Miracle-dz/UniFAR-FTUs huggingface dataset:
Acute Effects of Contract-Relax Stretching vs. TENS in Young Subjects With Anterior Knee Pain: A Randomized Controlled Trial

Abstract Valenza, MC, Torres-Sánchez, I, Cabrera-Martos, I, Valenza-Demet, G, and Cano-Cappellacci, M. Acute effects of contract-relax stretching vs. TENS in young subjects with anterior knee pain: A randomized controlled trial. J Strength Cond Res 30(8): 2271–2278, 2016—The aim of this study was to examine the immediate effects on pressure point tenderness, range of motion (ROM), and vertical jump (VJ) of contract-relax stretching vs. transcutaneous electrical nerve stimulation (TENS) therapy in individuals with anterior knee pain (AKP). Eighty-four subjects with AKP were randomly assigned to 1 of 3 different intervention groups: a contract-relax stretching group (n = 28), a TENS intervention group (n = 28), and a control group (n = 28). The participants included in the sample were both sex (37.5% men vs. 62.5% women) at a mean age of 21 years, with mean values of height and weight of 169 cm and 64 kg, respectively. The main outcome measures were knee ROM, pressure pain threshold (PPT), and VJ. The participants were assessed at baseline and immediately after treatment. In the case of VJ, at baseline, immediately after the intervention, at 3 and at 6 minutes posttreatment. The data analysis showed that PPT scores of participants in the stretching and TENS group significantly increased from pretest to posttest (p ⩽ 0.05). A significant increase pre- to posttreatment in ROM (p < 0.001) was also observed in both treatment groups. In VJ measures, TENS and stretching groups showed significant differences between preintervention and all postintervention values (p ⩽ 0.05), whereas no significant differences were found in the control group. In conclusion, the results show significant pre-to-post-treatment effects in PPT, ROM, and VJ from both contract-relax stretching and TENS in young subjects with AKP.
Delayed Subsidence After Rifting and a Record of Breakup for Northwestern Zealandia

Continental rifting and breakup of eastern Gondwana during the Cretaceous separated northern Zealandia from eastern Australia, but the processes leading to this highly extended and largely submerged block of continental crust are unknown. We acquired and processed multichannel seismic reflection data across northern Zealandia and examine the stratigraphy of the Middleton Basin. We identified a two‐phase formation process for the basin, as evidenced by an unconformity separating two postrift units. After initial basin formation and slow deposition of the lower postrift unit, deposition rates within the Middleton Basin rapidly increased in response to the latest stage of subsidence and to create the modern basin. We propose a tectonic model wherein the Middleton Basin initiated through oceanic spreading and the subsequent postrift subsidence of the newly created oceanic lithosphere was delayed due to thermal buoyancy associated with nearby oceanic spreading in the Tasman Basin. Our results provide new constraints on rifting and breakup processes of wide, magma‐poor, and asymmetric margins and indicate that multiple regions of weak lithosphere may have influenced the breakup.
STUDIES ON FEMALE GENITAL TRACT INFECTIONS AND THE ROLE OF NITRIC OXIDE IN DIAGNOSIS

Female genital tract infections are among the most common conditions causing individuals to seek medical care, and are associated with a high gynaecologic and reproductive morbidity. Thus, prompt and precise diagnosis of genital tract infections is essential to institute effective therapy and prevent sequelae. It is therefore desirable to develop an easily performed, objective test that can be useful to the practicing physician. Local measurement of nitric oxide (NO) gas in hollow organs has been used to detect and monitor inflammatory processes, but as of today NO has not been studied in the female genital tract. The aim of the present thesis was to investigate whether it is possible to measure accurate NO formation in the female genital tract. We also sought to explore the role of NO as a diagnostic marker for inflammation in the female genital tract. Finally we wanted to evaluate the effect of bacterial vaginosis (BV) on the pharmacokinetics (plasma concentration Cmax, Tmax and bioavailability measured as the AUC240) of vaginally administered misoprostol in the first trimester of pregnancy. NO has a very short half-life in biological tissues, but it is more stable in the gaseous phase, which makes it possible to measure this free radical in luminal structures. We have developed a new method for measurement of luminal NO formation involving the insertion of a silicon catheter in the vagina or the uterine cavity. The balloon is filled with air, which is incubated in the vagina or the uterine cavity for sampling of NO from the vagina or the uterus respectively. We observed an almost 100-fold increase in intrauterine concentration of NO in patients diagnosed with pelvic inflammatory disease compared to those diagnosed with appendicitis or healthy controls. Uterine NO levels were uniformly low in healthy women throughout the menstrual cycle. Intrauterine NO levels did not rise after manipulation in the uterine cavity, and after an incubation time of just two minutes, NO levels were significantly increased in patients with diagnosed PID compared to control subjects. Moreover, in patients with symptoms of vaginitis, NO concentration was almost 100-fold increased compared to healthy controls. Vaginal NO levels were uniformly low among healthy women, both of reproductive age and in menopause. Finally, we observed that the bioavailability of misoprostol was reduced in patients with BV, but there was no significant difference in the pharmacokinetics of vaginally administered misoprostol between the patients with BV and healthy control subjects. In conclusion, BV did not affect the pharmacokinetics of vaginally administered misoprostol in early pregnancy. Furthermore, we describe a simple, reliable, rapid, safe and well tolerated method for measuring NO in the female genital tract. We also suggest that NO could be further evaluated as a biomarker of inflammatory disease in the female genital tract.
Higher Order Partial Least Squares (HOPLS): A Generalized Multilinear Regression Method

A new generalized multilinear regression model, termed the higher order partial least squares (HOPLS), is introduced with the aim to predict a tensor (multiway array) Y from a tensor X through projecting the data onto the latent space and performing regression on the corresponding latent variables. HOPLS differs substantially from other regression models in that it explains the data by a sum of orthogonal Tucker tensors, while the number of orthogonal loadings serves as a parameter to control model complexity and prevent overfitting. The low-dimensional latent space is optimized sequentially via a deflation operation, yielding the best joint subspace approximation for both X and Y. Instead of decomposing X and Y individually, higher order singular value decomposition on a newly defined generalized cross-covariance tensor is employed to optimize the orthogonal loadings. A systematic comparison on both synthetic data and real-world decoding of 3D movement trajectories from electrocorticogram signals demonstrate the advantages of HOPLS over the existing methods in terms of better predictive ability, suitability to handle small sample sizes, and robustness to noise.
Medical Informatics Training and Research at Rennes University School of Medicine

Abstract The University of Rennes Medical School has offered Medical informatics training since 1988; at the same time, research in medical informatics was started. This paper describes the teaching programs at both the undergraduate level and at the graduate level. Research topics comprise fundamental research on biomedical models, ontologies for medical knowledge representations, natural language processing, and research for diagnosis and therapeutic aids. We have developed a local area network which communicates between the School of Medicine and the University Hospital, both internally (Intranet) and externally by access to the National Research network (RENA TER) in France, which is connected to the internet.
Soft computing approach for predictive blood glucose management using a fuzzy neural network

Challenges in the management of blood sugar for Type 1 Diabetes mellitus (T1DM) patients have emerged to be one of the major contributors for the increase in societal cost of the health care system. The objective of this work is to develop and assess a computerized model for predicting blood glucose in patients with T1DM using the insulin pump and continuous glucose sensor. This study draws upon a soft computing approach that tolerates imprecision, uncertainty, and partial truth. An adaptive network-based fuzzy inference system (ANFIS) was implemented in the framework of fuzzy inferences and adaptive networks using an artificial neural network. The goal for the predictive approach is to provide personalized aid to patients with T1DM to better manage their glucose levels.
Federal Reserve Bank of Minneapolis Research Department Staff Report 463 International Recessions *

The 2007—2009 crisis was characterized by an unprecedented degree of international synchronization as all major industrialized countries experienced large macroeconomic contractions around the date of Lehman bankruptcy. At the same time countries also experienced large and synchronized tightening of credit conditions. We present a two-country model with financial market frictions where a credit tightening can emerge as a self-fulfilling equilibrium caused by pessimistic but fully rational expectations. As a result of the credit tightening, countries experience large and endogenously synchronized declines in asset prices and economic activity (international recessions). The model suggests that these recessions are more severe if they happen after a prolonged period of credit expansion. excellent discussions. The views expressed herein are those of the authors and not necessarily those of the Federal Reserve Bank of Minneapolis or the Federal Reserve System.
CHAPTER IN : Foundations of Sport-Related Brain Injuries

This chapter is a review and analysis of quantitative EEG (qEEG) for the evaluation of the locations and extent of injury to the brain following rapid acceleration/deceleration trauma, especially in mild traumatic brain injury (TBI). The earliest use of qEEG was by Hans Berger in 1932 and since this time over 1,600 peer reviewed journal articles have been published in which qEEG was used to evaluate traumatic brain injury. Quantitative EEG is a direct measure of the electrical energies of the brain and network dynamics which are disturbed following a traumatic brain injury. The most consistent findings are: 1reduced power in the higher frequency bands (8 to 40 Hz) which is linearly related to the magnitude of injury to cortical gray matter, 2increased slow waves in the delta frequency band (1 to 4 Hz) in the more severe cases of TBI which is linearly related to the magnitude of cerebral white matter injury and, 3changes in EEG coherence and EEG phase delays which are linearly related to the magnitude of injury to both the gray matter and the white matter, especially in frontal and temporal lobes. A review of qEEG reliability and clinical validation studies showed high predictive and content validity as determined by correlations between qEEG and clinical measures such as neuropsychological test performance, Glasgow Coma Scores, length of coma and MRI biophysical measures. Inexpensive and high speed qEEG NeuroImaging methods were also discussed in which the locations of maximal deviations from normal in 3-dimensions were revealed. Evaluation of the sensitivity and specificity of qEEG with a reduced number of EEG channels offers the feasibility of real-time monitoring of the EEG using Blue Tooth technology inside of a football helmet so that immediate evaluation of the severity and extent of brain injury in athletes can be accomplished. Finally, qEEG biofeedback treatment for the amelioration of complaints and symptoms following TBI is discussed
Colonization factor antigen CFA/IV (PCF8775) of human enterotoxigenic Escherichia coli: nucleotide sequence of the CS5 determinant

Human enterotoxigenic Escherichia coli isolates expressing the colonization factor antigen CFA/IV (previously designated PCF8775) produce plasmid-encoded CS5 fimbriae. The nucleotide sequence of the region encoding the major CS5 fimbrial subunit was determined. The subunit is synthesized as a precursor of 203 amino acids (20.85 kDa) with a mature protein of 181 amino acids corresponding to a size of 18.6 kDa. The CS5 subunit shows homology to the corresponding component of porcine enterotoxigenic E. coli F41, particularly within the signal sequence and at the carboxy terminus.
Polycystic Echinococcosis in Pacas, Amazon Region, Peru

In the Peruvian Amazon, paca meat is consumed by humans. To determine human risk for polycystic echinococcosis, we examined wild pacas from 2 villages; 15 (11.7%) of 128 were infected with Echinococcus vogeli tapeworms. High E. vogeli prevalence among pacas indicates potential risk for humans living in E. vogeli–contaminated areas.
S2‐bundles over 2‐orbifolds

Let M be a closed 4‐manifold with π2(M)≅ℤ. Then M is homotopy equivalent to either CP2, or the total space of an orbifold bundle with general fibre S2 over an aspherical 2‐orbifold B, or the total space of an RP2‐bundle over an aspherical surface. If π=π1(M)≠1, then there are at most two such bundle spaces with given action u : π→Aut (π2(M)). The bundle space has the geometry 𝕊2×𝔼2 (if χ(M)=0) or 𝕊2×ℍ2 (if χ(M)<0) if B has an ‘untwisted’ reflector curve or if π is not generated by involutions. If B is orientable and π is generated by involutions, then the action is unique and there is one non‐geometric orbifold bundle.
Hierarchical synopses with optimal error guarantees

Hierarchical synopsis structures offer a viable alternative in terms of efficiency and flexibility in relation to traditional summarization techniques such as histograms. Previous research on such structures has mostly focused on a single model, based on the Haar wavelet decomposition. In previous work, we have introduced a more refined, wavelet-inspired hierarchical index structure for synopsis construction: the Haar+ tree. The chief advantages of this structure are twofold. First, it achieves higher synopsis quality at the task of summarizing data sets with sharp discontinuities than state-of-the-art histogram and Haar wavelet techniques. Second, thanks to its search space delimitation capacity, Haar+ synopsis construction operates in time linear in the size of the data set for any monotonic distributive error metric. Contemporaneous research has introduced another hierarchical synopsis structure, the compact hierarchical histogram (CHH). In this article, we elaborate on both these structures. First, we formally prove that the CHH, in its default binary-hierarchy form, is a simplified variant of a Haar+ tree. We then focus on the summarization problem, with both these hierarchical synopsis structures, in which an error guarantee expressed by a maximum-error metric is required. We show that this problem is most efficiently solved through its dual, space-minimization counterpart, which can also achieve optimal quality. In this case, there is a benefit to be gained by specializing the algorithm for each structure; hence, our algorithm for optimal-quality maximum-error CHH requires low polynomial time; on the other hand, optimal-quality Haar+ synopses for maximum-error metrics are constructed in exponential time; hence, we also develop a low-polynomial-time approximation scheme for the maximum-error Haar+ case. Furthermore, we extend our approach for both general-error and maximum-error Haar+ synopses to arbitrary dimensionality. In our experimental study, (i) we confirm the theoretically expected superiority of Haar+ synopses over Haar wavelet methods in both construction time and achieved quality for representative error metrics; (ii) we demonstrate that Haar+ synopses are also constructed faster than optimal plain histograms, and, moreover, achieve higher synopsis quality with highly discontinuous data sets; such an advantage of a hierarchical synopsis structure over a histogram had been intuitively expressed, but never experimentally verified; and (iii) we show that Haar+ synopsis quality supersedes that of a CHH.
α‐MSH tripeptide analogs activate the melanocortin 1 receptor and reduce UV‐induced DNA damage in human melanocytes

One skin cancer prevention strategy that we are developing is based on synthesizing and testing melanocortin analogs that reduce and repair DNA damage resulting from exposure to solar ultraviolet (UV) radiation, in addition to stimulating pigmentation. Previously, we reported the effects of tetrapeptide analogs of α‐melanocortin (α‐MSH) that were more potent and stable than the physiological α‐MSH, and mimicked its photoprotective effects against UV‐induced DNA damage in human melanocytes. Here, we report on a panel of tripeptide analogs consisting of a modified α‐MSH core His6‐d‐Phe7‐Arg8, which contained different N‐capping groups, C‐terminal modifications, or arginine mimics. The most potent tripeptides in activating cAMP formation and tyrosinase of human melanocytes were three analogs with C‐terminal modifications. The most effective C‐terminal tripeptide mimicked α‐MSH in reducing hydrogen peroxide generation and enhancing nucleotide excision repair following UV irradiation. The effects of these three analogs required functional MC1R, as they were absent in human melanocytes that expressed non‐functional receptor. These results demonstrate activation of the MC1R by tripeptide melanocortin analogs. Designing small analogs for topical delivery should prove practical and efficacious for skin cancer prevention.
Anticonvulsant activity of Hypoxis hemerocallidea Fisch. & C. A. Mey. (Hypoxidaceae) corm (‘African potato’) aqueous extract in mice

Extracts of Hypoxis hemerocallidea Fisch. & C. A. Mey. (Hypoxidaceae) corm (popularly known as ‘African potato’) are extensively used in South African traditional medicines for the treatment, management and/or control of an array of human ailments, including childhood convulsions and epilepsy. This study examined the anticonvulsant activity of the plant's corm aqueous extract (APE, 50–800 mg/kg i.p.) against pentylenetetrazole (PTZ)‐, picrotoxin (PCT)‐ and bicuculline (BCL)‐induced seizures in mice. Phenobarbitone and diazepam were used as reference anticonvulsant drugs for comparison. Like the reference antiseizure drugs used, Hypoxis hemerocallidea corm aqueous extract (APE, 100–800 mg/kg i.p.) significantly delayed (p < 0.05–0.001) the onset of, and antagonized, pentylenetetrazole (PTZ)‐induced seizures. The plant's corm aqueous extract (APE, 100–800 mg/kg i.p.) also profoundly antagonized picrotoxin (PCT)‐induced seizures, but only weakly antagonized bicuculline (BCL)‐induced seizures. Although the data obtained in the present study do not provide conclusive evidence, it would appear that ‘African potato’ aqueous extract (APE) produces its antiseizure effect by enhancing GABAergic neurotransmission and/or action in the brain. The results of this laboratory animal study indicate that APE possesses anticonvulsant activity in the mammalian experimental model used and, therefore, tend to suggest that the herb may be used as a natural supplementary remedy in the management, control and/or treatment of childhood convulsions and epilepsy. In conclusion, the findings of this study indicate that Hypoxis hemerocallidea corm aqueous extract possesses anticonvulsant activity, and thus lend pharmacological credence to the suggested folkloric, anecdotal ethnomedical uses of the herb in the management of childhood convulsions and epilepsy in some rural communities of South Africa. Copyright © 2007 John Wiley & Sons, Ltd.
Supplementary Material for the AISTATS 2016 Paper: Provable Tensor Methods for Learning Mixtures of Generalized Linear Models

which is a multilinear combination of the tensor mode-1 fibers. Similarly T (u, v, w) ∈ R is a multilinear combination of the tensor entries, and T (I, I, w) ∈ Rd×d is a linear combination of the tensor slices. Now, let us proceed with the proof. Proof: Let x′ := 〈u, x〉+ b. Define l(x) := y · x⊗ x. We have E[y · x⊗3] = E[l(x)⊗ x] = E[∇xl(x)], ∗Allen Institute for Artificial Intelligence. Email: hanies@allenai.org. This work was done while the author was a visiting researcher at University of California, Irvine. †University of California, Irvine. Email: mjanzami@uci.edu ‡University of California, Irvine. Email: a.anandkumar@uci.edu 1Compare with the matrix case where for M ∈ Rd×d, we have M(I, u) = Mu := ∑ j∈[d] ujM(:, j) ∈ Rd.
Doubly Selective Channel Estimation Using Superimposed Training and Exponential Bases Models

Channel estimation for single-input multiple-output (SIMO) frequency-selective time-varying channels is considered using superimposed training. The time-varying channel is assumed to be described by a complex exponential basis expansion model (CE-BEM). A periodic (nonrandom) training sequence is arithmetically added (superimposed) at a low power to the information sequence at the transmitter before modulation and transmission. A two-step approach is adopted where in the first step we estimate the channel using CE-BEM and only the first-order statistics of the data. Using the estimated channel from the first step, a Viterbi detector is used to estimate the information sequence. In the second step, a deterministic maximum-likelihood (DML) approach is used to iteratively estimate the SIMO channel and the information sequences sequentially, based on CE-BEM. Three illustrative computer simulation examples are presented including two where a frequency-selective channel is randomly generated with different Doppler spreads via Jakes' model.
The Applications of Deep Learning on Traffic Identification

Generally speaking, most systems of network traffic identification are based on features. The features may be port numbers, static signatures, statistic characteristics, and so on. The difficulty of the traffic identification is to find the features in the flow data. The process is very time ‐ consuming. Also, these approaches are invalid to unknown protocol. To solve these problems, we propose a method that is based on neural network and deep learning – a hotspot of research in machine learning. The results show that our approach works very well on the applications of feature learning, protocol identification and anomalous protocol detection.