Stony Brook University

The accelerating pace of emerging zoonotic diseases in the twenty-first century has motivated cross-disciplinary collaboration on One Health approaches, combining microbiology, veterinary and environmental sciences, and epidemiology for outbreak prevention and mitigation. Such outbreaks are often caused by spillovers attributed to human activities that encroach on wildlife habitats and ecosystems, such as land use change, industrialized food production, urbanization and animal trade. While the origin of anthropogenic effects on animal ecology and biogeography can be traced to the Late Pleistocene, the archaeological record—a long-term archive of human–animal–environmental interactions—has largely been untapped in these One Health approaches, thus limiting our understanding of these dynamics over time. In this review, we examine how humans, as niche constructors, have facilitated new host species and ‘disease-scapes’ from the Late Pleistocene to the Anthropocene, by viewing zooarchaeological, bioarchaeological and palaeoecological data with a One Health perspective. We also highlight how new biomolecular tools and advances in the ‘-omics’ can be holistically coupled with archaeological and palaeoecological reconstructions in the service of studying zoonotic disease emergence and re-emergence.

Organized mesoscale convective systems (MCSs) contribute a significant amount of precipitation in the Central and Eastern US during spring and summer, which impacts the availability of freshwater and flooding events. However, current global Earth system models cannot capture MCSs well and misrepresent the statistics of precipitation in the region. In this study, we investigate the representation of MCSs in three configurations of the Energy Exascale Earth System Model (E3SMv1) by tracking individual storms based on outgoing longwave radiation using a new application of TempestExtremes. Our results indicate that conventional parameterizations of convection, implemented in both low (LR; ∼150 km) and high (HR; ∼25 km) resolution configurations, fail to capture almost all MCS‐like events, in‐part because they underestimate high‐level cloud ice associated with deep convection. On the other hand, the multiscale modeling framework (MMF; cloud‐resolving models embedded in each grid‐column of ∼150 km resolution E3SMv1) configuration represents MCSs and their annual cycle better. Nevertheless, relative to observations, the E3SMv1‐MMF spatial distribution of MCSs and associated precipitation is shifted eastward, and the diurnal timing is lagged. A comparison between the large‐scale environment in E3SMv1‐MMF and ERA5 reanalysis suggests that the biases during the summer in E3SMv1‐MMF are associated with biases in low‐level humidity and meridional moisture transport within the low‐level jet. The fact that conventional parameterizations of convection, even with high‐resolution, cannot capture MCSs over the US suggests that methods with explicit representation of kilometer‐scale convective organization, such as the MMF, may be necessary for improving the simulation of these convective systems.

Accurate simulation of the present-day characteristics of mean and extreme precipitation at regional scales remains a challenge for Earth system models (ESMs), which is due in part to deficiencies in model physics such as convective parameterization (CP), and coarse resolution. High horizontal resolution (HR, ~25 km) and multiscale modeling framework (MMF, i.e., replacing conventional CP with embedded km-scale cloud-resolving models) are two promising directions that could help improve the interaction between subgrid-scale physical processes and large-scale climate. Here, we evaluate simulated extreme precipitation over the United States (US) across three configurations (low-resolution [LR], HR, and MMF) of the Energy Exascale Earth System Model (E3SMv1) and intercompare them against two gridded observation datasets (Climate Prediction Centre Daily US Precipitation and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement). We assess the model’s ability to simulate very heavy seasonal precipitation (illustrated by the difference between the 99th and 90th percentile values) as well as the spatial distributions of several extreme precipitation indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI). Our results show that both the dry (i.e., consecutive dry days) and wet (consecutive wet days, maximum 5-day precipitation, and very wet days) extremes evaluated herein show some improvement as well as degradation with MMF and HR relative to LR. These results vary across seasons and US subregions. For instance, only the very heavy precipitation of winter is improved with MMF and HR. Both configurations alleviate the well-known drizzling bias evident in LR across both winter and summer in many parts of the US, largely due to the overall improvement in intensity and frequency of precipitation. Additionally, our results suggest that while E3SMv1-MMF has higher intensity rates when it does rain, it has too many consecutive dry days during the summer, contributing to a low mean precipitation bias

Single-atom catalysts (SACs) are particularly sensitive to external conditions, complicating the identification of catalytically active species and active sites under in situ or operando conditions. We developed a methodology for tracing the structural evolution of SACs to nanoparticles, identifying the active species and their link to the catalytic activity for the reverse water gas shift (RWGS) reaction. The new method is illustrated by studying structure-activity relationships in two materials containing Pt SACs on ceria nanodomes, supported on either ceria or titania. These materials exhibited distinctly different activities for CO production. Multimodal operando characterization attributed the enhanced activity of the titania-supported catalysts at temperatures below 320 ˚C to the formation of unique Pt sites at the ceria-titania interface capable of forming Pt nanoparticles, the active species for the RWGS reaction. Migration of Pt nanoparticles to titania support was found to be responsible for the deactivation of titania-supported catalysts at elevated temperatures. Tracking the migration of Pt atoms provides a new opportunity to investigate the activation and deactivation of Pt SACs for the RWGS reaction.

Background The application of wastewater-based epidemiology to track the outbreak and prevalence of coronavirus disease (COVID-19) in communities has been tested and validated by several researchers across the globe. However, the RNA-based surveillance has its inherent limitations and uncertainties. Objective This study aims to complement the ongoing wastewater surveillance efforts by analyzing other chemical biomarkers in wastewater to help assess community response (hospitalization and treatment) during the pandemic (2020–2021). Methods Wastewater samples (n = 183) were collected from the largest wastewater treatment facility in Suffolk County, NY, USA and analyzed for COVID-19 treatment drugs (remdesivir, chloroquine, and hydroxychloroquine (HCQ)) and their human metabolites. We additionally monitored 26 pharmaceuticals including common over-the-counter (OTC) drugs. Lastly, we developed a Bayesian model that uses viral RNA, COVID-19 treatment drugs, and pharmaceuticals data to predict the confirmed COVID-19 cases within the catchment area. Results The viral RNA levels in wastewater tracked the actual COVID-19 case numbers well as expected. COVID-19 treatment drugs were detected with varying frequency (9–100%) partly due to their instability in wastewater. We observed a significant correlation (R = 0.30, p < 0.01) between the SARS-CoV-2 genes and desethylhydroxychloroquine (DHCQ, metabolite of HCQ). Remdesivir levels peaked immediately after the Emergency Use Authorization approved by the FDA. Although, 13 out of 26 pharmaceuticals assessed were consistently detected (DF = 100%, n = 111), only acetaminophen was significantly correlated with viral loads, especially when the Omicron variant was dominant. The Bayesian models were capable of reproducing the temporal trend of the confirmed cases. Impact In this study, for the first time, we measured COVID-19 treatment and pharmaceutical drugs and their metabolites in wastewater to complement ongoing COVID-19 viral RNA surveillance efforts. Our results highlighted that, although the COVID-19 treatment drugs were not very stable in wastewater, their detection matched with usage trends in the community. Acetaminophen, an OTC drug, was significantly correlated with viral loads and confirmed cases, especially when the Omicron variant was dominant. A Bayesian model was developed which could predict COVID-19 cases more accurately when incorporating other drugs data along with viral RNA levels in wastewater.

Although sexual communication’s association with sexual and relational functioning and satisfaction have been well established, to date only a small number of studies have used dyadic data, and all have used single time-point self-report measures of sexual outcomes. This study examined the unique association between sexual communication quality and daily levels of sexual satisfaction in couples. Participants included 81 couples comprised of mostly mixed-sex dyads of a diverse range of ages and relationship lengths. Using dyadic cross-sectional data on sexual communication quality in couples as well as 21-day daily diary data assessments of sexual satisfaction on days individuals reported having sex, our analyses revealed significant within-partner and cross-partner associations between individual’s perceived sexual communication quality in their relationship and average daily sexual satisfaction. Greater perceived sexual communication quality was also associated with less variability in individuals’ daily reports of sexual satisfaction on days they had sex. Additional models were run adding relationship satisfaction as a covariate and controlling for relationship length. Lastly, similarity in reports of sexual communication between partners did not moderate the association between sexual communication quality and average daily sexual satisfaction. Our findings suggest that greater quality sexual communication may be uniquely associated with better and more consistent daily sexual satisfaction in relationships. This research expands our current understanding of sexual communication’s impact in relationships day to day.

In this paper, we propose a new method, called DoubleCoverUDF, for extracting the zero level-set from unsigned distance fields (UDFs). DoubleCoverUDF takes a learned UDF and a user-specified parameter r (a small positive real number) as input and extracts an iso-surface with an iso-value r using the conventional marching cubes algorithm. We show that the computed iso-surface is the boundary of the r -offset volume of the target zero level-set S , which is an orientable manifold, regardless of the topology of S. Next, the algorithm computes a covering map to project the boundary mesh onto S , preserving the mesh's topology and avoiding folding. If S is an orientable manifold surface, our algorithm separates the double-layered mesh into a single layer using a robust minimum-cut post-processing step. Otherwise, it keeps the double-layered mesh as the output. We validate our algorithm by reconstructing 3D surfaces of open models and demonstrate its efficacy and effectiveness on synthetic models and benchmark datasets. Our experimental results confirm that our method is robust and produces meshes with better quality in terms of both visual evaluation and quantitative measures than existing UDF-based methods. The source code is available at https://github.com/jjjkkyz/DCUDF.

Kinase inhibitors are successful therapeutics in the treatment of cancers and autoimmune diseases and are useful tools in biomedical research. However, the high sequence and structural conservation of the catalytic kinase domain complicates the development of selective kinase inhibitors. Inhibition of off-target kinases makes it difficult to study the mechanism of inhibitors in biological systems. Current efforts focus on the development of inhibitors with improved selectivity. Here, we present an alternative solution to this problem by combining inhibitors with divergent off-target effects. We develop a multicompound-multitarget scoring (MMS) method that combines inhibitors to maximize target inhibition and to minimize off-target inhibition. Additionally, this framework enables optimization of inhibitor combinations for multiple on-targets. Using MMS with published kinase inhibitor datasets we determine potent inhibitor combinations for target kinases with better selectivity than the most selective single inhibitor and validate the predicted effect and selectivity of inhibitor combinations using in vitro and in cellulo techniques. MMS greatly enhances selectivity in rational multitargeting applications. The MMS framework is generalizable to other non-kinase biological targets where compound selectivity is a challenge and diverse compound libraries are available.

Class Demospongiae is the largest in the phylum Porifera (Sponges) and encompasses nearly 8,000 accepted species in three subclasses: Keratosa, Verongimorpha, and Heteroscleromorpha. Subclass Heteroscleromorpha contains ∼90% of demosponge species and is subdivided into 17 orders. The higher level classification of demosponges underwent major revision as the result of nearly three decades of molecular studies. However, because most of the previous molecular work only utilized partial data from a small number of nuclear and mitochondrial (mt) genes, this classification scheme needs to be tested by larger datasets. Here we compiled a mt dataset for 136 demosponge species—including 64 complete or nearly complete and six partial mt-genome sequences determined or assembled for this study—and used it to test phylogenetic relationships among Demospongiae in general and Heteroscleromorpha in particular. We also investigated the phylogenetic position of Myceliospongia araneosa , a highly unusual demosponge without spicules and spongin fibers, currently classified as Demospongiae incertae sedis , for which molecular data were not available. Our results support the previously inferred sister-group relationship between Heteroscleromorpha and Keratosa + Verongimorpha and suggest five main clades within Heteroscleromorpha: Clade C0 composed of order Haplosclerida; Clade C1 composed of Scopalinida, Sphaerocladina, and Spongillida; Clade C2 composed of Axinellida, Biemnida, Bubarida; Clade C3 composed of Tetractinellida; and Clade C4 composed of Agelasida, Clionaida, Desmacellida, Merliida, Suberitida, Poecilosclerida, Polymastiida, and Tethyida. The inferred relationships among these clades were (C0(C1(C2(C3+C4)))). Analysis of molecular data from M. araneosa placed it in the C3 clade as a sister taxon to the highly skeletonized tetractinellids Microscleroderma sp. and Leiodermatium sp. Molecular clock analysis dated divergences among the major clades in Heteroscleromorpha from the Cambrian to the Early Silurian, the origins of most heteroscleromorph orders in the middle Paleozoic, and the most basal splits within these orders around the Paleozoic to Mesozoic transition. Overall, the results of this study are mostly congruent with the accepted classification of Heteroscleromorpha, but add temporal perspective and new resolution to phylogenetic relationships within this subclass.

Human newborns are considered altricial compared with other primates because they are relatively underdeveloped at birth. However, in a broader comparative context, other mammals are more altricial than humans. It has been proposed that altricial development evolved secondarily in humans due to obstetrical or metabolic constraints, and in association with increased brain plasticity. To explore this association, we used comparative data from 140 placental mammals to measure how altriciality evolved in humans and other species. We also estimated how changes in brain size and gestation length influenced the timing of neurodevelopment during hominin evolution. Based on our data, humans show the highest evolutionary rate to become more altricial (measured as the proportion of adult brain size at birth) across all placental mammals, but this results primarily from the pronounced postnatal enlargement of brain size rather than neonatal changes. In addition, we show that only a small number of neurodevelopmental events were shifted to the postnatal period during hominin evolution, and that they were primarily related to the myelination of certain brain pathways. These results indicate that the perception of human altriciality is mostly driven by postnatal changes, and they point to a possible association between the timing of myelination and human neuroplasticity.

What does it mean for a boundary condition to be symmetric with respect to a noninvertible global symmetry? We discuss two possible definitions in 1+1D QFTs and lattice models. On the one hand, we call a boundary weakly symmetric if the symmetry defects can terminate topologically on it, leading to conserved operators for the Hamiltonian on an interval (in the open string channel). On the other hand, we call a boundary strongly symmetric if the corresponding boundary state is an eigenstate of the symmetry operators (in the closed string channel). These two notions of symmetric boundaries are equivalent for invertible symmetries, but bifurcate for noninvertible symmetries. We discuss the relation to anomalies, where we observe that it is sometimes possible to gauge a noninvertible symmetry in a generalized sense even though it is incompatible with a trivially gapped phase. The analysis of symmetric boundaries further leads to constraints on bulk and boundary renormalization group flows. In 2+1D, we study the action of noninvertible condensation defects on the boundaries of U(1) gauge theory and several TQFTs. Starting from the Dirichlet boundary of free Maxwell theory, the noninvertible symmetries generate infinitely many boundary conditions that are neither Dirichlet nor Neumann.

The correlations of electric currents in hot non-Abelian plasma are responsible for the experimental manifestations of the chiral magnetic effect in heavy-ion collisions. We evaluate these correlations using holography, and show that they are driven by large-scale topological fluctuations. In a non-Abelian plasma with chiral fermions, local axial charge can be generated either by topological fluctuations (creating domains with nonzero Chern-Simons number) or by thermal fluctuations. Within holography, we investigate the dynamical creation of the axial charge and isolate the imprint of the topological dynamics on the spatial correlations of electric current. In particular, we show that the spatial extent of the current correlation is quite large (∼1 fm) and grows with time, which is consistent with sphaleronlike dynamics. We provide numerical estimates for this spatial size that can be used as an input in phenomenological analyses.

Background Deep learning in medical applications is limited due to the low availability of large labeled, annotated, or segmented training datasets. With the insufficient data available for model training comes the inability of these networks to learn the fine nuances of the space of possible images in a given medical domain, leading to the possible suppression of important diagnostic features hence making these deep learning systems suboptimal in their performance and vulnerable to adversarial attacks. Purpose We formulate a framework to address this lack of labeled data problem. We test this formulation in computed tomographic images domain and present an approach that can synthesize large sets of novel CT images at high resolution across the full Hounsfield (HU) range. Methods Our method only requires a small annotated dataset of lung CT from 30 patients (available online at the TCIA) and a large nonannotated dataset with high resolution CT images from 14k patients (received from NIH, not publicly available). It then converts the small annotated dataset into a large annotated dataset, using a sequence of steps including texture learning via StyleGAN, label learning via U‐Net and semi‐supervised learning via CycleGAN/Pixel‐to‐Pixel (P2P) architectures. The large annotated dataset so generated can then be used for the training of deep learning networks for medical applications. It can also be put to use for the synthesis of CT images with varied anatomies that were nonexistent within either of the input datasets, enriching the dataset even further. Results We demonstrate our framework via lung CT‐Scan synthesis along with their novel generated annotations and compared it with other state of the art generative models that only produce images without annotations. We evaluate our framework effectiveness via a visual turing test with help of a few doctors and radiologists. Conclusions We gain the capability of generating an unlimited amount of annotated CT images. Our approach works for all HU windows with minimal depreciation in anatomical plausibility and hence could be used as a general purpose framework for annotated data augmentation for deep learning applications in medical imaging.

Background The purpose of this study was to investigate the relationship between preoperative aspartate aminotransferase-to-platelet ratio index (APRI) and postoperative complications following total hip arthroplasty (THA). Methods All THA for osteoarthritis patients from 2007 to 2020 within the American College of Surgeons (ACS) National Surgical Quality Improvement Program (NSQIP) database were included in this study. Subjects were subsequently divided into cohorts based on APRI. Four groups, including normal range, some liver damage, significant fibrosis, and cirrhosis groups, were created. Comparisons between groups were made for demographics, past medical history, and rate of major and minor complications. Other outcomes included readmission, reoperation, discharge destination, mortality, periprosthetic fracture, and postoperative hip dislocation. Multivariate logistic regression analysis was performed to determine the role of preoperative APRI in predicting adverse outcomes. Statistical significance was set at p < 0.05. Results In total, 104,633 primary THA patients were included in this study. Of these, 103,678 (99.1%) were in the normal APRI group, 444 (0.4%) had some liver damage, 256 (0.2%) had significant fibrosis, and 253 (0.2%) had cirrhosis. When controlling for demographics and relevant past medical history, the abnormal APRI groups had a significantly higher likelihood of major complication, minor complication, intraoperative or postoperative bleeding requiring transfusion, readmission, and non-home discharge (all p < 0.05) compared to normal APRI individuals. Conclusions Abnormal preoperative APRI is linked with an increasing number of adverse outcomes following THA for osteoarthritis for patients across the United States. Level of evidence Level I

Single object tracking (SOT) is a fundamental problem in computer vision, with a wide range of applications, including autonomous driving, augmented reality, and robot navigation. The robustness of SOT faces two main challenges: tiny target and fast motion. These challenges are especially manifested in videos captured by unmanned aerial vehicles (UAV), where the target is usually far away from the camera and often with significant motion relative to the camera. To evaluate the robustness of SOT methods, we propose BioDrone—the first bionic drone-based visual benchmark for SOT. Unlike existing UAV datasets, BioDrone features videos captured from a flapping-wing UAV system with a major camera shake due to its aerodynamics. BioDrone hence highlights the tracking of tiny targets with drastic changes between consecutive frames, providing a new robust vision benchmark for SOT. To date, BioDrone offers the largest UAV-based SOT benchmark with high-quality fine-grained manual annotations and automatically generates frame-level labels, designed for robust vision analyses. Leveraging our proposed BioDrone, we conduct a systematic evaluation of existing SOT methods, comparing the performance of 20 representative models and studying novel means of optimizing a SOTA method (KeepTrack Mayer et al. in: Proceedings of the IEEE/CVF international conference on computer vision, pp. 13444–13454, 2021) for robust SOT. Our evaluation leads to new baselines and insights for robust SOT. Moving forward, we hope that BioDrone will not only serve as a high-quality benchmark for robust SOT, but also invite future research into robust computer vision. The database, toolkits, evaluation server, and baseline results are available at http://biodrone.aitestunion.com.

A large-eddy simulation framework has been coupled with controller modules to systematically investigate the impacts of collective (CPC) and individual (IPC) pitch control strategies on utility-scale wind turbine energy production and fatigue loads. Wind turbine components were parameterized using an actuator surface model to simulate the rotor blades and the turbine nacelle. The baseline CPC and IPC algorithms, consisting of single-input single-output proportional–integral controllers and two integral controllers, respectively, were incorporated into the numerical framework. A series of simulations were carried out to investigate the relative performance of the two controllers under various turbulent inflow conditions, spanning hub-height velocities of 7 to 14 m/s. The numerical simulation results of this study showed that, in comparison to the CPC, the IPC controller could successfully reduce the damage equivalent loads of utility-scale turbines at regions 2 and 3 of turbine operation by about 3% and 40%, respectively, without any penalty on the power production of the turbine. It was also shown that, despite its minor impact on the turbulence kinetic energy of the wake, the IPC controller did not influence the recovery of the turbine wake.

Monolithic 3D (MONO3D) integration provides performance and power efficiency benefits over 2D circuits and, thus, is a potent technology for the design of Deep Neural Network (DNN) accelerators with enhanced energy efficiency. However, high IC temperatures are major challenges for the design of MONO3D systems. To this end, this paper focuses on designing temperature-aware MONO3D DNN accelerators. We propose a new automated method, called TREAD-M3D, that provides a near-optimal MONO3D DNN accelerator architecture in terms of systolic array size, SRAM organization, partition across 3D layers, and operating frequency, for a given DNN, optimization goal, and temperature constraint. TREAD-M3D incorporates circuit-and architecture-level models to evaluate the power and performance characteristics of different partitions. Our method reveals valuable insights and enables tradeoff analysis for achieving high energy efficiency in MONO3D systolic arrays. In comparison to recent works that adopt a fixed partition choice to design MONO3D DNN systems, TREAD-M3D yields up to 22% higher energy efficiency. Using TREAD-M3D, we further demonstrate that temperature unawareness not only leads to infeasible configurations due to temperature violations but also over-estimates energy-delay-product benefits by up to 24%.

Reachability analysis is a formal method to guarantee safety of dynamical systems under the influence of uncertainties. A substantial bottleneck of all reachability algorithms is the necessity to adequately tune specific algorithm parameters, such as the time step size, which requires expert knowledge. In this work, we solve this issue with a fully automated reachability algorithm that tunes all algorithm parameters internally such that the reachable set enclosure respects a user-defined approximation error bound in terms of the Hausdorff distance to the exact reachable set. Moreover, this bound can be used to extract an inner-approximation of the reachable set from the outer-approximation using the Minkowski difference. Finally, we propose a novel verification algorithm that automatically refines the accuracy of the outer-approximation and inner-approximation until specifications given by time-varying safe and unsafe sets can be verified or falsified. The numerical evaluation demonstrates that our verification algorithm successfully verifies or falsifies benchmarks from different domains without requiring manual tuning.