Miracle-dz/UniFAR-FTUs huggingface dataset:
The impact of chronic stress on intracellular redox balance: A systems level analysis

Chronic psychosocial stress is implicated in the onset and progression of noncommunicable diseases, and mechanisms underlying this relationship include alterations to the intracellular redox state. However, such changes are often investigated in isolation, with few studies adopting a system level approach. Here, male Wistar rats were exposed to 9.5 weeks of chronic unpredictable mild stress and redox status assays were subsequently performed on cardiac, hepatic, and brain tissues versus matched controls. The stressed rats displayed an anxious phenotype, with lowered plasma corticosterone levels (p = 0.04 vs. Controls) and higher plasma epinephrine concentrations (p = 0.03 vs. Controls). Our findings showed organ‐specific redox profiles, with stressed rats displaying increased myocardial lipid peroxidation (p = 0.04 vs. Controls) in the presence of elevated nonenzymatic antioxidant capacity (p = 0.04 vs. Controls). Conversely, hepatic tissues of stressed rats exhibited lowered nonenzymatic antioxidant capacity (p < 0.001 vs. Controls) together with increased superoxide dismutase (SOD) activity (p = 0.05 vs. Controls). The brain displayed region‐specific antioxidant perturbations, with increased SOD activity (p = 0.01 vs. Controls) in the prefrontal cortex of the stressed rats. These findings reveal distinct stress‐related organ‐specific vulnerability to redox perturbations and may provide novel insights into putative therapeutic targets.
The effect of tauroursodeoxycholic Acid (TUDCA) treatment on placental endoplasmic reticulum (ER) stress in a rat model of advanced maternal age

Advanced maternal age (≥35 years) is associated with an increased risk of pregnancy complications such as fetal growth restriction and preeclampsia. We previously demonstrated poor pregnancy outcomes (reduced fetal body weight), altered vascular function, and increased expression of endoplasmic reticulum (ER) stress markers (phospho-eIF2α and CHOP) in mesenteric arteries from a rat model of advanced maternal age. Further, treatment of aged dams during pregnancy with an ER stress inhibitor, tauroursodeoxycholic acid (TUDCA) increased fetal body weight (both male and female), tended to improve uterine artery function, and reduced expression of phospho-eIF2α and CHOP in systemic arteries. Placental ER stress has been linked to poor pregnancy outcomes in complicated pregnancies but whether placental ER stress is evident in advanced maternal age is not known. In addition, sex-specific changes in the placental labyrinth and junctional zones from male and female offspring in advanced maternal age have not been investigated. Therefore, the current study aimed to investigate the effect of TUDCA intervention on placental ER stress. We hypothesize that placental ER stress is increased in a rat model of advanced maternal age that is alleviated by TUDCA intervention for both sexes. Placental ER stress markers (GRP78, phospho-eIF2α, ATF-4, CHOP, ATF-6α, and sXBP-1) were quantified by Western blot in placentas from male and female offspring; the labyrinth and junction zones were analyzed separately. In the placental labyrinth zone from male offspring, only GRP78 (p = 0.007) was increased in aged dams compared to young dams; TUDCA treatment reduced the placental expression of GRP78 in aged dams (p = 0.003). In addition, TUDCA reduced the levels of phospho-eIF2α (p = 0.021), ATF-4 (p = 0.016), and CHOP (p = 0.012) in aged dams but no effect was observed in young TUDCA-treated dams. In the placental labyrinth zone from female offspring, an increased level of phospho-eIF2α (p = 0.005) was observed in aged dams compared to young dams, and TUDCA treatment had no effect in both young and aged groups. In the placental junctional zone from male and female offspring, no changes in the expression of GRP78, phospho-eIF2α, ATF-4, CHOP, and ATF-6α was observed with or without TUDCA treatment in both young and aged groups, however, a reduced expression of sXBP-1 protein was observed in from both male (p = 0.001) and female (p = 0.031) placentas from aged-TUDCA treated dams compared to aged control. In conclusion, our data highlight the complexity and sex-specificity of ER stress responses in advanced maternal age with TUDCA treatment maintaining ER stress proteins to basal levels and improving fetal growth in both male and female offspring.
Development and psychometric testing of an instrument to measure applicants’ desire to work in nursing

Background Nursing applicants’ desire to work in nursing has been identified as an important aspect to consider in nursing student selection, but relevant instruments are missing. Aims To describe the development and psychometric testing of the Desire to Work in Nursing instrument. Design A mixed-methods design. Methods The development phase included the collection and analysis of two types of data. First, three focus group interviews were organised with volunteer nursing applicants (n = 18) after the entrance exams of three universities of applied sciences (UAS) (in 2016). The interviews were analysed inductively. Second, scoping review data from four electronic databases were collected. Thirteen full-text articles (published between 2008 and 2019) were included in the review and analysed deductively based on the results of the focus group interviews. The items for the instrument were generated by synthesising the results of the focus group interviews and the scoping review. The testing phase included 841 nursing applicants who participated in the entrance exams of four UAS on 31 October 2018. The psychometric properties were analysed by examining internal consistency reliability and construct validity by principal component analysis (PCA). Results The desire to work in nursing was classified into four categories: nature of the work, career opportunities, suitability for nursing and previous experiences. The internal consistency reliability of the four subscales was satisfactory. The PCA found only one factor with an eigenvalue over one, explaining 76% of the total variance. Conclusions The instrument can be considered reliable and valid. Although theoretically the instrument contains four categories, a one-factor solution should be considered in the future. Impact statement Evaluation of applicants’ desire to work in nursing may provide a strategy to retain students. Plain Language summary Individuals choose the nursing profession for variety of reasons. However, there is very little understanding of why nursing applicants desire to work in nursing. With the current challenges in the adequate staffing in the nursing workforce, it is important to understand any aspects that may be associated with student recruitment and retention. In this study, it was identified that nursing applicants desire to work in nursing because of the nature of the work, career opportunities, being suitable for nursing and of previous experiences. Instrument to measure this desire was developed and tested. The tests revealed that the instrument can be used reliably in this context. It is suggested that the developed instrument could be used as a pre-screening or self-assessment tool before applying to nursing education to provide further insights to applicants about their reasons for applying and an opportunity to reflect on their decision.
Adsorption of Ibuprofen as a pharmaceutical pollutant from aqueous phase using zinc oxide nanoparticles: Green synthesis, batch adsorption, and biological activities

Recent interest has focused on the biosynthesis of metal nanoparticles (NPs), particularly from plants. The production of precipitate served as an early indicator of the presence in the present study's use of ZnO NPs green synthesis of these particles, which was further validated by; Fourier transform infrared spectroscopy, x‐ray diffraction. Additionally, the Brunauer–Emmett–Teller was used to calculate the surface area, which came out to be 119.12 m2/g. Since the true effects of new pollutants, including medicines, on the environment and human health are not well understood, their presence in aquatic systems poses a severe hazard. For this reason, the antibiotic Ibuprofen (IBP) was absorbable to ZnO‐NPs in this search. As opposed to fitting to Langmuir isothermally, the adsorption process was discovered to be pseudo‐second‐order kinetically, and the reaction was determined to be a chemisorption process. The process was endothermic and spontaneous, according to thermodynamic studies. Maximizing IBP removal from aqueous solution required the use of a Box–Behnken surface statistical design with four components, four levels, and response surface modeling. Solution pH, IBP concentration, duration, and dose were the four parameters that were utilized. The regeneration process, which is employed for five cycles with excellent efficiency, is the best benefit of using ZnO‐NPs. Examine the elimination of pollutants from actual samples as well. However, the adsorbent is quite effective at reducing biological activity. At high concentrations of ZnO‐NPs demonstrated notable antioxidant activity and Red Blood Cell (RBC) hemocompatibility and no discernible hemolysis was seen. ZnO‐NPs demonstrated a notable percent suppression of α‐amylase up to 53.6% at 400 μg/mL, and so displayed potential as an antidiabetic. Cyclooxygenase was suppressed by ZnO‐NPs in an anti‐inflammatory test (COX‐1 & COX‐2) up to 56.32% and 52.04% at a concentration of 400 μg/mL, respectively. Significant anti‐Alzheimer potential was demonstrated by ZnO‐NPs at 400 μg/mL by inhibiting Acetyl cholinesterase and Butyl cholinesterase up to 68.98 ± 1.62% and 62.36%, respectively. We concluded that guava extract is helpful for ZnO‐NP reduction and capping. The bioengineered NPs could prevent Alzheimer's, diabetes, and inflammation and were biocompatible.
The Concept of Forward-Forward Learning Applied to a Multi Output Perceptron

The concept of a recently proposed Forward-Forward learning algorithm for fully connected artificial neural networks is applied to a single multi output perceptron for classification. The parameters of the system are trained with respect to increased (decreased)"goodness"for correctly (incorrectly) labelled input samples. Basic numerical tests demonstrate that the trained perceptron effectively deals with data sets that have non-linear decision boundaries. Moreover, the overall performance is comparable to more complex neural networks with hidden layers. The benefit of the approach presented here is that it only involves a single matrix multiplication.
Predictive Coding as a Neuromorphic Alternative to Backpropagation: A Critical Evaluation

Abstract Backpropagation has rapidly become the workhorse credit assignment algorithm for modern deep learning methods. Recently, modified forms of predictive coding (PC), an algorithm with origins in computational neuroscience, have been shown to result in approximately or exactly equal parameter updates to those under backpropagation. Due to this connection, it has been suggested that PC can act as an alternative to backpropagation with desirable properties that may facilitate implementation in neuromorphic systems. Here, we explore these claims using the different contemporary PC variants proposed in the literature. We obtain time complexity bounds for these PC variants, which we show are lower bounded by backpropagation. We also present key properties of these variants that have implications for neurobiological plausibility and their interpretations, particularly from the perspective of standard PC as a variational Bayes algorithm for latent probabilistic models. Our findings shed new light on the connection between the two learning frameworks and suggest that in its current forms, PC may have more limited potential as a direct replacement of backpropagation than previously envisioned.
Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry

Bayesian inference in deep neural networks is challenging due to the high-dimensional, strongly multi-modal parameter posterior density landscape. Markov chain Monte Carlo approaches asymptotically recover the true posterior but are considered prohibitively expensive for large modern architectures. Local methods, which have emerged as a popular alternative, focus on specific parameter regions that can be approximated by functions with tractable integrals. While these often yield satisfactory empirical results, they fail, by definition, to account for the multi-modality of the parameter posterior. In this work, we argue that the dilemma between exact-but-unaffordable and cheap-but-inexact approaches can be mitigated by exploiting symmetries in the posterior landscape. Such symmetries, induced by neuron interchangeability and certain activation functions, manifest in different parameter values leading to the same functional output value. We show theoretically that the posterior predictive density in Bayesian neural networks can be restricted to a symmetry-free parameter reference set. By further deriving an upper bound on the number of Monte Carlo chains required to capture the functional diversity, we propose a straightforward approach for feasible Bayesian inference. Our experiments suggest that efficient sampling is indeed possible, opening up a promising path to accurate uncertainty quantification in deep learning.
A variational model for wrapped phase denoising

This paper presents a variational model for the denoising of wrapped phase images. By enforcing the required Pythagorean trigonometric identity between the real and imaginary components of the signal, this model improves the signal-to-noise ratio of the restored signal. To preserve phase map discontinuities, the model is based on total variation. The existence and uniqueness of the model’s solution are demonstrated using standard techniques. In addition, the convergence of a rapid fixed-point method to determine the numerical solution is demonstrated. Experiments on both synthetic and actual patterns validate the model’s performance.
Spectral Toolkit of Algorithms for Graphs: Technical Report (1)

Spectral Toolkit of Algorithms for Graphs (STAG) is an open-source library for efficient spectral graph algorithms, and its development starts in September 2022. We have so far finished the component on local graph clustering, and this technical report presents a user's guide to STAG, showcase studies, and several technical considerations behind our development.
Tracing and Visualizing Human-ML/AI Collaborative Processes through Artifacts of Data Work

Automated Machine Learning (AutoML) technology can lower barriers in data work yet still requires human intervention to be functional. However, the complex and collaborative process resulting from humans and machines trading off work makes it difficult to trace what was done, by whom (or what), and when. In this research, we construct a taxonomy of data work artifacts that captures AutoML and human processes. We present a rigorous methodology for its creation and discuss its transferability to the visual design process. We operationalize the taxonomy through the development of AutoML Trace a visual interactive sketch showing both the context and temporality of human-ML/AI collaboration in data work. Finally, we demonstrate the utility of our approach via a usage scenario with an enterprise software development team. Collectively, our research process and findings explore challenges and fruitful avenues for developing data visualization tools that interrogate the sociotechnical relationships in automated data work. Availability of Supplemental Materials: https://osf.io/3nmyj/? view_only=19962103d58b45d289b5c83421f48b36
Optimization of a probabilistic quantum search algorithm with 
a priori
 information

A quantum computer encodes information in quantum states and runs quantum algorithms to surpass the classical counterparts by exploiting quantum superposition and quantum correlation. Grover's quantum search algorithm is a typical quantum algorithm that proves the superiority of quantum computing over classical computing. It has a quadratic reduction in the query complexity of database search, and is known to be optimal when no a priori information about the elements of the database is provided. In this work, we consider a probabilistic Grover search algorithm allowing nonzero probability of failure for a database with a general a priori probability distribution of the elements, and minimize the number of oracle calls by optimizing the initial state of the quantum system and the reflection axis of the diffusion operator. The initial state and the reflection axis are allowed to not coincide, and thus the quantum search algorithm rotates the quantum system in a three-dimensional subspace spanned by the initial state, the reflection axis and the search target state in general. The number of oracle calls is minimized by a variational method, and formal results are obtained with the assumption of low failure probability. The results show that for a nonuniform a priori distribution of the database elements, the number of oracle calls can be significantly reduced given a small decrease in the success probability of the quantum search algorithm, leading to a lower average query complexity to find the solution of the search problem. The results are applied to a simple but nontrivial database model with two-value a priori probabilities to show the power of the optimized quantum search algorithm. The paper concludes with a discussion about the generalization to higher-order results that allows for a larger failure probability for the quantum search algorithm.
A matrix algebra approach to approximate Hessians

This work presents a novel matrix-based method for constructing an approximation Hessian using only function evaluations. The method requires less computational power than interpolation-based methods and is easy to implement in matrix-based programming languages such as MATLAB. As only function evaluations are required, the method is suitable for use in derivative-free algorithms. For reasonably structured sample sets, the method is proven to create an order-$1$ accurate approximation of the full Hessian. Under more specialized structures, the method is proved to yield order-$2$ accuracy. The undetermined case, where the number of sample points is less than required for full interpolation, is studied and error bounds are developed for the resulting partial Hessians.
Improving Reading Comprehension : Measuring Readability

A standardized method, called programed prose, has been developed which can b,e used to automatically convert prose training material into a form which forces trainees to read the material with at least a minimal level of _comprehension. From the results of a series of experimental studies, it was concluded that programed prose facilitates learning under the following conditions: (1) when individuals are not always highly motivated to learn; (2) when attention to the reading task wanes; (3) when the reading ability level of the individual exceeds the reading difficulty level of the prose material; and (4) when the time allowed for reading exceeds the time necessary to complete the programed prose. It was also found that programed prose is more effective and more efficient in facilitating learning than is the study question technique that is used in correspondence course material, and it was concluded that the Rauding scale was more valid as a measure of readability than either the Flesch or the Dale-Chall measures. (Author/RB) OC P.14 I. :UN( of . 4f . 1 LW( d 1 t L Improving Reading Comprehension: Measuring Readability
The secondary substrate problem in co-evolution and developmental-evolution

The performance of an Evolutionary Algorithm on a search problem is critically effected by the substrate used to encode the candidate solutions of the problem. In addition to the challenge of designing evolvable genetic substrates, two-population competitive coevolutionary algorithms (coEAs) and developmental Evolutionary Algorithms (devo-EAs) present another substrate-related design problem. Both involve an additional substrate with its own mechanism of change. In coEAs, test-cases are encoded with an independent genetic substrate having its own variation operators. In devo-EAs, phenotypes are composed of a distinct substrate with associated generative mechanisms capable of changing an individual's form and size during development. Though this “secondary” substrate is a distinctive feature of both algorithms, the design problem it poses remains poorly understood. This dissertation proposes novel formal models to characterize how the properties of the secondary substrate influences the performance devo-EAs and coEAs respectively. Firstly, we propose a computational model for devo-EAs which shows that the point in time at which the development of a phenotype halts can introduce selection biases that can cause an empirically measurable retardation in the performance of a devo-EA. Furthermore, a Genotype-Phenotype map that is bias-free is formally equivalent to a Nash equilibrium in a non-cooperative multi-player game, where each genotype is a player, the possible halting points are strategies and the payoffs are related to the fitness function. We show that algorithmic solutions to find this Nash map are expensive without a suitable secondary substrate. Secondly, we propose a novel search space model for Pareto coevolution that formally defines the evolvability properties required of the secondary substrate for pathology-free learning with a mutation-only coEA. With this model, we show that on boolean classification problems (a) the variational properties of the secondary substrate are a property of the problem class rather than tied to individual problems, and (b) the absence of coevolutionary pathologies does not imply success in finding high-quality solutions. Rather than being mysterious dynamical properties of coEAs, these findings are transparently explained using Machine Learning first principles.
Protected or Porous: A Comparative Analysis of Threat Detection Capability of IoT Safeguards

Consumer Internet of Things (IoT) devices are increasingly common, from smart speakers to security cameras, in homes. Along with their benefits come potential privacy and security threats. To limit these threats a number of commercial services have become available (IoT safeguards). The safeguards claim to provide protection against IoT privacy risks and security threats. However, the effectiveness and the associated privacy risks of these safeguards remains a key open question. In this paper, we investigate the threat detection capabilities of IoT safeguards for the first time. We develop and release an approach for automated safeguards experimentation to reveal their response to common security threats and privacy risks. We perform thousands of automated experiments using popular commercial IoT safeguards when deployed in a large IoT testbed. Our results indicate not only that these devices may be ineffective in preventing risks, but also their cloud interactions and data collection operations may introduce privacy risks for the households that adopt them.
Multiple-Population Discrete-Time Mean Field Games with Discounted and Total Payoffs: The Existence of Equilibria

In the paper we present a model of discrete-time mean-field game with several populations of players. Mean-field games with multiple populations of the players have only been studied in the literature in the continuous-time setting. The main results of this article are the first stationary and Markov mean-field equilibrium existence theorems for discrete-time mean-field games of this type. We consider two payoff criteria: discounted payoff and total payoff. The results are provided under some rather general assumptions on one-step reward functions and individual transition kernels of the players. In addition, the results for total payoff case, when applied to a single population, extend the theory of mean-field games also by relaxing some strong assumptions used in the existing literature.