Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional Constipation and the Gut Microbiome in Children: Preclinical and Clinical Evidence

Functional constipation is a common condition in childhood with significant impact on patients' quality of life and on health care resources. Functional constipation is characterized by decreased bowel movements and/or hard stools, which cause significant distress for children and their caregivers. While the term “functional” may imply the absence of organic causes with a focus on behavioral aspects, 40% of children continue to have symptoms beyond conventional management with one in four children continuing to experience constipation into adulthood. The refractory and chronic nature of constipation highlights the importance of considering a range of pathophysiological mechanisms, including the potential role of the gut microbiome. In this review, we provide an overview of preclinical and clinical studies that focus on the potential mechanisms through which the gut microbiome might contribute to the clinical presentation of functional constipation in pediatrics.
Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey

Here we summarize recent progress in machine learning model for diagnosis of Autism Spectrum Disorder (ASD) and Attention-deficit/Hyperactivity Disorder (ADHD). We outline and describe the machine-learning, especially deep-learning, techniques that are suitable for addressing research questions in this domain, pitfalls of the available methods, as well as future directions for the field. We envision a future where the diagnosis of ASD, ADHD, and other mental disorders is accomplished, and quantified using imaging techniques, such as MRI, and machine-learning models.
Genetic Structure and Evolutionary History of Rhinopithecus roxellana in Qinling Mountains, Central China

The Qinling mountainous region is one of the world's biodiversity hotspots and provides refuges for many endangered endemic animals. The golden snub-nosed monkeys (Rhinopithecus roxellana) are considered as a flagship species in this area. Here, we depicted the genetic structure and evolutionary history via microsatellite markers and combination with the ecological niche models (ENMs) to elucidate the intraspecific divergent and the impacts of the population demography on our focal species. Our results revealed three distinct subpopulations of R. roxellana and also uncovered asymmetric historical and symmetric contemporary gene flow that existed. Our evolutionary dynamics analyses based on diyabc suggested that the intraspecific divergence accompanied with effective population sizes changes. The ENM result implied that the distribution range of this species experienced expansion during the last glacial maximum (LGM). Our results highlighted that geological factors could contribute to the high genetic differentiation within the R. roxellana in the Qinling Mountains. We also provided a new insight into conservation management plans with endangered species in this region.
Poplar Autophagy Receptor NBR1 Enhances Salt Stress Tolerance by Regulating Selective Autophagy and Antioxidant System

Salt stress is an adverse environmental factor for plant growth and development. Under salt stress, plants can activate the selective autophagy pathway to alleviate stress. However, the regulatory mechanism of selective autophagy in response to salt stress remains largely unclear. Here, we report that the selective autophagy receptor PagNBR1 (neighbor of BRCA1) is induced by salt stress in Populus. Overexpression of PagNBR1 in poplar enhanced salt stress tolerance. Compared with wild type (WT) plants, the transgenic lines exhibited higher antioxidant enzyme activity, less reactive oxygen species (ROS), and higher net photosynthesis rates under salt stress. Furthermore, co-localization and yeast two-hybrid analysis revealed that PagNBR1 was localized in the autophagosome and could interact with ATG8 (autophagy-related gene). PagNBR1 transgenic poplars formed more autophagosomes and exhibited higher expression of ATG8, resulting in less accumulation of insoluble protein and insoluble ubiquitinated protein compared to WT under salt stress. The accumulation of insoluble protein and insoluble ubiquitinated protein was similar under the treatment of ConA in WT and transgenic lines. In summary, our results imply that PagNBR1 is an important selective autophagy receptor in poplar and confers salt tolerance by accelerating antioxidant system activity and autophagy activity. Moreover, the NBR1 gene is an important potential molecular target for improving stress resistance in trees.
Effects of organic matter and calcium on soil structural stability

The cationic bridging effect of the calcium ion (Ca2+) and the flocculating ability of clay and organic matter are crucial in the formation and stability of soil aggregates. They are therefore likely to influence the soil’s saturated hydraulic conductivity (Ks). We tested the individual effects of these factors on aggregate stability and related hydraulic properties, and studied the influence of clay mineralogy also. Samples from the surface (0–10 cm) of three contrasting soils in Trinidad were used. The soils were treated with three levels of Ca2+ and three levels of organic matter in a 3 × 3 × 3 factorial design and incubated for 14 days. Both aggregate stability and saturated hydraulic conductivity were influenced by all factor combinations. Interactions between soil type and Ca2+ revealed the importance of polyvalent cations in aggregate stability of soils with low activity minerals. The influence of organic matter varied with quantity; the more there was, the more stable the soil became, particularly in the soil containing little clay. Clay dispersion and slaking of expanding minerals occurred even with large additions of Ca2+ and organic matter, emphasizing the overall influence of mineralogy in determining the response of soils to stability treatments.
Perspectives of ESCAPE-Pain Programme for Older People With Knee Osteoarthritis in the Community Setting

Background: Functional limitationscommonly affect patients with knee osteoarthritis (OA) which reduces quality of life. The Enabling Self-management and Coping with Arthritic Pain using Exercise (ESCAPE-pain) is an evidence-based programme identified to be suitable for adaptation for the Malaysian health care system. It is important to understand the acceptance from a sociocultural context of the ESCAPE-pain programme from the perspectives of patients with knee OA and healthcare professionals. This qualitative study aims to explore the perspectives of stakeholders to inform the adaptation of the ESCAPE-pain programme into the Malaysian health care system. Method: Semi-structured interviews using interview guides were conducted with 18 patients with knee OA and 14 healthcare professionals including nurses, physiotherapists, occupational therapists, medical doctors, and orthopedic surgeons. The data were transcribed and analyzed using framework analysis. Results: The findings show that patients and healthcare professionals positively accept the programme into their daily living activities and recommend some modifications related to the Malaysian context. This study also highlights strategies to adopt when providing ESCAPE-pain to patients with knee OA. Conclusion: The findings reveal how sociocultural considerations could facilitate uptake and engagement with the ESCAPE-pain programme for home exercise among patients with knee osteoarthritis. These findings may benefit t patients with knee OA in the Malaysian healthcare system, although future research is recommended.
A Spatial-Channel Collaborative Attention Network for Enhancement of Multiresolution Classification

Recently, with the popularity of space-borne earth satellites, the resolution of high-resolution panchromatic (PAN) and multispectral (MS) remote sensing images is also increasing year by year, multiresolution remote sensing classification has become a research hotspot. In this paper, from the perspective of deep learning, we design a dual-branch interactive spatial-channel collaborative attention enhancement network (SCCA-net) for multiresolution classification. It aims to combine sample enhancement and feature enhancement to improve classification accuracy. In the part of sample enhancement, we propose an adaptive neighbourhood transfer sampling strategy (ANTSS). Different from the traditional pixel-centric sampling strategy with orthogonal sampling angle, our algorithm allows each patch to adaptively transfer the neighbourhood range by finding the homogeneous region of the pixel to be classified. And it also adaptively adjust the sampling angle according to the texture distribution of the homogeneous region to capture neighbourhood information that is more conducive for classification. Moreover, in the part of feature enhancement part, we design a local spatial attention module (LSA-module) for PAN data to highlight the spatial resolution advantages and a global channel attention module (GCA-module) for MS data to improve the multi-channel representation. It not only highlights the spatial resolution advantage of PAN data and the multi-channel advantage of MS data, but also improves the difference between features through the interaction between the two modules. Quantitative and qualitative experimental results verify the robustness and effectiveness of the method.
Semi-Automatic Method for Early Detection of Xylella fastidiosa in Olive Trees Using UAV Multispectral Imagery and Geostatistical-Discriminant Analysis

Xylella fastidiosa subsp. pauca (Xfp) is one of the most dangerous plant pathogens in the world. Identified in 2013 in olive trees in south–eastern Italy, it is spreading to the Mediterranean countries. The bacterium is transmitted by insects that feed on sap, and causes rapid wilting in olive trees. The paper explores the use of Unmanned Aerial Vehicle (UAV) in combination with a multispectral radiometer for early detection of infection. The study was carried out in three olive groves in the Apulia region (Italy) and involved four drone flights from 2017 to 2019. To classify Xfp severity level in olive trees at an early stage, a combined method of geostatistics and discriminant analysis was implemented. The results of cross-validation for the non-parametric classification method were of overall accuracy = 0.69, mean error rate = 0.31, and for the early detection class of accuracy 0.77 and misclassification probability 0.23. The results are promising and encourage the application of UAV technology for the early detection of Xfp infection.
Microcutting Redox Profile and Anatomy in Eucalyptus spp. With Distinct Adventitious Rooting Competence

Adventitious root (AR) development takes place in an intricate cellular environment. Reactive oxygen species (ROS) and antioxidant defenses, triggered by wounding in cuttings, can modulate this process. A comparative assessment of biochemical and anatomical parameters at critical rooting stages in hard- (Eucalyptus globulus Labill.) and easy- (Eucalyptus grandis W.Hill ex Maiden) to-root species was carried out. Microcuttings from seedlings were inoculated in auxin-free AR induction medium and, after 96 h, transferred to AR formation medium for a period of 24 h. Samples were collected upon excision (Texc) and at the 5th day post excision (Tform). Delayed xylem development, with less lignification, was recorded in E. globulus, when compared to E. grandis, suggesting lower activity of the cambium layer, an important site for AR development. Superoxide was more densely present around the vascular cylinder at both sampled times, and in greater quantity in E. globulus than E. grandis, declining with time in the former. Hydrogen peroxide was localized primarily along cell walls, more intensely in the primary xylem and phloem, and increased significantly at Tform in E. globulus. Ascorbate peroxidase (APX), superoxide dismutase (SOD), and catalase (CAT) activities were generally higher in E. grandis and varied as a function of time in E. globulus. Soluble guaiacol peroxidase (GPRX) activity increased from Texc to Tform in both species, whereas cell wall-bound GPRX activity increased with time in E. grandis, surpassing E. globulus. Flavonoid content increased with time in E. grandis and was higher than E. globulus at Tform. Principal component analysis showed that species- and time-derived differences contributed to almost 80% of the variance. Overall, data indicate that E. grandis shows higher cambium activity and tighter modulation of redox conditions than E. globulus. These features may influence ROS-based signaling and phytohormone homeostasis of cuttings, thereby impacting on AR development. Besides being players in the realm of AR developmental differences, the specific features herein identified could become potential tools for early clone selection and AR modulation aiming at improved clonal propagation of this forest crop.
Current Evidence on Traditional Chinese Exercises for Quality of Life in Patients With Essential Hypertension: A Systematic Review and Meta-Analysis

Essential hypertension is one of the most common chronic diseases seen in primary human health care that could lead to various health problems and reduce the quality of life (QOL). This study was performed to evaluate the effects of traditional Chinese exercises (TCE) on QOL in patients with essential hypertension. Three English databases and one Chinese database were searched for randomized controlled trials (RCTs) until August 2020. A total of 13 RCTs with 1,361 hypertensive patients met the inclusion criteria, 10 trials employed Tai Chi and 3 trials employed Qigong, including Dongeui Qinggong, Yijinjing, and Wuqinxi. Despite a large heterogeneity within studies, it is demonstrated that TCE may be an effective therapy to improve the QOL of hypertensive patients. More specific, compared with no intervention, the meta-analysis presented that Tai Chi significantly improved both the physical and mental component of the 36-item short-form health survey (SF-36) QOL scale, and it was found that the simplified 24-form Tai Chi with a frequency of 30–50 min per session at five to eight sessions per week was the most commonly used setting. The pooling also revealed that Tai Chi and Qigong had some significantly positive effects on QOL based on other scales. However, more rigorously designed RCTs are warranted for further verification. This systematic review and meta-analysis of RCTs demonstrated that Tai Chi may be an effective therapy to improve the QOL of patients with essential hypertension. All these findings provide helpful information for hypertensive patients and medical personnel.
Rating Iron Deficiency in Soybean Using Image Processing and Decision-Tree Based Models

The most efficient way of soybean (Glycine max (L.) Merrill) iron deficiency chlorosis (IDC) management is to select a tolerant cultivar suitable for the specific growing condition. These cultivars are selected by field experts based on IDC visual ratings. However, this visual rating method is laborious, expensive, time-consuming, subjective, and impractical on larger scales. Therefore, a modern digital image-based method using tree-based machine learning classifier models for rating soybean IDC at plot-scale was developed. Data were collected from soybean IDC cultivar trial plots. Images were processed with MATLAB and corrected for light intensity by using a standard color board in the image. The three machine learning models used in this study were decision tree (DT), random forest (RF), and adaptive boosting (AdaBoost). Calculated indices from images, such as dark green color index (DGCI), canopy size, and pixel counts into DGCI ranges and IDC visual scoring were used as input and target variables to train these models. Metrics such as precision, recall, and f1-score were used to assess the performance of the classifier models. Among all three models, AdaBoost had the best performance (average f1-score = 0.75) followed by RF and DT the least. Therefore, a ready-to-use methodology of image processing with AdaBoost model for soybean IDC rating was recommended. The developed method can be easily adapted to smartphone applications or scaled-up using images from aerial platforms.
Enhancing the Performance of Manifold Ranking in Image Retrieval using Combined Rank on Low-level Features and Embedded Vectors

The eﬀective manifold ranking (EMR), an extended graph-based algorithm, is used quite successfully in content-based image retrieval (CBIR) for large image databases where images are represented by multiple low-level features to describe the color, texture and shape. Moreover, image features and appropriate distance metric are the important factors for researchers to improve the performance of image retrieval. The success of Deep Metric Learning (DML), is based on deep architectures to learn a suitable metric from data and obtain embedded features that are more discriminative. The advantage of low-level features is the ability to quickly recognize diﬀerences in color, texture and shape without learning, while embedded features provide a higher discrimination but depend on the pre-trained DML model and a given image object detection algorithm. To increase the performance of the manifold ranking in image retrieval, in this paper we propose the use of a combined rank to take advantage of both low-level features and embedded vectors which represent high-level features derived from the pre-trained DML model. This new rank focuses on embedded vectors when they are not degraded and uses the rank values on low-level features instead if embedded vectors do not provide good retrieval results. Experiments have been conducted to demonstrate the eﬀectiveness of the proposed rank when increasing the quality of EMR.
Contrasting behavior of the structural and magnetic properties in Mn- and Fe-doped In2O3 films

We have observed room temperature ferromagnetism in In2O3 thin films doped with either 5 at. % Mn or Fe, prepared by pulsed laser deposition at substrate temperatures ranging from 300 to 600 °C. The dependence of saturation magnetization on grain size was investigated for both types of In2O3 films. It is revealed that, for the Mn-doped films, the magnetization was largest with small grains, indicating the importance of grain boundaries. In contrast, for Fe-doped films, the largest magnetization was observed with large grains.
Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications

Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (~$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R2 = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R2 = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management.
Diverse Roles of Previous Years' Water Conditions in Gross Primary Productivity in China

Gross primary productivity is one of the most important indicators of ecosystem function, which is related to water conditions and shown high interannual variation. Due to the time-lag effect, not only the current water condition but also the previous water conditions (e.g., one year before) impact the gross primary productivity (GPP). Revealing the impacts of current and previous years’ water status is currently a hot topic. In this study, we designed a series of water deficit scenarios based on the meteorological dataset of the Climatic Research Unit (CRU) and then analysed the responses of the remote sensing-based moderate resolution imaging spectroradiometer (MODIS) gross primary productivity (GPP) in China, from which the role of water deficit in time periods was evaluated. The results indicate that the impact of climate factors (i.e., water, temperature and radiation) on GPP has a high spatial heterogeneity and that water-limited regions that are primarily distributed in North and Northwestern China show a stronger water-GPP relationship than water-unlimited regions. The water deficit that occurred in different periods had a variable impact on GPP. Specifically, GPP was primarily controlled by the current year’s water conditions in the water-limited regions, with the importance value of 52.8% (the percentage of Increased Mean Square Error, %IncMSE) and 3.8 (the mean decrease in node impurity, IncNodePurity), but at the same time, it was conditionally affected by the water status in the previous year, with the importance value of 20.4% (%IncMSE) and 0.6 (IncNodePurity). The role of water in previous years is multifarious, which depends on the water conditions of the current year. The results revealed by the scenarios indicate that the influence of water conditions in the previous year was not statistically significant when the water conditions of the current year were in a drought. In contrast, when the current year’s water conditions were normal or wetter, the water conditions in the previous year (i.e., one-year time lag) were also important and the increase of GPP significantly depended on the water condition (p < 0.05). The diverse roles of water conditions in previous years on GPP and its non-ignorable time-lag effect revealed in this study imply that not only the current year’s water condition but also its dynamic changes in previous years should be considered when predicting changes in GPP caused by climate change.