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A standardised protocol for assessment of relative SARS-CoV-2 variant severity, with application to severity risk for COVID-19 cases infected with Omicron BA.1 compared to Delta variants in six European countries. (arXiv:2303.05541v1 [q-bio.PE]) arxiv.org/abs/2303.05541

A standardised protocol for assessment of relative SARS-CoV-2 variant severity, with application to severity risk for COVID-19 cases infected with Omicron BA.1 compared to Delta variants in six European countries

Several SARS-CoV-2 variants that evolved during the COVID-19 pandemic have appeared to differ in severity, based on analyses of single-country datasets. With decreased SARS-CoV-2 testing and sequencing, international collaborative studies will become increasingly important for timely assessment of the severity of newly emerged variants. The Joint WHO Regional Office for Europe and ECDC Infection Severity Working Group was formed to produce and pilot a standardised study protocol to estimate relative variant case-severity in settings with individual-level SARS-CoV-2 testing and COVID-19 outcome data during periods when two variants were co-circulating. To assess feasibility, the study protocol and its associated statistical analysis code was applied by local investigators in Denmark, England, Luxembourg, Norway, Portugal and Scotland to assess the case-severity of Omicron BA.1 relative to Delta cases. After pooling estimates using meta-analysis methods (random effects estimates), the risk of hospital admission (adjusted hazard ratio [aHR]=0.41, 95% CI 0.31-0.54), ICU admission (aHR=0.12, 95% CI 0.05-0.27), and death (aHR=0.31, 95% CI 0.28-0.35) was lower for Omicron BA.1 compared to Delta cases. The aHRs varied by age group and vaccination status. In conclusion, this study has demonstrated the feasibility of conducting variant severity analyses in a multinational collaborative framework. The results add further evidence for the reduced severity of the Omicron BA.1 variant.

arxiv.org

Exploration of the search space of Gaussian graphical models for paired data. (arXiv:2303.05561v1 [stat.ML]) arxiv.org/abs/2303.05561

Exploration of the search space of Gaussian graphical models for paired data

We consider the problem of learning a Gaussian graphical model in the case where the observations come from two dependent groups sharing the same variables. We focus on a family of coloured Gaussian graphical models specifically suited for the paired data problem. Commonly, graphical models are ordered by the submodel relationship so that the search space is a lattice, called the model inclusion lattice. We introduce a novel order between models, named the twin order. We show that, embedded with this order, the model space is a lattice that, unlike the model inclusion lattice, is distributive. Furthermore, we provide the relevant rules for the computation of the neighbours of a model. The latter are more efficient than the same operations in the model inclusion lattice, and are then exploited to achieve a more efficient exploration of the search space. These results can be applied to improve the efficiency of both greedy and Bayesian model search procedures. Here we implement a stepwise backward elimination procedure and evaluate its performance by means of simulations. Finally, the procedure is applied to learn a brain network from fMRI data where the two groups correspond to the left and right hemispheres, respectively.

arxiv.org

Novel Tetrahedral Human Phantoms for Space Radiation Dose Assessment. (arXiv:2303.05564v1 [physics.med-ph]) arxiv.org/abs/2303.05564

Novel Tetrahedral Human Phantoms for Space Radiation Dose Assessment

Space radiation remains one of the primary hazards to spaceflight crews. The unique nature of the intravehicular radiation spectrum makes prediction of biological outcomes difficult, with computational simulation-based efforts stymied by lack of computational resources or accurate modeling capabilities. Recent advancements in both Monte Carlo simulations and computational human phantom developments have allowed for complex radiation simulations and dosimetric calculations to be performed for numerous applications. In this work, advanced tetrahedral-type human phantoms were exposed to a simulated spectrum of particles equivalent to a single days exposure in the International Space Station in Low Earth Orbit. 3D Monte Carlo techniques were used to produce and simulate the radiation spectra. Organ absorbed dose, average energy deposition, and the whole-body integral dose was determined for a male and female phantom. Results were then extrapolated for two long-term scenarios: a 6-9 month mission on the International Space Station and a 3-year mission to Mars. The whole-body integral dose for the male and female models were found to be 0.2985 +- 0.0002 mGy/day 0.3050 +- 0.0002 mGy/day, respectively, which is within 10% of recorded dose values from the International Space Station. This work presents a novel approach to assess absorbed dose from space-like radiation fields using high-fidelity computational phantoms, highlighting the utility of complex models for space radiation research.

arxiv.org

International Vaccine Allocation: An Optimization Framework. (arXiv:2303.05917v1 [q-bio.PE]) arxiv.org/abs/2303.05917

International Vaccine Allocation: An Optimization Framework

The global SARS-CoV-2 (COVID-19) pandemic highlighted the challenge of equitable vaccine distribution between high- and low-income countries. High-income countries, such as the United States, were among the first to acquire the rapidly developed vaccines against COVID-19. However, many such high-income countries were reluctant or slow to distribute extra doses of the vaccine to lower-income countries via the COVID-19 Vaccines Global Access (COVAX) collaboration [Clinton and Yoo 2022]. In addition to moral objections to such vaccine nationalism, vaccine inequity during a pandemic could contribute to the evolution of new variants of the virus and possibly increase total deaths, including in the high-income countries. This paper uses the COVID-19 pandemic as a case study to identify scenarios under which it might be in a high-income nation's own interest to donate vaccine doses to another country before its own population has been fully vaccinated. Using an epidemiological model embedded in an optimization framework, we identify realistic scenarios under which a donor country prefers to donate vaccines before distributing them locally in order to minimize local deaths. We demonstrate that a nondonor-first vaccination policy can, under some circumstances, dramatically delay the emergence of more-contagious variants. Moreover, we find that vaccine distribution is not a zero-sum game between donor and nondonor countries: weighting the objective function even slightly in favor of minimizing total deaths can achieve dramatic reduction in total deaths with only a small increase in donor-country deaths. The insights yielded by this framework can be used to guide equitable vaccine distribution in future pandemics.

arxiv.org

Checking the Statistical Assumptions Underlying the Application of the Standard Deviation and RMS Error to Eye-Movement Time Series: A Comparison between Human and Artificial Eyes. (arXiv:2303.06004v1 [q-bio.NC]) arxiv.org/abs/2303.06004

Checking the Statistical Assumptions Underlying the Application of the Standard Deviation and RMS Error to Eye-Movement Time Series: A Comparison between Human and Artificial Eyes

Spatial precision is often measured using the standard deviation (SD) of the eye position signal or the RMS of the sample-to-sample differences (StoS) signal during fixation. As both measures emerge from statistical theory applied to time-series, there are certain statistical assumptions that accompany their use. It is intuitively obvious that the SD is most useful when applied to unimodal distributions. Both measures assume stationarity, which means that the statistical properties of the signals are stable over time. Both metrics assume the samples of the signals are independent. The presence of autocorrelation indicates that the samples in the time series are not independent. We tested these assumptions with multiple fixations from two studies, a publicly available dataset that included both human and artificial eyes ("HA Dataset", N=224 fixations), and data from our laboratory of 4 subjects ("TXstate", N=37 fixations). Many position signal distributions were multimodal (HA: median=32%, TXstate: median=100%). No fixation position signals were stationary. All position signals were statistically significantly autocorrelated (p < 0:01). Thus, the statistical assumptions of the SD were not met for any fixation. All StoS signals were unimodal. Some StoS signals were stationary (HA: 34%, TXstate: 24%). Almost all StoS signals were statistically significantly autocorrelated (p < 0:01). For TXstate, 3 of 37 fixations met all assumptions. Thus, the statistical assumptions of the RMS were generally not met. The general failure of these assumptions calls into question the appropriateness of the SD or the RMS-StoS as metrics of precision for eye-trackers.

arxiv.org

Scatter-based common spatial patterns -- a unified spatial filtering framework. (arXiv:2303.06019v1 [eess.SP]) arxiv.org/abs/2303.06019

Scatter-based common spatial patterns -- a unified spatial filtering framework

The common spatial pattern (CSP) approach is known as one of the most popular spatial filtering techniques for EEG classification in motor imagery (MI) based brain-computer interfaces (BCIs). However, it still suffers some drawbacks such as sensitivity to noise, non-stationarity, and limitation to binary classification.Therefore, we propose a novel spatial filtering framework called scaCSP based on the scatter matrices of spatial covariances of EEG signals, which works generally in both binary and multi-class problems whereas CSP can be cast into our framework as a special case when only the range space of the between-class scatter matrix is used in binary cases.We further propose subspace enhanced scaCSP algorithms which easily permit incorporating more discriminative information contained in other range spaces and null spaces of the between-class and within-class scatter matrices in two scenarios: a nullspace components reduction scenario and an additional spatial filter learning scenario.The proposed algorithms are evaluated on two data sets including 4 MI tasks. The classification performance is compared against state-of-the-art competing algorithms: CSP, Tikhonov regularized CSP (TRCSP), stationary CSP (sCSP) and stationary TRCSP (sTRCSP) in the binary problems whilst multi-class extensions of CSP based on pair-wise and one-versus-rest techniques in the multi-class problems. The results show that the proposed framework outperforms all the competing algorithms in terms of average classification accuracy and computational efficiency in both binary and multi-class problems.The proposed scsCSP works as a unified framework for general multi-class problems and is promising for improving the performance of MI-BCIs.

arxiv.org

Prevalence and major risk factors of non-communicable diseases: A Hospital-based Cross-Sectional Study in Dhaka, Bangladesh. (arXiv:2303.04808v1 [q-bio.QM]) arxiv.org/abs/2303.04808

Prevalence and major risk factors of non-communicable diseases: A Hospital-based Cross-Sectional Study in Dhaka, Bangladesh

Objective: The study aimed to determine the prevalence of several non-communicable diseases (NCD) and analyze risk factors among adult patients seeking nutritional guidance in Dhaka, Bangladesh. Result: Our study observed the relationships between gender, age groups, obesity, and NCDs (DM, CKD, IBS, CVD, CRD, thyroid). The most frequently reported NCD was cardiovascular issues (CVD), which was present in 83.56% of all participants. CVD was more common in male participants. Consequently, male participants had a higher blood pressure distribution than females. Diabetes mellitus (DM), on the other hand, did not have a gender-based inclination. Both CVD and DM had an age-based progression. Our study showed that chronic respiratory illness was more frequent in middle-aged participants than in younger or elderly individuals. Based on the data, every one in five hospitalized patients was obese. We analyzed the co-morbidities and found that 31.5% of the population has only one NCD, 30.1% has two NCDs, and 38.3% has more than two NCDs. Besides, 86.25% of all diabetic patients had cardiovascular issues. All thyroid patients in our study had CVD. Using a t-test, we found a relationship between CKD and thyroid (p-value 0.061). Males under 35 years have a statistically significant relationship between thyroid and chronic respiratory diseases (p-value 0.018). We also found an association between DM and CKD among patients over 65 (p-value 0.038). Moreover, there has been a statistically significant relationship between CKD and Thyroid (P < 0.05) for those below 35 and 35-65. We used a two-way ANOVA test to find the statistically significant interaction of heart issues and chronic respiratory illness, in combination with diabetes. The combination of DM and RTI also affected CKD in male patients over 65 years old.

arxiv.org

Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamics. (arXiv:2303.04863v1 [q-bio.QM]) arxiv.org/abs/2303.04863

Bayesian at heart: Towards autonomic outflow estimation via generative state-space modelling of heart rate dynamics

Recent research is revealing how cognitive processes are supported by a complex interplay between the brain and the rest of the body, which can be investigated by the analysis of physiological features such as breathing rhythms, heart rate, and skin conductance. Heart rate dynamics are of particular interest as they provide a way to track the sympathetic and parasympathetic outflow from the autonomic nervous system, which is known to play a key role in modulating attention, memory, decision-making, and emotional processing. However, extracting useful information from heartbeats about the autonomic outflow is still challenging due to the noisy estimates that result from standard signal-processing methods. To advance this state of affairs, we propose a paradigm shift in how we conceptualise and model heart rate: instead of being a mere summary of the observed inter-beat intervals, we introduce a modelling framework that views heart rate as a hidden stochastic process that drives the observed heartbeats. Moreover, by leveraging the rich literature of state-space modelling and Bayesian inference, our proposed framework delivers a description of heart rate dynamics that is not a point estimate but a posterior distribution of a generative model. We illustrate the capabilities of our method by showing that it recapitulates linear properties of conventional heart rate estimators, while exhibiting a better discriminative power for metrics of dynamical complexity compared across different physiological states.

arxiv.org

Structure-based approach can identify driver nodes in ensembles of biologically-inspired Boolean networks. (arXiv:2303.04888v1 [q-bio.QM]) arxiv.org/abs/2303.04888

Structure-based approach can identify driver nodes in ensembles of biologically-inspired Boolean networks

Because the attractors of biological networks reflect stable behaviors (e.g., cell phenotypes), identifying control interventions that can drive a system towards its attractors (attractor control) is of particular relevance when controlling biological systems. Driving a network's feedback vertex set (FVS) by node-state override into a state consistent with a target attractor is proven to force every system trajectory to the target attractor, but in biological networks, the FVS is typically larger than can be realistically manipulated. External control of a subset of a biological network's FVS was proposed as a strategy to drive the network to its attractors utilizing fewer interventions; however, the effectiveness of this strategy was only demonstrated on a small set of Boolean models of biological networks. Here, we extend this analysis to ensembles of biologically-inspired Boolean networks. On these models, we use three structural metrics -- PRINCE propagation, modified PRINCE propagation, and CheiRank -- to rank FVS subsets by their predicted attractor control strength. We validate the accuracy of these rankings using three dynamical measures: To Control, Away Control, and logical domain of influence. We also calculate the propagation metrics on effective graphs, which incorporate each Boolean model's functional information into edge weights. While this additional information increases the predicting power of structural metrics, we find that the increase with respect to the unweighted network is limited. The propagation metrics in conjunction with the FVS can be used to identify realizable driver node sets by emulating the dynamics that are prevalent in biological networks. This approach only uses the network's structure, and the driver sets are shown to be robust to the specific dynamical model.

arxiv.org

A variational synthesis of evolutionary and developmental dynamics. (arXiv:2303.04898v1 [q-bio.PE]) arxiv.org/abs/2303.04898

A variational synthesis of evolutionary and developmental dynamics

This paper introduces a variational formulation of natural selection, paying special attention to the nature of "things" and the way that different "kinds" of "things" are individuated from - and influence - each other. We use the Bayesian mechanics of particular partitions to understand how slow phylogenetic processes constrain - and are constrained by - fast, phenotypic processes. The main result is a formulation of adaptive fitness as a path integral of phenotypic fitness. Paths of least action, at the phenotypic and phylogenetic scales, can then be read as inference and learning processes, respectively. In this view, a phenotype actively infers the state of its econiche under a generative model, whose parameters are learned via natural (bayesian model selection). The ensuing variational synthesis features some unexpected aspects. Perhaps the most notable is that it is not possible to describe or model a population of conspecifics per se. Rather, it is necessary to consider populations - and nested meta-populations - of different natural kinds that influence each other. This paper is limited to a description of the mathematical apparatus and accompanying ideas. Subsequent work will use these methods for simulations and numerical analyses - and identify points of contact with related mathematical formulations of evolution.

arxiv.org

Dynamical Analysis of a Lotka-Volterra Competition Model with both Allee and Fear Effect. (arXiv:2303.04919v1 [q-bio.PE]) arxiv.org/abs/2303.04919

Dynamical Analysis of a Lotka-Volterra Competition Model with both Allee and Fear Effect

Population ecology theory is replete with density dependent processes. However trait-mediated or behavioral indirect interactions can both reinforce or oppose density-dependent effects. This paper presents the first two species competitive ODE and PDE systems where an Allee effect, which is a density dependent process and the fear effect, which is non-consumptive and behavioral are both present. The stability of the equilibria is discussed analytically using the qualitative theory of ordinary differential equations. It is found that the Allee effect and the fear effect change the extinction dynamics of the system and the number of positive equilibrium points, but they do not affect the stability of the positive equilibria. We also observe some special dynamics that induce bifurcations in the system by varying the Allee or fear parameter. Interestingly we find that the Allee effect working in conjunction with the fear effect, can bring about several qualitative changes to the dynamical behavior of the system with only the fear effect in place, in regimes of small fear. That is, for small amounts of the fear parameter, it can change a competitive exclusion type situation to a strong competition type situation. It can also change a weak competition type situation to a bi-stability type situation. However for large fear regimes the Allee effect reinforces the dynamics driven by the fear effect. The analysis of the corresponding spatially explicit model is also presented. To this end the comparison principle for parabolic PDE is used. The conclusions of this paper have strong implications for conservation biology, biological control as well as the preservation of biodiversity.

arxiv.org

SEAM: An Integrated Activation-Coupled Model of Sentence Processing and Eye Movements in Reading. (arXiv:2303.05221v1 [q-bio.NC]) arxiv.org/abs/2303.05221

SEAM: An Integrated Activation-Coupled Model of Sentence Processing and Eye Movements in Reading

Models of eye-movement control during reading, developed largely within psychology, usually focus on visual, attentional, lexical, and motor processes but neglect post-lexical language processing; by contrast, models of sentence comprehension processes, developed largely within psycholinguistics, generally focus only on post-lexical language processes. We present a model that combines these two research threads, by integrating eye-movement control and sentence processing. Developing such an integrated model is extremely challenging and computationally demanding, but such an integration is an important step toward complete mathematical models of natural language comprehension in reading. We combine the SWIFT model of eye-movement control (Seelig et al., 2020, doi:10.1016/j.jmp.2019.102313) with key components of the Lewis and Vasishth sentence processing model (Lewis & Vasishth, 2005, doi:10.1207/s15516709cog0000_25). This integration becomes possible, for the first time, due in part to recent advances in successful parameter identification in dynamical models, which allows us to investigate profile log-likelihoods for individual model parameters. We present a fully implemented proof-of-concept model demonstrating how such an integrated model can be achieved; our approach includes Bayesian model inference with Markov Chain Monte Carlo (MCMC) sampling as a key computational tool. The integrated Sentence-Processing and Eye-Movement Activation-Coupled Model (SEAM) can successfully reproduce eye movement patterns that arise due to similarity-based interference in reading. To our knowledge, this is the first-ever integration of a complete process model of eye-movement control with linguistic dependency completion processes in sentence comprehension. In future work, this proof of concept model will need to be evaluated using a comprehensive set of benchmark data.

arxiv.org

Molecular detection and antimicrobial activity of Endophytic fungi isolated from a medical plant Rosmarinus officinalis. (arXiv:2303.05242v1 [q-bio.GN]) arxiv.org/abs/2303.05242

Molecular detection and antimicrobial activity of Endophytic fungi isolated from a medical plant Rosmarinus officinalis

Endophytes are tiny organisms present in living tissues of distinct plants and have been extensively studied for their endophytic microbial complement. Roots of Rosmarinus officinalis were subjected to the isolation of endophytic fungi and screened for antimicrobial activity against Gram-positive (Staphylococcus aureus and Bacillus subtilis) and Gram-negative (Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae) bacteria. Genomic DNA from active fungal strain of Trichoderma harzianum was isolated, and the internal transcribed spacer (ITS) region was amplified using ITS4 and ITS5 primers and sequenced for genetic inference in fungus. The crude extract of T. harzianum isolate with Ethyl acetate was showed significant antimicrobial activity against P. aeruginosa, S. aureus, K. pneumonia, B. subtilis and E. coli. The antimicrobial activity was highest against P. aeruginosa at concentration of 40 microgram/ ml, followed by S. aureus and K. pneumonia at the same concentration. The lowest antimicrobial activity was against by S. aureus at concentration of 60 microgram/ ml. The current study is confirmed that the antimicrobial activity is due to bioactive compounds founded in endophytic fungi.

arxiv.org
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