Category: Nevin Manimala

Cad Saude Publica. 2021 Dec 1;37(11):e00224220. doi: 10.1590/0102-311X00224220. eCollection 2021.

ABSTRACT

This study aims to investigate whether the intersectional identities defined by race/skin color and gender are associated with smoking and excessive consumption of alcohol in a representative sample of Brazilian adults. This is a cross-sectional study with 48,234 participants in the Brazilian National Health Survey (PNS) – 2013. Crude and adjusted odds ratios (OR) and respective 95% confidence intervals (95%CI) were used to estimate the associations of intersectional categories of race/skin color and gender (white woman, brown woman, black woman, white man, brown man, black man) with smoking and excessive consumption of alcohol, based on the combination of weekly “days” and “servings”. The prevalence of smoking varied from 10.6% for white women to 23.1% for black men, while the prevalence of elevated consumption of alcohol ranged from 3.3% to 14%, respectively. In comparison to white women, only white, brown, and black men presented greater chances of smoking, reaching the OR of 2.04 (95%CI: 1.66-2.51) in black men. As to excessive consumption of alcohol, all intersectional categories showed greater chances of consumption than white women, with the greatest magnitude in black men (OR = 4.78; 95%CI: 3.66-6.23). These associations maintained statistical significance after adjustments made for sociodemographic, behavioral, and health characteristics. Results demonstrated differences in smoking habit and excessive consumption of alcohol when the intersectional categories were compared to traditional analyses. These findings reinforce the significance of including intersectionality of race/skin color and gender in epidemiological studies.

PMID:34877990 | DOI:10.1590/0102-311X00224220

J Appl Oral Sci. 2021 Dec 1;29:e20210290. doi: 10.1590/1678-7757-2021-0290. eCollection 2021.

ABSTRACT

OBJECTIVE: Non-human teeth have been commonly used in research as replacements for human teeth, and potential dissimilarities between the dental tissues should be considered when interpreting the outcomes. To compare the proteolytic activity and degradation rate of bovine and human dentin matrices.

METHODOLOGY: Dentin beam specimens were obtained from human molars (n=30) and bovine incisors (n=30). The beams were weighed hydrated and after complete dehydration to obtain the mineralized wet and dry masses. Then, the beams were demineralized in 10 wt% phosphoric acid. Next, 15 beams from each substrate were randomly selected and again dehydrated and weighed to obtain the initial demineralized dry mass (DM). Then, the beams were stored in saliva-like buffer solution (SLBS) for 7, 14 and 21 days. SLBS was used to evaluate hydroxyproline (HYP) release after each storage period. The remaining beams of each substrate (n=15) were tested for initial MMP activity using a colorimetric assay and then also stored in SLBS. DM and MMP activity were reassessed after 7, 14 and 21 days of incubation. The data were subjected to two-way ANOVA tests with repeated measures complemented by Bonferroni’s tests. Unpaired two-tailed t-tests were also used (p<0.05).

RESULTS: Similar water and inorganic fractions were found in human and bovine dentin, while human dentin had a higher protein content. The most intense proteolytic activity and matrix deterioration occurred short after dentin was demineralized. Both substrates exhibited a sharp reduction in MMP activity after seven days of incubation. Although human dentin had higher MMP activity levels, greater HYP release and DM loss after seven days than bovine dentin, after 14 and 21 days, the outcomes were not statistically different.

CONCLUSION: Bovine dentin is a suitable substrate for long-term studies involving the degradation of dentin matrices.

PMID:34878005 | DOI:10.1590/1678-7757-2021-0290

Sci Rep. 2021 Dec 7;11(1):23516. doi: 10.1038/s41598-021-02205-2.

ABSTRACT

Throughout the world, livestock predation by mammalian carnivores causes significant economic losses to poor farmers, and leads to human-wildlife conflicts. These conflicts result in a negative attitude towards carnivore conservation and often trigger retaliatory killing. In northern Pakistan, we investigated livestock depredation by large carnivores between 2014 and 2019, and subsequent Human-wildlife conflict, through questionnaire-based surveys (n = 100 households). We used a semi-structured questionnaire to collect data on livestock population, depredation patterns, predation count, and conservation approaches. We found a statistically significant increasing pattern of predation with influential factors such as age, gender, occupation, education of respondents, population of predators, threats index for predators and conservation efforts. Some 310 livestock heads with an average of 51 animals per year out of the total 9273 heads were killed by predators, and among them 168 (54%) were attributed to the wolf and 142 (45.8%) to snow leopard. Major threats to carnivores in the area included retaliatory killing, habitat destruction and climate change. Incentivization against depredation losses, guarded grazing and construction of predator-proof corral may reduce Human-wildlife conflict and both livelihood and predator can be safeguarded in the study area.

PMID:34876595 | DOI:10.1038/s41598-021-02205-2

Sci Rep. 2021 Dec 7;11(1):23571. doi: 10.1038/s41598-021-02676-3.

ABSTRACT

Traditional multivariate statistical-based process monitoring (MSPM) methods are effective data-driven approaches for monitoring large-scale industrial processes, but have a shortcoming in handling the redundant correlations between process variables. To address this shortcoming, this study proposes a new MSPM method called minimalist module analysis (MMA). MMA divides process data into several different minimalist modules and one more independent module. All variables in the minimalist module are strongly correlated, and no redundant variables exist; therefore, the extracted feature components in one minimalist module will not be disturbed by noise from the other modules. This study also proposes new monitoring indices and a fault localization strategy for MMA, and simulation tests demonstrate that MMA achieves superior performance in fault detection and localization.

PMID:34876575 | DOI:10.1038/s41598-021-02676-3

Nat Commun. 2021 Dec 7;12(1):7090. doi: 10.1038/s41467-021-27382-6.

ABSTRACT

The understanding of turbulent flows is one of the biggest current challenges in physics, as no first-principles theory exists to explain their observed spatio-temporal intermittency. Turbulent flows may be regarded as an intricate collection of mutually-interacting vortices. This picture becomes accurate in quantum turbulence, which is built on tangles of discrete vortex filaments. Here, we study the statistics of velocity circulation in quantum and classical turbulence. We show that, in quantum flows, Kolmogorov turbulence emerges from the correlation of vortex orientations, while deviations-associated with intermittency-originate from their non-trivial spatial arrangement. We then link the spatial distribution of vortices in quantum turbulence to the coarse-grained energy dissipation in classical turbulence, enabling the application of existent models of classical turbulence intermittency to the quantum case. Our results provide a connection between the intermittency of quantum and classical turbulence and initiate a promising path to a better understanding of the latter.

PMID:34876584 | DOI:10.1038/s41467-021-27382-6

Proc Natl Acad Sci U S A. 2021 Dec 14;118(50):e2102140118. doi: 10.1073/pnas.2102140118.

ABSTRACT

The form of political polarization where citizens develop strongly negative attitudes toward out-party members and policies has become increasingly prominent across many democracies. Economic hardship and social inequality, as well as intergroup and racial conflict, have been identified as important contributing factors to this phenomenon known as “affective polarization.” Research shows that partisan animosities are exacerbated when these interests and identities become aligned with existing party cleavages. In this paper, we use a model of cultural evolution to study how these forces combine to generate and maintain affective political polarization. We show that economic events can drive both affective polarization and the sorting of group identities along party lines, which, in turn, can magnify the effects of underlying inequality between those groups. But, on a more optimistic note, we show that sufficiently high levels of wealth redistribution through the provision of public goods can counteract this feedback and limit the rise of polarization. We test some of our key theoretical predictions using survey data on intergroup polarization, sorting of racial groups, and affective polarization in the United States over the past 50 y.

PMID:34876507 | DOI:10.1073/pnas.2102140118

BMJ Mil Health. 2021 Dec 7:e002006. doi: 10.1136/bmjmilitary-2021-002006. Online ahead of print.

NO ABSTRACT

PMID:34876478 | DOI:10.1136/bmjmilitary-2021-002006

Proc Natl Acad Sci U S A. 2021 Dec 14;118(50):e2021925118. doi: 10.1073/pnas.2021925118.

ABSTRACT

How can neural networks learn to efficiently represent complex and high-dimensional inputs via local plasticity mechanisms? Classical models of representation learning assume that feedforward weights are learned via pairwise Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity works only under unrealistic requirements on neural dynamics and input statistics. To overcome these limitations, we derive from first principles a learning scheme based on voltage-dependent synaptic plasticity rules. Here, recurrent connections learn to locally balance feedforward input in individual dendritic compartments and thereby can modulate synaptic plasticity to learn efficient representations. We demonstrate in simulations that this learning scheme works robustly even for complex high-dimensional inputs and with inhibitory transmission delays, where Hebbian-like plasticity fails. Our results draw a direct connection between dendritic excitatory-inhibitory balance and voltage-dependent synaptic plasticity as observed in vivo and suggest that both are crucial for representation learning.

PMID:34876505 | DOI:10.1073/pnas.2021925118

J Nucl Med Technol. 2021 Dec 7:jnmt.121.261998. doi: 10.2967/jnmt.121.261998. Online ahead of print.

ABSTRACT

Background and Aim: Discordance between histopathological grading and dual tracer PET-CT (⁶⁸Ga-DOTATATE and FDG) findings in neuroendocrine tumours (NETs), though not typical, can be encountered in real-world scenario. The aim of this study was to assess patients with discordance between WHO 2017 grade predicted molecular PET-CT imaging and the actual dual tracer PET-CT findings (by exploring their histopathological, immunohistochemical and molecular imaging characteristics), with a view to identifying the prognostic determinants effecting outcome in a peptide receptor radionuclide therapy (PRRT) set-up. Methods: Thirty six patients of histopathologically proven inoperable, locally advanced/metastatic NETs, referred for PRRT were included in this study. The cohort was divided into two broad population groups: (a) those with discordance (between WHO 2017 grade predicted molecular imaging and the dual tracer PET-CT findings) and (b) control (showing both FDG and ⁶⁸Ga-DOTATATE uptake). The cohort was divided based on dual tracer PET-CT into: (i) metabolically FDG non-avid and SSTR expressing tumors, (ii) metabolically active and non-⁶⁸Ga-DOTATATE concentrating (SSTR expressing) and (iii) matched imaging characteristics with WHO 2017 grading system (showing both FDG and ⁶⁸Ga-DOTATATE concentrating disease) for statistical analysis. Statistical analyses were done on SPSS 23.0. Descriptive statistics was used to analyze categorical data, multivariate analysis was used to assess the correlation between different variables with progression free survival (PFS) and overall survival (OS). Kaplan-Meier was used for survival analysis to calculate median survival and to analyze the survival based on WHO 2017 grading and dual tracer PET. Cox proportional hazards regression analysis was used to determine predictors of survival (OS and PFS). Results: In the entire cohort (n = 36), 24 patients (66.7%) showed discordance whereas 12 patients (33.3%) were in the control group. Among the patients showing discordance: 14 patients (38.9%) had metabolically inactive and SSTR expressing disease and remaining 10 patients (27.8%) had FDG concentrating and SSTR non-expressing disease. Those in the control group, 12 patients (33.3%) had intermediate grade NETs and showed matched (⁶⁸Ga-DOTATATE and FDG concentrating lesions) disease. Multivariate analysis in patients with discordant findings demonstrated significant correlation of dual tracer PET with overall survival while no significant correlation could be established between WHO grade and overall survival in the discordant subgroups. No significant correlation could be appreciated between PFS and either dual tracer PET or WHO grading. The Kaplan-Meier survival analysis and Cox proportional hazards regression analysis demonstrated dual tracer PET-CT imaging to be significant prognostic determinant and predictor of outcome respectively. Conclusion: In summary, in NET patients with discordance between the two parameters, dual tracer PET-CT with FDG and ⁶⁸Ga-DOTATATE performed better than WHO grading, differentiation status and immunohistochemistry in prognosticating and predicting outcome.

PMID:34876476 | DOI:10.2967/jnmt.121.261998

J Nucl Med Technol. 2021 Dec 7:jnmt.121.263081. doi: 10.2967/jnmt.121.263081. Online ahead of print.

ABSTRACT

Background: While normal ranges for ^99mTc thyroid percentage uptake vary, the seemingly intuitive evaluation of thyroid function does not reflect the complexity of thyroid pathology and biochemical status. The emergence of artificial intelligence (AI) in nuclear medicine has driven problem solving associated with logic and reasoning that warrant re-examination of established benchmarks in thyroid functional assessment. Methods: There were 123 patients retrospectively analysed in the study sample comparing scintigraphic findings to grounded truth established through biochemistry status. Conventional statistical approaches were used in conjunction with an artificial neural network (ANN) to determine predictors of thyroid function from data features. A convolutional neural network (CNN) was also used to extract features from the input tensor (images). Results: Analysis was confounded by sub-clinical hyperthyroidism, primary hypothyroidism, sub-clinical hypothyroidism and T3 toxicosis. Binary accuracy for identifying hyperthyroidism was highest for thyroid uptake classification using a threshold of 4.5% (82.6%), followed by pooled physician 6interpretation with the aid of uptake values (82.3%). Visual evaluation without quantitative values reduced accuracy to 61.0% for pooled physician determinations and 61.4% classifying on the basis of thyroid gland intensity relative to salivary glands. The machine learning (ML) algorithm produced 84.6% accuracy, however, this included biochemistry features not available to the semantic analysis. The deep learning (DL) algorithm had an accuracy of 80.5% based on image inputs alone. Conclusion: Thyroid scintigraphy is useful in identifying hyperthyroid patients suitable for radioiodine therapy when using an appropriately validated cut-off for the patient population (4.5% in this population). ML ANN algorithms can be developed to improve accuracy as second readers systems when biochemistry results are available. DL CNN algorithms can be developed to improve accuracy in the absence of biochemistry results. ML and DL do not displace the role of the physician in thyroid scintigraphy but could be used as second reader systems to minimize errors and increase confidence.

PMID:34876477 | DOI:10.2967/jnmt.121.263081