Search Results (1,564)

The aim of this work is to investigate drought variability in Padua, northern Italy, over a nearly 300-year period, from 1725 to 2023. Two well-established and widely used indices are calculated, the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI). They are compatible with a data series starting in the early instrumental period, as both can be estimated using only temperature and precipitation data. The Padua daily precipitation and temperature series from the early 18th century, which were recovered and homogenized with current observations, are used as datasets. The standard approach to estimate SPI and SPEI based on gamma and log-logistic probability distribution functions, respectively, is questioned, assessing the fitting performance of different distributions applied to monthly precipitation data. The best-performing distributions are identified for each index and accumulation period at annual and monthly scales, and their normality is evaluated. In general, they detect more extreme drought events than the standard functions. Moreover, the main statistical values of SPI are very similar, regardless of the approach type, as opposed to SPEI. The difference between SPI and SPEI time series calculated with the best-fit approach has increased since the mid-20th century, in particular in spring and summer, and can be related to ongoing global warming, which SPEI takes into account. The innovative trend analysis applied to SPEI12 indicates a general increasing trend in droughts, while for SPI12, it is significant only for severe events. Summer and fall are the most affected seasons. The critical drought intensity–duration–frequency curves provide an easily understandable relationship between the intensity, duration and frequency of the most severe droughts and allow for the calculation of return periods for the critical events of a certain duration. Moreover, the longest and most severe droughts over the 1725–2023 period are identified. Full article

Although effects of stress-induced anxiety on the gastrointestinal tract and enteric nervous system (ENS) are well studied, how ENS dysfunction impacts behaviour is not well understood. We investigated whether ENS modulation alters anxiety-related behaviour in rats. We used loperamide, a potent μ-opioid receptor agonist that does not cross the blood–brain barrier, to manipulate ENS function and assess changes in behaviour, gut and brain gene expression, and microbiota profile. Sprague Dawley (male/female) rats were acutely dosed with loperamide (subcutaneous) or control solution, and their behavioural phenotype was examined using open field and elevated plus maze tests. Gene expression in the proximal colon, prefrontal cortex, hippocampus, and amygdala was assessed by RNA-seq and caecal microbiota composition determined by shotgun metagenome sequencing. In female rats, loperamide treatment decreased distance moved and frequency of supported rearing, indicating decreased exploratory behaviour and increased anxiety, which was associated with altered hippocampal gene expression. Loperamide altered proximal colon gene expression and microbiome composition in both male and female rats. Our results demonstrate the importance of the ENS for communication between gut and brain for normo-anxious states in female rats and implicate corticotropin-releasing hormone and gamma-aminobutyric acid gene signalling pathways in the hippocampus. This study also sheds light on sexually dimorphic communication between the gut and the brain. Microbiome and colonic gene expression changes likely reflect localised effects of loperamide related to gut dysmotility. These results suggest possible ENS pharmacological targets to alter gut to brain signalling for modulating mood. Full article

Post-processing contamination of Listeria monocytogenes has remained a major concern for the safety of ready-to-eat (RTE) meat products that are not reheated before consumption. Mathematical models are rapid and cost-effective tools to predict pathogen behavior, product shelf life, and safety. The objective of this study was to develop and validate a comprehensive model to predict the Listeria growth rate in RTE meat products as a function of temperature, pH, water activity, nitrite, acetic, lactic, and propionic acids. The Listeria growth data in RTE food matrices, including RTE beef, pork, and poultry products (731 data sets), were collected from the literature and databases like ComBase. The growth parameters were estimated using the logistic-with-delay primary model. The good-quality growth rate data (n = 596, R² > 0.9) were randomly divided into 80% training (n = 480) and 20% testing (n = 116) datasets. The training growth rates were used to develop a secondary gamma model, followed by validation in testing data. The growth model’s performance was evaluated by comparing the predicted and observed growth rates. The goodness-of-fit parameter of the secondary model includes R² of 0.86 and RMSE of 0.06 (μ_max) during the development stage. During validation, the gamma model with interaction included an RMSE of 0.074 (μ_max), bias, and accuracy factor of 0.95 and 1.50, respectively. Overall, about 81.03% of the relative errors (RE) of the model’s predictions were within the acceptable simulation zone (RE ± 0.5 log CFU/h). In lag time model validation, predictions were 7% fail-dangerously biased, and the accuracy factor of 2.23 indicated that the lag time prediction is challenging. The model may be used to quantify the Listeria growth in naturally contaminated RTE meats. This model may be helpful in formulations, shelf-life assessment, and decision-making for the safety of RTE meat products. Full article

Gamma-glutamate is an important excitatory neurotransmitter in the central nervous system (CNS), which plays an important role in transmitting synapses, plasticity, and other brain activities. Nevertheless, alterations in the glutamatergic signaling pathway are now accepted as a central element in Alzheimer’s disease (AD) pathophysiology. One of the most prevalent types of dementia in older adults is AD, a progressive neurodegenerative illness brought on by a persistent decline in cognitive function. Since AD has been shown to be multifactorial, a variety of pharmaceutical targets may be used to treat the condition. N-methyl-D-aspartic acid receptor (NMDAR) antagonists and acetylcholinesterase inhibitors (AChEIs) are two drug classes that the Food and Drug Administration has authorized for the treatment of AD. The AChEIs approved to treat AD are galantamine, donepezil, and rivastigmine. However, memantine is the only non-competitive NMDAR antagonist that has been authorized for the treatment of AD. This review aims to outline the involvement of glutamate (GLU) at the molecular level and the signaling pathways that are associated with AD to demonstrate the drug target therapeutic potential of glutamate and its receptor. We will also consider the opinion of the leading authorities working in this area, the drawback of the existing therapeutic strategies, and the direction for the further investigation. Full article

Background: ⁶⁸Ga-DOTATOC PET/CT is a functional imaging modality that has revolutionized the evaluation of well-differentiated neuroendocrine tumors (NETs) by targeting somatostatin receptors. This technique has largely replaced conventional gamma camera imaging with 111In-labeled octreotide due to its superior sensitivity and resolution. While the physiologic distribution, normal variations, and common pitfalls associated with ⁶⁸Ga-DOTATOC imaging are well documented, rare but clinically significant pitfalls can still occur. Methods: We present a case highlighting one such pitfall: focal ⁶⁸Ga-DOTATOC uptake at the cervicothoracic junction, specifically within the stellate ganglia, which mimicked metastatic involvement of a NET. Results: Initially, the uptake was interpreted as a potential right cervical metastasis. To clarify this finding, a follow-up ⁶⁸Ga-DOTATOC PET/CT was performed, which demonstrated no evidence of cervical metastases, thereby confirming the initial uptake as a physiologic variation rather than pathological activity. This case underscores the dynamic variability of ⁶⁸Ga-DOTATOC uptake within the stellate ganglia in the same patient over time. On occasion, the intensity of physiologic uptake in these structures can be pronounced enough to mimic metastatic disease, posing a diagnostic challenge. Conclusions: Awareness of this rare phenomenon is essential to avoid misdiagnosis and unnecessary interventions. Full article

Background: Frozen shoulder (FS), or adhesive capsulitis, is a disabling condition characterized by pain and restricted shoulder mobility. Aims: This study investigates the relationship between metabolic biomarkers—liver enzymes and thyroid function—and pain and shoulder functionality in patients with FS. Methods: A total of 32 patients (22 women and 10 men) were included in this cross-sectional study. Participants underwent clinical evaluations and blood tests to assess metabolic biomarkers, including aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), and thyroid-stimulating hormone (TSH). Pain and functionality were measured using the Shoulder Pain and Disability Index (SPADI). Correlation and multiple regression analyses were performed to assess the associations between biomarkers, pain, and functionality. Results: Significant negative correlations were found between AST (r = −0.528, p = 0.029), ALT (r = −0.533, p = 0.027), GGT (r = −0.602, p = 0.011), and TSH (r = −0.556, p = 0.017) with total pain scores. A significant negative correlation was also observed between TSH and SPADI scores (r = −0.511, p = 0.039). Multiple regression analysis showed that GGT (β = −0.335, p = 0.008) and TSH (β = −0.298, p = 0.014) were the strongest predictors of pain. These findings suggest that metabolic biomarkers, particularly liver enzymes and thyroid function, play a significant role in the pathophysiology of frozen shoulder. The results highlight the importance of assessing these biomarkers for better understanding and managing pain and functionality in patients with FS. Conclusions: Further research is needed to explore the underlying mechanisms and potential therapeutic targets. Full article

Data on the distribution of the free flow speed (FFS) of cars are used to solve a wide range of tasks in the field of road transport, starting from road design and ending with the development of traffic modeling and simulation programs. The purpose of this study was to obtain the distribution of vehicle speeds on typical sections of the city road network, characterized by the presence of one-way traffic. The data were obtained by field observations using a portable radar. As a result, statistical characteristics and speed distribution laws for four sections of streets in the city of Kharkiv were analyzed. It was shown that the characteristics of FFS distributions differ depending on the functional class of the streets. Average FFS values on main street segments were on average 19 km/h higher. The one-way traffic has less impact on the FFS distribution, especially for arterial streets. The characteristics of FFS distributions differ depending on the type and functional class of streets; they can be described with sufficient accuracy by typical distribution laws, such as Normal, Log-normal, Gamma, and Chi-square. The results of this study can be useful for traffic modeling problems. Full article

Symbiotic microbiota significantly influence the development, physiology, and behavior of their hosts, and therefore, they are widely studied. However, very few studies have investigated the changes in symbiotic microbiota across generations. Daphnia magna originating from the Qinghai–Tibetan Plateau were cultured through seven generations in our laboratory, and the symbiotic microbiota of D. magna were sequenced using a 16S rRNA amplicon to analyze changes in the structure and functional properties of the symbiotic microbiota of D. magna from a harsh environment to an ideal environment. We detected substantial changes in the symbiotic microbiota of D. magna across generations. For example, the genus Nevskia, a member of the gamma-subclass Proteobacteria, had the highest abundance in the first generation (G1), followed by a decrease in abundance in the fourth (G4) and seventh (G7) generations. The gene functions of the microbiota in different generations of D. magna also changed significantly. The fourth generation was mainly rich in fatty acyl-CoA synthase, acetyl-CoA acyltransferase, phosphoglycerol phosphatase, etc. The seventh generation was mainly rich in osmotic enzyme protein and ATP-binding protein of the ABC transport system. This study confirms that the alterations in the structure and functional properties of the symbiotic microbiota of D. magna under changing environments are typical responses of D. magna to environmental changes. Full article

Kaposi’s sarcoma-associated herpesvirus (KSHV) is a variety of the human gamma-herpesvirus that often leads to the occurrence of malignant tumors. In addition, the occurrence of Kaposi’s sarcoma is a major cause of death among AIDS patients. Ganciclovir (GCV) is the most widely used drug against KSHV infection in the clinic. GCV can restrict the in vivo synthesis of DNA polymerase in KSHV, thereby inhibiting the replication of the herpesvirus. However, GCV still suffers from poor specificity and transmembrane capabilities, leading to many toxic side effects. Therefore, developing a drug delivery system that increases GCV concentrations in target cells remains a significant clinical challenge. In this study, zeolite imidazole salt framework-8 (ZIF-8), a biocompatible porous material constructed by coordinating zinc ions and 2-methylimidazole, was used to load GCV. A nano-delivery system with a microneedle structure was also constructed using a polydimethylsiloxane (PDMS) microneedle mold to fabricate MN/GCV@ZIF-8 arrays. These arrays not only offered good skin-piercing capabilities but also significantly inhibited the cleavage and replication of the virus in vivo, exerting an anti-KSHV function. For these reasons, the arrays were able penetrate the skin’s stratum corneum at the tumor site to deliver GCV and play an anti-KSHV role. Full article

A number of learning models have been suggested to analyze the repeated interaction of boundedly rational agents competing in oligopolistic markets. The agents form a model of the environment that they are competing in, which includes the market demand and price formation process, as well as their expectations of their rivals’ actions. The agents update their model based on the observed output and price realizations and then choose their next period output levels according to an optimization criterion. In previous works, the global dynamics of price movement have been analyzed when risk-neutral agents maximize their expected rewards at each round. However, in many practical settings, agents may be concerned with the risk or uncertainty in their reward stream, in addition to the expected value of the future rewards. Learning in oligopoly models for the case of risk-averse agents has received much less attention. In this paper, we present a novel learning model that extends fictitious play learning to continuous strategy spaces where agents combine their prior beliefs with market price realizations in previous periods to learn the mean and the variance of the aggregate supply function of the rival firms in a Bayesian framework. Next, each firm maximizes a linear combination of the expected value of the profit and a penalty term for the variance of the returns. Specifically, each agent assumes that the aggregate supply of the remaining agents is sampled from a parametric distribution employing a normal-inverse gamma prior. We prove the convergence of the proposed dynamics and present simulation results to compare the proposed learning rule to the traditional best response dynamics. Full article

This study addresses the challenges of estimating decay times for chemical components, focusing on hydroxylated fullerene $C_{60}{\left(OH\right)}_{29}$, which poses potential environmental risks due to its persistence and transformation in soil. Given the complexities of real-world experiments such as limited sample availability, time constraints, and the need for efficient resource use, a framework using the Gamma distribution based on hybrid Type-II censoring schemes was developed to model the decay time. The Gamma distribution’s flexibility and mathematical properties make it well-suited for reliability and decay analysis, capturing variable hazard rates and accommodating different censoring structures. We employ maximum likelihood estimation (MLE) and Bayesian methods to estimate the model’s parameters, consequently estimating the reliability and hazard functions. The large sample theory for MLE is used to approximate variances for constructing asymptotic confidence intervals. Additionally, we utilize the Markov chain Monte Carlo technique within the Bayesian framework to ensure robust parameter estimation. Through simulation studies and statistical tests—such as Chi-Square, Kolmogorov–Smirnov, and others—we assess the Gamma distribution’s fit and compare its performance with other distributions, validating the proposed model’s effectiveness. Full article

The Chinese monal (Lophophorus lhuysii) is a large-sized and vulnerable (VU in IUCN) bird from southwestern China. This study applied 16S rRNA high-throughput sequencing to comprehensively examine the gut microbiota of captive Chinese monals (located in Baoxing, Sichuan, China) across varying seasons and life stages. Dominant bacterial phyla identified included Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria. Significant seasonal and age-associated shifts were observed within specific bacterial groups, particularly marked by seasonal fluctuations in beta diversity. Moreover, linear discriminant analysis effect size (LEfSe) and functional predictions highlighted distinct winter signatures, indicating possible functional shifts in energy metabolism and disease resistance. In mid-aged adults, an expansion of Gamma-Proteobacteria suggested an elevated susceptibility of the gut microbiota of Chinese monals to chronic disorders and microbial imbalance. Putative pathogenic bacteria exhibited increased abundance in spring and summer, likely driven by temperature, host physiological cycles, interspecies interactions, and competition. These findings imply that the diversity, and structure of the gut microbiota in captive Chinese monals are strongly influenced by seasonal and age-related factors. The insights provided here are essential for improving breeding strategies and preventing gastrointestinal diseases in captivity. Full article

In order to address the problems of low spectrum efficiency in current communication systems and extend the lifetime of energy-constrained relay devices, this paper proposes a novel dual-hop free-space optical (FSO) system that integrates cognitive radio (CR) and energy harvesting (EH). In this system, the source node communicates with two users at the terminal via FSO and terahertz (THz) hard-switching links, as well as a multi-antenna relay for non-orthogonal multiple access (NOMA). There is another link whose relay acts as both the power beacon (PB) in the EH system and the primary network (PN) in the CR system, achieving the double function of auxiliary transmission. In addition, based on the three possible practical working scenarios of the system, three different transmit powers of the relay are distinguished, thus enabling three different working modes of the system. Closed-form expressions are derived for the interruption outage probability per user for these three operating scenarios, considering the Gamma–Gamma distribution for the FSO link, the $\alpha -\mu$ distribution for the THz link, and the Rayleigh fading distribution for the radio frequency (RF) link. Finally, the numerical results show that this novel system can be adapted to various real-world scenarios and possesses unique advantages. Full article

This paper investigates statistical methods for estimating unknown lifetime parameters using a progressive first-failure censoring dataset. The failure mode’s lifetime distribution is modeled by the odd-generalized-exponential–inverse-Weibull distribution. Maximum-likelihood estimators for the model parameters, including the survival, hazard, and inverse hazard rate functions, are obtained, though they lack closed-form expressions. The Newton–Raphson method is used to compute these estimations. Confidence intervals for the parameters are approximated via the normal distribution of the maximum-likelihood estimation. The Fisher information matrix is derived using the missing information principle, and the delta method is applied to approximate the confidence intervals for the survival, hazard rate, and inverse hazard rate functions. Bayes estimators were calculated with the squared error, linear exponential, and general entropy loss functions, utilizing independent gamma distributions for informative priors. Markov-chain Monte Carlo sampling provides the highest-posterior-density credible intervals and Bayesian point estimates for the parameters and reliability characteristics. This study evaluates these methods through Monte Carlo simulations, comparing Bayes and maximum-likelihood estimates based on mean squared errors for point estimates, average interval widths, and coverage probabilities for interval estimators. A real dataset is also analyzed to illustrate the proposed methods. Full article

This paper proposes and implements a methodology to fit a seven-parameter Generalized Tempered Stable (GTS) distribution to financial data. The nonexistence of the mathematical expression of the GTS probability density function makes maximum-likelihood estimation (MLE) inadequate for providing parameter estimations. Based on the function characteristic and the fractional Fourier transform (FRFT), we provide a comprehensive approach to circumvent the problem and yield a good parameter estimation of the GTS probability. The methodology was applied to fit two heavy-tailed data (Bitcoin and Ethereum returns) and two peaked data (S&P 500 and SPY ETF returns). For each historical data, the estimation results show that six-parameter estimations are statistically significant except for the local parameter, $\mu$. The goodness of fit was assessed through Kolmogorov–Smirnov, Anderson–Darling, and Pearson’s chi-squared statistics. While the two-parameter geometric Brownian motion (GBM) hypothesis is always rejected, the GTS distribution fits significantly with a very high p-value and outperforms the Kobol, Carr–Geman–Madan–Yor, and bilateral Gamma distributions. Full article