Mapping the mammalian ribosome quality control complex interactome using proximity labeling approaches.

Previous genetic and biochemical studies from Saccharomyces cerevisiae have identified a critical ribosome-associated quality control complex (RQC) that facilitates resolution of stalled ribosomal complexes. While components of the mammalian RQC have been examined in vitro, a systematic characterization of RQC protein interactions in mammalian cells has yet to be described. Here we utilize both proximity-labeling proteomic approaches, BioID and APEX, and traditional affinity-based strategies to both identify interacting proteins of mammalian RQC members and putative substrates for the RQC resident E3 ligase, Ltn1. Surprisingly, validation studies revealed that a subset of substrates are ubiquitylated by Ltn1 in a regulatory manner that does not result in subsequent substrate degradation. We demonstrate that Ltn1 catalyzes the regulatory ubiquitylation of ribosomal protein S6 kinase 1 and 2 (RPS6KA1, RPS6KA3). Further, loss of Ltn1 function results in hyperactivation of RSK1/2 signaling without impacting RSK1/2 protein turnover. These results suggest that Ltn1-mediated RSK1/2 ubiquitylation is inhibitory and establishes a new role for Ltn1 in regulating mitogen-activated kinase signaling via regulatory RSK1/2 ubiquitylation. Taken together, our results suggest that mammalian RQC interactions are difficult to observe and may be more transient than the homologous complex in S. cerevisiae and that Ltn1 has RQC-independent functions

A Prototypical First-Generation Electronic Cigarette Does Not Reduce Reports of Tobacco Urges or Withdrawal Symptoms Among Cigarette Smokers

It is unknown whether first-generation electronic cigarettes reduce smoking urges and withdrawal symptoms following a 24 h deprivation period. This study tested whether a first-generation electronic cigarette reduces smoking urges and withdrawal symptoms in cigarette smokers. Following 24 h of tobacco deprivation, using a within-subjects design, eight nontreatment seeking tobacco cigarette smokers (3 females) administered 10 puffs from a conventional cigarette or a first-generation electronic cigarette containing liquid with 0, 8 or 16 mg/ml nicotine. Conventional cigarettes ameliorated smoking urges and electronic cigarettes did not, regardless of nicotine concentration. First-generation electronic cigarettes may not effectively substitute for conventional cigarettes in reducing smoking urges, regardless of nicotine concentration

A Prototypical First-Generation Electronic Cigarette Does Not Reduce Reports of Tobacco Urges or Withdrawal Symptoms among Cigarette Smokers

It is unknown whether first-generation electronic cigarettes reduce smoking urges and withdrawal symptoms following a 24 h deprivation period. This study tested whether a first-generation electronic cigarette reduces smoking urges and withdrawal symptoms in cigarette smokers. Following 24 h of tobacco deprivation, using a within-subjects design, eight nontreatment seeking tobacco cigarette smokers (3 females) administered 10 puffs from a conventional cigarette or a first-generation electronic cigarette containing liquid with 0, 8 or 16 mg/ml nicotine. Conventional cigarettes ameliorated smoking urges and electronic cigarettes did not, regardless of nicotine concentration. First-generation electronic cigarettes may not effectively substitute for conventional cigarettes in reducing smoking urges, regardless of nicotine concentration

A Prototypical First-Generation Electronic Cigarette Does Not Reduce Reports of Tobacco Urges or Withdrawal Symptoms among Cigarette Smokers

It is unknown whether first-generation electronic cigarettes reduce smoking urges and withdrawal symptoms following a 24 h deprivation period. This study tested whether a first-generation electronic cigarette reduces smoking urges and withdrawal symptoms in cigarette smokers. Following 24 h of tobacco deprivation, using a within-subjects design, eight nontreatment seeking tobacco cigarette smokers (3 females) administered 10 puffs from a conventional cigarette or a first-generation electronic cigarette containing liquid with 0, 8 or 16 mg/ml nicotine. Conventional cigarettes ameliorated smoking urges and electronic cigarettes did not, regardless of nicotine concentration. First-generation electronic cigarettes may not effectively substitute for conventional cigarettes in reducing smoking urges, regardless of nicotine concentration

The Bacterial Community Structure and Microbial Activity in a Traditional Organic Milpa Farming System Under Different Soil Moisture Conditions

Agricultural practices affect the bacterial community structure, but how they determine the response of the bacterial community to drought, is still largely unknown. Conventional cultivated soil, i.e., inorganic fertilization, tillage, crop residue removal and maize (Zea mays L.) monoculture, and traditional organic farmed soil “milpa,” i.e., minimum tillage, rotation of maize, pumpkin (Cucurbita sp.) and beans (Phaseolus vulgaris L.) and organic fertilization were sampled. Both soils from the central highlands of Mexico were characterized and incubated aerobically at 5% field capacity (5%FC) and 100% field capacity (FC) for 45 days, while the C and N mineralization, enzyme activity and the bacterial community structure were monitored. After applying the different agricultural practices 3 years, the organic C content was 1.8-times larger in the milpa than in the conventional cultivated soil, the microbial biomass C 1.3-times, and C and N mineralization 2.0-times (mean for soil incubated at 5%FC and FC). The dehydrogenase, activity was significantly higher in the conventional cultivated soil than in the milpa soil when incubated at 5%FC, but not when incubated at FC. The relative abundance of Gemmatimonadetes was larger in the conventional cultivated soil than in the milpa soil in soil both at 5%FC and FC, while that of Bacteroidetes showed an opposite trend. The relative abundance of other groups, such as Nitrospirae and Proteobacteria, was affected by cultivation technique, but controlled by soil water content. The relative abundance of other groups, e.g., FBP, Gemmatimonadetes and Proteobacteria, was affected by water content, but the effect depended on agricultural practice. For soil incubated at FC, the xenobiotics biodegradation and metabolism related functions were higher in the milpa soil than in the conventional cultivated soil, and carbohydrate metabolism showed an opposite trend. It was found that agricultural practices and soil water content had a strong effect on soil characteristics, C and N mineralization, enzyme activity, and the bacterial community structure and its functionality. Decreases or increases in the relative abundance of bacterial groups when the soil water content decreased, i.e., from FC to 5%FC, was defined often by the cultivation technique, and the larger organic matter content in the milpa soil did not prevent large changes in the bacterial community structure when the soil was dried

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Research data supporting "Ureasil Organic-Inorganic Hybrids as Photoactive Waveguides for Conjugated Polyelectrolyte Luminescent Solar Concentrators"

The folder “Figure 2” contains the data for the steady-state optical properties of PBS-PFP-PDI in solution (water:1,4-dioxane (1:1(v/v)) and selected doped and undoped di- and tri-ureasil LSCs in the solid-state: (a) Absorption, excitation and emission spectra of PBS-PFP-PDI (10-6 mol dm-3, λex = 360 nm, λem = 660 nm). Emission spectra of (b) DU-CPE-0, (c) DU-CPE-08 and (d) TU-CPE-08 at different excitation wavelengths and excitation spectra of (e) DU-CPE-08 and (f) TU-CPE-08 at different emission wavelengths. The folder “Figure 3” contains the data for FTIR spectra and the corresponding Gaussian curve-fits of the amide I region of (a) DU-CPE-0 and (b) TU-CPE-0. The folder “Figure 4” contains the data for emission decay curves, the corresponding fits and the instrument response function (IRF) for PBS-PFP-PDI in solution (water/1,4-dioxane (1:1 (v/v)), DU-CPE-x and TU-CPE-x at selected excitation and emission wavelengths: (a) DU-CPE-0 and TU-CPE-0 (λex = 370 nm, λem = 420 and 500 nm). (b) PBS-PFP-PDI and DU-CPE-x (λex = 370 nm, λem = 420 nm) and (c) PBS-PFP-PDI and TU-CPE-x (λex = 370 nm, λem = 420 nm). As well as the weighted residuals for each fit. The folder “Figure 5” contains the data for (a) optical power spectra of DU-CPE-0, DU-CPE-08, TU-CPE-0, TU-CPE-08 with a dark absorbing background and (e) optical power spectra of DU-LR305 and DU-PBS-LR305 with a dark absorbing background. The folder “Figure S1” contains the data for the excitation spectra of (a) DU-CPE-0 and (c) TU-CPE-0 at different emission wavelengths and the emission spectra of (b) TU-CPE-0 at different excitation wavelengths. The folder “Figure S2” contains the data for the emission spectra of (a) DU-CPE-02, (b) DU-CPE-04, (c) TU-CPE-02 and (d) TU-CPE-04 at different excitation wavelengths. The folder “Figure S3” contains the data for the excitation spectra of (a) DU-CPE-02, (b) DU-CPE-04, (c) TU-CPE-02 and (d) TU-CPE-04 at different emission wavelengths. The folder “Figure S4” contains data for the FTIR spectra and the corresponding Gaussian curve0fits of the Amide I region of (a) DU-CPE-02, (b) DU-CPE-04, (c) DU-CPE-08, (d) TU-CPE-02, (e) TU-CPE-04 and (f) TU-CPE-08 The folder “Figure S5” contains data for the emission decay curve and the corresponding fir for TU-CPE-08 (λex = 466 nm and λem = 600 nm). The fitted decay times, weighted residuals and the instrument response function are also shown. The folder “Figure S6” contains data for the emission decay curves and the corresponding fits for a (a) DU-CPE-02 (b) DU-CPE-04 and (c) DU-CPE-08 upon excitation at 370 nm (λem = 500 nm). The fitted decay times, weighted residuals and the instrument response function are also shown. The folder “Figure S7” contains data for the emission decay curves and the corresponding fits for (a) TU-CPE-02, (b) TU-CPE-04 and (c) TU-CPE-08 upon excitation at 370 nm (λem = 500 nm). The fitted decay times, weighted residuals and the instrument response function are also shown. The folder “Figure S8” contains the data for the optical pwer spectrum of the solar simulator (AM1.5G)

Characterizing friction for fiber reinforced composites manufacturing: Method development and effect of process parameters

In automated layup manufacturing processes of fiber-reinforced polymer composites, the quality of the manufactured part is strongly dependent on frictional behavior. Improper control of frictional forces can lead to defect formation. Frictional sliding rheometry tests provide an innovative methodology to accurately characterize the tool-ply friction of unidirectional (UD) prepreg employing unique annular plate geometries. The effect of processing parameters (temperature, velocity, and normal force) on the frictional response of a carbon fiber prepreg was studied. Moreover, utilizing custom designed plate geometries coupled with optically transparent fixtures allowed for in-situ quantification of the prepreg-rigid surface contact area along with simultaneous characterization of the process parameter-dependent frictional mechanisms. Our findings highlight the reduction in frictional forces with increasing temperature, attributed to the increased resin flowability, while increases in sliding rates resulted in a pronounced increase in the frictional forces. The effect of applied load on the frictional characteristics was more complicated due to contributions from both the adhesive and normal forces. Finally, the results were interpreted in light of the contact area measurements performed at different temperatures, normal force, and sliding rate

Automatic quantification of white matter hyperintensities on T2-weighted fluid attenuated inversion recovery magnetic resonance imaging

https://pubmed.ncbi.nlm.nih.gov/34662699/White matter hyperintensities (WMH) are areas of increased signal visualized on T2-weighted fluid attenuated inversion recovery (FLAIR) brain magnetic resonance imaging (MRI) sequences. They are typically attributed to small vessel cerebrovascular disease in the context of aging. Among older adults, WMH are associated with risk of cognitive decline and dementia, stroke, and various other health outcomes. There has been increasing interest in incorporating quantitative WMH measurement as outcomes in clinical trials, observational research, and clinical settings. Here, we present a novel, fully automated, unsupervised detection algorithm for WMH segmentation and quantification. The algorithm uses a robust preprocessing pipeline, including brain extraction and a sample-specific mask that incorporates spatial information for automatic false positive reduction, and a half Gaussian mixture model (HGMM). The method was evaluated in 24 participants with varying degrees of WMH (4.9-78.6 cm3) from a community-based study of aging and dementia with dice coefficient, sensitivity, specificity, correlation, and bias relative to the ground truth manual segmentation approach performed by two expert raters. Results were compared with those derived from commonly used available WMH segmentation packages, including SPM lesion probability algorithm (LPA), SPM lesion growing algorithm (LGA), and Brain Intensity AbNormality Classification Algorithm (BIANCA). The HGMM algorithm derived WMH values that had a dice score of 0.87, sensitivity of 0.89, and specificity of 0.99 compared to ground truth. White matter hyperintensity volumes derived with HGMM were strongly correlated with ground truth values (r = 0.97, p = 3.9e-16), with no observable bias (-1.1 [-2.6, 0.44], p-value = 0.16). Our novel algorithm uniquely uses a robust preprocessing pipeline and a half-Gaussian mixture model to segment WMH with high agreement with ground truth for large scale studies of brain aging