Miracle-dz/UniFAR-FTUs huggingface dataset:
Benefits of Exercise Training for Children and Adolescents Undergoing Cancer Treatment: Results From the Randomized Controlled MUCKI Trial

Objective: In cancer patients, the impairment in muscle function is a frequently observed phenomenon. However, comprehensive evaluation of the effect of exercise training on muscle function in childhood cancer patients (CCPs) is sparse and therefore investigated in the MUCKI trial. Study Design: In the randomized controlled MUCKI trial, CCPs during intensive cancer treatment and aged 4–18 years were recruited. Eligible patients were enrolled soon after diagnosis as long as they were physically and mentally able to participate in exercise testing and training. Patients of the exercise group (n = 16) participated in average 2.7 ± 1.2 times per week in a combined resistance and endurance training with moderate exercise intensity, for a time period of 8.0 ± 2.1 weeks, while patients of the control group (n = 17) received usual care. Leg strength was evaluated as the primary endpoint. Secondary endpoints were 6-min walk performance, arm strength, body composition, fatigue, and health-related quality of life. Results: Comparisons of pre- and post-intervention results were evaluated by baseline and stratification criteria adjusted analysis and showed positive effects for the exercise group regarding leg strength [F(1, 20) = 5.733; p = 0.027*; ηp2 = 0.223], walking performance [F(1, 25) = 4.270; p = 0.049*; ηp2 = 0.146], fatigue [F(1, 13) = 8.353; p = 0.013*; ηp2 = 0.391], self-esteem [F(1, 6) = 6.823; p = 0.040*; ηp2 = 0.532], and self-reported strength and endurance capacity [F(1, 6) = 6.273; p = 0.046*; ηp2 = 0.511]. No significant differences were found for the other parameters. Conclusion: Within one of the first randomized controlled trials, the present study provides evidence for a positive effect of combined training in CCPs during intensive cancer treatment. Further research is needed to confirm these results and to evaluate their clinical impact. Clinical Trial Registration Number: NCT02612025.
The capacity to escape from predators in Passer domesticus: an experimental study

An experiment was performed simulating the effect of an aerial predator chasing house sparrows (Passer domesticus). For this, 82 sparrows were captured in an aviary using a sweep net and assigned a capture order. Morphological measurements were also taken. It was assumed that the capture order of the sparrows correlated positively with their flight ability. Older individuals and those with longer wings were captured later, while birds with longer tails were captured in the first trials. This suggests that predation could be a selective pressure on longer wings and shorter tails. The effect of age is probably due to the greater flight experience acquired by older sparrows. A relationship appeared between male badge size and flight ability, males with larger badge sizes having higher capture orders. Capture order did not correlate with sex, mass or tarsus length.
Proteomics-based discovery of biomarkers and therapeutic targets in neurodegenerative diseases: perspective of microglia and neuroinflammation

Proteomics is a field of study that characterises a complete profile of proteins present in a given cell, tissue or biological system. Recent advances in proteomics research made possible the identification of novel biomarkers and therapeutic targets in neurodegenerative diseases. The field of neurodegenerative diseases is particularly well suited for proteomic analysis, given the multifactorial nature of the diseases and complexity of the nervous system. As increasing evidence indicates that microglia-associated chronic neuroinflammation plays an important role in the pathophysiology of neurodegenerative diseases, microglial activation may be a potential drug target in CNS disorders. On the basis of both the neuroprotective and neurotoxic role of microglia, the proteomic analysis of microglial signalling and neuroinflammation may thus be used for the identification and validation of novel drug targets as well as for the discovery of useful biomarkers of disease diagnosis and progression in the CNS.
Implementable self-organized flocking algorithm for UAVs based on the emergence of virtual roads

Plenty of research has been published on algorithms for the coordinated operation of UAVs. However, little is known about these algorithms that go beyond the simulation domain. In the paper, we present a self-organized UAV flocking algorithm together with its practical validation with three real UAVs. We show how non-ideal aspects of real UAVs, as inaccurate sensor data, clocked sensor output instead of continuous data, as well as imperfect and delayed communication, can be handled. We provide a framework that allows us to develop these systems based on validated simulation models and show how the same framework can be directly implemented on real UAVs, closing the gap between abstract modeling and physical implementation on UAVs.
In-situ Scanning Tunneling Microscopy of Cobalt Phthalocyanine Catalyzed CO2 Reduction Reaction.

We report a molecular investigation of cobalt phthalocyanine (CoPc) catalyzed CO 2 reduction reaction by electrochemical scanning tunneling microscopy (ECSTM). An ordered adlayer of CoPc is prepared on Au(111). Approximately 14% of the adsorbed species appear with high contrast in CO 2 purged electrolyte environment. The ECSTM experiments indicate the proportion of high contrast species correlated with the reduction of Co II Pc (~-0.2 V). The high contrast species is ascribed to CoPc-CO 2 complex, which is further confirmed by theoretical simulation. T he sharp contrast change from CoPc-CO 2 to CoPc is revealed by in situ ECSTM in the reaction. Potential step experiments provide dynamic information for the initial stage of the reaction, which including the reduction of CoPc and the binding of CO 2 , and latter is the rate-limiting step. The rate constant of the formation and dissociation of CoPc-CO 2 is estimated on the basis of the in situ ECSTM experiment.
Focus and social contagion of environmental organization advocacy on Twitter

Agriculture, overexploitation, and urbanization remain the major threats to biodiversity in the Anthropocene. The attention these threats garner among leading environmental nongovernmental organizations (eNGOs) and the wider public is critical in fostering the political will necessary to reverse biodiversity declines worldwide. I analyzed the advocacy of leading eNGOs on Twitter by scraping account timelines, screening content for advocacy relating to biodiversity threats and, for prevalent threats, further screening content for positive and negative emotional language with a sentiment lexicon. Twitter advocacy was dominated by the major threats of climate change and overexploitation and the minor threat of plastic pollution. The major threats of agriculture, urbanization, invasions, and pollution were rarely addressed. Content relating to overexploitation and plastic pollution was more socially contagious than other content. Increasing emotional negativity further increased social contagion, whereas increasing emotional positivity did not. Scientists, policy makers, and eNGOs should consider how narrowly focused advocacy on platforms like Twitter will contribute to effective global biodiversity conservation.
Early Diagnosis and Management of Nitrogen Deficiency in Plants Utilizing Raman Spectroscopy

Nutrient deficiency alters growth and development of crop plants and compromises yield. Real-time non-invasive monitoring of the nutritional status of crops would allow timely applications of fertilizers to optimize for growth and yield at different times of the plant’s life cycle. Here, we used Raman spectroscopy to characterize Arabidopsis and two varieties of leafy vegetable crops under nitrogen sufficient and deficient conditions. We showed that the 1046 cm–1 Raman peak serves as a specific signature of nitrogen status in planta, which can be used for early diagnosis of nitrogen deficiency in plants before onset of any visible symptoms. Our research can be applied toward crop management for sustainable and precision agriculture.
Bacterial Communities in Stream Biofilms in a Degrading Grassland Watershed on the Qinghai–Tibet Plateau

Grassland is among the largest terrestrial biomes and is experiencing serious degradation, especially on the Qinghai–Tibet Plateau (QTP). However, the influences of grassland degradation on microbial communities in stream biofilms are largely unknown. Using 16S rRNA gene sequencing, we investigated the bacterial communities in stream biofilms in sub-basins with different grassland status in the Qinghai Lake watershed. Grassland status in the sub-basins was quantified using the normalized difference vegetation index (NDVI). Proteobacteria, Bacteroidetes, Cyanobacteria, and Verrucomicrobia were the dominant bacterial phyla. OTUs, 7,050, were detected in total, within which 19 were abundant taxa, and 6,922 were rare taxa. Chao 1, the number of observed OTUs, and phylogenetic diversity had positive correlations with carbon (C), nitrogen (N), and/or phosphorus (P) in biofilms per se. The variation of bacterial communities in stream biofilms was closely associated with the rate of change in NDVI, pH, conductivity, as well as C, N, P, contents and C:N ratio of the biofilms. Abundant subcommunities were more influenced by environmental variables relative to the whole community and to rare subcommunities. These results suggest that the history of grassland degradation (indicated as the rate of change in NDVI) influences bacterial communities in stream biofilms. Moreover, the bacterial community network showed high modularity with five major modules (>50 nodes) that responded differently to environmental variables. According to the module structure, only one module connector and 12 module hubs were identified, suggesting high fragmentation of the network and considerable independence of the modules. Most of the keystone taxa were rare taxa, consistent with fragmentation of the network and with adverse consequences for bacterial community integrity and function in the biofilms. By documenting the properties of bacterial communities in stream biofilms in a degrading grassland watershed, our study adds to our knowledge of the potential influences of grassland degradation on aquatic ecosystems.
Synthetic Forecasting Method for COVID-19: Applications to UK and Turkey

Synthetic control method (SCM) is generally used for producing counterfactual scenarios in policy evaluations. In the light and urgency of the recent developments regarding the COVID-19 virus and the related human and economic losses, I propose a synthetic forecasting method (SFM) in order to estimate the future path of virus-related outcomes in a country. The method takes advantage of the fact that the virus entered into different countries in slightly different points in time. An application to the cumulative COVID-19-related death toll (as of 13 April 2020) shows that the total numbers may increase by 50% in a week in UK and may double in two weeks in Turkey.
Motion opponency examined throughout visual cortex with multivariate pattern analysis of fMRI data

This study explores how the human brain solves the challenge of flicker noise in motion processing. Despite providing no useful directional motion information, flicker is common in the visual environment and exhibits omnidirectional motion energy which is processed by low-level motion detectors. Models of motion processing propose a mechanism called motion opponency that reduces the processing of flicker noise. Motion opponency involves the pooling of local motion signals to calculate an overall motion direction. A neural correlate of motion opponency has been observed in human area MT+/V5 using fMRI, whereby stimuli with perfectly balanced motion energy constructed from dots moving in counter-phase elicit a weaker BOLD response than non-balanced (in-phase) motion stimuli. Building on this previous work, we used multivariate pattern analysis to examine whether the patterns of brain activation elicited by motion opponent stimuli resemble the activation elicited by flicker noise across the human visual cortex. Robust multivariate signatures of opponency were observed in V5 and in V3A. Our results support the notion that V5 is centrally involved in motion opponency and in the reduction of flicker noise during visual processing. Furthermore, these results demonstrate the utility of powerful multivariate analysis methods in revealing the role of additional visual areas, such as V3A, in opponency and in motion processing more generally. Highlights Opponency is demonstrated in multivariate and univariate analysis of V5 data. Multivariate fMRI also implicates V3A in motion opponency. Multivariate analyses are useful for examining opponency throughout visual cortex.
E‐cigarettes and respiratory health: the latest evidence

The E‐cigarette market continues to expand at an alarming rate with thousands of flavours available for purchase and continuously evolving devices. Now that it is a multi‐billion dollar industry and one without stringent regulation, there is rising concern over the safety of vaping products. Since June 2019, over 2800 cases of E‐cigarette‐associated acute lung toxicity have been reported in the USA, over 60 of which resulted in death. Many argue that E‐cigarettes offer a safer alternative to smoking, but we are evidently far from fully understanding the potential hazards that they pose to respiratory health. Although the risk of an outbreak in the UK has been considered low due to tighter E‐cigarette regulations, we cannot fully eliminate the possibility of similar events occurring in the future. With evidence frequently emerging of the harmful effects of E‐cigarettes to pulmonary health, there is an urgent need to define the long‐term implications of vaping. Studies show that E‐cigarette exposure can disrupt pulmonary homeostasis, with reports of gas exchange disturbance, reduced lung function, increased airway inflammation and oxidative stress, downregulation of immunity, and increased risk of respiratory infection. In this review, the latest research on the effect of E‐cigarette use on respiratory health will be presented.
TLDCNN: A Triplet Low Dimensional Convolutional Neural Networks For High-Resolution Remote Sensing Image Retrieval

Remote Sensing Image Retrieval (RSIR) is quite a challenging topic due to the complexity of High-Resolution Remote Sensing (HRRS) imagery. Recently, most researches are focusing on feature extraction using learned features instead of traditional hand-crafted features for RSIR. However, on the one hand, learning discriminative feature representations is not sufficient due to the limited ability of the standard metric distances for matching similar images. On the other hand, the high dimensionality of the learned features makes the retrieval process not efficient for large-scale collections of HRRS images. To address these issues, we propose a Triplet Low-Dimensional Convolutional Neural Network (TLDCNN) which is based on the LDCNN model combined with triplet loss function. We use end-to-end training process to automatically learn fine-grained image similarity directly from input triplet images. The experiment results demonstrated the effectiveness of the proposed network on three publicly available HRRS image datasets for HRRSIR.
Mapping Land Subsidence in Urban Areas Using Esa’s G-POD and the P-SBAS Insar Technique: Examples in Asia, South America And North Africa

Ground stability and surface deformation in the urban areas of Zhengzhou (China), Osaka and Kyoto (Japan), Bogotá (Colombia) and Tunis (Tunisia) are investigated using satellite Interferometric Synthetic Aperture Radar (InSAR). The Parallel Small BAseline Subset (P-SBAS) workflow integrated within ESA’s Grid-Processing On Demand (G-POD) platform is used to process ENVISAT data. Distinctive land subsidence patterns are found in: suburban zones and new urban developments in Zhengzhou (-40 mm/year LOS rates), along the coastline of the Osaka Bay, onto the Yodo River alluvium (-28 mm/year), in the city of Bogotá and the Sabana de Bogotá basin (-44 mm/year), and the low-lying area of Tunis Marine and the shores of Tunis Lake (-20 mm/year). Most of these patterns are due to groundwater pumping for domestic, industrial and agricultural use, or sediment compaction after coastal reclamation and drainage for new urbanization.
Smart Health Predicting SystemUsing K-Means Algorithm

This main goal of this Appis mainly focus on the predicting the disease based on the given symptoms by the user through registration. The patient can able to get the doctor details and address of the hospital immediately by giving the disease and particular location where he requires. He gets the appointment from various doctors for respective treatments. The user can able to know the information regarding the acceptance and rejection of their appointment.If the appointment is acceptedsimultaneously, the bills can be sent to the respective user and he can pay the amount through this app.
Topic Detection and Tracking Based on Event Ontology

In recent years, Topic Detection and Tracking (TDT) has served as a core technology for searching, organizing and structuring news oriented textual materials from a variety of internet news and social media. The biggest challenges of TDT are the sparsity and complexity of data, organization of topic granularity, unexpectedness of emergency topics, and the unpredictability of topics evolution. This paper proposes a new TDT method based on event ontology for hierarchical topic detection and tracking topic evolution, named TDTEO. As domain-oriented event knowledge base, the event ontology provides event classes hierarchy based on domain common sense, as well a set of scenario models that describe the occurrence and evolution of different types of emergency events. The proposed method solves the problem that new emerging events are easy to be missed in the process of topic detection, and solves the problem that the topic model is easy to result in semantics drift due to the dynamic evolution of topic, and it can effectively improve the accuracy of topic detection and tracking. Experiments show our models achieve satisfactory performance of topic detection with a maximum macro-F1 value of 85.25%, and the ${({C_{Det}})_{Norm}}$ of topic tracking in the datasets is as low as 0.1028. Experimental results show that hierachical topic model can effectively detect the topics and tracking model based on event scenario can reflect the trend of topic evolution.
Prevalence and Diversity of Avian Influenza Virus Hemagglutinin Sero-Subtypes in Poultry and Wild Birds in Bangladesh

Highly pathogenic avian influenza H5 viruses have pandemic potential, cause significant economic losses and are of veterinary and public health concerns. This study aimed to investigate the distribution and diversity of hemagglutinin (HA) subtypes of avian influenza virus (AIV) in poultry and wild birds in Bangladesh. We conducted an avian influenza sero-surveillance in wild and domestic birds in wetlands of Chattogram and Sylhet in the winter seasons 2012–2014. We tested serum samples using a competitive enzyme-linked immunosorbent assay (c-ELISA), and randomly selected positive serum samples (170 of 942) were tested using hemagglutination inhibition (HI) to detect antibodies against the 16 different HA sero-subtypes. All AIV sero–subtypes except H7, H11, H14 and H15 were identified in the present study, with H5 and H9 dominating over other subtypes, regardless of the bird species. The diversity of HA sero-subtypes within groups ranged from 3 (in household chickens) to 10 (in migratory birds). The prevalence of the H5 sero-subtype was 76.3% (29/38) in nomadic ducks, 71.4% (5/7) in household chicken, 66.7% (24/36) in resident wild birds, 65.9% (27/41) in migratory birds and 61.7% (29/47) in household ducks. Moreover, the H9 sero-subtype was common in migratory birds (56%; 23/41), followed by 38.3% (18/47) in household ducks, 36.8% (14/38) in nomadic ducks, 30.6% (11/66) in resident wild birds and 28.5% (2/7) in household chickens. H1, H4 and H6 sero-subtypes were the most common sero-subtypes (80%; 8/10, 70%; 7/10 and 70%; 7/10, respectively) in migratory birds in 2012, H9 in resident wild birds (83.3%; 5/6) and H2 in nomadic ducks (73.9%; 17/23) in 2013, and the H5 sero-subtype in all types of birds (50% to 100%) in 2014. The present study demonstrates that a high diversity of HA subtypes circulated in diverse bird species in Bangladesh, and this broad range of AIV hosts may increase the probability of AIVs’ reassortment and may enhance the emergence of novel AIV strains. A continued surveillance for AIV at targeted domestic–wild bird interfaces is recommended to understand the ecology and evolution of AIVs.
Issues and Concepts of Graph Database and a Comparative Analysis on list of Graph Database tools

The work is review in nature and focuses on basic concepts and example on Graph Database with a special focus on list of standard computerized tools available for handling the queries using graph database structure. The implementation benefits of each tool and a comparative analysis on various functionalities has been presented in this work. This work also elaborates on popular Graph Databases tool that includes Allegro Graph, ArangoDB, OrientDB, Infinite Graph ,Neo4j, Titan, FlockDB, Bitsy, StarDog, MongoDB and investigate their acceptance for solving day to day problems.
Robust Control for Nonlinear Delta Parallel Robot With Uncertainty: An Online Estimation Approach

A series of fractional robust trajectory tracking controls are proposed for the Delta parallel robot with uncertainty. For the high speed and heavy load, the Delta parallel robot could not ignore the influences of the high nonlinearity (by the dynamics of the multiple closed-loops mechanism and the nonlinear joints friction) and the various kinds of uncertainties (i.e., the unknown dynamic parameters and external disturbances caused by the residual vibration). By formulating the motion equation of the Delta parallel robot, the nonlinearity is settled by a norm model based control design. The uncertainty considered in the paper is time-varying but unknown. An online estimation with an exponential type leakage term and dead-zone is construct to investigate the realtime information of the uncertainty. In virtue of the estimated information, two fractional robust trajectory tracking controls with the joints friction compensation are designed. Under the proposed controls, the system performance of the Delta parallel robot can be deterministically guaranteed (which includes uniform boundedness and uniform ultimate boundedness).