Structural Correlates of Personality Dimensions in Healthy Aging and MCI

The revised NEO Personality Inventory (NEOPI-R), popularly known as the five-factor model, defines five personality factors: Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness. The structural correlates of these personality factors are still a matter of debate. In this work, we examine the impact of subtle cognitive deficits on structural substrates of personality in the elderly using DTI derived white matter (WM) integrity measure, Fractional Anisotropy (FA). We employed canonical correlation analysis (CCA) to study the relationship between personality factors of the NEOPI-R and FA measures in two population groups: healthy controls and MCI. Agreeableness was the only personality factor to be associated with FA patterns in both groups. Openness was significantly related to FA data in the MCI group and the inverse was true for Conscientiousness. Furthermore, we generated saliency maps using bootstrapping strategy which revealed a larger number of positive correlations in healthy aging in contrast to the MCI status. The MCI group was found to be associated with a predominance of negative correlations indicating that higher Agreeableness and Openness scores were mostly related to lower FA values in interhemispheric and cortico-spinal tracts and a limited number of higher FA values in cortico-cortical and cortico-subcortical connection. Altogether these findings support the idea that WM microstructure may represent a valid correlate of personality dimensions and also indicate that the presence of early cognitive deficits led to substantial changes in the associations between WM integrity and personality factors

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

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

Trifluoromethyl substituted cyanostyrenes: fluorescent organogel fibrillar self-assemblies

by Jagadish Katla, Akshay J. M. Nair and Sriram Kanva

Utility of inter-frequency amplitude ratio of vestibular-evoked myogenic potentials in identifying meniere's disease : a systematic review and meta-analysis

Objectives: A recently devised parameter of vestibular-evoked myogenic potential (VEMP) based on the principles of frequency tuning is the inter-frequency amplitude ratio (IFAR). It refers to the ratio of the amplitude of 1000 Hz tone burst evoked VEMP to 500 Hz evoked tone burst. A pathology like Meniere's disease changes the frequency response and alters the frequency tuning of the otolith organs. Because IFAR is based on the principle of frequency tuning of VEMP, it is likely to help identify Meniere's disease. Few studies in the last decade have investigated the utility of IFAR in identifying Meniere's disease. However, a systematic review and a meta-analysis on IFAR in Meniere's disease are lacking. The present study investigates whether the IFAR of VEMP helps identify Meniere's disease and differentiates it from healthy ears and other vestibular pathologies. Design: The present study is a systematic review and a meta-analysis. The studies investigating the IFAR of cervical and ocular VEMPs in Meniere's disease, healthy controls, and other vestibular pathologies were searched across research databases such as PubMed, Science Direct, and Scopus. The search strategy was developed using the PICO (population, intervention, comparison, and outcomes) format, and Medical Subject Headings (MeSH) terms and Boolean operators were employed. The systematic review was performed using the Rayyan software, whereas the Review Manager software was used to carry out the meta-analysis. A total of 16,605 articles were retrieved from the databases. After the duplicate removal, 2472 articles remained. These were eliminated using title screening, abstract screening, and full-length inspections. A total of nine articles were found eligible for quality assessment and meta-analysis, and the New Castle-Ottawa Scale was used for quality assessment. After the data extraction, 24 six articles were found to have the desired data format for the meta-analysis. Results: The results showed significantly higher IFAR in the affected ears of individuals in the Meniere's disease group than in the control group's unaffected ears. There was no significant difference between the unaffected ears of individuals in the Meniere's disease group and the ears of the control group. The only study on Meniere's disease and benign paroxysmal positional vertigo found significantly larger ocular VEMP IFAR in ears with Meniere's disease than in benign paroxysmal positional vertigo. Conclusions: This systematic review and meta-analysis found IFAR efficient in differentiating Meniere's disease from healthy controls. We also found an enhanced IFAR as a potential marker for Meniere's disease. However, more investigations are needed to confirm the utility of an enhanced IFAR value in the exclusive identification of Meniere's disease

In-context learning in natural and artificial intelligence

In-context learning refers to the ability of a neural network to learn from information presented in its context. While traditional learning in neural networks requires adjusting network weights for every new task, in-context learning operates purely by updating internal activations without needing any updates to network weights. The emergence of this ability in large language models has led to a paradigm shift in machine learning and has forced researchers to reconceptualize how they think about learning in neural networks. Looking beyond language models, we can find in-context learning in many computational models relevant to cognitive science, including those that emerge from meta-learning

Zero-shot compositional reasoning in a reinforcement learning setting

People can easily evoke previously learned concepts, compose them, and apply the result to solve novel tasks on the first attempt. The aim of this paper is to improve our understanding of how people make such zero-shot compositional inferences in a reinforcement learning setting. To achieve this, we introduce an experimental paradigm where people learn two latent reward functions and need to compose them correctly to solve a novel task. We find that people have the capability to engage in zero-shot compositional reinforcement learning but deviate systematically from optimality. However, their mistakes are structured and can be explained by their performance in the sub-tasks leading up to the composition. Through extensive model-based analyses, we found that a meta-learned neural network model that accounts for limited computational resources best captures participants’ behaviour. Moreover, the amount of computational resources this model identified reliably quantifies how good individual participants are at zero-shot compositional reinforcement learning. Taken together, our work takes a considerable step towards studying compositional reasoning in agents – both natural and artificial – with limited computational resources

Artificial Bee Colony (ABC) based Variable Density Sampling Scheme for CS-MRI

The self-sustained dynamics of the bee population in nature is a result of their hierarchical working culture, efficient organizing skills and unique highly developed foraging ability, which enables them to interact effectively among each other as well as with their environment. In this paper, a novel algorithm utilizing the bee's swarm intelligence, and its heuristics based on quality and quantity of food sources (nectars) is proposed to generate a variable density sampling (VDS) scheme lOr compressive sampling (CS) based fast NMI data acquisition. The algorithm uses the scout-bees for global random selection process which is further fine-tuned by employed and onlooker-bees who forage locally in the neighborhood giving prime importance to points possessing high fitness values (or high energy) usually located around the center of k-space. The algorithm introduces the concept of searching for the high quality lOod sources in annular regions, called as bins, of varying widths. Retrospective CS-MRI simulations show that the proposed k-ABC based VDS scheme performs significantly better than other sampling schemes

Photophysical studies of pyrenyl cyanostyrenes: effect of trifluoromethyl substitution on gelation

α-Cyanostyrenes bearing a planar pyrene unit and electron withdrawing trifluoromethyl units were designed and synthesized. The conformational restriction due to the presence of the cyano group on the double bond favors aggregation induced emission in aqueous media. The styrylpyrenes aggregate to form microstructures influenced by π–π stacking, cyano and CF3 substituent interactions. Importantly confluence of the pyrene ring and simple trifluoromethyl (CF3) unit allows the formation of a stable organogel with bathochromic shifts in emission. The formation of aggregates and the gel was substantiated using 1H NMR spectroscopy and scanning electron microscopy. The stability of the gels was assessed using rheology investigations and rationalized by single crystal X-ray data.by Jagadish Kumar Katla, Abhijeet Ojha, Akshay J. M. Nair, Rangan Krishnan and Sriram Kanva