Surface density-of-states on semi-infinite topological photonic and acoustic crystals

Iterative Green's function, based on cyclic reduction of block tridiagonal matrices, has been the ideal algorithm, through tight-binding models, to compute the surface density-of-states of semi-infinite topological electronic materials. In this paper, we apply this method to photonic and acoustic crystals, using finite-element discretizations and a generalized eigenvalue formulation, to calculate the local density-of-states on a single surface of semi-infinite lattices. The three-dimensional (3D) examples of gapless helicoidal surface states in Weyl and Dirac crystals are shown and the computational cost, convergence and accuracy are analyzed.Comment: 7 pages, 4 figure

BAG5 Interacts with DJ-1 and Inhibits the Neuroprotective Effects of DJ-1 to Combat Mitochondrial Oxidative Damage

Loss-of-function mutations in gene encoding DJ-1 contribute to the pathogenesis of autosomal recessive early-onset familial forms of Parkinson’s disease (PD). DJ-1 is a multifunctional protein and plays a protective role against oxidative stress-induced mitochondrial damage and cell death, but the exact mechanism underlying this is not yet clearly understood. Here, using coimmunoprecipitation (Co-IP) and immunofluorescence methods, we prove that Bcl-2-associated athanogene 5 (BAG5), a BAG family member, interacts with DJ-1 in mammalian cells. Moreover, we show that BAG5 could decrease stability of DJ-1 and weaken its role in mitochondrial protection probably by influencing dimerization in stress condition. Our study reveals the relationship of BAG5 and DJ-1 suggesting a potential role for BAG5 in the pathogenesis of PD through its functional interactions with DJ-1

From hyper- to hypoinsulinemia and diabetes: effect of KCNH6 on insulin secretion

Glucose-stimulated insulin secretion from islet β cells is mediated by K channels. However, the role of non-K K channels in insulin secretion is largely unknown. Here, we show that a non-K K channel, KCNH6, plays a key role in insulin secretion and glucose hemostasis in humans and mice. KCNH6 p.P235L heterozygous mutation co-separated with diabetes in a four-generation pedigree. Kcnh6 knockout (KO) or Kcnh6 p.P235L knockin (KI) mice had a phenotype characterized by changing from hypoglycemia with hyperinsulinemia to hyperglycemia with insulin deficiency. Islets from the young KO mice had increased intracellular calcium concentration and increased insulin secretion. However, islets from the adult KO mice not only had increased intracellular calcium levels but also had remarkable ER stress and apoptosis, associated with loss of β cell mass and decreased insulin secretion. Therefore, dysfunction of KCNH6 causes overstimulation of insulin secretion in the short term and β cell failure in the long term.Yang et al. show that KCNH6 plays a key role in insulin secretion and glucose hemostasis in humans and mice. Dysfunction of KCNH6 results in a hyperinsulinemia phenotype in the short term and hypoinsulinemia and diabetes in the long term

The conscious processing of emotion in depression disorder: a meta-analysis of neuroimaging studies

BackgroundDepression is generally accompanied by a disturbed conscious processing of emotion, which manifests as a negative bias to facial/voice emotion information and a decreased accuracy in emotion recognition tasks. Several studies have proved that abnormal brain activation was responsible for the deficit function of conscious emotion recognition in depression. However, the altered brain activation related to the conscious processing of emotion in depression was incongruent among studies. Therefore, we conducted an activation likelihood estimation (ALE) analysis to better understand the underlying neurophysiological mechanism of conscious processing of emotion in depression.MethodElectronic databases were searched using the search terms “depression,” “emotion recognition,” and “neuroimaging” from inceptions to April 10th, 2023. We retrieved trials which explored the neuro-responses of depressive patients to explicit emotion recognition tasks. Two investigators independently performed literature selection, data extraction, and risk of bias assessment. The spatial consistency of brain activation in conscious facial expressions recognition was calculated using ALE. The robustness of the results was examined by Jackknife sensitivity analysis.ResultsWe retrieved 11,365 articles in total, 28 of which were included. In the overall analysis, we found increased activity in the middle temporal gyrus, superior temporal gyrus, parahippocampal gyrus, and cuneus, and decreased activity in the superior temporal gyrus, inferior parietal lobule, insula, and superior frontal gyrus. In response to positive stimuli, depressive patients showed hyperactivity in the medial frontal gyrus, middle temporal gyrus, and insula (uncorrected p < 0.001). When receiving negative stimuli, a higher activation was found in the precentral gyrus, middle frontal gyrus, precuneus, and superior temporal gyrus (uncorrected p < 0.001).ConclusionAmong depressive patients, a broad spectrum of brain areas was involved in a deficit of conscious emotion processing. The activation of brain regions was different in response to positive or negative stimuli. Due to potential clinical heterogeneity, the findings should be treated with caution.Systematic review registrationhttps://inplasy.com/inplasy-2022-11-0057/, identifier: 2022110057

Coinfection with influenza virus and non-typeable Haemophilus influenzae aggregates inflammatory lung injury and alters gut microbiota in COPD mice

BackgroundAcute exacerbation of chronic obstructive pulmonary disease (AECOPD) is associated with high mortality rates. Viral and bacterial coinfection is the primary cause of AECOPD. How coinfection with these microbes influences host inflammatory response and the gut microbiota composition is not entirely understood.MethodsWe developed a mouse model of AECOPD by cigarette smoke exposure and sequential infection with influenza H1N1 virus and non-typeable Haemophilus influenzae (NTHi). Viral and bacterial titer was determined using MDCK cells and chocolate agar plates, respectively. The levels of cytokines, adhesion molecules, and inflammatory cells in the lungs were measured using Bio-Plex and flow cytometry assays. Gut microbiota was analyzed using 16S rRNA gene sequencing. Correlations between cytokines and gut microbiota were determined using Spearman’s rank correlation coefficient test.ResultsCoinfection with H1N1 and NTHi resulted in more severe lung injury, higher mortality, declined lung function in COPD mice. H1N1 enhanced NTHi growth in the lungs, but NTHi had no effect on H1N1. In addition, coinfection increased the levels of cytokines and adhesion molecules, as well as immune cells including total and M1 macrophages, neutrophils, monocytes, NK cells, and CD4 + T cells. In contrast, alveolar macrophages were depleted. Furthermore, coinfection caused a decline in the diversity of gut bacteria. Muribaculaceae, Lactobacillus, Akkermansia, Lachnospiraceae, and Rikenella were further found to be negatively correlated with cytokine levels, whereas Bacteroides was positively correlated.ConclusionCoinfection with H1N1 and NTHi causes a deterioration in COPD mice due to increased lung inflammation, which is correlated with dysbiosis of the gut microbiota

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

When the lowest energy does not induce native structures: parallel minimization of multi-energy values by hybridizing searching intelligences.

BACKGROUND: Protein structure prediction (PSP), which is usually modeled as a computational optimization problem, remains one of the biggest challenges in computational biology. PSP encounters two difficult obstacles: the inaccurate energy function problem and the searching problem. Even if the lowest energy has been luckily found by the searching procedure, the correct protein structures are not guaranteed to obtain. RESULTS: A general parallel metaheuristic approach is presented to tackle the above two problems. Multi-energy functions are employed to simultaneously guide the parallel searching threads. Searching trajectories are in fact controlled by the parameters of heuristic algorithms. The parallel approach allows the parameters to be perturbed during the searching threads are running in parallel, while each thread is searching the lowest energy value determined by an individual energy function. By hybridizing the intelligences of parallel ant colonies and Monte Carlo Metropolis search, this paper demonstrates an implementation of our parallel approach for PSP. 16 classical instances were tested to show that the parallel approach is competitive for solving PSP problem. CONCLUSIONS: This parallel approach combines various sources of both searching intelligences and energy functions, and thus predicts protein conformations with good quality jointly determined by all the parallel searching threads and energy functions. It provides a framework to combine different searching intelligence embedded in heuristic algorithms. It also constructs a container to hybridize different not-so-accurate objective functions which are usually derived from the domain expertise