Miracle-dz/UniFAR-FTUs huggingface dataset:
Different Dietary Sources of Selenium Alleviate Hepatic Lipid Metabolism Disorder of Heat-Stressed Broilers by Relieving Endoplasmic Reticulum Stress

As global warming continues, the phenomenon of heat stress (HS) in broilers occurs frequently. The alleviating effect of different selenium (Se) sources on HS-induced hepatic lipid metabolism disorders in broilers remains unclear. This study compared the protective effects of four Se sources (sodium selenite; selenium yeast; selenomethionine; nano-Se) on HS-induced hepatic lipid metabolism disorder and the corresponding response of selenotranscriptome in the liver of broilers. The results showed that HS-induced liver injury and hepatic lipid metabolism disorder, which were reflected in the increased activity of serum alanine aminotransferase (ALT), the increased concentration of triacylglycerol (TG) and total cholesterol (TC), the increased activity of acetyl-CoA carboxylase (ACC), diacylglycerol O-acyltransferase (DGAT) and fatty acid synthase (FAS), and the decreased activity of hepatic lipase (HL) in the liver. The hepatic lipid metabolism disorder was accompanied by the increased mRNA expression of lipid synthesis related-genes, the decreased expression of lipidolysis-related genes, and the increased expression of endoplasmic reticulum (ER) stress biomarkers (PERK, IRE1, ATF6, GRP78). The dietary supplementation of four Se sources exhibited similar protective effects. Four Se sources increased liver Se concentration and promoted the expression of selenotranscriptome and several key selenoproteins, enhanced liver antioxidant capacity and alleviated HS-induced ER stress, and thus resisted the hepatic lipid metabolism disorders of broilers exposed to HS. In conclusion, dietary supplementation of four Se sources (0.3 mg/kg) exhibited similar protective effects on HS-induced hepatic lipid metabolism disorders of broilers, and the protective effect is connected to the relieving of ER stress.
Clinical Research in Prehospital Care: Current and Future Challenges

Prehospital care plays a critical role in improving patient outcomes, particularly in cases of time-sensitive emergencies such as trauma, cardiac failure, stroke, bleeding, breathing difficulties, systemic infections, etc. In recent years, there has been a growing interest in clinical research in prehospital care, and several challenges and opportunities have emerged. There is an urgent need to adapt clinical research methodology to a context of prehospital care. At the same time, there are many barriers in prehospital research due to the complex context, posing unique challenges for research, development, and evaluation. Among these, this review allows the highlighting of limited resources and infrastructure, ethical and regulatory considerations, time constraints, privacy, safety concerns, data collection and analysis, selection of a homogeneous study group, etc. The analysis of the literature also highlights solutions such as strong collaboration between emergency medical services (EMS) and hospital care, use of (mobile) health technologies and artificial intelligence, use of standardized protocols and guidelines, etc. Overall, the purpose of this narrative review is to examine the current state of clinical research in prehospital care and identify gaps in knowledge, including the challenges and opportunities for future research.
The application of air pressure difference in reducing indoor radon concentration

INTRODUCTION Radon mitigation methods and radon control measures have been well established and successfully applied in dwellings. Of these methods, the majority deal with detached and semi-detached houses where the main source of radon is the ground underneath the houses. For high-rise buildings where radon comes mainly from the concrete in the walls, increasing ventilation and applying radon barriers on wall surfaces seem to be the only ways to reduce the indoor radon concentration (1, 2). Out of these two methods, ventilation is the key factor in controlling radon level in high-rise buildings. According to surveys done in local commercial buildings and dwellings, the geometric mean indoor radon concentrations are 90 Bq m and 48 Bq m respectively. The major reason for high levels of radon in commercial buildings is the insufficient ventilation provided in the buildings because of various reasons, such energy saving, lack of maintenance, etc. Though ventilation is very efficient in reducing indoor radon level, the energy consumption can be a serious problem from an environmentalist’s point of view. The most effective way then is to reduce the exhalation of radon from the building materials. These can either be passive methods such as applying radon barriers on wall surfaces or active methods such as driving the radon away from the wall surfaces by air pressure difference. The sections that follow describe the active methods and discuss their applicability.
Efficient Spatial-Temporal Angle-Delay Analysis Scheme for Massive MIMO Indoor Tracking

Radio positioning is critical for many indoor applications, such as behavioral monitoring and autonomous robots. Mobile users, however, can also be exposed to surveillance risks due to this capability. This work presents a Spatial-Temporal Angle-Delay Analysis Scheme (STADAS) for massive MIMO wireless networks that can help the attacker to track a user without the need to enter buildings. First, we transform the channel state information (e.g., angle of arrival, time of arrival) from massive MIMO transmission gained over time into living Angle-Delay profiles (ADPs) with fixed objects (building walls, furniture) and a moving object (the mobile user). Second, a generative adversarial network learning model is used to remove distorted data points from Angle-Delay video frames. The processed ADPs are trained with a Deep Convolutional Neural Network (DCNN)-based model on estimating the user's location. Evaluations on an empirical dataset indicate that radio positioning capabilities in emerging wireless communication technologies such as mmWave MIMO can pose severe privacy and surveillance threats.
Native lamin A/C proteomes and novel partners from heart and skeletal muscle in a mouse chronic inflammation model of human frailty

Clinical frailty affects ∼10% of people over age 65 and is studied in a chronically inflamed (Interleukin-10 knockout; “IL10-KO”) mouse model. Frailty phenotypes overlap the spectrum of diseases (“laminopathies”) caused by mutations in LMNA. LMNA encodes nuclear intermediate filament proteins lamin A and lamin C (“lamin A/C”), important for tissue-specific signaling, metabolism and chromatin regulation. We hypothesized that wildtype lamin A/C associations with tissue-specific partners are perturbed by chronic inflammation, potentially contributing to dysfunction in frailty. To test this idea we immunoprecipitated native lamin A/C and associated proteins from skeletal muscle, hearts and brains of old (21–22 months) IL10-KO versus control C57Bl/6 female mice, and labeled with Tandem Mass Tags for identification and quantitation by mass spectrometry. We identified 502 candidate lamin-binding proteins from skeletal muscle, and 340 from heart, including 62 proteins identified in both tissues. Candidates included frailty phenotype-relevant proteins Perm1 and Fam210a, and nuclear membrane protein Tmem38a, required for muscle-specific genome organization. These and most other candidates were unaffected by IL10-KO, but still important as potential lamin A/C-binding proteins in native heart or muscle. A subset of candidates (21 in skeletal muscle, 30 in heart) showed significantly different lamin A/C-association in an IL10-KO tissue (p < 0.05), including AldoA and Gins3 affected in heart, and Lmcd1 and Fabp4 affected in skeletal muscle. To screen for binding, eleven candidates plus prelamin A and emerin controls were arrayed as synthetic 20-mer peptides (7-residue stagger) and incubated with recombinant purified lamin A “tail” residues 385–646 under relatively stringent conditions. We detected strong lamin A binding to peptides solvent exposed in Lmcd1, AldoA, Perm1, and Tmem38a, and plausible binding to Csrp3 (muscle LIM protein). These results validated both proteomes as sources for native lamin A/C-binding proteins in heart and muscle, identified four candidate genes for Emery-Dreifuss muscular dystrophy (CSRP3, LMCD1, ALDOA, and PERM1), support a lamin A-interactive molecular role for Tmem38A, and supported the hypothesis that lamin A/C interactions with at least two partners (AldoA in heart, transcription factor Lmcd1 in muscle) are altered in the IL10-KO model of frailty.
A Liquid-Metal-Based, Stretchable Inductive Loop Sensor for Muscle Atrophy

We present a sensor toward wearable monitoring of muscle atrophy with improved stretching capabilities and sensing resolution for practical implementation. The operation relies on our previously reported approach, where wrap-around transmit and receive loops at inductive frequencies monitor changes in muscle size via the magnitude and/or phase of the transmission coefficient. The novel aspects of this work entail: 1) a new fabrication approach with stretchable inductive loops made from Gallium Indium eutectic (EGaIn) injected into silicone tubing to improve stretchability, and 2) operation below the defined resonant frequency to improve magnitude resolution of the sensor. Simulation and in vitro measurement results are in excellent agreement. Compared to our previous sensor implementation, the proposed approach: 1) achieves 4.3 times higher stretchability, 2) measures 1.7 times larger muscle volume loss, and 3) improves magnitude resolution by 6 dB. Specifically, we achieve a magnitude and phase resolution of 1.25 dB and 3.8°, respectively, per centimeter of limb circumference, and 0.3 dB and 0.9° per a 1% volume reduction. We also demonstrate repeatable fabrication with a low standard error of 0.19 dB and 0.7°. Because of the improved stretching capabilities and resolution, we can now accurately measure the circumference at any location on the leg of any given member of the population with a single device. Concurrently, this work brings forward innovations in fabricating radio frequency electronics from liquid metals.
Association analysis in over 329,000 individuals identifies 116 independent variants 1 influencing neuroticism. 2

association (GWA) of neuroticism have identified up to 11 genetic loci 3,4 . Here we report 116 significant 30 independent loci from a GWA of neuroticism in 329,821 Biobank participants; 15 of 31 these replicated at P<. 00045 in an unrelated cohort (N = 122,867). Genetic signals in neuronal differentiation substantial genetic correlations were found between g . 82, SE=. 03), 35 The combination, in UK Biobank, of a large ethnically homogenous sample and a well-validated neuroticism scale has afforded the discovery of 15 stringently replicated genetic loci that influence neuroticism levels, four of them novel. Most lead variants were associated with gene regulation, with half of these expressed in the brain; single variant and gene associations overlapped substantially with MDD findings, and genes in antidepressant- targeted pathways were over-represented. There was also support for neuroticism having causal effects on socio-economic markers. These discoveries promise paths to understand the mechanisms whereby some people become depressed, and of broader human differences in 208 happiness, and they are a resource for those seeking novel drug targets for major depression. After millennia in which scholars and researchers have sought the sources of individual differences in proneness to dysphoria 46 , the present study adds significantly to explaining the (genetic) anatomy of melancholy.
Energy Efficient Secure Offloading in NOMA-aided Vehicular Networks Using A3C Learning

High-speed computation resources are provided by mobile edge computing (MEC) to boost various delay-sensitive vehicular applications. However, compared to computing tasks locally, the MEC approach consumes extra energy in the offloading process. In this paper, an asynchronous deep reinforcement learning-based energy-efficient secure offloading (EESO) is proposed to enhance the energy efficiency and security of the vehicular edge computing (VEC) network in the presence of multiple malicious eavesdropper vehicles. To secure the wireless offloading process of the information, a group of jammer vehicles is scheduled to form a NOMA cluster with each user vehicle for providing jamming signals to the eavesdropper vehicles while not interfering with the legitimate user vehicle. We minimize the system energy consumption with the computation delay constraint by jointly optimizing the transmit power, the computation resource allocation, and the selection of jammer vehicles in each NOMA cluster. Then we adopt an asynchronous advantage actor-critic (A3C) learning algorithm to solve the optimization problem. With proper training, the A3C-based EESO scheme can reduce the system energy consumption and improve offloading security.
Experimental Study on Double-Joint Soft Actuator and Its Dexterous Hand

In this paper, we propose a pneumatic double-joint soft actuator based on fiber winding and build a dexterous hand with 11 degrees of freedom. Firstly, soft actuator structural design is carried out according to the actuator driving principle and gives the specific manufacturing process. Then, an experimental analysis of the bending performance of a single soft actuator, including bending angle, speed, and force magnitude, is carried out by building a pneumatic control experimental platform. Finally, a series of dexterous robotic hand-grasping experiments is conducted. Different grasping methods are used to catch the objects and measure the objects’ change in height, length, and rotation angle during the experiment. The results show that the proposed soft actuator is more consistent with the bending rule of human fingers, and that the gestures of the dexterous hand are more imaginable and flexible when grasping objects. The soft actuator can carry out horizontal and vertical movements, and rotation of the object in the dexterous hand, thus achieving better human–computer interaction.
Winter wheat lodging resistance characteristics as affected by nitrogen application time and the underlying mechanism

Wheat lodging is an agricultural problem causing severe yield loss and quality degradation. Therefore, it is needed to investigate the impact of cultivation practices on lodging to develop effective solutions to reduce yield losses. This study conducted a two-yield field experiment with modifying the nitrogen application time treatments to analyse if it is possible to reduce the risk of lodging without yield loss. Transcriptomic analysis was performed to investigate changes in the expression levels of lignin and cellulose biosynthesis-related genes in node samples. The results of the experiment showed that delaying the nitrogen application time until the Zadoks (Z) 45 (booting) stage significantly reduced the risk of lodging due to the minimal yield loss by 3.9% and 2.9% for 2019–2020 and 2020–2021, respectively. The increased lodging resistance at the Z45 stage was associated with a lower plant height and gravity centre height, an increased wall thickness and stem thickness of the two basal internodes, which increased the strength resistance and lodging resistance index (LRI) of the stem. Transcriptomic analysis between the lodging (Z30) and the lodging-resistant (Z45) stages was conducted to illustrate the underlying mechanism on lodging resistance. The total number of differentially expressed genes (DEGs) was 9,000, and the gene ontology (GO) analysis of DEGs indicated that cellular component biogenesis was the most enriched GO term. Also, 30 and 14 DEGs involved in lignin and cellulose biosynthesis were identified, respectively, indicating that the primary mechanism resulting in a high lodging resistance is the high expression of key genes involved in the phases of lignin and cellulose synthesis processes. The findings of the study indicate not only a potential cultivation approach for reducing lodging risk, but also potential genes for developing lodging-resistant wheat cultivars.
A Novel Hybrid System of Detecting Brain Tumors in MRI

The growth of irregular brain cells leads to a disease called brain tumor (BT). It is difficult to predict a patient’s chance of survival due to the low rate and wide range of tumor shapes. Even though it is possible to manually detect cancer, doing so is difficult and time-consuming and runs the risk of producing false-positive results. This can be done via MRI, which is necessary for locating cancer. It is very difficult to reliably identify different illnesses from MRI images for successful therapy via a computer-aided diagnostic system. In the experiment, three openly accessible benchmark datasets were utilized. To perform feature extraction in our proposed method, a CNN model was employed, followed by the application of five machine learning classifiers: Decision tree (DT), Naive Bayes (NB), Adaptive Boosting (AdaBoost), K-nearest neighbor (KNN), and support vector machine (SVM). The outcomes show that the proposed CNN architecture with the KNN classifier performs better than previous CNN models by outperforming other cutting-edge DL models under various classification metrics. Finally, the achieved F1-Score, precision, recall, and accuracy values for the classification and detection of the proposed model were 99.58%, 99.59%, 99.58%, and 99.58%, respectively. For the comparison study, additional Transfer Learning models are utilized. Experimental findings support the strength of the proposed architecture, which has rapidly accelerated and improved the classifications of BTs. The designed method outperforms the body of existing knowledge, demonstrating that it is a quick and precise method for classifying BTs.
Determination of Production and Marketing Behaviors of Producers Selling Products to the Turkish Grain Board, (Kırşehir Province Mucur District Micro Field Study)

The aim of this study is to examine the socio-economic characteristics of producers who sell wheat to the Turkish Grain Board (TGB), which is an interventionist organization in the purchase and sale of grain in Türkiye, as well as some of their behaviors regarding production, marketing preferences, and their relations with TGB. The sample of the study consisted of 100 wheat producers in the Mucur district of Kırşehir province. The obtained data were interpreted by converting them into tables, cross-tabulations, and graphs. The data in the cross tables were statistically interpreted using Chi-square analysis. It can be said that producers are not satisfied with TGBs’ purchasing practices for some reasons. The most important reason for this is that quality-based purchasing practices have an extremely negative impact on prices. It was determined that they were not satisfied, and this resulted in high price reductions in quality-based purchasing, and as a result, there was distrust in analysis practices. Quality-based purchasing policy is a method that allows wheat quality characteristics to be determined with various devices in a short time. This method, which is decisive in quality classification and product pricing scale, must be explained correctly to producers. Otherwise, the producer ’s trust in the TGB may gradually decrease. This can be achieved through initiatives taken by the institution or through various publications and training activities. One of the most important results of this study is that TGBs and decision-makers are more sensitive to local producers. For decision-makers to maintain their influence on the producer, they must carefully examine the details of the processes with an inductive approach, starting from the bottom up. This situation is considered critical in terms of food security.
Healthcare Decision-Making with an ECG-Based Biometric System

Biometric systems that provide accurate identification are essential for making informed decisions in various fields. In this study, we propose a novel approach for identifying medical patients using the QRS waveform of their ECG signal. ECG signals are a highly secure biometric trait as they are difficult to spoof and reproduce. To derive a representative QRS waveform, the signals were subject to preprocessing via a 4th-order bandpass filter, amplitude normalization, and R-peak detection through the Pan-Tompkins algorithm. Subsequently, a vector encompassing 50 samples before and after each index was generated. An identification accuracy of 99.47% was achieved using 48 healthy subjects from the PTB Diagnostic ECG Database. To further test the system, we used 282 healthy and diseased subjects from the same database and achieved an accuracy of 97.60%. Our proposed approach provides a promising solution for biometric identification, which can improve decision-making in various fields such as security and healthcare. With the increasing demand for accurate identification, our study highlights the potential for ECG-based biometrics as a secure and reliable modality for decision-making.
Open Innovation System Theory Approach

This paper aims to explain the open innovation (OI) process using systems theory, showing (a) pathways towards their further development and (b) explaining evolutionary developments. We introduce a meta-view of the two disciplinary poles, i open innovation and ii evolutionary system, system order principles, and system stability. We apply then ii to i or OI using eight explaining Axioms. Finally, we give examples of how to use this axiomatic method in an innovation process and a practical OI method.
Dual PID Adaptive Variable Impedance Constant Force Control for Grinding Robot

High-precision and low-overshoot force control are important to guarantee the material removal rate and surface quality of robot grinding. However, traditional force control methods are subjected to positional disturbance, stiffness disturbance, contact process nonlinearity, and force-position coupling, leading to difficulties in robot constant force control. Therefore, how to achieve smooth, stable, and high-precision constant force control is an urgent problem. To address this problem, a dual PID adaptive variable impedance control is established (DPAVIC). Firstly, PD control is used to compensate for the force error, and PID is used to update the damping parameters to compensate for the disturbance. Secondly, a nonlinear tracking differentiator is used to smooth the desired force and reduce the contact force overshoot. Then, the stability, convergence, and effectiveness of the force control algorithm are verified via theoretical analysis, simulations, and experiments. The force tracking error and overshoot of a conventional impedance controller (CIC), adaptive variable impedance control (AVIC), and DPAVIC are analyzed. Finally, the algorithm is used in grinding experiments on a thin-walled workpiece. The force tracking error is controlled within ±0.2 N, and the surface roughness of the workpiece is improved to Ra 0.218 μm.