Miracle-dz/UniFAR-FTUs huggingface dataset:
: “Hyperparameter tuning: calibrating MCP and other sparse regression formulations”

Learning methods in a context where the number of features can be greater than the number of observations have been popularized by genomics applications. They are common for bio-statistics inference, where n the number of patients is limited, while p the number of features available are numerous (e.g., clinical or genetic features). This field has fostered the spread of regularized least-squares with sparsity inducing penalties.1 More recently, dictionary learning [8] or statistical inference (e.g., to provide faithful confidence intervals [13]) have relied on multiple regressors whose tuning time has become prohibitive. Yet, such regularized methods are sensitive to such tuning parameters, trading-off data fitting and sparsity. Improving tuning procedures is a major concern shared by most practitioners, might it be neuro-imaging or bio-statistics. For practitioners, tuning is a hurdle, and they usually resort to default settings from packaged methods: time constraints discourage them from investigating further settings. Hence, it is crucial to provide high dimensional regression methods that have automatic tuning properties. The standard tuning procedure in ML is cross-validation: 1) a grid of tuning parameters is created; 2) the dataset is divided into equal sized subsamples (folds), and alternatively each fold is left aside as a validation set, while training (for each parameter) is performed on the other folds. 3) A score aggregates the validation feedback and the parameter achieving the best one is selected. Though popular in practice, crossvalidation has important drawbacks: 1) theoretical efficiency analysis in high dimension is scarce [1]; 2) computational inefficiency: for 100 values of a 1D parameter, 10-fold cross-validation requires evaluating 1000 estimators, and is unpractical for more than 3 parameters. Tuning procedures satisfying jointly statistical and computational efficiency are so far still missing, despite recent advances [5, 11]. In a preliminary work with A. Gramfort and colleagues, we have shown that bi-level optimization techniques could help to handle many hyper-parameters to be tuned for Lasso-like methods [3]. Leveraging automatic differentiation and the underlying convex structure of the regressors, we have managed to efficiently tune p parameters, i.e., one per feature. Early results (see sparse-ho) have been obtained for standard left-out strategies, but vanilla cross-validation is not yet handled. A deeper investigation is required to generalize our strategy to more diverse learning frameworks.
Speech Synthesis from Stereotactic EEG using an Electrode Shaft Dependent Multi-Input Convolutional Neural Network Approach

Neurological disorders can lead to significant impairments in speech communication and, in severe cases, cause the complete loss of the ability to speak. Brain-Computer Interfaces have shown promise as an alternative communication modality by directly transforming neural activity of speech processes into a textual or audible representations. Previous studies investigating such speech neuroprostheses relied on electrocorticography (ECoG) or microelectrode arrays that acquire neural signals from superficial areas on the cortex. While both measurement methods have demonstrated successful speech decoding, they do not capture activity from deeper brain structures and this activity has therefore not been harnessed for speech-related BCIs. In this study, we bridge this gap by adapting a previously presented decoding pipeline for speech synthesis based on ECoG signals to implanted depth electrodes (sEEG). For this purpose, we propose a multi-input convolutional neural network that extracts speech-related activity separately for each electrode shaft and estimates spectral coefficients to reconstruct an audible waveform. We evaluate our approach on open-loop data from 5 patients who conducted a recitation task of Dutch utterances. We achieve correlations of up to 0.80 between original and reconstructed speech spectrograms, which are significantly above chance level for all patients (p < 0.001). Our results indicate that sEEG can yield similar speech decoding performance to prior ECoG studies and is a promising modality for speech BCIs.
Color fusion schemes for low-light CCD and infrared images of different properties

Although gray-level fused images can optimally integrate the modalities of low-light CCD and infrared imager, operators cannot tell from which modality the details originate. Thus the fundamental that human eyes can discern much more color categories than gray levels has been used to assigns a distinct color to each sensor modality. But the color fused image which has no natural appearance will fatigue operators greatly. Our approach is building on MIT scheme and aims at achieving natural appearance in the color fused image. MIT scheme derives its basis from biological models of color vision and utilizes the feed-forward center-surround shunting neural network to enhance and fuse low-light and infrared images. We bring forward linear fusion architecture, and composite architecture that comprises the enhancement part of MIT scheme and the linear fusion architecture. Furthermore, enhancement and combination methods for low-light and infrared images of different properties have been specified.
Immediate Effects of Vibrotactile Biofeedback Instructions on Human Postural Control

Vibrotactile biofeedback can improve balance and consequently be helpful in fall prevention. However, it remains unclear how different types of stimulus presentations affect not only trunk tilt, but also Center of Pressure (CoP) displacements, and whether an instruction on how to move contributes to a better understanding of vibrotactile feedback.Based on lower back tilt angles (L5), we applied individualized multi-directional vibrotactile feedback to the upper torso by a haptic vest in 30 healthy young adults. Subjects were equally distributed to three instruction groups (attractive - move in the direction of feedback, repulsive - move in the opposite direction of feedback & no instruction - with attractive stimuli). We conducted four conditions with eyes closed (feedback on/off, Narrow Stance with head extended, Semi-Tandem stance), with seven trials of 45s each. For CoP and L5, we computed Root Mean Square (RMS) of position/angle and standard deviation (SD) of velocity, and for L5 additionally, the percentage in time above threshold. The analysis consisted of mixed model ANOVAs and t-tests (α-level: 0.05). In the attractive and repulsive groups feedback significantly decreased the percentage above threshold (p<0.05). Feedback decreased RMS of L5, whereas RMS of CoP and SD of velocity in L5 and COP increased (p<0.05). Finally, an instruction on how to move contributed to a better understanding of the vibrotactile biofeedback.
A Multi-Modular System for the Visualization and Classification of MER Data During Neurostimulation Procedures

This paper proposes an interactive analysis and visualization tool for the accuracy improvement of electrode placement during neurostimulation therapy surgery. During the procedure, the presented system assists the surgeon in the crucial tissue type detection by providing a fused visualization of the current electrode location and the microelectrode recordings (MER). The system processes the MER in real-time and utilizes a convolutional neural network (CNN) to classify the targeted tissue type. In addition to presenting the MER in its raw waveform, the system also offers the visualization of the frequency domain and the result of the neural network. To further assist the decision-making process, additional visualization methods are integrated into the system. Using the pre-operative taken CT and MRI scans, the system offers 3D visualization in the form of direct volume rendering (DVR) and axis-aligned slice views in 2D. Both domains are enriched by the MER readings and the result of the machine learning classifier.
Estimated breeding values and genetic trend for 305-day milk yield in buffalo herd at les Chak Katora

Data on 1524 pedigrees, breeding and performance records of 447 Nili-Ravi buffaloes maintained at the Livestock Experiment Station, Chak Katora, (Bahawalpur), Punjab, Pakistan during the period 1989 to 2007 were utilized in this study to identify the high yielding elite buffaloes/bull mothers (dams) to retain for further breeding for the ongoing progeny testing program in Punjab. The lactation records up to 9th parity were used for the analysis. The data were analyzed through Best Linear Unbiased Prediction (BLUP) procedure. The breeding values were estimated by fitting an individual animal model. The least squares means for 305-day lactation milk yield was 2030.13±14.58 litre. The average lactation length was 260.42±1.35 days. Of 447 buffaloes, 284 had positive breeding values (EBVs) and 90 were declared as elite buffaloes. The estimated breeding values for milk yield from animal model evaluations ranged from -323.40 to +345.12 kg. The genetic trend for 305-day lactation milk yield depicted a deteriorating trend indicating that breeding strategy proved to be inefficient during the last 20 years.
Synergy Between Point-of-Care Testing and Laboratory Consolidations

Central or area laboratories will offer an improved number of diagnostic testing services, where drivers for change will involve chronic disease clinical care for an increasingly older population, new emerging diagnostic technologies and personalized medicine. Higher automation quality and ever more diagnostic field integration will lead to higher productivity by means of an improved throughput. At the same time Point of Care Testing (POCT) site of patient care allows for timely medical assessment, which can lead to improved patient outcomes, more effectiveness and patient satisfaction. POCT test introduction in clinical practice should be assessed by an outcome-based policy to avoid adverse events, failure to diagnose providing appropriate timed treatment. The use of POCT devices does not only require technological considerations for the production and management of acceptable tests possibly managed by central laboratory, but also implicates a shift in diagnostic practice across all health organizations. The interaction between laboratory professionals and clinicians will be enriched with new methods of evaluation of patient needs in the internet of things and mobile Health worlds, where boundaries between POCT and central laboratory or hospital and primary healthcare will no longer exist and where all data can be shared and disseminated among stakeholders in the healthcare system.
UBE2E3 regulates cellular senescence and osteogenic differentiation of BMSCs during aging

Background Osteoporosis has gradually become a public health problem in the world. However, the exact molecular mechanism of osteoporosis still remains unclear. Senescence and osteogenic differentiation inhibition of bone marrow mesenchymal stem cells (BMSCs ) are supposed to play an important part in osteoporosis. Methods We used two gene expression profiles (GSE35956 and GSE35958) associated with osteoporosis and selected the promising gene Ubiquitin-conjugating enzyme E2 E3 (UBE2E3). We then verified its function and mechanism by in vitro experiments. Results UBE2E3 was highly expressed in the bone marrow and positively associated with osteogenesis related genes. Besides, UBE2E3 expression reduced in old BMSCs compared with that in young BMSCs. In in vitro experiments, knockdown of UBE2E3 accelerated cellular senescence and inhibited osteogenic differentiation of young BMSCs. On the other hand, overexpression of UBE2E3 attenuated cellular senescence as well as enhanced osteogenic differentiation of old BMSCs. Mechanistically, UBE2E3 might regulate the nuclear factor erythroid 2-related factor (Nrf2) and control its function, thus affecting the senescence and osteogenic differentiation of BMSCs. Conclusion UBE2E3 may be potentially involved in the pathogenesis of osteoporosis by regulating cellular senescence and osteogenic differentiation of BMSCs.
Carotid plaque thickness is increased in chronic kidney disease and associated with carotid and coronary calcification

Background Chronic kidney disease accelerates both atherosclerosis and arterial calcification. The aim of the present study was to explore whether maximal carotid plaque thickness (cPTmax) was increased in patients with chronic kidney disease compared to controls and associated with cardiovascular disease and severity of calcification in the carotid and coronary arteries. Methods The study group consisted of 200 patients with chronic kidney disease stage 3 from the Copenhagen Chronic Kidney Disease Cohort and 121 age- and sex-matched controls. cPTmax was assessed by ultrasound and arterial calcification by computed tomography scanning. Results Carotid plaques were present in 58% of patients (n = 115) compared with 40% of controls (n = 48), p = 0.002. Among participants with plaques, cPTmax (median, interquartile range) was significantly higher in patients compared with controls (1.9 (1.4–2.3) versus 1.5 (1.2–1.8) mm), p = 0.001. Cardiovascular disease was present in 9% of patients without plaques (n = 85), 23% of patients with cPTmax 1.0–1.9 mm (n = 69) and 35% of patients with cPTmax >1.9 mm (n = 46), p = 0.001. Carotid and coronary calcium scores >400 were present in 0% and 4%, respectively, of patients with no carotid plaques, in 19% and 24% of patients with cPTmax 1.0–1.9 mm, and in 48% and 53% of patients with cPTmax >1.9 mm, p<0.001. Conclusions This is the first study showing that cPTmax is increased in patients with chronic kidney disease stage 3 compared to controls and closely associated with prevalent cardiovascular disease and severity of calcification in both the carotid and coronary arteries.
Misinformation effects in an online sample: results of an experimental study with a five day retention interval

Traditional face-to-face laboratory studies have contributed greatly to our understanding of how misinformation effects develop. However, an area of emerging concern that has been relatively under-researched is the impact of misinformation following exposure to traumatic events that are viewed online. Here we describe a novel method for investigating misinformation effects in an online context. Participants (N = 99) completed the study online. They first watched a 10-min video of a fictional school shooting. Between 5 and 10 days later, they were randomly assigned to receive misinformation or no misinformation about the video before completing a recognition test. Misinformed participants were less accurate at discriminating between misinformation and true statements than control participants. This effect was most strongly supported by ROC analyses (Cohen’s d = 0.59, BF10 = 8.34). Misinformation effects can be established in an online experiment using candid violent viral-style video stimuli.
Reactive oxygen species prevent lysosome coalescence during PIKfyve inhibition

Lysosomes are terminal, degradative organelles of the endosomal pathway that undergo repeated fusion-fission cycles with themselves, endosomes, phagosomes, and autophagosomes. Lysosome number and size depends on balanced fusion and fission rates. Thus, conditions that favour fusion over fission can reduce lysosome numbers while enlarging their size. Conversely, favouring fission over fusion may cause lysosome fragmentation and increase their numbers. PIKfyve is a phosphoinositide kinase that generates phosphatidylinositol-3,5-bisphosphate to modulate lysosomal functions. PIKfyve inhibition causes an increase in lysosome size and reduction in lysosome number, consistent with lysosome coalescence. This is thought to proceed through reduced lysosome reformation and/or fission after fusion with endosomes or other lysosomes. Previously, we observed that photo-damage during live-cell imaging prevented lysosome coalescence during PIKfyve inhibition. Thus, we postulated that lysosome fusion and/or fission dynamics are affected by reactive oxygen species (ROS). Here, we show that ROS generated by various independent mechanisms all impaired lysosome coalescence during PIKfyve inhibition and promoted lysosome fragmentation during PIKfyve re-activation. However, depending on the ROS species or mode of production, lysosome dynamics were affected distinctly. H2O2 impaired lysosome motility and reduced lysosome fusion with phagosomes, suggesting that H2O2 reduces lysosome fusogenecity. In comparison, inhibitors of oxidative phosphorylation, thiol groups, glutathione, or thioredoxin, did not impair lysosome motility but instead promoted clearance of actin puncta on lysosomes formed during PIKfyve inhibition. Additionally, actin depolymerizing agents prevented lysosome coalescence during PIKfyve inhibition. Thus, we discovered that ROS can generally prevent lysosome coalescence during PIKfyve inhibition using distinct mechanisms depending on the type of ROS.
Epidemiology of Clostridioides difficile in South Africa

Background Clostridioides difficile (CD) is the most common healthcare-associated enteric infection. There is currently limited epidemiological evidence on CD incidence in South Africa. Aim To estimate the burden of CD infection (CDI) in the South African public sector between 1 July 2016 and 30 June 2017. Methods A retrospective cohort study utilizing secondary data was conducted to describe the epidemiology of CD in South Africa. We assessed the patient-level association between variables of interest, CD, and CD recurrence, by undertaking both univariate and multivariable analysis. Adjusted incidence rate ratios (aIRR) were calculated utilizing multivariable Poisson regression. The incidence of CD, CD recurrence and CD testing was estimated by Poisson regression for various levels of care and provinces. Results A total of 14 023 samples were tested for CD during the study period. After applying exclusion criteria, we were left with a sample of 10 053 of which 1 860 (18.50%) tested CD positive. A positive and significant association between CDI and level of care is found, with patients treated in specialized tuberculosis (TB) hospitals having a five-fold increased adjusted incidence risk ratio (aIRR) for CDI (aIRR 4.96 CI95% 4.08–6.04,) compared to those managed in primary care. Patients receiving care at a secondary, tertiary, or central hospital had 35%, 66% and 41% increased adjusted incidence of CDI compared to those managed in primary care, respectively. National incidence of CDI is estimated at 53.89 cases per 100 000 hospitalizations (CI95% 51.58–56.29), the incidence of recurrence at 21.39 (CI95% 15.06–29.48) cases per 1 000 cases and a recurrence rate of 2.14% (CI95% 1.51–2.94). Conclusion Compared to European countries, we found a comparable incidence of CD. However, our estimates are lower than those for the United States. Compared to high-income countries, this study found a comparatively lower CD recurrence.
Comparison of anaerobic power between male and female with considering lean body mass

The purpose of current study is Comparison of anaerobic power between male and female with considering lean body mass. 30 volunteers (15 men and 15 women) took part in the research. Initially, the average of age, weight, fat-free mass and LBM was measured and some anaerobic power tests were conducted such as long jump, vertical jump and Margaria Kalamen stair test. Then, the average of anaerobic power tests was counted in relation to LBM average and independent groups were put into comparison by using Independent-samples T test. There was a vast difference between FFM and LBM of two groups while there was no considerable difference between age average and total weight of two groups (P<0.05). By counting the relation of anaerobic power records the difference between records of two genders was reduced compared to LBM. Nevertheless, there was still a significant difference. The difference between anaerobic power records of two genders can be reduced compared to LBM by counting anaerobic power records. Indeed, only the difference in body FFM and LBM is not adequate for evaluating anaerobic power differences of two genders and other factors of anthropometry and muscular-nervous factors may have some effects on anaerobic power data.
Implementation of Fuel Cell Dynamic Simulator

This paper presents a development of fuel cell dynamic simulator using a programmable DC power supply and LabVIEW graphical user interface. The developed simulator closely describes the static and dynamic characteristics of an actual fuel cell. The developed simulator can be used as a convenient and economic alternative of an actual fuel cell for developing and testing the fuel cell power conditioning system.
Building Object-based Causal Programs for Human-like Generalization

We present a novel task that measures how people generalize objects' causal powers based on observing a single (Experiment 1) or a few (Experiment 2) causal interactions between object pairs. We propose a computational modeling framework that can synthesize human-like generalization patterns in our task setting, and sheds light on how people may navigate the compositional space of possible causal functions and categories efficiently. Our modeling framework combines a causal function generator that makes use of agent and recipient objects' features and relations, and a Bayesian non-parametric inference process to govern the degree of similarity-based generalization. Our model has a natural"resource-rational"variant that outperforms a naive Bayesian account in describing participants, in particular reproducing a generalization-order effect and causal asymmetry observed in our behavioral experiments. We argue that this modeling framework provides a computationally plausible mechanism for real world causal generalization.
AlPSim ® : Simulation Software for Modeling of Aluminum Electrolysis Cell

Due to a limiting number of operational parameters that can be measured in an operating cell, the process is not fully understood and cell operation based largely on experience. In this work, the principles of the Hall-Heroult process are presented and a mathematical model developed to predict the behavior of an aluminum reduction cell. AlPSim is a Simulating model which is used for calculation of aluminium electrolysis properties (same as Electrical Conductivity, Electrolyte Viscosity, Electrolyte Density, Aluminium Density, Max Alumina Solubility in Electrolyte, Max Aluminium Solubility in Electrolyte and Liquidus Temperature) in order to optimize operational parameter in aluminium production systems. AlPSim is a mathematical-experimental model with a static simulation base with ability to evaluate the effect of additive same as Alumina, Carbonate Sodium and Aluminium Fluoride on the electrolysis properties and cell voltage. Cell Voltage in this model separated to decomposition voltage, over voltage and ohmic voltage drops which is part of the ohmic voltage measured manually with operators and will be used as an input parameters.
Optimization design of suspension biochip detection system

In this paper, we describe a new kind of micro fluidic channel used in suspension biochip detection system that is different from cytometry. The key indexes of the detection system are space resolution and sensitivity, which both depended on optical system and filtering system. At first, the key factors which have great effect to space resolution have been analyzed. Secondly the filtering system of suspension array is analyzed. At last the transmission ration curve of the filters used in our detection system has been tested in detail. The optimization design suspension biochip is also obtained.