Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling Daily Pan Evaporation in Humid Climates Using Gaussian Process Regression

Evaporation is one of the main processes in the hydrological cycle, and it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, the evaporation is a complex and nonlinear phenomenon; therefore, the data-based methods can be used to have precise estimations of it. In this regard, in the present study, Gaussian Process Regression (GPR), Nearest-Neighbor (IBK), Random Forest (RF) and Support Vector Regression (SVR) were used to estimate the pan evaporation (PE) in the meteorological stations of Golestan Province, Iran. For this purpose, meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W) and sunny hours (S) collected from the Gonbad-e Kavus, Gorgan and Bandar Torkman stations from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The outcome indicates that the optimum state of Gonbad-e Kavus, Gorgan and Bandar Torkman stations, Gaussian Process Regression (GPR) with the error values of 1.521, 1.244, and 1.254, the Nearest-Neighbor (IBK) with error values of 1.991, 1.775, and 1.577, Random Forest (RF) with error values of 1.614, 1.337, and 1.316, and Support Vector Regression (SVR) with error values of 1.55, 1.262, and 1.275, respectively, have more appropriate performances in estimating PE. It found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W, and S had the most accurate performances and proposed for precise estimation of PE. Due to the high rate of evaporation in Iran and the lack of measurement instruments, the findings of the current study indicated that the PE values might be estimated with few easily measured meteorological parameters accurately.
Modeling continuous-time stochastic processes using $\mathcal{N}$-Curve mixtures

Representations of sequential data are commonly based on the assumption that observed sequences are realizations of an unknown underlying stochastic process, where the learning problem includes determination of the model parameters. In this context the model must be able to capture the multi-modal nature of the data, without blurring between modes. This property is essential for applications like trajectory prediction or human motion modeling. Towards this end, a neural network model for continuous-time stochastic processes usable for sequence prediction is proposed. The model is based on Mixture Density Networks using B\'ezier curves with Gaussian random variables as control points (abbrev.: $\mathcal{N}$-Curves). Key advantages of the model include the ability of generating smooth multi-mode predictions in a single inference step which reduces the need for Monte Carlo simulation, as required in many multi-step prediction models, based on state-of-the-art neural networks. Essential properties of the proposed approach are illustrated by several toy examples and the task of multi-step sequence prediction. Further, the model performance is evaluated on two real world use-cases, i.e. human trajectory prediction and human motion modeling, outperforming different state-of-the-art models.
Floquet engineering of topological phases protected by emergent symmetries under resonant drives

Floquet engineering is one of the most vigorous fields in periodically driven (Floquet) systems, with which we can control phases of matter usually by high-frequency drives. In this paper, with Floquet engineering by a combination of high-frequency drives and resonant drives, we propose a way to realize nontrivial topological phases protected by a $\mathbb{Z}_2 \times \mathbb{Z}_2$ symmetry only in the presence of a $\mathbb{Z}_2$ symmetry, using a robust emergent $\mathbb{Z}_2$ symmetry induced by the resonant drives. Moreover, the symmetry protected topological (SPT) phases are switchable between nontrivial and trivial phases only by the direction of a static transverse field, and even perturbations on the resonant drive can be utilized to realize richer SPT phases. We also discuss the real-time dynamics of the model, and find that which topological phases the system lies in can be distinguished by a period doubling of a nonlocal order parameter, as with discrete time crystals. A realization or a control of nontrivial SPT phases without the required symmetries by resonant drives, proposed in this paper, would shed a new light on the observation of topological phenomena in nonequilibrium setups.
Hierarchical clusters in neuronal populations with plasticity

We report the phenomenon of frequency clustering in a network of Hodgkin-Huxley neurons with spike timing-dependent plasticity. The clustering leads to a splitting of a neural population into a few groups synchronized at diﬀerent frequencies. In this regime, the amplitude of the mean ﬁeld undergoes low-frequency modulations, which may contribute to the mechanism of the emergence of slow oscillations of neural activity observed in spectral power of local ﬁeld potentials or electroencephalographic signals at high frequencies. In addi-tion to numerical simulations of such multi-clusters, we investigate the mechanisms of the observed phenomena using the simplest case of two clusters. In particular, we propose a phenomenological model which describes the dynamics of two clusters taking into account the adaptation of coupling weights. We also determine the set of plasticity functions (update rules), which lead to multi-clustering. pre- and postsynaptic neuron. The adaptation is considered to be symmetric as experimentally found for hippocampal synapses [1] and can also be derived from asymmetric STDP under certain conditions [2]. In an adaptive network of Hodgkin-Huxley neurons we observe the emergence of clusters of neurons that are synchronized at diﬀerent frequencies in diﬀerent clusters. Our study shows that such a self-organized cluster formation is robust against changes of plasticity function. While the spiking frequency of each synchronized cluster appear to be on the timescale of individual neurons, the amplitude of the mean ﬁeld of such cluster system can evolve at a few orders of magnitude slower timescales. The reported slow modulations of the mean neural activity in a neural population with plasticity can explain the emergence of slow cortical oscillations detected in the empirical data of spectral power of local ﬁeld potentials (LFP) and electroencephalographic (EEG) signals, which correlate with spontaneous ﬂuctuations of the blood oxygen level-dependent (BOLD) signals measured by functional magnetic resonance imaging (fMRI) [3, 4].
Outage Analysis of Decode-and-Forward Two-Way Relay Selection With Different Coding and Decoding Schemes

In this paper, we consider a two-way relay with multirelays. We first review the joint decoding (JD), decoding by parts (DP), bit-level exclusive-or (XOR), and superposition coding (SC) schemes for the two-way decode-and-forward relay (TWDR), and then propose a new TWDR selection scheme for the two mentioned coding methods. In particular, we focus on the outage performance analysis of the relay selection strategy using the aforementioned coding and decoding methods. Based on an approximate analysis of the outage performance in the high signal-to-noise ratio, we present the diversity-multiplexing tradeoff for the considered decoding and coding schemes. Moreover, we present an optimal power allocation for relays with the SC mechanism. Our simulation results show that our proposed relay selection strategy is outage optimal for all the coding and decoding methods mentioned in this paper. Our results also show that the outage performance under the JD-Xor and JD-SC schemes is more competitive for the case in which the channel statistical characteristics satisfy <inline-formula><tex-math notation="LaTeX">${\bar{\gamma }_a}\sigma _{ka}^2 \approx {\bar{\gamma }_b}\sigma _{kb}^2$</tex-math></inline-formula> (<inline-formula><tex-math notation="LaTeX">$\bar{\gamma }_i = P_i/N_0$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$i \in \left\lbrace {a,b} \right\rbrace$</tex-math></inline-formula>, where <inline-formula><tex-math notation="LaTeX">$a$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$b$</tex-math></inline-formula> denote two sources exchanging information with each other, <inline-formula><tex-math notation="LaTeX">$P_i$</tex-math></inline-formula> is the average transmitting power from the <inline-formula><tex-math notation="LaTeX">$i$</tex-math></inline-formula> source, <inline-formula><tex-math notation="LaTeX">$N_0$</tex-math></inline-formula> is the variance of additive white Gaussian noise obtained by the relay <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$\sigma _{ki}^2$</tex-math></inline-formula> is the variance of the Rayleigh fading channel between <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$i$</tex-math></inline-formula>). When <inline-formula><tex-math notation="LaTeX">${\bar{\gamma }_a}\sigma _{ka}^2 \gg {\bar{\gamma }_b}\sigma _{kb}^2$</tex-math></inline-formula> or <inline-formula><tex-math notation="LaTeX">${\bar{\gamma }_a}\sigma _{ka}^2 \ll {\bar{\gamma }_b}\sigma _{kb}^2$</tex-math></inline-formula>, the outage performance using DP-Xor or DP-SC mechanisms is much better.
Expressing and Categorizing Motion in French and English : Verbal and Non-Verbal Cognition across Languages

Language-specific properties influence motion expression (Slobin 2004; Talmy 2000), but it is still debated whether they also influence non-verbal spatial cognition. We compare how English and French speakers perform three tasks involving motion events: non-verbal categorization based on cartoons during a dual task (articulatory suppression); verbal categorization involving target sentences; and descriptions of motion events. Descriptions show more manner expression in English and variation in both groups as a function of event properties. However, both groups frequently choose Path as categorization criterion, particularly in verbal categorization, but language effects do show that English speakers are more sensitive to event properties. Thus, typology has a strong impact on verbal cognition but also a partial influence on non-verbal cognition that must be taken into account in future research on the relation between language and thought.
Contextual priors do not modulate action prediction in children with autism

Bayesian accounts of autism suggest that this disorder may be rooted in an impaired ability to estimate the probability of future events, possibly owing to reduced priors. Here, we tested this hypothesis within the action domain in children with and without autism using a behavioural paradigm comprising a familiarization and a testing phase. During familiarization, children observed videos depicting a child model performing actions in diverse contexts. Crucially, within this phase, we implicitly biased action-context associations in terms of their probability of co-occurrence. During testing, children observed the same videos but drastically shortened (i.e. reduced amount of kinematics information) and were asked to infer action unfolding. Since during the testing phase movement kinematics became ambiguous, we expected children's responses to be biased to contextual priors, thus compensating for perceptual uncertainty. While this probabilistic effect was present in controls, no such modulation was observed in autistic children, overall suggesting an impairment in using contextual priors when predicting other peoples' actions in uncertain environments.
Insect brain plasticity: effects of olfactory input on neuropil size

Insect brains are known to express a high degree of experience-dependent structural plasticity. One brain structure in particular, the mushroom body (MB), has been attended to in numerous studies as it is implicated in complex cognitive processes such as olfactory learning and memory. It is, however, poorly understood to what extent sensory input per se affects the plasticity of the mushroom bodies. By performing unilateral blocking of olfactory input on immobilized butterflies, we were able to measure the effect of passive sensory input on the volumes of antennal lobes (ALs) and MB calyces. We showed that the primary and secondary olfactory neuropils respond in different ways to olfactory input. ALs show absolute experience-dependency and increase in volume only if receiving direct olfactory input from ipsilateral antennae, while MB calyx volumes were unaffected by the treatment and instead show absolute age-dependency in this regard. We therefore propose that cognitive processes related to behavioural expressions are needed in order for the calyx to show experience-dependent volumetric expansions. Our results indicate that such experience-dependent volumetric expansions of calyces observed in other studies may have been caused by cognitive processes rather than by sensory input, bringing some causative clarity to a complex neural phenomenon.
Mathematical modelling of the restenosis process after stent implantation

The stenting procedure has evolved to become a highly successful technique for the clinical treatment of advanced atherosclerotic lesions in arteries. However, the development of in-stent restenosis remains a key problem. In this work, a novel two-dimensional continuum mathematical model is proposed to describe the complex restenosis process following the insertion of a stent into a coronary artery. The biological species considered to play a key role in restenosis development are growth factors, matrix metalloproteinases, extracellular matrix, smooth muscle cells and endothelial cells. Diffusion–reaction equations are used for modelling the mass balance between species in the arterial wall. Experimental data from the literature have been used in order to estimate model parameters. Moreover, a sensitivity analysis has been performed to study the impact of varying the parameters of the model on the evolution of the biological species. The results demonstrate that this computational model qualitatively captures the key characteristics of the lesion growth and the healing process within an artery subjected to non-physiological mechanical forces. Our results suggest that the arterial wall response is driven by the damage area, smooth muscle cell proliferation and the collagen turnover among other factors.
Quantifying maladaptation during the evolution of sexual dimorphism

Females and males have distinct trait optima, resulting in selection for sexual dimorphism. However, most traits have strong cross-sex genetic correlations, which constrain evolutionary divergence between the sexes and lead to protracted periods of maladaptation during the evolution of sexual dimorphism. While such constraints are thought to be costly in terms of individual and population fitness, it remains unclear how severe such costs are likely to be. Building upon classical models for the ‘cost of selection’ in changing environments (sensu Haldane), we derived a theoretical expression for the analogous cost of evolving sexual dimorphism; this cost is a simple function of genetic (co)variances of female and male traits and sex differences in trait optima. We then conducted a comprehensive literature search, compiled quantitative genetic data from a diverse set of traits and populations, and used them to quantify costs of sexual dimorphism in the light of our model. For roughly 90% of traits, costs of sexual dimorphism appear to be modest, and comparable to the costs of fixing one or a few beneficial substitutions. For the remaining traits (approx. 10%), sexual dimorphism appears to carry a substantial cost—potentially orders of magnitude greater than costs of selection during adaptation to environmental changes.
Cdc42 Deficiency Leads To Epidermal Barrier Dysfunction by Regulating Intercellular Junctions and Keratinization of Epidermal Cells during Mouse Skin Development

Rationale: Cdc42 is a Rho GTPase that regulates diverse cellular functions. Here, we used genetic techniques to investigate the role of Cdc42 in epidermal development and epidermal barrier formation. Methods: Keratinocyte-restricted Cdc42 knockout mice were generated with the Cre-LoxP system under the keratin 14 (K14) promoter. The skin and other tissues were collected from mutant and wild-type mice, and their cellular, molecular, morphological, and physiological features were analyzed. Results: Loss of Cdc42 in the epidermis in vivo resulted in neonatal lethality and impairment of epidermal barrier formation. Cdc42 deficiency led to the loss of epidermal stem cells. The absence of Cdc42 led to increased thickening of the epidermis, which was associated with increased proliferation and reduced apoptosis of keratinocytes. In addition, Cdc42 deficiency damaged tight junctions, adherens junctions and desmosomes. RNA sequencing results showed that the most significantly altered genes were enriched by the terms of “keratinization” and “cornified envelope” (CE). Among the differentially expressed genes in the CE term, several members of the small proline-rich protein (SPRR) family were upregulated. Further study revealed that there may be a Cdc42-SPRR pathway, which may correlate with epidermal barrier function. Conclusions: Our study indicates that Cdc42 is essential for epidermal development and epidermal barrier formation. Defects in Cdc42-SPRR signaling may be associated with skin barrier dysfunction and a variety of skin diseases.
HIF-1α/Wnt signaling-dependent control of gene transcription regulates neuronal differentiation of glioblastoma stem cells

HIF-1α has been suggested to interplay with Wnt signaling components in order to activate a neuronal differentiation process in both normal brain and glioblastoma (GBM). Based on these data, we explored the molecular mechanisms underlying the observed capability of GBM cells to acquire a neuronal phenotype upon Wnt signaling stimulation and how the microenvironment, particularly hypoxia, modulates this process. Methods: here, the employment of ChIP-seq techniques together with co-immunoprecipitation approaches allowed to reconstruct the molecular interactions responsible for activating specific pro-differentiating transcriptional programs in GBM cells. Moreover, gene silencing/over-expression approaches coupled with the functional analysis of cell phenotype were applied to confirm ChIP-driven hypotheses. Finally, we combined the use of publicly available gene expression datasets with protein expression data by immunohistochemistry to test the clinical relevance of obtained results. Results: our data clearly suggest that HIF-1α is recruited by the β-catenin/TCF1 complex to foster neuronal differentiation gene transcription in hypoxic GBM cells. Conversely, at higher oxygen levels, the increased expression of TCF4 exerts a transcriptional inhibitory function on the same genomic regions, thus counteracting differentiation. Moreover, we demonstrate the existence of a positive correlation between the expression levels of HIF-1α, TCF1 and neuronal phenotype in GBM tumors, accompanied by the over-expression of several Wnt signaling components, finally affecting patient prognosis. Conclusion: we unveiled a peculiar mechanism by which TCF1 and HIF-1α can induce a reminiscent neuronal differentiation of hypoxic GBM cells, which is hampered, in normoxia, by high levels of TCF4, thus not only de facto controlling the balance between differentiation and stemness, but also impacting on intra-tumoral heterogeneity and eventually patient outcome.
Alzheimer’s disease: pathogenesis, diagnostics, and therapeutics

Abstract Currently, 47 million people live with dementia globally, and it is estimated to increase more than threefold (~131 million) by 2050. Alzheimer’s disease (AD) is one of the major causative factors to induce progressive dementia. AD is a neurodegenerative disease, and its pathogenesis has been attributed to extracellular aggregates of amyloid β (Aβ) plaques and intracellular neurofibrillary tangles made of hyperphosphorylated τ-protein in cortical and limbic areas of the human brain. It is characterized by memory loss and progressive neurocognitive dysfunction. The anomalous processing of APP by β-secretases and γ-secretases leads to production of Aβ40 and Aβ42 monomers, which further oligomerize and aggregate into senile plaques. The disease also intensifies through infectious agents like HIV. Additionally, during disease pathogenesis, the presence of high concentrations of Aβ peptides in central nervous system initiates microglial infiltration. Upon coming into vicinity of Aβ, microglia get activated, endocytose Aβ, and contribute toward their clearance via TREM2 surface receptors, simultaneously triggering innate immunoresponse against the aggregation. In addition to a detailed report on causative factors leading to AD, the present review also discusses the current state of the art in AD therapeutics and diagnostics, including labeling and imaging techniques employed as contrast agents for better visualization and sensing of the plaques. The review also points to an urgent need for nanotechnology as an efficient therapeutic strategy to increase the bioavailability of drugs in the central nervous system.
Development and in vitro evaluation of (β-cyclodextrin-g-methacrylic acid)/Na+-montmorillonite nanocomposite hydrogels for controlled delivery of lovastatin

Background: Hyperlipidemia is the elevation of low density lipoprotein levels resulting in fat deposites in arteries and their hardening and blockage. It is the leading cause of several life threatening pathological conditions like hypertension, cardiovascular diseases, diabetes etc. Purpose: The objective of this study was to prepare and optimize nontoxic, biocompatible β-CD-g-MAA/Na+-MMT nanocomposite hydrogels with varying content of polymer, monomer and montmorillonite. Moreover, lipid lowering potentials were determined and compared with other approaches. Methods: β-CD-g-MAA/Na+-MMT nanocomposite hydrogels (BM-1 to BM9) were prepared through free radical polymerization by using β-CD as polymer, MAA as monomer, MBA as crosslinker and montmorillonite as clay. Developed networks were evaluated for FTIR, DSC, TGA, PXRD, SEM, sol-gel fraction (%), swelling studies, antihyperlipidemic studies and toxicity studies. Results: Optimum swelling (94.24%) and release (93.16%) were obtained at higher pH values. Based on R2 and “n” value LVT release followed zero order kinetics with Super Case II transport release mechanism, respectively. Tensile strength and elongation at break were found to be 0.0283MPa and 94.68%, respectively. Gel fraction was between 80.55 – 98.16%. Antihyperlipidemic studies revealed that LDL levels were markedly reduced from 522.24 ± 21.88mg/dl to 147.63 ± 31.5mg/dl. Toxicity studies assured the safety of developed network. Conclusion: A novel pH responsive crosslinked network containing β-CD – g – poly (methacrylic acid) polymer and MMT was developed and optimized with excellent mechanical, swelling and release properties and lipid lowering potentials.