Category: Nevin Manimala

J Exp Biol. 2021 Apr 1;224(7):jeb240994. doi: 10.1242/jeb.240994. Epub 2021 Apr 6.

ABSTRACT

Heat tolerance plasticity is predicted to be an important buffer against global warming. Nonetheless, basal heat tolerance often correlates negatively with tolerance plasticity (‘trade-off hypothesis’), a constraint that could limit plasticity benefits. We tested the trade-off hypothesis at the individual level with respect to heat hardening in two lizard species, Anolis carolinensis and Anolis sagrei. Heat hardening is a rapid increase in heat tolerance after heat shock that is rarely measured in reptiles but is generally considered to be a first line of physiological defense against heat. We also employed a biophysical model of operative habitat temperatures to estimate the performance consequences of hardening under ecologically relevant conditions. Anolis carolinensis hardened by 2 h post-heat shock and maintained hardening for several hours. However, A. sagrei did not harden. Biophysical models showed that hardening in A. carolinensis reduces their overheating risk in the field. Therefore, while not all lizards heat harden, hardening has benefits for species that can. We initially found a negative relationship between basal tolerance and hardening within both species, consistent with the trade-off hypothesis. However, permutation analyses showed that the apparent trade-offs could not be differentiated from statistical artifact. We found the same result when we re-analyzed published data supporting the trade-off hypothesis in another lizard species. Our results show that false positives may be common when testing the trade-off hypothesis. Statistical approaches that account for this are critical to ensure that the hypothesis, which has broad implications for thermal adaptation and responses to warming, is assessed appropriately.

PMID:34424976 | DOI:10.1242/jeb.240994

J Exp Biol. 2021 Apr 1;224(7):jeb233544. doi: 10.1242/jeb.233544. Epub 2021 Apr 15.

ABSTRACT

Dynamic body acceleration (DBA), measured through animal-attached tags, has emerged as a powerful method for estimating field metabolic rates of free-ranging individuals. Following respirometry to calibrate oxygen consumption rate (ṀO2) with DBA under controlled conditions, predictive models can be applied to DBA data collected from free-ranging individuals. However, laboratory calibrations are generally performed on a relatively narrow size range of animals, which may introduce biases if predictive models are applied to differently sized individuals in the field. Here, we tested the mass dependence of the ṀO2-DBA relationship to develop an experimental framework for the estimation of field metabolic rates when organisms differ in size. We performed respirometry experiments with individuals spanning one order of magnitude in body mass (1.74-17.15 kg) and used a two-stage modelling process to assess the intraspecific scale dependence of the ṀO2-DBA relationship and incorporate such dependencies into the coefficients of ṀO2 predictive models. The final predictive model showed scale dependence; the slope of the ṀO2-DBA relationship was strongly allometric (M1.55), whereas the intercept term scaled closer to isometry (M1.08). Using bootstrapping and simulations, we evaluated the performance of this coefficient-corrected model against commonly used methods of accounting for mass effects on the ṀO2-DBA relationship and found the lowest error and bias in the coefficient-corrected approach. The strong scale dependence of the ṀO2-DBA relationship indicates that caution must be exercised when models developed using one size class are applied to individuals of different sizes.

PMID:34424983 | DOI:10.1242/jeb.233544

Cereb Cortex. 2021 Aug 23:bhab284. doi: 10.1093/cercor/bhab284. Online ahead of print.

ABSTRACT

Autism spectrum disorder (ASD) is characterized by atypical connectivity lateralization of functional networks. However, previous studies have not directly investigated if differences in specialization between ASD and typically developing (TD) peers are present in infancy, leaving the timing of onset of these differences relatively unknown. We studied the hemispheric asymmetries of connectivity in children with ASD and infants later meeting the diagnostic criteria for ASD. Analyses were performed in 733 children with ASD and TD peers and in 71 infants at high risk (HR) or normal risk (NR) for ASD, with data collected at 1 month and 9 months of age. Comparing children with ASD (n = 301) to TDs (n = 432), four regions demonstrated group differences in connectivity: posterior cingulate cortex (PCC), posterior superior temporal gyrus, extrastriate cortex, and anterior prefrontal cortex. At 1 month, none of these regions exhibited group differences between ASD (n = 10), HR-nonASD (n = 15), or NR (n = 18) infants. However, by 9 months, the PCC and extrastriate exhibited atypical connectivity in ASD (n = 11) and HR-nonASD infants (n = 24) compared to NR infants (n = 22). Connectivity did not correlate with symptoms in either sample. Our results demonstrate that differences in network asymmetries associated with ASD risk are observable prior to the age of a reliable clinical diagnosis.

PMID:34424949 | DOI:10.1093/cercor/bhab284

J Exp Biol. 2021 Apr 1;224(7):jeb231712. doi: 10.1242/jeb.231712. Epub 2021 Apr 15.

ABSTRACT

Characterizing sex and species differences in muscle physiology can contribute to a better understanding of proximate mechanisms underlying behavioral evolution. In Xenopus, the laryngeal muscle’s ability to contract rapidly and its electromyogram potentiation allows males to produce calls that are more rapid and intensity-modulated than female calls. Prior comparative studies have shown that some species lacking typical male features of vocalizations sometimes show reduced sex differences in underlying laryngeal physiology. To further understand the evolution of sexually differentiated laryngeal muscle physiology and its role in generating behavior, we investigated sex differences in the laryngeal physiology of X. muelleri, a species in which male and female calls are similar in rapidity but different with respect to intensity modulation. We delivered ethologically relevant stimulus patterns to ex vivo X. muelleri larynges to investigate their ability to produce various call patterns, and we also delivered stimuli over a broader range of intervals to assess sex differences in muscle tension and electromyogram potentiation. We found a small but statistically significant sex difference in laryngeal electromyogram potentiation that varied depending on the number of stimuli. We also found a small interaction between sex and stimulus interval on muscle tension over an ethologically relevant range of stimulus intervals; male larynges were able to produce similar tensions to female larynges at slightly smaller (11-12 ms) inter-stimulus intervals. These findings are consistent with behavioral observations and present a previously undescribed intermediate sex difference in Xenopus laryngeal muscle physiology.

PMID:34424964 | DOI:10.1242/jeb.231712

PLoS One. 2021 Aug 23;16(8):e0256521. doi: 10.1371/journal.pone.0256521. eCollection 2021.

ABSTRACT

OBJECTIVE: To examine subgroup-specific treatment preferences and characteristics of patients with hemophilia A.

METHODS: Best-Worst Scaling (BWS) Case 3 (four attributes: application type; bleeding frequencies/year; inhibitor development risk; thromboembolic events of hemophilia A treatment risk) conducted via online survey. Respondents chose the best and the worst option of three treatment alternatives. Data were analyzed via latent class model (LCM), allowing capture of heterogeneity in the sample. Respondents were grouped into a predefined number of classes with distinct preferences.

RESULTS: The final dataset contained 57 respondents. LCM analysis segmented the sample into two classes with heterogeneous preferences. Preferences within each were homogeneous. For class 1, the most decisive factor was bleeding frequency/year. Respondents seemed to focus mainly on this in their choice decisions. With some distance, inhibitor development was the second most important. The remaining attributes were of far less importance for respondents in this class. Respondents in class 2 based their choice decisions primarily on inhibitor development, also followed, by some distance, the second most important attribute bleeding frequency/year. There was statistical significance (P < 0.05) between the number of annual bleedings and the probability of class membership.

CONCLUSIONS: The LCM analysis addresses heterogeneity in respondents’ choice decisions, which helps to tailor treatment alternatives to individual needs. Study results support clinical and allocative decision-making and improve the quality of interpretation of clinical data.

PMID:34424920 | DOI:10.1371/journal.pone.0256521

PLoS One. 2021 Aug 23;16(8):e0256235. doi: 10.1371/journal.pone.0256235. eCollection 2021.

ABSTRACT

BACKGROUND: Malnutrition contributes to children’s morbidity and mortality, and the situation undermines the economic growth and development of Bangladesh. Malnutrition is associated with lower levels of education that decrease economic productivity and leads to poverty. The global burden of malnutrition continues to be unacceptably high amid social and economic growth, including in Bangladesh. Therefore, identifying the factors associated with childhood malnutrition and poverty is necessary to stop the vicious cycle of malnutrition leaded poverty.

METHODS: The study utilized the 2017-18 Bangladesh Demographic and Health Survey (BDHS), accumulating 7,738 mother-child pairs. Associations between potential risk factors and nutritional status were determined using chi-square tests, and multivariate logistic regression models were utilized on significant risk factors to measure their odds ratio (OR) with their 95% confidence intervals (CI).

RESULTS: The prevalence of moderate and severe wasting was 7.0% and 1.8%, respectively, whereas the prevalence of moderate and severe stunting was 19.2% and 8.0%, while 16.4% and 3.6% of children were moderately and severely underweight. Children from the poorest and poor households were suffering from at least one form of malnutrition. Adjusted ORs were estimated by controlling socio-economic and demographic risk factors, such as poor maternal body mass index, parents’ lower education level, use of unhygienic toilet, child age in months, and recent experience of diarrhea and fever. The pattern was almost similar for each malnutrition status (i.e., stunting, underweight, and wasting) in the poorest and poor households.

CONCLUSION: Bangladesh achieved the Millennium Development Goals, focusing primarily on health-related indicators and working to achieve the Sustainable Development Goals. Even considering this success, the prevalence of malnutrition and poverty in same household remains relatively high compared to other developing countries. Therefore, the study recommends the implementation of nationwide systematic measures to prevent poverty and malnutrition.

PMID:34424928 | DOI:10.1371/journal.pone.0256235

PLoS One. 2021 Aug 23;16(8):e0243595. doi: 10.1371/journal.pone.0243595. eCollection 2021.

ABSTRACT

Currently, the population dynamics of preclonal cancer cells before clonal expansion of tumors has not been sufficiently addressed thus far. By focusing on preclonal cancer cell population as a Darwinian evolutionary system, we formulated and analyzed the observed mutation frequency among tumors (MFaT) as a proxy for the hypothesized sequence read frequency and beneficial fitness effect of a cancer driver mutation. Analogous to intestinal crypts, we assumed that sample donor patients are separate culture tanks where proliferating cells follow certain population dynamics described by extreme value theory (EVT). To validate this, we analyzed three large-scale cancer genome datasets, each harboring > 10000 tumor samples and in total involving > 177898 observed mutation sites. We clarified the necessary premises for the application of EVT in the strong selection and weak mutation (SSWM) regime in relation to cancer genome sequences at scale. We also confirmed that the stochastic distribution of MFaT is likely of the Fréchet type, which challenges the well-known Gumbel hypothesis of beneficial fitness effects. Based on statistical data analysis, we demonstrated the potential of EVT as a population genetics framework to understand and explain the stochastic behavior of driver-mutation frequency in cancer genomes as well as its applicability in real cancer genome sequence data.

PMID:34424899 | DOI:10.1371/journal.pone.0243595

PLoS One. 2021 Aug 23;16(8):e0256496. doi: 10.1371/journal.pone.0256496. eCollection 2021.

ABSTRACT

BACKGROUND: While vaccines ensure individual protection against COVID-19 infection, delay in receipt or refusal of vaccines will have both individual and community impacts. The behavioral factors of vaccine hesitancy or refusal are a crucial dimension that need to be understood in order to design appropriate interventions. The aim of this study was to explore the behavioral determinants of COVID-19 vaccine acceptance and to provide recommendations to increase the acceptance and uptake of COVID-19 vaccines in Bangladesh.

METHODS: We employed a Barrier Analysis (BA) approach to examine twelve potential behavioral determinants (drawn from the Health Belief Model [HBM] and Theory of Reasoned Action [TRA]) of intended vaccine acceptance. We conducted 45 interviews with those who intended to take the vaccine (Acceptors) and another 45 interviews with those who did not have that intention (Non-acceptors). We performed data analysis to find statistically significant differences and to identify which beliefs were most highly associated with acceptance and non-acceptance with COVID-19 vaccines.

RESULTS: The behavioral determinants associated with COVID-19 vaccine acceptance in Dhaka included perceived social norms, perceived safety of COVID-19 vaccines and trust in them, perceived risk/susceptibility, perceived self-efficacy, perceived positive and negative consequences, perceived action efficacy, perceived severity of COVID-19, access, and perceived divine will. In line with the HBM, beliefs about the disease itself were highly predictive of vaccine acceptance, and some of the strongest statistically-significant (p<0.001) predictors of vaccine acceptance in this population are beliefs around both injunctive and descriptive social norms. Specifically, Acceptors were 3.2 times more likely to say they would be very likely to get a COVID-19 vaccine if a doctor or nurse recommended it, twice as likely to say that most people they know will get a vaccine, and 1.3 times more likely to say that most close family and friends will get a vaccine. The perceived safety of vaccines was found to be important since Non-acceptors were 1.8 times more likely to say that COVID-19 vaccines are “not safe at all”. Beliefs about one’s risk of getting COVID-19 disease and the severity of it were predictive of being a vaccine acceptor: Acceptors were 1.4 times more likely to say that it was very likely that someone in their household would get COVID-19, 1.3 times more likely to say that they were very concerned about getting COVID-19, and 1.3 times more likely to say that it would be very serious if someone in their household contracted COVID-19. Other responses of Acceptors on what makes immunization easier may be helpful in programming to boost acceptance, such as providing vaccination through government health facilities, schools, and kiosks, and having vaccinators maintain proper COVID-19 health and safety protocols.

CONCLUSION: An effective behavior change strategy for COVID-19 vaccines uptake will need to address multiple beliefs and behavioral determinants, reducing barriers and leveraging enablers identified in this study. National plans for promoting COVID-19 vaccination should address the barriers, enablers, and behavioral determinants found in this study in order to maximize the impact on COVID-19 vaccination acceptance.

PMID:34424913 | DOI:10.1371/journal.pone.0256496

PLoS Comput Biol. 2021 Aug 23;17(8):e1009303. doi: 10.1371/journal.pcbi.1009303. Online ahead of print.

ABSTRACT

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g. wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures, as well as knowledge of the temporal and spatial properties of the data. Thus, interpreting biomedical sensor data needs to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. This framework corrects for covariates to provide accurate assessments of the significance of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity and also compare the performance to other models. Specifically, we show how the framework is able to evaluate an exercise intervention’s effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.

PMID:34424894 | DOI:10.1371/journal.pcbi.1009303

Acta Otolaryngol. 2021 Aug 23:1-4. doi: 10.1080/00016489.2021.1963473. Online ahead of print.

ABSTRACT

BACKGROUND: Mastoid portion of the facial nerve plays an important role in the round window approach of cochlear implantation.

OBJECTIVES: This study aimed to predict the anterior displacement of the mastoid portion of the facial nerve in the preoperative HRCT coronal cuts. We also aimed to detect the implication of anterior displacement of MPFN on the R.W. accessibility through the posterior tympanotomy during cochlear implantation.

MATERIALS AND METHODS: It was a retrospective observational cohort study in tertiary referral hospitals. We included 246 pediatric patients who underwent cochlear implantation due to bilateral severe to profound SNHL through a posterior tympanotomy approach.

RESULTS: Type I MPFN was present in 84 cases, type II MPFN was present in 149 patients, and type III MPFN was present in 13 cases. R.W. was inaccessible in 3 cases with MPFN type II and in 11 subjects with MPFN type III. There was a statistically significant difference regarding the R.W. accessibility between the three types of MPFN (p-value <.05). There was a strong statistically significant correlation between R.W. accessibility and the radiological type of the MPFN.

CONCLUSION: Mandour radiological classification of the mastoid portion of the facial nerve in the preoperative HRCT coronal offers an easily applicable method to detect the anterior displacement of the facial nerve by using easy and well-known landmarks. This classification can also predict R.W. accessibility through posterior tympanotomy during cochlear implantation with 97.97% accuracy.

PMID:34424819 | DOI:10.1080/00016489.2021.1963473