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Low-cost particulate matter (PM) sensors are promising tools for supplementing existing air quality monitoring networks. However, the performance of the new generation of low-cost PM sensors under field conditions is not well understood. In this study, we characterized the performance capabilities of a new low-cost PM sensor model (Plantower model PMS3003) for measuring PM2.5 at 1 min, 1 h, 6 h, 12 h, and 24 h integration times. We tested the PMS3003 sensors in both low-concentration suburban regions (Durham and Research Triangle Park (RTP), NC, US) with 1 h PM2.5 (mean ± SD) of 9±9 and 10±3 µg m−3, respectively, and a high-concentration urban location (Kanpur, India) with 1 h PM2.5 of 36±17 and 116±57 µg m−3 during monsoon and post-monsoon seasons, respectively. In Durham and Kanpur, the sensors were compared to a research-grade instrument (environmental β attenuation monitor, E-BAM) to determine how these sensors perform across a range of PM2.5 concentrations and meteorological factors (e.g., temperature and relative humidity, RH). In RTP, the sensors were compared to three Federal Equivalent Methods (FEMs) including two Teledyne model T640s and a Thermo Scientific model 5030 SHARP to demonstrate the importance of the type of reference monitor selected for sensor calibration. The decrease in 1 h mean errors of the calibrated sensors using univariate linear models from Durham (201 %) to Kanpur monsoon (46 %) and post-monsoon (35 %) seasons showed that PMS3003 performance generally improved as ambient PM2.5 increased. The precision of reference instruments (T640: ±0.5 µg m−3 for 1 h; SHARP: ±2 µg m−3 for 24 h, better than the E-BAM) is critical in evaluating sensor performance, and β-attenuation-based monitors may not be ideal for testing PM sensors at low concentrations, as underscored by (1) the less dramatic error reduction over averaging times in RTP against optically based T640 (from 27 % for 1 h to 9 % for 24 h) than in Durham (from 201 % to 15 %); (2) the lower errors in RTP than the Kanpur post-monsoon season (from 35 % to 11 %); and (3) the higher T640–PMS3003 correlations (R2≥0.63) than SHARP–PMS3003 (R2≥0.25). A major RH influence was found in RTP (1 h RH =64±22 %) due to the relatively high precision of the T640 measurements that can explain up to ∼30 % of the variance in 1 min to 6 h PMS3003 PM2.5 measurements. When proper RH corrections are made by empirical nonlinear equations after using a more precise reference method to calibrate the sensors, our work suggests that the PMS3003 sensors can measure PM2.5 concentrations within ∼10 % of ambient values. We observed that PMS3003 sensors appeared to exhibit a nonlinear response when ambient PM2.5 exceeded ∼125 µg m−3 and found that the quadratic fit is more appropriate than the univariate linear model to capture this nonlinearity and can further reduce errors by up to 11 %. Our results have substantial implications for how variability in ambient PM2.5 concentrations, reference monitor types, and meteorological factors can affect PMS3003 performance characterization.

Next-generation sequencing technology is rapidly transforming the landscape of evolutionary biology, and has become a cost-effective and efficient means of collecting exome information for non-model organisms. Due to their taxonomic diversity, production of interesting venom and silk proteins, and the relative scarcity of existing genomic resources, spiders in particular are excellent targets for next-generation sequencing (NGS) methods. In this study, the transcriptomes of six entelegyne spider species from three genera (Cicurina travisae, C. vibora, Habronattus signatus, H. ustulatus, Nesticus bishopi, and N. cooperi) were sequenced and de novo assembled. Each assembly was assessed for quality and completeness and functionally annotated using gene ontology information. Approximately 100 transcripts with evidence of homology to venom proteins were discovered. After identifying more than 3,000 putatively orthologous genes across all six taxa, we used comparative analyses to identify 24 instances of positively selected genes. In addition, between ~ 550 and 1,100 unique orphan genes were found in each genus. These unique, uncharacterized genes exhibited elevated rates of amino acid substitution, potentially consistent with lineage-specific adaptive evolution. The data generated for this study represent a valuable resource for future phylogenetic and molecular evolutionary research, and our results provide new insight into the forces driving genome evolution in taxa that span the root of entelegyne spider phylogeny.

A concern in the field of autism electroencephalography (EEG) biomarker discovery is their lack of reproducibility. In the present study, we considered the problem of learning reproducible associations between multiple features of resting state (RS) neural activity and autism, using EEG data collected during a RS paradigm from 36 to 96 month-old children diagnosed with autism ( N  = 224) and neurotypical children ( N  = 69). Specifically, EEG spectral power and functional connectivity features were used as inputs to a regularized generalized linear model trained to predict diagnostic group (autism versus neurotypical). To evaluate our model, we proposed a procedure that quantified both the predictive generalization and reproducibility of learned associations produced by the model. When prioritizing both model predictive performance and reproducibility of associations, a highly reproducible profile of associations emerged. This profile revealed a distinct pattern of increased gamma power and connectivity in occipital and posterior midline regions associated with an autism diagnosis. Conversely, model selection based on predictive performance alone resulted in non-robust associations. Finally, we built a custom machine learning model that further empirically improved robustness of learned associations. Our results highlight the need for model selection criteria that maximize the scientific utility provided by reproducibility instead of predictive performance.

Background Tropical members of the sponge genus Ircinia possess highly complex microbiomes that perform a broad spectrum of chemical processes that influence host fitness. Despite the pervasive role of microbiomes in Ircinia biology, it is still unknown how they remain in stable association across tropical species. To address this question, we performed a comparative analysis of the microbiomes of 11 Ircinia species using whole-metagenomic shotgun sequencing data to investigate three aspects of bacterial symbiont genomes—the redundancy in metabolic pathways across taxa, the evolution of genes involved in pathogenesis, and the nature of selection acting on genes relevant to secondary metabolism. Results A total of 424 new, high-quality bacterial metagenome-assembled genomes (MAGs) were produced for 10 Caribbean Ircinia species, which were evaluated alongside 113 publicly available MAGs sourced from the Pacific species Ircinia ramosa . Evidence of redundancy was discovered in that the core genes of several primary metabolic pathways could be found in the genomes of multiple bacterial taxa. Across hosts, the metagenomes were depleted in genes relevant to pathogenicity and enriched in eukaryotic-like proteins (ELPs) that likely mimic the hosts’ molecular patterning. Finally, clusters of steroid biosynthesis genes (CSGs), which appear to be under purifying selection and undergo horizontal gene transfer, were found to be a defining feature of Ircinia metagenomes. Conclusions These results illustrate patterns of genome evolution within highly complex microbiomes that illuminate how associations with hosts are maintained. The metabolic redundancy within the microbiomes could help buffer the hosts from changes in the ambient chemical and physical regimes and from fluctuations in the population sizes of the individual microbial strains that make up the microbiome. Additionally, the enrichment of ELPs and depletion of LPS and cellular motility genes provide a model for how alternative strategies to virulence can evolve in microbiomes undergoing mixed-mode transmission that do not ultimately result in higher levels of damage (i.e., pathogenicity) to the host. Our last set of results provides evidence that sterol biosynthesis in Ircinia -associated bacteria is widespread and that these molecules are important for the survival of bacteria in highly complex Ircinia microbiomes. 2qsxhK4E14RHhUQ45Lc7Zo Video Abstract

Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Copy to clipboard Provided by the Springer Nature SharedIt content-sharing initiative [RAW_REF_TEXT] Correction [/RAW_REF_TEXT] [RAW_REF_TEXT] Open Access [/RAW_REF_TEXT] [RAW_REF_TEXT] Published:24 October 2022 [/RAW_REF_TEXT] Correction: Novel trends of genome evolution in highly complex tropical sponge microbiomes [RAW_REF_TEXT] Joseph B. Kelly 1,2, [/RAW_REF_TEXT] [RAW_REF_TEXT] David E. Carlson2, [/RAW_REF_TEXT] [RAW_REF_TEXT] Jun Siong Low3,4,5 & [/RAW_REF_TEXT] [RAW_REF_TEXT] … [/RAW_REF_TEXT] [RAW_REF_TEXT] Robert W. Thacker2,6 [/RAW_REF_TEXT] Show authors Microbiome volume 10, Article number: 182 (2022) Cite this article [RAW_REF_TEXT] 234 Accesses [/RAW_REF_TEXT] [RAW_REF_TEXT] 1 Altmetric [/RAW_REF_TEXT] [RAW_REF_TEXT] Metrics details [/RAW_REF_TEXT] The Original Article was published on 04 October 2022 Correction: Microbiome 10, 164 (2022) https://doi.org/10.1186/s40168-022-01359-z Following the publication of the original article [1], the author reported that the affiliations were incorrectly assigned. Robert W. Thacker2,6 [/RAW_REF_TEXT] Show authors Microbiome volume 10, Article number: 182 (2022) Cite this article [RAW_REF_TEXT] 234 Accesses 1 Altmetric Metrics details

Comprehensive descriptions of animal behavior require precise three-dimensional (3D) measurements of whole-body movements. Although two-dimensional approaches can track visible landmarks in restrictive environments, performance drops in freely moving animals, due to occlusions and appearance changes. Therefore, we designed DANNCE to robustly track anatomical landmarks in 3D across species and behaviors. DANNCE uses projective geometry to construct inputs to a convolutional neural network that leverages learned 3D geometric reasoning. We trained and benchmarked DANNCE using a dataset of nearly seven million frames that relates color videos and rodent 3D poses. In rats and mice, DANNCE robustly tracked dozens of landmarks on the head, trunk, and limbs of freely moving animals in naturalistic settings. We extended DANNCE to datasets from rat pups, marmosets, and chickadees, and demonstrate quantitative profiling of behavioral lineage during development.DANNCE enables robust 3D tracking of animals’ limbs and other features in naturalistic environments by making use of a deep learning approach that incorporates geometric reasoning. DANNCE is demonstrated on behavioral sequences from rodents, marmosets, and chickadees.

Abstract The evolution of intracellular endosymbiosis marks a major transition in the biology of the host and endosymbiont. Yet, how adaptation manifests in the genomes of the participants remains relatively understudied. We investigated this question by sequencing the genomes of Tetrahymena utriculariae, a commensal of the aquatic carnivorous bladderwort Utricularia reflexa, and its intracellular algae, Micractinium tetrahymenae. We discovered an expansion in copy number and negative selection in a TLD domain-bearing gene family in the genome of T. utriculariae, identifying it as a candidate for being an adaptive response to oxidative stress resulting from the physiology of its endosymbionts. We found that the M. tetrahymenae genome is larger than those of other Micractinium and Chlorella and contains a greater number of rapidly expanding orthogroups. These were enriched for Gene Ontology terms relevant to the regulation of intracellular signal transduction and cellular responses to stress and stimulus. Single-exon tandem repeats were overrepresented in paralogs belonging to these rapidly expanding orthogroups, which implicates long terminal repeat retrotransposons (LTRs) as potential agents of adaptation. We additionally performed a comparative transcriptomic analysis of M. tetrahymenae in a free-living state and in endosymbiosis with T. utriculariae and discovered that the genes that are differentially expressed were enriched for pathways that evidence shifts in energy generation and storage and in cellular protection strategies. Together, our results elucidate the axes along which the participants must adapt in this young endosymbiosis and highlight evolutionary responses to stress as a shared trend.

Wireless low-cost particulate matter sensor networks (WLPMSNs) are transforming air quality monitoring by providing particulate matter (PM) information at finer spatial and temporal resolutions. However, large-scale WLPMSN calibration and maintenance remain a challenge. The manual labor involved in initial calibration by collocation and routine recalibration is intensive. The transferability of the calibration models determined from initial collocation to new deployment sites is questionable, as calibration factors typically vary with the urban heterogeneity of operating conditions and aerosol optical properties. Furthermore, the stability of low-cost sensors can drift or degrade over time. This study presents a simultaneous Gaussian process regression (GPR) and simple linear regression pipeline to calibrate and monitor dense WLPMSNs on the fly by leveraging all available reference monitors across an area without resorting to pre-deployment collocation calibration. We evaluated our method for Delhi, where the PM2.5 measurements of all 22 regulatory reference and 10 low-cost nodes were available for 59 d from 1 January to 31 March 2018 (PM2.5 averaged 138±31 µg m−3 among 22 reference stations), using a leave-one-out cross-validation (CV) over the 22 reference nodes. We showed that our approach can achieve an overall 30 % prediction error (RMSE: 33 µg m−3) at a 24 h scale, and it is robust as it is underscored by the small variability in the GPR model parameters and in the model-produced calibration factors for the low-cost nodes among the 22-fold CV. Of the 22 reference stations, high-quality predictions were observed for those stations whose PM2.5 means were close to the Delhi-wide mean (i.e., 138±31 µg m−3), and relatively poor predictions were observed for those nodes whose means differed substantially from the Delhi-wide mean (particularly on the lower end). We also observed washed-out local variability in PM2.5 across the 10 low-cost sites after calibration using our approach, which stands in marked contrast to the true wide variability across the reference sites. These observations revealed that our proposed technique (and more generally the geostatistical technique) requires high spatial homogeneity in the pollutant concentrations to be fully effective. We further demonstrated that our algorithm performance is insensitive to training window size as the mean prediction error rate and the standard error of the mean (SEM) for the 22 reference stations remained consistent at ∼30 % and ∼3 %–4 %, respectively, when an increment of 2 d of data was included in the model training. The markedly low requirement of our algorithm for training data enables the models to always be nearly the most updated in the field, thus realizing the algorithm's full potential for dynamically surveilling large-scale WLPMSNs by detecting malfunctioning low-cost nodes and tracking the drift with little latency. Our algorithm presented similarly stable 26 %–34 % mean prediction errors and ∼3 %–7 % SEMs over the sampling period when pre-trained on the current week's data and predicting 1 week ahead, and therefore it is suitable for online calibration. Simulations conducted using our algorithm suggest that in addition to dynamic calibration, the algorithm can also be adapted for automated monitoring of large-scale WLPMSNs. In these simulations, the algorithm was able to differentiate malfunctioning low-cost nodes (due to either hardware failure or under the heavy influence of local sources) within a network by identifying aberrant model-generated calibration factors (i.e., slopes close to zero and intercepts close to the Delhi-wide mean of true PM2.5). The algorithm was also able to track the drift of low-cost nodes accurately within 4 % error for all the simulation scenarios. The simulation results showed that ∼20 reference stations are optimum for our solution in Delhi and confirmed that low-cost nodes can extend the spatial precision of a network by decreasing the extent of pure interpolation among only reference stations. Our solution has substantial implications in reducing the amount of manual labor for the calibration and surveillance of extensive WLPMSNs, improving the spatial comprehensiveness of PM evaluation, and enhancing the accuracy of WLPMSNs.

The significant advances in high-power lasers with the attainment of tens of kilowatts of optical power, high repetition rates (>MHz), reduction in size, lower cost per photon (<1$/watt), and high optical power conversion efficiency (>30%) are driving the use of lasers for material processing for renewable energy materials. The significant advances in high-power lasers with the attainment of tens of kilowatts of optical power, high repetition rates (>MHz), reduction in size, lower cost per photon (<1$/watt), and high optical power conversion efficiency (>30%) are driving the use of lasers for material processing with very high throughput. The use of renewable energy is also increasing as an alternative power source. This review examines the various aspects of laser processing for renewable energy materials and provides an overview of fundamentals of laser material interactions, advances in high-power lasers, and specific examples of laser processing of materials for photovoltaics, solar thermal energy, thermophotovoltaics, thermoelectrics, and thin films. High-power lasers have been adapted for solar cell manufacturing applications, and new processes such as laser doping, laser transfer of metal contacts, laser annealing, etc. are being advanced further for industrial applications. The future of laser processing for renewable energy materials looks very bright with further advances expected in high-power lasers, beam delivery systems, and decreasing cost with very high reliability. Lasers can provide noncontact localized energy deposition with the potential for all low-temperature processing of materials and a very low thermal budget.

In rodents, anxiety is charactered by heightened vigilance during low-threat and uncertain situations. Though activity in the frontal cortex and limbic system are fundamental to supporting this internal state, the underlying network architecture that integrates activity across brain regions to encode anxiety across animals and paradigms remains unclear. Here, we utilize parallel electrical recordings in freely behaving mice, translational paradigms known to induce anxiety, and machine learning to discover a multi-region network that encodes the anxious brain-state. The network is composed of circuits widely implicated in anxiety behavior, it generalizes across many behavioral contexts that induce anxiety, and it fails to encode multiple behavioral contexts that do not. Strikingly, the activity of this network is also principally altered in two mouse models of depression. Thus, we establish a network-level process whereby the brain encodes anxiety in health and disease.