Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Deep 1D Convolutional Grated Recurrent Unit Neural Network to Optimize Quantum Molecular Properties and Predict Intramolecular Coupling Constants of Molecules of Potential Health Medications and Other Generic Molecules

A molecule is the smallest particle in a chemical element or compound that possesses the element or compound’s chemical characteristics. There are numerous challenges associated with the development of molecular simulations of fluid characteristics for industrial purposes. Fluid characteristics for industrial purposes find applications in the development of various liquid household products, such as liquid detergents, drinks, beverages, and liquid health medications, amongst others. Predicting the molecular properties of liquid pharmaceuticals or therapies to address health concerns is one of the greatest difficulties in drug development. Computational tools for precise prediction can help speed up and lower the cost of identifying new medications. A one-dimensional deep convolutional gated recurrent neural network (1D-CNN-GRU) was used in this study to offer a novel forecasting model for molecular property prediction of liquids or fluids. The signal data from molecular properties were pre-processed and normalized. A 1D convolutional neural network (1D-CNN) was then built to extract the characteristics of the normalized molecular property of the sequence data. Furthermore, gated recurrent unit (GRU) layers processed the extracted features to extract temporal features. The output features were then passed through several fully-connected layers for final prediction. For both training and validation, we used molecular properties obtained from the Kaggle database. The proposed method achieved a better prediction accuracy, with values of 0.0230, 0.1517, and 0.0693, respectively, in terms of the mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE).
What is the optimal time interval between heart catheterization and surgery to prevent acute kidney injury in patients with isolated coronary artery bypass?

Aim The aim of the article was to study the role of the time between cardiac catheterization and cardiac surgery in the development of early postoperative acute kidney injury in patients who underwent isolated coronary artery bypass grafting was investigated. Material and methods A total of 1196 patients (832 males, 364 females; mean age 60.8 ±8.2 years; range: 32–74 years) operated between November 2006 and June 2014 at the same centre and by the same team for isolated coronary artery bypass grafting with cardiopulmonary bypass, whose preoperative serum creatinine level was < 1.5 mg/dl, were enrolled in the study. Patients were divided into group 1 – with acute kidney injury in the early postoperative period (n = 207) and group 2 – without (n = 989). Univariate analyses were done to determine significant clinical factors, and subsequent multiple logistic regression analysis was performed to determine independent predictors of acute kidney injury. Results A total of 207 (17.3%) patients developed acute kidney injury during 72 h postoperatively. Regarding the time interval between coronary angiography and coronary artery bypass grafting, there was a statistically highly significant difference between the patients with and without acute kidney injury (7.8 and 11.9 days, respectively; p = 0.0001). Postoperative C-reactive protein (p = 0.0001) and erythrocyte sedimentation rate (p = 0.0001) were significantly increased in group 1. Multivariate logistic regression analysis revealed the time between cardiac catheterization and surgery (p = 0.0001), increased postoperative C-reactive protein (p = 0.007 and p = 0.0001, respectively), and erythrocyte sedimentation rate (p = 0.0001) as independent predictors of early postoperative acute kidney injury in patients undergone isolated coronary artery bypass grafting. Conclusions If patients to be operated on are stable from a cardiac aspect, limitation of surgery in the early period following catheterization results in reduction of the incidence of postoperative acute kidney injury.
Behavioral genetics and genomics: Mendel’s peas, mice, and bees

The question of the heritability of behavior has been of long fascination to scientists and the broader public. It is now widely accepted that most behavioral variation has a genetic component, although the degree of genetic influence differs widely across behaviors. Starting with Mendel’s remarkable discovery of “inheritance factors,” it has become increasingly clear that specific genetic variants that influence behavior can be identified. This goal is not without its challenges: Unlike pea morphology, most natural behavioral variation has a complex genetic architecture. However, we can now apply powerful genome-wide approaches to connect variation in DNA to variation in behavior as well as analyses of behaviorally related variation in brain gene expression, which together have provided insights into both the genetic mechanisms underlying behavior and the dynamic relationship between genes and behavior, respectively, in a wide range of species and for a diversity of behaviors. Here, we focus on two systems to illustrate both of these approaches: the genetic basis of burrowing in deer mice and transcriptomic analyses of division of labor in honey bees. Finally, we discuss the troubled relationship between the field of behavioral genetics and eugenics, which reminds us that we must be cautious about how we discuss and contextualize the connections between genes and behavior, especially in humans.
Adaptive fixed‐time fuzzy control for output constrained nonlinear systems with unknown virtual control coefficients based on event‐triggered mechanism

The paper discusses a fixed‐time fuzzy adaptive event‐triggered control for the nonlinear systems with output constraint and unknown virtual control coefficients. Fuzzy logic systems are utilized to approximate the uncertain nonlinearities. The barrier Lyapunov function is employed to analyze the stability and avoid violating the given output constraint. The combination of the designed adaptive laws and fixed‐time control signal is used to reduce restriction on unknown virtual control coefficients and increase the robustness for control. An event‐triggered mechanism is proposed to update controllers to save communication resources. The control strategy makes sure that the tracking error can converge into a small neighborhood of the origin in a fixed time. Finally, the efficacy of the proposed control approach is verified by two simulation examples.
A Piecewise Monotonic Gait Phase Estimation Model for Controlling a Powered Transfemoral Prosthesis in Various Locomotion Modes

Gait phase-based control is a trending research topic for walking-aid robots, especially robotic lower-limb prostheses. Gait phase estimation is a challenge for gait phase-based control. Previous researches used the integration or the differential of the human's thigh angle to estimate the gait phase, but accumulative measurement errors and noises can affect the estimation results. In this letter, a more robust gait phase estimation method is proposed using a unified form of piecewise monotonic gait phase-thigh angle models for various locomotion modes. The gait phase is estimated from only the thigh angle, which is a stable variable and avoids phase drifting. A Kalman filter-based smoother is designed to further suppress the mutations of the estimated gait phase. Based on the proposed gait phase estimation method, a gait phase-based joint angle tracking controller is designed for a transfemoral prosthesis. The proposed gait estimation method, the gait phase smoother, and the controller are evaluated through offline analysis on walking data in various locomotion modes. And the real-time performance of the gait phase-based controller is validated in an experiment on the transfemoral prosthesis.
Preoperative Non-Invasive Prediction of Breast Cancer Molecular Subtypes With a Deep Convolutional Neural Network on Ultrasound Images

Purpose This study aimed to develop a deep convolutional neural network (DCNN) model to classify molecular subtypes of breast cancer from ultrasound (US) images together with clinical information. Methods A total of 1,012 breast cancer patients with 2,284 US images (center 1) were collected as the main cohort for training and internal testing. Another cohort of 117 breast cancer cases with 153 US images (center 2) was used as the external testing cohort. Patients were grouped according to thresholds of nodule sizes of 20 mm and age of 50 years. The DCNN models were constructed based on US images and the clinical information to predict the molecular subtypes of breast cancer. A Breast Imaging-Reporting and Data System (BI-RADS) lexicon model was built on the same data based on morphological and clinical description parameters for diagnostic performance comparison. The diagnostic performance was assessed through the accuracy, sensitivity, specificity, Youden’s index (YI), and area under the receiver operating characteristic curve (AUC). Results Our DCNN model achieved better diagnostic performance than the BI-RADS lexicon model in differentiating molecular subtypes of breast cancer in both the main cohort and external testing cohort (all p < 0.001). In the main cohort, when classifying luminal A from non-luminal A subtypes, our model obtained an AUC of 0.776 (95% CI, 0.649–0.885) for patients older than 50 years and 0.818 (95% CI, 0.726–0.902) for those with tumor sizes ≤20 mm. For young patients ≤50 years, the AUC value of our model for detecting triple-negative breast cancer was 0.712 (95% CI, 0.538–0.874). In the external testing cohort, when classifying luminal A from non-luminal A subtypes for patients older than 50 years, our DCNN model achieved an AUC of 0.686 (95% CI, 0.567–0.806). Conclusions We employed a DCNN model to predict the molecular subtypes of breast cancer based on US images. Our model can be valuable depending on the patient’s age and nodule sizes.
Applications of Circulating Tumor Cells and Circulating Tumor DNA in Precision Oncology for Breast Cancers

Liquid biopsies allow for the detection of cancer biomarkers such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA). Elevated levels of these biomarkers during cancer treatment could potentially serve as indicators of cancer progression and shed light on the mechanisms of metastasis and therapy resistance. Thus, liquid biopsies serve as tools for cancer detection and monitoring through a simple, non-invasive blood draw, allowing multiple longitudinal sampling. These circulating markers have significant prospects for use in assessing patients’ prognosis, monitoring response to therapy, and developing precision medicine. In addition, single-cell omics of these liquid biopsy markers can be potential tools for identifying tumor heterogeneity and plasticity as well as novel therapeutic targets. In this review, we focus on our current understanding of circulating tumor biomarkers, especially in breast cancer, and the scope of novel sequencing technologies and diagnostic methods for better prognostication and patient stratification to improve patient outcomes.
Visual Outcomes of Small-Incision Lenticule Extraction (SMILE) in Thin Corneas

We aimed to find out whether thin (≤500 μm) or normal (>500 μm, control) corneal thickness would impact efficacy and safety outcomes of small-incision lenticule extraction (SMILE). We retrospectively analyzed medical records of adult patients who had undergone SMILE. A total of 57 eyes were included in the “thin corneas” group and 180 eyes in the “control” group. At one month after surgery, rates of patients with uncorrected distance visual activity (UDVA) ≥ 0.8 were significantly higher in patients from the control group compared to the “thin corneas” group (87 vs. 71%, respectively p < 0.01), though rates were comparable at 3 months (87 vs. 76%, respectively, p > 0.05). SMILE had comparable safety in patients with thin and normal corneas. Procedure result predictability was comparable between groups. Regression analysis demonstrated that cap thickness impacted posterior corneal biomechanics, and the volume of removed tissue had a higher influence in patients with thin corneas. Moreover, an increase in cap thickness was associated with better final BCVA. Further study is needed for the evaluation of the impact of thin corneas on SMILE outcomes and planning. Our study also indicates that patients with thin corneas might require a different approach to nomogram calculation.
Optical Bistability in a Tunable Gourd-Shaped Silicon Ring Resonator

In this study, a tunable gourd-shaped ring resonator is demonstrated to generate optical bistability. The system consists of two sub-rings for a gourd shape configuration with a U-shaped wave guiding pathway. The transfer matrix method and FDTD simulation are used to acquire the spectral characteristics of the system. For the fabricated device, the spectra profile and extinction ratio can be effectively tuned by the microheater above the U-shaped waveguide, which matches with the theoretical results. Due to the gourd structure of the resonator, the light waves in two rings can be cross-coupled with each other, and the optical bistability could come out effectively with the change in the input optical power around 6 mW. The presented optical bistability devices have great application potential in optical information processing such as optical storage, switch and logic operation.
Carbon Nanofibers Grown in CaO for Self-Sensing in Mortar

Intelligent cementitious materials integrated with carbon nanofibers (CNFs) have the potential to be used as sensors in structural health monitoring (SHM). The difficulty in dispersing CNFs in cement-based matrices, however, limits the sensitivity to deformation (gauge factor) and strength. Here, we synthesise CNF by chemical vapour deposition on the surface of calcium oxide (CaO) and, for the first time, investigate this amphiphilic carbon nanomaterial for self-sensing in mortar. SEM, TEM, TGA, Raman and VSM were used to characterise the produced CNF@CaO. In addition, the electrical resistivity of the mortar, containing different concentrations of CNF with and without CaO, was measured using the four-point probe method. Furthermore, the piezoresistive response of the composite was quantified by means of compressive loading. The synthesised CNF was 5–10 μm long with an average diameter of ~160 nm, containing magnetic nanoparticles inside. Thermal decomposition of the CNF@CaO compound indicated that 26% of the material was composed of CNF; after CaO removal, 84% of the material was composed of CNF. The electrical resistivity of the material drops sharply at concentrations of 2% by weight of CNF and this drop is even more pronounced for samples with 1.2% by weight of washed CaO. This indicates a better dispersion of the material when the CaO is removed. The sensitivity to deformation of the sample with 1.2% by weight of CNF@CaO was quantified as a gauge factor (GF) of 1552, while all other samples showed a GF below 100. Its FCR amplitude can vary inversely up to 8% by means of cyclic compressive loading. The method proposed in this study provides versatility for the fabrication of carbon nanofibers on a tailored substrate to promote self-sensing in cementitious materials.
miR-10 and Its Negative Correlation with Serum IL-35 Concentration and Positive Correlation with STAT5a Expression in Patients with Rheumatoid Arthritis

Circulating free-cell miRNAs are increasingly important as potential non-invasive biomarkers due to the easy accessibility of clinical materials. Moreover, their epigenetic role may provide insight into the mechanisms of pathogenesis. Nevertheless, these aspects are mostly studied in the area of oncological diseases. Therefore, this research aimed to find the potential association of selected miRNAs in serum with the expression of Th17/Treg transcription factors and clinical features in RA patients. Accordingly, experiments was conducted on rheumatoid arthritis (RA), osteoarthritis (OA) and healthy subjects (HC). Analysis of miRNAs level in serum was performed using LNA miRNA PCR assays. mir-10 was detected only in RA patients. Furthermore, its expression was correlated with IL-35 serum concentration and the mRNA level of STAT5a in whole blood in RA. Additionally, a tendency of the raised level of miR-10 was noted in RA patients with high activity disease. miR-326 was significantly upregulated in RA patients with rheumatoid factor presence. In HC the correlation between miR-26 and IL-21 serum levels and expression of SMAD3 have been found. In OA patients, correlations between miR-126 and HIF1 expression and between miR-146 and RORc have been noted. The differential association of transcription factor expression with serum miRNA levels may be important in the diagnosis and progression of RA and OA.
Research on Power Load Forecasting Based on PSO-LSTM

In order to improve the prediction accuracy of electricity consumption, the particle swarm optimization algorithm was proposed to find the optimal hyperparameters of long-term and short-term memory (LSTM) neural networks, and the two models are combined to form a power load forecasting model. Aiming at the problem that it is difficult to manually select the LSTM hyperparameters, the PSO algorithm can effectively find the global optimal solution to find the hyperparameters of LSTM model. After continuous training, we find the appropriate hyperparameters and verify them The experimental results show that compared with the traditional LSTM network, the performance and prediction accuracy of the combined pso-lstm combination model have been significantly improved, which has certain academic value and application significance.
Fingerprint liveness map construction using convolutional neural network

With increasing markets for fingerprint authentication, there are also increasing concerns about spoofs or synthetically produced fingerprint identifications that can bypass the authentication process. In this Letter, the authors introduce a new convolutional neural networks (CNNs) architecture for fingerprint liveness detection problem that can provide a more robust framework for network training and detection than previous methods. The proposed method employs squared regression error for each receptive field without the usage of the fully connected layer. Such structure provides following advantages from the previous liveness fingerprint CNN. First, unlike the previous techniques which rely on the pre-trained features, the proposed CNN can be trained directly from fingerprints as the loss is minimised for each receptive field. Second, in contrast to the cross-entropy layer, the squared error layer allows them to set up a threshold value that can control the acceptable level of false positives or false negatives. Third, the absence of a fully connected layer allows them to crop the input fingerprints such that a trade-off between accuracy and computation time can be made without the negative effects of re-scaling. The proposed CNN is shown to provide higher accuracy for three out of four datasets when evaluated against the state-of-the-art method.
Effectiveness of Augmented Reality for Lower Limb Rehabilitation: A Systematic Review

Augmented reality- (AR-) based interventions have shown potential benefits for lower limb rehabilitation. However, current literature has not revealed these benefits as a whole. The main purposes of this systematic review were to determine the efficacy of AR-based interventions on lower limb recovery of the larger population based on the current process that has been made in this regard. Relevant studies were retrieved from five electronic databases (Web of Science, PubMed, ScienceDirect, Scopus, and Cochrane Library) using “augmented reality” OR “AR” AND “lower limb” OR “lower extremity” AND “intervention” OR “treatment”. Sixteen studies that met the eligibility criteria were included in this review, and they were further grouped into three categories based on the participant types. Seven studies focused on the elderly adults, six on the stroke patients, and the last three on Parkinson patients. Based on the findings of these trials, the significant effects of AR-based interventions on lower limb rehabilitation (i.e., balance, gait, muscle, physical performance, and fall efficacy) have been initially confirmed. Favorable results were achieved at least the same as the interventions without AR except for the turning and timing in the freezing of gait of Parkinson patients. However, given the infancy of this technology in clinical practices, more robust trials with larger sample sizes and greater homogeneity in terms of devices and treatment settings are warranted for further verification.
Elevated homocysteine levels, white matter abnormalities and cognitive impairment in patients with late-life depression

Background Cognitive impairment in late−life depression (LLD) is considered to be caused by neurodegenerative changes. Elevated homocysteine (Hcy) levels may be linked to cognitive abnormalities associated with LLD. The important role of white matter (WM) damage in cognitive impairment and pathogenesis in patients with LLD has been widely reported. However, no research has explored the interrelationships of these features in patients with LLD. Objective The goal of the study was to examine the interrelationship between Hcy levels, cognition, and variations in WM microstructure detected by diffusion tensor imaging (DTI) in patients with LLD. Methods We recruited 89 healthy controls (HCs) and 113 patients with LLD; then, we measured the plasma Hcy levels of participants in both groups. All individuals performed a battery of neuropsychological tests to measure cognitive ability. Seventy-four patients with LLD and 68 HCs experienced a DTI magnetic resonance imaging (MRI) scan. Results Patients with LLD showed significantly lower fractional anisotropy (FA) values in the bilateral inferior longitudinal fasciculus than those of healthy participants. Only in LLD patients was Hcy concentration inversely associated to FA values in the forceps minor. Finally, multiple regression analyses showed that an interaction between Hcy levels and FA values in the right cingulum of the cingulate cortex and right inferior longitudinal fasciculus were independent contributors to the executive function of patients with LLD. Conclusion Our results highlight the complex interplay between elevated homocysteine levels and WM abnormalities in the pathophysiology of LLD-related cognitive impairment, consistent with the neurodegeneration hypothesis.
Development of a Computational Model to Predict the In Vivo Contact Mechanics of Modern Total Knee Arthroplasty

v This dissertation focuses on the development of a computationally efficient and fast method that incorporates the kinematics obtained from fluoroscopy and extends it to the prediction of the in-vivo contact mechanics at the femoro-tibial articulation in modern knee implants for the deep knee bend activity. In this endeavor, this dissertation deals with the use of an inverse dynamic rigid body model characterizing the slip and roll behavior observed in the femoro-polyethylene articulation and a coupled deformation model where the polyethylene in knee implants are modeled as hexahedral discrete element networks. The performance of this method is tested by comparing force predictions from a telemetric knee and finite element analysis. Finally, the method is applied to study the in vivo contact mechanics and mechanics of the quadriceps mechanism in six popular knee designs. During the deep knee bend activity, the contact force generally increased with flexion. However, the medial lateral forces were not equally distributed and the medial lateral force distribution generally varied from 60%40% at full extension to as high as 75%-25% at full flexion in some patients. Also, the magnitude of axial force in the superior-inferior direction was the highest and was found to contribute around 98%-99% of the total load acting at the femorotibial joint. The forces in the medio-lateral and antero-posterior directions were low and the maximum