Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Real-Time Facial Landmark Detection in Depth Data Using Auxiliary Information

Modern facial motion capture systems employ a two-pronged approach for capturing and rendering facial motion. Visual data (2D) is used for tracking the facial features and predicting facial expression, whereas Depth (3D) data is used to build a series of expressions on 3D face models. An issue with modern research approaches is the use of a single data stream that provides little indication of the 3D facial structure. We compare and analyse the performance of Convolutional Neural Networks (CNN) using visual, Depth and merged data to identify facial features in real-time using a Depth sensor. First, we review the facial landmarking algorithms and its datasets for Depth data. We address the limitation of the current datasets by introducing the Kinect One Expression Dataset (KOED). Then, we propose the use of CNNs for the single data stream and merged data streams for facial landmark detection. We contribute to existing work by performing a full evaluation on which streams are the most effective for the field of facial landmarking. Furthermore, we improve upon the existing work by extending neural networks to predict into 3D landmarks in real-time with additional observations on the impact of using 2D landmarks as auxiliary information. We evaluate the performance by using Mean Square Error (MSE) and Mean Average Error (MAE). We observe that the single data stream predicts accurate facial landmarks on Depth data when auxiliary information is used to train the network. The codes and dataset used in this paper will be made available.
SleepGuard

Sleep is an important part of our daily routine -- we spend about one-third of our time doing it. By tracking sleep-related events and activities, sleep monitoring provides decision support to help us understand sleep quality and causes of poor sleep. Wearable devices provide a new way for sleep monitoring, allowing us to monitor sleep from the comfort of our own home. However, existing solutions do not take full advantage of the rich sensor data provided by these devices. In this paper, we present the design and development of SleepGuard, a novel approach to track a wide range of sleep-related events using smartwatches. We show that using merely a single smartwatch, it is possible to capture a rich amount of information about sleep events and sleeping context, including body posture and movements, acoustic events, and illumination conditions. We demonstrate that through these events it is possible to estimate sleep quality and identify factors affecting it most. We evaluate our approach by conducting extensive experiments involved fifteen users across a 2-week period. Our experimental results show that our approach can track a richer set of sleep events, provide better decision support for evaluating sleep quality, and help to identify causes for sleep problems compared to prior work.
Outlining a Novel Framework for Monitoring User's Vital Signs and Activity Data in Caregiving Facilities

Wearable sensors are consistently decreasing in size, while their computational power simultaneously rises. Current wearables enable high-quality measurements of vital signs and activity data, comparable to state-of-the-art technologies. However, they are rarely implemented in caregiving facilities, such as hospitals or elderly homes. A framework using a wearable sensing unit is proposed that mainly relies on an Inertial Measurement Unit (IMU), where an extension to a variety of sensor types are possible. This sensing unit is worn by the patient and connected to a base station. This will be locally distributed by placing one unit in each room. The sensor unit extracts various vital signs and activity parameters, transmitting such information wirelessly through a low-energy transmission standard. The data is easily accessible to any caregiver using a common mobile end-user device. The proposed approach is superior to previous attempts, as it overcomes common barriers that prevented implementation. By outlining current challenges, solutions are recommended to appropriately overcome these issues within a future system.
Comprehensively Profiling the Chromatin Architecture of Tissue Restricted Antigen Expression in Thymic Epithelial Cells Over Development

Thymic epithelial cells (TEC) effect crucial roles in thymopoiesis including the control of negative thymocyte selection. This process depends on their capacity to express promiscuously genes encoding tissue-restricted antigens. This competence is accomplished in medullary TEC (mTEC) in part by the presence of the transcriptional facilitator AutoImmune REgulator, AIRE. AIRE-regulated gene transcription is marked by repressive chromatin modifications, including H3K27me3. When during TEC development these chromatin marks are established, however, remains unclear. Here we use a comprehensive ChIP-seq dataset of multiple chromatin modifications in different TEC subtypes to demonstrate that the chromatin landscape is established early in TEC differentiation. Much of the chromatin architecture found in mature mTEC was found to be present already over earlier stages of mTEC lineage differentiation as well as in non-TEC tissues. This was reflected by the fact that a machine learning approach accurately classified genes as AIRE-induced or AIRE-independent both in immature and mature mTEC. Moreover, analysis of TEC specific enhancer elements identified candidate transcription factors likely to be important in mTEC development and function. Our findings indicate that the mature mTEC chromatin landscape is laid down early in mTEC differentiation, and that AIRE is not required for large-scale re-patterning of chromatin in mTEC.
U-Net for MAV-based Penstock Inspection: an Investigation of Focal Loss in Multi-class Segmentation for Corrosion Identification

Periodical inspection and maintenance of critical infrastructure such as dams, penstocks, and locks are of significant importance to prevent catastrophic failures. Conventional manual inspection methods require inspectors to climb along a penstock to spot corrosion, rust and crack formation which is unsafe, labor-intensive, and requires intensive training. This work presents an alternative approach using a Micro Aerial Vehicle (MAV) that autonomously flies to collect imagery which is then fed into a pretrained deep-learning model to identify corrosion. Our simplified U-Net trained with less than 40 image samples can do inference at 12 fps on a single GPU. We analyze different loss functions to solve the class imbalance problem, followed by a discussion on choosing proper metrics and weights for object classes. Results obtained with the dataset collected from Center Hill Dam, TN show that focal loss function, combined with a proper set of class weights yield better segmentation results than the base loss, Softmax cross entropy. Our method can be used in combination with planning algorithm to offer a complete, safe and cost-efficient solution to autonomous infrastructure inspection.
Finding Similar Patient Subpopulations in the ICU Using Laboratory Test Ordering Patterns

In this paper, we focus on phenotyping critically ill patients in intensive care units (ICUs). Various data types have been used to cluster patients. We introduce laboratory-test-ordering patterns as a source of information for finding clinically similar patients. We employed Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering method to find patient subpopulations based on the first 24 hours of laboratory test ordered. The DBSCAN identified 25 clusters, and we utilized t-Distributed Stochastic Neighbor Embedding (t-SNE) to visualize the subpopulations. Then, we evaluated the clinical interpretability of the clusters by using cluster characteristics and two outcomes: in-hospital mortality and 30-days post-discharge mortality. Our results demonstrate that laboratory-test-ordering patterns are informative and can be used to identify patient cohorts.
Roles of Post-translational Modifications in Spinocerebellar Ataxias

Post-translational modifications (PTMs), including phosphorylation, acetylation, ubiquitination, SUMOylation, etc., of proteins can modulate protein properties such as intracellular distribution, activity, stability, aggregation, and interactions. Therefore, PTMs are vital regulatory mechanisms for multiple cellular processes. Spinocerebellar ataxias (SCAs) are hereditary, heterogeneous, neurodegenerative diseases for which the primary manifestation involves ataxia. Because the pathogenesis of most SCAs is correlated with mutant proteins directly or indirectly, the PTMs of disease-related proteins might functionally affect SCA development and represent potential therapeutic interventions. Here, we review multiple PTMs related to disease-causing proteins in SCAs pathogenesis and their effects. Furthermore, we discuss these PTMs as potential targets for treating SCAs and describe translational therapies targeting PTMs that have been published.
CREST in the Nucleus Accumbens Core Regulates Cocaine Conditioned Place Preference, Cocaine-Seeking Behavior, and Synaptic Plasticity

Epigenetic mechanisms result in persistent changes at the cellular level that can lead to long-lasting behavioral adaptations. Nucleosome remodeling is a major epigenetic mechanism that has not been well explored with regards to drug-seeking behaviors. Nucleosome remodeling is performed by multi-subunit complexes that interact with DNA or chromatin structure and possess an ATP-dependent enzyme to disrupt nucleosome-DNA contacts and ultimately regulate gene expression. Calcium responsive transactivator (CREST) is a transcriptional activator that interacts with enzymes involved in both histone acetylation and nucleosome remodeling. Here, we examined the effects of knocking down CREST in the nucleus accumbens (NAc) core on drug-seeking behavior and synaptic plasticity in male mice as well as drug-seeking in male rats. Knocking down CREST in the NAc core results in impaired cocaine-induced conditioned place preference (CPP) as well as theta-induced long-term potentiation in the NAc core. Further, similar to the CPP findings, using a self-administration procedure, we found that CREST knockdown in the NAc core of male rats had no effect on instrumental responding for cocaine itself on a first-order schedule, but did significantly attenuate responding on a second-order chain schedule, in which responding has a weaker association with cocaine. Together, these results suggest that CREST in the NAc core is required for cocaine-induced CPP, synaptic plasticity, as well as cocaine-seeking behavior. SIGNIFICANCE STATEMENT This study demonstrates a key role for the role of Calcium responsive transactivator (CREST), a transcriptional activator, in the nucleus accumbens (NAc) core with regard to cocaine-induced conditioned place preference (CPP), self-administration (SA), and synaptic plasticity. CREST is a unique transcriptional regulator that can recruit enzymes from two different major epigenetic mechanisms: histone acetylation and nucleosome remodeling. In this study we also found that the level of potentiation in the NAc core correlated with whether or not animals formed a CPP. Together the results indicate that CREST is a key downstream regulator of cocaine action in the NAc.
Combinatorial lentiviral gene delivery of pro‐oligodendrogenic factors for improving myelination of regenerating axons after spinal cord injury

Spinal cord injury (SCI) results in paralysis below the injury and strategies are being developed that support axonal regrowth, yet recovery lags, in part, because many axons are not remyelinated. Herein, we investigated strategies to increase myelination of regenerating axons by overexpression of platelet‐derived growth factor (PDGF)‐AA and noggin either alone or in combination in a mouse SCI model. Noggin and PDGF‐AA have been identified as factors that enhance recruitment and differentiation of endogenous progenitors to promote myelination. Lentivirus encoding for these factors was delivered from a multichannel bridge, which we have previously shown creates a permissive environment and supports robust axonal growth through channels. The combination of noggin+PDGF enhanced total myelination of regenerating axons relative to either factor alone, and importantly, enhanced functional recovery relative to the control condition. The increase in myelination was consistent with an increase in oligodendrocyte‐derived myelin, which was also associated with a greater density of cells of an oligodendroglial lineage relative to each factor individually and control conditions. These results suggest enhanced myelination of regenerating axons by noggin+PDGF that act on oligodendrocyte‐lineage cells post‐SCI, which ultimately led to improved functional outcomes.
The prevalence of brucellosis and bovine tuberculosis in ruminants in Sidi Kacem Province, Morocco

Bovine tuberculosis (BTB) and brucellosis are major endemic zoonoses in ruminants in Morocco that impact on both animal and human health. This study presents an assessment of the epidemiological and socioeconomic burden of bacterial zoonoses in Sidi Kacem Province in Northern Morocco from a cross-sectional survey of 125 cattle and/or small ruminant-owning households. In total, 1082 sheep and goats were examined from 81 households. The single intradermal comparative cervical test to screen for bovine tuberculosis was undertaken on 1194 cattle from 123 households and all cattle were blood sampled. Cattle and small ruminant sera were tested for brucellosis using the standard Rose Bengal Test (sRBT) and the modified Rose Bengal Test (mRBT). Bacteriology was performed on 21 milk samples obtained from cattle that were seropositive for brucellosis for isolation and phenotyping of circulating Brucella strains. Individual and herd prevalence for BTB in cattle of 20.4% (95% CI 18%-23%) and 57.7% (95% CI 48%-66%), respectively, were observed in this study. The prevalence of brucellosis in cattle at individual and herd level was 1.9% (95% CI 1.2%-2.8%) and 9% (95% CI 4.5%-1.5%), respectively. Brucella pathogens were isolated from three cattle milk samples and were identified as B. abortus using Bruceladder® multiplex PCR and B. abortus biovar 1 by classical phenotyping. All small ruminants were seronegative to sRBT, two were positive to mRBT. A higher risk of BTB and brucellosis was observed in cattle in intensive livestock systems, in imported and crossed breeds and in animals from larger herds (>15). The three risk factors were usually present in the same herds, leading to higher transmission risk and persistence of both zoonoses. These results highlight the importance of implementing control strategies for both BTB and brucellosis to reduce productivity losses and the risk of transmission to humans. Prioritising control for BTB and brucellosis in intensive livestock production systems is essential for human and animal health.
Leveraging Zebrafish to Study Retinal Degenerations

Retinal degenerations are a heterogeneous group of diseases characterized by death of photoreceptors and progressive loss of vision. Retinal degenerations are a major cause of blindness in developed countries (Bourne et al., 2017; De Bode, 2017) and currently have no cure. In this review, we will briefly review the latest advances in therapies for retinal degenerations, highlighting the current barriers to study and develop therapies that promote photoreceptor regeneration in mammals. In light of these barriers, we present zebrafish as a powerful model to study photoreceptor regeneration and their integration into retinal circuits after regeneration. We outline why zebrafish is well suited for these analyses and summarize the powerful tools available in zebrafish that could be used to further uncover the mechanisms underlying photoreceptor regeneration and rewiring. In particular, we highlight that it is critical to understand how rewiring occurs after regeneration and how it differs from development. Insights derived from photoreceptor regeneration and rewiring in zebrafish may provide leverage to develop therapeutic targets to treat retinal degenerations.
Chatbots and New Audience Opportunities for Museums and Heritage Organisations

This paper explores how chatbots can offer opportunities for museums and galleries in engaging their audiences through recent developments, and through a case study approach focusing on the design and implementation of an audience development pilot in Milan involving four historic house museums ( Case Museo di Milano ). The pilot aimed to find new and interesting ways to engage teenagers in visiting these museums through visualising narrative using a convergence of chatbot technology and gamification.
On Misinformation Containment in Online Social Networks

The widespread online misinformation could cause public panic and serious economic damages. The misinformation containment problem aims at limiting the spread of misinformation in online social networks by launching competing campaigns. Motivated by realistic scenarios, we present the first analysis of the misinformation containment problem for the case when an arbitrary number of cascades are allowed. This paper makes four contributions. First, we provide a formal model for multi-cascade diffusion and introduce an important concept called as cascade priority. Second, we show that the misinformation containment problem cannot be approximated within a factor of $\Omega(2^{\log^{1-\epsilon}n^4})$ in polynomial time unless $NP \subseteq DTIME(n^{\polylog{n}})$. Third, we introduce several types of cascade priority that are frequently seen in real social networks. Finally, we design novel algorithms for solving the misinformation containment problem. The effectiveness of the proposed algorithm is supported by encouraging experimental results.
Implication of neurohormonal-coupled mechanisms of gastric emptying and pancreatic secretory function in diabetic gastroparesis

Recently, diabetic gastroparesis (DGP) has received much attention as its prevalence is increasing in a dramatic fashion and management of patients with DGP represents a challenge in the clinical practice due to the limited therapeutic options. DGP highlights an interrelationship between the gastric emptying and pancreatic secretory function that regulate a wide range of digestive and metabolic functions, respectively. It well documented that both gastric emptying and pancreatic secretion are under delicate control by multiple neurohormonal mechanisms including extrinsic parasympathetic pathways and gastrointestinal (GI) hormones. Interestingly, the latter released in response to various determinants that related to the rate and quality of gastric emptying. Others and we have provided strong evidence that the central autonomic nuclei send a dual output (excitatory and inhibitory) to the stomach and the pancreas in response to a variety of hormonal signals from the abdominal viscera. Most of these hormones released upon gastric emptying to provide feedback, and control this process and simultaneously regulate pancreatic secretion and postprandial glycemia. These findings emphasize an important link between gastric emptying and pancreatic secretion and its role in maintaining homeostatic processes within the GI tract. The present review deals with the neurohormonal-coupled mechanisms of gastric emptying and pancreatic secretory function that implicated in DGP and this provides new insights in our understanding of the pathophysiology of DGP. This also enhances the process of identifying potential therapeutic targets to treat DGP and limit the complications of current management practices.
Slow activation of fast mitochondrial Ca2+ uptake by cytosolic Ca2+

Mitochondrial Ca2+ uptake through the mitochondrial Ca2+ uniporter (MCU) is a tightly controlled process that sustains cell functions mainly by fine-tuning oxidative metabolism to cellular needs. The kinetics of Ca2+ fluxes across the mitochondrial membranes have been studied both in vitro and in vivo for many years, and the discovery of the molecular components of the MCU has further clarified that this Ca2+ uptake mechanism is based on a complex system subject to elaborate layers of controls. Alterations in the speed or capacity of the in-and-out pathways can have detrimental consequences for both the organelle and the cell, impairing cellular metabolism and ultimately causing cell death. Here, we report that pretreatment of deenergized mitochondria with low-micromolar Ca2+ concentrations for a few minutes markedly increases the speed of mitochondrial Ca2+ uptake upon re-addition of an oxidizable substrate. We found that this phenomenon is sensitive to alterations in the level of the MCU modulator proteins mitochondrial calcium uptake 1 (MICU1) and 2 (MICU2), and is accompanied by changes in the association of MICU1–MICU2 complexes with MCU. This increased Ca2+ uptake capacity, occurring under conditions mimicking those during ischemia/reperfusion in vivo, could lead to a massive amount of Ca2+ entering the mitochondrial matrix even at relatively low levels of cytosolic Ca2+. We conclude that the phenomenon uncovered here represents a potential threat of mitochondrial Ca2+ overload to the cell.
Emotion Recognition from Speech Signals by Using Evolutionary Algorithm and Empirical Mode Decomposition

Emotion status impacts massively the human’s health and the job performance of people. A system that can continuously and automatically monitor people’s emotion is worthy of development. Besides, the speech signals always contain some emotion features and are the most commonly used for communication between humans. The exploration of emotion recognition from speeches then becomes more important. In this paper, we propose a strategy for emotion recognition from speech by combining evolutionary algorithm (EA) with Empirical Mode Decomposition (EMD) to improve the emotion recognition rate. First, some emotional speeches were decomposed into several Intrinsic Mode Functions (IMFs) by using EMD process. The emotional part of a speech is then extracted by using these IMFs. In this paper, some weighted IMFs obtained from EMD are combined for the following recognition process. Hence, it is one of the goals of this paper to find the optimal weights corresponding to each IMF and to combine these weighted IMFs to make the recognition results as accurate as possible. The weights for each IMF are trained by evolutionary algorithm to find an optimal combination of IMFs. The reason why evolutional algorithm is used here is that evolutional algorithm always obtains some outstanding performances in many research concerning optimal design. The Mel-Frequency Cepstral Coefficients (MFCCs) are then computed and are used as the features for emotion recognition. An open database, eNTERFACE 2005 emotion database, is adopted in this paper as training and testing data for the experiments.
Comparing cognitive fusion and cognitive reappraisal as predictors of college student mental health

ABSTRACT Transdiagnostic cognitive behavioral interventions target different cognitive processes to promote mental health, including cognitive fusion and cognitive reappraisal. Determining the relative impact of cognitive fusion and reappraisal on a range of student mental health concerns could help interventions target psychopathological cognitive processes more effectively. Therefore, this study examined the longitudinal impact of cognitive fusion and reappraisal on mental health and functioning outcomes. A series of hierarchical regression models tested the effects of cognitive fusion and reappraisal in a sample of college students (n = 339). When controlling for reappraisal and baseline symptoms, fusion predicted distress, depression, generalized anxiety, social anxiety, hostility, academic distress, and student role problems 1 month later. Reappraisal predicted only student role problems longitudinally when controlling for fusion. These results suggest that cognitive fusion is a stronger predictor than reappraisal for a range of student mental health concerns and may be a particularly important target for improving student mental health.