Miracle-dz/UniFAR-FTUs huggingface dataset:
The Potential of Widespread UAV Cameras in the Identification of Conifers and the Delineation of Their Crowns

With the ever-improving advances in computer vision and Earth observation capabilities, Unmanned Aerial Vehicles (UAVs) allow extensive forest inventory and the description of stand structure indirectly. We performed several flights with different UAVs and popular sensors over two sites with coniferous forests of various ages and flight levels using the custom settings preset by solution suppliers. The data were processed using image-matching techniques, yielding digital surface models, which were further analyzed using the lidR package in R. Consumer-grade RGB cameras were consistently more successful in the identification of individual trees at all of the flight levels (84–77% for Phantom 4), compared to the success of multispectral cameras, which decreased with higher flight levels and smaller crowns (77–54% for RedEdge-M). Regarding the accuracy of the measured crown diameters, RGB cameras yielded satisfactory results (Mean Absolute Error—MAE of 0.79–0.99 m and 0.88–1.16 m for Phantom 4 and Zenmuse X5S, respectively); multispectral cameras overestimated the height, especially in the full-grown forests (MAE = 1.26–1.77 m). We conclude that widely used low-cost RGB cameras yield very satisfactory results for the description of the structural forest information at a 150 m flight altitude. When (multi)spectral information is needed, we recommend reducing the flight level to 100 m in order to acquire sufficient structural forest information. The study contributes to the current knowledge by directly comparing widely used consumer-grade UAV cameras and providing a clear elementary workflow for inexperienced users, thus helping entry-level users with the initial steps and supporting the usability of such data in practice.
Serum-Based Biomarkers in Neurodegeneration and Multiple Sclerosis

Multiple Sclerosis (MS) is a debilitating disease with typical onset between 20 and 40 years of age, so the disability associated with this disease, unfortunately, occurs in the prime of life. At a very early stage of MS, the relapsing-remitting mobility impairment occurs in parallel with a progressive decline in cognition, which is subclinical. This stage of the disease is considered the beginning of progressive MS. Understanding where a patient is along such a subclinical phase could be critical for therapeutic efficacy and enrollment in clinical trials to test drugs targeted at neurodegeneration. Since the disease course is uneven among patients, biomarkers are needed to provide insights into pathogenesis, diagnosis, and prognosis of events that affect neurons during this subclinical phase that shapes neurodegeneration and disability. Thus, subclinical cognitive decline must be better understood. One approach to this problem is to follow known biomarkers of neurodegeneration over time. These biomarkers include Neurofilament, Tau and phosphotau protein, amyloid-peptide-β, Brl2 and Brl2-23, N-Acetylaspartate, and 14-3-3 family proteins. A composite set of these serum-based biomarkers of neurodegeneration might provide a distinct signature in early vs. late subclinical cognitive decline, thus offering additional diagnostic criteria for progressive neurodegeneration and response to treatment. Studies on serum-based biomarkers are described together with selective studies on CSF-based biomarkers and MRI-based biomarkers.
Chronic Pain in Musculoskeletal Diseases: Do You Know Your Enemy?

Musculoskeletal pain is a condition that characterises several diseases and represents a constantly growing issue with enormous socio-economic burdens, highlighting the importance of developing treatment algorithms appropriate to the patient’s needs and effective management strategies. Indeed, the algic condition must be assessed and treated independently of the underlying pathological process since it has an extremely negative impact on the emotional and psychic aspects of the individual, leading to isolation and depression. A full understanding of the pathophysiological mechanisms involved in nociceptive stimulation and central sensitization is an important step in improving approaches to musculoskeletal pain. In this context, the bidirectional relationship between immune cells and neurons involved in nociception could represent a key point in the understanding of these mechanisms. Therefore, we provide an updated overview of the magnitude of the musculoskeletal pain problem, in terms of prevalence and costs, and summarise the role of the most important molecular players involved in the development and maintenance of pain. Finally, based on the pathophysiological mechanisms, we propose a model, called the “musculoskeletal pain cycle”, which could be a useful tool to counteract resignation to the algic condition and provide a starting point for developing a treatment algorithm for the patient with musculoskeletal pain.
Research on Sports Dance Video Recommendation Method Based on Style

At present, sports dance teaching still tends to “demonstration” training. Students have limited time and space for autonomous learning, and their enthusiasm for participation is not high, which leads to a decline in classroom learning efficiency. In view of this, video teaching has become popular in sports dance classrooms, providing a new model for sports dance teaching. Video recommendation is particularly important for the improvement of teaching quality. A sports dance video recommendation method based on style is proposed. The factorization machine model is used to combine features and process high-dimensional sparse features, the deep neural network model is adopted as the value function network of the deep Q-learning algorithm, and the deep Q-learning algorithm is used as the decision function to solve the recommendation accuracy and diversity question. Through the application experiment of sports dance video recommendation, it is resulted that the recommendation accuracy of the proposed model is slightly higher than that of traditional recommendation algorithm and the recommendation diversity is obviously better than that of traditional recommendation algorithm. The advantages and feasibility of the proposed model are verified.
Variations in the Gene Expression Profile in Atherosclerotic Patients with Non-Fatal ACS: A Preliminary Study

The pathophysiology of atherosclerosis and acute coronary syndrome (ACS) is related to interactions between immune cells, endothelium, and blood platelets. An increasing number of reports confirm the link between excessive immune activation and cellular cross-talk with ACS incidence. Our genetic and proteomic analysis was performed on strictly selected atherosclerotic patients with non-fatal ACS without typical risk factors and healthy donors. Results showed changes in the gene expression levels of the various inflammatory factors derived from the peripheral blood cells that drive the over-activation of the immune system. The enhanced activation of the immune system may lead to the overexpression of the pro-inflammatory mediators, which causes self-perpetuating machinery of processes associated with thrombosis. In our preliminary study, we confirmed an altered expression of genes associated with the inflammation and overall interaction of the vascular microenvironment. Furthermore, 5 of 92 analyzed genes, CCL2, CCR2, CSF2, GZMB, and ICOS, were expressed only in patients with ACS. In conclusion, the augmented expression of the pro-inflammatory genes from the peripheral blood cells may be a crucial genetic factor leading to the occurrence of acute inflammation and thus be significant in ACS pathogenesis.
Virtual Events in the Post-COVID-19 Pandemic Era in Medical Profession: A Narrative Review

The Coronavirus Disease 2019 (COVID-19) pandemic wreaked havoc on almost all aspects of life and has undeniably altered the landscape of the world forever. In the medical profession, everything from education to scheduling operations was affected. Educational events such as conferences, Continuing Medical Education (CME) and other events had to be converted into virtual conferences and meetings. However, now that the pandemic is slowly coming to an end and countries and borders have started to reopen, the question now remains- should we go back to our old ways or has this become the new normal? This review discusses the advantages and limitations of conducting virtual events and discusses future trends of holding such events in these uncertain times.
Beta HPV Deregulates Double-Strand Break Repair

Beta human papillomavirus (beta HPV) infections are common in adults. Certain types of beta HPVs are associated with nonmelanoma skin cancer (NMSC) in immunocompromised individuals. However, whether beta HPV infections promote NMSC in the immunocompetent population is unclear. They have been hypothesized to increase genomic instability stemming from ultraviolet light exposure by disrupting DNA damage responses. Implicit in this hypothesis is that the virus encodes one or more proteins that impair DNA repair signaling. Fluorescence-based reporters, next-generation sequencing, and animal models have been used to test this primarily in cells expressing beta HPV E6/E7. Of the two, beta HPV E6 appears to have the greatest ability to increase UV mutagenesis, by attenuating two major double-strand break (DSB) repair pathways, homologous recombination, and non-homologous end-joining. Here, we review this dysregulation of DSB repair and emerging approaches that can be used to further these efforts.
A generalized Wigner–Yanase skew information

We generalize a two-parameter extended Wigner–Yanase skew information given by Z. Zhang [J Math Anal Appl. 2021;497(1): 124851] to any general operator monotone function. We prove several fundamental properties including the joint concavity and an uncertainty relation for the proposed generalized Wigner–Yanase skew information.
A Multi-Attention Tensor Completion Network for Spatiotemporal Traffic Data Imputation

The widespread deployment of road sensors in the Internet of Things (IoT) allows for fine-grained data integration, which is a fundamental demand for data-driven applications. Sensing data with inevitable missing and substantial anomalies are unavoidable, due to unstable network communication, faulty sensors, etc. Recent tensor completion studies have demonstrated the superiority of deep learning in imputation tasks by precisely capturing the intricate spatiotemporal dependencies/correlations. However, ignoring the significance of initial interpolation in these methods results in unstable performance, especially for complicated missing scenarios across large-scale data. Additionally, the existing interpolation methods utilize recursive signal propagation along spatiotemporal dimensions, which produce noise accumulation where the dependencies are uncorrelated. In this study, we design a multiattention tensor completion network (MATCN) for modeling multidimensional representation in the presence of missing entries. MATCN sparsely sampled historical fragments and utilized a gated diffusion convolution layer to generate the initial schemes, which mitigate the exposure bias existing in previous traffic imputation models. In addition, we develop a spatial signal propagation module and a temporal self-attention module as the basic stack block of deep networks, which executes representation aggregation and dynamic dependencies extraction at the spatiotemporal level. This architecture empowers MATCN with progressive completion capacities for complex data missing scenarios. Numerical experiments on four real-world traffic data sets with various missing scenarios demonstrate the superiority of MATCN over multiple state-of-the-art imputation baselines.
Proactive thermal-aware management in cloud datacenters

OF THE DISSERTATION Proactive Thermal-aware Management in Cloud Datacenters by Eun Kyung Lee Dissertation Director: Professor Dario Pompili The complexity of modern datacenters is growing at an alarming rate due to the rising popularity of the cloud-computing paradigm as an effective means to cater to the ever increasing demand for computing and storage. The management of modern datacenters is rapidly exceeding human ability, making autonomic approaches essential. In the meanwhile, the increasing demand for faster computing and high storage capacity has resulted in an increase in energy consumption and heat generation in datacenters. Due to the increased heat generation, cooling requirements have become a critical concern, both in terms of growing operating costs as well as their environmental and societal impacts. (e.g., increase in CO2 emissions, overloading the electric supply grid resulting in power cuts, heavy water usage for cooling systems causing water scarcity) In this thesis, proactive thermal-aware datacenter management solutions, which include thermaland energy-aware resource provisioning, cooling system optimization, and anomaly detection, are proposed to help minimize both the impact on the environment and the Total Cost of Ownership (TCO) of datacenters, making them energy efficient and green. For the proactive thermal-aware solutions, a novel architecture endowed with different abstract components is introduced, which is composed of four
Effect of comprehensive yoga therapy on pulmonary function among various groups with chronic obstructive pulmonary disease

Introduction and Aim: COPD is a disease with involvement of multiple organ system. Our previous study has proved yoga to be useful in improving the lung function. The current study assesses the effect of yoga on lung function with respect to age, duration of illness and severity of disease due to COPD. Materials and Methods: Thirty cases of COPD were recruited in this study. Cases were assessed for lung function, peripheral oxygen saturation and exercise tolerance by 6MWT. Patients were exposed to yoga within their capacity of feasibility, and assessed for above parameters at the end of each month for about three months. A comparison study of various parameters following yoga based on age, duration of illness and severity of disease was done applying independent t test. Variables were expressed as Mean±SD. P<0.05 was considered the threshold for statistical significance. Results: there was an average increase in FEV1% by 5.2 in <65 years, compared to FEVI% by11.4 in >65yrs (p<0.05). FEV1/FVC (%) in both the age groups showed similar response following yoga intervention (p< 0.01 level). We did not get any significant results with duration of illness and severity of disease. Conclusion: Regular yoga practice improves the lung parameters and also the respiratory muscle strength. In this study, COPD patients falling under higher age groups showed more of benefit with yoga. We could not elucidate any response in lung function following yoga based on duration of illness and disease severity. Yoga practice is useful in improving lung parameters in higher age groups. Regular practice of yoga has helped patients with relief from frequent exacerbation of respiratory symptoms.
An Advanced Satisfaction-Based Home Energy Management System using Deep Reinforcement Learning

Home energy management (HEM) systems optimize electricity demand of appliances according to the price-based demand response (DR) programs. Undoubtedly, customer satisfaction is of such importance that if not taken into consideration, it prevents customers from participating in the DR. HEM systems suffer from high nonlinearity due to the variety of smart appliances and different criteria for customer satisfaction. In this paper, an advanced satisfaction-based HEM system using deep reinforcement learning is proposed to hourly schedule the controllable and time-shiftable appliances, including electric vehicle, air conditioner, and lighting system as controllable loads and washing machine, and dishwasher as time-shiftable loads. The proposed framework deploys a Deep Q-Network (DQN) method. Regarding customer dissatisfaction, this paper takes into consideration nonlinear precise functions. The Kano model for EV departure SoC, charging duration and lighting system satisfaction, desired temperature span for air conditioner, and the desirable operation period, waiting time, and consecutive mode of dishwasher and washing machine are taken into account. The proposed HEM system is applied to a smart home, and the results are compared with those of the Q-Learning algorithm. Numerical results prove the effectiveness of the proposed HEM system in reducing electricity cost and customer dissatisfaction, and the superiority of DQN over Q-Learning as well.
The impact of the Fundamental Critical Course on knowledge acquisition in Rwanda

Background Emerging critical care systems have gained little attention in low- and middle-income countries. In sub-Saharan Africa, only 4% of the healthcare workforce is trained in critical care, and mortality rates are unacceptably high in this patient population. Objectives We sought to retrospectively describe the knowledge acquisition and confidence improvement of practitioners who attend the Fundamental Critical Care Support (FCCS) course in Rwanda. Methods We conducted a retrospective study in which we assessed survey data and multiple-choice question data that were collected before and after course delivery. The purpose of these assessments at the time of delivery was to evaluate participants’ perception and acquisition of critical care knowledge. Results Thirty-six interprofessional clinicians completed the training. Performance on the multiple-choice questions improved overall after the course (mean score pre-course of 56.5% to mean score post-course of 65.8%, p-value <0.001) and improved in all content areas with the exception of diagnosis and management of acute coronary syndrome and acute respiratory failure/mechanical ventilation. Both physicians and nurses improved their scores significantly (68.9% to 75.6%, p-value = 0.031 and 52.0% to 63.5%, p-value <0.001, respectively). Self-reported confidence in level of knowledge also increased in all areas. Survey respondents indicated on open-answer questions that they would like the course offerings at least annually, and that further dissemination of the course in Rwanda was warranted. Conclusion Deploying the established FCCS course improved Rwandan healthcare provider knowledge and confidence across most critical care content areas. Therefore, this course represents a good first step in bridging the gaps noted in emerging critical care systems. Contributions of the study Critical care education in sub-Saharan Africa is limited and few staff have formal training. The aim of the study was to determine whether a focused course delivered in Rwanda on critical care management improved knowledge in key areas. Our retrospective study on results from a multiple choice question test and survey indicate that short courses may improve knowledge of critical care management.
Metaphor and movement: exploring the unspoken with a group of frontline workers

Abstract This study investigates metaphoric expression within the scope of dance movement therapy (DMT), cognitive linguistics and embodiment theory. It explores to what extent movement stimulates and enhances the metaphorical use of language. A workshop with frontline workers of a social integration programme for unaccompanied refugee minors in Greece was organised, following methodological procedures that intertwine writing and moving. A process of writing-moving-writing helped to elicit the recollection, reframing and clarification of prevalent thoughts and feelings about a significant event in the work with the refugees. The written data was scrutinised along the lines of ethnography inductive thematic analysis and summed up with the outcomes of a follow up focus group. The findings suggest that metaphors provide a valuable connection between mind and body, offering thus a valuable implication for clinical work in DMT and, perhaps, other embodied psychotherapies.
Transcriptomic and Metabolomic Analyses of the Effects of Exogenous Trehalose on Heat Tolerance in Wheat

Trehalose can improve the tolerance of plants to various types of environmental stress. Nonetheless, information respecting the molecular networks of wheat seedlings to exogenous trehalose under heat stress is limited. Here, two wheat varieties pretreated with exogenous trehalose were selected to explore the molecular mechanism by which trehalose improves the heat tolerance of wheat (Triticum aestivum L.). The results indicated that exogenous trehalose improved the physiological state of wheat seedlings under heat stress. Through RNA sequencing and metabolomics analysis, the genes and metabolites specifically expressed in trehalose pretreatment were identified. After heat stress, there were 18,352 differentially expressed genes (DEGs) in the control and trehalose-treated (H_vs_TreH) groups of Yangmai 18 and 9045 DEGs in Yannong 19. Functional annotation and enrichment analyses showed that the DEGs in the two wheat varieties were mainly involved in carbohydrate metabolism and biosynthesis of secondary metabolites. Through a liquid chromatography–mass spectrometry platform, 183 differential metabolites in H_vs_TreH groups of Yangmai 18 and 77 differential metabolites in Yannong 19 were identified. Compared with the control group, many protective metabolites, such as amino acids, purines, phenylpropanoids and flavonoids, showed significant differences under heat stress. The results indicated that exogenous trehalose protected the wheat biomembrane system, enhanced carbohydrate metabolism and signal transduction, strengthened the activity of the tricarboxylic acid cycle (TCA cycle), regulated purine metabolism, gene expression and metabolite accumulation in the phenylpropanoid biosynthesis and flavonoid biosynthesis pathways, thus improving the heat tolerance of wheat.
RF-UAVNet: High-Performance Convolutional Network for RF-based Drone Surveillance Systems

In recent years, the increasing popularity of unmanned aerial vehicles (UAVs) has arisen from the emergence of cutting-edge technologies deployed in small and low-cost devices. With the great capability of friendly uses and wide applications for multiple purposes, amateur drones can be piloted to effortlessly access any geographical area. This poses some difficulties in monitoring and managing drones that may invade private or limited-access areas. In this paper, we propose a radio-frequency (RF)-based surveillance solution to effectively detect and classify drones, and recognize operations by leveraging a high-performance convolutional neural network. The proposed network, namely RF-UAVNet, is specified with grouped one-dimensional convolution to significantly reduce the network size and computational cost. Besides, a novel structure of multi-level skip-connection, for the preservation of gradient flow, incorporating multi-level pooling, for the collection of informative deep features, is proposed to achieve high accuracy via learning efficiency improvement. In the experiments, RF-UAVNet yields the accuracy of 99.85% for drone detection, 98.53% for drone classification, and 95.33% for operation mode recognition, numbers which outperform the current state-of-the-art deep learning-based methods on DroneRF, a publicly available dataset for RF-based drone surveillance systems.