Category: Nevin Manimala

J Opt Soc Am A Opt Image Sci Vis. 2022 Jun 1;39(6):1104-1110. doi: 10.1364/JOSAA.457223.

ABSTRACT

Color theory is based on the exclusive activation of cones. However, since the discovery of melanopsin expressing cells in the human retina, evidence of its intrusion in brightness and color vision is increasing. We aimed to assess if differences between peripheral or large field and foveal color matches can be accounted for by melanopsin activation or rod intrusion. Photopic color matches by young observers showed that differences between extrafoveal and foveal results cannot be explained by rod intrusion. Furthermore, statistical analyses on existing color-matching functions suggest a role of melanopsin activation, particularly, in large field S fundamentals.

PMID:36215541 | DOI:10.1364/JOSAA.457223

J Opt Soc Am A Opt Image Sci Vis. 2022 Jun 1;39(6):1014-1024. doi: 10.1364/JOSAA.451074.

ABSTRACT

In an actual scene, underwater optical wireless communication (UOWC) transceivers may not be perfectly aligned from the start due to imprecise operation or disturbances such as water flow, and thus outdated pointing errors can no longer reliably reflect precise channel conditions. In this paper, for the first time, to our knowledge, we formulate a comprehensive misalignment errors model by taking into account both random jitter and initial misalignment errors. Furthermore, we deduce an effective receiving area due to the deflection of the receiver with three rotation angles in three-dimensional space. Moreover, we also apply the above findings to the composite fading channel model, which is more accurate and practical than the previous. Finally, we develop closed-form results for the bit error rate (BER) in terms of the Meijer G-function of UOWC systems. The performance is also analyzed by the multiplicative statistical channel model. Results demonstrate that comprehensive misalignment errors exacerbate performance degradation in terms of both average BER and outage probability, compared to pointing errors considering only random jitter. It indicates that the initial misalignment errors are not negligible, and analyzing scenes with comprehensive misalignment errors is of great importance in practice.

PMID:36215531 | DOI:10.1364/JOSAA.451074

Proc Natl Acad Sci U S A. 2022 Oct 18;119(42):e2205772119. doi: 10.1073/pnas.2205772119. Epub 2022 Oct 10.

ABSTRACT

The power grid is going through significant changes with the introduction of renewable energy sources and the incorporation of smart grid technologies. These rapid advancements necessitate new models and analyses to keep up with the various emergent phenomena they induce. A major prerequisite of such work is the acquisition of well-constructed and accurate network datasets for the power grid infrastructure. In this paper, we propose a robust, scalable framework to synthesize power distribution networks that resemble their physical counterparts for a given region. We use openly available information about interdependent road and building infrastructures to construct the networks. In contrast to prior work based on network statistics, we incorporate engineering and economic constraints to create the networks. Additionally, we provide a framework to create ensembles of power distribution networks to generate multiple possible instances of the network for a given region. The comprehensive dataset consists of nodes with attributes, such as geocoordinates; type of node (residence, transformer, or substation); and edges with attributes, such as geometry, type of line (feeder lines, primary or secondary), and line parameters. For validation, we provide detailed comparisons of the generated networks with actual distribution networks. The generated datasets represent realistic test systems (as compared with standard test cases published by Institute of Electrical and Electronics Engineers (IEEE)) that can be used by network scientists to analyze complex events in power grids and to perform detailed sensitivity and statistical analyses over ensembles of networks.

PMID:36215503 | DOI:10.1073/pnas.2205772119

Proc Natl Acad Sci U S A. 2022 Oct 18;119(42):e2204405119. doi: 10.1073/pnas.2204405119. Epub 2022 Oct 10.

ABSTRACT

Ecosystems are complex systems of various physical, biological, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity, handling these data is a challenge for existing methods of time series-based causal inferences. Here, we show that, by harnessing contemporary machine learning approaches, the concept of Granger causality can be effectively extended to the analysis of complex ecosystem time series and bridge the gap between dynamical and statistical approaches. The central idea is to use an ensemble of fast and highly predictive artificial neural networks to select a minimal set of variables that maximizes the prediction of a given variable. It enables decomposition of the relationship among variables through quantifying the contribution of an individual variable to the overall predictive performance. We show how our approach, EcohNet, can improve interaction network inference for a mesocosm experiment and simulated ecosystems. The application of the method to a long-term lake monitoring dataset yielded interpretable results on the drivers causing cyanobacteria blooms, which is a serious threat to ecological integrity and ecosystem services. Since performance of EcohNet is enhanced by its predictive capabilities, it also provides an optimized forecasting of overall components in ecosystems. EcohNet could be used to analyze complex and hybrid multivariate time series in many scientific areas not limited to ecosystems.

PMID:36215500 | DOI:10.1073/pnas.2204405119

Proc Natl Acad Sci U S A. 2022 Oct 18;119(42):e2211178119. doi: 10.1073/pnas.2211178119. Epub 2022 Oct 10.

ABSTRACT

Intrinsically disordered regions (IDRs) can function as autoregulators of folded enzymes to which they are tethered. One example is the bacterial cell division protein FtsZ. This includes a folded core and a C-terminal tail (CTT) that encompasses a poorly conserved, disordered C-terminal linker (CTL) and a well-conserved 17-residue C-terminal peptide (CT17). Sites for GTPase activity of FtsZs are formed at the interface between GTP binding sites and T7 loops on cores of adjacent subunits within dimers. Here, we explore the basis of autoregulatory functions of the CTT in Bacillus subtilis FtsZ (Bs-FtsZ). Molecular simulations show that the CT17 of Bs-FtsZ makes statistically significant CTL-mediated contacts with the T7 loop. Statistical coupling analysis of more than 1,000 sequences from FtsZ orthologs reveals clear covariation of the T7 loop and the CT17 with most of the core domain, whereas the CTL is under independent selection. Despite this, we discover the conservation of nonrandom sequence patterns within CTLs across orthologs. To test how the nonrandom patterns of CTLs mediate CTT-core interactions and modulate FtsZ functionalities, we designed Bs-FtsZ variants by altering the patterning of oppositely charged residues within the CTL. Such alterations disrupt the core-CTT interactions, lead to anomalous assembly and inefficient GTP hydrolysis in vitro and protein degradation, aberrant assembly, and disruption of cell division in vivo. Our findings suggest that viable CTLs in FtsZs are likely to be IDRs that encompass nonrandom, functionally relevant sequence patterns that also preserve three-way covariation of the CT17, the T7 loop, and core domain.

PMID:36215496 | DOI:10.1073/pnas.2211178119

Proc Natl Acad Sci U S A. 2022 Oct 18;119(42):e2212930119. doi: 10.1073/pnas.2212930119. Epub 2022 Oct 10.

ABSTRACT

Bacterial secondary metabolites are a major source of antibiotics and other bioactive compounds. In microbial communities, these molecules can mediate interspecies interactions and responses to environmental change. Despite the importance of secondary metabolites in human health and microbial ecology, little is known about their roles and regulation in the context of multispecies communities. In a simplified model of the rhizosphere composed of Bacillus cereus, Flavobacterium johnsoniae, and Pseudomonas koreensis, we show that the dynamics of secondary metabolism depend on community species composition and interspecies interactions. Comparative metatranscriptomics and metametabolomics reveal that the abundance of transcripts of biosynthetic gene clusters (BGCs) and metabolomic molecular features differ between monocultures or dual cultures and a tripartite community. In both two- and three-member cocultures, P. koreensis modified expression of BGCs for zwittermicin, petrobactin, and other secondary metabolites in B. cereus and F. johnsoniae, whereas the BGC transcriptional response to the community in P. koreensis itself was minimal. Pairwise and tripartite cocultures with P. koreensis displayed unique molecular features that appear to be derivatives of lokisin, suggesting metabolic handoffs between species. Deleting the BGC for koreenceine, another P. koreensis metabolite, altered transcript and metabolite profiles across the community, including substantial up-regulation of the petrobactin and bacillibactin BGCs in B. cereus, suggesting that koreenceine represses siderophore production. Results from this model community show that bacterial BGC expression and chemical output depend on the identity and biosynthetic capacity of coculture partners, suggesting community composition and microbiome interactions may shape the regulation of secondary metabolism in nature.

PMID:36215464 | DOI:10.1073/pnas.2212930119

J Opt Soc Am A Opt Image Sci Vis. 2022 May 1;39(5):979-986. doi: 10.1364/JOSAA.453689.

ABSTRACT

Expressions for the temporal covariance function and temporal frequency spectrum for a plane wave propagation in an underwater turbulent medium are developed analytically. Temporal correlation in moving natural water is presented, which is shown to be dependent upon the moving velocity, the delay between two instants of time, propagation distance, average temperature, and average salinity concentration. Coherence time and zero crossing time also are calculated. The results show that the velocity of the moving natural water has a significant impact on the temporal correlation of irradiance fluctuations. Additionally, the propagation distance, average temperature, average salinity concentration, and temperature-salinity gradient ratio also impact the temporal correlation up to a certain level.

PMID:36215459 | DOI:10.1364/JOSAA.453689

J Opt Soc Am A Opt Image Sci Vis. 2022 May 1;39(5):820-828. doi: 10.1364/JOSAA.455823.

ABSTRACT

A model of multivariate Gaussian statistics has been applied to study the higher-order statistics of the polarization speckle at two spatial or temporal points. Based on the Gaussian assumption for the random electric field, the joint probability density functions of the Stokes parameters and the parameters characterizing the polarization ellipse for the produced random polarization fields at two points are obtained. Subsequently, the corresponding statistics of an isotropic polarization speckle at two points have been investigated to obtain the joint and conditional probability densities of these random variables.

PMID:36215443 | DOI:10.1364/JOSAA.455823

J Opt Soc Am A Opt Image Sci Vis. 2022 May 1;39(5):959-968. doi: 10.1364/JOSAA.443326.

ABSTRACT

There are two types of uncertainty in image reconstructions from list-mode data: statistical and deterministic. One source of statistical uncertainty is the finite number of attributes of the detected particles, which are sampled from a probability distribution on the attribute space. A deterministic source of uncertainty is the effect that null functions of the imaging operator have on reconstructed pixel or voxel values. Quantifying the reduction in this deterministic source of uncertainty when more attributes are measured for each detected particle is the subject of this work. Specifically, upper bounds on an error metric are derived to quantify the error introduced in the reconstruction by the presence of null functions, and these upper bounds are shown to be reduced when the number of attributes is increased. These bounds are illustrated with an example of a two-dimensional single photon emission computed tomography (SPECT) system where the depth of interaction in the scintillation crystal is added to the attribute vector.

PMID:36215457 | DOI:10.1364/JOSAA.443326

Stat Appl Genet Mol Biol. 2022 Oct 10;21(1). doi: 10.1515/sagmb-2022-0003. eCollection 2022 Jan 1.

ABSTRACT

Batch effect Reduction of mIcroarray data with Dependent samples usinGEmpirical Bayes (BRIDGE) is a recently developed statistical method to address the issue of batch effect correction in batch-confounded microarray studies with dependent samples. The key component of the BRIDGE methodology is the use of samples run as technical replicates in two or more batches, “bridging samples”, to inform batch effect correction/attenuation. While previously published results indicate a relationship between the number of bridging samples, M, and the statistical power of downstream statistical testing on the batch-corrected data, there is of yet no formal statistical framework or user-friendly software, for estimating M to achieve a specific statistical power for hypothesis tests conducted on the batch-corrected data. To fill this gap, we developed pwrBRIDGE, a simulation-based approach to estimate the bridging sample size, M, in batch-confounded longitudinal microarray studies. To illustrate the use of pwrBRIDGE, we consider a hypothetical, longitudinal batch-confounded study whose goal is to identify Alzheimer’s disease (AD) progression-associated genes from amnestic mild cognitive impairment (aMCI) to AD in human blood after a 5-year follow-up. pwrBRIDGE helps researchers design and plan batch-confounded microarray studies with dependent samples to avoid over- or under-powered studies.

PMID:36215429 | DOI:10.1515/sagmb-2022-0003