Category: Nevin Manimala

PLoS One. 2022 Mar 8;17(3):e0264803. doi: 10.1371/journal.pone.0264803. eCollection 2022.

ABSTRACT

Traffic is one of the major contributors to PM2.5 in cities worldwide. Quantifying the role of traffic is an important step towards understanding the impact of transport policies on the possibilities to achieve cleaner air and accompanying health benefits. With the aim of estimating potential health benefits of eliminating traffic emissions, we carried out a meta-analysis using the World Health Organisation (WHO) database of source apportionment studies of PM2.5 concentrations. Specifically, we used a Bayesian meta-regression approach, modelling both overall and traffic-related (tailpipe and non-tailpipe) concentrations simultaneously. We obtained the distributions of expected PM2.5 concentrations (posterior densities) of different types for 117 cities worldwide. Using the non-linear Integrated Exposure Response (IER) function of PM2.5, we estimated percent reduction in different disease endpoints for a scenario with complete removal of traffic emissions. We found that eliminating traffic emissions results in achieving the WHO-recommended concentration of PM2.5 only for a handful of cities that already have low concentrations of pollution. The percentage reduction in premature mortality due to cardiovascular and respiratory diseases increases up to a point (30-40 ug/m3), and above this concentration, it flattens off. For diabetes-related mortality, the percentage reduction in mortality decreases with increasing concentrations-a trend that is opposite to other outcomes. For cities with high concentrations of pollution, the results highlight the need for multi-sectoral strategies to reduce pollution. The IER functions of PM2.5 result in diminishing returns of health benefits at high concentrations, and in case of diabetes, there are even negative returns. The results show the significant effect of the shape of IER functions on health benefits. Overall, despite the diminishing results, a significant burden of deaths can be prevented by policies that aim to reduce traffic emissions even at high concentrations of pollution.

PMID:35259180 | DOI:10.1371/journal.pone.0264803

PLoS One. 2022 Mar 8;17(3):e0264957. doi: 10.1371/journal.pone.0264957. eCollection 2022.

ABSTRACT

Physician stress is associated with near misses and adverse medical events. However, little is known about physiological mechanisms linking stress to such events. We explored the utility of machine learning to determine whether the catabolic stress hormone cortisol and the anabolic, anti-stress hormone dehydroepiandrosterone sulfate (DHEA-S), as well as the cortisol to DHEA-S ratio relate to near misses in emergency medicine residents during active duty in a trauma 1 emergency department. Compared to statistical models better suited for inference, machine learning models allow for prediction in situations that have not yet occurred, and thus better suited for clinical applications. This exploratory study used multiple machine learning models to determine possible relationships between biomarkers and near misses. Of the various models tested, support vector machine with radial bias function kernels and support vector machine with linear kernels performed the best, with training accuracies of 85% and 79% respectively. When evaluated on a test dataset, both models had prediction accuracies of around 80%. The pre-shift cortisol to DHEA-S ratio was shown to be the most important predictor in interpretable models tested. Results suggest that interventions that help emergency room physicians relax before they begin their shift could reduce risk of errors and improve patient and physician outcomes. This pilot demonstrates promising results regarding using machine learning to better understand the stress biology of near misses. Future studies should use larger groups and relate these variables to information in electronic medical records, such as objective and patient-reported quality measures.

PMID:35259166 | DOI:10.1371/journal.pone.0264957

PLoS One. 2022 Mar 8;17(3):e0263618. doi: 10.1371/journal.pone.0263618. eCollection 2022.

ABSTRACT

The observation and assessment of animal biodiversity using acoustic technology has developed considerably in recent years. Current eco-acoustic research focuses on automatic audio recorder arrays and acoustic indices, which may be used to study the spatial and temporal dynamics of local animal communities in high resolution. While such soundscapes have often been studied above ground, their applicability in soils has rarely been tested. For the first time, we applied acoustic and statistical methods to explore the spatial, diurnal, and seasonal dynamics of the soundscape in soils. We studied the dynamics of acoustic complexity in forest soils in the alpine Pfynwald forest in the Swiss canton of Valais and related them to meteorological and microclimatic data. To increase microclimatic variability, we used a long-term irrigation experiment. We also took soil samples close to the sensors on 6 days in different seasons. Daily and seasonal patterns of acoustic complexity were predicted to be associated with abiotic parameters-that is, meteorological and microclimatic conditions-and mediated by the dynamics of the diversity and activity of the soil fauna. Seasonal patterns in acoustic complexity showed the highest acoustic complexity values in spring and summer, decreasing in fall and winter. Diurnal acoustic complexity values were highest in the afternoon and lowest during the night. The measurement of acoustic diversity at the sampling site was significantly associated with soil communities, with relationships between taxa richness or community composition and acoustic complexity being strongest shortly before taking the soil samples. Our results suggest that the temporal and spatial dynamics of the diversity and community composition of soil organisms can be predicted by the acoustic complexity of soil soundscapes. This opens up the possibility of using soil soundscape analysis as a noninvasive and easy-to-use method for soil biodiversity monitoring programs.

PMID:35259175 | DOI:10.1371/journal.pone.0263618

PLoS Comput Biol. 2022 Mar 8;18(3):e1009557. doi: 10.1371/journal.pcbi.1009557. Online ahead of print.

ABSTRACT

Perception is often characterized computationally as an inference process in which uncertain or ambiguous sensory inputs are combined with prior expectations. Although behavioral studies have shown that observers can change their prior expectations in the context of a task, robust neural signatures of task-specific priors have been elusive. Here, we analytically derive such signatures under the general assumption that the responses of sensory neurons encode posterior beliefs that combine sensory inputs with task-specific expectations. Specifically, we derive predictions for the task-dependence of correlated neural variability and decision-related signals in sensory neurons. The qualitative aspects of our results are parameter-free and specific to the statistics of each task. The predictions for correlated variability also differ from predictions of classic feedforward models of sensory processing and are therefore a strong test of theories of hierarchical Bayesian inference in the brain. Importantly, we find that Bayesian learning predicts an increase in so-called “differential correlations” as the observer’s internal model learns the stimulus distribution, and the observer’s behavioral performance improves. This stands in contrast to classic feedforward encoding/decoding models of sensory processing, since such correlations are fundamentally information-limiting. We find support for our predictions in data from existing neurophysiological studies across a variety of tasks and brain areas. Finally, we show in simulation how measurements of sensory neural responses can reveal information about a subject’s internal beliefs about the task. Taken together, our results reinterpret task-dependent sources of neural covariability as signatures of Bayesian inference and provide new insights into their cause and their function.

PMID:35259152 | DOI:10.1371/journal.pcbi.1009557

PLoS Comput Biol. 2022 Mar 8;18(3):e1009925. doi: 10.1371/journal.pcbi.1009925. Online ahead of print.

ABSTRACT

A central goal in sensory neuroscience is to understand the neuronal signal processing involved in the encoding of natural stimuli. A critical step towards this goal is the development of successful computational encoding models. For ganglion cells in the vertebrate retina, the development of satisfactory models for responses to natural visual scenes is an ongoing challenge. Standard models typically apply linear integration of visual stimuli over space, yet many ganglion cells are known to show nonlinear spatial integration, in particular when stimulated with contrast-reversing gratings. We here study the influence of spatial nonlinearities in the encoding of natural images by ganglion cells, using multielectrode-array recordings from isolated salamander and mouse retinas. We assess how responses to natural images depend on first- and second-order statistics of spatial patterns inside the receptive field. This leads us to a simple extension of current standard ganglion cell models. We show that taking not only the weighted average of light intensity inside the receptive field into account but also its variance over space can partly account for nonlinear integration and substantially improve response predictions of responses to novel images. For salamander ganglion cells, we find that response predictions for cell classes with large receptive fields profit most from including spatial contrast information. Finally, we demonstrate how this model framework can be used to assess the spatial scale of nonlinear integration. Our results underscore that nonlinear spatial stimulus integration translates to stimulation with natural images. Furthermore, the introduced model framework provides a simple, yet powerful extension of standard models and may serve as a benchmark for the development of more detailed models of the nonlinear structure of receptive fields.

PMID:35259159 | DOI:10.1371/journal.pcbi.1009925

PLoS One. 2022 Mar 8;17(3):e0255753. doi: 10.1371/journal.pone.0255753. eCollection 2022.

ABSTRACT

While peptides can be excellent therapeutics for several conditions, their limited in vivo half-lives have been a major bottleneck in the development of therapeutic peptides. Conjugating the peptide to an inert chemical moiety is a strategy that has repeatedly proven to be successful in extending the half-life of some therapeutics. This systematic review and meta-analysis was conducted to examine the available literature and assess it in an unbiased manner to determine which conjugates, both biological and synthetic, provide the greatest increase in therapeutic peptide half-life. Systematic searches run on PubMed, Scopus and SciFinder databases resulted in 845 studies pertaining to the topic, 16 of these were included in this review after assessment against pre-specified inclusion criteria registered on PROSPERO (#CRD42020222579). The most common reasons for exclusion were non-IV administration and large peptide size. Of the 16 studies that were included, a diverse suite of conjugates that increased half-life from 0.1 h to 33.57 h was identified. Amongst these peptides, the largest increase in half-life was seen when conjugated with glycosaminoglycans. A meta-analysis of studies that contained fatty acid conjugates indicated that acylation contributed to a statistically significant extension of half-life. Additionally, another meta-analysis followed by a sensitivity analysis suggested that conjugation with specifically engineered recombinant peptides might contribute to a more efficient extension of peptide half-life as compared to PEGylation. Moreover, we confirmed that while polyethylene glycol is a good synthetic conjugate, its chain length likely has an impact on its effectiveness in extending half-life. Furthermore, we found that most animal studies do not include as much detail when reporting findings as compared to human studies. Inclusion of additional experimental detail on aspects such as independent assessment and randomization may be an easily accomplished strategy to drive more conjugated peptides towards clinical studies.

PMID:35259149 | DOI:10.1371/journal.pone.0255753

Plast Reconstr Surg. 2022 Mar 9. doi: 10.1097/PRS.0000000000008990. Online ahead of print.

ABSTRACT

BACKGROUND: Hyaluronic acid soft-tissue augmentation fillers are commonly injected into multiple areas of the face, including the tear trough. Despite well-documented risks, there is no standardized, evidence-based approach to inject filler in this area, be it using a hypodermic needle or a microcannula. The authors, therefore, sought to establish a preference between the two methods to facilitate progression toward standardization and prevention of adverse events.

METHODS: This is a systematic review of articles discussing hyaluronic acid tear trough injection techniques performed in vivo and related outcomes. Searches were conducted across The Cochrane Library, PubMed, Scopus, Web of Science, and Embase to yield relevant articles published before February of 2020. All selected articles incorporated discrete patient cases and were analyzed by a variety of variables assessing evidence strength, outcomes, technique, and patient safety.

RESULTS: After appraisal, 42 articles met eligibility criteria: 20 using needles, 12 using cannulas, and 10 focusing on adverse events. Level III was the most commonly afforded evidence grade, corresponding to retrospective, nonexperimental descriptive studies. There were no statistically significant differences in reported aesthetic results, patient satisfaction, or incidence of adverse events across the needle-based and cannula-based articles. Some technique trends, such as targeted anatomical plane and needle position, emerged in subsequent articles.

CONCLUSION: Given that there were no statistically significant differences in patient safety or outcomes, an evidence-based preference for needle or cannula injection into the tear trough cannot be made at this time. Current inconsistencies make tear trough injection procedures difficult to replicate, making standardization based on avoidance of adverse events not feasible.

PMID:35259144 | DOI:10.1097/PRS.0000000000008990

Plast Reconstr Surg. 2022 Mar 9. doi: 10.1097/PRS.0000000000009058. Online ahead of print.

ABSTRACT

BACKGROUND: Only a few studies have directly compared outcomes of different arm contouring techniques across matched cohorts of patients. In this study, the authors present preliminary data comparing outcomes of conventional open suction-assisted brachioplasty (using the Pascal and Le Louarn procedure) versus combined liposuction/laser skin tightening in (grade 2b arm ptosis per El Khatib classification).

METHODS: Thirty patients (60 arms) (28 women, two men) with moderate brachial ptosis (severe upper arm adiposity and a moderate degree of skin laxity) (grade 2b arm ptosis per El Khatib classification) were included. Objective and subjective measures were used in the assessment of results.

RESULTS: There were no statistically significant differences in objective measurements (arm circumference reduction ratio and percentage of ptosis elimination) between the groups. Patient satisfaction scores were higher with liposuction/laser skin tightening and found to be statistically significant (p < 0.05). Patients in this latter cohort reported less pain and earlier return to work (mean less than a week) (p < 0.05). Four patients complained of residual ptosis in each group.

CONCLUSIONS: Liposuction/laser skin tightening is a safe and effective alternative to open suction-assisted brachioplasty (using the Pascal and Le Louarn technique) in patients with severe arm adiposity and moderate brachial ptosis (grade 2b arm ptosis as described by El Khatib classification). Proper patient selection remains critical for the success of this treatment strategy and requires precise clinical analysis as described.

CLINICAL QUESTION/LEVEL OF EVIDENCE: Therapeutic, II.

PMID:35259140 | DOI:10.1097/PRS.0000000000009058

IEEE Trans Neural Netw Learn Syst. 2022 Mar 8;PP. doi: 10.1109/TNNLS.2022.3151683. Online ahead of print.

ABSTRACT

Domain adaptation is concerned with the problem of generalizing a classification model to a target domain with little or no labeled data, by leveraging the abundant labeled data from a related source domain. The source and target domains possess different joint probability distributions, making it challenging for model generalization. In this article, we introduce domain neural adaptation (DNA): an approach that exploits nonlinear deep neural network to 1) match the source and target joint distributions in the network activation space and 2) learn the classifier in an end-to-end manner. Specifically, we employ the relative chi-square divergence to compare the two joint distributions, and show that the divergence can be estimated via seeking the maximal value of a quadratic functional over the reproducing kernel hilbert space. The analytic solution to this maximization problem enables us to explicitly express the divergence estimate as a function of the neural network mapping. We optimize the network parameters to minimize the estimated joint distribution divergence and the classification loss, yielding a classification model that generalizes well to the target domain. Empirical results on several visual datasets demonstrate that our solution is statistically better than its competitors.

PMID:35259116 | DOI:10.1109/TNNLS.2022.3151683

IEEE Trans Pattern Anal Mach Intell. 2022 Mar 8;PP. doi: 10.1109/TPAMI.2022.3157444. Online ahead of print.

ABSTRACT

The integral probability metric (IPM) equips generative adversarial nets (GANs) with the necessary theoretical support for comparing statistical moments in an embedded domain of the critic, while stabilising their training and mitigating the mode collapse issues. For enhanced intuition and physical insight, we introduce a generalisation of IPM-GANs which operates by directly comparing probability distributions rather than their moments. This is achieved through characteristic functions (CFs), a powerful tool that uniquely comprises all information about any general distribution. For rigour, we first theoretically prove the ability of the CF loss to compare probability distributions, and proceed to establish the physical meaning of the phase and amplitude of CFs. An optimal sampling strategy is then developed to calculate the CFs, and an equivalence between the embedded and data domains is proved under the reciprocal theory. This makes it possible to seamlessly combine IPM-GAN with an auto-encoder structure by an advanced anchor architecture, which adversarially learns a semantic low-dimensional manifold for both generation and reconstruction. This efficient reciprocal CF GAN (RCF-GAN) structure, uses only two modules and a simple training strategy to achieve the state-of-the-art bi-directional generation. Experiments demonstrate the superior performance of RCF-GAN on both regular (images) and irregular (graph) domains.

PMID:35259097 | DOI:10.1109/TPAMI.2022.3157444