Spatial-temporal diffusion model of aggregated infectious diseases based on population life characteristics: a case study of COVID-19

Outbreaks of infectious diseases pose significant threats to human life, and countries around the world need to implement more precise prevention and control measures to contain the spread of viruses. In this study, we propose a spatial-temporal diffusion model of infectious diseases under a discrete grid, based on the time series prediction of infectious diseases, to model the diffusion process of viruses in population. This model uses the estimated outbreak origin as the center of transmission, employing a tree-like structure of daily human travel to generalize the process of viral spread within the population. By incorporating diverse data, it simulates the congregation of people, thus quantifying the flow weights between grids for population movement. The model is validated with some Chinese cities with COVID-19 outbreaks, and the results show that the outbreak point estimation method could better estimate the virus transmission center of the epidemic. The estimated location of the outbreak point in Xi'an was only 0.965 km different from the actual one, and the results were more satisfactory. The spatiotemporal diffusion model for infectious diseases simulates daily newly infected areas, which effectively cover the actual patient infection zones on the same day. During the mid-stage of viral transmission, the coverage rate can increase to over 90%, compared to related research, this method has improved simulation accuracy by approximately 18%. This study can provide technical support for epidemic prevention and control, and assist decision-makers in developing more scientific and efficient epidemic prevention and control policies

Multi-omics analysis reveals the prognostic and tumor micro-environmental value of lumican in multiple cancer types

Background: Lumican (LUM), a proteoglycan of the extracellular matrix, has been reported to be involved in the regulation of immune escape processes, but the data supporting this phenomenon are not sufficient. In this study, we aimed to explore the links among LUM expression, survival, tumor microenvironment (TME), and immunotherapy in 33 cancer types.Methods: Data from several databases, such as UCSC Xena, GTEx, UALCAN, HPA, GEPIA2, TISIDB, PrognoScan, TIMER2, and GEO, as well as published studies, were used to determine the relationship between LUM expression and clinical features, TME, heterogeneity, and tumor stemness.Results: The expression of LUM was statistically different in most tumors versus normal tissues, both at the RNA and protein expression levels. High expression of LUM was typically associated with a poor prognosis in tumors. Additionally, immune scores, six immune cells, four immunosuppressive cells, cancer-associated fibroblasts (CAFs)-associated and immunosuppressive factors, tumor mutation burden (TMB), microsatellite instability (MSI), DNAss, and RNAss were all significantly associated with LUM. Among them, LUM expression displayed a significant positive correlation with CAFs and their factors, and exhibited immunosuppressive effects in six independent immunotherapy cohorts.Conclusion: Multi-omics analysis suggests that LUM may have been a prognostic marker, contributed to immunosuppression in the TME, and decreased the effectiveness of immune checkpoint inhibitors

Analisis Portofolio Optimal Dengan Single Index Model Untuk Meminimumkan Risiko Bagi Investor Di Bursa Efek Indonesia (Studi Pada Saham Indeks Kompas 100 Periode Februari 2010-juli 2014)

Investments can be made in the capital market, capital market instruments which are mostly attractive for investors is stock. Stock provides a return in the form of capital gains and dividends yield, not only noticing the return, investors need to pay attention to the investments risk. Unsystematis risk can be minimized by forming the optimal portfolio using one of the methods that is single index model. Study purpose is to knowing the stocks forming the optimal portfolio, the proportion of funds allocated to each stocks, the level of expectation return and risk.The method used in this research is descriptive research method with a quantitative approach. The samples used were 46 stocks in Kompas 100 Index, which meets the criteria for sampling. The results showed that 12 stocks of forming optimal portfolio, the stocks of which are UNVR, TRAM, MNCN, BHIT, JSMR, BMTR, GJTL, KLBF, AALI, CPIN, AKRA, and ASRI. Stock with highest proportion of funds is TRAM (23,52%), stock with lowest proportion of funds is AALI (0,62%). Portfolio which are formed will give return expectations by 3,05477% and carry the risk for about 0,1228%

Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures

Abstract: Major depressive disorder (MDD) has been shown to be associated with structural abnormalities in a variety of spatially diverse brain regions. However, the correlation between brain structural changes in MDD and gene expression is unclear. Here, we examine the link between brain-wide gene expression and morphometric changes in individuals with MDD, using neuroimaging data from two independent cohorts and a publicly available transcriptomic dataset. Morphometric similarity network (MSN) analysis shows replicable cortical structural differences in individuals with MDD compared to control subjects. Using human brain gene expression data, we observe that the expression of MDD-associated genes spatially correlates with MSN differences. Analysis of cell type-specific signature genes suggests that microglia and neuronal specific transcriptional changes account for most of the observed correlation with MDD-specific MSN differences. Collectively, our findings link molecular and structural changes relevant for MDD

Magnetoelectric interaction and transport behaviours in magnetic nanocomposite thermoelectric materials

How to suppress the performance deterioration of thermoelectric materials in the intrinsic excitation region remains a key challenge. The magnetic transition of permanent magnet nanoparticles from ferromagnetism to paramagnetism provides an effective approach to finding the solution to this challenge. Here, we have designed and prepared magnetic nanocomposite thermoelectric materials consisting of BaFe12O19 nanoparticles and Ba0.3In0.3Co4Sb12 matrix. It was found that the electrical transport behaviours of the nanocomposites are controlled by the magnetic transition of BaFe12O19 nanoparticles from ferromagnetism to paramagnetism. BaFe12O19 nanoparticles trap electrons below the Curie temperature (TC) and release the trapped electrons above the TC, playing an ‘electron repository’ role in maintaining high figure of merit ZT. BaFe12O19 nanoparticles produce two types of magnetoelectric effect—electron spiral motion and magnon-drag thermopower—as well as enhancing phonon scattering. Our work demonstrates that the performance deterioration of thermoelectric materials in the intrinsic excitation region can be suppressed through the magnetic transition of permanent magnet nanoparticles

MUSiC : a model-unspecific search for new physics in proton-proton collisions at root s=13TeV

Results of the Model Unspecific Search in CMS (MUSiC), using proton-proton collision data recorded at the LHC at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 35.9 fb(-1), are presented. The MUSiC analysis searches for anomalies that could be signatures of physics beyond the standard model. The analysis is based on the comparison of observed data with the standard model prediction, as determined from simulation, in several hundred final states and multiple kinematic distributions. Events containing at least one electron or muon are classified based on their final state topology, and an automated search algorithm surveys the observed data for deviations from the prediction. The sensitivity of the search is validated using multiple methods. No significant deviations from the predictions have been observed. For a wide range of final state topologies, agreement is found between the data and the standard model simulation. This analysis complements dedicated search analyses by significantly expanding the range of final states covered using a model independent approach with the largest data set to date to probe phase space regions beyond the reach of previous general searches.Peer reviewe

Measurement of prompt open-charm production cross sections in proton-proton collisions at root s=13 TeV

The production cross sections for prompt open-charm mesons in proton-proton collisions at a center-of-mass energy of 13TeV are reported. The measurement is performed using a data sample collected by the CMS experiment corresponding to an integrated luminosity of 29 nb(-1). The differential production cross sections of the D*(+/-), D-+/-, and D-0 ((D) over bar (0)) mesons are presented in ranges of transverse momentum and pseudorapidity 4 < p(T) < 100 GeV and vertical bar eta vertical bar < 2.1, respectively. The results are compared to several theoretical calculations and to previous measurements.Peer reviewe