Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality and acceptability of measures of exercise adherence in musculoskeletal settings: a systematic review

Objective. To recommend robust and relevant measures of exercise adherence for application in the musculoskeletal field. Method. A systematic review of measures was conducted in two phases. Phase 1 sought to identify all reproducible measures used to assess exercise adherence in a musculoskeletal setting. Phase 2 identified published evidence of measurement and practical properties of identified measures. Eight databases were searched (from inception to February 2016). Study quality was assessed against the Consensus-based Standards for the Selection of Health Measurement Instruments guidelines. Measurement quality was assessed against accepted standards. Results. Phase 1: from 8511 records, 326 full-text articles were reviewed; 45 reproducible measures were identified. Phase 2: from 2977 records, 110 full-text articles were assessed for eligibility; 10 articles provided evidence of measurement/practical properties for just seven measures. Six were exercise adherence-specific measures; one was specific to physical activity but applied as a measure of exercise adherence. Evidence of essential measurement and practical properties was mostly limited or not available. Assessment of relevance and comprehensiveness was largely absent and there was no evidence of patient involvement during the development or evaluation of any measure. Conclusion. The significant methodological and quality issues encountered prevent the clear recommendation of any measure; future applications should be undertaken cautiously until greater clarity of the conceptual underpinning of each measure is provided and acceptable evidence of essential measurement properties is established. Future research should seek to engage collaboratively with relevant stakeholders to ensure that exercise adherence assessment is high quality, relevant and acceptable.
A short-term double-blind randomized controlled pilot trial with active or placebo pindolol in patients treated with venlafaxine for major depression

Background: Pindolol has been widely investigated as an augmenter of antidepressant drug response. Results have been inconsistent. In this study, we used pindolol together with venlafaxine because of its ability to achieve a rapid onset of serotonin transporter blockade. Aims: The object of this study was thus to investigate if pindolol augments the antidepressant response to venlafaxine. Methods: Patients with major depression were randomized to either active or placebo pindolol 20 mg retard daily dosage and concomitantly treated with venlafaxine for 19 days. Depression severity was evaluated at four visits. Plasma concentrations of venlafaxine and its major metabolites O-desmethylvenlafaxine (ODV) and N-desmethylvenlafaxine (NDV) and pindolol were analysed. The ratio of ODV/venlafaxine was calculated. A low ratio corresponds to patients being poor metabolizers and a high ratio corresponds to patients being extensive metabolizers. Results: No statistically significant difference in depression outcome was found between treatment groups. A statistically significant effect was, however, found of the ratio of ODV/venlafaxine on depression outcome, showing an augmenting effect of pindolol in patients with a low ratio, and the reverse in patients with a high ratio. Conclusion: The differential effect of pindolol, on depression outcome, in patients with varying degrees of venlafaxine metabolism into ODV, corresponds to patients being poor or extensive metabolizers of venlafaxine. From this finding, we conclude that only patients who are poor metabolizers of venlafaxine might benefit from pindolol augmentation. This mechanism might explain some of the variability of outcome in pindolol augmentation studies.
Viral attack exacerbates the susceptibility of a bloom‐forming alga to ocean acidification

Both ocean acidification and viral infection bring about changes in marine phytoplankton physiological activities and community composition. However, little information is available on how the relationship between phytoplankton and viruses may be affected by ocean acidification and what impacts this might have on photosynthesis‐driven marine biological CO2 pump. Here, we show that when the harmful bloom alga Phaeocystis globosa is infected with viruses under future ocean conditions, its photosynthetic performance further decreased and cells became more susceptible to stressful light levels, showing enhanced photoinhibition and reduced carbon fixation, up‐regulation of mitochondrial respiration and decreased virus burst size. Our results indicate that ocean acidification exacerbates the impacts of viral attack on P. globosa, which implies that, while ocean acidification directly influences marine primary producers, it may also affect them indirectly by altering their relationship with viruses. Therefore, viruses as a biotic stressor need to be invoked when considering the overall impacts of climate change on marine productivity and carbon sequestration.
An Implantable Seizure-Onset Detector Based on a Dual-Path Single-Window Count-Based Technique for Closed-Loop Applications

In this paper, we present a single voltage-window count-based seizure onset detection algorithm and its associated hardware implementation. The proposed algorithm combines the advantages associated with voltage-window count-based and event-based threshold-voltage detections. The result is an algorithm that is more tolerant to noise, dc offsets, baseline energy variations, and seconds-long nonseizure related sharp activities. In addition, only one parameter (one threshold voltage) needs to be optimized per patient, and for that, only one seizure per patient is used for training, making the process of optimizing the patient-specific detector a simple task. The time evaluation period when counting is performed is kept constant across all patients studied, and is fixed at 5 s in this work. A novel dual path digital signal processing unit in the back-end of the detector is included and is shown to decrease the detection latency by 14%. Experimental results on a printed circuit board using commercially available discrete components confirm the correct functionality of the proposed detector. The proposed algorithm achieves 100% sensitivity, 10.7 s average detection delay, and a single false alarm when evaluated on a total of 25 seizures and 24 nonseizure datasets of intracerebral electroencephalographic (icEEG) recordings from five patients from the epilepsy monitoring unit of Notre-Dame Hospital in Montréal. In addition, monolithic integration of the overall system, including bio-amplification and comparison, is also carried out in a TSMC 0.18-$\mu$ m complementary metal–oxide–semiconductor technology. Simulations show that a static power dissipation of 7 $\mu {\rm W}$, 99% of which is consumed by the front-end bio-amplifier, is achieved, showing the potential of using the proposed seizure detector in a closed-loop brain–prosthesis device interface for seizure control and treatment.
Real-time cloth simulation for garment CAD

For decades, Computer Graphics has played an important role in many kinds of design works, especially, industrial design of solid objects. However, most available methods can not work well for soft and deformable object like cloth. The quality of works mostly depends on the efficiency of hardware as same as the cloth and garment pattern modeling methods. In this research, real time cloth modeling for costume design and making is proposed using mass-spring model and cellular structure space model. For representing realistic cloth, mass-spring model is parameterized under Newton laws, while cellular structured space model is defined for keeping the structure of each garment pattern. Then, the pattern is scaled to fit the input user's body size and clothed on user's virtual body. Furthermore, this pattern can be printed out as a set of ready-to-cut patterns. Proposed 3D modeling for costume design can reduce material cost and computational time for pattern making, and also enable dress remaking and refitting process in real time.
5-Bromo-N 3-phenylpyrazine-2,3-diamine

In the title compound, C10H9BrN4, the dihedral angle between the benzene and pyrazine rings is 61.34 (5)°. Intermolecular N—H⋯N hydrogen bonds and N—H⋯π interactions assemble the molecules into a three-dimensional network structure.
Efficient real-time face detection for high resolution surveillance applications

This paper presents an efficient face detection method suitable for real-time surveillance applications. Improved efficiency is achieved by constraining the search window of an AdaBoost face detector to pre-selected regions. Firstly, the proposed method takes a sparse grid of sample pixels from the image to reduce whole image scan time. A fusion of foreground segmentation and skin colour segmentation is then used to select candidate face regions. Finally, a classifier-based face detector is applied only to selected regions to verify the presence of a face (the Viola-Jones detector is used in this paper). The proposed system is evaluated using 640×480 pixels test images and compared with other relevant methods. Experimental results show that the proposed method reduces the detection time to 42 ms, where the Viola-Jones detector alone requires 565 ms (on a desktop processor). This improvement makes the face detector suitable for real-time applications. Furthermore, the proposed method requires 50% of the computation time of the best competing method, while reducing the false positive rate by 3.2% and maintaining the same hit rate.
Fuzzy neural network model for a class of nonlinear systems

For a class of nonlinear systems that the entire operation region can be divided into several operating regions and the property of each subsystem is linear, a modified fuzzy neural network (FNN) is designed as identifier. The prior knowledge of the system is considered adequately in the FNN model. The local model network of FNN is used to approximate the subsystem of each zone according to ARMAX model. The output of FNN model is the interpolation of local model network outputs with the output of the fuzzy decision-making layer. A new cost function is used when training FNN model. It can reduce over-fit of FNN model.
Simultaneous Quantification of Viral Antigen Expression Kinetics Using Data-Independent (DIA) Mass Spectrometry*

The generation of antigen-specific reagents is a significant bottleneck in the study of complex pathogens that express many hundreds to thousands of different proteins or to emerging or new strains of viruses that display potential pandemic qualities and therefore require rapid investigation. In these instances the development of antibodies for example can be prohibitively expensive to cover the full pathogen proteome, or the lead time may be unacceptably long in urgent cases where new highly pathogenic viral strains may emerge. Because genomic information on such pathogens can be rapidly acquired this opens up avenues using mass spectrometric approaches to study pathogen antigen expression, host responses and for screening the utility of therapeutics. In particular, data-independent acquisition (DIA) modalities on high-resolution mass spectrometers generate spectral information on all components of a complex sample providing depth of coverage hitherto only seen in genomic deep sequencing. The spectral information generated by DIA can be iteratively interrogated for potentially any protein of interest providing both evidence of protein expression and quantitation. Here we apply a solely DIA mass spectrometry based methodology to profile the viral antigen expression in cells infected with vaccinia virus up to 9 h post infection without the need for antigen specific antibodies or other reagents. We demonstrate deep coverage of the vaccinia virus proteome using a SWATH-MS acquisition approach, extracting quantitative kinetics of 100 virus proteins within a single experiment. The results highlight the complexity of vaccinia protein expression, complementing what is known at the transcriptomic level, and provide a valuable resource and technique for future studies of viral infection and replication kinetics. Furthermore, they highlight the utility of DIA and mass spectrometry in the dissection of host-pathogen interactions.
Priming for Steganography : Undermining Steganalysis through the Introduction of Superfluous Variation

This paper examines the effects upon steganalysis classifier performance of introducing superfluous variation into digital cover images . By using fixed steganography and steganalysis tools (Steghide 0.5.1 for former and Stegdetect 0.6 for latter), a number of different image filters are individually applied to cover images prior to the introduction of steganographic content; the effect of these filters are then measured in terms of the classifier performance (AUC) of steganalysis software Stegdetect.
A Content-based Probabilistic Correction Model for OCR Document Retrieval

The diff iculty with information retrieval for OCR documents lies in the fact that OCR documents comprise of a significant amount of erroneous words and unfortunately most information retrieval techniques rely heavily on word matching between documents and queries. In this paper, we propose a general content-based correction model that can work on top of an existing OCR correction tool to “boost” retrieval performance. The basic idea of this correction model is to exploit the whole content of a document to supplement any other useful information provided by an existing OCR correction tool for word correction. Instead of making an explicit correction decision for each erroneous word as typically done in a traditional approach, we consider the uncertainties in such correction decisions and compute an estimate of the original “uncorrupted” document language model accordingly. The document language model can then be used for retrieval with a language modeling retrieval approach. Evaluation using a TREC standard testing collection indicates that our method significantly improves the performance when compared with simple word correction approaches such as using only the top ranked correction.
3-D linearized scattering of surface waves and a formalism for surface wave holography

Summary. Scattering of surface waves by lateral heterogeneities is analysed in the Born approximation. It is assumed that the background medium is either laterally homogeneous, or smoothly varying in the horizontal direction. A dyadic representation of the Green's function simplifies the theory tremendously. Several examples of the theory are presented. The scattering and mode conversion coefficients are shown for scattering of surface waves by the root of an Alpine-like crustal structure. Furthermore a ‘great circle theorem’in a plane geometry is derived. A new proof of Snell's law is given for surface wave scattering by a quarter-space. It is shown how a stationary phase approximation can be used to simplify the Fourier synthesis of the scattered wave in the time domain. Finally a procedure is suggested to do ‘surface wave holography'.
CDNsim: A simulation tool for content distribution networks

Content distribution networks (CDNs) have gained considerable attention in the past few years. Hence there is need for developing frameworks for carrying out CDN simulations. In this article we present a modeling and simulation framework for CDNs, called CDNsim. CDNsim has been designated to provide a realistic simulation for CDNs, simulating the surrogate servers, the TCP/IP protocol, and the main CDN functions. The main advantages of this tool are its high performance, its extensibility, and its user interface, which is used to configure its parameters. CDNsim provides an automated environment for conducting experiments and extracting client, server, and network statistics. The purpose of CDNsim is to be used as a testbed for CDN evaluation and experimentation. This is quite useful to both the research community (to experiment with new CDN data management techniques), and for CDN developers (to evaluate profits on prior certain CDN installations).
Successful Schools and Risky Behaviors Among Low-Income Adolescents

OBJECTIVES: We examined whether exposure to high-performing schools reduces the rates of risky health behaviors among low-income minority adolescents and whether this is due to better academic performance, peer influence, or other factors. METHODS: By using a natural experimental study design, we used the random admissions lottery into high-performing public charter high schools in low-income Los Angeles neighborhoods to determine whether exposure to successful school environments leads to fewer risky (eg, alcohol, tobacco, drug use, unprotected sex) and very risky health behaviors (eg, binge drinking, substance use at school, risky sex, gang participation). We surveyed 521 ninth- through twelfth-grade students who were offered admission through a random lottery (intervention group) and 409 students who were not offered admission (control group) about their health behaviors and obtained their state-standardized test scores. RESULTS: The intervention and control groups had similar demographic characteristics and eighth-grade test scores. Being offered admission to a high-performing school (intervention effect) led to improved math (P < .001) and English (P = .04) standard test scores, greater school retention (91% vs 76%; P < .001), and lower rates of engaging in ≥1 very risky behaviors (odds ratio = 0.73, P < .05) but no difference in risky behaviors, such as any recent use of alcohol, tobacco, or drugs. School retention and test scores explained 58.0% and 16.2% of the intervention effect on engagement in very risky behaviors, respectively. CONCLUSIONS: Increasing performance of public schools in low-income communities may be a powerful mechanism to decrease very risky health behaviors among low-income adolescents and to decrease health disparities across the life span.
The Great Leap Backward: Exploring the Differences in Development Paths Between the Dominican Republic and Haiti

This thesis examines the variance in human development paths and policies pursed on the island of Hispaniola by the governments of the Dominican Republic and Haiti. The different paths have resulted in significant dissimilarities in contemporary levels of economic and social development across the island. Starting from the theoretical perspective of Acemplgu and Robinson (2012), I find that institutional explanations can only explain part of this divergence. I argue that a more complete explanation needs to take into consideration the role played by class, color, and race. I also find that foreign intervention, particularly the occupation of both countries by the US Marines in the 20th century, helped direct the development strategies of each country in different directions.
Graphical Models for Ordinal Data

This article considers a graphical model for ordinal variables, where it is assumed that the data are generated by discretizing the marginal distributions of a latent multivariate Gaussian distribution. The relationships between these ordinal variables are then described by the underlying Gaussian graphical model and can be inferred by estimating the corresponding concentration matrix. Direct estimation of the model is computationally expensive, but an approximate EM-like algorithm is developed to provide an accurate estimate of the parameters at a fraction of the computational cost. Numerical evidence based on simulation studies shows the strong performance of the algorithm, which is also illustrated on datasets on movie ratings and an educational survey.
A new 'slope-modulated' PWM strategy for implementation in a single chip gate array

The task of designing a gate-array-based, dedicated, three-phase PWM (pulsewidth-modulated) modulator for electric drive control is addressed. A novel slope-modulated PWM strategy is presented. Sine-wave and square-wave signal references, under several operating modes, are used to demonstrate the modulation properties. Performance parameters, such as reliable inverter switching, minimum on/off times, peak inverter switching frequency, total harmonic distortion, and pulse dropping are described. The modulator is shown to generate high-resolution PWM waveforms, with excellent performance when a variable-frequency ratio strategy is used with a sine-wave reference signal. The square-wave reference signal uses less complex digital circuitry, with a reliable operation of the inverter bridge. <<ETX>>
Predicting root biomass for semiarid grassland species of the southern Chihuahuan Desert

Most of carbon in grasslands comes from underground biomass, particularly in arid grassland ecosystems. However, estimation of root biomass in these ecosystems has been poorly studied. In this study was analyzed the correlation between above ground plant variables and root biomass to develop statistical models for reliable root biomass estimations. Twenty-six plant species were collected within grazing-excluded grasslands. Linear, exponential and logarithmic regression models were performed for each species and for the whole data set to determine the variables that best predicted root biomass. Only Frankenia gypsophila and Dalea gypsophila showed root/shoot ratio (RSR) higher than one. Enneapogon desvauxii and Atriplex acantocarpha had a RSR close to one. Eight species showed statistical significance in at least one of the correlation analyses but only Tiquilia canescens, Bouteloua gracilis, Machaerantera pinnatifida, Lesquerella fendleri, and Atriplex acanthocarpa had both statistical significance and acceptable coefficient of determination (r2 ≥0.50). Using the Marquardt exponential method, 14 out of 15 studied species showed a high determination coefficient and statistical significance. This method was adequate (r2=0.853) to estimate root biomass for the whole set of plants from plant height and crown diameter.
Subcarrier allocation and partner selection algorithms for cooperative multicarrier systems

Subcarrier allocation, power allocation, and partner selection algorithms are examined for amplify-and-forward cooperative multicarrier systems. Consider a network that consists of multiple cooperative pairs employing a two-phase cooperation scheme where the users first transmit their own messages on each of their subcarriers in Phase I and either retransmit their own messages or relay the messages of their partners in Phase II, i.e., the cooperative phase. In general, subcarrier allocation and partner selection problems are known to be intractable for systems with large number of users and subcarriers. In this work, we propose a mathematically tractable approach to address these issues and show its effectiveness compared to existing algorithms. Specifically, we consider a relaxed problem formulation, where each subcarrier is able to retransmit or relay for all other subcarriers in Phase II over orthogonal channels, and derive the optimal power allocation for this scenario. Efficient subcarrier allocation policies can then be derived based on the power allocation result and a sufficient condition is obtained to determine when cooperation is helpful among two users. A partner selection algorithm is then devised based on the sufficient condition. Given the subcarrier allocation scheme, the optimal power allocation among subcarriers in the cooperative phase can be shown to be a convex optimization problem and, thus, can be solved efficiently using standard convex optimization tools. The efficacy of the proposed algorithms is demonstrated through numerical simulations.