Miracle-dz/UniFAR-FTUs huggingface dataset:
Glucocorticoid-induced formation of cross-linked actin networks in cultured human trabecular meshwork cells.

PURPOSE To determine the effects of glucocorticoid treatment on the microfilament structure of cultured human trabecular meshwork cells. Topical or systemic administration of glucocorticoids can lead to the development of ocular hypertension and to the development of vision loss, which is clinically similar to primary open angle glaucoma. However, the mechanism(s) by which glucocorticoids cause ocular hypertension is not well defined. Alterations in the trabecular meshwork, the site of drainage of aqueous humor from the eye, have been linked to the development of ocular hypertension. METHODS Human trabecular meshwork cells were cultured in the presence and absence of glucocorticoids for 0 to 21 days. The microfilament organization of the cultured trabecular meshwork cells was examined by epifluorescent and transmission electron microscopy. RESULTS Glucocorticoids caused a progressive change in the organization of microfilaments in the trabecular meshwork cells, but not in other cultured ocular cells. By fluorescence microscopic analysis, the actin stress fibers found in control trabecular meshwork cells were reorganized on treatment with glucocorticoids into cross-linked actin networks that resembled geodesic-dome-like polygonal lattices. The cross-linked actin networks were reversible on withdrawal of the glucocorticoid treatment. Dose-response data for dexamethasone, relative ranking of activity with glucocorticoid potency, and partial inhibition with glucocorticoid antagonists all suggest the involvement of the trabecular meshwork glucocorticoid receptor in cross-linked actin network formation. The reorganization of the trabecular meshwork cytoskeleton alters cell function because glucocorticoid treatment of cultured trabecular meshwork cells also inhibited trabecular meshwork cell migration and proliferation. CONCLUSION The steroid-induced alteration in trabecular meshwork cytoskeleton may be an important factor in the development of steroid-induced ocular hypertension and may play a role in the ocular hypertension associated with primary open angle glaucoma.
Intrasentential Centering: A Case Study

One of the necessary extensions to the centering model is a mechanism to handle pronouns with intrasentential antecedents. Existing centering models deal only with discourses consisting of simple sentences. It leaves unclear how to delimit center-updating utterance units and how to process complex utterances consisting of multiple clauses. In this paper, I will explore the extent to which a straightforward extension of an existing intersentential centering model contributes to this effect. I will motivate an approach that breaks a complex sentence into a hierarchy of center-updating units and proposes the preferred interpretation of a pronoun in its local context arbitrarily deep in the given sentence structure. This approach will be substantiated with examples from naturally occurring written discourses.
Carry-Along Trade

Large multi-product firms dominate international trade flows. Using novel linked production and export data at the firm-product level, we find that the overwhelming majority of manufacturing firms export products that they do not produce. Three quarters of the exported products and 30% of export value from Belgian manufacturers are in goods that are not produced by the firm, so-called Carry-Along Trade (CAT). The number of CAT products is strongly increasing in firm productivity while the number of produced products that are exported is weakly increasing in firm productivity. We propose a general model of production and sourcing at multi-product firms and explore new demand- and supply-side modelling features capable of generating predictions consistent with the empirical findings. Export price data and company interviews offer suggestive evidence for the presence of demand-scope complementarities.
General Loss Bounds for Universal Sequence Prediction

The Bayesian framework is ideally suited for induction problems. The probability of observing $x_t$ at time $t$, given past observations $x_1. x_{t-1}$ can be computed with Bayes'' rule if the true distribution $\mu$ of the sequences $x_1x_2x_3. $ is known. The problem, however, is that in many cases one does not even have a reasonable estimate of the true distribution. In order to overcome this problem a universal distribution $\xi$ is defined as a weighted sum of distributions $\mu_i\! \in\! M$, where $M$ is any countable set of distributions including $\mu$. This is a generalization of Solomonoff induction, in which $M$ is the set of all enumerable semi-measures. Systems which predict $y_t$, given $x_1. x_{t-1}$ and which receive loss $l_{x_t y_t}$ if $x_t$ is the true next symbol of the sequence are considered. It is proven that using the universal $\xi$ as a prior is nearly as good as using the unknown true distribution $\mu$. Furthermore, games of chance, defined as a sequence of bets, observations, and rewards are studied. The time needed to reach the winning zone is bounded in terms of the relative entropy of $\mu$ and $\xi$. Extensions to arbitrary alphabets, partial and delayed prediction, and more active systems are discussed.
Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation

We present a probabilistic model that uses both prosodic and lexical cues for the automatic segmentation of speech into topically coherent units. We propose two methods for combining lexical and prosodic information using hidden Markov models and decision trees. Lexical information is obtained from a speech recognizer, and prosodic features are extracted automatically from speech waveforms. We evaluate our approach on the Broadcast News corpus, using the DARPA-TDT evaluation metrics. Results show that the prosodic model alone is competitive with word-based segmentation methods. Furthermore, we achieve a significant reduction in error by combining the prosodic and word-based knowledge sources.
WHY SO? or WHY NO? Functional Causality for Explaining Query Answers

In this paper, we propose causality as a unified framework to explain query answers and non-answers, thus generalizing and extending several previously proposed approaches of provenance and missing query result explanations. We develop our framework starting from the well-studied definition of actual causes by Halpern and Pearl. After identifying some undesirable characteristics of the original definition, we propose functional causes as a refined definition of causality with several desirable properties. These properties allow us to apply our notion of causality in a database context and apply it uniformly to define the causes of query results and their individual contributions in several ways: (i) we can model both provenance as well as non-answers, (ii) we can define explanations as either data in the input relations or relational operations in a query plan, and (iii) we can give graded degrees of responsibility to individual causes, thus allowing us to rank causes. In particular, our approach allows us to explain contributions to relational aggregate functions and to rank causes according to their respective responsibilities. We give complexity results and describe polynomial algorithms for evaluating causality in tractable cases. Throughout the paper, we illustrate the applicability of our framework with several examples. Overall, we develop in this paper the theoretical foundations of causality theory in a database context.
Order-Optimal Data Aggregation in Wireless Sensor Networks - Part I: Regular Networks

The predominate traffic patterns in a wireless sensor network are many-to-one and one-to-many communication. Hence, the performance of wireless sensor networks is characterized by the rate at which data can be disseminated from or aggregated to a data sink. In this paper, we consider the data aggregation problem. We demonstrate that a data aggregation rate of O(log(n)/n) is optimal and that this rate can be achieved in wireless sensor networks using a generalization of cooperative beamforming called cooperative time-reversal communication.
Wireless Information-Theoretic Security - Part I: Theoretical Aspects

In this two-part paper, we consider the transmission of confi dential data over wireless wiretap channels. The first part presents an information-theoretic problem formulation in which two legitimate partners communicate over a quasi-static fading channel and an eavesdropper observes their transmissions through another independent quasi-static fading channel. We define the secrecy capacity in terms of outage probability and provide a complete characterization of the maximum transmission rate at which the eavesdropper is unable to decode any information. In sharp contrast with known results for Gaussian wiretap channels (without feedback), our contribution shows that in the presence of fading informationtheoretic security is achievable even when the eavesdropper has a better average signal-to-noise ratio (SNR) than the legitimate receiver — fading thus turns out to be a friend and not a foe. The issue of imperfect channel state information is also addressed. Practical schemes for wireless information-theoretic security are presented in Part II, which in some cases comes close to the secrecy capacity limits given in this paper.
Using Text Segmentation Algorithms for the Automatic Generation of E-Learning Courses

With the advent of e-learning, there is a strong demand for tools that help to create e-learning courses in an automatic or semi-automatic way. While resources for new courses are often freely available, they are generally not properly structured into easy to handle units. In this paper, we investigate how state of the art text segmentation algorithms can be applied to automatically transform unstructured text into coherent pieces appropriate for e-learning courses. The feasibility to course generation is validated on a test corpus specifically tailored to this scenario. We also introduce a more generic training and testing method for text segmentation algorithms based on a Latent Dirichlet Allocation (LDA) topic model. In addition we introduce a scalable random text segmentation algorithm, in order to establish lower and upper bounds to be able to evaluate segmentation results on a common basis.
Multi-class probabilistic classification using inductive and cross Venn-Abers predictors

Inductive (IVAP) and cross (CVAP) Venn–Abers predictors are computationally efficient algorithms for probabilistic prediction in binary classification problems. We present a new approach to multi-class probability estimation by turning IVAPs and CVAPs into multiclass probabilistic predictors. The proposed multi-class predictors are experimentally more accurate than both uncalibrated predictors and existing calibration methods.
Characterization of asphalt binder containing hydrothermal liquefied composition extracted from food waste

The use of biological wastes to extract an alternative substance to the asphalt binder could potentially reduce the depletion of crude oil. This also could address the environmental pollution resulting from these wastes and develop the asphalt pavement economically and environmentally. Liquefied product extracted from food waste by hydrothermal liquefaction process can be used as an alternative bio-binder in asphalt pavement. This study evaluates the performance of the base asphalt binder of 60/70 pen after adding the hydrothermal liquefaction product of food waste. This involves a set of laboratory trials in liquefying the food waste via hydrothermal process under various selected variables. The physical properties of the base and modified asphalt binder were then evaluated using the penetration, softening point and viscosity test. The results conclude that liquefied food waste (LFW) product has a potential to be used as bio-binder in asphalt pavement.
Preparation and In Vitro/In Vivo Evaluation of Puerarin Solid Self-Microemulsifying Drug Delivery System by Spherical Crystallization Technique

The aim of this work was to establish a method for preparing stable and controllable solid self-microemulsifying drug delivery system (S-SMEDDS) by spherical crystallization technique, which was explored for promoting the dissolution, oral bioavailability, and process efficiency. Solubility test, preparation of liquid self-microemulsifying drug delivery system (L-SMEDDS), and the obtained ternary phase diagrams test have been performed to screen and optimize the composition of LSMEDDS. The optimized formulation was used to prepare puerarin solid self-microemulsifying drug delivery system (Pue-SSMEDDS) by spherical crystallization technique. Droplet size and morphological analysis of the optimal Pue-SSMEDDS were determined to evaluate the final formulation. And the Pue-SSMEDDS was also assessed by flowability study, angle of repose, Carr’s index, and flow velocity. Furthermore, the vitro dissolution and pharmacokinetic profile in vivo were analyzed. The study in vitro showed the Pue-SSMEDDS could disperse in the dispersion medium within 60 s and was spherical with the particle size of 19.66 nm and zeta potential of −28.3 mV. It could keep stable at low temperature and seal condition for 3 months. In vivo pharmacokinetic experiments of rats, the mean plasma concentration of self-microemulsion group was much higher than that of conventional tablets and could play a long-lasting efficacy, while there was no significant difference between the LSMEDDS and S-SMEDDS. The results suggested the potential of S-SMEDDS could improve the oral bioavailability of poorly water-soluble drug, such as puerarin.
Standardized Patient Methodology to Assess Refractive Error Reproducibility

Purpose. Standardized patient (SP) methodology is the gold standard for evaluating clinical practice. This approach was used to investigate the content of typical optometric eyecare in England and the reproducibility of refractive error measurement using prescriptions obtained by three SPs. Methods. The three SPs were independently examined by three to four expert optometric clinicians to obtain “benchmark” estimates of refractive error. One hundred two community optometrists consented to be visited by three SPs who were trained to provide accurate responses during the examinations. The spectacle prescriptions obtained by the SPs were analyzed for spherical equivalent refraction, spherical power and cylindrical power using astigmatic decomposition. Results. The spherical equivalent refractions were found to be within ±0.25 D of the benchmark on average 81% of the time and within ±0.50 D 97% of the time. The spherical power was within ±0.25 D 90% of the time and within ±0.50 D 98% of the time. The cylindrical power agreed within ±0.25 D 93% of the time and within ±0.50 D 100% of the time. Based on reproducibility limits data obtained for all six eyes, any two optometrists would differ in their estimation of spherical equivalent refraction by no more than 0.75 D in 95% of repeated measures. The astigmatic data (C0 and C45) show that optometrists will differ in their estimation of the C0 component by between 0.25 and 0.61 D and for the C45 component by between 0.22 and 0.47 D in 95% of repeated measures. Conclusions. The agreement between our data and the results of other similar studies support the conclusions that subjective refractive findings are reproducible to approximately ±0.75 D when performed by multiple optometrists in patients of different age groups and levels of ametropia. SPs are an effective way of measuring reproducibility of refractive error and should be considered for further comparative analysis in different age groups and different levels of ametropia.
Appearing anxious leads to negative judgments by others.

OBJECTIVES To investigate people's perceptions of anxiety symptoms and the disclosure of anxiety by others, and how one's own level of social anxiety affects these perceptions. DESIGN Undergraduate students and community members high (n = 83) and low (n = 80) in social anxiety, as measured by the Social Interaction Anxiety Scale (SIAS; Mattick & Clark, 1998), were randomly assigned to watch 1 of 4 videos in which the target individual either appeared or did not appear anxious, and either disclosed or did not disclose her anxiety. Participants rated the target individual on various characteristics on which socially anxious individuals often fear being judged, and completed measures of their own level of social anxiety, perceived similarity to the target individual, and reasons for their ratings. RESULTS Participants negatively evaluated others who looked anxious on qualities related to awkwardness, social skills, and weakness, and the disclosure of anxiety was associated with fewer negative judgments. Participants' own levels of social anxiety were not related to their judgments. CONCLUSIONS This study provided evidence that the concerns held by socially anxious individuals that others will judge them negatively based on their signs of anxiety is accurate to a degree. These findings might inform the process of cognitive restructuring for social anxiety.
Pyridoxal 5′-Phosphate Biosynthesis by Pyridox-(am)-ine 5′-Phosphate Oxidase: Species-Specific Features

Enzymes reliant on pyridoxal 5′-phosphate (PLP), the metabolically active form of vitamin B6, hold significant importance in both biology and medicine. They facilitate various biochemical reactions, particularly in amino acid and neurotransmitter metabolisms. Vitamin B6 is absorbed by organisms in its non-phosphorylated form and phosphorylated within cells via pyridoxal kinase (PLK) and pyridox-(am)-ine 5′-phosphate oxidase (PNPOx). The flavin mononucleotide-dependent PNPOx enzyme converts pyridoxine 5′-phosphate and pyridoxamine 5′-phosphate into PLP. PNPOx is vital for both biosynthesis and salvage pathways in organisms producing B6 vitamers. However, for those depending on vitamin B6 as a nutrient, PNPOx participates only in the salvage pathway. Transferring the PLP produced via PNPOx to client apo-enzymes is indispensable for their catalytic function, proper folding and targeting of specific organelles. PNPOx activity deficiencies due to inborn errors lead to severe neurological pathologies, particularly neonatal epileptic encephalopathy. PNPOx maintains PLP homeostasis through highly regulated mechanisms, including structural alterations throughout the catalytic cycle and allosteric PLP binding, influencing substrate transformation at the active site. Elucidation at the molecular level of the mechanisms underlying PNPOx activity deficiencies is a requirement to develop personalized approaches to treat related disorders. Finally, despite shared features, the few PNPOx enzymes molecularly and functionally studied show species-specific regulatory properties that open the possibility of targeting it in pathogenic organisms.
Adaptive Online Prediction by Following the Perturbed Leader

When applying aggregating strategies to Prediction with Expert Advice (PEA), the learning rate must be adaptively tuned. The natural choice of sqrt(complexity/current loss) renders the analysis of Weighted Majority (WM) derivatives quite complicated. In particular, for arbitrary weights there have been no results proven so far. The analysis of the alternative Follow the Perturbed Leader (FPL) algorithm from Kalai and Vempala (2003) based on Hannan's algorithm is easier. We derive loss bounds for adaptive learning rate and both finite expert classes with uniform weights and countable expert classes with arbitrary weights. For the former setup, our loss bounds match the best known results so far, while for the latter our results are new.
What Can We Learn Privately?

Learning problems form an important category of computational tasks that generalizes many of the computations researchers apply to large real-life data sets. We ask: what concept classes can be learned privately, namely, by an algorithm whose output does not depend too heavily on any one input or specific training example? More precisely, we investigate learning algorithms that satisfy differential privacy, a notion that provides strong confidentiality guarantees in the contexts where aggregate information is released about a database containing sensitive information about individuals. We present several basic results that demonstrate general feasibility of private learning and relate several models previously studied separately in the contexts of privacy and standard learning.
MAPPING OF GENDER INEQUALITY IN INDONESIA BASED ON INFLUENCING FACTORS USING GEOGRAPHICALLY WEIGHTED ORDINAL LOGISTIC REGRESSION

Gender inequality is a condition of discrimination between men and women that results from unequal social systems and structures. Gender inequality is measured based on the gender inequality index (IKG). This research aims to map gender inequality in Indonesia based on influencing factors and compare classification accuracy results between the GWOLR and ordinal logistic regression model. Data was obtained from the Indonesian Central Statistics Agency (BPS RI) and KemenPPPA in the year of 2022. The Gender Inequality Index data as the response variable is categorized using an ordinal data scale, namely IKG (1) Low, IKG (2) Middle, and IKG (3) High with ten predictor variables from the dimensions of health, education, human empowerment, socio-culture, and employment, with the amount of data is 34 observation data. The research method uses geographically weighted ordinal logistic regression (GWOLR) based on exponential kernel weighting. In the data analysis stage, ordinal logistic regression is performed before applying GWOLR, and after the model is formed, the classification accuracy will be calculated. The results of this study indicate that mapping gender inequality in Indonesia based on influencing factors using the GWOLR model forms three groups. The first mapping location labeled as low inequality is influenced by women whose birth was attended by a health worker (X1), women who have a pre-employment card (X7), women who are employed (X8), and the percentage of women who married before the age of 17 (X10). The second mapping location labeled with middle inequality is influenced by women whose delivery is attended by a health worker (X1), women's net enrolment in higher education (X2), and women married before the age of 17 (X10). The three locations categorized as high inequality are influenced by female birth attendance by health personnel (X1), Women's Human Development Index (X3), female rape offenses (X4), female domestic violence offenses (X6), and female marriage under the age of 17 (X10). Modeling the Gender Inequality Index using the GWOLR model resulted in higher classification accuracy than the ordinal logistic regression model, which was 94.11%.
Improving Student Grade Prediction Using Hybrid Stacking Machine Learning Model

With increasing technical procedures, academic institutions are adapting to a data-driven decision-making approach of which grade prediction is an integral part. The purpose of this study is to propose a hybrid model based on a stacking approach and compare its accuracy with those of the individual base models. The model hybridizes K-nearest neighbours, Random forests, XGBoost and multi-layer perceptron networks to improve the accuracy of grade prediction by enabling a combination of strengths of different algorithms for the creation of a more robust and accurate model. The proposed model achieved an average overall accuracy of around 90.9% for 10 epochs, which is significantly higher than that achieved by any of the individual algorithms of the stack. The results demonstrate the improvement of prediction results but using a stacking approach. This study has significant implications for academic institutions which can help them make informed grade predictions for the improvement of student outcomes.