Miracle-dz/UniFAR-FTUs huggingface dataset:
Pathological Roles of Mitochondrial Oxidative Stress and Mitochondrial Dynamics in Cardiac Microvascular Ischemia/Reperfusion Injury

Mitochondria are key regulators of cell fate through controlling ATP generation and releasing pro-apoptotic factors. Cardiac ischemia/reperfusion (I/R) injury to the coronary microcirculation has manifestations ranging in severity from reversible edema to interstitial hemorrhage. A number of mechanisms have been proposed to explain the cardiac microvascular I/R injury including edema, impaired vasomotion, coronary microembolization, and capillary destruction. In contrast to their role in cell types with higher energy demands, mitochondria in endothelial cells primarily function in signaling cellular responses to environmental cues. It is clear that abnormal mitochondrial signatures, including mitochondrial oxidative stress, mitochondrial fission, mitochondrial fusion, and mitophagy, play a substantial role in endothelial cell function. While the pathogenic role of each of these mitochondrial alterations in the endothelial cells I/R injury remains complex, profiling of mitochondrial oxidative stress and mitochondrial dynamics in endothelial cell dysfunction may offer promising potential targets in the search for novel diagnostics and therapeutics in cardiac microvascular I/R injury. The objective of this review is to discuss the role of mitochondrial oxidative stress on cardiac microvascular endothelial cells dysfunction. Mitochondrial dynamics, including mitochondrial fission and fusion, are critically discussed to understand their roles in endothelial cell survival. Finally, mitophagy, as a degradative mechanism for damaged mitochondria, is summarized to figure out its contribution to the progression of microvascular I/R injury.
The Use of Hyperscanning to Investigate the Role of Social, Affective, and Informative Gestures in Non-Verbal Communication. Electrophysiological (EEG) and Inter-Brain Connectivity Evidence

Communication can be considered as a joint action that involves two or more individuals transmitting different information. In particular, non-verbal communication involves body movements used to communicate different information, characterized by the use of specific gestures. The present study aims to investigate the electrophysiological (EEG) correlates underlying the use of affective, social, and informative gestures during a non-verbal interaction between an encoder and decoder. From the results of the single brain and inter-brain analyses, an increase of frontal alpha, delta, and theta brain responsiveness and inter-brain connectivity emerged for affective and social gestures; while, for informative gestures, an increase of parietal alpha brain responsiveness and alpha, delta, and theta inter-brain connectivity was observed. Regarding the inter-agents’ role, an increase of frontal alpha activity was observed in the encoder compared to the decoder for social and affective gestures. Finally, regarding gesture valence, an increase of theta brain responsiveness and theta and beta inter-brain connectivity was observed for positive gestures on the left side compared to the right one. This study, therefore, revealed the function of the gesture type and valence in influencing individuals’ brain responsiveness and inter-brain connectivity, showing the presence of resonance mechanisms underlying gesture execution and observation.
Degenerated Cones in Cultured Human Retinas Can Successfully Be Optogenetically Reactivated

Biblical references aside, restoring vision to the blind has proven to be a major technical challenge. In recent years, considerable advances have been made towards this end, especially when retinal degeneration underlies the vision loss such as occurs with retinitis pigmentosa. Under these conditions, optogenetic therapies are a particularly promising line of inquiry where remaining retinal cells are made into “artificial photoreceptors”. However, this strategy is not without its challenges and a model system using human retinal explants would aid its continued development and refinement. Here, we cultured post-mortem human retinas and show that explants remain viable for around 7 days. Within this period, the cones lose their outer segments and thus their light sensitivity but remain electrophysiologically intact, displaying all the major ionic conductances one would expect for a vertebrate cone. We optogenetically restored light responses to these quiescent cones using a lentivirus vector constructed to express enhanced halorhodopsin under the control of the human arrestin promotor. In these ‘reactivated’ retinas, we show a light-induced horizontal cell to cone feedback signal in cones, indicating that transduced cones were able to transmit their light response across the synapse to horizontal cells, which generated a large enough response to send a signal back to the cones. Furthermore, we show ganglion cell light responses, suggesting the cultured explant’s condition is still good enough to support transmission of the transduced cone signal over the intermediate retinal layers to the final retinal output level. Together, these results show that cultured human retinas are an appropriate model system to test optogenetic vision restoration approaches and that cones which have lost their outer segment, a condition occurring during the early stages of retinitis pigmentosa, are appropriate targets for optogenetic vision restoration therapies.
Color Calibration of Proximal Sensing RGB Images of Oilseed Rape Canopy via Deep Learning Combined with K-Means Algorithm

Plant color is a key feature for estimating parameters of the plant grown under different conditions using remote sensing images. In this case, the variation in plant color should be only due to the influence of the growing conditions and not due to external confounding factors like a light source. Hence, the impact of the light source in plant color should be alleviated using color calibration algorithms. This study aims to develop an efficient, robust, and cutting-edge approach for automatic color calibration of three-band (red green blue: RGB) images. Specifically, we combined the k-means model and deep learning for accurate color calibration matrix (CCM) estimation. A dataset of 3150 RGB images for oilseed rape was collected by a proximal sensing technique under varying illumination conditions and used to train, validate, and test our proposed framework. Firstly, we manually derived CCMs by mapping RGB color values of each patch of a color chart obtained in an image to standard RGB (sRGB) color values of that chart. Secondly, we grouped the images into clusters according to the CCM assigned to each image using the unsupervised k-means algorithm. Thirdly, the images with the new cluster labels were used to train and validate the deep learning convolutional neural network (CNN) algorithm for an automatic CCM estimation. Finally, the estimated CCM was applied to the input image to obtain an image with a calibrated color. The performance of our model for estimating CCM was evaluated using the Euclidean distance between the standard and the estimated color values of the test dataset. The experimental results showed that our deep learning framework can efficiently extract useful low-level features for discriminating images with inconsistent colors and achieved overall training and validation accuracies of 98.00% and 98.53%, respectively. Further, the final CCM provided an average Euclidean distance of 16.23 ΔΕ and outperformed the previously reported methods. This proposed technique can be used in real-time plant phenotyping at multiscale levels.
Genome Sequence of a Human Norovirus GII.4 Hong Kong[P31] Variant in Hong Kong, China

We report the nearly complete genome of a norovirus GII.4 Hong Kong[P31] variant (GII strain Hu/HK/2019/GII.4 Hong Kong[P31]/CUHK-NS-2200) that was detected in a patient with gastroenteritis in August 2019. The genome was sequenced by metagenomic next-generation sequencing and was found to have 92.8% nucleotide similarity to the closest GII.4 norovirus sequence in GenBank. ABSTRACT We report the nearly complete genome of a norovirus GII.4 Hong Kong[P31] variant (GII strain Hu/HK/2019/GII.4 Hong Kong[P31]/CUHK-NS-2200) that was detected in a patient with gastroenteritis in August 2019. The genome was sequenced by metagenomic next-generation sequencing and was found to have 92.8% nucleotide similarity to the closest GII.4 norovirus sequence in GenBank.
Lazy object copy as a platform for population-based probabilistic programming

This work considers dynamic memory management for population-based probabilistic programs, such as those using particle methods for inference. Such programs exhibit a pattern of allocating, copying, potentially mutating, and deallocating collections of similar objects through successive generations. These objects may assemble data structures such as stacks, queues, lists, ragged arrays, and trees, which may be of random, and possibly unbounded, size. For the simple case of $N$ particles, $T$ generations, $D$ objects, and resampling at each generation, dense representation requires $O(DNT)$ memory, while sparse representation requires only $O(DT+DN\log DN)$ memory, based on existing theoretical results. This work describes an object copy-on-write platform to automate this saving for the programmer. The core idea is formalized using labeled directed multigraphs, where vertices represent objects, edges the pointers between them, and labels the necessary bookkeeping. A specific labeling scheme is proposed for high performance under the motivating pattern. The platform is implemented for the Birch probabilistic programming language, using smart pointers, hash tables, and reference-counting garbage collection. It is tested empirically on a number of realistic probabilistic programs, and shown to significantly reduce memory use and execution time in a manner consistent with theoretical expectations. This enables copy-on-write for the imperative programmer, lazy deep copies for the object-oriented programmer, and in-place write optimizations for the functional programmer.
Deep Learning for MIR Tutorial

Deep Learning has become state of the art in visual computing and continuously emerges into the Music Information Retrieval (MIR) and audio retrieval domain. In order to bring attention to this topic we propose an introductory tutorial on deep learning for MIR. Besides a general introduction to neural networks, the proposed tutorial covers a wide range of MIR relevant deep learning approaches. \textbf{Convolutional Neural Networks} are currently a de-facto standard for deep learning based audio retrieval. \textbf{Recurrent Neural Networks} have proven to be effective in onset detection tasks such as beat or audio-event detection. \textbf{Siamese Networks} have been shown effective in learning audio representations and distance functions specific for music similarity retrieval. We will incorporate both academic and industrial points of view into the tutorial. Accompanying the tutorial, we will create a Github repository for the content presented at the tutorial as well as references to state of the art work and literature for further reading. This repository will remain public after the conference.
Determination of Plant Nitrogen Content in Wheat Plants via Spectral Reflectance Measurements: Impact of Leaf Number and Leaf Position

The determination of plant nitrogen (N) content (%) in wheat via destructive lab analysis is expensive and inadequate for precision farming applications. Vegetation indices (VI) based on spectral reflectance can be used to predict plant N content indirectly. For these VI, reflectance from space-borne, airborne, or ground-borne sensors is captured. Measurements are often taken at the canopy level for practical reasons. Hence, translocation processes of nutrients that take place within the plant might be ignored or measurements might be less accurate if nutrient deficiency symptoms occur on the older leaves. This study investigated the impact of leaf number and measurement position on the leaf itself on the determination of plant N content (%) via reflectance measurements. Two hydroponic experiments were carried out. In the first experiment, the N fertilizer amount and growth stage for the determination of N content was varied, while the second experiment focused on a secondary induction of N deficiency due to drought stress. For each plant, reflectance measurements were taken from three leaves (L1, L2, L3) and at three positions on the leaf (P1, P2, P3). In addition, the N content (%) of the whole plant was determined by chemical lab analysis. Reflectance spectrometer measurements (400–1650 nm) were used to calculate 16 VI for each combination of leaf and position. N content (%) was predicted using each VI for each leaf and each position. Significant lower mean residual error variance (MREV) was found for leaves L1 and L3 and for measurement position on P3 in the N trial, but the difference of MREV between the leaves was very low and therefore considered as not relevant. The drought stress trial also led to no significant differences in MREV between leaves and positions. Neither the position on the leaf nor the leaf number had an impact on the accuracy of plant nitrogen determination via spectral reflectance measurements, wherefore measurements taken at the canopy level seem to be a valid approach.
Assessment of a Bayesian Vitrea CT Perfusion Analysis to Predict Final Infarct and Penumbra Volumes in Patients with Acute Ischemic Stroke: A Comparison with RAPID

Data were retrospectively collected for 105 patients with acute ischemic stroke (55 patients with successful recanalization [TICI 2b/2c/3] and large-vessel occlusions and 50 patients without interventions). Final infarct volumes were calculated using DWI and FLAIR 24 hours following CTP imaging. RAPID and the Vitrea Bayesian CTP algorithm (with 3 different settings) predicted infarct and penumbra volumes for comparison with final infarct volumes to assess software performance. RAPID and Vitrea default setting had the most accurate final infarct volume prediction in patients with interventions. Default Vitrea and RAPID were the most and least accurate in determining final infarct volume for patients without an intervention, respectively. Compared with RAPID, the Vitrea default setting was noninferior for patients with interventions and superior in penumbra estimation for patients without interventions as indicated by mean infarct differences and correlations with final infarct volumes. BACKGROUND AND PURPOSE: Brain CTP is used to estimate infarct and penumbra volumes to determine endovascular treatment eligibility for patients with acute ischemic stroke. We aimed to assess the accuracy of a Bayesian CTP algorithm in determining penumbra and final infarct volumes. MATERIALS AND METHODS: Data were retrospectively collected for 105 patients with acute ischemic stroke (55 patients with successful recanalization [TICI 2b/2c/3] and large-vessel occlusions and 50 patients without interventions). Final infarct volumes were calculated using DWI and FLAIR 24 hours following CTP imaging. RAPID and the Vitrea Bayesian CTP algorithm (with 3 different settings) predicted infarct and penumbra volumes for comparison with final infarct volumes to assess software performance. Vitrea settings used different combinations of perfusion maps (MTT, TTP, CBV, CBF, delay time) for infarct and penumbra quantification. Patients with and without interventions were included for assessment of predicted infarct and penumbra volumes, respectively. RESULTS: RAPID and Vitrea default setting had the most accurate final infarct volume prediction in patients with interventions ([Spearman correlation coefficient, mean infarct difference] default versus FLAIR: [0.77, 4.1 mL], default versus DWI: [0.72, 4.7 mL], RAPID versus FLAIR: [0.75, 7.5 mL], RAPID versus DWI: [0.75, 6.9 mL]). Default Vitrea and RAPID were the most and least accurate in determining final infarct volume for patients without an intervention, respectively (default versus FLAIR: [0.76, –0.4 mL], default versus DWI: [0.71, –2.6 mL], RAPID versus FLAIR: [0.68, −49.3 mL], RAPID versus DWI: [0.65, –51.5 mL]). CONCLUSIONS: Compared with RAPID, the Vitrea default setting was noninferior for patients with interventions and superior in penumbra estimation for patients without interventions as indicated by mean infarct differences and correlations with final infarct volumes.
Sarcopenia and Heart Failure

Modifications of lean mass are a frequent critical determinant in the pathophysiology and progression of heart failure (HF). Sarcopenia may be considered one of the most important causes of low physical performance and reduced cardiorespiratory fitness in older patients with HF. Sarcopenia is frequently misdiagnosed as cachexia. However, muscle wasting in HF has different pathogenetic features in sarcopenic and cachectic conditions. HF may induce sarcopenia through common pathogenetic pathways such as hormonal changes, malnutrition, and physical inactivity; mechanisms that influence each other. In the opposite way, sarcopenia may favor HF development by different mechanisms, including pathological ergoreflex. Paradoxically, sarcopenia is not associated with a sarcopenic cardiac muscle, but the cardiac muscle shows a hypertrophy which seems to be “not-functional. ” First-line agents for the treatment of HF, physical activity and nutritional interventions, may offer a therapeutic advantage in sarcopenic patients irrespective of HF. Thus, sarcopenia is highly prevalent in patients with HF, contributing to its poor prognosis, and both conditions could benefit from common treatment strategies based on pharmacological, physical activity, and nutritional approaches.
Bimodal Nd-Doped LuVO4 Nanoprobes Functionalized with Polyacrilic Acid for X-Ray Computed Tomography and NIR Luminescent Imaging

Uniform Nd3+-doped LuVO4 nanophosphors have been synthesized for the first time in literature by using a poliol-based method at 120 °C from Nd3+ and vanadate precursors. After optimizing the Nd doping level, these phosphors present intense luminescence in the near-infrared biological windows. The X-ray attenuation capacity of the optimum nanophosphor has been found to be higher than that of a commercial X-ray computed tomography contrast agent. After surface coating with polyacrylic acid, such nanoparticles present high colloidal stability in physiological pH medium and high cell viability. Because of these properties, the developed Nd3+-doped LuVO4 nanoparticles have potential applications as a bimodal probe for NIR luminescent bioimaging and X-ray computed tomography.
Stereocilia Rootlets: Actin-Based Structures That Are Essential for Structural Stability of the Hair Bundle

Sensory hair cells of the inner ear rely on the hair bundle, a cluster of actin-filled stereocilia, to transduce auditory and vestibular stimuli into electrical impulses. Because they are long and thin projections, stereocilia are most prone to damage at the point where they insert into the hair cell’s soma. Moreover, this is the site of stereocilia pivoting, the mechanical movement that induces transduction, which additionally weakens this area mechanically. To bolster this fragile area, hair cells construct a dense core called the rootlet at the base of each stereocilium, which extends down into the actin meshwork of the cuticular plate and firmly anchors the stereocilium. Rootlets are constructed with tightly packed actin filaments that extend from stereocilia actin filaments which are wrapped with TRIOBP; in addition, many other proteins contribute to the rootlet and its associated structures. Rootlets allow stereocilia to sustain innumerable deflections over their lifetimes and exemplify the unique manner in which sensory hair cells exploit actin and its associated proteins to carry out the function of mechanotransduction.
Dépistage systématique de la dénutrition protéino-énergétique du sujet âgé de plus de 70 ans par les médecins généralistes picards : place du MNA

INTRODUCTION - Le depistage de la denutrition du sujet âge en medecine generale est un enjeu de sante publique. Le but du travail est de determiner le pourcentage de medecins generalistes picards qui le realisent, de savoir quelle etait la place du MNA et connaitre les habitudes de pratique. METHODE - Il s’agit d’une etude transversale descriptive par l’envoi de questionnaires a 600 medecins generalistes tires au sort. RESULTATS/DISCUSSION - Nous avons obtenu 17% de reponses. 1/4 des medecins estime le depistage difficile et rencontre des obstacles : manque de temps, doute de la fiabilite de certaines reponses. Des moyens d’amelioration sont decrits : developper un outil de depistage adapte, ameliorer la formation, sensibiliser la population. Le poids est l’indicateur utilise le plus souvent. Les moyens evalues (poids, IMC, estimation des facteurs de risque, appetit et apports alimentaires) sont majoritairement utilises quand cela semble utile. Les dosages biologiques sont realises annuellement ou quand cela semble utile. 1/3 des medecins (plutot des femmes, âges de moins de 45 ans) declare realiser le depistage systematique et utilise les indicateurs un peu plus souvent que l’echantillon total. Le MNA est l’outil le moins utilise, connu par 28% des medecins, majoritairement des hommes, âges de plus de 55ans. 16% des medecins l’utilisent, dont 88% quand cela semble utile. Ils preferent utiliser la forme courte (88%), jugee facile et adaptee. CONCLUSION - Le depistage de la denutrition est realise, mais le MNA peu utilise malgre les recommandations. Sa diffusion version courte et la sensibilisation des medecins et de l’entourage permettraient un meilleur depistage.
Show, Recall, and Tell: Image Captioning with Recall Mechanism

Generating natural and accurate descriptions in image captioning has always been a challenge. In this paper, we propose a novel recall mechanism to imitate the way human conduct captioning. There are three parts in our recall mechanism : recall unit, semantic guide (SG) and recalled-word slot (RWS). Recall unit is a text-retrieval module designed to retrieve recalled words for images. SG and RWS are designed for the best use of recalled words. SG branch can generate a recalled context, which can guide the process of generating caption. RWS branch is responsible for copying recalled words to the caption. Inspired by pointing mechanism in text summarization, we adopt a soft switch to balance the generated-word probabilities between SG and RWS. In the CIDEr optimization step, we also introduce an individual recalled-word reward (WR) to boost training. Our proposed methods (SG+RWS+WR) achieve BLEU-4 / CIDEr / SPICE scores of 36.6 / 116.9 / 21.3 with cross-entropy loss and 38.7 / 129.1 / 22.4 with CIDEr optimization on MSCOCO Karpathy test split, which surpass the results of other state-of-the-art methods.