Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning in Urological Images Using Convolutional Neural Networks: An Artificial Intelligence Study

Objective: Using artificial intelligence and a deep learning algorithm can differentiate vesicoureteral reflux and hydronephrosis reliably. Material and Methods: An online dataset of vesicoureteral reflux and hydronephrosis images were abstracted. We developed image analysis and deep learning workflow. The images were trained to distinguish between vesicoureteral reflux and hydronephrosis. The discriminative capability was quantified using receiver-operating characteristic curve analysis. We used Scikit learn to interpret the model. Results: Thirty-nine of the hydronephrosis and 42 of the vesicoureteral reflux images were abstracted from an online dataset. First, we randomly divided the images into training and validation. In this example, we put 68 cases into training and 13 into validation. We did inference on 2 cases and in return their predictions were predicted: [[0.00006]] hydronephrosis, predicted: [[0.99874]] vesicoureteral reflux on 2 test cases. Conclusion: This study showed a high-level overview of building a deep neural network for urological image classification. It is concluded that using artificial intelligence with deep learning methods can be applied to differentiate all urological images.
When to Use Antibiotics in COVID-19: A Proposal Based on Questions

The COVID-19 pandemic has affected millions of people, including hundreds of deaths. The search for adequate treatments and interventions that influence poor prognostic factors and reduce mortality has led to excessive use of antibiotics based on the possible existence of bacterial co-infection. However, there is no evidence to justify the systematic use of antimicrobials in COVID-19. The recommendations seek to provide knowledge regarding treatment; standardizing a management algorithm requires validation in clinical trials and studies of greater methodological rigor.
Comprehensive Analysis of Differentially Expressed mRNAs, lncRNAs and circRNAs Related to Intramuscular Fat Deposition in Laiwu Pigs

Long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) are important classes of small noncoding RNAs that can regulate numerous biological processes. To understand the role of message RNA (mRNAs, lncRNAs and circRNAs) in the regulation of intramuscular fat (IMF) deposition, in this study the expression profiles of longissimus dorsi (LD) muscle from six Laiwu pigs (three with extremely high and three with extremely low IMF content) were sequenced based on rRNA-depleted library construction. In total, 323 differentially expressed protein-coding genes (DEGs), 180 lncRNAs (DELs) and 105 circRNAs (DECs) were detected between the high IMF and low IMF groups. Functional analysis indicated that most DEGs, and some target genes of DELs, were enriched into GO terms and pathways related to adipogenesis, suggesting their important roles in regulating IMF deposition. In addition, 12 DELs were observed to exhibit a positive relationship with stearoyl-CoA desaturase (SCD), phosphoenolpyruvate carboxykinase 1 (PCK1), and adiponectin (ADIPOQ), suggesting they are highly likely to be the target genes of DELs. Finally, we constructed a source gene-circRNA-miRNA connective network, and some of miRNA of the network have been reported to affect lipid metabolism or adipogenesis. Overall, this work provides a valuable resource for further research and helps to understand the potential functions of lncRNAs and circRNAs in IMF deposition.
Model reduction using the genetic algorithm and Routh approximations

A new method of model reduction combining the genetic algorithm(GA) with the Routh approximation method is presented. It is suggested that a high-order system can be approximated by a low-order model with a time delay. The denominator parameters of the reduced-order model are determined by the Routh approximation method, then the numerator parameters and time delay are identified by the GA. The reduced-order models obtained by the proposed method will always be stable if the original system is stable and produce a good approximation to the original system in both the frequency domain and time domain. Two numerical examples show that the method is computationally simple and efficient.
Lower-Limb Kinematic Change during Pelvis Anterior and Posterior Tilt in Double-Limb Support in Healthy Subjects with Knee Malalignment

This study aimed to investigate lower-limb kinematic changes during pelvic tilting in participants with knee malalignment. To define participants with lower-limb malalignment, the quadriceps angle (Q-angle) was used in this study. The sample population was divided into two groups in accordance with the Q-angle: the experimental group (ABQ) consisted of participants with an abnormal Q-angle greater than the normal range, and the control group (CON) consisted of participants with a normal Q-angle. All participants performed anterior and posterior pelvic tilts in double-limb support. Kinematic change in the lower limb was evaluated using a three-dimensional motion analysis system (Motion Analysis, Santa Rosa, CA, USA). The horizontal plane hip angle in the ABQ was significantly different compared with that in the CON in all positions (p < 0.05), and no significant difference was observed in the other lower-limb kinematic variables (p > 0.05). A significant correlation was identified only between the Q-angle and horizontal plane hip angle in all positions. Based on the results, the Q-angle was strongly related to the thigh, although it may not be related to malalignment with other segments during double-limb support.
A Displaced Cosmology. (Book Reviews: The Milky Way Galaxy and Statistical Cosmology, 1890-1924.)

List of illustrations Acknowledgments Abbreviations of manuscript sources Introduction Part I. The Nineteenth-Century Background: 1. Early nineteenth-century statistical astronomy 2. Statistical astronomy and the Milky Way Galaxy Part II. Statistical Cosmology, 1890-1924: 3. Seeliger and stellar density 4. Kapteyn and the distribution of stars 5. Statistical astronomy as a research program, 1900-15 6. Statistical cosmology as a research program, 1915-22 7. Internationalization of astronomy Part III. Statistical Cosmology and the Second Astronomical Revolution: 8. The decline of a research program 9. Conclusion: research programs in transition Appendix 1: Seeliger's star-ratio function Appendix 2: Seeliger's density theorem Bibliographical notes Index.
FalsifAI: Falsification of AI-Enabled Hybrid Control Systems Guided by Time-Aware Coverage Criteria

Modern Cyber-Physical Systems (CPSs) that need to perform complex control tasks (e.g., autonomous driving) are increasingly using AI-enabled controllers, mainly based on deep neural networks (DNNs). The quality assurance of such types of systems is of vital importance. However, their verification can be extremely challenging, due to their complexity and uninterpretable decision logic. Falsification is an established approach for CPS quality assurance, which, instead of attempting to prove the system correctness, aims at finding a time-variant input signal violating a formal specification describing the desired behavior; it often employs a search-based testing approach that tries to minimize the <italic>robustness</italic> of the specification, given by its quantitative semantics. However, guidance provided by robustness is mostly black-box and only related to the system output, but does not allow to understand whether the temporal internal behavior determined by multiple consecutive executions of the neural network controller has been explored sufficiently. To bridge this gap, in this paper, we make an early attempt at exploring the temporal behavior determined by the repeated executions of the neural network controllers in hybrid control systems and first propose eight time-aware coverage criteria specifically designed for neural network controllers in the context of CPS, which consider different features by design: the simple temporal activation of a neuron, the continuous activation of a neuron for a given duration, and the differential neuron activation behavior over time. Second, we introduce a falsification framework, named <inline-formula><tex-math notation="LaTeX">$\mathtt {FalsifAI}$</tex-math><alternatives><mml:math><mml:mi mathvariant="monospace">FalsifAI</mml:mi></mml:math><inline-graphic xlink:href="zhang-ieq1-3194640.gif"/></alternatives></inline-formula>, that exploits the coverage information for better falsification guidance. Namely, inputs of the controller that increase the coverage (so improving the <italic>exploration</italic> of the DNN behaviors), are prioritized in the <italic>exploitation</italic> phase of robustness minimization. Our large-scale evaluation over a total of 3 typical CPS tasks, 6 system specifications, 18 DNN models and more than 12,000 experiment runs, demonstrates 1) the advantage of our proposed technique in outperforming two state-of-the-art falsification approaches, and 2) the usefulness of our proposed time-aware coverage criteria for effective falsification guidance.
Estudo de modelos distribucionais para detecção de discurso de ódio em português

SILVA, Adriano dos Santos Rodrigues da. Study of distributional models for the detection of hate speech in Portuguese. 2022. 76 p. Dissertation (Master of Science) – School of Arts, Sciences and Humanities, University of São Paulo, São Paulo, 2021. With the emergence of social networks, users have moved from consumers to content producers, allowing any user the freedom to express their opinion. Due to the large amount of content that users publish on social networks, it is impossible for the monitoring to be done by a human agent, so it is necessary that this supervision be done automatically. However, the problem of identifying hate speech in Portuguese is little explored, and most of the research is dedicated to the English language. In addition to performing classification, it is necessary to analyze which factors influenced the classifier’s decision making, since the mistakes made can contribute to discrimination rather than fighting. It distributional models can be used in this task and, in the experiments carried out, models with a multilingual strategy and exclusive to Portuguese were used. These models achieved superior performance over traditional methods like N-Gram combined with SVM.
Tightly Coupled Integration of GNSS, INS, and LiDAR for Vehicle Navigation in Urban Environments

The emerging Internet of Things (IoT) applications, such as driverless cars, have a growing demand for high-precision positioning and navigation. Nowadays, the global navigation satellite system (GNSS) is recognized as an important approach for worldwide positioning services. However, its application is limited in urban areas due to severe signal attenuation, reflections, and blockages. Inertial navigation system (INS) can provide high-precision navigation outputs within a short period, but its accuracy suffers from error accumulation, especially when equipped with the low-cost microelectromechanical system (MEMS) inertial measurement units (IMUs). In addition, light detection and ranging (LiDAR) is becoming more common as an option in vehicles, which can detect rich geometric information in the environment for ego-motion estimation. Aiming at taking advantage of the complementary characteristics of these onboard technologies to navigate in urban environments, a tightly coupled multi-GNSS precise point positioning (PPP)/INS/LiDAR integrated system is proposed. We also develop an LiDAR sliding-window plane-feature tracking method to further improve navigation accuracy and computational efficiency. The performance of the proposed integrated system was evaluated in vehicular experiments with different GNSS observation conditions. Results indicate that our proposed GNSS/INS/LiDAR integration can maintain submeter level horizontal positioning accuracy in GNSS-challenging environments, with improvements of (73.3%, 59.7%, and 64.2%) compared to traditional GNSS/INS integration. Moreover, the plane-feature tracking method is proved to outperform traditional point-to-line and point-to-plane scan matching in terms of accuracy and efficiency.
The New Zealand Genetic Frontotemporal Dementia Study (FTDGeNZ): a longitudinal study of pre-symptomatic biomarkers

ABSTRACT The New Zealand Genetic Frontotemporal Dementia Study (FTDGeNZ) is an emerging longitudinal study of a large New Zealand pedigree with genetic frontotemporal dementia (FTD). Natural history studies of genetic FTD cohorts provide a unique opportunity to identify biomarkers of pre-symptomatic dementia, as carriers can be identified and studied decades before expected symptom onset. FTDGeNZ was established in 2016 with the aim of identifying the earliest pre-symptomatic biomarkers of FTD, in collaboration with international multi-centre cohorts. We enrolled 25 participants from a single family between April 2016 and August 2018. Participants were genotyped to determine whether they were pre-symptomatic carriers of the mutation (MAPT IVS 10 + 16 C > T), or non-carrier controls. Participants have undergone clinical assessments including neuropsychological and mood assessment; olfactory testing; assessment of social cognition; and blood collection for analyses of microRNA and protein fluid biomarkers annually. We have also performed structural and functional MRI of the brain and assessment of autobiographical memory biennially, and retinal imaging at baseline. Here, we describe the full study protocol and the baseline demographic and clinical characteristics of the FTDGeNZ cohort, and we highlight the latest findings in the field.
Advantages of blue InGaN light‐emitting diodes with composition‐graded barriers and electron‐blocking layer

Blue InGaN light‐emitting diodes (LEDs) with composition‐graded barriers and composition‐garded electron‐blocking layer (EBL) are studied theoretically. Simulation results show that the employment of composition‐graded barriers and compositon‐graded EBL simultaneously can benefit from the enhanced hole‐injection efficiency and more uniform carrier distribution among the quantum wells. Moreover, the optical and electrical properties of the InGaN blue LEDs are markedly promoted. The graded barriers are investigated systematically in an attempt to obtain the optimal situation.
Advances in bismuth-based topological quantum materials by scanning tunneling microscopy

In recent years, topological quantum materials (TQMs) have attracted intensive attention in the area of condensed matter physics due to their novel topologies and their promising applications in quantum computing, spin electronics and next-generation integrated circuits. Scanning tunneling microscopy/spectroscopy (STM/STS) is regarded as a powerful technique to characterize the local density of states with atomic resolution, which is ideally suited to the measurement of the bulk-boundary correspondence of TQMs. In this review, using STM/STS, we focus on recent research on bismuth-based TQMs, including quantum-spin Hall insulators, 3D weak topological insulators (TIs), high-order TIs, topological Dirac semi-metals and dual TIs. Efficient methods for the modulation of the topological properties of the TQMs are introduced, such as interlayer interaction, thickness variation and local electric field perturbation. Finally, the challenges and prospects for this field of study are discussed.
Coping Style and Resilience Mediate the Effect of Childhood Maltreatment on Mental Health Symptomology

Background: A well-known distal risk factor for mental health symptomology is childhood maltreatment. Previous research revealed that several mediators, such as coping style and resilience, might be connected to the psychological mechanism of childhood maltreatment on mental health symptomology. Objective: The purpose of this study was to assess how coping style and resilience affect the relationship between childhood maltreatment and mental health symptomology of college students. Methods: With the method of cross-sectional survey, 740 college students from China (Gansu Province) completed the Childhood Trauma Questionnaire (CTQ), the Simplified Coping Style Questionnaire (SCSQ), the Connor–Davidson Resilience Scale (CD-RISC), and the Symptom Checklist 90 (SCL-90). Structural equation modeling (SEM) was used to reveal the link between childhood maltreatment, coping style, resilience, and mental health symptomology. Results: The results showe that childhood maltreatment was significantly positively correlated with mental health symptomology and significantly negatively correlated with coping style and resilience. Coping style was significantly negatively correlated with mental health symptomology and significantly positively correlated with resilience. Resilience was significantly negatively correlated with mental health symptomology. Coping style and resilience played a partially mediating role in the relationship between childhood maltreatment and college students’ mental health symptomology. Through a chain of intermediary effects on coping style and resilience, childhood maltreatment not only had a direct impact on mental health symptomology but also had an indirect impact. Conclusion: Childhood maltreatment could affect college students’ mental health symptomology through the chain mediating effect of coping style and resilience. Therefore, it is an effective way to reduce the influence of childhood maltreatment on mental health symptomology through some intervention measures to cultivate positive coping style and improve resilience.
Land Cover Classification from Very High-Resolution UAS Data for Flood Risk Mapping

Monitoring the world’s areas that are more vulnerable to natural hazards has become crucial worldwide. In order to reduce disaster risk, effective tools and relevant land cover (LC) data are needed. This work aimed to generate a high-resolution LC map of flood-prone rural villages in southwest Niger using multispectral drone imagery. The LC was focused on highly thematically detailed classes. Two photogrammetric flights of fixed-wing unmanned aerial systems (UAS) using RGB and NIR optical sensors were realized. The LC input dataset was generated using structure from motion (SfM) standard workflow, resulting in two orthomosaics and a digital surface model (DSM). The LC system is composed of nine classes, which are relevant for estimating flood-induced potential damages, such as houses and production areas. The LC was generated through object-oriented supervised classification using a random forest (RF) classifier. Textural and elevation features were computed to overcome the mapping difficulties due to the high spectral homogeneity of cover types. The training-test dataset was manually defined. The segmentation resulted in an F1_score of 0.70 and a median Jaccard index of 0.88. The RF model performed with an overall accuracy of 0.94, with the grasslands and the rocky clustered areas classes the least performant.
Research on automatic detection of text-oriented counterfactual statements

Counter-fact is the highest form of causal reasoning. Focusing on counter-fact will provide new ideas for higher order causal reasoning. For counterfactual text detection, two detection methods are proposed. One is the automatic detection method based on classification model, that is, counterfactual detection is carried out on the given annotated text data. In this way, the data imbalance problem is solved first, then word vector is generated by doc2vec, and finally the binary SVM training model is adopted.The second is automatic detection method based on sequence annotation model, namely the counterfactual statements of reason and result orientation, to this sequence labeling method is adopted, the individual words in the text annotation, after processing the data to be included in the BiLSTM after training, and through a layer of conditions with the airport, the resulting text labeling information. Experiments show that the proposed method can accurately detect and locate the cause and result in the counterfactual text.
Depression with Chronic Disease Is Associated with Increased Use of Medical Services and Medical Expenses in Hardcore Smokers

We aimed to investigate the association of chronic disease and depression with medical service use and expenses in hardcore smokers and provide basic data for health management system of hardcore smokers. This was a secondary data study involving 1735 smokers. Propensity score matching (PSM) was conducted to match hardcore smokers with regular smokers, and a two part model (TPM) was used based on the matched groups. In the case of general smokers, subjects with both depression and chronic disease had a significant relation to medical service use. In the case of hardcore smokers, subjects without depression and with chronic disease or with both depression and chronic disease had increased the use of medical services. The depression and chronic disease of general smokers did not affect the use of medical services. In the case of hardcore smokers, subjects who do not have depression and have only chronic disease (β = 0.20, p = 0.002) or with depression and chronic disease (β = 0.20, p = 0.014) significantly related the use of medical services. Conclusion: It is necessary to establish a health management system that considers both emotional states and chronic disease for hardcore smokers.
Effect Evaluation of Preoperative Psychological Nursing Intervention on Sinusitis Patients Undergoing General Anesthesia by Multiplanner Reformation-Based CT

The aim of this study was at exploring the clinical effect of CT images based on multiplanner reformation (MPR) combined with a preoperative psychological nursing intervention model in sinusitis patients undergoing general anesthesia. Sixty sinusitis patients who received MPR-based CT examination and general anesthesia were selected as the study subjects and randomly divided into the control group (n = 30) and the experimental group (n = 30). The control group used traditional preoperative education. The experimental group added the psychological nursing intervention based on traditional preoperative education. The blood pressure and heart rate before and after the operation, the self-rating anxiety scale (SAS) score before and after intervention, and satisfaction were comprehensively assessed. The results showed that CT based on MPR could observe the lesions and anatomical structures of the sinus wall and sinus in detail from multiple angles. The blood pressure (systolic blood pressure 135.12 ± 14.89 mmHg, diastolic blood pressure 87.05 ± 11.24 mmHg), heart rate (78.42 ± 12.19 beats/min), SAS score (45.85 ± 4.97 points), and nursing satisfaction (78.9%) of the experimental group were significantly better than those of the control group (145.83 ± 15.62 mmHg, 94.21 ± 10.86 mmHg, 86.44 ± 13.65 beats/min, 56.44 ± 5.12 points, 56.4%), and the differences were statistically significant (P < 0.05). In summary, the preoperative psychological care model has a positive role in reducing the tension and anxiety of patients before general anesthesia surgery and CT based on MPR is important for the clinical diagnosis and treatment of sinusitis. This study provides a theoretical reference for the clinical treatment of patients with sinusitis.