Miracle-dz/UniFAR-FTUs huggingface dataset:
Attractiveness and nutritional effects of flowers on the survival of a dipteran pest and its parasitoids

Flower addition is commonly used worldwide to benefit parasitoids and improve conservation biological control of pests. However, biological control enhancement via flower addition depends on both flower attractiveness and the responses of parasitoid and pest species to flower resources. Here, we experimentally compared the effects of two Apiaceae flowering species (Conium maculatum and Coriandrum sativum) with those of water, honey and no resource treatments on longevity and survival of adults of the leafminer Liriomyza huidobrensis and two hymenopteran parasitoids (Chrysocharis flacilla and Phaedrotoma scabriventris). We also examined the attractiveness of flowers of both plant species to the leafminer and P. scabriventris adults. The provision of flowers increased longevity and survival of the three insect species with respect to water and no resource treatments. The effects of honey on longevity were similar to or even higher than those of flowers, depending on the insect species, whereas water only increased the longevity of P. scabriventris in relation to no resource treatments. In attractiveness trials, P. scabriventris preferred C. maculatum flowers, whereas L. huidobrensis showed no preference for flower species. Our findings indicate that both plant species have potential as floral resources in biological control, since two parasitoid species benefited from flowers. However, these plant species must be cautiously considered because L. huidobrensis also improved its fitness when provided with both floral resources. The present work highlights the need to simultaneously study parasitoid and herbivore food needs and flower attractiveness, especially in dipteran pests, in order to achieve a successful biological control.
Application of Genetically Encoded Photoconvertible Protein SAASoti for the Study of Enzyme Activity in a Single Live Cell by Fluorescence Correlation Microscopy

Fluorescent Correlation Spectroscopy (FCS) allows us to determine interactions of labeled proteins or changes in the oligomeric state. The FCS method needs a low amount of fluorescent dye, near nanomolar concentrations. To control the amount of fluorescent dye, we used new photoconvertible FP SAASoti. This work is devoted to the proof of principle of using photoconvertible proteins to measure caspase enzymatic activity in a single live cell. The advantage of this approach is that partial photoconversion of the FP makes FCS measurements possible when studying enzymatic reactions. To investigate the process, in vivo we used HeLa cell line expressing the engineered FRET sensor, SAASoti-23-KFP. This FRET sensor has a cleavable (DEVD) sequence in the linker between two FPs for the detection of one of the key enzymes of apoptosis, caspase-3. Caspase-3 activity was detected by registering the increase in the fluorescent lifetimes of the sensor, whereas the diffusion coefficient of SAASoti decreased. This can be explained by an increase in the total cell viscosity during apoptosis. We can suppose that in the moment of detectible caspase-3 activity, cell structure already has crucial changes in viscosity.
Cross-border Effects between the Spanish and French Electricity Markets: Asymmetric Dynamics and Benefits in the Light of European Market Integration

We investigate cross-border dynamics between the Spanish and French electricity markets in the light of EU’s market integration. The analysis is highly relevant given the isolation of the Iberian Peninsula from the rest of European markets and its unique electricity market design. Results show the shift in the merit order curve in each market. Electricity prices in Spain follow the dynamics of the French ones, fostered by Spain’s import-dependence. Benefits of grid integration are asymmetric, with Spanish users taking advantage of the market integration with France. The substitution effect between domestic fuel-based units and imported green electricity allows Spain to further decarbonize its energy mix and increase the security of supply. The expansion of the cross-border grid until 2030 will be key for a successful phasing out of the remaining coal-based units. Our findings should be considered in support schemes for integration projects to align the incentives of participants.
Circuit-Based Design of Microfluidic Drop Networks

Microfluidic-drop networks consist of several stable drops—interconnected through microfluidic channels—in which organ models can be cultured long-term. Drop networks feature a versatile configuration and an air–liquid interface (ALI). This ALI provides ample oxygenation, rapid liquid turnover, passive degassing, and liquid-phase stability through capillary pressure. Mathematical modeling, e.g., by using computational fluid dynamics (CFD), is a powerful tool to design drop-based microfluidic devices and to optimize their operation. Although CFD is the most rigorous technique to model flow, it falls short in terms of computational efficiency. Alternatively, the hydraulic–electric analogy is an efficient “first-pass” method to explore the design and operation parameter space of microfluidic-drop networks. However, there are no direct electric analogs to a drop, due to the nonlinear nature of the capillary pressure of the ALI. Here, we present a circuit-based model of hanging- and standing-drop compartments. We show a phase diagram describing the nonlinearity of the capillary pressure of a hanging drop. This diagram explains how to experimentally ensure drop stability. We present a methodology to find flow rates and pressures within drop networks. Finally, we review several applications, where the method, outlined in this paper, was instrumental in optimizing design and operation.
Developing Textbooks to Improve Math Learning in Primary Education: Empirical Evidence from El Salvador

Although primary school enrollment in developing countries has rapidly increased since 1990, the quality of education has stagnated over the years. In teaching and learning practices, textbooks are an important intermediate that links curriculum, teachers, and students. Through a randomized controlled trial in El Salvador, this study evaluated the effect of a package of interventions including the distribution of textbooks that were carefully designed to improve math learning. This study targeted second-grade students and tracked them for 2 years. To capture their progress in learning over the period, this study linked math test scores at the end of the second grade and third grade by using the item response theory (IRT). The average 1-year effect of the package is estimated at 0.49 standard deviations of the IRT scores. Schools in the control group began to receive the package in the second year of research. After 2 years of exposure to the package—as compared with students in the control group who received only 1 year of exposure—the effect is estimated at 0.13 standard deviations. Students in the treatment group advanced their math learning in the second year of research because of the improved understanding in the previous year.
Demand-Aware Cooperative Content Caching in 5G/6G Networks With MEC-Enabled Edges

Today, billions of smart devices are interconnected via wireless networks, leading to large volumes of video contents circulating through the bandwidth-limited backhaul. This causes network performance to deteriorate. As a mitigation mechanism, caching of highly popular contents to network edges is deployed. We propose a cooperative and demand-aware caching strategy, which is modelled using the Separable Assignment Problem, to maximize the cache hit ratio. This problem is solved with a recursive enumeration method, where dynamic programming is used to fill each edge. The extensive application-level evaluations show that the proposed strategy outperforms existing caching policies.
Poly(ADP-ribose) Polymerase 1 Mediates Rab5 Inactivation after DNA Damage

Parthanatos is programmed cell death mediated by poly(ADP-ribose) polymerase 1 (PARP1) after DNA damage. PARP1 acts by catalyzing the transfer of poly(ADP-ribose) (PAR) polymers to various nuclear proteins. PAR is subsequently cleaved, generating protein-free PAR polymers, which are translocated to the cytoplasm where they associate with cytoplasmic and mitochondrial proteins, altering their functions and leading to cell death. Proteomic studies revealed that several proteins involved in endocytosis bind PAR after PARP1 activation, suggesting endocytosis may be affected by the parthanatos process. Endocytosis is a mechanism for cellular uptake of membrane-impermeant nutrients. Rab5, a small G-protein, is associated with the plasma membrane and early endosomes. Once activated by binding GTP, Rab5 recruits its effectors to early endosomes and regulates their fusion. Here, we report that after DNA damage, PARP1-generated PAR binds to Rab5, suppressing its activity. As a result, Rab5 is dissociated from endosomal vesicles, inhibiting the uptake of membrane-impermeant nutrients. This PARP1-dependent inhibition of nutrient uptake leads to cell starvation and death. It thus appears that this mechanism may represent a novel parthanatos pathway.
Using SystemVerilog Packages in Real Verification Projects

This paper details some of the key features and frustrations of using the package construct in SystemVerilog. The package construct is compared to similar features in other languages such as the identically named construct in VHDL and namespaces in C++. Valuable lessons learned over the course of multiple projects in the development of verification environments are described
Modeling the RXTE/ASM X-ray Light Curve of GX301-2

GX301-2 is an accreting neutron star in an eccentric orbit ($e=0.462$) with a massive early-type stellar companion. GX301-2 accretes from the stellar wind and exhibits regular X-ray flares approximately 1.4 days prior to periastron passage. Long-term continuous X-ray monitoring of GX301-2 by the RXTE/All-Sky-Monitor has now been carried out for a period of 5 years. These data now comprise the best observations of the orbital X-ray light curve. The main flux peak just prior to periastron is clearly seen, as well as a secondary broad peak near apastron. CGRO/BATSE observations have provided new improved orbital parameters. The constraint on the orbital inclination is examined. Then the GX301-2 orbital light curve is modeled using wind, disk and stream models. The conclusion is that only a stellar wind plus mass stream is consistent with the data. The wind model is further supported by calculations of the stream flow from the primary and the expected orbital phases that the neutron star crosses the stream.
Trust in Fintech: Risk, Governance, and Continuance Intention

ABSTRACT The rapid development of Fintech (financial technology) urges Fintech platforms to better understand users’ perceptions and behaviors toward Fintech use. Based on the theoretical framework of trust, risk perception, platform governance, and information system continuous use theories, we construct a model to explore how users’ different trust perceptions on Fintech platforms affect their risk perception, platform governance perception, and continuance intention of using Fintech. Our findings indicate users’ institutional trust, technology trust, and interpersonal trust all impact their risk perception, perception of Fintech platform governance, and continuance intention to use Fintech in different ways and magnitudes. Furthermore, platform governance perception mediates between institutional trust, interpersonal trust, and continuance intention. Our research helps the practitioners of Fintech platforms attract and retain users in the highly competitive market and provides a reference for the governance of their platforms.
The Role and Therapeutic Potential of the Integrated Stress Response in Amyotrophic Lateral Sclerosis

In amyotrophic lateral sclerosis (ALS) patients, loss of cellular homeostasis within cortical and spinal cord motor neurons triggers the activation of the integrated stress response (ISR), an intracellular signaling pathway that remodels translation and promotes a gene expression program aimed at coping with stress. Beyond its neuroprotective role, under regimes of chronic or excessive stress, ISR can also promote cell/neuronal death. Given the two-edged sword nature of ISR, many experimental attempts have tried to establish the therapeutic potential of ISR enhancement or inhibition in ALS. This review discusses the complex interplay between ISR and disease progression in different models of ALS, as well as the opportunities and limitations of ISR modulation in the hard quest to find an effective therapy for ALS.
Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction

While enabling accelerated acquisition and improved reconstruction accuracy, current deep MRI reconstruction networks are typically supervised, require fully sampled data, and are limited to Cartesian sampling patterns. These factors limit their practical adoption as fully-sampled MRI is prohibitively time-consuming to acquire clinically. Further, non-Cartesian sampling patterns are particularly desirable as they are more amenable to acceleration and show improved motion robustness. To this end, we present a fully self-supervised approach for accelerated non-Cartesian MRI reconstruction which leverages self-supervision in both k-space and image domains. In training, the undersampled data are split into disjoint k-space domain partitions. For the k-space self-supervision, we train a network to reconstruct the input undersampled data from both the disjoint partitions and from itself. For the image-level self-supervision, we enforce appearance consistency obtained from the original undersampled data and the two partitions. Experimental results on our simulated multi-coil non-Cartesian MRI dataset demonstrate that DDSS can generate high-quality reconstruction that approaches the accuracy of the fully supervised reconstruction, outperforming previous baseline methods. Finally, DDSS is shown to scale to highly challenging real-world clinical MRI reconstruction acquired on a portable low-field (0.064 T) MRI scanner with no data available for supervised training while demonstrating improved image quality as compared to traditional reconstruction, as determined by a radiologist study.
The Use of Mobile-Based Ecological Momentary Assessment (mEMA) Methodology to Assess Dietary Intake, Food Consumption Behaviours and Context in Young People: A Systematic Review

Mobile-based ecological momentary assessment (mEMA) offers a novel method for dietary assessment and may reduce recall bias and participant burden. This review evaluated mEMA methodology and the feasibility, acceptability and validity as a dietary assessment method in young people. Five databases were searched from January 2008 to September 2021 for studies including healthy young people aged 16–30 years and used mEMA for obtaining dietary intake data, food consumption behaviours and/or contextual factors. Data on the method used to administer mEMA, compliance with recording and validation were extracted. A total of 46 articles from 39 independent studies were included, demonstrating a wide variation in mEMA methods. Signal-contingent prompting (timed notification to record throughout the day) was used in 26 studies, 9 used event-contingent (food consumption triggered recordings), while 4 used both. Monitoring periods varied and most studies reported a compliance rate of 80% or more. Two studies found mEMA to be burdensome and six reported mEMA as easy to use. Most studies (31/39) reported using previously validated questions. mEMA appears to be a feasible and acceptable methodology to assess dietary intake and food consumption in near real time.
Effects Of Source Distributions And Correlation On Fractionally Spaced Blind Constant Modulus Algori

A common assumption in blind equalization schemes using the Constant Mod-ulus Algorithm (CMA) is that the source sequence is drawn from an independent, uniform distribution. Much of the analysis demonstrating the global convergence of CMA to an open-eye setting uses such an assumption. Also, much of this analysis has been performed in the baud-spaced equalizer setting, in which an innnite order equalizer is necessarily assumed to avoid the existence of local minima. However , recent results in the literature show that a nite fractionally-spaced equalizer allows for perfect equalization of moving average channels (under certain channel conditions known as equalizability). Furthermore, CMA has been shown to converge to a perfect equalizing setting under independent, uniformly distributed source. This thesis work investigates the eeect of source statistics on the location of CMA stationary points in the fractionally-sampled equalizer case under the conditions of equalizability. The work identiies the stationary points as the solution set of a system of multivariate polynomial equations with monomial coeecients given by the source moments. The work is divided into three main areas. First, an investigation of the properties of the CMA error surface is performed assuming source independence. This section revisits some previously known results and then extends them by quantifying aspects of the error surface based on the source kurtosis. The relevancy of the results here point to the tradeoos between coding gain (a function of the source distribution) and the error surface curvature (with its eeects on convergence). Next, features of a topological nature of the CMA error surface are discussed. Such properties, which hold independent of source statistics assumptions, form a bridge to the third section describing stationary point locations under temporal source correlation. The mathematical tools used here (e.g. Groebner bases and Homotopy Methods) are just starting to appear in applications in signal processing. The results include a Monte-Carlo study of the eeects of source correlation due to periodic inputs as well as an example of source sequences resulting from Markov models. iii To MeeYoung iv Acknowledgements I wish to express my sincere gratitude to MeeYoung for the endless support she has given me over the years. Her dedication, cheerfulness, and patience has made all this possible. Her sidekick for the past 18 months, Pascal, deserves high praise for being such a joyful baby and giving me the drive to nish. Thanks are also due to my mother and father, not just …
Comparison of Tau and Amyloid-β Targeted Immunotherapy Nanoparticles for Alzheimer’s Disease

Alzheimer’s disease (AD) is a rapidly growing global concern associated with the accumulation of amyloid-β plaques and intracellular neurofibrillary tangles in the brain combined with a high acetylcholinesterase activity. AD diagnosis is usually made too late, when patients have an extensive neuronal death, and brain damage is irreversible. Several therapeutic targets have been defined mainly related to two hypotheses of AD: the tau hypothesis and the amyloid-β hypothesis. Here, we intend to investigate and to compare different therapeutic approaches for AD, mainly based on nanoparticles (NPs) targeted at the brain and at the pathological hallmarks of the disease. We analyzed preclinical trials that have successfully improved drug bioavailability in the brain by using targeted nanocarriers towards either tau, amyloid-β, or both. We then compared these trials to find out which protein is more efficient in therapeutic targeting. We found that the search for a cure was mostly based on the amyloid-β hypothesis, with Aβ dysplasia emerging as the most confirmed and convincing therapeutic target. Targeted NPs have proven useful to enhance both the bioavailability and the performance of therapies against AD in animal models. A better understanding of AD mechanisms will help the successful application of targeted NPs for combined therapies.
The Association Between Salivary FGF2 and Physiological and Psychological Components of the Human Stress Response

Background Fibroblast Growth Factor 2 (FGF2) is a neurotrophic protein that has been implicated as a biomarker for anxiety and depressive disorders, which comprise a significant component of the global burden of disease. Research using rodents has indicated that FGF2 is part of the stress response, but whether this translates to humans has yet to be investigated. In this study, we aimed to explore the potential role of FGF2 in the human stress response by examining its association with physiological and psychological processes during and following the Trier Social Stress Test (TSST). Methods Participants in the active stress experiment (N = 87) underwent the TSST, provided saliva samples to obtain levels of cortisol and FGF2, and reported on post-event rumination related to the TSST task over the following week. Participants in the no-stress experiment (N = 25) provided saliva samples for measurement of FGF2 and cortisol across a corresponding time period. Results Salivary FGF2 levels changed after the TSST and were associated with the pattern of change in salivary cortisol. Cortisol responses in the active stress condition were blunted in females (relative to males), however, sex did not interact with any other effect. FGF2 reactivity (ie, the magnitude of change over time) was not correlated with cortisol reactivity. Lower FGF2 reactivity following the TSST, but not overall FGF2 levels, or cortisol, was associated with higher fear of negative evaluation, repetitive negative thinking and post-event processing, as well as repetitive negative thinking in the week following the TSST. Participants in the no-stress experiment showed a decrease in cortisol, yet no change in their FGF2 levels. Conclusion These findings suggest that FGF2 is involved in the human stress response and higher levels of FGF2 reactivity may be associated with protective cognitive processes following stress exposure.
Algorithmic enhancements to identify predictable components from users’ data and a framework to detect misinformation in social media

ABSTRACT The flow of distorted information on social media platforms cannot always be handled. As a result, digital misinformation has become a significant social, political, and technological risk factor. Extant research on detecting misinformation in social networks has focused on using metadata or characteristics of influential actors (users) and their group dynamics in isolation, but less on the act (information content) itself and on developing an integrated approach. We unify them to produce a data science framework to detect valid instances of misinformation from social media such as Twitter. Here we develop novel and efficient algorithmic improvements to extract predictable components from users’ data. The model results demonstrate a significant increase in performance beyond typical incremental improvements. This research proposes a novel term weighting scheme, clique-based features, and a metadata-based feature. These contributions to the data science literature can be helpful for future studies in the social media context.