioning Subjects, Framing Selves, Making Worlds Chapter 6 Mapping The Politics: a rhetoric of circumstances, motives and action Chapter 7 Truth, Witness and betrayal: The ethical framing of interview based research Chapter 8 From Anecdote to Narrative Case Studies Chapter 9 From Interviews to Writing Conclusion
Whole-farmphosphorus management on dairy farms

Most dairy farms in Wisconsin continue to follow a fairly generalized formula of how to produce milk. Cows and replacement heifers are fed primarily homegrown feed from crop rotations comprising alfalfa (Medicago sativa L.), corn (Zea mays L.), oats (Avena spp.) and soybean (Glycine max L. Merr.). Protein and mineral supplements are purchased to compliment dairy diets. However, the dairy industry is undergoing rapid change to remain economically viable. Many farms are expanding herd size and increasing the importation of feed. Greater livestock numbers on a fixed land base has increased the risk of soil nutrient buildup and environmental pollution.
Music Genre Classification via Joint Sparse Low-Rank Representation of Audio Features

A novel framework for music genre classification, namely the joint sparse low-rank representation (JSLRR) is proposed in order to: 1) smooth the noise in the test samples, and 2) identify the subspaces that the test samples lie onto. An efficient algorithm is proposed for obtaining the JSLRR and a novel classifier is developed, which is referred to as the JSLRR-based classifier. Special cases of the JSLRR-based classifier are the joint sparse representation-based classifier and the low-rank representation-based one. The performance of the three aforementioned classifiers is compared against that of the sparse representation-based classifier, the nearest subspace classifier, the support vector machines, and the nearest neighbor classifier for music genre classification on six manually annotated benchmark datasets. The best classification results reported here are comparable with or slightly superior than those obtained by the state-of-the-art music genre classification methods.
Hormonal treatment for endometriosis associated pelvic pain

Background: Endometriosis is a common gynecological problem associated with chronic pelvic pain. Objective: To evaluate the effectiveness of current hormonal treatments of endometriosis associated pain. Materials and Methods: Randomized Controlled studies identified from databases of Medline and Cochrane Systemic Review groups were pooled. 7 RCTs were recruited for evaluation in this review. Data from these studies were pooled and meta-analysis was performed in three comparison groups: 1) Progestogen versus GnRHa; 2) Implanon versus Progestogen (injection); 3) Combined oral contraceptive pills versus placebo and progestogen. Response to treatment was measured as a reduction in pain score. Pain improvement was defined as improvement ≥1 at the end of treatment. Results: There was no significant difference between treatment groups of progestogen and GnRHa (RR: 0.036; CI:-0.030-0.102) for relieving endometriosis associated pelvic pain. Long acting progestogen (Implanon) and Mirena are not inferior to GnRHa and depot medroxy progesterone acetate (DMPA) (RR: 0.006; CI:-0.142-0.162). Combined oral contraceptive pills demonstrated effective treatment of relieving endometriosis associated pelvic pain when compared with placebo groups (RR:0.321CI-0.066-0.707). Progestogen was more effective than combined oral contraceptive pills in controlling dysmenorrhea (RR:-0.160; CI:-0.386-0.066), however, progestogen is associated with more side effects like spotting and bloating than the combined contraceptive pills. Conclusion: Combined oral contraceptive pills (COCP), GnRHa and progestogens are equally effective in relieving endometriosis associated pelvic pain. COCP and progestogens are relatively cheap and more suitable for long-term use as compared to GnRHa. Long-term RCT of medicated contraceptive devices like Mirena and Implanon are required to evaluate their long-term effects on relieving the endometriosis associated pelvic pain.
Can Evolutionary Sampling Improve Bagged Ensembles?

Perturb and Combine (P&C) group of methods generate multiple versions of the predictor by perturbing the training set or construction and then combining them into a single predictor (Breiman, 1996b). The motive is to improve the accuracy in unstable classification and regression methods. One of the most well known method in this group is Bagging. Arcing or Adaptive Resampling and Combining methods like AdaBoost are smarter variants of P&C methods. In this extended abstract, we lay the groundwork for a new family of methods under the P&C umbrella, known as Evolutionary Sampling (ES). We employ Evolutionary algorithms to suggest smarter sampling in both the feature space (sub-spaces) as well as training samples. We discuss multiple fitness functions to assess ensembles and empirically compare our performance against randomized sampling of training data and feature sub-spaces.
A Robust Optical Flow Estimation under Varying Illumination

This paper proposes a new method for robust and accurate optical flow estimation. The robustness of the new method is the ability of weakening the disturbance of varying illumination and preserving motion boundaries. The significance of this work is twofold: In the first part the generalized dynamic image model (GDIM) is adopted based on gradient field to encourage robustness to non-uniform brightness variations; otherwise, a Bayesian selecting scheme is adopted to encourage the ability of preserving motion boundaries and reduce the error, bias generated by smoothness constraint. We validated our results on a set of real and synthetic sequences. The qualitative and quantitative results indicate that the new method produces more accurate and robust results, which better sustain our conclusions.
TID and Displacement Damage Effects in Vertical and Lateral Power MOSFETs for Integrated DC-DC Converters

TID and displacement damage effects are studied for vertical and lateral power MOSFETs in five different technologies in view of the development of radiation-tolerant fully integrated DC-DC converters. Investigation is pushed to the very high level of radiation expected for an upgrade to the LHC experiments. TID induces threshold voltage shifts and, in n-channel transistors, source-drain leakage currents. Wide variability in the magnitude of these effects is observed. Displacement damage increases the on-resistance of both vertical and lateral high-voltage transistors. In the latter case, degradation at high particle fluence might lead to a distortion of the output characteristics curve. HBD techniques to limit or eliminate the radiation-induced leakage currents are successfully applied to these high-voltage transistors, but have to be used carefully to avoid consequences on the breakdown voltage.
Is There a Relationship between Language Switching and Executive Functions in Bilingualism? Introducing a within group Analysis Approach

Several studies have suggested a bilingual advantage in executive functions, presumably due to bilinguals’ massive practice with language switching that requires executive resources, but the results are still somewhat controversial. Previous studies are also plagued by the inherent limitations of a natural groups design where the participant groups are bound to differ in many ways in addition to the variable used to classify them. In an attempt to introduce a complementary analysis approach, we employed multiple regression to study whether the performance of 30- to 75-year-old Finnish–Swedish bilinguals (N = 38) on tasks measuring different executive functions (inhibition, updating, and set shifting) could be predicted by the frequency of language switches in everyday life (as measured by a language switching questionnaire), L2 age of acquisition, or by the self-estimated degree of use of both languages in everyday life. Most consistent effects were found for the set shifting task where a higher rate of everyday language switches was related to a smaller mixing cost in errors. Mixing cost is thought to reflect top-down management of competing task sets, thus resembling the bilingual situation where decisions of which language to use has to be made in each conversation. These findings provide additional support to the idea that some executive functions in bilinguals are affected by a lifelong experience in language switching and, perhaps even more importantly, suggest a complementary approach to the study of this issue.
Commodity Financialization: Risk Sharing and Price Discovery in Commodity Futures Markets

We study how commodity financialization affects trading behavior, prices, and welfare through affecting risk sharing and price discovery in futures markets. Our analysis highlights a supply channel through which the futures price feeds back into the later spot price. This feedback effect tends to reduce price efficiency but improve welfare. Consistent with recent evidence, we show that financial traders either provide or demand liquidity in the futures market, depending on the information environment, and that commodity financialization reduces the futures price bias through broadening risk sharing and injecting information into the market. Commodity financialization also has important welfare implications.
Validation of PET..Acquired Input Functions for CardiacStudies

with noninvasive positron emission tomography (PET) measurements utilizing bolus injections requires the time-activity curve ofthe tracer in arterial blood (1,2). The arterial input function can be determined from a sequence ofwithdrawn arterial blood samples by count ing in a well counter. Other methods yielding an arterial input function ofhigh temporal resolution include con tinuously monitored withdrawn arterial blood (for brain imaging)(3-5), and region ofinterest (ROl) calculation from dynamicallyâ€”acquired PET images of the left ventricular (LV) chamber in cardiac studies (2). The aim of this study was the validation of the noninvasive measurement of arterial time-activity curves obtained with dynamic PET cardiac imaging, by comparison with activity curves obtained independ ently with a beta probe that measured radioactivity in blood continuously withdrawn from the LV. The low sensitivity of the beta probe to background radiation allowed measurement of blood activity in the immedi ate vicinity of the probe without contamination from gamma raysoriginatingin the body ofthe experimental
Human Action Poselets Estimation via Color G-Surf in Still Images

Human activity is a persistent subject of interest in the last decade. On the one hand, video sequences provide a huge volume of motion information in order to recognize the human active actions. On the other hand, the spatial information about static human poses is valuable for human action recognition. Poselets were introduced as latent variables representing a configuration for mutual locations of body parts and allowing different views of description. In current research, some modifications of Speeded-Up Robust Features (SURF) invariant to affine geometrical transforms and illumination changes were tested. First, a grid of rectangles is imposed on object of interest in a still image. Second, sparse descriptor based on Gauge-SURF (G-SURF) invariant to color/lighting changes is constructed for each rectangle separately. A common Spatial POselet Descriptor (SPOD) aggregates the SPODs of rectangles with following random forest classification in order to receive fast classification results. The proposed approach was tested on samples from PASCAL Visual Object Classes (VOC) Dataset and Challenge 2010 providing accuracy 61-68% for all possible 3D poses locations and 82-86% for front poses locations regarding to nine action categories.
Shiga Toxin-Mediated Hemolytic Uremic Syndrome: Time to Change the Diagnostic Paradigm?

Background Hemolytic uremic syndrome (HUS) is caused by enterohemorrhagic Escherichia coli (EHEC) which possess genes encoding Shiga toxin (stx), the major virulence factor, and adhesin intimin (eae). However, the frequency of stx-negative/eae-positive E. coli in stools of HUS patients and the clinical significance of such strains are unknown. Methodology/Principal Findings Between 1996 and 2006, we sought stx-negative/eae-positive E. coli in stools of HUS patients using colony blot hybridization with the eae probe and compared the isolates to EHEC causing HUS. stx-negative/eae-positive E. coli were isolated as the only pathogens from stools of 43 (5.5%) of 787 HUS patients; additional 440 (55.9%) patients excreted EHEC. The majority (90.7%) of the stx-negative/eae-positive isolates belonged to serotypes O26:H11/NM (nonmotile), O103:H2/NM, O145:H28/NM, and O157:H7/NM, which were also the most frequent serotypes identified among EHEC. The stx-negative isolates shared non-stx virulence and fitness genes with EHEC of the corresponding serotypes and clustered with them into the same clonal complexes in multilocus sequence typing, demonstrating their close relatedness to EHEC. Conclusions/Significance At the time of microbiological analysis, ∼5% of HUS patients shed no longer the causative EHEC, but do excrete stx-negative derivatives of EHEC that lost stx during infection. In such patients, the EHEC etiology of HUS is missed using current methods detecting solely stx or Shiga toxin; this can hamper epidemiological investigations and lead to inappropriate clinical management. While maintaining the paradigm that HUS is triggered by Shiga toxin, our data demonstrate the necessity of considering genetic changes of the pathogen during infection to adapt appropriately diagnostic strategies.
Transient receptor potential vanilloid type 4 channels mediate Na‐K‐Cl‐co‐transporter‐induced brain edema after traumatic brain injury

Na+‐K+‐2Cl− co‐transporter (NKCC1) plays an important role in traumatic brain injury (TBI)‐induced brain edema via the MAPK cascade. The transient receptor potential vanilloid type 4 (TRPV4) channel participates in neurogenic inflammation, pain transmission, and edema. In this study, we investigated the relationship between NKCC1 and TRPV4 and the related signaling pathways in TBI‐induced brain edema and neuronal damage. TBI was induced by the calibrated weight‐drop device. Adult male Wistar rats were randomly assigned into sham and experimental groups for time‐course studies of TRPV4 expression after TBI. Hippocampal TRPV4, NKCC1, MAPK, and PI‐3K cascades were analyzed by western blot, and brain edema was also evaluated among the different groups. Expression of hippocampal TRPV4 peaked at 8 h after TBI, and phosphorylation of the MAPK cascade and Akt was significantly elevated. Administration of either the TRPV4 antagonist, RN1734, or NKCC1 antagonist, bumetanide, significantly attenuated TBI‐induced brain edema through decreasing the phosphorylation of MEK, ERK, and Akt proteins. Bumetanide injection inhibited TRPV4 expression, which suggests NKCC1 activation is critical to TRPV4 activation. Our results showed that hippocampal NKCC1 activation increased TRPV4 expression after TBI and then induced severe brain edema and neuronal damage through activation of the MAPK cascade and Akt‐related signaling pathway.
How to solve classification and regression problems on high-dimensional data with a supervised extension of slow feature analysis

Supervised learning from high-dimensional data, for example, multimedia data, is a challenging task. We propose an extension of slow feature analysis (SFA) for supervised dimensionality reduction called graph-based SFA (GSFA). The algorithm extracts a label-predictive low-dimensional set of features that can be post-processed by typical supervised algorithms to generate the final label or class estimation. GSFA is trained with a so-called training graph, in which the vertices are the samples and the edges represent similarities of the corresponding labels. A new weighted SFA optimization problem is introduced, generalizing the notion of slowness from sequences of samples to such training graphs. We show that GSFA computes an optimal solution to this problem in the considered function space and propose several types of training graphs. For classification, the most straightforward graph yields features equivalent to those of (nonlinear) Fisher discriminant analysis. Emphasis is on regression, where four different graphs were evaluated experimentally with a subproblem of face detection on photographs. The method proposed is promising particularly when linear models are insufficient as well as when feature selection is difficult.