Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetics of Monogenic Disorders of Calcium and Bone Metabolism

Disorders of calcium homeostasis are the most frequent metabolic bone and mineral disease encountered by endocrinologists. These disorders usually manifest as primary hyperparathyroidism (PHPT) or hypoparathyroidism (HP), which have a monogenic aetiology in 5%–10% of cases, and may occur as an isolated endocrinopathy, or as part of a complex syndrome. The recognition and diagnosis of these disorders is important to facilitate the most appropriate management of the patient, with regard to both the calcium‐related phenotype and any associated clinical features, and also to allow the identification of other family members who may be at risk of disease. Genetic testing forms an important tool in the investigation of PHPT and HP patients and is usually reserved for those deemed to be an increased risk of a monogenic disorder. However, identifying those suitable for testing requires a thorough clinical evaluation of the patient, as well as an understanding of the diversity of relevant phenotypes and their genetic basis. This review aims to provide an overview of the genetic basis of monogenic metabolic bone and mineral disorders, primarily focusing on those associated with abnormal calcium homeostasis, and aims to provide a practical guide to the implementation of genetic testing in the clinic.
Systematic Review and Meta-analysis of the Association Between Metabolic Syndrome and Androgenetic Alopecia

The association of androgenetic alopecia with metabolic syndrome has been investigated in several studies, with conflicting results. We conducted a meta-analysis to quantitatively evaluate the risk grade of metabolic syndrome and the metabolic profile in patients with androgenetic alopecia compared with controls. In total, 19 articles (2,531 participants) satisfied the inclusion criteria. The pooled odds ratio for the prevalence rate of metabolic syndrome between the group with androgenetic alopecia and controls was 3.46 (95% CI 2.38–5.05; p < 0.001). Female sex, early onset, and African ethnicity were associated with an increased risk of metabolic syndrome. Furthermore, patients with androgenetic alopecia had significantly poorer metabolic profiles, such as body mass index, waist circumference, fasting glucose, blood lipids, and blood pressure. It is important for physicians to screen metabolism-related indicators in patients with androgenetic alopecia. More rigorously designed studies and larger sample sizes are required in future studies.
Top-down, bottom-up, and history-driven processing of multisensory attentional cues in intellectual disability: An experimental study in virtual reality

Models of attention demonstrated the existence of top-down, bottom-up, and history-driven attentional mechanisms, controlled by partially segregated networks of brain areas. However, few studies have examined the specific deficits in those attentional mechanisms in intellectual disability within the same experimental setting. The aim of the current study was to specify the attentional deficits in intellectual disability in top-down, bottom-up, and history-driven processing of multisensory stimuli, and gain insight into effective attentional cues that could be utilized in cognitive training programs for intellectual disability. The performance of adults with mild to moderate intellectual disability (n = 20) was compared with that of typically developing controls (n = 20) in a virtual reality visual search task. The type of a spatial cue that could aid search performance was manipulated to be either endogenous or exogenous in different sensory modalities (visual, auditory, tactile). The results identified that attentional deficits in intellectual disability are overall more pronounced in top-down rather than in bottom-up processing, but with different magnitudes across cue types: The auditory or tactile endogenous cues were much less effective than the visual endogenous cue in the intellectual disability group. Moreover, the history-driven processing in intellectual disability was altered, such that a reversed priming effect was observed for immediate repetitions of the same cue type. These results suggest that the impact of intellectual disability on attentional processing is specific to attentional mechanisms and cue types, which has theoretical as well as practical implications for developing effective cognitive training programs for the target population.
Facile one pot microbe-mediated in situ synthesis and antibacterial activity of reduced graphene oxide-silver nanocomposite

The present research deals with the development of a novel bioinspired in situ fabrication of reduced graphene oxide (rGO)-silver nanoparticle (AgNPs) nanocomposite (rGO@AgNCs) using microbes namely Pseudomonas aeruginosa (PA) and Staphylococcus aureus (SA). The fabricated rGO@AgNCs were characterized using Ultraviolet-visible (UV–Vis) spectroscopy, Fourier-transform infrared spectroscopy (FTIR), particle size analysis, polydispersity index (PDI), zeta potential analysis, energy dispersive x-ray analysis (EDAX), Raman spectroscopy, powder x-ray diffraction (PXRD), high-resolution transmission electron microscopy (HR-TEM) analysis, etc. Furthermore, the rGO@AgNCs-PA and rGO@AgNCs-SA interaction with serum protein, pH stability study, and in vitro dissolution of AgNPs were also performed. The research findings of the proposed study demonstrated the simultaneous reduction of graphene oxide (GO) and AgNPs and the formation of rGO@AgNCs in the presence of microbes. The in vitro dissolution studies of rGO@AgNCs composites showed better AgNPs dissolution with controlled release and offered remarkable matrix integrity throughout the dissolution period. The size and stability of rGO@AgNCs-PA and rGO@AgNCs-SA had no significant changes at physiological pH 7.4. A minimal decrease in the zeta potential of rGO@AgNCs was observed, which may be due to the weak interaction of nanocomposites and albumin. The antibacterial application of the synthesized nanocomposite was evaluated against a pathogenic mastitis-forming bacterium. The obtained results suggested an admirable antibacterial activity of synthesized nanocomposites against the tested microbes. This knowledge will assist the scientific fraternity in designing novel antibacterial agents with enhanced antibacterial activity against various veterinary pathogens in near future.
The Perception of Ramped Pulse Shapes in Cochlear Implant Users

The electric stimulation provided by current cochlear implants (CI) is not power efficient. One underlying problem is the poor efficiency by which information from electric pulses is transformed into auditory nerve responses. A novel stimulation paradigm using ramped pulse shapes has recently been proposed to remedy this inefficiency. The primary motivation is a better biophysical fit to spiral ganglion neurons with ramped pulses compared to the rectangular pulses used in most contemporary CIs. Here, we tested the hypotheses that ramped pulses provide more efficient stimulation compared to rectangular pulses and that a rising ramp is more efficient than a declining ramp. Rectangular, rising ramped and declining ramped pulse shapes were compared in terms of charge efficiency and discriminability, and threshold variability in seven CI listeners. The tasks included single-channel threshold detection, loudness-balancing, discrimination of pulse shapes, and threshold measurement across the electrode array. Results showed that reduced charge, but increased peak current amplitudes, was required at threshold and most comfortable levels with ramped pulses relative to rectangular pulses. Furthermore, only one subject could reliably discriminate between equally-loud ramped and rectangular pulses, suggesting variations in neural activation patterns between pulse shapes in that participant. No significant difference was found between rising and declining ramped pulses across all tests. In summary, the present findings show some benefits of charge efficiency with ramped pulses relative to rectangular pulses, that the direction of a ramped slope is of less importance, and that most participants could not perceive a difference between pulse shapes.
The potential benefit of leptin therapy against amyotrophic lateral sclerosis (ALS)

Targeting leptin could represent a rational strategy to treat amyotrophic lateral sclerosis (ALS), as previously clinical studies have shown its levels to be associated with a lower risk of ALS disease. However, very little is known about the potential influence of leptin in altering disease progression in ALS, as it has thus far been correlated with the protection exerted by increased fat mass stores.
Crossing Symmetric Spinning S-matrix Bootstrap: EFT bounds

We develop crossing symmetric dispersion relations for describing 2-2 scattering of identical external particles carrying spin. This enables us to import techniques from Geometric Function Theory and study two sided bounds on low energy Wilson coefficients. We consider scattering of photons, gravitons in weakly coupled effective field theories. We provide general expressions for the locality/null constraints. Consideration of the positivity of the absorptive part leads to an interesting connection with the recently conjectured weak low spin dominance. We also construct the crossing symmetric amplitudes and locality constraints for the massive neutral Majorana fermions and parity violating photon and graviton theories. The techniques developed in this paper will be useful for considering numerical S-matrix bootstrap in the future.
Method of reducing the effects of a delay mismatch variation in a linearisation loop

The effects of a delay variation in a linearisation loop on the linearisation performance of the loop is studied, especially when the loop uses a one-pilot control method. A delay variation in a linearisation loop caused by ageing and environmental changes is inevitable and unadjustable. Therefore, a two-pilot control method and a control algorithm having stable and fast convergence characteristics are proposed to minimise the deterioration of the linearisation performance due to a delay variation. The experimental results of the proposed control method and algorithm are presented. The ability to reduce the performance degradation caused by a delay variation in the linearisation loop is also demonstrated.
Cheap robust learning of data anomalies with analytically solvable entropic outlier sparsification

Entropic outlier sparsification (EOS) is proposed as a cheap and robust computational strategy for learning in the presence of data anomalies and outliers. EOS dwells on the derived analytic solution of the (weighted) expected loss minimization problem subject to Shannon entropy regularization. An identified closed-form solution is proven to impose additional costs that depend linearly on statistics size and are independent of data dimension. Obtained analytic results also explain why the mixtures of spherically symmetric Gaussians—used heuristically in many popular data analysis algorithms—represent an optimal and least-biased choice for the nonparametric probability distributions when working with squared Euclidean distances. The performance of EOS is compared to a range of commonly used tools on synthetic problems and on partially mislabeled supervised classification problems from biomedicine. Applying EOS for coinference of data anomalies during learning is shown to allow reaching an accuracy of 97%±2% when predicting patient mortality after heart failure, statistically significantly outperforming predictive performance of common learning tools for the same data.
Validation Solutions to the Full-sky Radio Interferometry Measurement Equation for Diffuse Emission

Low-frequency radio observatories are reaching unprecedented levels of sensitivity in an effort to detect the 21 cm signal from the Cosmic Dawn. High precision is needed because the expected signal is overwhelmed by foreground contamination, largely from so-called diffuse emission—a nonlocalized glow comprising Galactic synchrotron emission and radio galaxies. The impact of this diffuse emission on observations may be better understood through detailed simulations, which evaluate the Radio Interferometry Measurement Equation (RIME) for a given instrument and sky model. Evaluating the RIME involves carrying out an integral over the full sky, which is naturally discretized for point sources but must be approximated for diffuse emission. The choice of integration scheme can introduce errors that must be understood and isolated from the instrumental effects under study. In this paper, we present several analytically defined patterns of unpolarized diffuse sky emission for which the RIME integral is manageable, yielding closed-form or series visibility functions. We demonstrate the usefulness of these RIME solutions for validation by comparing them to simulated data and show that the remaining differences behave as expected with varied sky resolution and baseline orientation and length.
Community Detection in Medical Image Datasets: Using Wavelets and Spectral Methods

Medical image datasets can have large number of images representing patients with diﬀerent health conditions and various disease severity. When dealing with raw unlabeled image datasets, the large number of samples often makes it hard for experts and non-experts to understand the variety of images present in a dataset. Supervised learning methods rely on labeled images which requires a considerable eﬀort by medical experts to ﬁrst understand the communities of images present in the data and then labeling the images. Here, we propose an algorithm to facilitate the automatic identiﬁcation of communities in medical image datasets. We further demonstrate that such analysis can be insightful in a supervised setting when the images are already labeled. Such insights are useful because, health and disease severity can be considered a continuous spectrum, and within each class, there usually are ﬁner communities worthy of investigation, especially when they have similarities to communities in other classes. In our approach, we use wavelet decomposition of images in tandem with spectral methods. We show that the eigenvalues of a graph Laplacian can reveal the number of notable communities in an image dataset. Moreover, analyzing the similarities may be used to infer a spectrum representing the severity of the disease. In our experiments, we use a dataset of images labeled with diﬀerent conditions for COVID patients. We detect 25 communities in the dataset and then observe that only 6 of those communities contain patients with pneumonia. We also investigate the contents of a colorectal cancer histology dataset.
Lifetime estimation of MMC for offshore wind power HVDC application

Due to a series of unique merits, Modular Multilevel Converters (MMCs) have been developing dramatically in the last decade. However in some applications like offshore power transmission the MMC modules are subjected to a variety of tough temperature profiles with adverse working condition, all of these could lead to thermo-mechanical fatigues in the components and joints of the power modules and thereby causing reliability challenge. This study focuses on the lifetime evaluation of MMC based on power cycling and thermal cycling considering the mission profiles in the High Voltage Direct Current (HVDC) application for offshore wind power. The mission profiles as well as the topology of the MMC are first analyzed in order to better understand the loading profiles of MMC. Then the power loss calculation along with the electro-thermal simulation is conducted to uncover the thermal loading of MMC. At last, the lifetime of MMC is studied based on rain flow counting and existing lifetime models for power semiconductors.
Multi-impact source localisation on aircraft composite structure using uniform linear PZT sensors array

Composite structures are often struck by foreign objects at multiple locations. Multiple dents may be found on the composite structures such that it is hard to determine the number of impact events. This research presents a multiple impacts localisation method based on the near-field 2D multiple signal classification (2D-MUSIC) method for the composite structure. When multiple impacts are loaded on the plate, it is difficult to localise the positions of multiple impacts because of the overlapping of elastic waves produced by multi-impacts and the edge reflection of the plate. The MUSIC approach can provide estimation of the parameters of multiple emitters without any signal separation of mixed multi-impacts. It is well known that the performance of the MUSIC algorithm largely depends on the successful determination of the number of impacts. Gerschgorin disc theorem is applied for estimating the number of impacts. To verify the proposed method, experiments were preformed on the composite structure. The estimation of the number of impacts and localisation results are in good agreement with the actual impact occurrence. Finally, the proposed method was applied to a real aircraft composite oil tank which showed its successful performance on complex composite structures.
Long-Term Healthcare Utilization and the Cost of Bipolar Disorder Among Participants in a Large Employer's Health Benefit Plan

This study found that BD individuals were similar in longitudinal costs when compared to other costly conditions. Because BD lingers and commonly develops at a younger age, the cumulative cost may be greater for this population. The need for employer-sponsored programs is necessary and can mitigate excess medical costs. Objective: Bipolar disorder (BD) is a chronic illness with recurrent exacerbations. The objective was to evaluate longitudinal costs related to BD in an employer-sponsored medical plan. Methods: This analysis utilized 5 years of administrative claims data. Claimants with a diagnosis of BD were matched to plan members (1:5) based on age, sex, and years of follow-up. Results: Medical costs for hospitalized BD members were 3.5 times more expensive than the general population (BDhosp = $92.2K vs General population = $26.8K). Average 5-year paid costs among hospitalized members with BD was $107K, $105.4K with cancer, and $103.3K with myocardial infarction (MI). Conclusions: Hospitalized BD plan members consumed more than 3.5 times the medical resources and were similar in longitudinal costs when compared with members with other costly conditions. These findings highlight the need for novel employer-sponsored programs to help manage BD.
Molecular Dynamics Studies on the Bacterial Membrane Pore Formation by Small Molecule Antimicrobial Agents

Antimicrobial peptides (AMPs) act on the membrane bilayer of pathogens, causing leakage in the membrane and cell death. Amphiphilic kaempferol derivatives possessing basic functional groups show excellent antibacterial activities, which has been proven through experimental techniques. These compounds are known to target negatively charged bacterial membranes. However, the detailed mechanism of action and their structure-activity relationship are not clear. In this work, we reported theoretical investigation on the mechanism of action of two previously reported kaempferol derivatives on a DMPC/DMPG mixed bilayer. Despite the rigid structure of the compounds when compared to AMPs, spontaneous pore formation in the membrane was not observed in 400 ns molecular dynamics (MD) simulations. MD simulations with biasing forces resulted in the formation of pores in the bilayer for the derivatives and not for kaempferol. The stability of the pores was assessed by pore closure timescales in unbiased MD simulations, which was found to be 5.3 and 17.0 ns for 2 and 3, respectively. Free energy change for the permeation into the bilayer for kaempferol (1), tertiary amine derivative (2), and arginine derivative (3) was calculated to be -1.5, -48.2, and -100.3 kJ/mol, respectively, which correlate with their antibacterial activity. Furthermore, our results indicate that compound 3 forms a stable toroidal pore in the membrane when multiple molecules are oriented in a transmembrane configuration. Our work sheds light on the mechanism of action of small molecule antimicrobial agents, which can be exploited for the rational design of drug candidates.
New Variable Hot Subdwarf Stars Identified from Anomalous Gaia Flux Errors, Observed by TESS, and Classified via Fourier Diagnostics

Hot subdwarf stars are mostly stripped red giants that can exhibit photometric variations due to stellar pulsations, eclipses, the reflection effect, ellipsoidal modulation, and Doppler beaming. Detailed studies of their light curves help constrain stellar parameters through asteroseismological analyses or binary light-curve modeling and generally improve our capacity to draw a statistically meaningful picture of this enigmatic stage of stellar evolution. From an analysis of Gaia DR2 flux errors, we have identified around 1200 candidate hot subdwarfs with inflated flux errors for their magnitudes—a strong indicator of photometric variability. As a pilot study, we obtained 2 minute cadence TESS Cycle 2 observations of 187 candidate hot subdwarfs with anomalous Gaia flux errors. More than 90% of our targets show significant photometric variations in their TESS light curves. Many of the new systems found are cataclysmic variables, but we report the discovery of several new variable hot subdwarfs, including HW Vir binaries, reflection-effect systems, pulsating sdBV s stars, and ellipsoidally modulated systems. We determine atmospheric parameters for select systems using follow-up spectroscopy from the 3 m Shane telescope. Finally, we present a Fourier diagnostic plot for classifying binary light curves using the relative amplitudes and phases of their fundamental and harmonic signals in their periodograms. This plot makes it possible to identify certain types of variables efficiently, without directly investigating their light curves, and may assist in the rapid classification of systems observed in large photometric surveys.
A scoping review of the application of motor learning principles to optimize myoelectric prosthetic hand control

Although prosthetic hand rejection rates remain high, evidence suggests that effective training plays a major role in device acceptance. Receiving training early in the rehabilitation process also enhances functional prosthetic use, decreases the likelihood of developing an overreliance on the intact limb, and reduces amputation-related pain. Despite these obvious benefits, there is a current lack of evidence regarding the most effective training techniques to facilitate myoelectric prosthetic hand control, and it remains unknown whether training is effective in facilitating the acquisition and transfer of prosthetic skill. In this scoping review, we introduced and summarized key motor learning principles related to attentional focus, implicit motor learning, training eye–hand coordination, practice variability, motor imagery, and action observation, and virtual training and biofeedback. We then reviewed the existing literature that has applied these principles for training prosthetic hand control before outlining future avenues for further research. The importance of optimizing early and appropriate training cannot be overlooked. While the intuition and experience of clinicians holds enormous value, evidence-based guidelines based on well-established motor learning principles will also be crucial for training effective prosthetic hand control. While it is clear that more research is needed to form the basis of such guidelines, it is hoped that this review highlights the potential avenues for this work.