Miracle-dz/UniFAR-FTUs huggingface dataset:
Weak housing demand in a shrinking city: the potential effects of the water crisis on Flint, Michigan’s housing market and homeowners’ perceived mobility

ABSTRACT This study explores perceived and real effects of a human-made environmental crisis (the Flint water crisis) on residential mobility and housing demand in the weak market city of Flint, Michigan, USA. We surveyed Flint residents in summer 2016 and found that a large proportion of homeowners believed the water crisis negatively affected their property values; more than half wanted to leave the city but felt constrained by the inability to sell their homes; and one-fifth considered abandoning their homes to leave the city. We used regression models to examine predictors of these responses, and spatial statistics to examine whether responses clustered in the city. Then, to see if the perceptions of homeowners reflected actual changes in the housing market caused by the crisis, we used descriptive statistics, maps, and repeated measures ANOVA models to examine changes in home sales prices and mortgages originations in Flint from 2012–2017, before and after the start of the crisis. The results indicate the water crisis had no discernable effect on the city’s housing market, likely because the market was incredibly weak prior to the crisis. The severe lack of housing demand irrespective of the crisis potentially contributed to residents’ perceptions of constrained mobility.
Event‐triggered and self‐triggered gain scheduling control of input constrained systems with applications to the spacecraft rendezvous

This article studies event‐triggered and self‐triggered gain scheduling control design for semi‐global stabilization of input constrained linear systems. Using the parametric Lyapunov equation (PLE), a event‐triggered gain scheduling control, which can increase the convergence rate of the closed‐loop systems by designing a gain scheduling scheme and save the communication resources by building an event generator, is designed. In order to avoid continuously monitoring the system states, the self‐triggered gain scheduling control algorithms are also designed. The non‐occurrence of the Zeno phenomenon and the boundedness of the parameter in the PLE are proved. The impact of the parameter on the minimal inter‐event time is analyzed, which allows us to find easily a trade‐off between the inter‐event times and the control performance. Another advantage of the designed methods is that the controller is linear. Finally, the proposed control algorithms are applied to stabilize the spacecraft rendezvous control system and the numerical simulation result shows the efficacy of the control algorithms.
Dendritic and Spine Loss in Epilepsy: What Seizures Got to Do With It?

Dendritic Pathology, Spine Loss and Synaptic Reorganization in Human Cortex From Epilepsy Patients Rossini L, De Santis D, Mauceri RR, Tesoriero C, Bentivoglio M, Maderna E, Maiorana A, Deleo F, de Curtis M, Tringali G, Cossu M, Tumminelli G, Bramerio M, Spreafico R, Tassi L, Garbelli R. Brain. 2021;144(1):251-265. doi:10.1093/brain/awaa387 Neuronal dendritic arborizations and dendritic spines are crucial for a normal synaptic transmission and may be critically involved in the pathophysiology of epilepsy. Alterations in dendritic morphology and spine loss mainly in hippocampal neurons have been reported both in epilepsy animal models and in human brain tissues from patients with epilepsy. However, it is still unclear whether these dendritic abnormalities relate to the cause of epilepsy or are generated by seizure recurrence. We investigated fine neuronal structures at the level of dendritic and spine organization using Golgi impregnation, and analyzed synaptic networks with immunohistochemical markers of glutamatergic (vGLUT1) and GABAergic (vGAT) axon terminals in human cerebral cortices derived from epilepsy surgery. Specimens were obtained from 28 patients with different neuropathologically defined etiologies: type Ia and type II focal cortical dysplasia, cryptogenic (no lesion) and temporal lobe epilepsy with hippocampal sclerosis. Autoptic tissues were used for comparison. Three-dimensional reconstructions of Golgi-impregnated neurons revealed severe dendritic reshaping and spine alteration in the core of the type II focal cortical dysplasia. Dysmorphic neurons showed increased dendritic complexity, reduction of dendritic spines, and occasional filopodia-like protrusions emerging from the soma. Surprisingly, the intermingled normal-looking pyramidal neurons also showed severe spine loss and simplified dendritic arborization. No changes were observed outside the dysplasia (perilesional tissue) or in neocortical postsurgical tissue obtained in the other patient groups. Immunoreactivities of vGLUT1 and vGAT showed synaptic reorganization in the core of type II dysplasia characterized by the presence of abnormal perisomatic baskets around dysmorphic neurons, in particular those with filopodia-like protrusions, and changes in vGLUT1/vGAT expression. Ultrastructural data in type II dysplasia highlighted the presence of altered neuropil engulfed by glial processes. Our data indicate that the fine morphological aspect of neurons and dendritic spines are normal in epileptogenic neocortex, with the exception of type II dysplastic lesions. The findings suggest that the mechanisms leading to this severe form of cortical malformation interfere with the normal dendritic arborization and synaptic network organization. The data argue against the concept that long-lasting epilepsy and seizure recurrence per se unavoidably produce a dendritic pathology.
Accelerated parameter tuning of antenna structures using inverse and feature‐based forward kriging surrogates

Tuning of geometry parameters is one of the essential stages of contemporary antenna design. It is necessary because available design methods, whether based on theoretical considerations or on engineering experience, are only capable of yielding initial designs that need further adjustment in order to boost the performance parameters as much as possible. Numerical optimization is also imperative for antenna re‐design with respect to different operating conditions (e.g., center frequency) or material parameters (e.g., substrate permittivity/thickness). As the tuning process normally involves full‐wave electromagnetic (EM) analysis, it may incur considerable computational expenses. Reducing these costs is highly desirable from the perspective of both academic research and industry. This paper proposes a simple surrogate‐assisted framework for accelerated antenna parameter tuning that allows for reusing the previously acquired design data. The first (inverse) surrogate serves as a reliable predictor for generating a reasonable initial design, whereas the second (forward) model encodes antenna sensitivities at the level of so‐called response features. The involvement of the response feature technology leads to a more accurate rendition of the antenna gradients, which speeds up the design refinement as compared to the forward model constructed at the level of original antenna characteristics. Our methodology is demonstrated using two microstrip antennas and compared to the previously reported warm‐start optimization procedures.
Dose-Dependent Effects of Protein Ingestion and Resistance Exercise on Muscle Protein Synthesis in Aging Adults: A Literature Review

Introduction: Sarcopenia can lead to physical disability and lower quality of life, but increasing muscle protein synthesis in older adults may reduce its effects. Dose-response curves may be used to determine the optimal protein dose in rested and exercised muscle to elicit maximal muscle protein synthesis. Methods: A literature review was conducted to explore and summarize the findings on the following topics: the mechanisms of muscle protein synthesis, anabolic resistance, and the dose-responses of muscle protein synthesis to anabolic stimuli in both younger and older individuals. Results: Reduced phosphorylation in downstream targets of the mammalian target of rapamycin complex 1 pathway is characteristic of muscle protein synthesis in older muscle. Compared to younger muscle, older muscle can elicit a similar maximal muscle protein synthesis response, but is less sensitive to lower doses of protein ingestion. With ingestion of 40g of whey protein, the fractional synthetic rate in older muscle is similar to that of younger muscle with 20g of whey protein ingestion. Marked increases in amino acid oxidation are also observed. Discussion: Anabolic resistance can be biochemically explained by reduced phosphorylation in the mammalian target of rapamycin complex 1 pathway. Due to this phenomenon, older individuals require greater anabolic stimuli to achieve maximal muscle protein synthesis. However, the most effective protein dose for maximal muscle protein synthesis in older muscle is not well-established. Conclusion: The muscle protein synthesis dose-response curve for older individuals reveals blunted responses to stimuli due to anabolic resistance. Further research is warranted to determine the optimal protein dose for maximal muscle protein synthesis in older adults.
High n-type conductivity and carrier concentration in Si-implanted homoepitaxial AlN

We demonstrate Si-implanted AlN with high conductivity (>1 Ω−1 cm−1) and high carrier concentration (5 × 1018 cm−3). This was enabled by Si implantation into AlN with a low threading dislocation density (TDD) (<103 cm−2), a non-equilibrium damage recovery and dopant activation annealing process, and in situ suppression of self-compensation during the annealing. Low TDD and active suppression of VAl-nSiAl complexes via defect quasi Fermi level control enabled low compensation, while low-temperature, non-equilibrium annealing maintained the desired shallow donor state with an ionization energy of ∼70 meV. The realized n-type conductivity and carrier concentration are over one order of magnitude higher than that reported thus far and present a major technological breakthrough in doping of AlN.
Effects of different management options of Norway spruce on radiative forcing through changes in carbon stocks and albedo

Norway spruce (Picea abies Karst. (L.)) in the boreal zone can be managed as even-aged or uneven-aged stands, or be grown with no management at all. Here, we investigated how these management options affect carbon dynamics, particularly the carbon stocks in the forest ecosystem (trees and soil), and albedo, and their combined effect on radiative forcing compared to a reference case, clear-cut site before planting seedlings. This allowed us to assess the potential of different management regimes to mitigate global warming. We ran long-term simulations under the current climate on a sub-mesic site in central Finland (62oN) using an eco-physiological forest-ecosystem model. Compared to even-aged management, no management (old-growth forest) increased ecosystem carbon stocks by 47 per cent and decreased albedo by 15 per cent, whereas uneven-aged management reduced ecosystem carbon stocks by 16 per cent and increased albedo by 10 per cent. Only the no management option resulted in a significant net cooling effect whereas for even-aged and uneven-aged management, the opposing effects of changes in albedo and carbon stocks largely cancelled each other with little remaining net effect. On the other hand, the latter one even made a small net warming contribution. Overall, maintaining higher ecosystem carbon stocks implied the larger cooling benefits. This was evident even though lower albedo enhanced radiation absorption, and thus warming. Increasing use of the no management option by forest owners may require proper incentives such as compensation for lost harvest incomes.
Turn of the decade: versatility of 2D hexagonal boron nitride

The era of two-dimensional (2D) materials, in its current form, truly began at the time that graphene was first isolated just over 15 years ago. Shortly thereafter, the use of 2D hexagonal boron nitride had expanded in popularity, with use of the thin isolator permeating a significant number of fields in condensed matter and beyond. Due to the impractical nature of cataloguing every use or research pursuit, this review will cover ground in the following three subtopics relevant to this versatile material: growth, electrical measurements, and applications in optics and photonics. Through understanding how the material has been utilized, one may anticipate some of the exciting directions made possible by the research conducted up through the turn of this decade.
The Putative RNA-Binding Protein Dri1 Promotes the Loading of Kinesin-14/Klp2 to the Mitotic Spindle and Is Sequestered into Heat-Induced Protein Aggregates in Fission Yeast

Cells form a bipolar spindle during mitosis to ensure accurate chromosome segregation. Proper spindle architecture is established by a set of kinesin motors and microtubule-associated proteins. In most eukaryotes, kinesin-5 motors are essential for this process, and genetic or chemical inhibition of their activity leads to the emergence of monopolar spindles and cell death. However, these deficiencies can be rescued by simultaneous inactivation of kinesin-14 motors, as they counteract kinesin-5. We conducted detailed genetic analyses in fission yeast to understand the mechanisms driving spindle assembly in the absence of kinesin-5. Here, we show that deletion of the dri1 gene, which encodes a putative RNA-binding protein, can rescue temperature sensitivity caused by cut7-22, a fission yeast kinesin-5 mutant. Interestingly, kinesin-14/Klp2 levels on the spindles in the cut7 mutants were significantly reduced by the dri1 deletion, although the total levels of Klp2 and the stability of spindle microtubules remained unaffected. Moreover, RNA-binding motifs of Dri1 are essential for its cytoplasmic localization and function. We have also found that a portion of Dri1 is spatially and functionally sequestered by chaperone-based protein aggregates upon mild heat stress and limits cell division at high temperatures. We propose that Dri1 might be involved in post-transcriptional regulation through its RNA-binding ability to promote the loading of Klp2 on the spindle microtubules.
M♮-Convexity and Its Applications in Operations

A new approach for structural analysis of operations models with substitutability structures. In many operations models with substitutability structures, one often ends up with parametric optimization models that maximize submodular objective functions, and it is desirable to derive structural properties including monotone comparative statics of the optimal solutions or preservation of submodularity under the optimization operations. Yet, this task is challenging because the classical and commonly used results in lattice programming, applicable to optimization models with supermodular objective function maximization, do not apply. Using a key concept in discrete convex analysis, M♮-convexity, Chen and Li establish conditions under which the optimal solutions are nonincreasing in the parameters and the preservation property holds for parametric maximization models with submodular objectives, together with the development of several new fundamental properties of M♮-convexity. Their approach is powerful as demonstrated by applications in a classical multiproduct stochastic inventory model and a portfolio contract model.
The rice SCFOsFBK1 E3 ligase mediates jasmonic acid induced degradation of a RING-H2 protein and the cinnamoyl-CoA reductase, OsCCR14

We had previously shown the rice F-box, OsFBK1, plays a role in anther development by mediating the turnover of OsCCR14, a cinnamoyl CoA-reductase regulating lignification. Another substrate identified in the same Y2H library screening was OsATL53, a member of the ATL family of RING-H2 proteins that is primarily localized to the cytoplasm. We found OsATL53 to be a component and substrate of SCFOsFBK1 by immunoprecipitation and cell-free studies. Incidentally, OsATL53 was found to interact with OsCCR14 in the cytoplasm and form a stable complex in cell-free experiments and bimolecular fluorescence complementation assays. Biochemically, OsATL53 was found to influence the enzymatic activity of OsCCR14 by decreasing its efficiency. Degradation studies have shown OsFBK1 mediates turnover of OsCCR14 in the nucleus, while OsATL53 is degraded in both cytoplasm and nucleus. The degradation of ATLs by F-box proteins has not been reported before. In presence of jasmonic acid (JA), which plays a role in anther dehiscence, OsATL53 has been found to gather around the nucleus, and this property enables the translocation of the OsATL53-OsCCR14 complex from a cytoplasmic localization to accumulate around the nuclear periphery. FLIM analyses revealed OsCCR14-OsATL53 complex undergoing conformational changes in presence of JA and this triggers OsFBK1 to mediate the targeted degradation of OsATL53 in the cytoplasm, thereby dissociating the cytoplasmic OsCCR14-OsATL53 complex and enabling OsCCR14 to enter the nucleus and eventually get degraded by SCFOsFBK1 E3 ligase. We have thus studied the signalling mechanism of a variant JA-induced E3 ligase-mediated substrate turnover in plants at the molecular level.
A Deep Graph-Embedded LSTM Neural Network Approach for Airport Delay Prediction

Due to the strong propagation causality of delays between airports, this paper proposes a delay prediction model based on a deep graph neural network to study delay prediction from the perspective of an airport network. We regard airports as nodes of a graph network and use a directed graph network to construct airports’ relationship. For adjacent airports, weights of edges are measured by the spherical distance between them, while the number of flight pairs between them is utilized for airports connected by flights. On this basis, a diffusion convolution kernel is constructed to capture characteristics of delay propagation between airports, and it is further integrated into the sequence-to-sequence LSTM neural network to establish a deep learning framework for delay prediction. We name this model as deep graph-embedded LSTM (DGLSTM). To verify the model’s effectiveness and superiority, we utilize the historical delay data of 325 airports in the United States from 2015 to 2018 as the model training set and test set. The experimental results suggest that the proposed method is superior to the existing mainstream methods in terms of accuracy and robustness.
Gestionar la Autonomía Pedagógica: Un Factor de Calidad Reconocido

Today's society develops in democratic contexts based on valuation and respect for difference, as a source of mutual enrichment and global progress. As a result, diversity is recognized as an inherent characteristic of people, which, transferred to schools and classrooms, requires a model of inclusive education, capable of training different people in the same groups, but responding to the uniqueness of each of them. It is the valid formula for mutual knowledge to be given and prejudices arising from segregation during the stages of compulsory education to disappear. To implement the right answers in each school, pedagogical, organizational and management autonomy is required in educational institutions, which must have the possibility of making their own decisions to adapt the established system to the contextual realities and the particular population that they must attend to. The exercise of committed and responsible autonomy will be the basis of an educational quality relevant to the current requirements of society. To this end, the training of managers and teachers, inclusive leadership and internal institutional evaluation, are basic elements that will allow to achieve the objectives envisaged for the individual and for the whole of society. *Contacto: macasanova@gmail.com ISSN: 1696-4713 www.rinace. net/reice revistas.uam. es/reice Recibido: 1 de septiembre 2020 1a Evaluación: 15 de diciembre 2020 2a Evaluación: 27 de enero 2021 Aceptado: 3 de febrero 2021 CÓMO CITAR: Casanova, M. A. (2021). Gestionar la autonomía pedagógica: un factor de calidad reconocido. REICE. Revista Iberoamericana sobre Calidad, Eficacia y Cambio en Educación, 19(2), 9-22. https://doi.org/10.15366/reice2021.19. 2.001
Diverse Metal–Organic Framework Architectures for Electromagnetic Absorbers and Shielding

Metal–organic frameworks (MOFs) and their derivatives, featuring unique 3D microstructures and enhanced electromagnetic properties, are illuminating infinite possibilities for electromagnetic functional materials and devices, receiving significant attention from domestic and foreign researchers. Herein, the design strategy of the MOF monomer is investigated, and the electromagnetic response mechanism is systematically analyzed. Research is emphatically introduced regarding MOF‐based materials in microwave absorption and electromagnetic interference shielding. Finally, a clear insight on the quickly growing field is given, and the current challenges and future research directions are summarized and predicted.
REVIEW ON PREDICTING DISEASE SEVERITY – LEARNING ALGORITHMS AS CLASSIFIERS FOR DATA WAREHOUSE ENVIRONMENTS

When research fields such as biological and medical arenas are considered, computer science-based applications have been utilized on an increasing basis. In the process, data has accumulated in significant amounts. In particular, most of the big data that has resulted has accrued from experimental and medical results that are reported. The eventuality is that in the healthcare platforms, data mining techniques have been employed towards timely predictions of conditions with which patients present, a process achieved through data analysis. Also, the data analyses have strived to discern correlations among disease-related variables or parameters in otherwise voluminous information. It is also notable that data mining has received increasing adoption in the healthcare field because of the perceived ability to provide room for informed decision-making, reliable treatment decisions, and the detection of potential fraudulence when it comes to health care service payments. While these interplays point to a promising trend regarding the utilization of data mining and disease severity prediction, the eventual bid data has proved to be so complex that the use of traditional methods of data processing and analyzing is deemed less effective. In addition, given data warehouse, the use of traditional methods towards data extraction makes it difficult to discern some of the hidden intersections, pointing to the importance of a new approach for data classification and disease severity prediction. The aim of this review paper was to demonstrate how the use of classifiers in the form of machine learning algorithms could be utilized in the prediction of the severity of diseases. Indeed, the context of the research entailed warehouse environments. To have an ideal kernel established towards data classification (that would then enable additional diagnostic tests), some of the machine learning algorithms that were investigated included SVM (support vector machine) and ANN (artificial neural network). From the review outcomes, it was established that when machine learning algorithms are used to obtain trend similarities in disease severity through predictive analytics, similar severity magnitudes of the given diseases could be projected in terms of their probabilities of and trends in occurrence. Also, the review outcomes demonstrated that disease severity prediction through machine learning algorithms is an insightful practice because it allows for the prediction of patients’ conditions, as well as the prevision of room for optimal decision-making regarding treatment modalities to be adopted by healthcare firms.