Miracle-dz/UniFAR-FTUs huggingface dataset:
Advances in current in vitro models on neurodegenerative diseases

Many neurodegenerative diseases are identified but their causes and cure are far from being well-known. The problem resides in the complexity of the neural tissue and its location which hinders its easy evaluation. Although necessary in the drug discovery process, in vivo animal models need to be reduced and show relevant differences with the human tissues that guide scientists to inquire about other possible options which lead to in vitro models being explored. From organoids to organ-on-a-chips, 3D models are considered the cutting-edge technology in cell culture. Cell choice is a big parameter to take into consideration when planning an in vitro model and cells capable of mimicking both healthy and diseased tissue, such as induced pluripotent stem cells (iPSC), are recognized as good candidates. Hence, we present a critical review of the latest models used to study neurodegenerative disease, how these models have evolved introducing microfluidics platforms, 3D cell cultures, and the use of induced pluripotent cells to better mimic the neural tissue environment in pathological conditions.
Thermal Conductivity of Frozen and Unfrozen Gas-Saturated Soils

Arctic permafrost often contains gas-saturated horizons. The gas component in freezing and frozen soils can exist under different pressures, which are expected to affect their properties and behavior. The effect of pore gas pressure on the thermal conductivity of frozen and unfrozen silt loam saturated with methane or carbon dioxide at pressures below the hydrate formation conditions is observed in the current study. The variable gas pressure and temperature conditions are simulated in a specially designed pressure cell, which allows thermal conductivity measuring in pressurized samples at positive and negative temperatures. The experiments using natural samples collected near the gas emission crater (Yamal Peninsula) show that thermal conductivity is sensitive to pore gas pressure even at high moisture contents. The thermal conductivity of methane-saturated soil becomes 4% and 6% higher in frozen and unfrozen samples, respectively, as the gas pressure increases from 0.1 MPa to 2 MPa. In the case of CO2 saturation, the respective thermal conductivity increase in frozen and unfrozen samples reaches 25% and 15% upon pressure change from 0.1 to 0.9 MPa. The results stimulate further special studies of the effects of gas type and pressure on the thermal properties of closed gas-saturated taliks, of which the pore pressure is increasing during freezing up.
Deep Learning Approach for Runoff Prediction: Evaluating the Long-Short-Term Memory Neural Network Architectures for Capturing Extreme Discharge Events in the Ouergha Basin, Morocco

Rainfall-runoff modeling plays a crucial role in achieving efficient water resource management and flood forecast - ing, particularly in the context of increasing intensity and frequency of extreme meteorological events induced by climate change. Therefore, the aim of this research is to assess the accuracy of the Long-Short-Term Memory (LSTM) neural networks and the impact of its architecture in predicting runoff, with a particular focus on capturing extreme hydrological discharges in the Ouergha basin; a Moroccan Mediterranean basin with historical implica - tions in many cases of flooding; using solely daily rainfall and runoff data for training. For this purpose, three LSTM models of different depths were constructed, namely LSTM 1 single-layer, LSTM 2 bi-layer, and LSTM 3 tri-layer, their window size and hyperparameters were first tuned, and on seven years of daily data they were trained, then validated and tested on two separate years to ensure the generalization on unseen data. The performance of the three models was compared using hydrogram-plots, Scatter-plots, Taylor diagrams, and several statistical metrics. The results indicate that the single-layer LSTM 1 outperforms the other models, it consistently achieves higher overall performance on the training, validation, and testing periods with a coefficient of determination R-squared of 0.92, 0.97, and 0.95 respectively; and with Nash-Sutcliffe efficiency metric of 0.91, 0.94 and 0.94 respectively, challenging the conventional beliefs about the direct link between complexity and effectiveness. Furthermore, all the models are capable of capturing the extreme discharges, although, with a moderate underprediction trend for LSTM 1 and 2 as it does not exceed -25% during the test period. For LSTM 3, even if its underestimation is less pronounced, its increased error rate reduces the confidence in its performance. This study highlights the impor - tance of aligning model complexity with data specifications and suggests the necessity of considering unaccounted factors like upstream dam releases to enhance the efficiency in capturing the peaks of extreme events.
Emotion Recognition from Physiological Signals Collected with a Wrist Device and Emotional Recall

Implementing affective engineering in real-life applications requires the ability to effectively recognize emotions using physiological measurements. Despite being a widely researched topic, there seems to be a lack of systems that translate results from data collected in a laboratory setting to higher technology readiness levels. In this paper, we delve into the feasibility of emotion recognition beyond controlled laboratory environments. For this reason, we create a minimally-invasive experimental setup by combining emotional recall via autobiographical emotion memory tasks with a user-friendly Empatica wristband measuring blood volume pressure, electrodermal activity, skin temperature, and acceleration. We employ standard practices of feature-based supervised learning and specifically use support vector machines to explore subject dependency through various segmentation methods. We collected data from 45 participants. After preprocessing, using a data set of 134 segments from 40 participants, the accuracy of the classifier after 10-fold cross-validation was barely better than random guessing (36% for four emotions). However, when extracting multiple segments from each emotion task per participant using 10-fold cross-validation (i.e., including subject-dependent data in the training set), the classification rate increased to up to 75% for four emotions but was still as low as 32% for leave-one-subject-out cross-validation (i.e., subject-independent training). We conclude that highly subject-dependent issues might pose emotion recognition.
Uncovering the Mental World of Children: Attachment Quality, Mental State Talk, and Children’s Drawings

ABSTRACT The present study utilized archival data to uncover trends associated with children’s drawings, attachment quality, and mental state talk (MST). Two samples of 5–12-year-old children and their caregivers were recruited: one child sample from a public elementary school (n = 54), and the other from consecutive admissions to a child psychiatric inpatient unit (n = 45). Children completed drawings of family, primary caregiver, and self. Drawings were coded using the Formal Elements (FE) and Content rating scales designed to identify selective content and organizational qualities. Attachment quality and MST were obtained using the Attachment Story Completion Task and Children’s Apperception Test. Consistent with the first hypothesis, attachment quality demonstrated a marginally significant positive relationship with Content scores of inpatient children’s family drawings. Consistent with the second hypothesis, attachment quality demonstrated a significant positive relationship with MST in both samples. Consistent with the third hypothesis, MST demonstrated a significant positive relationship with Content scores of nonpatient children’s caregiver drawings, FE scores of nonpatient children’s family drawings, and both Content and FE scores of inpatient children’s family drawings. Drawings hold promise as tools to access children’s internal working models and mental states. Clinical implications are discussed.
Recurrent Neural Network Methods for Extracting Dynamic Balance Variables during Gait from a Single Inertial Measurement Unit

Monitoring dynamic balance during gait is critical for fall prevention in the elderly. The current study aimed to develop recurrent neural network models for extracting balance variables from a single inertial measurement unit (IMU) placed on the sacrum during walking. Thirteen healthy young and thirteen healthy older adults wore the IMU during walking and the ground truth of the inclination angles (IA) of the center of pressure to the center of mass vector and their rates of changes (RCIA) were measured simultaneously. The IA, RCIA, and IMU data were used to train four models (uni-LSTM, bi-LSTM, uni-GRU, and bi-GRU), with 10% of the data reserved to evaluate the model errors in terms of the root-mean-squared errors (RMSEs) and percentage relative RMSEs (rRMSEs). Independent t-tests were used for between-group comparisons. The sensitivity, specificity, and Pearson’s r for the effect sizes between the model-predicted data and experimental ground truth were also obtained. The bi-GRU with the weighted MSE model was found to have the highest prediction accuracy, computational efficiency, and the best ability in identifying statistical between-group differences when compared with the ground truth, which would be the best choice for the prolonged real-life monitoring of gait balance for fall risk management in the elderly.
Biopolymers for Tissue Engineering: Crosslinking, Printing Techniques, and Applications

Currently, tissue engineering has been dedicated to the development of 3D structures through bioprinting techniques that aim to obtain personalized, dynamic, and complex hydrogel 3D structures. Among the different materials used for the fabrication of such structures, proteins and polysaccharides are the main biological compounds (biopolymers) selected for the bioink formulation. These biomaterials obtained from natural sources are commonly compatible with tissues and cells (biocompatibility), friendly with biological digestion processes (biodegradability), and provide specific macromolecular structural and mechanical properties (biomimicry). However, the rheological behaviors of these natural-based bioinks constitute the main challenge of the cell-laden printing process (bioprinting). For this reason, bioprinting usually requires chemical modifications and/or inter-macromolecular crosslinking. In this sense, a comprehensive analysis describing these biopolymers (natural proteins and polysaccharides)-based bioinks, their modifications, and their stimuli-responsive nature is performed. This manuscript is organized into three sections: (1) tissue engineering application, (2) crosslinking, and (3) bioprinting techniques, analyzing the current challenges and strengths of biopolymers in bioprinting. In conclusion, all hydrogels try to resemble extracellular matrix properties for bioprinted structures while maintaining good printability and stability during the printing process.
Realizing a Life Well Lived: The Design of a Home Robot to Assist Older Adults with Self-Reflection and Intentional Living

Previous work suggests that older adults’ meaning and happiness may be increased simply by having them engage in self-reflection exercises. Therefore, we design four modules to promote self-reflection, delivered by the QT robot. These modules were created with reference to three different time orientations — past, present, and future — and by incorporating aspects of life satisfaction and the PERMA model of well-being. Results show that about half of our older adult participants experienced subjective changes in meaning, happiness, and desire to make positive life changes during each module, with 11 of 15 participants experiencing changes to one of these measures after engaging in all four interactions. We make several suggestions for updating these modules for autonomous and longer-term deployment using the robot.
Maximum Nash Social Welfare Under Budget-Feasible EFX

Fair division is a crucial problem in many research fields, such as computer science, economics, and cloud computing. In particular, the problem of allocating indivisible goods has been extensively studied, and the allocation satisfying the maximum <italic>Nash Social Welfare</italic> (NSW) is known to be <italic>envy-free up to one good</italic> (EF1), which is a widely-used fairness concept. However, EF1's fairness guarantee is limited to just one most valuable good, indicating a weakness in the scope. In this article, we explore the relationship between maximum NSW and a stronger concept, <italic>envy-free up to any good</italic> (EFX), where envy disappears after removing any good. Our concentrate is on the budget-feasible setting, which ensures that every agent is allocated goods within her budget. When agents' values are in the <italic>Interval</italic> <inline-formula><tex-math notation="LaTeX">$[1, k]$</tex-math></inline-formula>, any maximum NSW allocation ensures a <inline-formula><tex-math notation="LaTeX">$\frac{1}{k \cdot {(k-1)}}$</tex-math></inline-formula> approximation of EFX, and in the <italic>Binary</italic> <inline-formula><tex-math notation="LaTeX">$\lbrace 0,1\rbrace$</tex-math></inline-formula> case, it guarantees <inline-formula><tex-math notation="LaTeX">$\frac{1}{4}$</tex-math></inline-formula>-EFX. Besides, we offer an algorithm for identifying a budget-feasible EFX allocation when agents have <italic>Binary</italic> valuations. Furthermore, we also improve the approximation ratio guarantee of budget-feasible EFX allocations by assuming the agents have large budgets.
Typical Characteristics of Soil Heavy Metal Pollution of a Land Where Deserted Electronic Equipment was Taken Apart and Assessment of Its Ecological Risk

[Objective] The study aims to discuss the correlation between historical pollution and potential ecological risk. [Method] Through field sampling and laboratory analysis,the content of heavy metals(Cd,Ni,Cr,Cu,Pb,As and Hg) in the surface soil of an old electronic waste land and their pollution characteristics were analyzed,and then its ecological risk was assessed by using geo-accumulation index and potential ecological harm index. [Result] The study farmland was polluted by heavy metals to some extent,and the contents of various heavy metals were above the background values,and they were arranged in order of content as follows: CdHgCuCrPbNiAs.The potential ecological risk of the soil ranked Class D,and its ecological harm was very high.Among the seven heavy metals,the ecological risk index of Cd reached the top. [Conclusion] There existed potential ecological risk caused by historical pollutants in the study farmland.
Mechanisms and Patterns of Leachate Flow in Municipal Solid Waste Landfills

Vertical moisture flow through municipal solid waste landfills has been represented primarily as one-dimensional Darcian flow in homogeneous media, though channeling of flow through large pores in the waste has been shown to be an important flow mechanism at high loading rates. Channeling of flow through test cells containing compacted municipal solid waste appears to be a significant flow mechanism even at low infiltration rates (0.17 mm/hr) and after steady-state conditions (infiltration = discharge) have been reached. Practical field capacity is significantly lower at 0.0996 than the HELP model field capacity of 0.294, while the average experimental porosity is identical to the HELP default value (0.52). Experimental unsaturated hydraulic conduc­ tivity values are one to two orders of magnitude higher than the HELP default value at field capacity (1.2 x 10" cm/s), however, these values appear to be influenced by the experimental loading rate. In order to better understand the mechanisms and patterns of moisture flow in solid waste, more detailed information on the channels such as nature of flow in the channels and the spatial distribution of the channels is needed. Also, to more accurately repre­ sent the physical system, any new leachate generation models should account for both Darcian and channeled flow. ♦This research was funded through a grant from the Natural Science and Engineering Research Council (NSERC) of Canada. The research was further made possible by gracious in-kind support by management and staff at the West Edmonton Landfill of Waste Management Inc. of Edmonton. The comments in this article are for information and discussion purposes only and are not to be relied upon in any particular situation without the expressed written consent of the authors. 247 © 1995, Baywood Publishing Co., Inc. doi: 10.2190/JW1M-A901-L1X9-T15F http://baywood.com 248 / ZEISS AND UGUCCION
The Atlantic Pacific Seesaw.

Abstract A global, oceanic teleconnection of salinity, meridional overturning circulation (MOC), and climate of the North Atlantic and North Pacific is proposed. Simulations with a global climate model show that an extraction of freshwater from the Pacific results not only in an increase of salinity there, but also in a decrease of salinity in the Atlantic. As a result, a Pacific MOC develops while the Atlantic MOC collapses without freshwater perturbation in the Atlantic. Similarly, an input of freshwater to the Atlantic leads not only to a decrease of salinity there, but also to an increase of salinity in the Pacific. The Atlantic MOC collapses, whereas the Pacific MOC develops without freshwater perturbation in the Pacific. The mechanism behind this antiphase Atlantic– Pacific relationship is the positive feedback between ocean circulation and salinity contrasts, originally proposed by Stommel to operate between low and high latitudes. Here the authors show that the same mechanism operates on the Atlan.
An improved multi-scale gradient generative adversarial network for enhancing classification of colorectal cancer histological images

Introduction Deep learning-based solutions for histological image classification have gained attention in recent years due to their potential for objective evaluation of histological images. However, these methods often require a large number of expert annotations, which are both time-consuming and labor-intensive to obtain. Several scholars have proposed generative models to augment labeled data, but these often result in label uncertainty due to incomplete learning of the data distribution. Methods To alleviate these issues, a method called InceptionV3-SMSG-GAN has been proposed to enhance classification performance by generating high-quality images. Specifically, images synthesized by Multi-Scale Gradients Generative Adversarial Network (MSG-GAN) are selectively added to the training set through a selection mechanism utilizing a trained model to choose generated images with higher class probabilities. The selection mechanism filters the synthetic images that contain ambiguous category information, thus alleviating label uncertainty. Results Experimental results show that compared with the baseline method which uses InceptionV3, the proposed method can significantly improve the performance of pathological image classification from 86.87% to 89.54% for overall accuracy. Additionally, the quality of generated images is evaluated quantitatively using various commonly used evaluation metrics. Discussion The proposed InceptionV3-SMSG-GAN method exhibited good classification ability, where histological image could be divided into nine categories. Future work could focus on further refining the image generation and selection processes to optimize classification performance.
A Vegetable Oil-based Green Process for the Membrane Extraction of Ionic Solutes

Extraction or removal of the polluting components can be accomplished by a variety of physical, chemical and biological methods. Solvent extraction has demonstrated as one of the promising methods to accomplish the extraction or removal of ions more or less selectively from the source streams. The processes, especially when operated in membrane modules, have been successful and effective in reducing the pollutant concentration. In the development of these processes in to industrial operations the solvents (or the extracting phase) used or recommended in the literature are from non-sustainable source, they have undesirable properties (toxic, corrosive and health and safety issues). In recent years there have been research work to examine the feasibility of other solvents from sustainable sources and with good characteristics to overcome or minimize some of the above adverse effects. In this paper, the effectiveness of sunflower oil (as a model for vegetable oil), is described in extraction (or removal) of a polluting component in a small-scale membrane contactor. The components are: chromate ion and ammonium ion and the process is developed using a carrier molecule (di-2-ethylhexyl phosphate for ammonium and Aliquat 336 for chromate) in sunflower oil. The removal percentages are very good (in the range of 43-99%) and obtained at their natural conditions (with no chemical added for the adjustment of pH) of the aqueous feed. The main attraction of the process is the good performance of sunflower oil, which can be considered a â€œgreenâ€ solvent system as it has the benefit of being environmentally friendly, less costly, non-toxic and sustainable.
Modelling of synchronous generators for the analysis of frequency transients above 1 Hz/s

: Large synchronous generators are of high importance for the stability of power systems. They generate the frequency of the system and stabilize it in case of severe grid faults like trips of large in-feeders or loads. In distributed energy systems, in-feed via inverters will replace this generation in large parts. Modern inverters are capable of supporting grid frequency during severe faults by diﬀerent means on the one hand. On the other hand, higher Rates of Change of Frequency (RoCoF) after incidents need to be accustomed by future systems. To be able to analyse the RoCoF withstand capability of synchronous or induction generators, suitable models need to be developed. Especially the control and excitation system model need enhancements compared to models proposed in standards like IEEE Std 421.5. This paper elaborates on the necessary modelling depth and validates the approach with example results.
GOLDEYE RECRUITMENT AND GROWTH IN TWO MISSOURI RIVER BACKWATERS

Goldeye Hiodon alosoides was found to be an abundant fish species in the Missouri River system and interactions with the lotic community at all levels likely occur. Therefore, understanding the population dynamics (recruitment, growth, and mortality) of goldeye in this system has the potential to be important. Goldeye were sampled in two backwater habitats in western North Dakota with experimental gill nets during August and September of 1997 and scales were collected. From the scales, we determined age structure and back-calculated length at age. Mean back-calculated length at age was found to be 121-, 189, 234-, 276-, 298-, 305-, and 314-mm total length at ages 1-7, respectively. The age-frequency histogram indicated the presence of goldeye in each age group through age 7 and the stair-step decline in goldeye frequency from ages 2 through 7 indicated that recruitment of goldeye in this system was relatively consistent. A total mortality rate of 47% for age 2-6 goldeye was determined using the Chapman-Robson method. The population dynamics of goldeye in upper Missouri River backwaters were similar to goldeye studies in other Missouri River segments and both lotic and lentic populations in Canada.
Using geophysical techniques to trace active faults in the urbanized northern Hueco Bolson, West Texas, USA, and northern Chihuahua, Mexico

The Hueco Bolson serves as one of the primary groundwater sources for the El Paso–Ciudad Juarez metropolitan region of over 2.5 million residents. The bolson lies at the point where the strike of the southern Rio Grande rift changes from north-south to northwest-southeast, likely due to its interaction with preexisting Mesozoic and Paleozoic structures. The region is tectonically active with recent (