Distinct Transcriptomic and Tumor Microenvironment Profiles in Sinonasal Mucosal Melanoma and Aggressive Cutaneous Melanomas.

: Sinonasal mucosal melanoma (SNMM) is a rare and aggressive melanoma subtype with a notably poor prognosis compared to cutaneous melanoma (CM). Despite advances in molecular characterization, SNMM remains underexplored, posing a clinical challenge and highlighting the need for detailed molecular profiling. This study aimed to identify the molecular features of SNMM, elucidate its clinical behavior and prognostic implications, and provide insights for improved therapeutic strategies.

Evaluation of the Biological Effect of a Nicotinamide-Containing Broad-Spectrum Sunscreen on Photodamaged Skin.

Introduction: UVA-UVB increases skin matrix metalloproteinases and breaks down extracellular proteins and fibrillar type 1 collagen, leading to photodamage. Topical application of nicotinamide prevents UV-induced immunosuppression. Several studies have demonstrated the importance of protection against UV.

A brief review and guidance on the spatiotemporal sampling designs for disease vector surveillance.

Obtaining a representative sample of disease vectors (mosquitoes, flies, ticks, etc.) is essential for researchers to draw meaningful conclusions about the entire vector population in a target study area and during a specific study period. To achieve this, a carefully chosen surveillance design is required to ensure that the sample captures essential spatial and temporal variations in the target vector population(s) and/or that the study results can be generalized to the entire population.

Multivariate Poisson cokriging: A geostatistical model for health count data.

Multivariate disease mapping is important for public health research, as it provides insights into spatial patterns of health outcomes. Geostatistical methods that are widely used for mapping spatially correlated health data encounter challenges when dealing with spatial count data. These include heterogeneity, zero-inflated distributions and unreliable estimation, and lead to difficulties when estimating spatial dependence and poor predictions.

The ctenophore Mnemiopsis leidyi deploys a rapid injury response dating back to the last common animal ancestor.

Regenerative potential is widespread but unevenly distributed across animals. However, our understanding of the molecular mechanisms underlying regenerative processes is limited to a handful of model organisms, restricting robust comparative analyses. Here, we conduct a time course of RNA-seq during whole body regeneration in Mnemiopsis leidyi (Ctenophora) to uncover gene expression changes that correspond with key events during the regenerative timeline of this species.

Quasi-3D Model for Lateral Resonances on Homogeneous BAW Resonators.

Lateral modes are responsible for the in-band spurious resonances that appear on BAW resonators, degrading the in-band filter response. In this work, a fast computational method based on the transmission line matrix (TLM) method is employed to model the lateral resonances of BAW resonators. Using the precomputed dispersion curves of Lamb waves and an equivalent characteristic impedance for the TE mode, a network of transmission lines is used to calculate the magnitude of field distributions on the electrodes.

A spatio-temporal Dirichlet process mixture model for coronavirus disease-19.

Understanding the spatio-temporal patterns of the coronavirus disease 2019 (COVID-19) is essential to construct public health interventions. Spatially referenced data can provide richer opportunities to understand the mechanism of the disease spread compared to the more often encountered aggregated count data. We propose a spatio-temporal Dirichlet process mixture model to analyze confirmed cases of COVID-19 in an urban environment.

Analysis of spurious peaks at series resonance in solidly mounted resonators by combined BVD-Mason modelling.

Solidly Mounted Resonators (SMRs) for high frequency RF filters and sensing applications often display spurious resonances that distort their frequency response. In this work, we try to identify the origin of spurious resonances accompanying the main series resonances in AlN-based SMRs with the help of modified Butterworth Van Dyke (BVD) and Mason's models. By manufacturing SMRs of different sizes and shapes and studying the influence of the position of the electrical probing spot, we have demonstrated both theoretically and experimentally that devices with larger areas are more likely to display these additional peaks.

COVID-19 transmission risk in Surabaya and Sidoarjo: an inhomogeneous marked Poisson point process approach.

Understanding the spatio-temporal dynamics of COVID-19 transmission is necessary to plan better strategies for controlling the spread of the disease. However, only a few studies explore the COVID-19 transmission risk over a fine spatial resolution while considering relevant spatial and temporal factors. To this aim, we consider an inhomogeneous marked Poisson point process model to assess COVID-19 transmission risk using data of home addresses of confirmed cases, in relation to locations of sources of crowd (enterprise, market, and place of worship) and population density in Surabaya and Sidoarjo, Indonesia.

Development and validation of a minimally invasive and image-guided tape stripping method to sample atopic skin in children.

Background: Molecular skin profiling techniques, typically performed on skin samples taken by punch biopsy, have enhanced the understanding of the pathophysiology of atopic dermatitis (AD), thereby enabling the development of novel targeted therapeutics. However, punch biopsies are not always feasible or desirable, and novel minimally invasive methods such as skin tape stripping have been developed.

Aim: To develop, optimize and validate a novel tape stripping method guided by noninvasive in vivo skin imaging to sample atopic skin in children.

Spatio-temporal stochastic differential equations for crime incidence modeling.

We propose a methodology for the quantitative fitting and forecasting of real spatio-temporal crime data, based on stochastic differential equations. The analysis is focused on the city of Valencia, Spain, for which 90247 robberies and thefts with their latitude-longitude positions are available for a span of eleven years (2010-2020) from records of the 112-emergency phone. The incidents are placed in the 26 zip codes of the city (46001-46026), and monthly time series of crime are built for each of the zip codes.

Spatially informed Bayesian neural network for neurodegenerative diseases classification.

Magnetic resonance imaging (MRI) plays an increasingly important role in the diagnosis and prognosis of neurodegenerative diseases. One field of extensive clinical use of MRI is the accurate and automated classification of degenerative disorders. Most of current classification studies either do not mirror medical practice where patients may exhibit early stages of the disease, comorbidities, or atypical variants, or they are not able to produce probabilistic predictions nor account for uncertainty.

Modeling noisy time-series data of crime with stochastic differential equations.

We develop and calibrate stochastic continuous models that capture crime dynamics in the city of Valencia, Spain. From the emergency phone, data corresponding to three crime events, aggressions, stealing and women alarms, are available from the year 2010 until 2020. As the resulting time series, with monthly counts, are highly noisy, we decompose them into trend and seasonality parts.

A phenomenological model for COVID-19 data taking into account neighboring-provinces effect and random noise.

We model the incidence of the COVID-19 disease during the first wave of the epidemic in Castilla-Leon (Spain). Within-province dynamics may be governed by a generalized logistic map, but this lacks of spatial structure. To couple the provinces, we relate the daily new infections through a density-independent parameter that entails positive spatial correlation.

Modeling infectious disease dynamics: Integrating contact tracing-based stochastic compartment and spatio-temporal risk models.

Major infectious diseases such as COVID-19 have a significant impact on population lives and put enormous pressure on healthcare systems globally. Strong interventions, such as lockdowns and social distancing measures, imposed to prevent these diseases from spreading, may also negatively impact society, leading to jobs losses, mental health problems, and increased inequalities, making crucial the prioritization of riskier areas when applying these protocols. The modeling of mobility data derived from contact-tracing data can be used to forecast infectious trajectories and help design strategies for prevention and control.

A mechanistic spatio-temporal modeling of COVID-19 data.

Understanding the evolution of an epidemic is essential to implement timely and efficient preventive measures. The availability of epidemiological data at a fine spatio-temporal scale is both novel and highly useful in this regard. Indeed, having geocoded data at the case level opens the door to analyze the spread of the disease on an individual basis, allowing the detection of specific outbreaks or, in general, of some interactions between cases that are not observable if aggregated data are used.

Spatio-temporal modeling of traffic accidents incidence on urban road networks based on an explicit network triangulation.

Traffic deaths and injuries are one of the major global public health concerns. The present study considers accident records in an urban environment to explore and analyze spatial and temporal in the incidence of road traffic accidents. We propose a spatio-temporal model to provide predictions of the number of traffic collisions on any given road segment, to further generate a risk map of the entire road network.

Multivariate Kalman filtering for spatio-temporal processes.

Unlabelled: An increasing interest in models for multivariate spatio-temporal processes has been noted in the last years. Some of these models are very flexible and can capture both marginal and cross spatial associations amongst the components of the multivariate process. In order to contribute to the statistical analysis of these models, this paper deals with the estimation and prediction of multivariate spatio-temporal processes by using multivariate state-space models.

Distributed Nonlinear Model and Fast Analysis for In-Band IMD3 Prediction of Surface Acoustic Wave Resonators.

The motivation of this work is to analyze the in-band intermodulation distortion (IMD) occurring in surface acoustic wave (SAW) devices, using a recently developed fast method based on the input-output equivalent sources (IOES). The method calculates the equivalent current sources of a given harmonic (H) or IMD, which when applied at the boundaries of any uniform nonlinear region produce the same nonlinearities as the full distributed circuit. The accuracy of the method is validated with a very simplified SAW resonator with ten digits, which is modeled by a discretized Mason-based circuit.

A Bayesian machine learning approach for spatio-temporal prediction of COVID-19 cases.

Modeling the spread of infectious diseases in space and time needs to take care of complex dependencies and uncertainties. Machine learning methods, and neural networks, in particular, are useful in modeling this sort of complex problems, although they generally lack of probabilistic interpretations. We propose a neural network method embedded in a Bayesian framework for modeling and predicting the number of cases of infectious diseases in areal units.

A stochastic Bayesian bootstrapping model for COVID-19 data.

We provide a stochastic modeling framework for the incidence of COVID-19 in Castilla-Leon (Spain) for the period March 1, 2020 to February 12, 2021, which encompasses four waves. Each wave is appropriately described by a generalized logistic growth curve. Accordingly, the four waves are modeled through a sum of four generalized logistic growth curves.

A spatial randomness test based on the box-counting dimension.

Statistical modelling of a spatial point pattern often begins by testing the hypothesis of spatial randomness. Classical tests are based on quadrat counts and distance-based methods. Alternatively, we propose a new statistical test of spatial randomness based on the fractal dimension, calculated through the box-counting method providing an inferential perspective contrary to the more often descriptive use of this method.

Spatio-temporal small area surveillance of the COVID-19 pandemic.

The emergence of COVID-19 requires new effective tools for epidemiological surveillance. Spatio-temporal disease mapping models, which allow dealing with small units of analysis, are a priority in this context. These models provide geographically detailed and temporally updated overviews of the current state of the pandemic, making public health interventions more effective.