Miracle-dz/UniFAR-FTUs huggingface dataset:
Some characterizations based on the Bhattacharya matrix

Laha and Lukacs (1960) have studied distributions with the property that a quadratic statistic has quadratic regression on the sample mean. In doing this, they have arrived at some interesting characterizations for the normal, Poisson, gamma, binomial and negative binomial distributions. Starting with an exponential-type probability density function, the present paper investigates all the distributions for which the 3 × 3 Bhattacharya matrix is diagonal. It is found that the normal, Poisson, gamma, binomial and negative binomial distributions can be characterized by this property. Further, it is observed that for these distributions an s × s Bhattacharya matrix is defined for all s and is also diagonal.
Testing constancy of the Hurst exponent of some long memory stationary Gaussian time series

Long-range dependence is often observed in stationary time series. The Hurst exponent then characterizes the long term features of the data, which implies that changes in its value could have implications for the long term behaviour of the series. In this paper we propose and apply tests to detect changes over time in the Hurst exponent of long memory Gaussian time series, in particular fractional Gaussian noise and fractionally integrated Gaussian white noise.
Transmembrane Mucin Response in Conjunctival Epithelial Cells Exposed to Wall Shear Stresses

Human conjunctival epithelium cells (HCEC) line the inner surface of the eyelid and cover the sclera and are continuously subjected to wall shear stresses (WSS). The effects of external forces on the conjunctival epithelium are not fully known. The conjunctival epithelium contains stratified squamous cells that synthesize the membrane-spanning mucins MUC1 and MUC16, which play important roles in protecting the ocular surface. Alterations in both gel-forming and membrane-tethered mucins occur in drying ocular surface diseases. The aim of this study was to explore the mechanobiological characteristics of transmembrane mucin secretion and cellular alterations of primary HCEC exposed to airflow-induced WSS perturbations. We exposed the HCEC to a steady WSS of 0.5 dyne/cm2 for durations of 15 and 30 min. Cytoskeletal alterations and MUC1 secretions were studied using immunohistochemically fluorescent staining with specific antibodies. We investigated for the first time an in vitro model of membrane-tethered mucin secretion by HCEC in response to WSS. The exposure of HCEC to WSS increased the polymerization of F-actin, altered the cytoskeletal shape and reduced the secretion of membrane-tethered MUC1.
Advances in the application of molecular diagnostic techniques for the detection of infectious disease pathogens (Review)

Infectious diseases are a major global cause of morbidity and mortality, seriously affecting public health and socioeconomic stability. Since infectious diseases can be caused by a wide variety of pathogens with similar clinical manifestations and symptoms that are difficult to accurately distinguish, selecting the appropriate diagnostic techniques for the rapid identification of pathogens is crucial for clinical disease diagnosis and public health management. However, traditional diagnostic techniques have low detection rates, long detection times and limited automation, which means that they do not meet the requirements for rapid diagnosis. Recent years have seen continuous developments in molecular detection technology, which has a higher sensitivity and specificity, shorter detection time and increased automation, and performs an important role in the early and rapid detection of infectious disease pathogens. The present study summarizes recent progress in molecular diagnostic technologies such as PCR, isothermal amplification, gene chips and high-throughput sequencing for the detection of infectious disease pathogens, and compares the technical principles, advantages and disadvantages, applicability and costs of these diagnostic techniques.
Vision-Based System for Measuring the Diameter of Wood Logs

Detecting and measuring objects with vision-based systems in uncontrolled environments is a difficult task that today, thanks to the development of increasingly advanced artificial intelligence-based techniques, can be solved with greater ease. In this context, this article proposes a novel approach for the vision-based measurement of objects in uncontrolled environments using a specific type of convolutional neural network (CNN) named you only look once (YOLO) and a direct linear transformation (DLT) process. The case study concerned designing a novel vision-based system for measuring the diameter of wood logs cut and loaded onto trucks. This problem has been occurring in the Swedish forestry industry. In fact, this operation is not carried out with computer vision algorithms because of the high variability of environmental conditions caused by the changing position of the sun, weather conditions, and the variability of truck positioning. To solve this problem, the YOLO network is proposed to locate logs while attempting to maintain a high Intersection over Union (IoU) value for the correct estimation of log size. Furthermore, in order to obtain accurate measurements, the DLT is used to convert into world coordinates the dimensions of the logs themselves. The proposed CNN-based solution is described after briefly introducing today’s methodologies adopted for wood bundle analysis. Particular attention is paid to both the training and the calibration steps. Results report that for 80% of cases, the error reported has been smaller than 4 cm, representing only 8% of the measurement, considering a mean log diameter for the application of 50 cm.
Evaluating Model Fit of Measurement Models in Confirmatory Factor Analysis

Confirmatory factor analyses (CFA) are often used in psychological research when developing measurement models for psychological constructs. Evaluating CFA model fit can be quite challenging, as tests for exact model fit may focus on negligible deviances, while fit indices cannot be interpreted absolutely without specifying thresholds or cutoffs. In this study, we review how model fit in CFA is evaluated in psychological research using fit indices and compare the reported values with established cutoff rules. For this, we collected data on all CFA models in Psychological Assessment from the years 2015 to 2020 (NStudies=221) . In addition, we reevaluate model fit with newly developed methods that derive fit index cutoffs that are tailored to the respective measurement model and the data characteristics at hand. The results of our review indicate that the model fit in many studies has to be seen critically, especially with regard to the usually imposed independent clusters constraints. In addition, many studies do not fully report all results that are necessary to re-evaluate model fit. We discuss these findings against new developments in model fit evaluation and methods for specification search.
Experimental Formation and Mechanism Study for Super-High Dielectric Constant AlOx/TiOy Nanolaminates

Super-high dielectric constant (k) AlOx/TiOy nanolaminates (ATO NLs) are deposited by an atomic layer deposition technique for application in next-generation electronics. Individual multilayers with uniform thicknesses are formed for the ATO NLs. With an increase in AlOx content in each ATO sublayer, the shape of the Raman spectrum has a tendency to approach that of a single AlOx layer. The effects of ATO NL deposition conditions on the electrical properties of the metal/ATO NL/metal capacitors were investigated. A lower deposition temperature, thicker ATO NL, and lower TiOy content in each ATO sublayer can lead to a lower leakage current and smaller loss tangent at 1 kHz for the capacitors. A higher deposition temperature, larger number of ATO interfaces, and higher TiOy content in each ATO sublayer are important for obtaining higher k values for the ATO NLs. With an increase in resistance in the capacitors, the ATO NLs vary from semiconductors to insulators and their k values have a tendency to decrease. For most of the capacitors, the capacitances reduce with increments in absolute measurement voltage. There are semi-circular shapes for the impedance spectra of the capacitors. By fitting them with the equivalent circuit, it is observed that with the increase in absolute voltage, both parallel resistance and capacitance decrease. The variation in the capacitance is explained well by a novel double-Schottky electrode contact model. The formation of super-high k values for the semiconducting ATO NLs is possibly attributed to the accumulation of charges.
A Novel based Hybrid smart System for detecting the cyber-attacks on IOT’s

The Iot technology (IoT) is rapidly advancing sufficiently that it might have a larger effect on huge industrial infrastructure and everyday routines. However, this became the focus of fraudsters, whom made IoT into such a prey for their misbehaviour, thereby potentially opening the door for an assault on the computer systems. Have used a traditional intrusion detection device to manage the Iot network is problematic because of the enormous variety and quantity of IoT devices. A revolutionary aggregation Interdisciplinary Detection System (HIDS) that merges a C5 predictor with the a One Category Classification Algorithm is built to guard IoT devices. It project's goal is to find threats that can be inhibited with high classification certainty and low completely bogus rates, for both storing and accessing and negligible vulnerabilities. The User database, consisting includes include legal IoT data stream and other varieties of assaults, is employed to assess the specified HIDS. Studies show that when combined with SIDS and Assistance methodologies, the proposed hybrid IDS provides a higher accuracy results and a smaller percentage of false positives.
SARS-CoV-2 ORF3A interacts with the Clic-like chloride channel-1 (CLCC1) and triggers an unfolded protein response

Understanding the interactions between SARS-CoV-2 and host cell machinery may reveal new targets to treat COVID-19. We focused on an interaction between the SARS-CoV-2 ORF3A accessory protein and the CLIC-like chloride channel-1 (CLCC1). We found that ORF3A partially co-localized with CLCC1 and that ORF3A and CLCC1 could be co-immunoprecipitated. Since CLCC1 plays a role in the unfolded protein response (UPR), we hypothesized that ORF3A may also play a role in the UPR. Indeed, ORF3A expression triggered a transcriptional UPR that was similar to knockdown of CLCC1. ORF3A expression in 293T cells induced cell death and this was rescued by the chemical chaperone taurodeoxycholic acid (TUDCA). Cells with CLCC1 knockdown were partially protected from ORF3A-mediated cell death. CLCC1 knockdown upregulated several of the homeostatic UPR targets induced by ORF3A expression, including HSPA6 and spliced XBP1, and these were not further upregulated by ORF3A. Our data suggest a model where CLCC1 silencing triggers a homeostatic UPR that prevents cell death due to ORF3A expression.
Covariance Estimation for Pose Graph Optimization in Visual-Inertial Navigation Systems

Pose graph optimization helps reduce drift accumulated in pure odometry of visual simultaneous localization and mapping (SLAM) systems by solving a nonlinear least square problem, including both sequential constraints and loop-closing constraints. However, the covariances of all constraints are set to constant matrices or by manual setting. In this paper, we propose a novel approach to approximate covariances of constraints in pose graph optimization to better represent the true uncertainty of the underlying visual-inertial navigation system (VINS) that fuses inertial measurements and visual observations. Specifically, for sequential constraints, we propose to utilize nonlinear factor recovery to optimally extract covariance matrices from the accumulated visual-inertial odometry (VIO). For loop-closing constraints, we propose a dynamic scale estimation method to approximate the scales of the information matrices. To evaluate the effectiveness and robustness of the proposed method, we conduct extensive experiments on public and self-collected datasets in various environments. Results show that our proposed method achieves higher accuracy compared with naively-formulated pose graph optimization adopted by several state-of-the-art visual-inertial navigation systems.
Homologie des ensembles ordonnés et des espaces topologiques

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Carbon Nanomaterials: Emerging Roles in Immuno-Oncology

Cancer immunotherapy has made breakthrough progress in cancer treatment. However, only a subset of patients benefits from immunotherapy. Given their unique structure, composition, and interactions with the immune system, carbon nanomaterials have recently attracted tremendous interest in their roles as modulators of antitumor immunity. Here, we focused on the latest advances in the immunological effects of carbon nanomaterials. We also reviewed the current preclinical applications of these materials in cancer therapy. Finally, we discussed the challenges to be overcome before the full potential of carbon nanomaterials can be utilized in cancer therapies to ultimately improve patient outcomes.
Effect of Various Protocols of Pre-Emptive Pulpal Laser Analgesia on Enamel Surface Morphology Using Scanning Electron Microscopy: An Ex Vivo Study

Achieving local anaesthesia for various clinical dental applications is a challenge that we encounter in our daily practice. Pre-emptive pulpal laser analgesia (PPLA) treatment strategy could be a promising non-pharmacological modality. Hence, our ex vivo laboratory study is aimed at evaluating the changes in enamel surface morphology when irradiated with various published PPLA protocols using scanning electron microscopy (SEM). To do so, 24 extracted healthy human permanent premolar teeth were collected, and each tooth was divided into equal halves randomised into six groups. The following laser parameter protocols based on published protocols of clinical Er:YAG laser-induced PPLA were randomly assigned for each group: 0.2 W/10 Hz/3 J/cm2 (Group A—100% water spray; Group B—no water); 0.6 W/15 Hz/10 J/cm2 (Group C—100% water spray; Group D—no water); 0.75 W/15 Hz/12 J/cm2 (Group E—100% water spray; Group F—no water); 1 W/20 Hz/17 J/cm2 (Group G—100% water spray; Group H—no water). Each sample was irradiated at an angle of 90° to the dental pulp, with a sweeping speed of 2 mm/s for a 30 s exposure time. Our results have shown, for the first time, no alteration to the mineralised tooth structure when irradiated with the following protocols: 0.2 W/10 Hz/3 J/cm2 with 100% water spray or without water spray with an irradiated area fixed at a 10 mm tip-to-tissue distance, sweeping motion with 2 mm/s speed of movement; average power output of 0.6 W/15 Hz/10 J/cm2, maximum water cooling of 100%, tip-to-tooth distance fixed at 10 mm, 30 s exposure time, sweeping motion with 2 mm/s speed of movement. The authors concluded that the current available proposed PPLA protocols in the literature might cause an alteration to the enamel surface. Hence, future clinical studies are warranted to validate our study’s PPLA protocols.
On the Estimation Accuracy of Causal Effects using Supplementary Variables

This paper focuses on a situation in which a set of treatments is associated with a response through a set of supplementary variables in linear models as well as discrete models. Under the situation, we demonstrate that the causal effect can be estimated more accurately from the set of supplementary variables. In addition, we show that the set of supplementary variables can include selection variables and proxy variables as well. Furthermore, we propose selection criteria for supplementary variables based on the estimation accuracy of causal effects. From graph structures based on our results, we can judge certain situations under which the causal effect can be estimated more accurately by supplementary variables and reliably evaluate the causal effects from observed data.
A Concise Diastereoselective Total Synthesis of α-Ambrinol

(−)-cis-α-Ambrinol is a natural product present in ambergris, a substance of marine origin that has been highly valued by perfumers. In this paper, we present a new approach to its total synthesis. The starting material is commercially available α-ionone and the key step is an intramolecular Barbier-type cyclization induced by CpTiCl2, an organometallic compound prepared in situ by a CpTiCl3 reduction with Mn.
Nonbank lenders as global shock absorbers: evidence from US monetary policy spillovers

We show that nonbank lenders act as global shock absorbers from US monetary policy spillovers. For identification, we exploit loan-level data from the global syndicated lending market and US monetary policy surprises. We find that when US monetary policy tightens, nonbanks increase dollar credit supply to non-US corporate borrowers, relative to banks. This partially mitigates the total reduction in dollar lending. The substitution is stronger for emerging market borrowers, riskier borrowers, and borrowers in countries subject to stronger capital inflow restrictions. Results suggest that our findings are not driven by borrower-lender matching, zombie lending, or destabilising lending. Moreover, the credit substitution has significant real effects, as firms with existing relationships with nonbank lenders increase total debt, investment, and employment relative to firms without such relationships. Our findings suggest that access to nonbank credit reduces the volatility in capital flows and economic activity associated with US monetary policy spillovers.
Craniomandibular Osteology of Manidens condorensis (Ornithischia: Heterodontosauridae) from the Upper Lower Jurassic of Argentina

ABSTRACT Heterodontosauridae is a clade that appears early in the ornithischian fossil record, and includes small-bodied, highly specialized species characterized by an unusual heterodont dentition. Although known from relatively few taxa, the early representation of the clade and unsolved phylogenetic relationships within heterodontosaurids and among early ornithischians implies that novel information has a marked effect on broader phylogenetic hypotheses and our understanding of early diversification patterns within Ornithischia. This paper describes the cranial osteology of the heterodontosaurid Manidens condorensis based on computed micro-tomographic scans of MPEF-PV 3211 and MPEF-PV 3809. This enabled more detailed descriptions of previously recognized bones, corrections to the literature, and the identification of undescribed elements. We present a new skull reconstruction and propose an emended diagnosis in light of novel anatomical information. Areas of jaw muscle attachment were identified and compared with Heterodontosaurus and Lesothosaurus, and mandibular function among heterodontosaurids is discussed. Our results indicate that diverse skull morphologies and functions existed among Early Jurassic ornithischians, with Manidens being intermediate between the plesiomorphic cranial shape and function associated with a generalist diet in ornithischians such as Tianyulong and Lesothosaurus, and the more derived cranial construction specialized for herbivory identified in heterodontosaurids from South Africa such as Heterodontosaurus.
Preparation of CNT@NiM-OH nanocomposites for high-performance supercapacitors

Abstract Herein, an effective method of preparing CNT@NiM-OH nanocomposites by reacting dopamine-modified carbon nanotubes (CNT@PDA) with M(NO3)2 (M = Zn,Co) and 2-methylimidazole is reported. The research results show that CNTs are intertwined to form a network structure surrounded by NiM-OH in CNT@NiM-OH, which provides a more electrolyte contact area and reaction active sites. Electrochemical research indicates that CNT@NiM-OH exhibits excellent electrochemical properties for supercapacitors. The specific capacitances of CNT@NiCoZn-OH, CNT@NiZn-OH and CNT@NiCo-OH were 2039.63, 1930.03 and 1664.73 F/g, respectively, at 1 A/g. In addition, the specific capacitance retention rates of the three electrode materials after 1000 cycles were 97.7%, 99.0% and 85.4%, respectively. Graphical Abstract
Application of an Interpretable Machine Learning for Estimating Severity of Graves’ Orbitopathy Based on Initial Finding

(1) Background: We constructed scores for moderate-to-severe and muscle-predominant types of Graves’ orbitopathy (GO) risk prediction based on initial ophthalmic findings. (2) Methods: 400 patients diagnosed with GO and followed up at both endocrinology and ophthalmology clinics with at least 6 months of follow-up. The Score for Moderate-to-Severe type of GO risk Prediction (SMSGOP) and the Score for Muscle-predominant type of GO risk Prediction (SMGOP) were constructed using the machine learning-based automatic clinical score generation algorithm. (3) Results: 55.3% were classified as mild type and 44.8% were classified as moderate-to-severe type. In the moderate-to-severe type group, 32.3% and 12.5% were classified as fat-predominant and muscle-predominant type, respectively. SMSGOP included age, central diplopia, thyroid stimulating immunoglobulin, modified NOSPECS classification, clinical activity score and ratio of the inferior rectus muscle cross-sectional area to total orbit in initial examination. SMGOP included age, central diplopia, amount of eye deviation, serum FT4 level and the interval between diagnosis of GD and GO in initial examination. Scores ≥46 and ≥49 had predictive value, respectively. (4) Conclusions: This is the first study to analyze factors in initial findings that can predict the severity of GO and to construct scores for risk prediction for Korean. We set the predictive scores using initial findings.