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Background In the early stages of a novel infectious disease outbreak, when vaccines, treatments, and herd immunity are lacking, non-pharmaceutical interventions—particularly self-reporting and contact tracing—play a critical role in suppressing transmission. During the first wave of COVID-19 in Korea, a large outbreak centered around a religious community in Daegu rapidly escalated, thus highlighting the transmission risks associated with a closed and low-reporting high-risk group. This study aimed to quantitatively assess the effectiveness of self-reporting and contact tracing strategies across heterogeneous risk groups. Methods The population of Daegu was stratified into two groups: a high-risk group characterized by high transmissibility and low reporting rate, and a low-risk group with lower transmissibility and higher reporting compliance. We developed a stochastic model and applied a modified Gillespie algorithm incorporating both Markovian and non-Markovian processes. Scenario-based simulations were conducted to evaluate the impact of changes in self-reporting rates and delays in contact tracing. We simulated each scenario 10,000 times to estimate the mean and 95% credible intervals for the number of infections. Results When the self-reporting rate in the high-risk group was lowered to 0.1, the total infections increased by approximately 22%, while unreported infections rose by 164% compared to the baseline. Conversely, increasing the self-reporting rate in the high-risk group to 0.8 reduced the total cases by approximately 21% and unreported infections by 86%. Notably, unreported infections in the low-risk group increased by approximately 416% when their reporting rate declined to 0.4, although this group had a lower transmission potential. Even a modest contact tracing delay of 4–7 d resulted in an 85% increase in unreported cases, with diminishing returns for longer delays, highlighting the critical importance of timely tracing in outbreak control. Conclusions In situations with heterogeneous risk groups, improving the self-reporting behavior of high-risk populations and maintaining high compliance in the low-risk group are essential for effective outbreak control. Contact tracing should be completed within 1–4 d to prevent further spread. Our study which accounts for behavioral heterogeneity, provides a scientific foundation for designing group-specific intervention strategies in future outbreaks of emerging infectious diseases.

Several NOD2 mutations are associated with a greater risk of Crohn's disease. Ma and colleagues show that the 3020insC Nod2 mutant actively suppresses IL10 transcription by interfering with phosphorylation of the nuclear ribonucleoprotein hRNP-A1. A common mutation in the gene encoding the cytoplasmic sensor Nod2, involving a frameshift insertion at nucleotide 3020 ( 3020insC ), is strongly associated with Crohn's disease. How 3020insC contributes to this disease is a controversial issue. Clinical studies have identified defective production of interleukin 10 (IL-10) in patients with Crohn's disease who bear the 3020insC mutation, which suggests that 3020insC may be a loss-of-function mutation. However, here we found that 3020insC Nod2 mutant protein actively inhibited IL10 transcription. The 3020insC Nod2 mutant suppressed IL10 transcription by blocking phosphorylation of the nuclear ribonucleoprotein hnRNP-A1 via the mitogen-activated protein kinase p38. We confirmed impairment in phosphorylation of hnRNP-A1 and binding of hnRNP-A1 to the IL10 locus in peripheral blood mononuclear cells from patients with Crohn's disease who bear the 3020insC mutation and have lower production of IL-10.

Background Heterogeneous nuclear ribonucleoproteins (hnRNPs) of the A/B type (hnRNP A1, A2/B1, A3) are highly related multifunctional proteins participating in alternative splicing by antagonising other splicing factors, notably ASF/SF2. The altered expression pattern of hnRNP A2/B1 and/or splicing variant B1 alone in human lung cancer and their potential to serve as molecular markers for early diagnosis remain issues of intense investigation. The main objective of the present study was to use paired tumour/non-tumour biopsies from patients with non-small cell lung cancer (NSCLC) to investigate the expression profiles of hnRNP A1, A2/B1 and A3 in conjunction with ASF/SF2. Methods We combined western blotting of tissue homogenates with immunohistochemical examination of fixed tissue sections and quantification of mRNA expression levels in tumour versus adjacent normal-looking areas of the lung in the same patient. Results Our study, in addition to clear evidence of mostly uncoupled deregulation of hnRNPs A/B, has revealed hnRNP A1 to be the most deregulated protein with a high frequency of over-expression (76%), followed by A3 (52%) and A2/B1 (43%). Moreover, direct comparison of protein/mRNA levels showed a lack of correlation in the case of hnRNP A1 (as well as of ASF/SF2), but not of A2/B1, suggesting that different mechanisms underlie their deregulation. Conclusion Our results provide strong evidence for the up-regulation of hnRNP A/B in NSCLC, and they support the existence of distinct mechanisms responsible for their deregulated expression.

Trade liberalization changes the volatility of returns by reducing the negative correlation between local prices and productivity shocks. In this paper, we explore these second-moment effects of trade. Using forty years of agricultural micro-data from India, we show that falling trade costs due to expansions of the Indian highway network reduced the responsiveness of local prices to local yields but increased the responsiveness of local prices to yields elsewhere. In response, farmers shifted their production toward crops with less volatile yields, especially so for those with poor access to risk mitigating technologies such as banks. We then characterize how volatility affects farmers’ crop allocation using a portfolio choice framework where returns are determined in general equilibrium by a many-location, many-good Ricardian trade model with flexible trade costs. Finally, we structurally estimate the model—recovering farmers’ risk-return preferences from the gradient of the mean-variance frontier at their observed crop choices—to quantify the second-moment effects of trade. The simultaneous expansion of both the highway and rural bank networks increased the mean and the variance of farmer real income, with the first-moment effect dominating such that expected welfare rose 4.4%. But had rural bank access remained unchanged, welfare gains would have been only half as great, as risk mitigating technologies allowed farmers to take advantage of higher-risk higher-return allocations.

The lack of affordable, reliable, and resilient energy services remains a challenge for many U.S. households. Few studies have investigated how temperature makes already vulnerable Black, low-income, and less-educated households more likely to experience energy poverty. We construct a unique 8-year historical panel dataset to unpack the relationship between temperatures and energy burdens, paying specific attention to additional burdens among the most vulnerable groups. We find that hot and cold temperatures have further exacerbated the disproportionate impact on energy burdens across regions and multiple vulnerable groups. Extremely low-income groups are ∼6 times more adversely affected by temperatures than high-income groups. Temperatures also put other already marginalized groups, such as those less-educated/unemployed/living in energy-inefficient old houses, at higher risk of falling into an energy poverty trap. Considering temperatures are the dominant feature differentiating households in their ability to meet basic electricity needs, we recommend more equitable and inclusive electrification strategies and compensation mechanisms for affected communities to improve energy equity.

HNRNPA2B1 is a member of the HNRNP family, which is associated with telomere function, mRNA translation, and splicing, and plays an important role in tumor development. To date, there have been no pan‐cancer studies of HNRNPA2B1, particularly within the TME. Therefore, we conducted a pan‐cancer analysis of HNRNPA2B1 using TCGA data. Based on datasets from TCGA, TARGET, Genotype‐Tissue Expression, and Human Protein Atlas, we employed a range of bioinformatics approaches to explore the potential oncogenic role of HNRNPA2B1. This included analyzing the association of HNRNPA2B1 expression with prognosis, tumor mutation burden (TMB), microsatellite instability (MSI), immune response, and immune cell infiltration of individual tumors. We further validated the bioinformatic findings using immunohistochemistry techniques. HNRNPA2B1 was found to be differentially expressed across most tumor types in TCGA’s pan‐cancer database and was predictive of poorer clinical staging and survival status. HNRNPA2B1 expression was also closely linked to TMB, MSI, tumor stemness, and chemotherapy response. HNRNPA2B1 plays a significant role in the TME and is involved in the regulation of novel immunotherapies. Its expression is significantly associated with the infiltration of macrophages, dendritic cells, NK cells, and T cells. Furthermore, HNRNPA2B1 is closely associated with immune checkpoints, immune‐stimulatory genes, immune‐inhibitory genes, MHC genes, chemokines, and chemokine receptors. We performed a comprehensive evaluation of HNRNPA2B1, revealing its potential role as a prognostic indicator for patients and its immunomodulatory functions.

The present study examined the relationships between ethnicity, peer-reported bullying and victimization, and whether these relationships were moderated by the ethnic composition of the school classes. Participants were 2386 adolescents (mean age: 13 years and 10 months; 51.9% boys) from 117 school classes in the Netherlands. Multilevel analyses showed that, after controlling for the ethnic composition of school class, ethnic minority adolescents were less victimized, but did not differ from the ethnic majority group members on bullying. Victimization was more prevalent in ethnically heterogeneous classes. Furthermore, the results revealed that ethnic minority adolescents bully more in ethnically heterogeneous classes. Our findings suggest that, in order to understand bullying and victimization in schools in ethnically diverse cultures, the ethnic background of adolescents and the ethnic composition of school classes should be taken into account.

We consider optimal group testing of individuals with heterogeneous risks for an infectious disease. Our algorithm significantly reduces the number of tests needed compared to Dorfman (Ann Math Stat 14(4):436–440, 1943). When both low-risk and high-risk samples have sufficiently low infection probabilities, it is optimal to form heterogeneous groups with exactly one high-risk sample per group. Otherwise, it is not optimal to form heterogeneous groups, but homogeneous group testing may still be optimal. For a range of parameters including the U.S. Covid-19 positivity rate for many weeks during the pandemic, the optimal size of a group test is four. We discuss the implications of our results for team design and task assignment.

Risk assessment and management are some of the major tasks of urban power-grid management. The growing amount of data from, e.g., prediction systems, sensors, and satellites has enabled access to numerous datasets originating from a diversity of heterogeneous data sources. While these advancements are of great importance for more accurate and trustable risk analyses, there is no guidance on selecting the best information available for power-grid risk analysis. This paper addresses this gap on the basis of existing standards in risk assessment. The key contributions of this research are twofold. First, it proposes a method for reinforcing data-related risk analysis steps. The use of this method ensures that risk analysts will methodically identify and assess the available data for informing the risk analysis key parameters. Second, it develops a method (named the three-phases method) based on metrology for selecting the best datasets according to their informative potential. The method, thus, formalizes, in a traceable and reproducible manner, the process for choosing one dataset to inform a parameter in detriment of another, which can lead to more accurate risk analyses. The method is applied to a case study of vegetation-related risk analysis in power grids, a common challenge faced by power-grid operators. The application demonstrates that a dataset originating from an initially less valued data source may be preferred to a dataset originating from a higher-ranked data source, the content of which is outdated or of too low quality. The results confirm that the method enables a dynamic optimization of dataset selection upfront of any risk analysis, supporting the application of dynamic risk analyses in real-case scenarios.

Background Ovine footrot caused by Dichelobacter nodosus ( D. nodosus ) is a contagious disease with serious economic and welfare impacts in sheep production systems worldwide. A better understanding of the host genetic architecture regarding footrot resistance/susceptibility is crucial to develop disease control strategies that efficiently reduce infection and its severity. A genome-wide association study was performed using a customized SNP array (47,779 SNPs in total) to identify genetic variants associated to footrot resistance/susceptibility in two Portuguese native breeds, i.e. Merino Branco and Merino Preto, and a population of crossbred animals. A cohort of 1375 sheep sampled across 17 flocks, located in the Alentejo region (southern Portugal), was included in the analyses. Results Phenotypes were scored from 0 (healthy) to 5 (severe footrot) based on visual inspection of feet lesions, following the Modified Egerton System. Using a linear mixed model approach, three SNPs located on chromosome 24 reached genome-wide significance after a Bonferroni correction ( p  < 0.05). Additionally, six genome-wide suggestive SNPs were identified each on chromosomes 2, 4, 7, 8, 9 and 15. The annotation and KEGG pathway analyses showed that these SNPs are located within regions of candidate genes such as the nonsense mediated mRNA decay associated PI3K related kinase (SMG1) (chromosome 24) and the RALY RNA binding protein like (RALYL) (chromosome 9), both involved in immunity, and the heparan sulfate proteoglycan 2 (HSPG2) (chromosome 2) and the Thrombospodin 1 (THBS1) (chromosome 7) implicated in tissue repair and wound healing processes. Conclusion This is the first attempt to identify molecular markers associated with footrot in Portuguese Merino sheep. These findings provide relevant information on a likely genetic association underlying footrot resistance/susceptibility and the potential candidate genes affecting this trait. Genetic selection strategies assisted on the information obtained from this study could enhance Merino sheep-breeding programs, in combination with farm management strategies, for a more effective and sustainable long-term solution for footrot control.