Miracle-dz/UniFAR-FTUs huggingface dataset:
Large-Scale Expansion of Human Umbilical Cord-Derived Mesenchymal Stem Cells in a Stirred Suspension Bioreactor Enabled by Computational Fluid Dynamics Modeling

Human umbilical cord-derived mesenchymal stem cells (hUCMSCs) hold great potential to generate novel and curative cell therapy products. However, the current large-scale cultivation of hUCMSCs is based on empirical geometry-dependent methods, limiting the generation of high-quantity and high-quality hUCMSCs for clinical therapy. Herein, we develop a novel scale-up strategy based on computational fluid dynamics (CFD) to effectively expand the hUCMSCs in a 3D tank bioreactor. Using a standardized hUCMSCs line on microcarriers, we successfully translated and expanded the hUCMSCs from a 200 mL spinner flask to a 1.5 L computer-controlled bioreactor by matching the shear environment and suspending the microcarrier. Experimental results revealed that the batch-cultured hUCMSCs in bioreactors with an agitation speed of 40 rpm shared a more favorable growth and physiological state, similar to that run at 45 rpm in a 200 mL spinner flask, showing comparability in both culture systems. Notably, the maximum cell density reached up to 27.3 × 105 cells/mL in fed-batch culture, 2.9 folds of that of batch culture and 20.2 times of seeding cells. As such, efficient process optimization and scale-up expansion of hUCMSCs were achieved in the microcarrier-based bioreactor system by the developed CFD simulation strategy, which provided an alternative toolbox to generate massive and standardized curative cell therapy products.
Mammography Classification Based on Convolutional Neural Network

Limited by various conditions, the features of mammography images are difficult to extract, so it is hard to classify them. The paper proposed a method based on deep learning method to classify benign and malignant mammography images. The convolutional neural network concludes four convolution layers, four pool layers, and two full-connection layers, and a Softmax layer. The paper designed a new network architecture to improve the traditional one. As a result, we have done an experiment on the DDSM database. Compared with other classification methods, it shows that the method proposed in this paper is more effective than other methods. Keywords—mammography; classification; convolutional neural network; DDSM
Using Sentiment Analysis to Identify Student Emotional State to Avoid Dropout in E-Learning

Dropping out of school comes from a long-term disengagement process with social and economic consequences. Being able to predict students' behavior earlier can minimize their failures and disengagement. This article presents the SASys architecture, based on a lexical approach and a polarized frame network. Its main goal is to define the author's sentiment in texts and increase the assertiveness of detecting the sentence's emotional state by adding authors' information and preferences. The author's emotional state begins with the phrase extraction from Virtual Learning Environments; then, pre-processing techniques are applied in the text, which is submitted to the complex frame network to identify words with polarity and the author's text sentiment. The flow ends with the identification of the author's emotional state. The proposal was evaluated by a case study, applying the Sentiment Analysis approach to the students' school dropout problem. The results point to the feasibility of the proposal for asserting the student's emotional state and detection of students' risks of dropout.
Design and Modeling of a High Sensitivity Fiber Bragg Grating-Based Accelerometer

Use of a detailed theoretical model has allowed the optimization of the design of a high sensitivity accelerometer, based on a fiber Bragg grating (FBG) and an accelerometer based on this design has been demonstrated experimentally. With a universal model based on double-point encapsulation established, the performance of the device in terms of its optimal sensitivity and frequency distribution has been analyzed, with an optimization ‘figure of merit’ using the product of the sensitivity and the resonant frequency being presented. The experimental results obtained indicate that the FBG-based accelerometer thus developed shows a broad, flat frequency band, a corresponding flat range sensitivity of ~152.0pm/G, a resonant frequency of 441.0Hz, and a cross-axis sensitivity of less than 3.6% of the main-axis sensitivity. An accelerometer of this type and with this performance thus has the potential for the important field of low frequency oil-gas seismic exploration.
Drug-induced autoimmune hepatitis: A minireview

Drug-induced autoimmune hepatitis (DIAIH) is a specific phenotype of drug-induced liver injury that may lead to the devastating outcome of acute liver failure requiring liver transplantation. Drugs implicated in DIAIH include antimicrobials such as nitrofurantoin and minocycline, non-steroidal anti-inflammatory drugs, statins as well as anti-tumor necrosis agents. The clinical features of drug-induced liver injury are indistinguishable from idiopathic autoimmune hepatitis (AIH) as both may have positive AIH-related autoantibodies, elevated immunoglobulin G, as well as similar histopathological findings. In patients who show no clinical improvement, or there is progressive liver injury despite cessation of the suspected drug, a liver biopsy should be considered, whereby the presence of advance fibrosis on histology favors the diagnosis of idiopathic AIH. Empirical treatment with corticosteroids may be required in patients with non-resolving liver injury. A typical clinical scenario supportive of DIAIH includes a history of drug exposure with spontaneous resolution of liver injury after drug withdrawal and the absence of relapse after rapid steroid taper. In this article we report two cases of DIAIH secondary to Sorafenib and Atorvastatin along with a review of currently available literature. Early identification and treatment often lead to a favorable outcome in DIAIH.
CO in mergers. III. NGC 1614 and NGC 3256.

12 CO(1−0) and 12 CO(2−1) maps of two southern merger remnants, NGC 1614 and NGC 3256, obtained with the SEST 15 m telescope are presented. For NGC 1614, the 12 CO(2−1) beam (22″ = 7 kpc) does not resolve the emission, while in NGC 3256 the CO emission is extended with a diametre of 3-4 kpc. The molecular masses of these galaxies are 6.5 10 9 M ⊙ and 1.3 10 10 M ⊙ respectively, with a standard conversion factor from CO emissivities to H 2 column densities of 2.3 10 20 cm −2 /(K km/s −1 )
A Novel Solution for Day-Ahead Scheduling Problems Using the IoT-Based Bald Eagle Search Optimization Algorithm

Advances in technology and population growth are two factors responsible for increasing electricity consumption, which directly increases the production of electrical energy. Additionally, due to environmental, technical and economic constraints, it is challenging to meet demand at certain hours, such as peak hours. Therefore, it is necessary to manage network consumption to modify the peak load and tackle power system constraints. One way to achieve this goal is to use a demand response program. The home energy management system (HEMS), based on advanced internet of things (IoT) technology, has attracted the special attention of engineers in the smart grid (SG) field and has the tasks of demand-side management (DSM) and helping to control equality between demand and electricity supply. The main performance of the HEMS is based on the optimal scheduling of home appliances because it manages power consumption by automatically controlling loads and transferring them from peak hours to off-peak hours. This paper presents a multi-objective version of a newly introduced metaheuristic called the bald eagle search optimization algorithm (BESOA) to discover the optimal scheduling of home appliances. Furthermore, the HEMS architecture is programmed based on MATLAB and ThingSpeak modules. The HEMS uses the BESOA algorithm to find the optimal schedule pattern to reduce daily electricity costs, reduce the PAR, and increase user comfort. The results show the suggested system’s ability to obtain optimal home energy management, decreasing the energy cost, microgrid emission cost, and PAR (peak to average ratio).
Arbuscular Mycorrhizal Fungi Mediated Enhanced Biomass, Root Morphological Traits and Nutrient Uptake under Drought Stress: A Meta-Analysis

Drought stress remains the major constraint in affecting crop productivity in several arid and semi-arid areas highlighting climate change perspectives. Arbuscular mycorrhizal fungi (AMF) belong to a versatile class of plant–fungal symbiotic associations establishing drought stress alleviation. Nevertheless, the mechanistic mode of sustainable agriculture necessitates rigorous assessment for authentic and reproducible plant growth parameters. Understanding the plant growth promotion, root morphological changes, and nutrient uptake response in AMF-inoculated plants to drought is very important for sustainable agriculture. Therefore, conducted a meta-analysis of published research articles for determining the efficacy of AMF in alleviating drought stress. Overall analysis showed that AM inoculated plants had 49% higher plant growth promotion than the non-mycorrhizal plants under drought stress. Biomass analysis depicted the root dry weight increase by 49%, shoot dry weight increase by 54%, and total dry weight increase by 58% indicating plant biomass traits augmentation. Root morphological traits analysis corresponded to increased root length (37%), root surface (31%), and root volume (65%). Notably, nutrient uptake assessment showed variable increases in uptake patterns such as P uptake by 86%, N uptake by 35%, and K uptake by 46%. Furthermore, the prominent efficacy of AMF was significantly larger under drought for P uptake (p < 0.001) and root volume (p < 0.001) indicating the linear relationship between root length and P uptake. Thus, the present meta-analysis confirms that drought stress alleviation emancipated by AMF is mediated by root traits modification and phosphorous acquisition efficacy. Hence, meta-analyses along with experimental validations with field trial evaluations will certainly provide the AMF research for escalated applications for better plant productivity, stress alleviation, and sustainable agriculture.
Modeling Driving Behavior of Human Drivers for Trajectory Planning

Extracted driving behavior of human driven vehicles can benefit the development of various applications like trajectory prediction or planning, abnormal driving detection, driving behavior classification, traffic simulation modeling, etc. In this paper, we focus on modeling human driving behavior in order to find simplifications for trajectory planning. Using a time-discrete kinematic bicycle model with the vehicle’s acceleration and steering rate as inputs, we model the human driven trajectories of an urban intersection drone dataset for different input sampling times. While most planning algorithms are using input sampling times below 0.33s, we are able to model 98.2% of the human driven trajectories of the investigated dataset with a sampling time of 0.6s. Using longer input sampling times can result in smoother trajectories and longer planning horizons, and thus more efficient trajectories. In a next step, we analyze the correlations between the input of our model and the current state/last input. Such a priori knowledge could simplify common planning algorithms like model predictive control or tree-search based planners by limiting the action space of the ego-vehicle. We propose nonlinear transformations for steering rate and steering angle to represent correlations between speed, acceleration, steering angle and steering rate. In the transformed space the statistics are very well modeled by multivariate Gaussian distributions. Using a multivariate Gaussian, a fast usable behavior model is extracted which is independent of the environment.
Immunohistochemical Profile of Parathyroid Tumours: A Comprehensive Review

Immunohistochemistry remains an indispensable tool in diagnostic surgical pathology. In parathyroid tumours, it has four main applications: to detect (1) loss of parafibromin; (2) other manifestations of an aberrant immunophenotype hinting towards carcinoma; (3) histogenesis of a neck mass and (4) pathogenetic events, including features of tumour microenvironment and immune landscape. Parafibromin stain is mandatory to identify the new entity of parafibromin-deficient parathyroid neoplasm, defined in the WHO classification (2022). Loss of parafibromin indicates a greater probability of malignant course and should trigger the search for inherited or somatic CDC73 mutations. Aberrant immunophenotype is characterised by a set of markers that are lost (parafibromin), down-regulated (e.g., APC protein, p27 protein, calcium-sensing receptor) or up-regulated (e.g., proliferation activity by Ki-67 exceeding 5%) in parathyroid carcinoma compared to benign parathyroid disease. Aberrant immunophenotype is not the final proof of malignancy but should prompt the search for the definitive criteria for carcinoma. Histogenetic studies can be necessary for differential diagnosis between thyroid vs. parathyroid origin of cervical or intrathyroidal mass; detection of parathyroid hormone (PTH), chromogranin A, TTF-1, calcitonin or CD56 can be helpful. Finally, immunohistochemistry is useful in pathogenetic studies due to its ability to highlight both the presence and the tissue location of certain proteins. The main markers and challenges (technological variations, heterogeneity) are discussed here in the light of the current WHO classification (2022) of parathyroid tumours.
An Improved Faster RCNN Marine Fish Classification Identification Algorithm

For the efficient identification of marine fish species, a fast neural network recognition algorithm based on improved Faster RCNN is proposed.The algorithm first selects residual network (Resnet) with strong feature extraction capability for feature extraction; then generates candidate target regions through 12 different Anchors to further improve the accuracy of detection; finally, the resulting features are transmitted to two subnetworks to achieve classification and positioning respectively.The classification networks are based on the full connectivity structure, while the localization network is mainly composed of convolutional neural networks.This paper verifies the effectiveness of the algorithm on the marine fish (holothurian, echinus, scallop, starfish) image dataset. The results show that the proposed algorithm is more accurate recognition than Faster RCNN while efficiently detecting the target.
Morpho-Metric and Specialized Metabolites Modulation of Parsley Microgreens through Selective LED Wavebands

Plant factories and high-tech greenhouses offer the opportunity to modulate plant growth, morphology and qualitative content through the management of artificial light (intensity, photoperiod and spectrum). In this study, three Light Emitting Diode (LED) lighting systems, with blue (B, 460 nm), red (R, 650 nm) and mixed red + green-yellow + blue (RGB) light were used to grow parsley microgreens to understand how light quality could change the phenotype and the profile of secondary metabolites. Plants showed altered morphological characteristics and higher amounts of secondary metabolites under RGB LEDs treatment. The results demonstrated that microgreens under red light showed the highest fresh yield, petiole length, coumaric acid content but also the highest nitrate content. Plants under RGB light showed the highest dry matter percentage and highest content of total and single polyphenols content, while blue light showed the highest ascorbic acid and ABTS antioxidant activity. Moreover, microgreens under red light showed more compact leaves with less intercellular spaces, while under blue and RGB light, the leaves displayed ticker spongy mesophyll with higher percentage of intercellular spaces. Therefore, the specific spectral band was able to modify not only the metabolic profile, but also it could modulate the differentiation of mesophyll cells. Light quality as a preharvest factor helps to shape the final parsley microgreens product as a whole, not only in terms of yield and quality, but also from a morpho-anatomical point of view.
METHOD FOR EXTRACTING DIAGNOSTIC FEATURES OF THE FACILITIES TECHNICAL CONDITION IN THE SYSTEM FOR MONITORING

Technical diagnostics of facilities is an urgent problem during its operation. An integral part of the implementation of diagnostic monitoring systems is the development of a decision support system (DSS) based on the analysis of acoustic emission (AE) diagnostic data and machine learning methods. A necessary condition for the application of machine learning methods in the development of DSS is the process of extracting diagnostic features from the AE signal. In the present work, an improved method is proposed for extracting diagnostic features from time series of AE signals. This includes two successive steps. At the first step, the frequency and frequency-time characteristics are calculated in a sliding window of short duration, which describe local changes in the shape and structure of single pulses. At the second step, the resulting matrix of informative features is aggregated by calculating statistical moments of various orders, which makes it possible to effectively detect long-term trends in the AE signal changes emitted by the defect. Verification of the proposed method was carried out on a full-scale control object of the oil tank RVS No. 3 ("NTEK LLC"). Based on the results obtained, a conclusion was made about the effectiveness of the proposed method in the development of diagnostic monitoring systems based on acoustic emission data.
Role of stem cells-based in facial nerve reanimation: A meta-analysis of histological and neurophysiological outcomes

BACKGROUND Treatments involving stem cell (SC) usage represent novel and potentially interesting alternatives in facial nerve reanimation. Current literature includes the use of SC in animal model studies to promote graft survival by enhancing nerve fiber growth, spreading, myelinization, in addition to limiting fibrotic dege neration after surgery. However, the effectiveness of the clinical use of SC in facial nerve reanimation has not been clarified yet. AIM To investigate the histological, neurophysiological, and functional outcomes in facial reanimation using SC, compared to autograft. METHODS Our study is a systematic review of the literature, consistently conducted according to the preferred reporting items for systematic reviews and meta-analyses statement guidelines. The review question was: In facial nerve reanimation on rats, has the use of stem cells revealed as effective when compared to autograft, in terms of histological, neurophysiological, and functional outcomes? Random-effect meta-analysis was conducted on histological and neurophysiological data from the included comparative studies. RESULTS After screening 148 manuscript, five papers were included in our study. 43 subjects were included in the SC group, while 40 in the autograft group. The meta-analysis showed no significative differences between the two groups in terms of myelin thickness [CI: -0.10 (-0.20, 0.00); I2 = 29%; P = 0.06], nerve fibers diameter [CI: 0.72 (-0.93, 3.36); I2 = 72%; P = 0.6], compound muscle action potential amplitude [CI: 1.59 (0.59, 3.77); I2 = 89%; P = 0.15] and latency [CI: 0.66 (-1.01, 2.32); I2 = 67%; P = 0.44]. The mean axonal diameter was higher in the autograft group [CI: 0.94 (0.60, 1.27); I2 = 0%; P ≤ 0.001]. CONCLUSION The role of stem cells in facial reanimation is still relatively poorly studied, in animal models, and available results should not discourage their use in future studies on human subjects.
R&D on Robotics for Decommissioning of Fukushima Daiich

search Institute for Nuclear Decommissioning,略して IRID)が設立された. 目的は第一に「廃止措置に関する研 究開発」であり,加えて,「廃止措置に関する国際・国内関 係機関との協力」ならびに「研究開発に関する人材育成」 の三つである. まず IRID の紹介を簡単にしておこう. IRID は電力会 社,原子力関連企業,プラントメーカー,国立研究開発法 人などの組合員によって構成される技術研究組合である. IRIDが使用する研究費の大半は国からの補助金(100%と 50%がある)で成り立っており,一方,組合の運営費は組 合員への賦課金で賄われている. 研究開発期間は現在 2年 を標準としている. 補助金のテーマは国が立案し,研究資 金管理団体から競争的な研究開発の公募として公示される. IRIDでは組合員間で協力し,開発目標・開発計画を立案し て,応募する. 専門家による審査が行われ,管理団体が採 択企業体を決める. それゆえ,基本的な要求仕様が補助金 募集要項内に記述され,提案書には開発目的・内容などが 詳細化されている. 通常の補助金による研究開発ではこの
Design, Synthesis, and Biological Evaluation of N14-Amino Acid-Substituted Tetrandrine Derivatives as Potential Antitumor Agents against Human Colorectal Cancer

As a typical dibenzylisoquinoline alkaloid, tetrandrine (TET) is clinically used for the treatment of silicosis, inflammatory pulmonary, and cardiovascular diseases in China. Recent investigations have demonstrated the outstanding anticancer activity of this structure, but its poor aqueous solubility severely restricts its further development. Herein, a series of its 14-N-amino acid-substituted derivatives with improved anticancer effects and aqueous solubility were designed and synthesized. Among them, compound 16 displayed the best antiproliferative activity against human colorectal cancer (HCT-15) cells, with an IC50 value of 0.57 μM. Compared with TET, 16 was markedly improved in terms of aqueous solubility (by 5-fold). Compound 16 significantly suppressed the colony formation, migration, and invasion of HCT-15 cells in a concentration-dependent manner, with it being more potent in this respect than TET. Additionally, compound 16 markedly impaired the morphology and motility of HCT-15 cells and induced the death of colorectal cancer cells in double-staining and flow cytometry assays. Western blot results revealed that 16 could induce the autophagy of HCT-15 cells by significantly decreasing the content of p62/SQSTM1 and enhancing the Beclin-1 level and the ratio of LC3-II to LC3-I. Further study showed that 16 effectively inhibited the proliferation, migration, and tube formation of umbilical vein endothelial cells, manifesting in a potent anti-angiogenesis effect. Overall, these results revealed the potential of 16 as a promising candidate for further preclinical studies.
Detection and Localization of Open-Phase Fault in Three-Phase Induction Motor Drives Using Second Order Rotational Park Transformation

The open-phase fault in vector-controlled drives is known as one of the most prevalent failures that require immediate detection. It is still challenging to develop a trustable fault diagnosis scheme at light load, transients, and low-speed regions. This paper presents a novel open-phase fault diagnosis strategy for three-phase induction motor drives over the entire speed range and for different load conditions. By taking advantage of the second-order rotational park transformation (SORP), new fault indices are proposed to identify the open-phase location without using any additional sensors or electric equipment. Comparing with root-mean-square (RMS) calculation-based methods, the proposed method detects the fault at least twice faster, with minimal system resources. The presented scheme may be implemented simply and is robust against the operating point changes. Proof of the proposed fault detection and localization method has been shown through experimental tests on a 250-W three-phase induction machine which is controlled by TI-DSP F28335.