Miracle-dz/UniFAR-FTUs huggingface dataset:
Pose Estimation and Behavior Classification of Jinling White Duck Based on Improved HRNet

Simple Summary The behavior and pose of ducks during the breeding process are directly related to their welfare and health status. The real-time and accurate monitoring of their different behaviors and poses is significant. In this study, we proposed a duck pose estimation method using computer vision techniques, achieving accurate detection of eight keypoints, including the head, beak, chest, left leg, right leg, left foot, right foot, and tail, under six behaviors: standing, drinking, preening, spreading wings, walking, and resting, and forming correct body expressions. Furthermore, the model’s generalization capability was tested with different lighting intensity levels. Ducks of various ages, breeds, and farming methods were used to validate the model’s comprehensive pose estimation capability. The model’s comprehensive detection capability was compared with mainstream pose estimation methods, proving its superiority. The results of this study are considered instrumental in smart poultry farming. Abstract In breeding ducks, obtaining the pose information is vital for perceiving their physiological health, ensuring welfare in breeding, and monitoring environmental comfort. This paper proposes a pose estimation method by combining HRNet and CBAM to achieve automatic and accurate detection of duck’s multi-poses. Through comparison, HRNet-32 is identified as the optimal option for duck pose estimation. Based on this, multiple CBAM modules are densely embedded into the HRNet-32 network to obtain the pose estimation model based on HRNet-32-CBAM, realizing accurate detection and correlation of eight keypoints across six different behaviors. Furthermore, the model’s generalization ability is tested under different illumination conditions, and the model’s comprehensive detection abilities are evaluated on Cherry Valley ducklings of 12 and 24 days of age. Moreover, this model is compared with mainstream pose estimation methods to reveal its advantages and disadvantages, and its real-time performance is tested using images of 256 × 256, 512 × 512, and 728 × 728 pixel sizes. The experimental results indicate that for the duck pose estimation dataset, the proposed method achieves an average precision (AP) of 0.943, which has a strong generalization ability and can achieve real-time estimation of the duck’s multi-poses under different ages, breeds, and farming modes. This study can provide a technical reference and a basis for the intelligent farming of poultry animals.
Extracellular Vesicles as Source of Biomarkers in Glomerulonephritis

Kidney disease is a global health and healthcare burden. Glomerulonephritis (Gn), both primary and secondary, is generally characterized by an inflammatory glomerular injury and may lead to end-stage renal disease. Kidney biopsy is fundamental to the diagnosis; however, kidney biopsy presents some concerns that may partly hamper the clinical process. Therefore, more accurate diagnostic tools are needed. Extracellular vesicles (EVs) are membranous vesicles released by cells and found in bodily fluids, including urine. EVs mediate intercellular signaling both in health and disease. EVs can have both harmful and cytoprotective effects in kidney diseases, especially Gn. Previous findings reported that the specific cargo of urinary EV contains an aerobic metabolic ability that may either restore the recipient cell metabolism or cause oxidative stress production. Here, we provide an overview of the most recent proteomic findings on the role of EVs in several aspects of glomerulopathies, with a focus on this metabolic and redox potential. Future studies may elucidate how the ability of EVs to interfere with aerobic metabolism and redox status can shed light on aspects of Gn etiology which have remained elusive so far.
Multifractality of Complex Networks Is Also Due to Geometry: A Geometric Sandbox Algorithm

Over the past three decades, describing the reality surrounding us using the language of complex networks has become very useful and therefore popular. One of the most important features, especially of real networks, is their complexity, which often manifests itself in a fractal or even multifractal structure. As a generalization of fractal analysis, the multifractal analysis of complex networks is a useful tool for identifying and quantitatively describing the spatial hierarchy of both theoretical and numerical fractal patterns. Nowadays, there are many methods of multifractal analysis. However, all these methods take into account only the fact of connection between nodes (and eventually the weight of edges) and do not take into account the real positions (coordinates) of nodes in space. However, intuition suggests that the geometry of network nodes’ position should have a significant impact on the true fractal structure. Many networks identified in nature (e.g., air connection networks, energy networks, social networks, mountain ridge networks, networks of neurones in the brain, and street networks) have their own often unique and characteristic geometry, which is not taken into account in the identification process of multifractality in commonly used methods. In this paper, we propose a multifractal network analysis method that takes into account both connections between nodes and the location coordinates of nodes (network geometry). We show the results for different geometrical variants of the same network and reveal that this method, contrary to the commonly used method, is sensitive to changes in network geometry. We also carry out tests for synthetic as well as real-world networks.
Dense Depth Estimation for Surgical Endoscope Robot with Multi-Baseline Depth Map Fusion

Dense depth estimation in endoscopic images can provide surgeons with important information for performing accurate minimally invasive surgeries. However, it is difficult to estimate the absolute depth of the scene based on monocular endoscope. Depth values in endoscopic images change drastically during the operation, which make it hard to estimate them with a fixed baseline. In this paper, we propose a depth estimation scheme with multiple baselines. The monocular endoscope is moved horizontally by a robotic endoscope holder to generate stereo images. A pixel-level depth map fusion algorithm is designed to combine depth values estimated with different baselines. Experimental results show that the proposed method improves the accuracy of depth estimation and the visual quality of depth maps.
Relationships between test results at 3 and 4 years of age for Swedish riding horses.

INTRODUCTION In Sweden, riding horses can participate in tests at the age of three and four years. The Riding Horse Quality Test (RHQT) for 4-year-old riding horses was introduced in 1973 and annually about one third of all 4-year-old riding horses participate in the test. Brood mares may be tested at either four or five years of age. The aims of this one-day field test are to provide information to the genetic evaluations of stallions and mares, and to evaluate overall quality of young sporthorses, especially their talents for dressage and show jumping. Traits tested are conformation, gaits under rider, jumping and health status.
COCCA: Point Cloud Completion through Cad Cross-Attention

3D scene- and object-level scans typically result in sparse and incomplete point clouds. Since dense point clouds of high quality are essential for the 3D reconstruction process, a promising approach is to improve the scan quality by point cloud completion. In this paper, we present COCCA, an extension of point cloud completion networks for scan-to-CAD use cases. The proposed extension is based on cross-attention of features extracted from a scan with rotation-, translation-, and scale-invariant features extracted from a sampled CAD point cloud. With the proposed cross-attention operation, we improve the learning of scan features and the subsequent decoding to a complete shape. We demonstrate the effectiveness of COCCA on the ShapeNet dataset in quantitative and qualitative experiments. COCCA improves the overall completion performance of point cloud completion networks by up to 11.8% for Chamfer Distance and up to 2.2% for F-Score. Our qualitative experiments visualize how COCCA completes point clouds with higher geometric detail. In addition, we demonstrate how completion by COCCA improves the point cloud registration task required for scan-to-CAD alignment.
Is Surgical Treatment for Obesity Able to Cure Urinary Incontinence in Women?—A Prospective Single-Center Study

There is enough evidence to support weight loss in order to improve urinary incontinence. Nevertheless, weight loss and maintaining a lower weight are not easy to achieve in the general population. Our study aims to evaluate whether bariatric surgery has a positive effect on the symptoms of urinary incontinence in female patients. We performed a prospective study on obese female patients before and after bariatric surgery, over a period of 9 years. Patients with a BMI ≥ 33 kg/m2 were included if they described involuntary loss of urine and no previous surgery for urinary incontinence was performed. The patients underwent laparoscopic surgery, either gastric sleeve, bypass or banding, performed by four surgeons in our hospital. The type of incontinence was not assessed at the initial visit carried out by the surgeon. All patients who declared being incontinent were referred to the urologist where they received the ICIQ—UI-SF questionnaire before their bariatric surgery and during follow -up visits. The sum of points obtained at questions 3, 4 and 5 was used to evaluate the severity of incontinence, as well as the impact on the quality of life. Our evaluation collected data on age, time since onset of symptoms, pad usage, number and type of deliveries, concomitant conditions and medications. The type of incontinence was assessed by the urologist before bariatric surgery as urge, stress or mixed incontinence. At follow-up visits, the patients were also asked to fill out a 10-point VAS questionnaire evaluating their perception on the evolution of incontinence symptoms. Data were analyzed using t-test statistical analysis. Our objective defined changes in incontinence as cure, improved, no change and worse. We included 54 women from whom initial data and at least 18 months of follow-up were available. We observed that about 50% of all women undergoing bariatric surgery have some degree of urinary incontinence. The ICIQ score improved from 13.31 ± 5.18 before surgery to 8.30 ± 4.49 points after surgery (p < 0.0001). Before surgery, 38 patients (70%) described severe incontinence compared to only 20 patients (37%) after surgery. A total of 16 women (31%) reported complete cure of urinary incontinence after bariatric surgery. Data from the VAS questionnaire show improvement in 46 cases (85%). Pad usage improved from 7.04 ± 2.79 to 3.42 ± 2.77 (p < 0.001) per day. The number of patients using more than one pad per day decreased from 35 (65%) to 9 (17%). The type of incontinence did not seem to be relevant, but our sample size was too small to lead to statistically significant results. There was no impact on the outcome of incontinence of number/type of delivery, age or BMI. Our data show that bariatric surgery is able to cure urinary incontinence in one of three obese women. A significant improvement was obtained in more than two-thirds of the patients, regardless of the type of incontinence. For an obese female with urinary incontinence, treatment for obesity should prevail and incontinence should be treated only if symptoms remain.
A Social-Ecological systems perspective on water management in South Africa

Conventional approaches to water management have traditionally treated social systems and ecosystems as distinct, and to a large degree have failed to achieve outcomes that are simultanously efficient, equitable, and sustainable. Perhaps nowhere has the need to reform the way water is managed and even conceived been more apparent than in South Africa in the last decade, where a tremendous opportunity for change has been created in the form of the National Water Act of 1998. In this thesis I propose that water management in South Africa – which encompasses its water resources, ecosystems and their services, people they support, and institutions that govern them – is a social-ecological system: a coupled, inseparable system of human beings and nature. Using a combination of approaches, I demonstrate that a social-ecological systems perspective is needed to understand the true nature of these challenges. First, drawing from the experience of the Southern African Millennium Ecosystem Assessment (SAfMA), I construct and apply a framework for evaluating past water management responses. Second, I review a scenario planning exercise as an approach for identifying robust decisions amid high levels of uncertainty about future ecosystem services. Third, I use an agent-based model to explore the evolution of decision-making and learning by water managers under alternative paradigms. Lastly, I compare the ability of two existing frameworks to increase understanding of resilience as it applies to South African water management. Results of this work indicate that: congruence of impacts, awareness, and power is key to achieving effective water management in South Africa; future water management planning needs to take account of cross-scale trade-offs; decentralisation holds most promise when supported by a national policy framework but which allows for local learning; learning may be constrained by temporal variability, water stress, access to learning University of Pretoria etd, Bohensky E L (2006)
Truncated Weighted Nuclear Norm Regularization and Sparsity for Image Denoising

The attribute of signal sparsity is widely used to sparse representaion. The existing nuclear norm minimization and weighted nuclear norm minimization may achieve a suboptimal in real application with the inaccurate approximation of rank function. This paper presents a novel denoising method that preserves fine structures in the image by imposing L1 norm constraints on the wavelet transform coefficients and low rank on high-frequency components of group similar patches. An efficient proximal operator of Truncated Weighted Nuclear Norm (TWNN) is proposed to accurately recover the underlying high-frequency components of low rank patches. By combining a wavelet domain sparse preservation prior with TWNN, the proposed method significantly improves the reconstruction accuracy, leading to a higher PSNR/SSIM and visual quality than state of the art approaches.
Chinese clinical practice guidelines for the prevention and treatment of radiation‐induced dermatitis

Radiation‐induced dermatitis is one of the most prevalent complications in patients undergoing cancer radiotherapy and poses a significant challenge to cancer therapy. The symptoms include erythema, dry desquamation, and moist desquamation, which are frequently observed in patients with breast, head and neck, anal, and vulvar cancers. Early skin reactions typically manifest within 2–4 weeks following the initiation of radiotherapy. In severe cases, acute dermatitis can cause radiotherapy interruptions, prolong treatment time, and ultimately affect patient outcomes and quality of life. Currently, there are numerous guidelines on radiation dermatitis, including the Multinational Association of Supportive Care in Cancer (MASCC), British Columbia Cancer Agency (BCCA), Oncology Nursing Society (ONS), and UK Society of Radiographers (SCoR) guidelines. In China, dermatology experts have drafted a consensus. However, due to the differing backgrounds of experts, recommendations among guidelines vary. These guidelines were first developed by Chinese radiation oncologists. The evidence‐based guideline in this paper fully considers and adopts China's national conditions; hence, it can be easily applied in daily practice.
Role of Lnc-RNAs in the Pathogenesis and Development of Diabetic Retinopathy

Important advances in diabetic retinopathy (DR) research and management have occurred in the last few years. Neurodegenerative changes before the onset of microvascular alterations have been well established. So, new strategies are required for earlier and more effective treatment of DR, which still is the first cause of blindness in working age. We describe herein gene regulation through Lnc-RNAs as an interesting subject related to DR. Long non-coding RNAs (Lnc-RNAs) are non-protein-coding transcripts larger than 200 nucleotides. Lnc-RNAs regulate gene expression and protein formation at the epigenetic, transcriptional, and translational levels and can impact cell proliferation, apoptosis, immune response, and oxidative stress. These changes are known to take part in the mechanism of DR. Recent investigations pointed out that Lnc-RNAs might play a role in retinopathy development as Metastasis-Associated Lung Adenocarcinoma Transcript (Lnc-MALAT1), Maternally expressed gene 3 (Lnc-MEG3), myocardial-infarction-associated transcript (Lnc-MIAT), Lnc-RNA H19, Lnc-RNA HOTAIR, Lnc-RNA ANRIL B-Raf proto-oncogene (Lnc-RNA BANCR), small nucleolar RNA host gene 16 (Lnc-RNA SNHG16) and others. Several molecular pathways are impacted. Some of them play a role in DR pathophysiology, including the PI3K-Akt signaling axis, NAD-dependent deacetylase sirtuin-1 (Sirti1), p38 mitogen-activated protein kinase (P38/mapk), transforming growth factor beta signaling (TGF-β) and nuclear factor erythroid 2-related factor 2 (Nrf2). The way Lnc-RNAs affect diabetic retinopathy is a question of great relevance. Performing a more in-depth analysis seems to be crucial for researchers if they want to target Lnc-RNAs. New knowledge on gene regulation and biomarkers will enable investigators to develop more specialized therapies for diabetic retinopathy, particularly in the current growing context of precision medicine.
Aluminum film over nanosphere surface for Deep Ultraviolet plasmonic nanosensors

Deep ultraviolet (DUV) plasmonic nanosensors are emerging as potential candidates to detect biomolecules with enhanced sensitivity, as these molecules strongly absorb light in the DUV region. In the present work, we fabricated aluminium film over nanospheres surface (AlFON) and successfully demonstrated, for the first time, an AlFON based DUV plasmonic nanosensor for detecting volatile organic compounds (VOCs) such as ethanol, isopropyl alcohol (IPA) and toluene, and bovine serum albumin (BSA) protein. The AlFON was fabricated by thermally evaporating aluminium thin film onto a two-dimensional photonic crystal (2DPC). The 2DPC was prepared by drop casting colloidal polystyrene spheres (PSS) on quartz substrates that self-assembled to form a monolayer of hexagonally close packed PSS with periodic interstices. The optical reflection spectrum of 70 nm AlFON exhibited a sharp minimum at 289 nm in the DUV wavelength range which corresponds to strong absorption in V-shaped nanocavities due to excitation of localized cavity plasmons. The reflection minimum—or LSPR wavelength—is sensitive to the changes in the refractive index of the surrounding medium. The LSPR mode of the V-shaped nanocavities in 70 nm AlFON was exploited to sense VOCs and BSA. The 70 nm AlFON exhibited 244 nm/RIU sensitivity in detecting VOCs and was capable of sensing 1 mM concentration of BSA in water. The AlFON is physically stable and has a self-limiting Al2O3 oxide layer of less than 5 nm thickness as elucidated from x-ray photoelectron spectroscopy measurements. The effect of higher aluminum film thickness, i.e. 100 nm and 120 nm on the sensitivity of AlFON also was studied. The performance of AlFON based DUV plasmonic nanosensors is better than the previously reported Al nanostructure based nanosensors and can still be enhanced by optimizing the structure of 2DPC and AlFON.
Community building in virtual participation charity sport events

ABSTRACT The study aims to explore the drivers of community building in virtual participation charity sport events. The authors conducted a case study of virtual charity events governed by Team World Vision, the sports arm of a global not-for-profit service organisation. They conducted semi-structured interviews with World Vision marketing managers and virtual running race participants, analysed survey and podcast interview data, and performed a document analysis. Four overarching themes were revealed as drivers of community building: community engagement, social networking, impression management, and fitness philanthropy practicing. The study uncovers the peculiarities of the virtual format that helped build virtual communities and create excitement around the cause, such as digitised communication plans, social media- and technology-facilitated opportunities to connect both locally and globally, and the adapted fundraising strategies in the virtual format.
Uncertainties and variations in the carbon footprint of livestock products

Livestock production is a major contributor to anthropogenic climate change, being responsible for 18% of global greenhouse gas (GHG) emissions. In the quest to reduce emissions, the amount of GHG released during the production of livestock products is commonly quantified by calculating the carbon footprint (CF), which includes all GHG emitted during the life cycle of the product. Quantification of the CF is challenging for several reasons. The majority of GHG emissions from agricultural systems arise from complex microbial processes that are difficult to fully understand and highly variable in time and space. Changes in carbon pools above and below ground can have huge impacts on GHG emissions from agricultural systems. The increasing demand for food, feed and biofuel on the global market is leading to deforestation and thus increased emissions of GHG. In addition, there is great diversity between livestock systems, e.g. in feeding strategies, animal growth and production, housing systems and manure handling. This report describes uncertainties and variations in input data and models used to calculate the CF of livestock products. It discusses when uncertainty assessments are important and how uncertainty can be included in CF calculations. Uncertainty in the CF of livestock products arises from: 1) uncertain input data; 2) the choice of model used for calculating emissions of e.g. N2O from soil, CH4 from enteric fermentation in ruminants, CO2 emissions or sequestration in soils and emissions as a result of land use change, as well as uncertainties in these models; and 3) uncertainty due to scenario choices in modelling the livestock system, e.g. how system boundaries are drawn and how allocation between co-products is handled. It is important to account for uncertainty when comparing different production systems where emissions arise from different sources. However, when similar production systems are compared, e.g. when they only differ in the amount of feed used, it is possible to draw solid conclusions without comprehensive uncertainty assessments. Uncertainty in input data and model parameters can be propagated through the CF model using stochastic simulation, which gives an uncertainty range for the resulting CF. Sensitivity analysis can be used to test how different modelling choices affect the results, thus providing a measure of their robustness. In a full sustainability assessment, it is important not to focus solely on the CF, but to include other environmental impact categories and social and economic aspects.