Miracle-dz/UniFAR-FTUs huggingface dataset:
Stereo Feature Learning Based on Attention and Geometry for Absolute Hand Pose Estimation in Egocentric Stereo Views

Egocentric hand pose estimation is significant for wearable cameras since the hand interactions are captured from an egocentric viewpoint. Several studies on hand pose estimation have recently been presented based on RGBD or RGB sensors. Although these methods provide accurate hand pose estimation, they have several limitations. For example, RGB-based techniques have intrinsic difficulty in converting relative 3D poses into absolute 3D poses, and RGBD-based techniques only work in indoor environments. Recently, stereo-sensor-based techniques have gained increasing attention owing to their potential to overcome these limitations. However, to the best of our knowledge, there are few techniques and no real datasets available for egocentric stereo vision. In this paper, we propose a top-down pipeline for estimating absolute 3D hand poses using stereo sensors, as well as a novel dataset for training. Our top-down pipeline consists of two steps: hand detection and hand pose estimation. Hand detection detects hand areas and then is followed by hand pose estimation, which estimates the positions of the hand joints. In particular, for hand pose estimation with a stereo camera, we propose an attention-based architecture called StereoNet, a geometry-based loss function called StereoLoss, and a novel 2D disparity map called StereoDMap for effective stereo feature learning. To collect the dataset, we proposed a novel annotation method that helps reduce human annotation efforts. Our dataset is publicly available at https://github.com/seo0914/SEH. We conducted comprehensive experiments to demonstrate the effectiveness of our approach compared with the state-of-the-art methods.
Bend, Push, Stretch: Remarkable Structure and Mechanics of Single Intermediate Filaments and Meshworks

The cytoskeleton of the eukaryotic cell provides a structural and functional scaffold enabling biochemical and cellular functions. While actin and microtubules form the main framework of the cell, intermediate filament networks provide unique mechanical properties that increase the resilience of both the cytoplasm and the nucleus, thereby maintaining cellular function while under mechanical pressure. Intermediate filaments (IFs) are imperative to a plethora of regulatory and signaling functions in mechanotransduction. Mutations in all types of IF proteins are known to affect the architectural integrity and function of cellular processes, leading to debilitating diseases. The basic building block of all IFs are elongated α-helical coiled-coils that assemble hierarchically into complex meshworks. A remarkable mechanical feature of IFs is the capability of coiled-coils to metamorphize into β-sheets under stress, making them one of the strongest and most resilient mechanical entities in nature. Here, we discuss structural and mechanical aspects of IFs with a focus on nuclear lamins and vimentin.
Problem Gambling ‘Fuelled on the Fly’

Problem gambling is a gambling disorder often described as continued gambling in the face of increasing losses. In this article, we explored problem gambling behaviour and its psychological determinants. We considered the assumption of stability in risky preferences, anticipated by both normative and descriptive theories of decision making, as well as recent evidence that risk preferences are in fact ‘constructed on the fly’ during risk elicitation. Accordingly, we argue that problem gambling is a multifaceted disorder, which is ‘fueled on the fly’ by a wide range of contextual and non-contextual influences, including individual differences in personality traits, hormonal and emotional activations. We have proposed that the experience of gambling behaviour in itself is a dynamic experience of events in time series, where gamblers anchor on the most recent event—typically a small loss or rare win. This is a highly adaptive, but erroneous, decision-making mechanism, where anchoring on the most recent event alters the psychological representations of substantial and accumulated loss in the past to a representation of negligible loss. In other words, people feel better while they gamble. We conclude that problem gambling researchers and policy makers will need to employ multifaceted and holistic approaches to understand problem gambling.
Genetic Evaluation of Inpatient Neonatal and Infantile Congenital Heart Defects: New Findings and Review of the Literature

The use of clinical genetics evaluations and testing for infants with congenital heart defects (CHDs) is subject to practice variation. This single-institution cross-sectional study of all inpatient infants with severe CHDs evaluated 440 patients using a cardiovascular genetics service (2014–2019). In total, 376 (85.5%) had chromosome microarray (CMA), of which 55 (14.6%) were diagnostic in syndromic (N = 35) or isolated (N = 20) presentations. Genetic diagnoses were made in all CHD classes. Diagnostic yield was higher in syndromic appearing infants, but geneticists’ dysmorphology exams lacked complete sensitivity and 6.5% of isolated CHD cases had diagnostic CMA. Interestingly, diagnostic results (15.8%) in left ventricular outflow tract obstruction (LVOTO) defects occurred most often in patients with isolated CHD. Geneticists’ evaluations were particularly important for second-tier molecular testing (10.5% test-specific yield), bringing the overall genetic testing yield to 17%. We assess these results in the context of previous studies. Cumulative evidence provides a rationale for comprehensive, standardized genetic evaluation in infants with severe CHDs regardless of lesion or extracardiac anomalies because genetic diagnoses that impact care are easily missed. These findings support routine CMA testing in infants with severe CHDs and underscore the importance of copy-number analysis with newer testing strategies such as exome and genome sequencing.
Peptide-Assisted Nucleic Acid Delivery Systems on the Rise

Concerns associated with nanocarriers’ therapeutic efficacy and side effects have led to the development of strategies to advance them into targeted and responsive delivery systems. Owing to their bioactivity and biocompatibility, peptides play a key role in these strategies and, thus, have been extensively studied in nanomedicine. Peptide-based nanocarriers, in particular, have burgeoned with advances in purely peptidic structures and in combinations of peptides, both native and modified, with polymers, lipids, and inorganic nanoparticles. In this review, we summarize advances on peptides promoting gene delivery systems. The efficacy of nucleic acid therapies largely depends on cell internalization and the delivery to subcellular organelles. Hence, the review focuses on nanocarriers where peptides are pivotal in ferrying nucleic acids to their site of action, with a special emphasis on peptides that assist anionic, water-soluble nucleic acids in crossing the membrane barriers they encounter on their way to efficient function. In a second part, we address how peptides advance nanoassembly delivery tools, such that they navigate delivery barriers and release their nucleic acid cargo at specific sites in a controlled fashion.
Automatic Meningioma Segmentation and Grading Prediction: A Hybrid Deep-Learning Method

The purpose of this study was to determine whether a deep-learning-based assessment system could facilitate preoperative grading of meningioma. This was a retrospective study conducted at two institutions covering 643 patients. The system, designed with a cascade network structure, was developed using deep-learning technology for automatic tumor detection, visual assessment, and grading prediction. Specifically, a modified U-Net convolutional neural network was first established to segment tumor images. Subsequently, the segmentations were introduced into rendering algorithms for spatial reconstruction and another DenseNet convolutional neural network for grading prediction. The trained models were integrated as a system, and the robustness was tested based on its performance on an external dataset from the second institution involving different magnetic resonance imaging platforms. The results showed that the segment model represented a noteworthy performance with dice coefficients of 0.920 ± 0.009 in the validation group. With accurate segmented tumor images, the rendering model delicately reconstructed the tumor body and clearly displayed the important intracranial vessels. The DenseNet model also achieved high accuracy with an area under the curve of 0.918 ± 0.006 and accuracy of 0.901 ± 0.039 when classifying tumors into low-grade and high-grade meningiomas. Moreover, the system exhibited good performance on the external validation dataset.
Restoration of KCC2 Membrane Localization in Striatal Dopamine D2 Receptor-Expressing Medium Spiny Neurons Rescues Locomotor Deficits in HIV Tat-Transgenic Mice

People infected with HIV (PWH) are highly susceptible to striatal and hippocampal damage. Motor and memory impairments are common among these patients, likely as behavioral manifestations of damage to these brain regions. GABAergic dysfunction from HIV infection and viral proteins such as transactivator of transcription (Tat) have been well documented. We recently demonstrated that the neuron specific Cl− extruder, K+ Cl− cotransporter 2 (KCC2), is diminished after exposure to HIV proteins, including Tat, resulting in disrupted GABAAR-mediated hyperpolarization and inhibition. Here, we utilized doxycycline (DOX)-inducible, GFAP-driven HIV-1 Tat transgenic mice to further explore this phenomenon. After two weeks of Tat expression, we found no changes in hippocampal KCC2 levels, but a significant decrease in the striatum that was associated with hyperlocomotion in the open field assay. We were able to restore KCC2 activity and baseline locomotion with the KCC2 enhancer, CLP290. Additionally, we found that CLP290, whose mechanism of action has yet to be described, acts to restore phosphorylation of serine 940 resulting in increased KCC2 membrane localization. We also examined neuronal subpopulation contributions to the noted effects and found significant differences. Dopamine D2 receptor-expressing medium spiny neurons (MSNs) were selectively vulnerable to Tat-induced KCC2 loss, with no changes observed in dopamine D1 receptor-expressing MSNs. These results suggest that disinhibition/diminished hyperpolarization of dopamine D2 receptor-expressing MSNs can manifest as increased locomotion in this context. They further suggest that KCC2 activity might be a therapeutic target to alleviate motor disturbances related to HIV.
(Dys)Prosody in Parkinson’s Disease: Effects of Medication and Disease Duration on Intonation and Prosodic Phrasing

The phonology of prosody has received little attention in studies of motor speech disorders. The present study investigates the phonology of intonation (nuclear contours) and speech chunking (prosodic phrasing) in Parkinson’s disease (PD) as a function of medication intake and duration of the disease. Following methods of the prosodic and intonational phonology frameworks, we examined the ability of 30 PD patients to use intonation categories and prosodic phrasing structures in ways similar to 20 healthy controls to convey similar meanings. Speech data from PD patients were collected before and after a dopaminomimetic drug intake and were phonologically analyzed in relation to nuclear contours and intonational phrasing. Besides medication, disease duration and the presence of motor fluctuations were also factors included in the analyses. Overall, PD patients showed a decreased ability to use nuclear contours and prosodic phrasing. Medication improved intonation regardless of disease duration but did not help with dysprosodic phrasing. In turn, disease duration and motor fluctuations affected phrasing patterns but had no impact on intonation. Our study demonstrated that the phonology of prosody is impaired in PD, and prosodic categories and structures may be differently affected, with implications for the understanding of PD neurophysiology and therapy.
Assessing Prevalence and Factors Related to Frailty in Community-Dwelling Older Adults: A Multinomial Logistic Analysis

Frailty is an age-related clinical condition that typically involves a deterioration in the physiological capacity of various organ systems and heightens the patient’s susceptibility to stressors. For this reason, one of the main research goals currently being addressed is that of characterising the impact of frailty in different settings. The main aim of this study is to determine the prevalence of Fried’s frailty phenotype among community-dwelling older people and to analyse the factors associated with frailty. In this research study, 582 persons aged 65 years or more participated in this cross-sectional study that was conducted at primary healthcare centres in Málaga, Spain. Sociodemographic, clinical, functional and comprehensive drug therapy data were compiled. The relationship between the independent variables and the different states of frailty was analysed by using a multinomial logistic regression model. Frailty was present in 24.1% of the study sample (95% CI = 20.7–27.6) of whom 54.3% were found to be pre-frail and 21.6% were non-frail. The study variable most strongly associated with frailty was the female gender (OR = 20.54, 95% CI = 9.10–46.3). Other factors found to be associated with the state of frailty included age, dependence for the instrumental activities of daily living (IADL), polymedication, osteoarticular pathology and psychopathology. This study confirms the high prevalence of frailty among community-dwelling older people. Frailty may be associated with many factors. Some of these associated factors may be preventable or modifiable and, thus, provide clinically relevant targets for intervention. This is particularly the case for depressive symptoms, the clinical control of osteoarthritis and the use of polypharmacy.
A simple, semi-empirical performance modeling approach for partially transparent tracking-integrated concentrator photovoltaics

While concentrator photovoltaics have long promised high power densities compared to standard photovoltaics, in practice, the requirement of sun tracking and the loss of the diffuse component of the solar resource have prevented this from being realized, especially in space-constrained settings such as building integration where it would have most value. Schemes for tracking integration, in which sun-tracking capability is incorporated into the CPV module, rather than being supplied by an external tracker, have thus far failed to change this state of affairs, due to the limited tracking range of practical integrated tracking systems, and the higher diffuse fraction on a fixed-tilt as opposed to a sun-oriented surface. Recently proposed systems promise to finally resolve this situation by combining tracking integration (TI) with partial transparency of the CPV module, which allows the module to simultaneously collect sunlight for illumination without degrading electricity production. This concept, which is currently undergoing commercialization, has substantial application potential in building-integrated and agricultural settings. In order to adequately evaluate the technical and economic potential of these technologies, a straightforward method for estimating their performance is required. Standard photovoltaic modeling tools are of limited value for this emerging and unique class of technologies, although significant development is ongoing. In this manuscript, we present a simple, semi- empirical method for performance modeling of semi-transparent tracking-integrated concentrator photovoltaics based on the outputs of a standard photovoltaic system model and experimental characterizations of actual modules. Optical and electrical and performances are considered in an easily-implemented performance model that avoids resorting to detailed module-level simulations. Based on the model, the economic viability of TI-CPVs is discussed. We have previously used this modeling approach to evaluate module performance in several target applications, which will be summarized, and we furthermore discuss ongoing work to increase the rigor and precision of the modeling approach while retaining simplicity of use.
Cavity Resonance Sound Absorptive Material Parameter Optimization Based Image Recognition and Deep Learning

Traditional modelling and optimization methods for sound absorptive materials require manual testing and huge computation effort. Artificial intelligence is an ideal method for the prediction and identification of complex systems in these cases. This study presents an optimization approach based on image recognition combined with deep learning for cavity resonance sound absorptive material. The sound absorptive material model is conducted using the acoustic-structure coupling model in COMSOL Multiphysics for the sound pressure distribution and behaviour during different frequency excitation. The cavity material plane wave image recognition model was obtained using the CNN method of multiscale layered features. The image arrangement was then processed and sent to the convolutional layer for feature extraction. Subsequently, the feature record was entered into the loop. The association between the kinematic parameters of the cavity material and the sound pressure distribution is obtained through the prediction layer. Using the prediction of CRNN, the results indicate that the range of the cover thickness should be between 50~70 mm and the angle should be between 45~60°. It also showed that this optimization method could be successfully applied for similar materials and components, with certain universality.
Electrochemical Determination of Chlorogenic Acid in Nutraceuticals Using Voltammetric Sensors Based on Screen-Printed Carbon Electrode Modified with Graphene and Gold Nanoparticles

The present study describes the electrochemical properties of three screen-printed electrodes (SPEs), the first electrode being carbon-based (C), the second graphene-based (GPH), and the third based on GPH modified with gold nanoparticles (GNP). These electrodes were used for the study of the electrochemical behavior of chlorogenic acid in different aqueous solutions, at pH = 7. In chlorogenic acid solution, a redox process was noticed in the case of all three electrodes; GPH and GNP significantly improved the sensor response regarding sensitivity and reversibility, a fact demonstrated by characterizing the sensor by cyclic voltammetry in potassium ferrocyanide, which corresponds to the exchange of two electrons and two protons. Moreover, the calibration curves for each sensor were developed, subsequently calculating the detection limits (LOD) and the quantification limits (LOQ). Low LOD and LOQ were obtained, the best—of the order of 10−7 M (LOD = 0.62 × 10−7 M; LOQ = 1.97 × 10−7 M)—being obtained in the case of GPH-GNP-SPE, which demonstrates that the method may be applied for determining chlorogenic acid in real samples. Thus, the sensors were successfully used for the quantitative determination of chlorogenic acid in three nutraceutical products. The validation of the results was done using the FTIR method. The results obtained by cyclic voltammetry were in accordance with those obtained by the spectrometric method, without significant differences from a statistical point of view.
In Vitro Effects of Paralytic Shellfish Toxins and Lytic Extracellular Compounds Produced by Alexandrium Strains on Hemocyte Integrity and Function in Mytilus edulis

Harmful effects caused by the exposure to paralytic shellfish toxins (PSTs) and bioactive extracellular compounds (BECs) on bivalves are frequently difficult to attribute to one or the other compound group. We evaluate and compare the distinct effects of PSTs extracted from Alexandrium catenella (Alex5) cells and extracellular lytic compounds (LCs) produced by A. tamarense (NX-57-08) on Mytilus edulis hemocytes. We used a 4 h dose–response in vitro approach and analyzed how these effects correlate with those observed in a previous in vivo feeding assay. Both bioactive compounds caused moderated cell death (10–15%), being dose-dependent for PST-exposed hemocytes. PSTs stimulated phagocytic activity at low doses, with a moderate incidence in lysosomal damage (30–50%) at all tested doses. LCs caused a dose-dependent impairment of phagocytic activity (up to 80%) and damage to lysosomal membranes (up to 90%). PSTs and LCs suppressed cellular ROS production and scavenged H2O2 in in vitro assays. Neither PSTs nor LCs affected the mitochondrial membrane potential in hemocytes. In vitro effects of PST extracts on M. edulis hemocytes were consistent with our previous study on in vivo exposure to PST-producing algae, while for LCs, in vivo and in vitro results were not as consistent.
Variable Frequency – Perturb and Observe Controlled PV source with Series Resonant Converter for Induction Heating System

This paper presented a Series Resonant Load con-figuration of a full bridge inverter for Induction Heating application, powered by a Photovoltaic (PV) source. A Variable Frequency-Perturb and Observe (VFPO) control of Series Resonant Full bridge Converter is proposed in this paper, to track the Maximum Power Point (MPP) of the system and to control the power output of the converter against the variation in PV source output due to climatic changes. The converter performance is evaluated under unfavourable conditions in the aspect of MPP tracking and regulating the power output to induction heating load. Impedance matching analysis of series resonant converter fed induction heating load through variation of frequency has been presented. Operating constraints are determined to incorporate in the control strategy to extract maximum power from PV source. The relevant theoretical analysis and control strategy is verified using simulation results.
Insulin Receptor Isoforms Differently Regulate Cell Proliferation and Apoptosis in the Ligand-Occupied and Unoccupied State

The insulin receptor (IR) presents two isoforms (IR-A and IR-B) that differ for the α-subunit C-terminal. Both isoforms are expressed in all human cells albeit in different proportions, yet their functional properties-when bound or unbound to insulin-are not well characterized. From a cell model deprived of the Insulin-like Growth Factor 1 Receptor (IGF1-R) we therefore generated cells exhibiting no IR (R-shIR cells), or only human IR-A (R-shIR-A), or exclusively human IR-B (R-shIR-B) and we studied the specific effect of the two isoforms on cell proliferation and cell apoptosis. In the absence of insulin both IR-A and IR-B similarly inhibited proliferation but IR-B was 2–3 fold more effective than IR-A in reducing resistance to etoposide-induced DNA damage. In the presence of insulin, IR-A and IR-B promoted proliferation with the former significantly more effective than the latter at increasing insulin concentrations. Moreover, only insulin-bound IR-A, but not IR-B, protected cells from etoposide-induced cytotoxicity. In conclusion, IR isoforms have different effects on cell proliferation and survival. When unoccupied, IR-A, which is predominantly expressed in undifferentiated and neoplastic cells, is less effective than IR-B in protecting cells from DNA damage. In the presence of insulin, particularly when present at high levels, IR-A provides a selective growth advantage.