Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamics of follicle growth in the sheep

The terminal differentiation of ovarian follicles is acutely regulated by gonadotrophins, the secretion of which is carefully controlled by steroid and peptide hormones produced by the follicles in a short term feed-back system. The endocrine changes during the oestrous cycle and anoestrous season have been extensively documented in sheep, but very little is known about the growth dynamics of large antral follicles and its hormonal regulation. In many species, correlation of the dynamics of follicle growth as measured by serial ultrasound with the endocrine environment has increased the understanding of the factors regulating terminal stages of follicular development. Using ewes with an ovarian autotransplant, the diameter of individual follicles and corpora lutea was measured by ultrasound scanning, to characterise the pattern of development of antral follicles over 2.5mm, in relation to gonadotrophin and ovarian hormone concentrations in jugular and ovarian venous blood. Studies were conducted to investigate the effects of season, age and the action of a major gene involved in increased prolificacy in sheep (FecB) on the dynamics of follicular growth and its hormonal regulation. The results showed that the final stages of maturation of dominant follicles in sheep during the oestrous cycle and anoestrous occurs in a succession of waves of follicular growth. During the luteal phase 3 waves emerge on days 2, 7 and 11 postLH surge. During the follicular phase, most of the follicles that ovulate after the LH surge originate from follicles above 5mm in diameter at the time of luteal regression, but additional smaller follicles can be recruited during the follicular phase. During the follicular and early luteal phase, when LH pulse frequency is elevated, follicles achieved dominance by secreting large amounts of oestradiol and inhibin A, depressing FSH concentrations below threshold levels. During the second and third waves of follicular development of the luteal phase a similar pattern of FSH secretion was evident but the relationship between the secretion of oestradiol and inhibin A was less clear. Despite these endocrine differences, the mechanism of follicular selection operated in each of these waves as the number of dominant follicles was similar to the ovulation rate. Similar investigations in ewes carrying the Booroola FecB gene showed that gene carrier ewes produce more follicles, that mature earlier at smaller size, without any differences in the concentration of FSH and the ovarian secretion of oestradiol, androstenedione and inhibin A compared to non-carrier animals. This suggests that the Booroola gene acts at an ovarian level modulating the gonadotrophin signal to increase the number of follicles selected. In order to study the effect of age in
Wind turbine converter control interaction with complex wind farm systems

This study presents wind turbine converter stability analysis of wind farms in frequency domain. The interaction between the wind turbine control system and the wind farm structure in wind farms is deeply investigated. Two wind farms (i.e. Horns Rev II and Karnice) are taken into consideration in this study. It is shown that wind farm components, such as long high-voltage alternating current cables and park transformers, can introduce significant low-frequency series resonances seen from the wind turbine terminals that can affect wind turbine control system operation and overall wind farm stability. The same wind turbine converter control strategy is evaluated in two different wind farms. It is emphasised that the grid-side converter controller should be characterised by sufficient harmonic/noise rejection and adjusted depending on wind farms to which it is connected. Various stability indices such as gain margin, vector gain margin and phase margin are used in order to emphasise the differences between the two wind farms.
Persistent Homology based Graph Convolution Network for Fine-grained 3D Shape Segmentation

Fine-grained 3D segmentation is an important task in 3D object understanding, especially in applications such as intelligent manufacturing or parts analysis for 3D objects. However, many challenges involved in such problem are yet to be solved, such as i) interpreting the complex structures located in different regions for 3D objects; ii) capturing fine-grained structures with sufficient topology correctness. Current deep learning and graph machine learning methods fail to tackle such challenges and thus provide inferior performance in fine-grained 3D analysis. In this work, methods in topological data analysis are incorporated with geometric deep learning model for the task of fine-grained segmentation for 3D objects. We propose a novel neural network model called Persistent Homology based Graph Convolution Network (PHGCN), which i) integrates persistent homology into graph convolution network to capture multi-scale structural information that can accurately represent complex structures for 3D objects; ii) applies a novel Persistence Diagram Loss (ℒPD) that provides sufficient topology correctness for segmentation over the fine-grained structures. Extensive experiments on fine-grained 3D segmentation validate the effectiveness of the proposed PHGCN model and show significant improvements over current state-of-the-art methods.
Locating in Supermalls with SSD

: The need for a fast and accurate indoor positioning solution is increasing rapidly since people are spending more time indoors, mostly for shopping and entertaining. Being aware of their own positions is not only vital to consumers’ safety, but also necessary to improve their shopping experience. In this paper, an alternative way for indoor positioning is proposed, which is based on convolutional neural network, particularly an algorithm called Single Shot Multi-Box Detector (SSD). Different from other indoor positioning technologies, this image-based technique does not depend on special detecting devices or communication networks. Testing shows that positioning with SSD in indoor spaces has great advantages (both convenience and accuracy) over other methods, indicating its potential to be applied in real-life situations.
Shape Reconstruction for Abdominal Organs based on a Graph Convolutional Network

Computed tomography and magnetic resonance imaging produce high-resolution images; however, during surgery or radiotherapy, only low-resolution cone-beam CT and low-dimensional X-ray images can be obtained. Furthermore, because the duodenum and stomach are filled with air, even in high-resolution CT images, it is hard to accurately segment their contours. In this paper, we propose a method that is based on a graph convolutional network (GCN) to reconstruct organs that are hard to detect in medical images. The method uses surrounding detectable-organ features to determine the shape and location of the target organ and learns mesh deformation parameters, which are applied to a target organ template. The role of the template is to establish an initial topological structure for the target organ. We conducted experiments with both single and multiple organ meshes to verify the performance of our proposed method.
Molecular Mechanisms of Mechanical Stress Response during Chondrogenesis

Muscloskeletal tissues, bone, cartilage, muscles and tendons regulate and support the body's actions and are differentiated from mesenchymal stem cells. These organs generate and/or respond to mechanical stress, which is inevitable in daily life. Among these tissues, cartilages play roles in articular function in joints where shear stress is loaded in combination with cyclical or intermittent compressive force by joint action, and provides a template for bone growth under compressive stress directed along the long axis of long bones. Along with soluble factors, such as cytokines and growth factors, mechanical stress has been recognized as one of the epigenetic factors that regulates the gene expression of various types of cells. Thus, the molecular mechanisms of the mechano-sensing, mechano-transduction, and mechano-response of cells have become a focus of connective tissue biology. Here, we will discuss the mechanisms through which differentiated chondrocytes and mesenchymal cells that are differentiating into chondrocytes respond to various types of mechanical stress by altering their phenotype and how these phenotypic changes are molecularly regulated. We will focus on the roles of cell-extracellular matrix interactions through integrins and downstream signaling pathways involving mitogen-activated protein kinases.
An Economic Valuation of Forest Ecosystem Services: A Choice Modeling Application to the Mekong Delta Project in Vietnam*

This study is the application of a choice experiment to assess Mekong Delta urban households’ preferences and motivations for ecosystem conservation in the U Minh forest. The study applied a choice modeling approach to estimate the economic values of the proposed ecosystem conservation program in the U Minh forest by accessing urban consumer preferences and their willingness to pay for the project. Discrete choice experimental data was collected from 450 residents in the cities of the Vietnamese Mekong Delta. The multinomial logit model was employed to identify consumer’s stated preferences for the environmental and sustainability attributes of the conservation project. The results showed that Mekong Delta urban residents paid much attention to the proposed project to protect and develop the U Minh forest. In addition, the results showed that higher education, income, and knowledge of the U Minh forest revealed a higher likelihood of selecting the project, while the older residents would select the status quo more than the younger ones. The study also proved that the effect of participation had a strong impact on the willingness to pay for the project. The findings could be useful for policymakers to take action to raise resident’s awareness and willingness to pay for the U Minh forest project.
Comparison of optimized interferential stimulation using two pairs of electrodes and two arrays of electrodes

Interferential stimulation (IFS) or Temporal Interference (TI) has recently generated considerable interest as computational models show that it can focally stimulate deep brain regions with non-invasive transcranial electrical currents [1]. However, the proposed solution in [1] requires two arrays, involving dozens of electrodes in each, to achieve optimal focality in the deep brain regions. Implementation of this approach is usually not feasible in practice due to the limited number of channels and the associated accuracy and precision needed in current stimulation devices. Alternative method [2] focuses on using only two pairs of electrodes as proposed in the conventional IFS approach and searching exhaustively in the parameter space for the optimal montage that maximizes the focality of the modulation at the deep target. Here we compare these two methods in terms of the quality of the solutions (focality versus modulation depth) and the practicality (speed and number of electrodes needed). We then give general guidelines for optimal IFS in practice for future studies.
Skin lesion classification using features of 3D border lines

Machine learning algorithms are progressively assuming important roles as computational tools to support clinical diagnosis, namely in the classification of pigmented skin lesions using RGB images. Most current classification methods rely on common 2D image features derived from shape, colour or texture, which does not always guarantee the best results. This work presents a contribution to this field, by exploiting the lesions’ border line characteristics using a new dimension – depth, which has not been thoroughly investigated so far. A selected group of features is extracted from the depth information of 3D images, which are then used for classification using a quadratic Support Vector Machine. Despite class imbalance often present in medical image datasets, the proposed algorithm achieves a top geometric mean of 94.87%, comprising 100.00% sensitivity and 90.00% specificity, using only depth information for the detection of Melanomas. Such results show that potential gains can be achieved by extracting information from this often overlooked dimension, which provides more balanced results in terms of sensitivity and specificity than other settings.
Conditional Generative Adversarial Networks for low-dose CT image denoising aiming at preservation of critical image content

X-ray Computed Tomography (CT) is an imaging modality where patients are exposed to potentially harmful ionizing radiation. To limit patient risk, reduced-dose protocols are desirable, which inherently lead to an increased noise level in the reconstructed CT scans. Consequently, noise reduction algorithms are indispensable in the reconstruction processing chain. In this paper, we propose to leverage a conditional Generative Adversarial Networks (cGAN) model, to translate CT images from low-to-routine dose. However, when aiming to produce realistic images, such generative models may alter critical image content. Therefore, we propose to employ a frequency-based separation of the input prior to applying the cGAN model, in order to limit the cGAN to high-frequency bands, while leaving low-frequency bands untouched. The results of the proposed method are compared to a state-of-the-art model within the cGAN model as well as in a single-network setting. The proposed method generates visually superior results compared to the single-network model and the cGAN model in terms of quality of texture and preservation of fine structural details. It also appeared that the PSNR, SSIM and TV metrics are less important than a careful visual evaluation of the results. The obtained results demonstrate the relevance of defining and separating the input image into desired and undesired content, rather than blindly denoising entire images. This study shows promising results for further investigation of generative models towards finding a reliable deep learning-based noise reduction algorithm for low-dose CT acquisition.
High Density Impulse noise removal from Digital Images by Fast and Enhanced Tolerance based Selective Arithmetic Mean Filtering Technique

— As we all know digital images holds detailed information which play very important role in various applications. Due to environmental effects, errors while transmission, while acquiring image by sensors their might be noise interference in the image and to get expected or detailed information from noise interfaced image we need to apply effective denoising algorithm or technique to get back effective information associated with that image. As we know today there are superior techniques which are associated with technology. Even though denoising concept is challenging role for researchers. As we know for less salt and pepper noise median filters works well and enhanced techniques associated with median filters and mean filters gives impact results. Tolerance based Selective Arithmetic Mean Filtering is among one which work good for certain level of high noise density to restore image information. In this paper fast and enhanced Tolerance based Selective Arithmetic Mean Filtering Technique is proposed for effective denoising concept. Simulated results show that proposed technique performs effectively for highly corrupted salt and pepper noise.
Wideband surface‐mount chip antenna for eight‐band LTE/WWAN slim mobile phone application

Surface‐mount chip antenna with a thin thickness of 3 mm and a small volume of 3 × 4 × 50 mm3 (0.6 cm3) for eight‐band long term evolution/wireless wide area network (LTE/WWAN) operation in the slim mobile phone is presented.The antenna is a simple two‐strip shorted monopole, yet it can provide two wide operating bands of at least 698–960/1710–2690 MHz to cover the three LTE bands (LTE700/2300/2500) and five WWAN bands (GSM850/900/1800/1900/UMTS). The antenna is suitable to be mounted on the no‐ground board space of the system circuit board of the mobile phone. Wideband operation is achieved by incorporating an on‐board feeding structure formed by a short shorting strip and a chip capacitor–loaded feeding strip, both are of 8 mm in length and disposed on the no‐ground board space, to the proposed chip antenna. Details of the antenna including the on‐board feeding structure are presented. © 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52:2554–2560, 2010; View this article online at wileyonlinelibrary.com. DOI 10.1002/mop.25543
Development of a wearable system with In-Ear EEG electrodes for the monitoring of brain activities: An application to epilepsy

Epilepsy is a chronic noncommunicable disease of the brain characterized by recurrent seizures, which are brief episodes of excessive and synchronized electrical discharges of neuronal activities. The main technique used for the diagnosis of seizures is the electroencephalography (EEG), a noninvasive method which records the electrical activity of the brain. The conventional EEG monitoring to diagnose epilepsy has some problems: First, the short monitoring time generates misdiagnosis. Second, the long wires needed for the connection of electrodes are uncomfortable for the patients. Third, the patients are not monitored in a natural environment where different conditions can trigger seizures and last, a conventional EEG monitoring is too expensive. A solution is the development of a wearable monitoring EEG where the recording system is a device of small dimensions with continuous and long-term recording capabilities. In this work, we developed an In-Ear wearable system to allow a long-term recording of EEG signals in a nonclinical environment. For this, we designed In-ear electrodes with soft foam earplugs and a knit conductive fabric of silver. We performed different tests to validate the signal quality of the acquisition system including one ambulatory recording in an epileptic patient.
Parallel MRI Reconstruction Using Broad Learning System

As an inverse problem, parallel magnetic resonance imaging (pMRI) reconstruction accelerates imaging speed by interpolating missing k-space data from a group of phased-array coils. Deep learning methods have been used for improving pMRI reconstruction quality in recent years. However, deep learning approaches need a large amount of training data that are acquired from different hardware configurations and anatomical areas. Data distributions may be different between training data and testing data, and a long-time training is needed. In this work, we proposed a broad learning system based parallel MRI reconstruction that exploits approximation capability of one-layer neural network through broadening network structure with expanded nodes. Experimental results show that the proposed method is able to suppress noise in compared to the conventional pMRI reconstruction.
Highly reliable Au-Sn eutectic bonding with background GaAs LSI chips

The authors investigate the reliability of mechanically ground chips through die-shear and thermal shock tests. The bonded chips, which were ground to 450 mu m thickness by an original technology, were proved to be as reliable as polished chips through 1000 cycles of thermal shock between -65 degrees C and +150 degrees C. No chip fracture occurred and no induced void was observed with scanning acoustic microscopy. The shear strength of the chips after thermal shocks remained at more than 10 kg, passing the MIL-STD-883C test. Surface flaws due to backgrinding, which would cause chip fracture, were eliminated by the slight chemical etching after backgrinding. Scrubbing action has been confirmed to be necessary to obtain void-free bondings consistently in low-cost production. The Sn in the Au-Sn preform easily forms on oxide film at the surface, which tends to prevent wetting at the bonding interface. This tin oxide film (300-400 AA) was observed through Auger electron spectroscopy (AES) to be broken down by scrubbing action. >
2D Ultrasound Validation to Assess the Accuracy of Hip Displacement Measurement: A Phantom Study

Hip displacement is a common orthopedic abnormality in children with cerebral palsy and is assessed on anteroposterior pelvic radiographs during surveillance. Repeated exposure to ionizing radiation is a major concern of cancer risks for children. Ultrasound (US) has been proposed to image the hips. The severity of hip displacement is measured by the Reimers’ migration percentage (MP), which is calculated by the ratio of the femoral head distance from the acetabulum to the width of the femoral head. Methods have been published to estimate MP from the US hip images in literature; however, validation for accuracy has not been reported. This study aimed to determine the accuracy of the 2D ultrasound techniques using two 3D printed hip phantoms with known MP values. The MPs estimated from the US images were compared with those measured from the X-ray images. Based on the experimental results, the US measurements had a maximum absolute discrepancy of 2.2% as compared to 9.8% from the X-ray measurements for the MP. The study on phantoms has showed the proposed US approach is promising with better accuracy and without ionizing radiation.Clinical Relevance — If the accuracy is proved to be at least as good as the current X-ray gold standard, the proposed US method will provide a modality of choice to pediatric patients for hip displacement diagnostics and hip surveillance, especially those with cerebral palsy. The method will be free of ionizing radiation and therefore significantly improve the pediatric patient care.
Economic sanctions

Economic sanctions have been referred to as a blunt instrument that the international community has often wielded without full consideration of the impact that these measures will have on the population of the targeted countries, particularly the weakest elements of society. Case studies of sanctions against Cuba, Iraq, and Yugoslavia have demonstrated the impact that sanctions can have on the availability of food, clean water, and medicine, causing many to conclude that all sanctions have extensive public health consequences. In this article, we examine the generalizability of these conclusions in a quantitative cross-national study of sanctions and their public health effects. Additionally, we compare these effects to those associated with both civil and interstate conflicts as critics have recently suggested that sanctions are not a humane alternative to armed warfare. We find that when sanctions have a large economic effect on the target they can have severe public health consequences. These consequences are substantively similar to those associated with major military conflicts. However, when sanctions have little or no economic effect on the target, they also have no substantive effect on public health. Building on recent work to explore the human consequences of war, this work also helps to demonstrate the importance of smart sanctions and humanitarian exemptions in sanctions policy.