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Abstract Salinity regimes in coastal ecosystems are highly dynamic and driven by complex geomorphic and hydrological processes. Estuarine biota are generally adapted to salinity fluctuation, but are vulnerable to salinity extremes. Characterizing coastal salinity regime for ecological studies therefore requires representing extremes of salinity ranges at time scales relevant to ecology (e.g., daily, monthly, and seasonally). Here, we propose a framework for modeling coastal salinity with these overall goals: (1) quantify uncertainty in salinity associated with important terrestrial and oceanographic drivers, (2) examine time scales of salinity response to river streamflow events, and (3) predict salinity continuously over space at key time scales. Salinity is modeled as quantile surfaces related to river discharge, tidal dynamics, wind, and spatial location, applied to Suwannee Sound estuary, FL, USA, where salinity has been monitored spatially since 1981. Each quantile level is regressed independently, and together they comprise a distribution of salinity uncertainty across space, with upper and lower quantiles describing salinity extremes. Effects of physical drivers on salinity are compared through four base models with various combinations of tide and wind variables, each including spatial coordinates and a single streamflow metric (in cubic meters per second). Multiple time scales of streamflow are considered by taking means across various periods, from 1 to 12 days, and at various lagged intervals prior to salinity sample, totaling 144 streamflow metrics. We found that the Suwannee coastal salinity regime is dynamic at multiple time scales and varies nonlinearly across space from the river effluence outward. Salinity increases nonlinearly with decreasing river flow rates below 200 m3/s, most prominently in the lower quantiles of salinity (τ = 0.05–0.25). Wind appears to have a stronger influence on salinity than astronomic tides for this estuary. The regression approach developed here can be applied to any coastal system that has sufficient spatial and temporal monitoring coverage to capture multiple flood and drought events. It is implemented with a simple R routine, and is less computationally‐intensive than finite difference hydrodynamic modeling. The characterizations of salinity uncertainty developed in these analyses can be directly applied to future studies of fish and wildlife responses to changes in watershed management.

Abstract Removal sampling data are the primary source of monitoring information for many populations (e.g., invasive species, fisheries). Population dynamics, temporary emigration, and imperfect detection are common sources of variation in monitoring data and are key parameters for informing management. We developed two open robust‐design removal models for simultaneously modeling population dynamics, temporary emigration, and imperfect detection: a random walk linear trend model (estimable without ancillary information), and a 2‐age class informed population model (InfoPM, closely related to integrated population models) that incorporated prior information for age‐structured vital rates and relative juvenile availability. We applied both models to multiyear, removal trapping time‐series of a large invasive lizard (Argentine black and white tegu, Salvator merianae) in three management areas of South Florida to evaluate the effectiveness of management programs. Although estimates of the two models were similar, the InfoPMs generally returned more precise estimates, partitioned dynamics into births, deaths, net migration, and provided a decision support tool to predict population dynamics under different effort scenarios while accounting for uncertainty. Trends in tegu superpopulation abundance estimates were increasing in two management areas despite generally high removal rates. However, tegu abundance appeared to decline in the Core management area, where trapping density was the highest and immigration the lowest. Finally, comparing abundance predictions of no‐removal scenarios to those estimated in each management area suggested significant population reductions due to management. These results suggest that local tegu population control via systematic trapping may be feasible with high enough trap density and limited immigration; and highlights the value of these trapping programs. We provided the first estimates of tegu abundance, capture probabilities, and population dynamics, which is critical for effective management. Furthermore, our models are applicable to a wide range of monitoring programs (e.g., carcass recovery or removal point‐counts).

The loss of ecosystem services due to climate change and coastal development is projected to have significant impacts on local economies and conservation of natural resources. Consequently, there has been an increase in coastal management activities such as living shorelines, oyster reef restoration, marsh restoration, beach and dune nourishment, and revegetation projects. Coastal management decisions are complex and include challenging trade-offs. Decision science offers a useful framework to address such complex problems. Here, we provide a synthesis about how decision science can help to integrate research from multiple disciplines (physical and life sciences) with management of coastal and marine systems. Specifically, we discuss the importance of considering concepts and techniques from ecology, coastal geology, geomorphology, climate science, oceanography, and decision analysis when developing conservation plans for coastal restoration. We illustrate the process with several coastal restoration studies. Our capstone example is based on a recent barrier island restoration assessment project at Dauphin Island, Alabama, which included the development of geomorphological and ecological models. We show how decision science can be used as a framework to combine geomorphological and ecological modeling to help inform management decisions while considering uncertainty about system changes and risk tolerance. We also build on our examples through a review of recently developed techniques for spatial conservation planning for land acquisition decisions and the application of adaptive management for sequential decisions.

Abstract Machine learning (ML) systems have enabled the modelling of quantitative structure–property relationships (QSPR) and structure-activity relationships (QSAR) using existing experimental data to predict target properties for new molecules. These property predictors hold significant potential in accelerating drug discovery by guiding generative artificial intelligence (AI) agents to explore desired chemical spaces. However, they often struggle to generalize due to the limited scope of the training data. When optimized by generative agents, this limitation can result in the generation of molecules with artificially high predicted probabilities of satisfying target properties, which subsequently fail experimental validation. To address this challenge, we propose an adaptive approach that integrates active learning (AL) and iterative feedback to refine property predictors, thereby improving the outcomes of their optimization by generative AI agents. Our method leverages the Expected Predictive Information Gain (EPIG) criterion to select additional molecules for evaluation by an oracle. This process aims to provide the greatest reduction in predictive uncertainty, enabling more accurate model evaluations of subsequently generated molecules. Recognizing the impracticality of immediate wet-lab or physics-based experiments due to time and logistical constraints, we propose leveraging human experts for their cost-effectiveness and domain knowledge to effectively augment property predictors, bridging gaps in the limited training data. Empirical evaluations through both simulated and real human-in-the-loop experiments demonstrate that our approach refines property predictors to better align with oracle assessments. Additionally, we observe improved accuracy of predicted properties as well as improved drug-likeness among the top-ranking generated molecules. Scientific contribution We present an adaptable framework that integrates AL and human expertise to refine property predictors for goal-oriented molecule generation. This approach is robust to noise in human feedback and ensures that navigating chemical space with human-refined predictors leverages human insights to identify molecules that not only satisfy predicted property profiles but also score highly on oracle models. Additionally, it prioritizes practical characteristics such as drug-likeness, synthetic accessibility, and a favorable balance between exploring diverse chemical space and exploiting similarity to existing training data.

Abstract The endosymbiotic bacteria Wolbachia can invade insect populations by modifying host reproduction through cytoplasmic incompatibility (CI), an effect that results in embryonic lethality when Wolbachia-carrying males mate with Wolbachia-free females. Here we describe a transgenic system for recreating CI in the major arbovirus vector Aedes aegypti using CI factor (cif) genes from wAlbB, a Wolbachia strain currently being deployed to reduce dengue transmission. CI-like sterility is induced when cifA and cifB are co-expressed in testes; this sterility is rescued by maternal cifA expression, thereby reproducing the pattern of Wolbachia-induced CI. Expression of cifB alone is associated with extensive DNA damage and disrupted spermatogenesis. The strength of rescue by maternal cifA expression is dependent on the comparative levels of cifA/cifB expression in males. These findings are consistent with CifB acting as a toxin and CifA as an antitoxin, with CifA attenuating CifB toxicity in both the male germline and in developing embryos. These findings provide important insights into the interactions between cif genes and their mechanism of activity and provide a foundation for the building of a cif gene-based drive system in Ae. aegypti.

Abstract Some strains of the inherited bacterium Wolbachia have been shown to be effective at reducing the transmission of dengue virus (DENV) and other RNA viruses by Aedes aegypti in both laboratory and field settings and are being deployed for DENV control. The degree of virus inhibition varies between Wolbachia strains. Density and tissue tropism can contribute to these differences but there are also indications that this is not the only factor involved: for example, strains wAu and wAlbA are maintained at similar intracellular densities but only wAu produces strong DENV inhibition. We previously reported perturbations in lipid transport dynamics, including sequestration of cholesterol in lipid droplets, with strains wMel/wMelPop in Ae. aegypti. To further investigate the cellular basis underlying these differences, proteomic analysis of midguts was carried out on Ae. aegypti lines carrying strains wAu and wAlbA: with the hypothesis that differences in perturbations may underline Wolbachia-mediated antiviral activity. Surprisingly, wAu-carrying midguts not only showed distinct proteome perturbations when compared to non-Wolbachia carrying and wAlbA-carrying midguts but also wMel-carrying midguts. There are changes in RNA processing pathways and upregulation of a specific set of RNA-binding proteins in the wAu-carrying line, including genes with known antiviral activity. Lipid transport and metabolism proteome changes also differ between strains, and we show that strain wAu does not produce the same cholesterol sequestration phenotype as wMel. Moreover, in contrast to wMel, wAu antiviral activity was not rescued by cyclodextrin treatment. Together these results suggest that wAu could show unique features in its inhibition of arboviruses compared to previously characterized Wolbachia strains.

Abstract Background Some people produce specific body odours that make them more attractive than others to mosquitoes, and consequently are at higher risk of contracting vector-borne diseases. The skin microbiome can break down carbohydrates, fatty acids and peptides on the skin into volatiles that mosquitoes can differentiate. Results Here, we examined how skin microbiome composition of women differs in relation to level of attractiveness to Anopheles coluzzii mosquitoes, to identify volatiles in body odour and metabolic pathways associated with individuals that tend to be poorly-attractive to mosquitoes. We used behavioural assays to measure attractiveness of participants to An. coluzzii mosquitoes, 16S rRNA amplicon sequencing of the bacteria sampled from the skin and gas chromatography of volatiles in body odour. We found differences in skin microbiome composition between the poorly- and highly-attractive groups, particularly eight Amplicon Sequence Variants (ASVs) belonging to the Proteobacteria, Actinobacteria and Firmicutes phyla. Staphylococcus 2 ASVs are four times as abundant in the highly-attractive compared to poorly-attractive group. Associations were found between these ASVs and volatiles known to be attractive to Anopheles mosquitoes. Propanoic pathways are enriched in the poorly-attractive participants compared to those found to be highly-attractive. Conclusions Our findings suggest that variation in attractiveness of people to mosquitoes is related to the composition of the skin microbiota, knowledge that could improve odour-baited traps or other next generation vector control tools.

PURPOSE: Dual HER2 blockade chemotherapy is the standard of care for localized HER2+ breast cancer (BC). However, despite the efficacy of neoadjuvant therapy, relapses occurring in around 10% of patients highlight the need to improve its clinical approach. Therefore, this study aimed to evaluate the effectiveness/safety of neoadjuvant therapy with subcutaneous (SC) trastuzumab- pertuzumab chemotherapy (real world) to extend the evidence, which comes mainly from clinical trials (selected population; intravenous [IV] trastuzumab).MATERIALS AND METHODS: A prospective, longitudinal, observational study in a Cuban hospital. Population: women aged ≥18 years with histologically confirmed HER2+ early-stage BC (2017–2021) eligible for neoadjuvant treatment (IV pertuzumab, SC trastuzumab, taxane-based chemotherapy). The aim was to determine the pathological complete response (pCR) rate to this scheme, its safety, and the impact of patient's characteristics on the outcomes.RESULTS: Eighty-seven women were included: n=29 (DPT [docetaxel-IV pertuzumab- SC trastuzumab 600 mg; 4 cycles]); n=58 (ddAC-DPT [dose-dense anthracycline-based scheme+DPT]; 8 cycles). The median age was 57 years (range 30–83), ECOG 0: 97%. Time from diagnosis to treatment (median) was 28 days. The overall pCR rate was 62.1% (55.2%, DPT; 66.5%, ddAC-DPT; p =0.351); HR+, 47.7% vs. HR-, 76.7% (p=0.006). There were no statistically significant differences based on nodal status, stage, or Ki-67 levels. Overall, 94.2% of patients experienced ≥1 adverse event related to treatment, all of them grade 1-3 and more common with ddAC-DPT. The main cause of treatment delays (n=19; ddAC-DPT, 16; DPT, 3) was treatment-related toxicities.CONCLUSION: Neoadjuvant trastuzumab (SC) and pertuzumab plus chemotherapy for HER2+ early-stage BC showed benefits in a real-life setting, with an acceptable safety profile.

Wolbachia are widespread maternally-transmitted bacteria of arthropods that often spread by manipulating their host's reproduction through cytoplasmic incompatibility (CI). Their invasive potential is currently being harnessed in field trials aiming to control mosquito-borne diseases. Wolbachia genomes commonly harbour prophage regions encoding the cif genes which confer their ability to induce CI. Recently, a plasmid-like element was discovered in wPip, a Wolbachia strain infecting Culex mosquitoes; however, it is unclear how common such extra-chromosomal elements are in Wolbachia. Here we sequenced the complete genome of wAlbA, a strain of the symbiont found in Aedes albopictus, after eliminating the co-infecting and higher density wAlbB strain that previously made sequencing of wAlbA challenging. We show that wAlbA is associated with two new plasmids and identified additional Wolbachia plasmids and related chromosomal islands in over 20% of publicly available Wolbachia genome datasets. These plasmids encode a variety of accessory genes, including several phage-like DNA packaging genes as well as genes potentially contributing to host-symbiont interactions. In particular, we recovered divergent homologues of the cif genes in both Wolbachia- and Rickettsia-associated plasmids. Our results indicate that plasmids are common in Wolbachia and raise fundamental questions around their role in symbiosis. In addition, our comparative analysis provides useful information for the future development of genetic tools to manipulate and study Wolbachia symbionts.