Nevin Manimala

Ecol Appl. 2021 Jul 13:e02408. doi: 10.1002/eap.2408. Online ahead of print.

ABSTRACT

Estimating bird and bat fatalities caused by wind-turbine facilities is challenging when carcasses are rare and produce counts that are either exactly or very near zero. The rarity of found carcasses is exacerbated when live members of a particular species are rare and when carcasses degrade quickly, are removed by scavengers, or are not detected by observers. With few observed carcass counts, common statistical methods like logistic, Poisson, or negative binomial regression are unreliable (statistically biased) and often fail to provide answers (i.e., fail to converge). Here, we propose a binomial N-mixture model that estimates fatality rates as well as the total number of carcasses when rates are expanded. Our model extends the ‘evidence of absence’ model (Huso et al., 2015; Dalthorp, Huso, and Dail, 2017) by relating carcass deposition rates to study covariates and by incorporating terms that naturally scale counts from facilities of different sizes. Our model, which we call Evidence of Absence Regression (EoAR), can estimate the total number of birds or bats killed at a single wind energy facility or a fleet of wind energy facilities based on covariate values. Furthermore, with accurate prior distributions the model’s results are extremely robust to sparse data and unobserved combinations of covariate values. In this paper, we describe the model, show its low bias and high precision via computer simulation, and apply it to bat carcass counts observed at 21 wind energy facilities in Iowa.

PMID:34256420 | DOI:10.1002/eap.2408

Physiol Meas. 2021 Jul 13. doi: 10.1088/1361-6579/ac13ce. Online ahead of print.

ABSTRACT

OBJECTIVE: There is emerging evidence that analysing the entropy and complexity of biomedical signals can detect underlying changes in physiology which may be reflective of disease pathology. This approach can be used even when only short recordings of biomedical signals are available. This study aimed to determine whether entropy and complexity measures can detect differences between subjects with Parkinson’s disease and healthy controls.

APPROACH: A method based on a diagram of entropy versus complexity, named complexity-entropy plane, was used to re-analyse a dataset of cerebral haemodynamic signals from subjects with Parkinson’s disease and healthy controls obtained under poikilocapnic conditions. A probability distribution for a set of ordinal patterns, designed to capture regularities in a time series, was computed from each signal under analysis. Four types of entropy and ten types of complexity measures were estimated from these distributions. Mean values of entropy and complexity were compared and their classification power was assessed by evaluating the best linear separator on the corresponding complexity-entropy planes.

MAIN RESULTS: Few linear separators obtained significantly better classification, evaluated as the area under the receiver operating characteristic curve, than signal mean values. However, significant differences in both entropy and complexity were detected between the groups of participants.

SIGNIFICANCE: Measures of entropy and complexity were able to detect differences between healthy volunteers and subjects with Parkinson’s disease, in poikilocapnic conditions, even though only short recordings were available for analysis. Further work is needed to refine this promising approach, and to help understand the findings in the context of specific pathophysiological changes.

PMID:34256359 | DOI:10.1088/1361-6579/ac13ce

Phys Med Biol. 2021 Jul 13. doi: 10.1088/1361-6560/ac13fe. Online ahead of print.

ABSTRACT

Conventional PET image reconstruction is achieved by the statistical iterative method. Deep learning provides another opportunity for speeding up the image reconstruction process. However, conventional deep learning-based image reconstruction requires a fully connected network for learning the Radon transform. The use of fully connected networks greatly complicated the network and increased hardware cost. In this study, we proposed a novel deep learning-based image reconstruction method by utilizing the DIRECT data partitioning method. The U-net structure with only convolutional layers was used in our approach. Patch-based model training and testing were used to achieve 3D reconstructions within current hardware limitations. TOF histoimages were first generated from the listmode data to replace conventional sinograms. Different projection angles were used as different channels in the input. A total of 15 patient data were used in this study. For each patient, the dynamic whole-body scanning protocol was used to expand the training dataset and a total of 372 separate scans were included. The leave-one-patient-out validation method was used. Two separate studies were carried out. In the first study, the measured TOF histoimages were directly used for model training and testing, to study the performance of the method in real-world applications. In the second study, TOF histoimages were simulated from already reconstructed images to exclude the scatters, randoms, attenuation activity mismatch effects. This study was used to evaluate the optimal performance when all other corrections are ideal. Volumes of interests (VOI) were placed in the liver and lesion region to study image noise and lesion quantitations. The reconstructed images using the proposed deep learning method showed similar image quality when compared with the conventional EM approach. A minimal difference was observed when the simulated TOF histoimages were used as model input and testing, suggesting the deep learning model can indeed learn the reconstruction process. Some quantitative difference was observed when the measured TOF histoimages were used. The two studies suggested that the major difference is caused by inaccurate corrections performed by the network itself, which indicated that physics-based corrections are still required for better quantitative performance. In conclusion, we have proposed a novel deep learning-based image reconstruction method for TOF PET. With the help of the DIRECT data partitioning method, no fully connected layers were used and 3D image reconstruction can be directly achieved within the limits of the current hardware.

PMID:34256356 | DOI:10.1088/1361-6560/ac13fe

Epilepsy Behav. 2021 Jul 10;122:108194. doi: 10.1016/j.yebeh.2021.108194. Online ahead of print.

ABSTRACT

BACKGROUND: Epilepsy is associated with a high prevalence of sleep disturbance. However, population-based studies on the burden of sleep disturbance in people with epilepsy are limited. This study assessed sleep duration and sleep quality by epilepsy status in the general U.S. adult population aged ≥ 18 years.

METHODS: We pooled data of cross-sectional National Health Interview Surveys in 2013, 2015, and 2017 to compare the prevalence of sleep duration and quality among those without epilepsy (N = 93,126) with those with any epilepsy (a history of physician-diagnosed epilepsy) (N = 1774), those with active epilepsy (those with a history of physician-diagnosed epilepsy who were currently taking medication to control it, had one or more seizures in the past year, or both) (N = 1101), and those with inactive epilepsy (those with a history of physician-diagnosed epilepsy who were neither taking medication for epilepsy nor had had a seizure in the past year) (N = 673). We also compared these measures between those with active and those with inactive epilepsy. The prevalences were adjusted for sociodemographics, behaviors, and health covariates, with multivariable logistic regression. We used Z-tests to compare prevalences of sleep duration and quality at the statistical significance level of 0.05.

RESULTS: Adults with any epilepsy reported significantly higher adjusted prevalences of short sleep duration (<7 h) (36.0% vs. 31.8%) and long sleep duration (>9 h per day) (6.7% vs. 3.7%) but a lower prevalence of healthy sleep duration (7-9 h per day) (57.4% vs.64.6%) than those without epilepsy. In the past week, adults with any epilepsy reported significantly higher adjusted prevalences than adults without epilepsy of having trouble falling asleep (25.0% vs. 20.3%), staying asleep (34.4% vs. 26.3%), nonrestorative sleep (adults did not wake up feeling well rested) (≥3days) (50.3% vs. 44.3%), and taking medication to help themselves fall asleep or stay asleep (≥1 times) (20.9% vs. 13.5%). However, adults with active epilepsy did not differ from adults with inactive epilepsy with respect to these sleep duration and quality measures.

CONCLUSIONS: Adults with epilepsy reported more short or long sleep duration and worse sleep quality than those without epilepsy. Neither seizure occurrence nor antiepileptic drug use accounted for these differences in sleep duration and quality. Careful screening for sleep complaints as well as identifying and intervening on the modifiable risk factors associated with sleep disturbances among people with epilepsy could improve epilepsy outcomes and quality of life.

PMID:34256341 | DOI:10.1016/j.yebeh.2021.108194

Parkinsonism Relat Disord. 2021 Jul 8;89:90-92. doi: 10.1016/j.parkreldis.2021.07.002. Online ahead of print.

ABSTRACT

BACKGROUND: Parkinson’s disease (PD) patients may be at increased risk of Covid-19 mortality due to the nature of their disease or underlying conditions.

METHOD: The information of 12,909 Covid-19 patients who were hospitalized during the last eleven months were collected from the data depository of two referral university hospitals. Eighty-seven of these patients were diagnosed with PD, and thirty-one of these PD patients died because of Covid-19. 2132 other deaths occurred in these centers, related to Covid-19 of non-PD patients. Fisher exact test, Chi-square test, and Principle component analysis were used for statistical analysis.

RESULTS: The mortality among PD patients and other hospitalized patients was 35.6% and 19.8%, respectively, and the difference between the mortality of these two groups was found to be statistically significant (p-value<0.01). The mean age of PD patients who passed away was 77.06 ± 7.46, and it was not significantly different from that of alive PD patients (p-value>0.05). Alzheimer’s disease as an underlying condition was more frequent in deceased PD patients in comparison to survived PD patients, and this difference was found to be statistically significant (p-value<0.01).

CONCLUSION: PD patients possess a higher rate of Covid-19 mortality in comparison with other patients hospitalized for Covid-19. PD pathophysiology, advanced age, underlying conditions, and health systems’ efficacy may play an essential role in such an outcome.

PMID:34256334 | DOI:10.1016/j.parkreldis.2021.07.002

Early Hum Dev. 2021 Jul 4;160:105421. doi: 10.1016/j.earlhumdev.2021.105421. Online ahead of print.

ABSTRACT

BACKGROUND: Weight gain in the first week of life is indicative of fluid excess in preterm neonates.

AIMS: To determine if morbidity and/or mortality of extremely low birthweight (ELBW) infants was lower in those who did not have excess weight gain in the first week of life, compared with those who did.

STUDY DESIGN: Retrospective cohort study.

SUBJECTS: ELBW infants born from 1st May 2014 – 31st May 2019.

EXCLUSIONS: major congenital abnormalities (including hydrops), died within the first 7 days, no recorded weight on day 6, 7 or 8.

OUTCOME MEASURES: We compared infants whose weight was greater than birthweight by day 7 and infants whose weight remained at, or below, birthweight by day 7.

RESULTS: There were 312 ELBW infants in the study population: 15 (5%) died before discharge from hospital. Holding birthweight and gestational age (GA) constant, the odds of death in neonates with day 7 weight >birthweight was about 3 times the odds of death in neonates with day 7 weight ≤birthweight (adjusted odds ratio 3.18, 95% confidence interval 0.66-15.26, p = 0.15). Neonates with day 7 weight >birthweight were more likely to have had a PDA that required treatment than those with day 7 weight ≤birthweight (65% versus 43% respectively; p <0.001).

CONCLUSIONS: ELBW infants who gain weight in the first week of postnatal life, have a greater risk of PDA requiring treatment and may have a higher risk of mortality than infants who lose weight in the first week of life.

PMID:34256312 | DOI:10.1016/j.earlhumdev.2021.105421

Eur Neuropsychopharmacol. 2021 Jul 10;53:4-6. doi: 10.1016/j.euroneuro.2021.06.008. Online ahead of print.

ABSTRACT

Bipolar disorder (BD) is a complex illness with variability at the level of symptom presentation, clinical course, cognitive capacity, and everyday function. Cognition is a key predictor of functional disability in BD, however, much remains unknown about the development and presentation of cognitive dysfunction in BD. Studies have shown that 30-50% of affectively stable people with BD are indistinguishable from healthy individuals in terms of cognitive presentation. In contrast, many people with BD have moderate to severe cognitive deficits, in some cases on par with what is typically observed in schizophrenia (SZ). Recent research efforts have aimed to parse this cognitive heterogeneity using unsupervised statistical techniques, resulting in more homogeneous subgroups. This method has provided new insights into the clinical and biological predictors of a potentially – neuroprogressive – declinin – gcognitive course in BD. Future studies that include detailed longitudinal follow-up in large BD cohorts hold promise for guiding the development of novel treatments that reach beyond the primary affective symptoms and target functionally relevant outcomes to promote full recovery.

PMID:34256309 | DOI:10.1016/j.euroneuro.2021.06.008

Environ Int. 2021 Jul 10;156:106750. doi: 10.1016/j.envint.2021.106750. Online ahead of print.

ABSTRACT

BACKGROUND: Birthweight is an indicator of fetal growth and environmental-related alterations of birthweight have been linked with multiple disorders and conditions progressing into adulthood. Although a few studies have assessed the association between birthweight and the totality of exogenous exposures and their downstream molecular responses in maternal urine and cord blood; no prior research has considered a) the maternal serum prenatal metabolome, which is enriched for hormones, and b) non-linear and synergistic associations among exposures.

METHODS: We measured the maternal serum metabolome during pregnancy using an untargeted metabolomics approach and birthweight for gestational age (BWGA) z-score in 410 mother-child dyads enrolled in the PRogramming of Intergenerational Stress Mechanisms (PRISM) cohort. We leveraged a Bayesian factor analysis for interaction to select the most important metabolites associated with BWGA z-score and to evaluate their linear, non-linear and non-additive associations. We also assessed the primary biological functions of the identified proteins using the MetaboAnalyst, a centralized repository of curated functional information. We compared our findings with those of a traditional metabolite-wide association study (MWAS) in which metabolites are individually associated with BWGA z-score.

RESULTS: Among 1110 metabolites, 46 showed evidence of U-shape associations with BWGA z-score. Most of the identified metabolites (85%) were lipids primarily enriched for pathways central to energy production, immune function, and androgen and estrogen metabolism, which are essential for pregnancy and parturition processes. Metabolites within the same class, i.e. steroids and phospholipids, showed synergistic relationships with each other.

CONCLUSIONS: Our results support that the aspects of the maternal metabolome during pregnancy contribute linearly, non-linearly and synergistically to variation in newborn birthweight.

PMID:34256302 | DOI:10.1016/j.envint.2021.106750

Environ Pollut. 2021 Jul 7;288:117715. doi: 10.1016/j.envpol.2021.117715. Online ahead of print.

ABSTRACT

Maricultured organisms are chronically exposed to water containing antibiotics but the bioaccumulative behavior of antibiotics in exposed organisms at different growth stages has received little attention. Here, we investigated the concentrations and tissue-specific bioaccumulation characteristics of 19 antibiotics during three growth stages (youth stage, growth stage, and adult stage) of various organisms (Scophthalmus maximus, Penaeus vannamei, Penaeus japonicus, and Apostichopus japonicus) cultivated in typical marine aquaculture regions, and explored the factors that could affect the bioaccumulation of antibiotics. Tetracyclines (TCs) and fluoroquinolones (FQs) were the dominant antibiotics in all organisms, and the total concentrations of the target antibiotics in fish (S. maximus) were significantly higher than those in shrimp (P. vannamei and P. japonicus) and sea cucumber (A. japonicus) (p < 0.01). The bioaccumulation capacity of a class of statistically significant antibiotics in most samples was strongest during the youth stage and weakest during the adult stage. The antibiotics exhibited higher bioaccumulation capacity in lipid-rich tissues (fish liver and shrimp head) or respiratory organs (fish gill) than muscle. Our results also reveal significant metabolic transformation of enrofloxacin in fish. Different from previous studies, the logarithm bioaccumulation factor (log BAF) was positively correlated with log D_lipw in low-biotransformation tissues (fish gill and muscle) rather than lipid-rich tissues (fish liver). Based on the calculated hazard quotients (HQ), doxycycline in fish muscle may pose a distinct risk to human health, which deserves special attention. Overall, these results provide insight into the bioaccumulation patterns of antibiotics during different growth stages and tissues of maricultured organisms.

PMID:34256288 | DOI:10.1016/j.envpol.2021.117715

Comput Biol Med. 2021 Jun 9;135:104563. doi: 10.1016/j.compbiomed.2021.104563. Online ahead of print.

ABSTRACT

Cytomegalovirus infection is a significant health concern and need further exploration in immunologic response mechanisms during primary and reactivated CMV infection. In this work, we evaluated the whole genomes and proteomes of different CMV species and developed an integrated open-access platform, CytomegaloVirusDb, a multi-Omics knowledge database for Cytomegaloviruses. The resource is categorized into the main sections “Genomics,” “Proteomics,” “Immune response,” and “Therapeutics,”. The database is annotated with the list of all CMV species included in the study, and available information is freely accessible at http://www.cmvdb.dqweilab-sjtu.com/index.php. Various parameters used in the analysis for each section were primarily based on the whole genome or proteome of each specie. The platform provided datasets are open to access for researchers to obtain CMV species-specific information. This will help further to explore the dynamics of CMV-specific immune response and therapeutics. This platform is a useful resource to aid in advancing research against Cytomegaloviruses.

PMID:34256256 | DOI:10.1016/j.compbiomed.2021.104563