Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of dexamethasone administration on gonadal-fertility functions in female albino rats

Introduction dexamethasone is misused for skin whitening with broad effects on steroidogenesis and ovarian functions. Here in we investigated the impact of dexamethasone administration on gonadotropin Luteinizing Hormone (LH) and Follicle Stimulating Hormone (FSH), prolactin, and ovarian tissues in albino rats. Methods in the experimental study, 36 female albino rats weighting (140-162 g) were divided into three groups: control, normal dose received dexamethasone (8.3 μg/kg/day) and high dose (24.9 μg/kg/day), for 30 and 60 days. follicle stimulating hormone, luteinizing hormone, and prolactin (PRL) were measured. Histological ovarian sections were examined. Results luteinizing hormone, follicle stimulating hormone and prolactin significantly increased (p-value < 0.05) following dexamethasone treatment compared to control. The ovary sections showed degenerative tissue with necrosis of the Graafian follicles, stromal fibrosis, and vacuolation of the interstitial cells. Conclusion the study concludes that dexamethasone administration has a potentially adverse effect on gonadotropin, prolactin, and ovarian follicle cells in female albino rats.
Optimizing Service Redeployment in Migration-Oriented IoT Networks

The Internet of Things (IoT) paradigm has established an effective platform to promote the collaboration of resource-limited and duty-cycle IoT nodes, in order to support relative complex service requests that can hardly be achieved by any single IoT node. The functionalities of IoT nodes are typically encapsulated into IoT services, and the satisfaction of service requests is implemented as IoT service composition. Generally, IoT nodes work in turn in terms of their prespecific working cycles, and IoT network topology is constantly varied due to their active/sleeping behavior switching, causing unscheduled response latency. IoT service composition instantiation should be dynamically adjusted and partially redeployed on-demand for supporting request processing efficiently. To remedy this issue, this article proposes a migration-oriented service redeployment (MSrD) mechanism by migrating certain IoT services from their hosted IoT nodes to neighboring ones, in order to support functionally continuous availability. We formulate this problem as a game-theoretic approach, which is reduced to a potential game, where a Nash equilibrium solution is searched for optimizing this service redeployment game. Extensive experiments are conducted, and numerical results show that our MSrD mechanism is promising, compared with the state-of-art techniques, in achieving service redeployment optimization with efficient energy consumption and timely response latency simultaneously.
Sensor-less active uncertainty attenuation control for DC–DC buck power converters with the presence of involved parameter variations

This paper explores the semi-global active uncertainty attenuation control problem for the DC–DC buck power converters. Taking consideration that the involved components may be subject to uncertainties and unknown time variation, a robust control result has been developed by utilizing a sensor-less control method via an output feedback domination technique and a generalized extended state observer. Explicit stability analysis shows the theoretical justification of the proposed design procedure. Numerical simulation results are presented to show that with different application requirements, the proposed control algorithm could perform with strong robustness against internal parameter uncertainties and has a nice disturbance rejection ability against internal/external disturbances. Moreover, the proposed control scheme consists of a set of linear equations with more flexibility of the gain selections but without losing its high-precision voltage-tracking performance, hence it will provide the engineers with convenient and easy implementations.
Predicting ICO Prices Using Artificial Neural Network and Ridge Regression Algorithm

An Initial Coin Offering (ICO) is a method of raising funds for digital currency projects. Investors purchase these coins at a very low initial price before they are released. These coins are then listed on the trading platform, and their prices may increase rapidly if the currency performs well. After six months of release, ICO evaluation is the expected time for investors to profit. A dataset consisting of 109 ICOs was constructed from reputable websites after data preprocessing. Correlation analysis of 12 inputs revealed issues of multicollinearity, leading to biased regression model results. Overfitting occurred when using the regression model. To address these limitations, the Ridge regression method resolved the issues with the ICO data. An artificial neural network model addressed the complex nonlinear relationships between inputs and ICO prices. By adjusting parameters to achieve the best performance according to the Root Mean Square Error, R-squares, and Mean Absolute Error metrics, the results showed that the Ridge regression algorithm with a test set of three ICOs achieved accuracy ranging from 63% to 92% of ICO prices, while the artificial neural network model predicted with 98% accuracy depending on the metric used.
Deep Learning Based Multiresponse Optimization Methodology for Dual-Axis MEMS Accelerometer

This paper presents a deep neural network (DNN) based design optimization methodology for dual-axis microelectromechanical systems (MEMS) capacitive accelerometer. The proposed methodology considers the geometric design parameters and operating conditions of the MEMS accelerometer as input parameters and allows to analyze the effect of the individual design parameters on the output responses of the sensor using a single model. Moreover, a DNN-based model allows to simultaneously optimize the multiple output responses of the MEMS accelerometers in an efficient manner. The efficiency of the proposed DNN-based optimization model is compared with the design of the computer experiments (DACE) based multiresponse optimization methodology presented in the Literature, which showed a better performance in terms of two output performance metrics, i.e., mean absolute error (MAE) and root mean squared error (RMSE).
An FPGA-based edge computing and accelerating platform for fast diabetic retinopathy diagnosis

Diabetic retinopathy (DR) is one of the leading causes of blindness. Early DR screening may significantly reduce the risk of vision impairments. Traditional DR diagnosis relies on a professional ophthalmologist to examine the fundus image of a potential patient. However, in remote and underdeveloped areas, medical resources are relatively limited, hindering professional and timely DR diagnosis. In such context, there is an urgent need to develop convenient and fast DR screening methods to provide effective intervention plans. In this work, we implemented and validated a real-time DR diagnosis system on field-programmable gate array (FPGA) board. We first trained a DR grading CNN of interest on GPUs, and then deployed it on FPGA after quantization. Two CNN architectures, ResNet50 and MobileNetV2, are investigated. With 16-bit quantization, ResNet50 and MobileNetV2 respectively achieve 0.828 and 0.753 Kappa scores on the publicly-accessible EyePACS test set. Improvements in average processing time have also been observed: 16.92ms over CPU and 0.21ms over GPU.
A systematic review of medicinal plants and compounds for restoring pancreatic β-cell mass and function in the management of diabetes mellitus

Diabetes mellitus is among the most challenging public health problems worldwide due to its increasing prevalence and its associated complications, affecting individuals, healthcare systems, and economies. Insulin, a crucial hormone for maintaining glucose homeostasis, is secreted by pancreatic β-cells. The progression of both type 1 (T1D) and type 2 (T2D) diabetes is aided by dysfunction/reduced functional β-cell mass, which results in decreased insulin secretion and long-term complications. There has been a surge in interest in drug discovery programs targeted at augmenting both the quality and quantity of pancreatic islets. Several small-molecule drugs and biologics have already been proposed, and newer entities are being investigated. Importantly, plant and plant-derived compounds have a high potential for increasing the number, volume, and functionality of pancreatic islet cells. An update on the current research on the capacity of Aloe vera , Momordica charantia , Tinospora cordifolia , Gymnema sylvestre , Panax Ginseng, and plant-derived compounds including geniposide, berberine, phenylpropenoic acid glucoside, genistein, naringenin, baicalein, and apigenin to restore β-cell mass and function is given in this systematic review. Since β-cell deficiency is a defining feature of both T1D and T2D, it is critical to comprehend the role of some of these natural products in restoring pancreatic β-cell functionality.
Particle swarm optimization based on uncertain knowledge

Basic particle swarm optimization(PSO) algorithm only depends on individual and social cognition,but the cognitive part of PSO is uncertain for random behavior of cognitive factors.Therefore,uncertain knowledge is introduced to the basic PSO to improve the knowledge structure of each particle.Random cognitive factors normalization is also considered in the PSO-UK algorithm to ensure complete knowledge for the PSO-UK algorithm.Theoretical analysis and simulations show that the PSO-UK algorithm has the performance of global convergence and better precision value of local convergence than the basic PSO.
Checkpoint Kinase 1 Is a Key Signal Transducer of DNA Damage in the Early Mammalian Cleavage Embryo

After fertilization, remodeling of the oocyte and sperm genome is essential for the successful initiation of mitotic activity in the fertilized oocyte and subsequent proliferative activity of the early embryo. Despite the fact that the molecular mechanisms of cell cycle control in early mammalian embryos are in principle comparable to those in somatic cells, there are differences resulting from the specific nature of the gene totipotency of the blastomeres of early cleavage embryos. In this review, we focus on the Chk1 kinase as a key transduction factor in monitoring the integrity of DNA molecules during early embryogenesis.
Pomegranate Extract Affects Gut Biofilm Forming Bacteria and Promotes Intestinal Mucosal Healing Regulating the Crosstalk between Epithelial Cells and Intestinal Fibroblasts

Background: Pomegranate (Punica granatum) can be used to prepare a bioactive extract exerting anti-inflammatory activities. Clinical studies demonstrated an improvement in clinical response in inflammatory bowel disease (IBD) patients when pomegranate extract (PG) was taken as a complement to standard medications. However, the molecular mechanisms underlying its beneficial effects are still scarcely investigated. This study investigates the effect of PG on bacterial biofilm formation and the promotion of mucosal wound healing. Methods: The acute colitis model was induced in C57BL/6N mice by 3% dextran sodium sulfate administration in drinking water for 5 days. During the recovery phase of colitis, mice received saline or PG (200 mg/kg body weight) by oral gavage for 11 days. Colitis was scored daily by evaluating body weight loss, bleeding, and stool consistency. In vivo intestinal permeability was evaluated by fluorescein isothiocyanate-conjugated dextran assay, bacterial translocation was assessed by fluorescence in situ hybridization on tissues, whereas epithelial and mucus integrity were monitored by immunostaining for JAM-A and MUC-2 markers. Bacterial biofilm formation was assessed using microfluidic devices for 24 or 48 h. Primary fibroblasts were isolated from healthy and inflamed areas of 8 IBD patients, and Caco-2 cells were stimulated with or without PG (5 μg/mL). Inflammatory mediators were measured at the mRNA and protein level by RT-PCR, WB, or Bio-plex multiplex immunoassay, respectively. Results: In vivo, PG boosted the recovery phase of colitis, promoting a complete restoration of the intestinal barrier with the regeneration of the mucus layer, as also demonstrated by the absence of bacterial spread into the mucosa and the enrichment of crypt-associated fibroblasts. Microfluidic experiments did not highlight a specific effect of PG on Enterobacterales biofilm formation, even though Citrobacter freundii biofilm was slightly impaired in the presence of PG. In vitro, inflamed fibroblasts responded to PG by downregulating the release of metalloproteinases, IL-6, and IL-8 and upregulating the levels of HGF. Caco-2 cells cultured in a medium supplemented with PG increased the expression of SOX-9 and CD44, whereas in the presence of HGF or plated with a fibroblast-conditioned medium, they displayed a decrease in SOX-9 and CD44 expression and an increase in AXIN2, a negative regulator of Wnt signaling. Conclusions: These data provide new insight into the manifold effects of PG on promoting mucosal homeostasis in IBD by affecting pathogen biofilm formation and favoring the regeneration of the intestinal barrier through the regulation of the crosstalk between epithelial and stromal cells.
Co-Unet-GAN: a co-learning domain adaptation model on echocardiography segmentation

Convolutional neural networks (CNN) are a powerful deep learning method for medical image segmentation. However performance in clinical practice often deteriorates when attempting to generalize models trained from a particular source domain to a different target domain (e.g. different vendor, acquisition parameters, protocols). To address this issue, domain adaptation has attracted increasing attention because it can minimize distribution differences among different but related domains. Extending from this prior work, we introduce Co-Unet-GAN, a co-learning domain adaptation and segmentation model addressing the domain shift problem. In this model, we train a Unet segmentation network and an image translation generative adversarial network (GAN) together to generalize performance across domains given supervised data only in the source domain. We evaluate our model on two large open echocardiography datasets, using the CAMUS set as supervised source domain and EchoNet-Dynamic as the unsupervised target. In this context, we obtain mean absolute error on ejection fraction of 9.67% on Co-Unet-GAN compared to 11.28% for a previously published Unet-GAN. Our Co-Unet-GAN for image translation and segmentation is a promising solution to the domain shift problem.
The conserved quantities in general theorems ofthe Cosserat elastic rod dynamics

Conserved quantities of the Cosserat elastic rod dynamics are studied according to the general theorems of dynamics. The rod dynamical equation takes the cross section of the rod as its objective of study and is expressed by two independent variables, the arc coordinate of the rod and the time, so the conserved quantities are written in the integral forms and there exist the arc coordinate conservation and the time conservation. The existence conditions and the formulas of conservations of momentum and moment of momentum are derived from the theorem of momentum and the theorem of moment of momentum respectively, which contain two cases of conserved quanties, one is the time and the other is arc coordinate. Also existence conditions and formulas of conservations of energy about time and are coordinate, which contain mechanical energy conservation, are derived from energy equations about the time and arc coordinate of the rod respectively. All of conservative motions of the rod are explained by examples. The conserved quantities in the integral form are of practical significance in both theoretical and numerical analysis for the Cosserat elastic rod dynamics.
Logical inference for inverse problems

Estimating a deterministic single value for model parameters when reconstructing the system response has a limited meaning if one considers that the model used to predict its behaviour is just an idealization of reality, and furthermore, the existence of measurements errors. To provide a reliable answer, probabilistic instead of deterministic values should be provided, which carry information about the degree of uncertainty or plausibility of those model parameters providing one or more observations of the system response. This is widely-known as the Bayesian inverse problem, which has been covered in the literature from different perspectives, depending on the interpretation or the meaning assigned to the probability. In this paper, we revise two main approaches: the one that uses probability as logic, and an alternative one that interprets it as information content. The contribution of this paper is to provide an unifying formulation from which both approaches stem as interpretations, and which is more general in the sense of requiring fewer axioms, at the time the formulation and computation is simplified by dropping some constants. An extension to the problem of model class selection is derived, which is particularly simple under the proposed framework. A numerical example is finally given to illustrate the utility and effectiveness of the method.
Biosynthesis of Gold and Silver Nanoparticles Using Phytochemical Compounds

Gold and silver nanoparticles are nanoparticles that have been widely used in various fields and have shown good benefits. The method of nanoparticle biosynthesis utilizing plant extracts, also known as green synthesis, has become a promising method considering the advantages it has compared to other synthesis methods. This review aims to give an overview of the phytochemical compounds in plants used in the synthesis of gold and silver nanoparticles, the nanoparticle properties produced using plant extracts based on the concentration and structure of phytochemical compounds, and their applications. Phytochemical compounds play an important role as reducing agents and stabilizers in the stages of the synthesis of nanoparticles. Polyphenol compounds, reducing sugars, and proteins are the main phytochemical compounds that are responsible for the synthesis of gold and silver nanoparticles. The concentration of phytochemical compounds affects the physical properties, stability, and activity of nanoparticles. This is important to know to be able to overcome limitations in controlling the physical properties of the nanoparticles produced. Based on structure, the phytochemical compounds that have ortho-substituted hydroxyl result in a smaller size and well-defined shape, which can lead to greater activity and stability. Furthermore, the optimal condition of the biosynthesis process is required to gain a successful reaction that includes setting the metal ion concentration, temperature, reaction time, and pH.
Objets algébriquement clos et injectifs dans les catégories localement présentables

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Towards Macroscopic Quantum Effects Using Optomechanical and Optical Systems

In this thesis, I studied two different approaches which are promising for creating macroscopic quantum superpositions. The first approach, which is the main focus of this thesis, is based on optomechanical systems. The basic challenge to realize quantum effects in optomechanical systems, is considered to be the weak optomechanical coupling rate compared to the decoherence rates of the system caused by the cavity and mechanical damping. To enhance the optomechanical coupling it has been suggested to pump the cavity with coherent laser light [1]. By this method the strong coupling regime in which the optomechanical coupling rate exceeds all decoherence rates has been demonstrated very recently [2, 3]. It is also known that the optomechanical systems may exhibit nonlinear behaviours such as bistability. After introducing the basic ideas of optomechanical systems in chapter 1, in chapter 2 we studied the connection between optomechanical entanglement and bistability. In chapter 3, we studied the drawback of using a laser to enhance the interaction. We studied the effect of the laser phase noise, as an additional decoherence channel, on the optomechanical entanglement. We have shown that the system is very sensitive to this new source of decoherence and the sensitivity is maximum in the bistable threshold. We discussed that our results are independent of how we model the laser phase noise. In chapter 4, we studied single photon optomechanics. In this regime, we proposed a new scheme to enhance the optomechanical coupling based on post selection. Our scheme is potentially promising to create a superposition of the massive oscillator in different motional states. We also discussed the potential application of our scheme to test some decoherence models. The second approach that we consider in this thesis is based on amplifying single photons with high gain to create micro-macro superpositions . In chapter 5, we discussed the De
STRONG CONVERGENCE OF AN ITERATIVE METHOD FOR THE LASSO

The lasso of Tibshiranis(1996) is a least-squares regularized by the l 1 norm. In recent years, the lasso has been paid much attention due to the involvement of the l 1 norm, which it’s property is promoted by the sparseness of the norm. Now, we have mostly article studied it’s weak convergence to a solution of the lasso. It is the purpose of this paper to show that under certain conditions, the iterative sequence { x_ n } converges strongly to a solution of the lasso, is also the unique solution of the following variational inequality: ⟨ ( I − h ) x^ ∗ ,x − x^ ∗ ⟩ ≥ 0 , ∀ x^ ∗ ∈ Fix ( V λ ), where h : H → H is a contractive mapping and V λ : H → H is an averaged mapping.
Sigma functions for a space curve (3, 4, 5) type with an appendix by J. Komeda

In this article, a generalized Kleinian sigma function for an affine (3, 4, 5) space curve of genus 2 was constructed as the simplest example of sigma function for an affine space curves, by applying the construction method of sigma function for (r, s) plane curve provided by Eilbeck Enolskii and Leykin (SIDEIII, CRM Proc. Lecture Notes, 25 2000) to the space curve. By defining the fundamental differential of the second kind over it, the Legendre relations was obtained as the symplectic structure over it. It was showed that with the abelian map to C, the symplectic structure determines the sigma function. Using the sigma function, the Jacobi inversion formulae for the curve are obtained. It means that the generalization of the sigma functions for the affine plane curves to ones for the space curves is basically possible. An interesting relation between a semigroup generated by (6, 13, 14, 15, 16) and Norton number associated with Monster group is also mentioned with an Appendix by Komeda. sigma function, space curve, Jacobi inversion formula MSC 2010: 14H05, 14H42, 14H50, 14H55, 20M07
On the use of Doppler bandwidth for more accurate blood flow estimation

If the range cell or measurement volume of a Doppler flow measurement system is small enough so that the flow through it is of uniform velocity, the resulting Doppler spectrum will be relatively narrow, with a mean frequency related to the velocity in the range cell by the Doppler equation. If the range cell is now enlarged so as to encompass a range of velocities, the resulting Doppler spectrum will broaden correspondingly. It is shown theoretically and confirmed by experiment, that this broadening of the Doppler spectrum which accompanies an increase in length of the range cell, does not affect the maximum frequency of the spectrum which remains constant, being a function only of the maximum velocity passing through the range cell. Applications of this phenomenon to blood flow estimation in narrow vessels is discussed