Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiplex Cytokine Detection versus ELISA for Aqueous Humor: IL-5, IL-10, and IFN (cid:1) Proﬁles in Uveitis

P URPOSE . The purpose of this study was to determine levels of IL-2, -4, -5, -10, TNF- (cid:1) , and IFN- (cid:2) in aqueous humor (AH) from patients with active panuveitis, anterior uveitis (AU), and non- inﬂammatory controls by using a ﬂow cytometric mutiplex array (CBA) and to compare with results from ELISA. M ETHODS . Pooled normal AH was spiked with six cytokines at decreasing concentrations for evaluating the CBA. AH was also obtained from 10 controls (cataract patients) and 36 patients with active uveitis. Cell-free supernatants were added to a cocktail of capture beads and detector antibodies or to anti- body-coated wells for CBA and ELISA determination, respec-tively. R ESULTS . CBA demonstrated greater sensitivity for detecting IL-4, IL-10, and TNF- (cid:1) than with ELISA. Increased IFN- (cid:2) was detected in both AU and panuveitis groups compared with controls ( P (cid:1) 0.01). IL-10 was higher in the panuveitis group on steroids ( P (cid:1) 0.01). IL-5 was detected in the control ( P (cid:1) 0.01) and AU groups ( P (cid:1) 0.05) but was undetectable in the panuveitis group ( n (cid:2) 10). Correlations between IFN- (cid:2) and IL-10 were found in all uveitis groups ( P (cid:1) 0.01) but not in controls, whereas TNF- (cid:1) correlations with IL-4/IFN- (cid:2) were obtained in controls but not in the uveitis groups ( P (cid:1) 0.01). C ONCLUSIONS . It was possible to measure cytokines titrated into normal AH specimens by CBA, and a greater number of cytokines were detected with increased sensitivity than with ELISA. Elevated IFN- (cid:2) in active uveitis and decreased IL-5 in posterior uveitis suggest Th1 polarity is more marked, with greater uveal tract involvement. The increased IL-10 in the steroid treated group suggests glucocorticoid-induced IL-10 upregulation. ( In- vest Ophthalmol Vis Sci.
An Eye Tracking Study of the Visual Behavior of Children in Social Interaction

— The paper presents an eye tracking analysis study to help us understand the visual behavior and pattern of normal developing children and autistic children while viewing a socially rich stimulus consisting of human and social interactions, as well as the factors that influence their behavior. Eye tracking is a technology that allows the assessment of one’s spontaneous visual attention and eye gaze preference and pattern. An eye tracking experiment consists of displaying different images with social stimuli (containing human faces) to the child. The eye tracker captures and tracks the child’s eye gaze movements, then analyzes the data to identify where specifically in the stimulus is the child looking at. Sixty-four participants (normal and autistic) were divided into two groups. The participants were asked to view a socially rich information stimulus for a limited and set time. Based on the data analysis conducted in the study, the findings show a significant difference between the two groups viewing patterns and behavior when the subjects were presented with a scene included material with human and social interaction content. The study also reveals that a large percentage of autistic participants expressed minimum interest and time looking at the face area, evident by a significant time spent fixating on non-face regions. This is linked to a lack of interest in socially relevant information, especially the two small areas of interest which are the eyes and the mouth regions, when compared to the normal developing children. The results can be used to help improve the life style of other children who have a potential to develop autism as well as discover earlier signs of autism spectrum disorder.
Unscented Kalman Filtering for Hybrid Estimation of Spacecraft Attitude Dynamics and Rate Sensor Alignment

Requirements of highly precise pointing performance have been imposed on recently developed spacecrafts for a variety of missions. The stringent requirements have called on on-orbit estimation of spacecraft dynamics parameters and calibration of on-board sensors as indispensible practices. Consequently, on-orbit estimation of the mass moment of inertia of spacecraft has been a major issue mostly due to the changes by solar panel deployment and a large portion of fuel consumption (Creamer et al., 1996; Ahmed et al., 1998; Bordany et al., 2000; VanDyke et al., 2004; Myung et al., 2007; Myung & Bang, 2008; Sekhavat et al., 2009). As for measurement sensors, on-board calibration of alignment and bias errors of attitude and rate sensors is one of the main concerns of attitude sensor calibration researches (Pittelkau, 2001 & 2002, Lai et al., 2003). Pittelkau (2002) proposed an attitude estimator based on the Kalman filter (Kalman, 1960), in which spacecraft attitude quaternion, rate sensor misalignment and bias, and star tracker misalignments are taken into consideration as states, whereas the body rate is dealt as a synthesized signal by the estimates. Lai at al. (2003) derived a method for alignment estimation of attitude and rate sensors based on the unscented Kalman filter (UKF) (Julier and Uhlmann, 1997). Ma and Jiang (2005) presented spacecraft attitude estimation and calibration based only magnetometer measurements using an UKF. An interesting point is that we need predesigned 3-axis excitation manoeuvres of spacecraft for both dynamics parameter estimation and sensor calibration. Therefore, this study is motivated to merge above estimation and calibration processes into a single filtering problem. It is noteworthy that poor information of moments of inertia is to be treated as a system uncertainty while the rate sensor model errors are to be incorporated into the measurement process. As a filtering algorithm, this study employs a UKF. Extended Kalman filters (EKFs) have been successfully applied to the nonlinear attitude estimation problem (Crassidis et al., 2007). Hybrid estimation using the EKF has been reported by Myung at al. (2007). However, the EKF estimates using the first order linearization, which may lead to instability of the filter (ValDyke et al., 2004). The UKF approximates the nonlinear model to the second order by spreading points 1 sigma apart from the a priori mean. Performing nonlinear
Social remembering in the digital age: Implications for virtual study, work, and social engagement

Abstract Technology has transformed how people interact with one another. According to two recent Pew Research Center surveys (2021a; 2021b), 97 per cent of United States adults have a cell phone, 85 per cent have a smartphone, 93 per cent use the Internet, and 77 per cent have broadband Internet access at home. The Internet has opened countless doors by providing unprecedented access to information and connecting people. While we know from laboratory research that context and collaboration can influence memory, little is known about how virtual collaboration affects memory and whether in-person studies generalise to virtual contexts. In this article, we discuss the challenges, value, and broader relevance of extending laboratory-based memory research to online platforms. In doing so, we report a virtual collaborative memory paradigm, where we examine individual and social remembering in a fully online, chat-based setting, and present data from two completely virtual experiments. In Experiment 1, online participants studied a word list and, in a chatroom, recalled the words either alone (as controls) or with two other participants. Surprisingly, collaborative inhibition – the robust finding of lower recall in collaborative groups than controls – disappeared. This outcome occurred because participants who worked alone recalled less than what we see in in-person studies. In Experiment 2, where instructions were modified and an experimenter was present, individual performance improved, resulting in collaborative inhibition. We reflect on the contextualised nature of this effect in online settings, for both collaborative and individual remembering, and on the implications for the study of memory in the digital age.
COVID-19 Diagnosis System Based on Chest X-ray Images Using Optimized Convolutional Neural Network

It is worth noting that this 21st century has experienced so many economic, social, cultural and political turbulences throughout the world. The 2019 novel coronavirus (COVID-19) outbreak has been regarded by the World Health Organization (WHO) as a public health crisis of global concern. Nowadays, the chest X-ray (CXR) and chest computed tomography (CT) are a more effective imaging technique for diagnosing lung related problems. Deep learning has been more mature in the field of supervised learning, but other areas of machine learning have just started, especially for the areas of unsupervised learning and reinforcement learning. Deep learning has very good performance in speech recognition and image recognition. Using deep learning approaches to diagnose COVID-19 can achieve better cures and treatments. This research presents the data augmentation and L2 regularization approach for transfer learning in several state-of-the-art deep learning models such as VGG16, VGG19, ResNet, and AlexNet, with Convolutional Block Attention Module (CBAM) to perform binary classification ( such as normal and COVID-19/pneumonia cases or COVID-19 and pneumonia cases) and also multi-class classification (such as COVID-19, pneumonia, and normal cases) of covid-chestxray-dataset and NIH datasets. In addition, the performance evaluation adopted the confused matrix to evaluate the results of these models. To sum up, the CBAM can improve the accuracy of all deep learning models to achieve better performance in contrast to that without this architecture. The findings can be a reference for the related COVID-19 diagnosis researches especially during the post-pandemic era.
WipeOutR: automated redundancy detection for feature models

Feature models are used to specify variability and commonality properties of software artifacts. In order to assure high-quality models, different feature model analysis and testing operations can be applied. In this paper, we present two new algorithms that help to make feature model configuration as well as different kinds of analysis operations more efficient. Specifically, we focus on the automated identification of redundancies in feature models and cor-responding test suites. Redundant constraints in feature models can lead to low-performing configuration (solution) search and also to additional efforts in feature model debugging. Redundant feature model test cases can trigger inefficiencies in testing operations. In this paper, we introduce WipeOutR which is an algorithmic approach to support the automated identification of redundancies. This approach has the potential to significantly improve the quality of feature model development and configuration.
Influence of the heating rate on sintering shrinkage of plain iron

The effect of heating rate on shrinkage of plain iron was investigated on green specimens with different green densities: 6.5, 6.9 and 7.3 g cm−3. On increasing the heating rate, the anisothermal shrinkage in the alpha field starts at higher temperatures but its amount does not change. The activation energy corresponds to that of dislocation pipe diffusion, confirming the role of the structural defects accumulated in the particles during cold compaction. The isothermal shrinkage increases on increasing the heating rate, likely due to the effect of surface diffusion at lower temperatures that reduce the driving force, without a measurable effect on shrinkage. The effect of green density is the same for anisothermal and isothermal shrinkage, with a minimum (in absolute value) at the intermediate green density.
Influence of application mode of universal adhesive on the surface morphology, elemental composition and bond strength of calcium silicate-based cements to composite resin: a SEM-EDX microanalysis study

Abstract Maintaining pulp vitality is one of the most important issues in dentistry and the quality of the bond strength of calcium silicate-based cements (CSCs) used for this purpose to restorative material directly affects the long-term success of the treatment. This in vitro study aimed to evaluate microshear bond strength (µSBS) of several CSCs to composite resin using a universal adhesive in both etch-and-rinse and self-etch modes and to examine surface characteristics after acid etching. Five CSCs, namely Angelus MTA, Biodentine, NeoMTA, TheraCal LC, and Well Root ST were evaluated. Fifteen specimens per group were used for determining the µSBS to composite resin. The composite resin was applied over CSCs using a universal adhesive either in etch-and-rinse or self-etch modes, and µSBS of CSCs to composite resin was assessed. The effect of acid etching on the surface morphology and Ca/Si ratio of CSCs were examined by SEM-EDX microanalysis. The highest µSBS was obtained for TheraCal LC in both etch-and-rinse and self-etch modes. No significant difference was found between Biodentine and MTA-based cements in the etch-and-rinse mode, whereas in the self-etch mode, Biodentine displayed a significantly superior bond strength. The results of SEM demonstrated distinct morphological alterations after acid etching. Acid etching decreased the Ca/Si ratio of the substrate surface by approximately 4-fold for all CSCs. TheraCal LC is more suitable for vital pulp therapy with its superior bond strength to composite resin. Furthermore, it may be suggested that adequate adhesive performance may also be achieved without an acid etching.
Thermal stability augmentation of poly(vinyl chloride) by blending with carboxymethyl cellulose

ABSTRACT With the objective of providing thermal support to PVC while enhancing the biodegradability of the PVC side-effects, blend polyvinyl chloride (PVC) with biodegradable carboxymethyl cellulose (CMC) was fabricated. The nucleophilic replacement of chlorine in PVC was observed for the first time. The FT-IR spectra presented a significant vibrational band for the acetate groups at 1600 cm−1. However, the crystalline regions of CMC were enhanced, as evidenced by the XRD results. Therefore, it was demonstrated that the CMC/PVC composite formation led to improved thermal stability due to the increase in the composite’s glass temperature. Graphical Abstract
Survival of mineral-bound peptides into the Miocene

The oldest authenticated peptide sequences to date were reported in 2016 from 3.8 Ma old ostrich eggshell (OES) from the site of Laetoli, Tanzania (Demarchi et al., 2016). Here we demonstrate survival of the same sequences in 6.5-9 Ma OES recovered from a palaeosteppe setting in northwestern China. The eggshell is thicker than those observed in extant species and consistent with the Liushu Struthio sp. ootaxon. These findings push the preservation of ancient proteins back to the Miocene and highlight their potential for paleontology, paleoecology and evolutionary biology.
Egocentric value maps of the near-body environment

Body-part centric response fields are pervasive: they are observed in single neurons, fMRI, EEG, and multiple behavioural measures. This prevalence across scales and measures makes them excellent candidates for studying systems-level neuroscience. Nonetheless, they remain poorly understood because we lack a unifying formal explanation of their origins and role in wider brain function. Here, we provide such explanation. We use reinforcement learning to analytically explain the existence of body-part centric receptive fields, also known as peripersonal field. We then simulate multiple experimental findings considered foundational in the peripersonal space literature. Our results demonstrate that peripersonal fields naturally arise from two simple and plausible assumptions about living agents: 1) they experience reward when they contact objects in the environment, and 2) they act to maximise reward. These simple assumptions are enough to explain empirical findings on stimulus kinematics, tool use, valence, and network-architecture. Our explanation provides further insight. First, it offers multiple empirically testable predictions. Second, it offers a formal description of the notion that the world-agent state is encoded in parieto-premotor cortices, using motor primitives: peripersonal fields provide building blocks that together create a short-term model of the world near the agent in terms of its future states; a successor representation. This short-term, close-range egocentric peripersonal map is analogous to the long-term, long-range allocentric spatial map of place and grid cells, which underlie locomotion and navigation to reach distant objects. Together, these allocentric and egocentric maps allow efficient interactions with a changing environment across multiple spatial and temporal scales.
Maser Pulse Emission Mechanisms

The difficulties with "coherent" curvature emission by bunches of electrons formed through a streaming instability suggest that it cannot account for the observed radio emission for existing polar-cap models of pulsars. Amongst alternative maser mechanisms, that involving acceleration emission due to the electric fields in the waves generated by the bunching instability can account for the gross properties of the observed emission, provided that the relativistic electrons have y £ 10 in the wave frame.
Dauer fate in a Caenorhabditis elegans Boolean network model

Cellular fates are determined by genes interacting across large, complex biological networks. A critical question is how to identify causal relationships spanning distinct signaling pathways and underlying organismal phenotypes. Here, we address this question by constructing a Boolean model of a well-studied developmental network and analyzing information flows through the system. Depending on environmental signals Caenorhabditis elegans develop normally to sexual maturity or enter a reproductively delayed, developmentally quiescent ‘dauer’ state, progressing to maturity when the environment changes. The developmental network that starts with environmental signal and ends in the dauer/no dauer fate involves genes across 4 signaling pathways including cyclic GMP, Insulin/IGF-1, TGF-β and steroid hormone synthesis. We identified three stable motifs leading to normal development, each composed of genes interacting across the Insulin/IGF-1, TGF-Beta and steroid hormone synthesis pathways. Three genes known to influence dauer fate, daf-2, daf-7 and hsf-1, acted as driver nodes in the system. Using causal logic analysis, we identified a five gene cyclic subgraph integrating the information flow from environmental signal to dauer fate. Perturbation analysis showed that a multifactorial insulin profile determined the stable motifs the system entered and interacted with daf-12 as the switchpoint driving the dauer/no dauer fate. Our results show that complex organismal systems can be distilled into abstract representations that permit full characterization of the causal relationships driving developmental fates. Analyzing organismal systems from this perspective of logic and function has important implications for studies examining the evolution and conservation of signaling pathways.
Credit Score Prediction Relying on Machine Learning

Financial institutions use a variety of methodologies to define their commercial and strategic policies, and a significant role is played by credit risk assessment. In recent years, different credit risk assessment services arose, providing Social Lending platforms to connect lenders and borrowers in a direct way without assisting of financial institutions. Despite the pros of these platforms in supporting fundraising process, there are different stems from multiple factors including lack of experience of lenders, missing or uncertain information about the borrower’s credit history. In order to handle these problems, credit risk assessments of financial transactions are usually modeled as a binary problem based on debt repayment, going to apply Machine Learning (ML) techniques. The paper represents an extended abstract of a recent work, where some of the authors performed a benchmarking among the most used credit risk assessment ML models in the field of predicting whether a loan will be repaid in a P2P platform. The experimental analysis is based on a real dataset of Social Lending (Lending Club), going to evaluate several evaluation metrics including AUC, sensitivity, specificity and explainability of the models.
Comet Science: The Study of Remnants from the Birth of the Solar System

1. The history of the comets 2. What is a comet? 3. Visual observations 4. Visible and ultraviolet spectroscopy 5. The 1986 exploration of comet Halley 6. New techniques: infrared and radio 7. Cometary nuclei and their activity 8. The collision of comet Shoemaker-Levy and Jupiter 9. The nature of comets 10. Comets and the history of the solar system 11. The future of cometary research.
Histone modi¢cations and chromatin dynamics: a focus on ¢lamentous fungi

The readout of the genetic information of eukaryotic organisms is signiﬁcantly regulated by modiﬁcations of DNA and chromatin proteins. Chromatin alterations induce genome-wide and local changes in gene expression and affect a variety of processes in response to internal and external signals during growth, differentiation, development, in metabolic processes, diseases, and abiotic and biotic stresses. This review aims at summarizing the roles of histone H1 and the acetylation and methylation of histones in ﬁlamentous fungi and links this knowledge to the huge body of data from other systems. Filamentous fungi show a wide range of morphologies and have developed a complex network of genes that enables them to use a great variety of substrates. This fact, together with the possibility of simple and quick genetic manipulation, highlights these organisms as model systems for the investigation of gene regulation. However, little is still known about regulation at the chromatin level in ﬁlamentous fungi. Understanding the role of chromatin in transcriptional regulation would be of utmost importance with respect to the impact of
Edinburgh Explorer Direct maternal deaths attributable to HIV in the era of antiretroviral therapy

Objective: To assess whether HIV is associated with an increased risk of mortality from direct maternal complications. Design: Population-based cohort study using data from three demographic surveillance sites in Eastern and Southern Africa. Methods: We use verbal autopsy data, with cause of death assigned using the InSilicoVA algorithm, to describe the association between HIV and direct maternal deaths amongst women aged 20–49 years. We report direct maternal mortality rates by HIV status, and crude and adjusted rate ratios comparing HIV-infected and uninfected women, by study site and by ART availability. We pool the study-speciﬁc rate ratios using random-effects meta-analysis. Results: There was strong evidence that HIV increased the rate of direct maternal mortality across all the study sites in the period ART was widely available, with the rate ratios varying from 4.5 in Karonga, Malawi [95% conﬁdence interval (CI) 1.6–12.6] to 5.2 in Kisesa, Tanzania (95% CI 1.7–16.1) and 5.9 in uMkhanyakude, South Africa (95% CI 2.3–15.2) after adjusting for sociodemographic confounders. Combining these adjusted results across the study sites, we estimated that HIV-infected women have 5.2 times the rate of direct maternal mortality compared with HIV-uninfected women (95% CI 2.9–9.5). Conclusion: HIV-infected women face higher rates of mortality from direct maternal causes, which suggests that we need to improve access to quality maternity care for these women. These ﬁndings also have implications for the surveillance of HIV/AIDS-related mortality, as not all excess mortality attributable to HIV will be explicitly attributed to HIV/AIDS on the basis of a verbal autopsy interview.