Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Minimalist Footwear Strategies for Simulating Barefoot Running: A Randomized Crossover Study

Possible benefits of barefoot running have been widely discussed in recent years. Uncertainty exists about which footwear strategy adequately simulates barefoot running kinematics. The objective of this study was to investigate the effects of athletic footwear with different minimalist strategies on running kinematics. Thirty-five distance runners (22 males, 13 females, 27.9 ± 6.2 years, 179.2 ± 8.4 cm, 73.4 ± 12.1 kg, 24.9 ± 10.9 km.week-1) performed a treadmill protocol at three running velocities (2.22, 2.78 and 3.33 m.s-1) using four footwear conditions: barefoot, uncushioned minimalist shoes, cushioned minimalist shoes, and standard running shoes. 3D kinematic analysis was performed to determine ankle and knee angles at initial foot-ground contact, rate of rear-foot strikes, stride frequency and step length. Ankle angle at foot strike, step length and stride frequency were significantly influenced by footwear conditions (p<0.001) at all running velocities. Posthoc pairwise comparisons showed significant differences (p<0.001) between running barefoot and all shod situations as well as between the uncushioned minimalistic shoe and both cushioned shoe conditions. The rate of rear-foot strikes was lowest during barefoot running (58.6% at 3.33 m.s-1), followed by running with uncushioned minimalist shoes (62.9%), cushioned minimalist (88.6%) and standard shoes (94.3%). Aside from showing the influence of shod conditions on running kinematics, this study helps to elucidate differences between footwear marked as minimalist shoes and their ability to mimic barefoot running adequately. These findings have implications on the use of footwear applied in future research debating the topic of barefoot or minimalist shoe running.
Infrared switching electrochromic devices based on tungsten oxide

Different types of electrochromic devices for thermal emittance modulation were developed in the spectral region from mid- to far-infrared (2–40 μm). In all devices polycrystalline and amorphous tungsten oxide have been used as electrochromic and ion storage layer, respectively. Two types of all-solid-state devices were designed, one with a metal grid for the top and bottom electrode deposited on a highly emissive glass substrate, and another with a top metal grid electrode and a highly reflecting bottom metal electrode layer. Tantalum oxide is used as an ion conductor in both device types. The third device type consists of a polymeric ion conductor. All solid-state constituent layers were grown by either reactive or nonreactive dc or rf magnetron sputtering in a high vacuum environment. Modulation of the emittance is accomplished by reversible insertion of Li ions into polycrystalline WO3 by applying and switching a small voltage across the structure. Spectrally dependent measured reflectance modulation .
Gene-Specific Substitution Profiles Describe the Types and Frequencies of Amino Acid Changes during Antibody Somatic Hypermutation

Somatic hypermutation (SHM) plays a critical role in the maturation of antibodies, optimizing recognition initiated by recombination of V(D)J genes. Previous studies have shown that the propensity to mutate is modulated by the context of surrounding nucleotides and that SHM machinery generates biased substitutions. To investigate the intrinsic mutation frequency and substitution bias of SHMs at the amino acid level, we analyzed functional human antibody repertoires and developed mGSSP (method for gene-specific substitution profile), a method to construct amino acid substitution profiles from next-generation sequencing-determined B cell transcripts. We demonstrated that these gene-specific substitution profiles (GSSPs) are unique to each V gene and highly consistent between donors. We also showed that the GSSPs constructed from functional antibody repertoires are highly similar to those constructed from antibody sequences amplified from non-productively rearranged passenger alleles, which do not undergo functional selection. This suggests the types and frequencies, or mutational space, of a majority of amino acid changes sampled by the SHM machinery to be well captured by GSSPs. We further observed the rates of mutational exchange between some amino acids to be both asymmetric and context dependent and to correlate weakly with their biochemical properties. GSSPs provide an improved, position-dependent alternative to standard substitution matrices, and can be utilized to developing software for accurately modeling the SHM process. GSSPs can also be used for predicting the amino acid mutational space available for antigen-driven selection and for understanding factors modulating the maturation pathways of antibody lineages in a gene-specific context. The mGSSP method can be used to build, compare, and plot GSSPs1; we report the GSSPs constructed for 69 common human V genes (DOI: 10.6084/m9.figshare.3511083) and provide high-resolution logo plots for each (DOI: 10.6084/m9.figshare.3511085).
An Exploratory Study of Innovation Effectiveness Measurement in Australian and Thai SMEs

The advantages of using a balanced approach to measurement of overall organisational performance are well-known. We examined the effects of a balanced approach in the more specific domain of measuring innovation effectiveness in 144 small to medium-sized companies in Australia and Thailand. We found that there were no differences in the metrics used by Australian and Thai companies. In line with our hypotheses, we found that those SMEs that took a balanced approach were more likely to perceive benefits of implemented innovations than those that used only a financial approach to measurement. The perception of benefits then had a subsequent effect on overall attitudes towards innovation. The study shows the importance of measuring both financial and non-financial indicators of innovation effectiveness within SMEs and discusses ways in which these can be conducted with limited resources.
SleepAp: An automated obstructive sleep apnoea screening application for smartphones

Obstructive Sleep Apnoea (OSA) is a sleep disorder with long term consequences. It is often diagnosed with an overnight sleep study or polysomnogram. Monitoring can be costly with long wait times for diagnosis. In this paper we describe a novel OSA screening framework and prototype phone application (app). A database of 856 patients that underwent at-home polysomnography was collected. Features were derived from audio, actigraphy, photoplethysmography (PPG) and demographics, and used as the inputs of a support vector machine (SVM) classifier. The SVM was trained on 735 patients (368 non-OSA and 567 OSA) and tested on 121 patients (44-77 split). Classification on the test set had an accuracy of up to 92.3%. The signal processing and machine learning algorithms were ported to Java and integrated into the phone app. The app records the audio, actigraphy and PPG signals, implements the clinically validated STOP-BANG questionnaire, derives features from the signals, and finally classifies the patient as needing treatment or not using the trained SVM. The resulting software could provide a new, easy-to-use, low-cost and widely available modality for OSA screening.
BGRAF2: a real-time graphics language with modular objects and implicit dynamics

The BGRAF2 language for interactive real-time 2D graphics was designed as a user-oriented language emphasizing ease of use rather than of implementation. Procedural statements allow straightforward computation while classlike characteristics encourage modular graphics programming. The clock and event data types facilitate creation of parallel and synchronous procedures automatically acting on structures and display files. A graphic procedure, which includes procedural, drawing and event-driven statements, is a graphic prototype. Such procedures may be nested and iteratively or recursively called. An individual invocation of a procedure, called a graphic object, shares code while commanding its own data structures and images.
Identifying Cell Cycle Modulators That Selectively Target ARID1A Deficiency Using High-Throughput Image-Based Screening

ARID1A, a component of the chromatin remodeling complex SWI/SNF, is an evolutionarily conserved complex that uses the energy of adenosine triphosphate hydrolysis to remodel chromatin structure and functions as a master regulator of gene transcription. Recent genomic studies have revealed that ARID1A is one of the most frequently mutated genes in human cancers. However, therapeutic approaches that selectively target ARID1A-mutant tumors are not yet clinically available. Our previous study showed that ARID1A facilitates chromatin response and cell cycle checkpoint activation after DNA damage. Therefore, an ARID1A deficiency may result in therapeutic vulnerabilities in cell cycle modulators. The goals of our study were to develop a novel screening approach, based on fluorescent ubiquitination-based cell cycle indicators (FUCCI), and to identify chemical agents that can selectively modulate the cell cycle transition in ARID1A-deficient cancer cells. Using this high-throughput assay, we screened 2643 compounds and identified six potential chemical modulators that can selectively modulate the cell cycle in ARID1A-deficient cells; these agents may be useful for developing new therapeutics for ARID1A-mutant tumors. In summary, our study demonstrates that FUCCI cell-based high-content screening is a powerful and effective approach for identifying cell cycle modulators and can be applied to multigenotypic screening for targeted cancer therapeutics.
Interactions across Multiple Stimulus Dimensions in Primary Auditory Cortex

Visual Abstract Although sensory cortex is thought to be important for the perception of complex objects, its specific role in representing complex stimuli remains unknown. Complex objects are rich in information along multiple stimulus dimensions. The position of cortex in the sensory hierarchy suggests that cortical neurons may integrate across these dimensions to form a more gestalt representation of auditory objects. Yet, studies of cortical neurons typically explore single or few dimensions due to the difficulty of determining optimal stimuli in a high dimensional stimulus space. Evolutionary algorithms (EAs) provide a potentially powerful approach for exploring multidimensional stimulus spaces based on real-time spike feedback, but two important issues arise in their application. First, it is unclear whether it is necessary to characterize cortical responses to multidimensional stimuli or whether it suffices to characterize cortical responses to a single dimension at a time. Second, quantitative methods for analyzing complex multidimensional data from an EA are lacking. Here, we apply a statistical method for nonlinear regression, the generalized additive model (GAM), to address these issues. The GAM quantitatively describes the dependence between neural response and all stimulus dimensions. We find that auditory cortical neurons in mice are sensitive to interactions across dimensions. These interactions are diverse across the population, indicating significant integration across stimulus dimensions in auditory cortex. This result strongly motivates using multidimensional stimuli in auditory cortex. Together, the EA and the GAM provide a novel quantitative paradigm for investigating neural coding of complex multidimensional stimuli in auditory and other sensory cortices.
LSH-RANSAC: An incremental scheme for scalable localization

This paper addresses the problem of feature-based robot localization in large-size environments. With recent progress in SLAM techniques, it has become crucial for a robot to estimate the self-position in real-time with respect to a large-size map that can be incrementally build by other mapper robots. Self-localization using large-size maps have been studied in litelature, but most of them assume that a complete map is given prior to the self-localization task. In this paper, we present a novel scheme for robot localization as well as map representation that can successfully work with large-size and incremental maps. This work combines our two previous works on incremental methods, iLSH and iRANSAC, for appearance-based and position-based localization.
The Effects of a Moderate Exercise Program on Knee Osteoarthritis in Male Wistar Rats

Objective(s): Osteoarthritis (OA) or degenerative joint disease is the commonest form of arthritis and can lead to joint pain, decrease in joint’s range of motion, loss of function, and ultimately disability. Exercise is considered as one of the non-pharmacological treatments of OA. But the effects of exercise on knee joint cartilage remain ambiguous. The aim of the present study was to investigate the effect of a four-week moderate treadmill exercise on rats’ knee osteoarthritis. Materials and Methods: Eighteen male Wistar rats (173 ± 1 g, 8 weeks old) were randomly divided into three groups (n = 6): Intact control, monosodium iodoacetate (MIA) only (OA), and training. The osteoarthritis model was induced by intra-articular injection of monosodium iodoacetate (MIA). Subjects followed a moderate-intensity exercise program for 28 days. Rats were killed after 28 days and histological assessment was done on their knee joints. One-way ANOVA (P<0.05) and post-hoc Tukey test was used for the statistical analysis. Results: Histological assessment on 3 measurements of, depth ratio of lesions (P=0.001), total cartilage degeneration width (P=0.001), and significant cartilage degeneration width (P=0.001), demonstrated that moderate exercise for 4 weeks could surprisingly almost treat OA symptoms of rats’ knee joints. Conclusion: The findings of the present study indicate that a moderate treadmill exercise program exert a beneficial influence on rats’ knee osteoarthritis.
Significance of Plankton Community Structure and Nutrient Availability for the Control of Dinoflagellate Blooms by Parasites: A Modeling Approach

Dinoflagellate blooms are frequently observed under temporary eutrophication of coastal waters after heavy rains. Growth of these opportunistic microalgae is believed to be promoted by sudden input of nutrients and the absence or inefficiency of their natural enemies, such as grazers and parasites. Here, numerical simulations indicate that increasing nutrient availability not only promotes the formation of dinoflagellate blooms but can also stimulate their control by protozoan parasites. Moreover, high abundance of phytoplankton other than dinoflagellate hosts might have a significant dilution effect on the control of dinoflagellate blooms by parasites, either by resource competition with dinoflagellates (thus limiting the number of hosts available for infection) or by affecting numerical-functional responses of grazers that consume free-living parasite stages. These outcomes indicate that although both dinoflagellates and their protozoan parasites are directly affected by nutrient availability, the efficacy of the parasitic control of dinoflagellate blooms under temporary eutrophication depends strongly on the structure of the plankton community as a whole.
What is simulated in the action observation network when we observe actions?

In the last decade there has been a great amount of research investigating the role of simulation in our ability to infer the underlying intentions of any observed action. The majority of studies have focussed on the role of mirror neurons and the network of cortical areas active during action observation (AON) in inferring the goal of an observed action. However, it remains unclear what precisely is simulated when we observe an action and how such simulations can enable the observer to infer the underlying intention of that action. In particular it is not known how simulation in the AON enables the inference of the same goal when the kinematics observed to achieve that goal differ, such as when reaching to grasp an object with the left or right hands. Here we performed a behavioural study with healthy human subjects to address this question. We show that the subjects were able to detect very subtle changes in the kinematics of an observed action. In addition, we fitted the behavioural responses with a model based on the predictive coding account of mirror neurons. This is a Bayesian account of action observation that can be explained by the free‐energy principle. Here we show that we can model all the effects observed when the action observation system is considered within a predictive coding framework.
Modulation of mediotemporal and ventrostriatal function in humans by Delta9-tetrahydrocannabinol: a neural basis for the effects of Cannabis sativa on learning and psychosis.

CONTEXT Cannabis sativa use can impair verbal learning, provoke acute psychosis, and increase the risk of schizophrenia. It is unclear where C. sativa acts in the human brain to modulate verbal learning and to induce psychotic symptoms. OBJECTIVES To investigate the effects of 2 main psychoactive constituents of C. sativa, Delta9-tetrahydrocannabinol (Delta9-THC) and cannabidiol, on regional brain function during verbal paired associate learning. DESIGN Subjects were studied on 3 separate occasions using a block design functional magnetic resonance imaging paradigm while performing a verbal paired associate learning task. Each imaging session was preceded by the ingestion of Delta9-THC (10 mg), cannabidiol (600 mg), or placebo in a double-blind, randomized, placebo-controlled, repeated-measures, within-subject design. SETTING University research center. PARTICIPANTS Fifteen healthy, native English-speaking, right-handed men of white race/ethnicity who had used C. sativa 15 times or less and had minimal exposure to other illicit drugs in their lifetime. MAIN OUTCOME MEASURES Regional brain activation (blood oxygen level-dependent response), performance in a verbal learning task, and objective and subjective ratings of psychotic symptoms, anxiety, intoxication, and sedation. RESULTS Delta9-Tetrahydrocannabinol increased psychotic symptoms and levels of anxiety, intoxication, and sedation, whereas no significant effect was noted on these parameters following administration of cannabidiol. Performance in the verbal learning task was not significantly modulated by either drug. Administration of Delta9-THC augmented activation in the parahippocampal gyrus during blocks 2 and 3 such that the normal linear decrement in activation across repeated encoding blocks was no longer evident. Delta9-Tetrahydrocannabinol also attenuated the normal time-dependent change in ventrostriatal activation during retrieval of word pairs, which was directly correlated with concurrently induced psychotic symptoms. In contrast, administration of cannabidiol had no such effect. CONCLUSION The modulation of mediotemporal and ventrostriatal function by Delta9-THC may underlie the effects of C. sativa on verbal learning and psychotic symptoms, respectively.
Estimated life expectancy in a Scottish cohort with type 1 diabetes, 2008-2010.

IMPORTANCE Type 1 diabetes has historically been associated with a significant reduction in life expectancy. Major advances in treatment of type 1 diabetes have occurred in the past 3 decades. Contemporary estimates of the effect of type 1 diabetes on life expectancy are needed. OBJECTIVE To examine current life expectancy in people with and without type 1 diabetes in Scotland. We also examined whether any loss of life expectancy in patients with type 1 diabetes is confined to those who develop kidney disease. DESIGN, SETTING, AND PARTICIPANTS Prospective cohort of all individuals alive in Scotland with type 1 diabetes who were aged 20 years or older from 2008 through 2010 and were in a nationwide register (n=24,691 contributing 67,712 person-years and 1043 deaths). MAIN OUTCOMES AND MEASURES Differences in life expectancy between those with and those without type 1 diabetes and the percentage of the difference due to various causes. RESULTS Life expectancy at an attained age of 20 years was an additional 46.2 years among men with type 1 diabetes and 57.3 years among men without it, an estimated loss in life expectancy with diabetes of 11.1 years (95% CI, 10.1-12.1). Life expectancy from age 20 years was an additional 48.1 years among women with type 1 diabetes and 61.0 years among women without it, an estimated loss with diabetes of 12.9 years (95% CI, 11.7-14.1). Even among those with type 1 diabetes with an estimated glomerular filtration rate of 90 mL/min/1.73 m2 or higher, life expectancy was reduced (49.0 years in men, 53.1 years in women) giving an estimated loss from age 20 years of 8.3 years (95% CI, 6.5-10.1) for men and 7.9 years (95% CI, 5.5-10.3) for women. Overall, the largest percentage of the estimated loss in life expectancy was related to ischemic heart disease (36% in men, 31% in women) but death from diabetic coma or ketoacidosis was associated with the largest percentage of the estimated loss occurring before age 50 years (29.4% in men, 21.7% in women). CONCLUSIONS AND RELEVANCE Estimated life expectancy for patients with type 1 diabetes in Scotland based on data from 2008 through 2010 indicated an estimated loss of life expectancy at age 20 years of approximately 11 years for men and 13 years for women compared with the general population without type 1 diabetes.
Multitasking Support for Dynamically Reconfig Urable Systems

Reconfigurable hardware is often used in an attempt to boost performance of an embedded system while minimizing the cost penalty of additional hardware. The key method applied to achieve this goal is the re-use of hardware resources for different tasks. However, reconfigurable systems tend to have a large internal state, which complicates rapid task switching, as it is required for multitasking in real-time systems, significantly. We present a control technique enabling a fast and efficient preemption of dynamically reconfigurable systems which is largely performed by hardware. The technique handles the problem of internal state preservation and reduces the preemption effort by applying the concept of preemption points. The implementation of the system control and an evaluation of the performance is given, showing the high efficiency of the concept.
UML Metamodel-based Workflow Modeling and Execution

In this paper, we present a UML metamodel-based approach for creating and executing workflow models. The workflow modeling language is introduced through its abstract syntax, and an evaluation shows how this language supports known workflow patterns. Some patterns can be expressed easier compared to established languages like EPCs or BPMN. Organizational and data aspects in workflow models can be described on the basis of the presented metamodel. The workflow models can be instantiated and executed with a tool realizing parts of the UML action semantics. At an early stage of design, our workflow models can be evaluated by testing scenarios with the used tool in combination with the developed workflow plug in. Employing the tool, dynamic aspects of the workflow process models together with data and organizational aspects can be evaluated. During execution of the workflow scenarios, the workflow models can be adaptively changed, and data can be captured and evaluated by formulating process mining queries with UML's OCL (Object Constraint Language).