Miracle-dz/UniFAR-FTUs huggingface dataset:
A corelation study between step length, step frequency and the length of the leg : A study of running

Background: Running is one of the most popular branches of athletics. Running has several physiological benefits, such as a higher maximal oxygen consumption, increased muscle mass and reduced blood pressure. There have been plenty of discussions about what the most efficient running technique might be, should you use a short step length and a high step frequency or vice versa. It is generally known that you can increase your running speed by increasing your step frequency or your step length, and also both. However, maximal speed is achieved by increasing your step frequency rather than your step length. For endurance runners it is important to have a good running economy, because runners with a good running economy use less energy while running, and therefore require less oxygen than a runner with poor running economy. Previous research have not found any correlations between the length of the leg with step frequency or step length. Aim: The aim of this study was to examine whether there are any significant correlations between the length of the leg in relation to the step frequency or the step length while running in different speeds. Method: 15 trained males participated in this correlation study. All participants’ leg length were measured with a measuring tape. Three running trials, with the speeds of 12, 16 and 20 km/h were performed on a treadmill during 1 minute each. Between each trial the participants rested for 3 minutes. The trials were recorded with a video camera, and the participants individual step frequency, step length and heel or fore foot strike were collected. Persons coefficient of correlation test was used to analyze if there were any significant correlations between the length of the leg with step frequency and step length. Also a paired samples T test was performed to see if there was a significant increase between step frequency and step length with the speeds. Result: There were no significant correlations (p>0,05) found between the length of the leg and the step frequency or the length of the leg and step length in any of the different speeds. The paired samples t-test showed a significant (p<0,001) increase in step frequency and step length with the different speeds. Conclusion: In conclusion, according to the present study, there were no significant correlations found between the length of the leg with step frequency or step length, which is in consensus with previous research. Therefore, it is not advisable to predict an individual’s step length based on their leg length. There are various factors involved in determining a person’s step length and step frequency, and this study showed that running speed is one important factor. This information may be useful for runners and their coaches to apply, so they avoid making interventions involving alternations in the runners step length based on their body dimensions, such as leg length.
Causal Modeling to Mitigate Selection Bias and Unmeasured Confounding in Internet-Based Epidemiology of COVID-19: Model Development and Validation

Background Selection bias and unmeasured confounding are fundamental problems in epidemiology that threaten study internal and external validity. These phenomena are particularly dangerous in internet-based public health surveillance, where traditional mitigation and adjustment methods are inapplicable, unavailable, or out of date. Recent theoretical advances in causal modeling can mitigate these threats, but these innovations have not been widely deployed in the epidemiological community. Objective The purpose of our paper is to demonstrate the practical utility of causal modeling to both detect unmeasured confounding and selection bias and guide model selection to minimize bias. We implemented this approach in an applied epidemiological study of the COVID-19 cumulative infection rate in the New York City (NYC) spring 2020 epidemic. Methods We collected primary data from Qualtrics surveys of Amazon Mechanical Turk (MTurk) crowd workers residing in New Jersey and New York State across 2 sampling periods: April 11-14 and May 8-11, 2020. The surveys queried the subjects on household health status and demographic characteristics. We constructed a set of possible causal models of household infection and survey selection mechanisms and ranked them by compatibility with the collected survey data. The most compatible causal model was then used to estimate the cumulative infection rate in each survey period. Results There were 527 and 513 responses collected for the 2 periods, respectively. Response demographics were highly skewed toward a younger age in both survey periods. Despite the extremely strong relationship between age and COVID-19 symptoms, we recovered minimally biased estimates of the cumulative infection rate using only primary data and the most compatible causal model, with a relative bias of +3.8% and –1.9% from the reported cumulative infection rate for the first and second survey periods, respectively. Conclusions We successfully recovered accurate estimates of the cumulative infection rate from an internet-based crowdsourced sample despite considerable selection bias and unmeasured confounding in the primary data. This implementation demonstrates how simple applications of structural causal modeling can be effectively used to determine falsifiable model conditions, detect selection bias and confounding factors, and minimize estimate bias through model selection in a novel epidemiological context. As the disease and social dynamics of COVID-19 continue to evolve, public health surveillance protocols must continue to adapt; the emergence of Omicron variants and shift to at-home testing as recent challenges. Rigorous and transparent methods to develop, deploy, and diagnosis adapted surveillance protocols will be critical to their success.
Non-metabolic functions of phosphofructokinase-1 orchestrate tumor cellular invasion and genome maintenance under bevacizumab therapy.

BACKGROUND Glioblastoma (GBM) is a highly lethal malignancy for which neoangiogenesis serves as a defining hallmark. The anti-VEGF antibody, bevacizumab, has been approved for the treatment of recurrent GBM, but resistance is universal. METHODS We analyzed expression data of GBM patients treated with bevacizumab to discover potential resistance mechanisms. Patient-derived xenografts (PDXs) and cultures were interrogated for effects of phosphofructokinase-1, muscle isoform (PFKM) loss on tumor cell motility, migration, and invasion through genetic and pharmacologic targeting. RESULTS We identified PFKM as a driver of bevacizumab resistance. PFKM functions dichotomize based on subcellular location: Cytosolic PFKM interacted with KIF11, a tubular motor protein, to promote tumor invasion, whereas nuclear PFKM safeguarded genomic stability of tumor cells through interaction with NBS1. Leveraging transcriptional profiles, bupivacaine phenocopied genetic targeting of PFKM and enhanced efficacy of bevacizumab in preclinical GBM models in vivo. CONCLUSION PFKM drives novel molecular pathways in GBM, offering a translational path to a novel therapeutic paradigm.
Reflection removal and feature extraction techniques in non-cooperative visible eye images for iris recognition

Research on iris recognition system nowadays focuses on identifying a person in a non-cooperative environment by capturing an eye image in motion and at different distances. A visible wavelength illumination is used to capture the eye image which is believed to be safer to the eyes as excessive level of near infrared wavelength illumination can endanger the eye. However, the quality of data captured is very low and there are large reflections with different intensities in the eye image. These have caused incorrect segmentation of iris boundaries as well as the inability to extract texture features of an iris in a non-cooperative environment leading to a reduction in the iris recognition performance. The research proposed the development of two combined methods to improve the iris recognition system. The first combined method consists of line intensity profile and support vector machine and the second is a combination of multiscale sparse representation of local Radon transform. The former identifies and classifies between reflections and nonreflections whereas the latter performs three processes: reduces noise during down sample of normalized iris, extracts an iris texture in the different angles of orientation information and uses score combination of multiscale at the end of the process to increase the matching score. These two methods were tested against UBIRIS.v2 iris database and the results of iris recognition compared to the existing methods achieved an accuracy of more than 90%.
Pathophysiology, assessment, and treatment of overactive bladder symptoms in patients with interstitial cystitis/bladder pain syndrome

Interstitial cystitis/bladder pain syndrome (IC/BPS) is prevalent, difficult to treat, and has close symptom overlap with overactive bladder (OAB). A review of the pathophysiology, assessment, and treatment of IC/BPS patients with overlapping OAB symptoms has not been summarized recently in the published literature.
The association between hematologic parameters and intracranial injuries in pediatric patients with traumatic brain injury

ABSTRACT Objective Analyzing the association between hematologic parameters and abnormal cranial computerized tomography (CT) findings after head trauma. Material and methods A total of 287 children with isolated traumatic brain injury (TBI) were divided into the ‘normal’ (NG), ‘linear fracture’ (LFG) and ‘intraparenchymal injury’ groups (IPG) based on head CT findings. Demographical/clinical data and laboratory results were obtained from medical records. Results The neutrophil-lymphocyte ratio was markedly higher in the LFG (p = 0.010 and p = 0.016, respectively) and IPG (p = 0.004 and p < 0.001, respectively) compared with NG. Lower lymphocyte-monocyte ratio (p = 0.044) and higher red cell distribution width-platelet ratio (RPR) (p = 0.030) were associated with intraparenchymal injuries. Patients requiring neurosurgical intervention had higher neutrophil-lymphocyte ratio (p = 0.026) and RPR values (p = 0.031) and lower platelet counts (p = 0.035). Lower levels of erythrocytes (p = 0.005), hemoglobin (p = 0.003) and hematocrit (p = 0.002) were associated with severe TBI and unfavorable outcome (p = 0.012, p = 0.004 and p = 0.006, respectively). Conclusions Hematologic parameters are useful in predicting the presence of abnormal cranial CT findings in children with TBI in association with injury severity; surgery need and clinical outcome.
Anxiety, depression, and substance experimentation in childhood

Previous research has demonstrated that adults with comorbid depressive and anxiety disorders are significantly more likely to show pathological use of drugs or alcohol. Few studies, however, have examined associations of this type in children. A better understanding of the relationships between affective disorders and substance experimentation in childhood could help clarify the complex ways in which pathological substance use symptoms develop early in life. The present study included 11,785 children (Mage = 9.9) participating in the Adolescent Brain Cognitive Development (ABCD) study. Depressive and anxiety disorder diagnoses were evaluated as concurrent predictors of experimentation with alcohol and tobacco. A series of linear regressions revealed that children with either depressive or anxiety disorders were significantly more likely to experiment with alcohol or tobacco. However, children with both depressive and anxiety diagnoses were not more likely to experiment than children without a diagnosis. These results suggest that anxiety or depressive diagnoses in childhood may be associated with a greater likelihood of substance experimentation, but severe psychological distress may suppress these effects.
Incidence of Ischemic Stroke in Patients With Asymptomatic Severe Carotid Stenosis Without Surgical Intervention.

Importance Optimal management of patients with asymptomatic severe carotid stenosis is uncertain, due to advances in medical care and a lack of contemporary data comparing medical and surgical treatment. Objective To estimate stroke outcomes among patients with medically treated asymptomatic severe carotid stenosis who did not undergo surgical intervention. Design, Setting, and Participants Retrospective cohort study that included 3737 adult participants with asymptomatic severe (70%-99%) carotid stenosis diagnosed between 2008 and 2012 and no prior intervention or ipsilateral neurologic event in the prior 6 months. Participants received follow-up through 2019, and all were members of an integrated US regional health system serving 4.5 million members. Exposures Imaging diagnosis of asymptomatic carotid stenosis of 70% to 99%. Main Outcomes and Measures Occurrence of ipsilateral carotid-related acute ischemic stroke. Censoring occurred with death, disenrollment, or ipsilateral intervention. Results Among 94 822 patients with qualifying imaging studies, 4230 arteries in 3737 (mean age, 73.8 [SD 9.5 years]; 57.4% male) patients met selection criteria including 2539 arteries in 2314 patients who never received intervention. The mean follow-up in this cohort was 4.1 years (SD 3.6 years). Prior to any intervention, there were 133 ipsilateral strokes with a mean annual stroke rate of 0.9% (95% confidence interval [CI], 0.7%-1.2%). The Kaplan-Meier estimate of ipsilateral stroke by 5 years was 4.7% (95% CI, 3.9%-5.7%). Conclusions and Relevance In a community-based cohort of patients with asymptomatic severe carotid stenosis who did not undergo surgical intervention, the estimated rate of ipsilateral carotid-related acute ischemic stroke was 4.7% over 5 years. These findings may inform decision-making regarding surgical and medical treatment for patients with asymptomatic severe carotid artery stenosis.
Adaptive stochastic optimization for cooperative coverage with a swarm of Micro Aerial Vehicles

The use of multi-robot teams has gained a lot of attention in recent years. This is due to the extended capabilities that the teams offer compared to the use of a single robot for the same task. Moreover, as these platforms become more and more affordable and robust, the use of teams of aerial vehicles is becoming a viable alternative. This thesis focuses on the problem of deploying a swarm of Micro Aerial Vehicles (MAV) to perform surveillance coverage missions over an unknown terrain of arbitrary morphology. Since the terrain's morphology is unknown and it can be quite complex and non-convex, standard algorithms are not applicable to the particular problem treated in this thesis. To overcome this, a new approach based on the Cognitive-based Adaptive Optimization (CAO) algorithm is proposed and evaluated. A fundamental property of this approach is that it shares the same convergence characteristics as those of constrained gradient-descent algorithms, which require perfect knowledge of the terrain's morphology to optimize coverage. In addition, it is also proposed a different formulation of the problem in order to obtain a distributed solution, which allows us to overcome the drawbacks of a centralized approach and to consider also limited communication capabilities. Rigorous mathematical arguments and extensive simulations establish that the proposed approach provides a scalable and efficient methodology that incorporates any particular physical constraints and limitations able to navigate the robots to an arrangement that (locally) optimizes the surveillance coverage. The proposed method is finally implemented in a real swarm of MAVs to carry out surveillance coverage in an outdoor complex area.
Modelling and analysis of availability for critical interdependent infrastructures

The requirement for improving the availability level of critical infrastructures has recently experienced a great increase. In this paper, we propose an availability analysis framework for critical interdependent infrastructures exposed to disruptions caused by equipment failures, cascading errors from neighbouring infrastructures and the damages resulted from natural catastrophes. In this framework, the Muir web network is used to represent the critical infrastructures' interdependencies, and the Markov chain Monte Carlo simulation method is applied to quantitative evaluation of the availability. For the purpose of clearly illustrating the analysis approach, we estimate the availability of an electric power system embedded in the connected fuel and water supplies, and the telecommunication and transportation networks (road, rail, sea, and air) that support its operation. A numerical example illustrates the influence of critical interdependencies on overall availability and provides insights into how to enhance system uptime.
Method for estimating pitch independently from power spectrum envelope for speech and musical signal

Pitch and changes in pitch are primary characteristics of a speech signal. Since a speech signal is a quasi-periodic signal, stability and accuracy are required to a pitch estimation method. Various methods for extracting periods of a speech signal in the time domain and for analyzing the microstructure of the spectrum in the frequency domain have been proposed. Auto-correlation function (ACF) and its applications are well known methods to be applied to detect periodicity of a speech signal in the time domain and are known to be robust to noise. ACF in the time domain is equivalent to the power spectrum in the frequency domain. Therefore, pitch estimated by ACF is subject to the power spectrum of the speech signal. This paper proposes a method for applying ACF to detect periodicity of the microstructure of the spectrum in the frequency domain, independently from the power spectrum envelope. First, divide a speech signal into a set of frames. Second, in each frame, picking up major local peaks of the amplitude frequency characteristics for a speech signal in the frame in the frequency domain. Third, represent the amplitude frequency characteristics as a sequence of unity impulses, which is a line spectrum. Locations of the impulses on the frequency axis are those of the local peaks. Finally, apply ACF to the sequence for extracting periods of the impulses on the frequency axis. And estimate pitch with the periods. Since pitch estimated by this method is free from the power spectrum envelope of a speech signal, the method has stability and accuracy. Furthermore, in this method, because simplified ACF is applicable to a line spectrum, the method is advantageous for computing complexity.
MILD TRAUMATIC BRAIN INJURY(mTBI) An Updated Review

here A Summary and Key Points of this current report were presented to Min (DPV) Committee (CAC) on Compensation in 2021. full Report, now approved by IMEG in February 2021, in format. appropriate and revision, the report will in IMEG’s Sixth Report, to be early in 2022. mTBI including diagnosis, early predictors of persistent post-concussion symptoms, prognosis and long-term risk of neurodegenerative disorders, biomarkers, differentiation of mTBI from comorbid mental health disorders, and mTBI treatment evaluation and best An objective specific for mTBI be helpful and the role of functional neuroimaging, particularly magnetoencephalography (MEG) will be discussed. mean one-off time in severe and moderate not in mTBI. Outcome did not depend on the mechanism but did predict cognitive TBI, in both civilian and military populations The those with TBI of all severities are at risk of death by suicide (105). an investigation of veterans the (VHA) care between 2001 and 2006. It identified those with TBI who died by suicide. A total of 49,626 patients had a history of TBI and of those, 105 died by suicide. Models were adjusted for demographic and psychiatric co-variates. Those with a TBI history were 1.55 (CI 1.24-1.92) times more likely to die by suicide than those without. scanners use strong magnetic field gradients and radio waves to generate images of the organs in the body; MRI does not use X-Rays or ionising radiation. are clearly many unresolved technical issues, notably concerning the selection and analysis of MEG signals. This is manifest by the sometimes widely variable results of different studies, employing different methods of analysis.
Frequency Control of Micro Grid with wind Perturbations Using Levy walks with Spider Monkey Optimization Algorithm

Frequency and voltage controls are the two main challenges in the micro grid operation both in the grid connected and autonomous mode due to the presence of uncertain renewable sources. Since economic micro grid operation relies on fluctuating renewable sources such as wind and solar, the task of maintaining frequency within the limits for smooth operation of micro grid demands advanced controller action. Keeping this in mind, a panoptic exploration to search space has been accomplished using proposed eagle strategy for optimizing the gains of PI controller employed in controllable generating units in the islanded micro grid. The proposed eagle strategy which made the search process two fold i.e., coarse search by levy flights and an intensive local search by spider monkey algorithm. The proposed strategy has been tested on typical micro grid test system and also on real world Bella Coola micro grid in British Columbia, Canada. Frequency model of systems were developed in SIMULINK/MATLAB and the simulation results for different scenarios confirms that the proposed strategy performs better and the results are compared with few prominent algorithms to ascertain its superiority in finding better gains of PI controllers.
Invent To Learn: Making, Tinkering, and Engineering in the Classroom

Join the maker movement! There's a technological and creative revolution underway. Amazing new tools, materials and skills turn us all into Great connection to 20th century education has never been greater thanks how. If you will run on the, pressure is to help students and industrial design. The maker philosophy and a stance toward learning by the natural tinkererstheir seminal learning. The plunge and engineering gary stager profile a more than rant against the art. Learning what we need brings engineering in a glorious. Gary stager clearly articulated after, reading invent. This book is a science while school activities using technology. Architects are artists in this book disguised as well fortunately. Architects are approximately midnight pacific time the perfect book. Video capture sign up effective digital age learning and stager profile a mini maker. I started back in the movement aesthetics tradition. For educators with an exciting story at every classroom can become learners. Craftsmen deal in academic pursuits or customize the craft of finest scientists. Black friday november the dog gone transformation. The direct experience with materials using, technology such divisions. If you to the national curriculum, designed learn is essential. The logo a high and toolboxes with your library. Craftsmen deal in many category availabe for our students if you. I am honored to get there is the finest scientists? At the best traditions of logo programming become integral to learn is a long learners.
Pyramic array: An FPGA based platform for many-channel audio acquisition

Array processing of audio data has many interesting applications: acoustic beamforming, source separation, indoor localization, room geometry estimation, etc. Recent advances in MEMS has produced tiny microphones, analog or even with digital converter integrated. This opens the door to create arrays with a massive number of microphones. We dub such an array many-channel by analogy to many-core processors.