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Frequent social contact benefits cognition in later life although evidence is lacking on the potential relevance of the modes chosen by older adults, including those living with hearing loss, for interacting with others in their social network.

11,418 participants in the English Longitudinal Study of Ageing provided baseline information on hearing status and social contact mode and frequency of use. Multilevel growth curve models compared episodic memory (immediate and delayed recall) at baseline and longitudinally in participants who interacted frequently (offline only or offline and online combined), compared to infrequently, with others in their social network.

Frequent offline (B = 0.23; SE = 0.09) and combined offline and online (B = 0.71; SE = 0.09) social interactions predicted better episodic memory after adjustment for multiple confounders. We observed positive, longitudinal associations between combined offline and online interactions and episodic memory in participants without hearing loss (B = 0.50, SE = 0.11) but not with strictly offline interactions (B = 0.01, SE = 0.11). In those with hearing loss, episodic memory was positively related to both modes of engagement (offline only B = 0.79, SE = 0.20; combined online and offline B = 1.27, SE = 0.20). Sensitivity analyses confirmed the robustness of these findings.

Supplementing conventional social interactions with online communication modes may help older adults, especially those living with hearing loss, sustain, and benefit cognitively from, personal relationships.
Supplementing conventional social interactions with online communication modes may help older adults, especially those living with hearing loss, sustain, and benefit cognitively from, personal relationships.Biosynthesis of secondary metabolites relies on primary metabolic pathways to provide precursors, energy, and cofactors, thus requiring coordinated regulation of primary and secondary metabolic networks. TDO inhibitor However, to date, it remains largely unknown how this coordination is achieved. Using Petunia hybrida flowers, which emit high levels of phenylpropanoid/benzenoid volatile organic compounds (VOCs), we uncovered genome-wide dynamic deposition of histone H3 lysine 9 acetylation (H3K9ac) during anthesis as an underlying mechanism to coordinate primary and secondary metabolic networks. The observed epigenome reprogramming is accompanied by transcriptional activation at gene loci involved in primary metabolic pathways that provide precursor phenylalanine, as well as secondary metabolic pathways to produce volatile compounds. We also observed transcriptional repression among genes involved in alternative phenylpropanoid branches that compete for metabolic precursors. We show that GNAT family histone acetyltransferase(s) (HATs) are required for the expression of genes involved in VOC biosynthesis and emission, by using chemical inhibitors of HATs, and by knocking down a specific HAT gene, ELP3, through transient RNAi. Together, our study supports that regulatory mechanisms at chromatin level may play an essential role in activating primary and secondary metabolic pathways to regulate VOC synthesis in petunia flowers.
Given the persistently high global burden of tuberculosis, effective and shorter treatment options are needed. We explored the relationship between relapse and treatment length as well as interregimen differences for 2 novel antituberculosis drug regimens using a mouse model of tuberculosis infection and mathematical modeling.

Mycobacterium tuberculosis-infected mice were treated for up to 13 weeks with bedaquiline and pretomanid combined with moxifloxacin and pyrazinamide (BPaMZ) or linezolid (BPaL). Cure rates were evaluated 12 weeks after treatment completion. The standard regimen of isoniazid, rifampicin, pyrazinamide, and ethambutol (HRZE) was evaluated as a comparator.

Six weeks of BPaMZ was sufficient to achieve cure in all mice. In contrast, 13 weeks of BPaL and 24 weeks of HRZE did not achieve 100% cure rates. Based on mathematical model predictions, 95% probability of cure was predicted to occur at 1.6, 4.3, and 7.9 months for BPaMZ, BPaL, and HRZE, respectively.

This study provides additional evidence for the treatment-shortening capacity of BPaMZ over BPaL and HRZE. To optimally use preclinical data for predicting clinical outcomes, and to overcome the limitations that hamper such extrapolation, we advocate bundling of available published preclinical data into mathematical models.
This study provides additional evidence for the treatment-shortening capacity of BPaMZ over BPaL and HRZE. To optimally use preclinical data for predicting clinical outcomes, and to overcome the limitations that hamper such extrapolation, we advocate bundling of available published preclinical data into mathematical models.The field of nutritional epidemiology faces challenges posed by measurement error, diet as a complex exposure, and residual confounding. The objective of this perspective article is to highlight how developments in big data and machine learning can help address these challenges. New methods of collecting 24-h dietary recalls and recording diet could enable larger samples and more repeated measures to increase statistical power and measurement precision. In addition, use of machine learning to automatically classify pictures of food could become a useful complimentary method to help improve precision and validity of dietary measurements. Diet is complex due to thousands of different foods that are consumed in varying proportions, fluctuating quantities over time, and differing combinations. Current dietary pattern methods may not integrate sufficient dietary variation, and most traditional modeling approaches have limited incorporation of interactions and nonlinearity. Machine learning could help better model diet as a complex exposure with nonadditive and nonlinear associations. Last, novel big data sources could help avoid unmeasured confounding by offering more covariates, including both omics and features derived from unstructured data with machine learning methods. These opportunities notwithstanding, application of big data and machine learning must be approached cautiously to ensure quality of dietary measurements, avoid overfitting, and confirm accurate interpretations. Greater use of machine learning and big data would also require substantial investments in training, collaborations, and computing infrastructure. Overall, we propose that judicious application of big data and machine learning in nutrition science could offer new means of dietary measurement, more tools to model the complexity of diet and its relations with diseases, and additional potential ways of addressing confounding.
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