Miracle-dz/UniFAR-FTUs huggingface dataset:
Preliminary Study on the Feasibility of Using Permanent Magnetic Elastomer as a Stretchable Skin Sensor

Sensor technology will greatly accelerate the development of automation, especially in the field of robotics, particularly for safe interaction with both humans and the environment. In previous work, permanent magnet elastomers (PMEs), which were produced by mixing Neodymium powders into a silicone base and subsequently magnetizing the material, were integrated in skin sensors, and force detection experiments based on magnetic field changes were performed. This study investigates the changes in magnetic fields of PMEs made from silicone bases of various softness when stretched, as well as performs cyclic tests to analyze the elongation of each PME sample. The experimental results show that a linear relationship exists between the magnetic field of PMEs and the stretched distance, which was evaluated using a linear fit with a slope in the range of -0.006 to -0.057 mT/mm. Moreover, the findings demonstrate that the PMEs can be used to measure pressing forces even when they are being stretched. The elongation of PME changes by 4% for sample made from Dragon Skin™ 10 MEDIUM (D10) and 1.4% for sample made from Ecoflex™ 00–50 (E50). The results confirmed the feasibility of using PMEs as stretchable sensors.
A Machine Learning Model for Survival Prediction in Secondary Liver Cancer

Survival prediction in the context of secondary liver cancer represents a significant challenge within the field of oncology, yet it is an area that has received limited attention. To address this gap, our paper introduces an innovative approach that harnesses the power of machine learning to enhance the accuracy of survival predictions. Our work leverages a comprehensive dataset that encompasses a wide array of patient information, pre-treatment data, and essential clinical variables. A meticulous process of data preprocessing, along with the application of advanced techniques, ensures that this data is optimally prepared for in-depth analysis. Through the application of cutting-edge machine learning methodologies, our predictive model adeptly stratifies patients into two distinct categories: those who have 'Survived' and those who have unfortunately 'Deceased. ' Our model's efficacy and precision are underscored by rigorous testing, suggesting the potential to significantly improve the decision-making process regarding treatments and, ultimately, patient outcomes.
Evaluation of Crowd Models Under Various Environments for Robot Navigation Simulator

Robot simulators are commonly employed in the study of autonomous mobile robots operating in human-robot coexistence environments. In these simulators, it is essential to place moving pedestrians as dynamic obstacles, which requires the application of various crowd models developed in the field of crowd simulation. However, existing crowd models often assume that all agents, including both pedestrians and robots, follow the same navigation algorithms. The validity of using such models for robot simulators remains unclear. This paper aims to explore the impact of robots with different navigation methods than pedestrians and the environment in which they are placed on the behavior and performance of crowd models in a robot navigation simulator. The experimental environment is constructed within a mobile robot simulator in which multiple agents, including robots, are deployed and crowd simulations are performed. These simulations are performed by moving the agents through multiple crowd models. In addition, the impact of the presence of robots and environmental factors on the performance of the crowd models is evaluated. The results indicate that the performance of the crowd models is highly dependent on the environment in which the robot operates, and that it is necessary to select the appropriate model for a given environment.
Construction of Enterprise Digital Intelligent Management Platform Based on Improved Neural Network

The purpose of studying the digital intelligent management platform for enterprises is to help them better carry out digital transformation and intelligent management, thereby improving their operational efficiency and market competitiveness. This paper aims to solve a series of problems encountered in the intelligent management platform through the improvement of neural network technology, so as to promote the digital transformation and intelligent management of enterprises. Through experiments on the accuracy and stability of this method, the results show that the accuracy based on improved neural networks is generally above 85%, with the highest stability reaching 85.72%. This indicates that the improved neural network algorithm can process complex data more accurately and produce more stable and reliable results. Adopting this method can provide higher quality and more effective management services for enterprises, thereby providing strong support for their sustainable development.
Vision-Based Software System for Indoor Quadrupedal Locomotion: Integrated with SLAM, Foothold Planning, and Multimodal Gait

This paper presents a software system designed for indoor quadrupedal locomotion using vision-based techniques. It incorporates established technologies such as Simultaneous Localization and Mapping (SLAM), global planning, and composite Center of Gravity (COG) trajectory planning methods to achieve both autonomous and semi-autonomous quadrupedal movement. The software solution offers a comprehensive approach to locomotion challenges, encompassing vision-based foothold planning and obstacle avoidance. With a modular architecture that supports diverse controller implementations, the software demonstrates its versatility. Overall, this research contributes a comprehensive software framework for indoor quadrupedal locomotion, exhibiting promising results in simulation and offering potential for real-world deployment.
Characterizing Manipulator Motion Using an Evolving Type 2 Quantum Fuzzy Neural Network

This paper presents a control algorithm based on the Evolving Type 2 Quantum Fuzzy Neural Network (ET2QFNN) for characterizing and controlling the joint-space motion of a manipulator. The approach integrates ET2QFNN, a hybrid neural network model that combines principles from quantum fuzzy sets and neural networks, to address the inherent complexity and uncertainty associated with manipulator systems. Unlike traditional control methods, the ET2QFNN-based approach learns and adapts through simulations of closed-loop torque control, incorporating joint position commands and corresponding motion data. By utilizing data from previous cycles, the ET2QFNN enhances the manipulator's performance and stability, particularly in repetitive tasks. To assess its effectiveness, a comparative analysis is conducted, contrasting the approach with finely tuned proportional-derivative (PD) control and integral sliding mode control (iSMC) in scenarios with and without non-parametric uncertainties. The results demonstrate the unique advantages and superior performance of the ET2QFNN-based control algorithm in addressing these challenges.
Soil Moisture Variations with Land Use along the Precipitation Gradient in the North–South Transect of the Loess Plateau

Knowledge of soil moisture spatial variation with land use along the precipitation gradient is necessary to improve land management and guide restoration practice in the water‐limited Chinese Loess Plateau. This study selected 45 sampling points at 11 sites across the north–south transect of the Loess Plateau based on the precipitation gradient and land use. Results showed that the vertical profiles of soil moisture revealed large variations with the precipitation gradient changing, especially in the surface layer (0–100 cm). Significant linear correlation existed between the average soil moisture of the profile and the mean annual precipitation (MAP) for each land use type (p < 0·05). Hereinto, the soil moisture under the grassland was affected more greatly by precipitation. The soil moisture under each land use commonly revealed the trend as farmland > grassland > shrubland > woodland, while it might be higher under the woodland than the shrubland in the surface layer in regions with MAP <500 mm. The soil moisture of woodland or shrubland at the selected points was below or approximate to the permanent wilting point in regions with MAP <520 mm. Covariance analysis confirmed the effects of land use and MAP on the soil moisture in depth of 100–300 cm, and it showed land use did not pose significant effects in the surface layer. In addition, our study indicated that it is necessary to reconsider and re‐evaluate the current vegetation restoration strategy in the perspective of vegetation sustainability and soil water availability, in which woodland and shrubland were selected on a large scale in the arid and semi‐arid regions. Copyright © 2016 John Wiley & Sons, Ltd.
Identifying and modulating neural signatures of stress susceptibility and resilience enables control of anhedonia

Anhedonia is a core aspect of major depressive disorder. Traditionally viewed as a blunted emotional state in which individuals are unable to experience joy, anhedonia also diminishes the drive to seek rewards and the ability to value and learn about them 1–4.The neural underpinnings of anhedonia and how this emotional state drives related behavioral changes remain unclear. Here, we investigated these questions by taking advantage of the fact that when mice are exposed to traumatic social stress, susceptible animals become socially withdrawn and anhedonic, where they cease to seek high-value rewards, while others remain resilient. By performing high density electrophysiological recordings and comparing neural activity patterns of these groups in the basolateral amygdala (BLA) and ventral CA1 (vCA1) of awake behaving animals, we identified neural signatures of susceptibility and resilience to anhedonia. When animals actively sought rewards, BLA activity in resilient mice showed stronger discrimination between upcoming reward choices. In contrast, susceptible mice displayed a rumination-like signature, where BLA neurons encoded the intention to switch or stay on a previously chosen reward. When animals were at rest, the spontaneous BLA activity of susceptible mice was higher dimensional than in controls, reflecting a greater number of distinct neural population states. Notably, this spontaneous activity allowed us to decode group identity and to infer if a mouse had a history of stress better than behavioral outcomes alone. Finally, targeted manipulation of vCA1 inputs to the BLA in susceptible mice rescued dysfunctional neural dynamics, amplified dynamics associated with resilience, and reversed their anhedonic behavior. This work reveals population-level neural signatures that explain individual differences in responses to traumatic stress, and suggests that modulating vCA1-BLA inputs can enhance resilience by regulating these dynamics.
Improving phage therapy by evasion of phage resistance mechanisms

Abstract Antibiotic failure is one of the most worrisome threats to global health. Among the new therapeutic efforts that are being explored, the use of bacteriophages (viruses that kill bacteria), also known as ‘phages’, is being extensively studied as a strategy to target bacterial pathogens. However, one of the main drawbacks of phage therapy is the plethora of defence mechanisms that bacteria use to defend themselves against phages. This review aims to summarize the therapeutic approaches that are being evaluated to overcome the bacterial defence systems, including the most innovative therapeutic approaches applied: circumvention of phage receptor mutations; modification of prophages; targeting of CRISPR-Cas systems and the biofilm matrix; engineering of safer and more efficacious phages; and inhibition of the anti-persister strategies used by bacteria.
Noninvasive radiomic biomarkers for predicting pseudoprogression and hyperprogression in patients with non-small cell lung cancer treated with immune checkpoint inhibition

ABSTRACT This study aimed to develop a computed tomography (CT)-based radiomics model capable of precisely predicting hyperprogression and pseudoprogression (PP) in patients with non-small cell lung cancer (NSCLC) treated with immunotherapy. We retrospectively analyzed 105 patients with NSCLC, from three institutions, treated with immune checkpoint inhibitors (ICIs) and categorized them into training and independent testing set. Subsequently, we processed CT scans with a series of image-preprocessing techniques, and 6008 radiomic features capturing intra- and peritumoral texture patterns were extracted. We used the least absolute shrinkage and selection operator logistic regression model to select radiomic features and construct machine learning models. To further differentiate between progressive disease (PD) and hyperprogressive disease (HPD), we developed a new radiomics model. The logistic regression (LR) model showed optimal performance in distinguishing PP from HPD, with areas under the receiver operating characteristic curve (AUC) of 0.95 (95% confidence interval [CI]: 0.91-0.99) and 0.88 (95% CI: 0.66-1) in the training and testing sets, respectively. Additionally, the support vector machine model showed optimal performance in distinguishing PD from HPD, with AUC of 0.97 (95% CI: 0.93-1) and 0.87 (95% CI: 0.72-1) in the training and testing sets, respectively. Kaplan‒Meier survival curves showed clear stratification between PP predicted by the radiomics model and true progression (HPD and PD) (hazard ratio = 0.337, 95% CI: 0.200–0.568, p < 0.01) in overall survival. Our study demonstrates that radiomic features extracted from baseline CT scans are effective in predicting PP and HPD in patients with NSCLC treated with ICIs.
Evaluating the Morphology of Unique Superficial Fissured Cartilage Lesions at the Femoral Head-Neck Junction in Patients with Femoroacetabular Impingement Syndrome

Background: While an association between femoroacetabular impingement (FAI) and osteoarthritis (OA) has been reported, the mechanistic differences and transition between the 2 conditions is not fully understood. In FAI, cartilage lesions at the femoral head-neck junction can sometimes be visualized during hip arthroscopy. Purpose/Hypothesis: The purpose of this study was to describe a unique dimpled pattern of superficial fissured cartilage lesions on the femoral head-neck junction at impingement site in patients with FAI syndrome (FAIS) and to evaluate the clinical, histological, and genetic phenotype of this cartilage. We hypothesized that the cartilage lesions may indicate risk for, or predict occurrence of, OA. Study Design: Controlled laboratory study. Methods: Six hips (6 patients; mean age, 34.2 ± 12.9 years; range, 19-54 years) with dimpled or fissured cartilage were included among patients who underwent hip arthroscopy for treatment of FAIS from October 2020 through December 2021. This affected cartilage (dimple-pattern group) and normal cartilage (control group) on the femoral head-neck junction were collected from the same patients and evaluated for histological quantification by Mankin scores and expression of proteins related to cartilage degeneration (eg, matrix metalloproteinase [MMP]-1, MMP-2, MMP-3, MMP-10, and MMP-12, tissue inhibitor of metalloproteinase [TIMP]-1 and TMP-2, aggrecan neopepitope CS846, and hyaluronic acid [HA]) with the use of Milliplex Multiplex Assays. Results: All 6 hips were of the mixed FAI subtype. Preoperatively, 4 of 6 hips had Tönnis grade 1 radiographic changes, which was associated with greater femoral head chondral damage visualized intraoperatively. Mankin scores for the normal cartilage group and the dimple-pattern group were 0.67 ± 0.82 and 3.3 ± 0.82, respectively. Dimple pattern fissured cartilage showed a significant increase in Mankin score (P = .031) and a significant increase in protein expression of CS846 (P = .031) compared with normal cartilage. There were no significant differences in MMPs, TIMPs, or HA levels between the 2 groups. Conclusion: The dimple pattern fissured cartilage, compared to normal cartilage, showed histologically significant cartilage degeneration and a significant increase in protein expression of CS846, a biomarker for early OA. Clinical Relevance: This lesion serves as helpful visual indicator of early degeneration of the cartilage of femoral head-neck junction caused by FAIS.
DNA barcoding, identification, and validation of the pufferfish (Order: Tetraodontiformes) in China coastal waters

Abstract The order Tetraodontiformes are one of the most unique groups of teleostean fish, exhibiting highly derived and greatly diversified phenotypes. It is a difficult task for both professionals and nonprofessionals to accurately identify these species only according to morphological characteristics. DNA barcoding can identify species at the molecular level to overcome the limitations of morphological classification. In this study, we collected 616 specimens of pufferfish from the coastal waters of China. According to the morphological characteristics, they were preliminarily identified as 50 species. Further analysis using DNA barcodes identified these specimens as 46 species, belonging to 23 genera, 6 families. According to the species classification results of DNA barcoding, the three species identified by morphology as Takifugu pseudommus, Takifugu chinensis, and Takifugu rubripes should be the same species. Similarly, Lagocephalus wheeleri is the synonym of Lagocephalus spadiceus. Another important discovery of DNA barcoding analysis is that there are closer interspecific genetic distances within the genus Takifugu. If T. rubripes, T. pseudommus, and T. chinensis are taken as one species, the average interspecific to intraspecific genetic distance ratio of Takifugu is only 6.21 times, which does not reach the DNA barcoding threshold of more than 10 times proposed previously. Although the interspecific genetic distance in the genus Takifugu is relatively small, each species can be clustered into independent clades in the NJ tree. In conclusion, this study not only found that there are synonymous phenomena in the order Tetraodontiformes but also provided molecular evidence for the valid species names of Takifugu rubripes and Lagocephalus Spadiceus. The results can provide reliable DNA barcoding information for the identification of pufferfish species, help solve the problem of classification confusion in this order, and provide technical support for the identification of the original components of related commodities on the aquatic product market.
Biofilm therapy for chronic wounds

Abstract Chronic wounds have been a major factor of serious harm to global public health. At present, it is known that almost all chronic wounds contain biofilms, which seriously hinder the healing process. Removal of biofilms can effectively promote the healing of chronic wounds. As the study of wound biofilms deepens, many new treatment methods have emerged, thus bringing revolutionary means for the treatment of chronic wound biofilm. This review summarizes various methods for the treatment of chronic wound biofilm worldwide to provide a theoretical summary and practical basis for the selection of suitable wound biofilm treatment methods in clinical practice.
Soil Moisture Remote Sensing: State‐of‐the‐Science

Satellites, particularly at L‐band frequency, can globally map near‐surface soil moisture. Near‐surface moisture is extended to the root zone using models and data assimilation. Validation uses core monitoring sites, monitoring networks, field campaigns, and multi‐satellite comparisons. Efforts are underway to associate soil moisture variability dynamics with land surface attributes.
Diagnostic value of angiography-derived index of microcirculatory resistance (AMR) for coronary microcirculatory dysfunction (CMD) and its prognostic significance in patients with chronic coronary syndromes in the smoking population

This study aims to analyze the prognostic significance of angiographic microvascular resistance (AMR) derived from functional coronary angiography in diagnosing coronary microvascular dysfunction (CMD) among patients with chronic coronary syndrome (CCS), particularly focusing on the prognostic significance of CMD in smokers. Additionally, this study assesses the prognostic significance of the diagnostic accuracy of the AMR index for CMD in CCS patients within the smoking population. Seventy-five CCS patients were studied for IMR measurement between June 2018 and December 2020, classified into CMD and non-CMD groups. Pearson correlation analyzed AMR and IMR relationship, while ROC curves determined AMR’s predictive value for CMD with optimal cutoff value. An additional 223 CCS patients were included for AMR measurements. The study’s primary endpoint was MACE occurrence, followed up through telephone calls. COX one-way analysis and Kaplan–Meier analysis evaluated the association between AMR and MACE risk. In the study, 52% of patients were diagnosed with CMD, and the AUC under the AMR curve for predicted IMR ≥ 25 was 0.90 (95% CI: 0.79–0.95). The CMD group had a higher proportion of female patients, smokers, and those with hypertension and diabetes mellitus compared to the non-CMD group (all P < .05). The CMD group exhibited higher AMR values than the non-CMD group (3.20 ± 0.5 vs 1.95 ± 0.45, P < .01), with a significant correlation between AMR and IMR (R2 = 0.81, P < .001). The ROC curve illustrated AMR’s effectiveness in diagnosing CMD in CCS patients, using IMR as the gold standard, with the optimal cutoff value of AMR = 2.6mmHgs/cm (sensitivity: 90.5%, specificity: 81.5%). Patients with AMR ≥ 2.6mmHgs/cm had a higher risk of MACE events (HR = 6.00; 95% CI: 1.59–22.67). AMR in patients with CCS was significantly correlated with IMR and demonstrated good diagnostic accuracy. AMR ≥ 2.6 mm Hg*s/cm was associated with an increased risk of MACE occurrence and served as a valuable prognostic factor. In patients with CCS, AMR has a high diagnostic performance, In patients with CCS who smoke, impaired coronary microvascular function as assessed by AMR is significant and is an independent predictor of MACE.
Retrospective study on the effect of Niaoduqing particles on outcome of non-diabetic patients with stage IV chronic kidney disease

This study was to investigate the improvement value of Niaoduqing particles in the outcome of non-diabetic patients with stage IV chronic kidney disease (CKD). The non-diabetic patients with stage IV CKD who were to receive Niaoduqing particles were set as the study group (252 cases), and the patients with the same disease who only received Western medicine in the public database were set as the control group (220 cases). The follow-up visits were 3 months/time for 1 year. Deaths due to various causes, doubling of creatinine levels, and end-stage renal disease were used as hard end points to stop follow-up. The clinical indexes of the 2 groups were observed and compared. The results showed that the rate of compound outcome was significantly lower in the study group (28.17%) than in the control group (36.82%), the glomerular filtration rate was significantly higher than that in the control group, and the levels of uric acid and urea were significantly lower than that in the control group (P < .05). Niaoduqing particles can reduce creatinine and urea nitrogen, stabilize renal function, delay dialysis time, and improve the incidence of compound outcome in patients with non-diabetic stage IV CKD, which is worthy of clinical promotion.