Miracle-dz/UniFAR-FTUs huggingface dataset:
βKlotho Inhibits Cell Proliferation by Downregulating ELK4 and Predicts Favorable Prognosis in Prostate Cancer

Objective Prostate cancer (PCa) ranks as the second common malignancy in males worldwide. Although conspicuous progressions in diagnosis and treatment have been achieved in the past decades, the prognosis expectation of PCa remains unsatisfied yet. To improve the prognosis prediction of PCa, more specific biomarkers are needed. In this retrospective research, we focused on βKlotho and ETS-like transcription factor 4 (ELK4), aiming to identify potential prognostic biomarkers for PCa. Methods Western blotting was used to determine the expression of βKlotho, ELK4, and PARP in C4-2B and PC3 PCa cell lines. CCK-8 assay and colony formation assay were applied to examine the roles of βKlotho and ELK4 in the proliferation of PCa cells. The expression of βKlotho and ELK4 in PCa tissue samples was determined by immunochemistry. Pearson’s χ2 test and Fisher’s exact test were performed to investigate the associations among βKlotho, ELK4 and various clinical factors. Kaplan–Meier curves and Cox regression model were established to reveal the correlation among βKlotho, ELK4 expression and the prognosis of patients. Results βKlotho overexpression down-regulated the ELK4 expression, induced apoptosis and inhibited cell proliferation in both C4-2B and PC3 cells, which were reversed by ELK4 overexpression. βKlotho expression in PCa tissue samples had negative correlation with the ELK4 expression, and higher βKlotho expression was associated with lower Gleason score, absent distant metastasis and lower prostate-specific antigen (PSA) level. On the contrast, higher ELK4 expression was correlated with distant metastasis and higher PSA level. Moreover, βKlotho and ELK4 were both recognized as independent factors for the prognosis of patients with PCa. Conclusion βKlotho inhibits proliferation of prostate cancer cells by downregulating ELK4. Both βKlotho and ELK4 expressions correlate with the prognosis of PCa, which may serve as potential biomarkers for follow-up surveillance and prognostic assessments.
Methylation of the RIN3 Promoter is Associated with Transient Ischemic Stroke/Mild Ischemic Stroke with Early Cognitive Impairment

Background Early cognitive impairment after transient ischemic stroke (TIA)/mild ischemic stroke (MIS) is common but easily overlooked. It has been demonstrated that DNA methylation plays a significant role in cognitive impairment and ischemic stroke. Furthermore, it has been reported that the RIN3 gene influences transportation of the amyloid β-protein. However, to our knowledge, there has been no research related to correlations between RIN3 methylation and early-onset cognitive impairment after TIA/MIS. Therefore, this study aimed to investigate this relationship in TIA/MIS patients. Methods This study include 28 control subjects and 84 patients with TIA/MIS who were evaluated within 7 days of TIA/MIS onset using four single-domain cognitive scales. In addition, DNA methylation of whole blood was tested. RIN3 methylation was compared between TIA/MIS and control groups and between TIA/MIS patients with early cognitive impairment and those without early cognitive impairment. Clinical variables and RIN3 methylation sites with statistical differences were then used to construct a predictive model. Results Hypomethylation of the RIN3 gene was observed in the whole blood of TIA/MIS patients relative to healthy controls. Furthermore, patients with early cognitive impairment after TIA/MIS had hypomethylation of RIN3 relative to those without early cognitive impairment. Conclusion RIN3 methylation is strongly associated with TIA/MIS and TIA/MIS with early cognitive impairment. It is possible to influence the disease process by methylation via appropriate lifestyle and clinical interventions, and methylation of RIN3 gene sites may predict the occurrence of TIA/MIS with early cognitive impairment.
A New Way of Evaluating Effectiveness of URM Summer Pipeline Programs

Purpose Many academic medical centers fund educational opportunities (pipeline programs) for students who are underrepresented in medicine (URM). However, there is a sparsity of published literature on pipeline programs and an even smaller body of published literature that investigates program effectiveness. Methods In a retrospective cohort study (n=12) of the Provost’s Summer Mentorship Program-Medicine (SMPM), we evaluated students’ rating of program effectiveness, students’ rating of the program’s impact on their mindsets, and SAT scores. Several program mindsets, including sense of belonging (inclusiveness) in the health professions and connection to mentors in the medical field, reflect common barriers that prevent URM students from pursuing careers in medicine as outlined in pipeline literature. We describe program effectiveness using mean and median ratings of SMPM effectiveness, ratings of mindsets, and SAT scores. We used Wilcoxon Rank Sum to assess pre and post program differences in ratings of mindsets and SAT scores. Results SMPM was effective for learners. The overall mean rating for SMPM effectiveness was 4.27. Mindsets for confidence, interest, sense of belonging, college mentorship, and physician mentorship were statistically different from the start to the end of SMPM (p<0.05), with mean improvement of about 34%, 41%, 44%, 180%, and 140% respectively. The mean pre and post SMPM SAT scores as well as 4-month follow-up SAT mean scores were 713 (SD:155), 813 (SD:83), and 1058 (SD:147), respectively. There was a statistically significant difference between all three SAT scores (p<0.05). Conclusion In addition to providing educational support, our pipeline program effectively increased students’ sense of belonging in the medical field and their connections to physician mentors, which are two common barriers for URM students who are interested in medicine.
In vivo inducing collagen regeneration of biodegradable polymer microspheres

Abstract Biodegradable polymer particles have been used as dermal fillers for pre-clinical and clinical trials. The impact of material properties of polymers is very important to develop products for aesthetic medicine such as dermal fillers. Herein, eight biodegradable polymers with different molecular weights, chemical compositions or hydrophilic-hydrophobic properties were prepared and characterized for systematical study for aesthetic medicine applications. Polymer microspheres with 20–100 μm were prepared. The in vitro degradation study showed that poly (L-lactic-co-glycolic acid) 75/25 microspheres degraded the fastest, whereas poly (L-lactic acid) (PLLA) microspheres with intrinsic viscosity of 6.89 ([η] = 6.89) with the highest molecular weight showed the slowest degradation rate. After these microspheres were fabricated dermal fillers according to the formula of Sculptra®, they were injected subcutaneously into the back skin of rabbits. In vivo results demonstrated that the degradation rate of microspheres strongly correlated with the foreign body reaction and collagen regeneration was induced by microspheres. The microspheres with faster degradation rate induced inflammatory response and the collagen regeneration maintained in shorter time. PLLA ([η] = 3.80) microsphere with a moderate molecular weight and degradation rate could strongly regenerate Type I and III collagen to maintain a long-term aesthetic medicine effect. These properties of size, morphology and degradation behavior would influence the foreign body reaction and collagen regeneration.
Refractive Outcomes in Fuchs’ Endothelial Corneal Dystrophy: Conventional and Femtosecond Laser-Assisted Cataract Surgery

Purpose To investigate the refractive outcomes of eyes with Fuchs’ endothelial corneal dystrophy (FECD) following phacoemulsification. Methods This is a retrospective chart review of patients with FECD who underwent phacoemulsification. Manifest refraction at the early postoperative period (1–6 weeks) and late postoperative period (3–12 months) was collected. The spherical equivalent (SE) and variance of SE from target in diopters (D) were analyzed. Results A total of 219 eyes from 175 FECD patients (73 FLACS, 146 conventional phacoemulsification) were included. In the early postoperative period, when comparing variance from intended target, 62% (n=126) had a SE variance of ≤0.5 D, 22% (n=44) >0.5 D and ≤1 D, and 17% (n=34) >1 D. In the late postoperative period, 62% (n=85) had a SE variance of ≤0.5 D, 19% (n=26) >0.5 D and ≤1 D, and 20% (n=27) >1 D. There was no difference in the variance of SE comparing FLACS versus conventional phacoemulsification in either the early postoperative period (p=0.78) or the late postoperative period (p=0.29). Conclusion Patients with mild-to-moderate FECD had favorable refractive outcomes with phacoemulsification. There was no difference in refractive outcomes in eyes with FECD between the group that underwent FLACS versus the group that underwent conventional phacoemulsification.
Physical Inactivity as a Pandemic: Daily Activities and Dietary Practices

Background Numerous studies have linked a decline in physical activity (PA) around the world to an increase in chronic diseases. There were two key goals for this study. The first was to observe how daily activities affected physical health in terms of VO2max. The second aim was to investigate the effect of university students' dietary practices on levels of PA. Methods A repeated cross-sectional study was conducted on 75 subjects (41 males and 34 females) with an average age of 27.3 [SD 5.8] years. The respondents’ physical activity in the previous seven days was classified as vigorous activity, moderate activity, walking, and sitting using the International Physical Activity Questionnaire (IPAQ). Anthropometric measurements such as height, weight, and body mass index (BMI) were reported at the baseline, during Lent (week 7 of the vegan diet), and seven weeks later (week 14). To determine Maximum Oxygen Consumption-the VO2max, the Queen’s College Step Test [QCST] was used. Results There was a major difference in mean VO2max between males and females, with males getting a higher VO2max. There was no connection between VO2max and changes in dietary adherence/transition. Based on physical activity study, there were no major variations between subjects (walking Metabolic Equivalent Task (MET) and moderate MET). However, a thorough Vigorous MET showed major gender gaps. The majority of the participants in the current study spent 35 (46.7%) of their time studying and 19 (28%) of their time attending class lectures, with some students using more time than others. Conclusion Researches that may conduct in university and/or college students may provide early information to help the students understand their physical fitness. According to the findings of this limited prospective study, students spend the majority of their time engaging in sitting for various purposes. In turn, regardless of whether they followed a vegan or omnivorous diet, these study participants had low VO2 max.
Relentless multi-wavelength variability of Markarian 421 and Markarian 501

Department of Astronomy, University of Geneva, Chemin d’Ecogia 16, CH-1290 Versoix, Switzerland Department of Physics, ETH Zurich, Otto Stern Weg 5, CH-8093 Zurich, Switzerland Fakultät Physik, Technische Universität Dortmund, Otto Hahn Str. 4a, D-44227 Dortmund, Germany Institut für Theoretische Physik und Astrophysik, Universität Würzburg, Emil Fischer Str. 31, 97074 Würzburg, Germany Physikalisches Institut III A, RWTH Aachen University, Otto Blumenthal Str. , D-52074 Aachen, Germany E-mail: vitalii.sliusar@unige.ch
On the Validity of the Tight-Binding Method for Describing Systems of Subwavelength Resonators

The goal of this paper is to relate the capacitance matrix formalism to the tight-binding approximation. By doing so, we open the way to the use of mathematical techniques and tools from condensed matter theory in the mathematical and numerical analysis of metamaterials, in particular for the understanding of their topological properties. We firstly study how the capacitance matrix formalism, both when the material parameters are static and modulated, can be posed in a Hamiltonian form. Then, we use this result to compare this formalism to the tight-binding approximation. We prove that the correspondence between the capacitance formulation and the tight-binding approximation holds only in the case of dilute resonators. On the other hand, the tight-binding model is often coupled with a nearest-neighbour approximation, whereby long-range interactions are neglected. Even in the dilute case, we show that long-range interactions between subwavelength resonators are relatively strong and nearest-neighbour approximations are not generally appropriate.
Combining Local and Global Viewpoint Planning for Fruit Coverage

Obtaining 3D sensor data of complete plants or plant parts (e.g., the crop or fruit) is difficult due to their complex structure and a high degree of occlusion. However, especially for the estimation of the position and size of fruits, it is necessary to avoid occlusions as much as possible and acquire sensor information of the relevant parts. Global viewpoint planners exist that suggest a series of viewpoints to cover the regions of interest up to a certain degree, but they usually prioritize global coverage and do not emphasize the avoidance of local occlusions. On the other hand, there are approaches that aim at avoiding local occlusions, but they cannot be used in larger environments since they only reach a local maximum of coverage. In this paper, we therefore propose to combine a local, gradient-based method with global viewpoint planning to enable local occlusion avoidance while still being able to cover large areas. Our simulated experiments with a robotic arm equipped with a camera array as well as an RGB-D camera show that this combination leads to a significantly increased coverage of the regions of interest compared to just applying global coverage planning.
Measurements of the Magnetic Field Strengths at the Bases of Stellar Coronae Using the Magnetic-field-induced Transition Theory

Measurements of the magnetic field in the stellar coronae are extremely difficult. Recently, it was proposed that the magnetic-field-induced transition (MIT) of the Fe x 257 Å line can be used to measure the coronal magnetic field of the Sun. We performed forward modeling with a series of global stellar magnetohydrodynamics models to investigate the possibility of extending this method to other late-type stars. We first synthesized the emissions of several Fe x lines for each stellar model, then calculated the magnetic field strengths using the intensity ratios of Fe x 257 Å to several other Fe x lines based on the MIT theory. Finally, we compared the derived field strengths with those in the models, and concluded that this method can be used to measure at least the magnetic field strengths at the coronal bases of stars with a mean surface magnetic flux density about one order of magnitude higher than that of the Sun. Our investigation suggests the need for an extreme ultraviolet spectrometer to perform routine measurements of the stellar coronal magnetic field.
Effects of organic cations on the defect physics of tin halide perovskites

Tin (Sn) halide perovskite absorbers have attracted much interest because of their nontoxicity as compared to their lead (Pb) halide perovskite counterparts. Recent progress shows that the power conversion efficiency of FASnI3 (FA = HC(NH2)2) solar cells prevails over that of MASnI3 (MA = CH3NH3). In this paper, we show that the organic cations, i.e., FA and MA, play a vital role in the defect properties of Sn halide perovskites. The antibonding coupling between Sn-5s and I-5p is clearly weaker in FASnI3 than in MASnI3 due to the larger ionic size of FA, leading to higher formation energies of Sn vacancies in FASnI3. Subsequently, the conductivity of FASnI3 can be tuned from p-type to intrinsic by varying the growth conditions of the perovskite semiconductor; in contrast, MASnI3 shows unipolar high p-type conductivity independent of the growth conditions. This provides a reasonable explanation for the better performance of FASnI3-based solar cells in experiments with respect to the MASnI3-based solar cells.
Deep Graph Memory Networks for Forgetting-Robust Knowledge Tracing

Tracing a student's knowledge is vital for tailoring the learning experience. Recent knowledge tracing methods tend to respond to these challenges by modelling knowledge state dynamics across learning concepts. However, they still suffer from several inherent challenges including: modelling forgetting behaviors and identifying relationships among latent concepts. To address these challenges, in this paper, we propose a novel knowledge tracing model, namely Deep Graph Memory Network (DGMN). In this model, we incorporate a forget gating mechanism into an attention memory structure in order to capture forgetting behaviors dynamically during the knowledge tracing process. Particularly, this forget gating mechanism is built upon attention forgetting features over latent concepts considering their mutual dependencies. Further, this model has the capability of learning relationships between latent concepts from a dynamic latent concept graph in light of a student's evolving knowledge states. A comprehensive experimental evaluation has been conducted using four well-established benchmark datasets. The results show that DGMN consistently outperforms the state-of-the-art KT models over all the datasets. The effectiveness of modelling forgetting behaviors and learning latent concept graphs has also been analyzed in our experiments.
Adversarial Relighting against Face Recognition

Deep face recognition (FR) has achieved significantly high accuracy on several challenging datasets and fosters successful real-world applications, even showing high robustness to the illumination variation that is usually regarded as a main threat to the FR system. However, in the real world, illumination variation caused by diverse lighting conditions cannot be fully covered by the limited face dataset. In this paper, we study the threat of lighting against FR from a new angle, i.e., adversarial attack, and identify a new task, i.e., adversarial relighting. Given a face image, adversarial relighting aims to produce a naturally relighted counterpart while fooling the state-of-the-art deep FR methods. To this end, we first propose the physical modelbased adversarial relighting attack (ARA) denoted as albedoquotient-based adversarial relighting attack (AQ-ARA). It generates natural adversarial light under the physical lighting model and guidance of FR systems and synthesizes adversarially relighted face images. Moreover, we propose the auto-predictive adversarial relighting attack (AP-ARA) by training an adversarial relighting network (ARNet) to automatically predict the adversarial light in a one-step manner according to different input faces, allowing efficiency-sensitive applications. More importantly, we propose to transfer the above digital attacks to physical ARA (PhyARA) through a precise relighting device, making the estimated adversarial lighting condition reproducible in the real world. We validate our methods on three state-of-the-art deep FR methods, i.e., FaceNet, ArcFace, and CosFace, on two public datasets. The extensive and insightful results demonstrate our work can generate realistic adversarial relighted face images fooling face recognition tasks easily, revealing the threat of specific light directions and strengths.
On the Variability of the Sample Covariance Matrix Under Complex Elliptical Distributions

We derive the form of the variance-covariance matrix for any affine equivariant matrix-valued statistics when sampling from complex elliptical distributions. We then use this result to derive the variance-covariance matrix of the sample covariance matrix (SCM) as well as its theoretical mean squared error (MSE) when finite fourth-order moments exist. Finally, illustrative examples of the formulas are presented.
Affect-Aware Deep Belief Network Representations for Multimodal Unsupervised Deception Detection

Automated systems that detect the social behavior of deception can enhance human well-being across medical, social work, and legal domains. Labeled datasets to train supervised deception detection models can rarely be collected for real-world, high -stakes contexts. To address this challenge, we propose the first unsupervised approach for detecting realworld, high-stakes deception in videos without requiring labels. This paper presents our novel approach for affect-aware unsupervised Deep Belief Networks (DBN) to learn discriminative representations of deceptive and truthful behavior. Drawing on psychology theories that link affect and deception, we experimented with unimodal and multimodal DBN-based approaches trained on facial valence, facial arousal, audio, and visual features. In addition to using facial affect as a feature on which DBN models are trained, we also introduce a DBN training procedure that uses facial affect as an aligner of audio-visual representations. We conducted classification experiments with unsupervised Gaussian Mixture Model clustering to evaluate our approaches. Our best unsupervised approach (trained on facial valence and visual features) achieved an AVC of 80%, outperforming human ability and performing comparably to fully-supervised models. Our results motivate future work on unsupervised, affect-aware computational approaches for detecting deception and other social behaviors in the wild.
Confidence Adaptive Regularization for Deep Learning with Noisy Labels

Recent studies on the memorization effects of deep neural networks on noisy labels show that the networks first fit the correctly-labeled training samples before memorizing the mislabeled samples. Motivated by this early-learning phenomenon, we propose a novel method to prevent memorization of the mislabeled samples. Unlike the existing approaches which use the model output to identify or ignore the mislabeled samples, we introduce an indicator branch to the original model and enable the model to produce a confidence value for each sample. The confidence values are incorporated in our loss function which is learned to assign large confidence values to correctly-labeled samples and small confidence values to mislabeled samples. We also propose an auxiliary regularization term to further improve the robustness of the model. To improve the performance, we gradually correct the noisy labels with a well-designed target estimation strategy. We provide the theoretical analysis and conduct the experiments on synthetic and real-world datasets, demonstrating that our approach achieves comparable results to the state-of-the-art methods.