Brantleythorhauge1531

Defects in mitochondrial function activate compensatory responses in the cell. Mitochondrial stress that is caused by unfolded proteins inside the organelle induces a transcriptional response (termed the "mitochondrial unfolded protein response" [UPRmt]) that is mediated by activating transcription factor associated with stress 1 (ATFS-1). The UPRmt increases mitochondrial protein quality control. Mitochondrial dysfunction frequently causes defects in the import of proteins, resulting in the accumulation of mitochondrial proteins outside the organelle. In yeast, cells respond to mistargeted mitochondrial proteins by increasing activity of the proteasome in the cytosol (termed the "unfolded protein response activated by mistargeting of proteins" [UPRam]). The presence and relevance of this response in higher eukaryotes is unclear. Here, we demonstrate that defects in mitochondrial protein import in Caenorhabditis elegans lead to proteasome activation and life span extension. Both proteasome activation and life span prolongation partially depend on ATFS-1, despite its lack of influence on proteasomal gene transcription. Importantly, life span prolongation depends on the fully assembled proteasome. Our data provide a link between mitochondrial dysfunction and proteasomal activity and demonstrate its direct relevance to mechanisms that promote longevity.Surveillance is critical to mounting an appropriate and effective response to pandemics. However, aggregated case report data suffers from reporting delays and can lead to misleading inferences. Different from aggregated case report data, line list data is a table contains individual features such as dates of symptom onset and reporting for each reported case and a good source for modeling delays. Current methods for modeling reporting delays are not particularly appropriate for line list data, which typically has missing symptom onset dates that are non-ignorable for modeling reporting delays. In this paper, we develop a Bayesian approach that dynamically integrates imputation and estimation for line list data. Specifically, this Bayesian approach can accurately estimate the epidemic curve and instantaneous reproduction numbers, even with most symptom onset dates missing. The Bayesian approach is also robust to deviations from model assumptions, such as changes in the reporting delay distribution or incorrect specification of the maximum reporting delay. We apply the Bayesian approach to COVID-19 line list data in Massachusetts and find the reproduction number estimates correspond more closely to the control measures than the estimates based on the reported curve.Circadian rhythms are nearly ubiquitous throughout nature, suggesting they are critical for survival in diverse environments. Organisms inhabiting largely arrhythmic environments, such as caves, offer a unique opportunity to study the evolution of circadian rhythms in response to changing ecological pressures. Populations of the Mexican tetra, Astyanax mexicanus, have repeatedly invaded caves from surface rivers, where individuals must contend with perpetual darkness, reduced food availability, and limited fluctuations in daily environmental cues. To investigate the molecular basis for evolved changes in circadian rhythms, we investigated rhythmic transcription across multiple independently-evolved cavefish populations. Our findings reveal that evolution in a cave environment has led to the repeated disruption of the endogenous biological clock, and its entrainment by light. The circadian transcriptome shows widespread reductions and losses of rhythmic transcription and changes to the timing of the activation/repression of core-transcriptional clock. this website In addition to dysregulation of the core clock, we find that rhythmic transcription of the melatonin regulator aanat2 and melatonin rhythms are disrupted in cavefish under darkness. Mutants of aanat2 and core clock gene rorca disrupt diurnal regulation of sleep in A. mexicanus, phenocopying circadian modulation of sleep and activity phenotypes of cave populations. Together, these findings reveal multiple independent mechanisms for loss of circadian rhythms in cavefish populations and provide a platform for studying how evolved changes in the biological clock can contribute to variation in sleep and circadian behavior.

Schools play a key role in supporting the well-being and resettlement of refugee children, and parental engagement with the school may be a critical factor in the process. Many resettlement countries have policies in place to support refugee parents' engagement with their children's school. However, the impact of these programs lacks systematic evaluation. This study first aimed to validate self-report measures of parental school engagement developed specifically for the refugee context, and second, to identify parent characteristics associated with school engagement, so as to help tailor support to families most in need.

The report utilises 2016 baseline data of a cohort study of 233 Arabic-speaking parents (77% response rate) of 10- to 12-year-old schoolchildren from refugee backgrounds across 5 schools in Sydney, Australia. Most participants were born in Iraq (81%) or Syria (11%), and only 25% spoke English well to very well. Participants' mean age was 40 years old, and 83% were female. Confirmatory fad support parents with school-attending children from refugee backgrounds who are experiencing psychological distress or resettlement stressors. At the school level, the findings suggest that cultural brokers may be effective in targeting newly arrived families.Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.