Miller College

ABSTRACT We present the first joint analysis of the kinetic Sunyaev–Zeldovich (kSZ) effect with galaxy–galaxy lensing (GGL) for CMASS galaxies in the Baryon Oscillation Spectroscopic Survey. We show these complementary probes can disentangle baryons from dark matter in the outskirts of galactic haloes by alleviating model degeneracies that are present when fitting to kSZ or GGL measurements alone. In our joint kSZ + GGL analysis we show that the baryon density profile is well constrained on scales from 0.3 to 50 Mpc$\,h^{-1}$. With our well-constrained profile of the baryon density, we provide direct comparisons to simulations. For our model we find an outer slope of the baryon distribution that is shallower than predicted by some hydrodynamical simulations, consistent with enhanced baryonic feedback. We also show that not including baryons in a model for GGL can shift halo mass estimates by $\sim 20~{{\ \rm per\ cent}}$ compared to a model that includes baryons and is jointly fit to kSZ + GGL measurements. Our modelling code GGL and kSZ (glasz) is publicly available at https://github.com/James11222/glasz.

Understanding the extent of parallelism across adaptive radiations remains a central problem in evolutionary biology. We used whole-genome resequencing of 123 individuals to compare the adaptive radiation of Cyprinodon pupfishes in Lake Chichancanab, Mexico, to an independent radiation of San Salvador Island (SSI) pupfishes in the Bahamas, and assess the repeatability of adaptive genetic architecture, sources of adaptive variation, and stages of selection. Despite rapid craniofacial divergence of trophic specialists within 8–15 kya, only two candidate genes (0.5%; 2/426) were shared between Caribbean radiations. Although adaptive introgression played a major role in SSI, we found minimal evidence of adaptive introgression in Chichancanab, likely due to the geographic isolation of this inland lake. Instead, de novo mutations provided a substantial source of adaptive variation (30.6%) for the endemic zooplanktivore, 15 times higher than the endemic scale-eater on SSI. However, in parallel with SSI, we found strong evidence that adaptive divergence occurred in stages, first on regulatory and standing genetic variation, then on de novo and nonsynonymous mutations. Consistent with adaptive variants near opsin and spermatogenesis genes, functional categories unique to Chichancanab, we found greater visual acuity and divergent sperm morphology in lab-reared zooplanktivores relative to generalists using laboratory assays. Consistent with extensive adaptive de novo mutations in WNT10A and rapid diversification of tooth size in the zooplanktivore, we found that experimental inhibition of the Wnt pathway in generalists resulted in narrower oral teeth. We conclude that de novo mutations, not introgression, can drive rapid adaptive radiations in isolated environments.

An improved understanding of the materials that will sustain the future of energy production, storage, and delivery calls for better characterization tools. Operando characterization methods have thus become essential for investigating electrocatalytic materials. Without their resulting insights, the study of highly performing catalysts post-mortem cannot viably facilitate the further development of functional catalysts. Herein, we present an operando electrochemical cell designed for hard X-ray absorption spectroscopy (XAS) and specifically adapted to the study of an electrocatalytically active Cu nanoparticle ensemble. So far, this nanocatalyst has proven to pose quite a challenge to characterize due to its unique structural dynamics. Adopting a design comparable to the H-cell employed for all activity testing, we report the satisfactory translation of the active site formation into an XAS-compatible cell. The simultaneous collection of CO2-derived products during XAS characterization enabled the operando characterization of this CO2-reducing active structure. We report a Cu–Cu coordination number of the first scattering path higher than suggested in our previous studies, highlighting the importance of monitoring metastable nanoelectrocatalysts in operando. This study illustrates important caveats for the electrocatalysis community when considering the application of operando XAS. Our results highlight that the sample size, homogeneity, and stability determine how to interpret the measured signal. Considering these parameters carefully, the operando EXAFS results confirm the exceptional undercoordinated character of the Cu nanoparticle ensemble during CO2 reduction to C2+ products.

Influenza viruses encountered in childhood can leave a lasting immunological imprint. To disentangle the mortality effects of age, the circulating seasonal strain, and immune history, we fit statistical mortality models to 54 years of influenza mortality data from the U.S. We find strong signatures of subtype-level imprinting in H1N1-dominated seasons following the 2009 pandemic – cohorts imprinted with more similar H1N1 strains experience greater protection. Furthermore, we find large differences in age-specific mortality risk across cohorts based on their imprinted strain and the seasonal strains that were dominant throughout their lifetime. In contrast to older H1N1- and H2N2-imprinted cohorts, our results show that more recent cohorts imprinted with H3N2 have experienced substantially higher mortality through most of their lifetime. Our results highlight the long-term consequences of immune imprinting, and its impact on period and cohort influenza mortality. Overall, our findings have important implications for vaccination efforts and future influenza mortality burden.

ZusammenfassungHintergrund und ZielsetzungDie vorliegende Untersuchung befasste sich mit der In‐vivo‐ und Ex‐vivo‐Anwendung von hochfrequentem Ultraschall (HFUS) während mikrographisch kontrollierter Chirurgie (MKC) bei nichtmelanozytärem Hautkrebs (NMSC) im Kopf‐ und Gesichtsbereich, insbesondere im Hinblick auf die perioperative Beurteilung der Resektionsränder zur potenziellen Reduktion von Operationsschritten und der Hospitalisierungsdauer der Patienten.Patienten und MethodikDie präoperative In‐vivo‐ und Ex‐vivo‐Sonografie von 136 nichtmelanozytären Hauttumoren (NMSC) bei 111 Patienten wurde retrospektiv im Hinblick auf die Tumorrandbeurteilung während mikrographisch kontrollierter Chirurgie (MKC) durch einen erfahrenen Dermatochirurgen ausgewertet, während die Präparate unabhängig durch einen Histopathologen beurteilt wurden.ErgebnisseUnsere Ex‐vivo‐Beurteilung der Tumorränder zeigte eine Spezifität von >98 % für das Plattenepithelkarzinom (SCC) und das Basalzellkarzinom (BCC), mit lediglich fünf falsch‐negativen und zwei falsch‐positiven Befunden im Vergleich zur Histopathologie. HFUS identifizierte tumorfreie Ränder korrekt bei 89% der untersuchten Tumoren nach der ersten Resektion sowie acht unvollständige Exzisionen (6%), bei denen eine Nachresektion durchgeführt und Tumorreste histologisch nachgewiesen wurden. Insgesamt zeigte HFUS eine sehr hohe Genauigkeit sowohl in der Detektion tumorfreier Resektionsränder als auch von Tumorresten.SchlussfolgerungenHFUS ist eine hochpräzise Methode zur schnellen Beurteilung der Tumorränder während der MKC bei NMSC in ästhetisch besonders sensiblen Arealen. Sie hat das Potenzial, die Anzahl der Operationsschritte und die Hospitalisierungsdauer bei dermatochirurgischen Patienten signifikant zu reduzieren und damit die Qualität der Patientenversorgung zu verbessern.

Wastewater and environmental surveillance (WES) is a promising method of detecting infectious diseases in human and animal populations and offers significant advantages over traditional surveillance methods in the early detection of outbreaks. However, WES involves financial and human resources, and public policy decisions must determine whether the benefits of WES outweigh the costs, particularly in low-resource areas. The selection of surveillance sites, sampling frequency, and test sensitivity and specificity are crucial determinants of WES effectiveness and cost-efficiency. We created an analytical model and numerical simulations of disease arrival, spread, and WES strategies to determine the optimal sampling frequency for two interacting patches, each represented by a different sampling site. We show that it is optimal to test in one patch more frequently than it is to test in both patches less frequently if the patches are sufficiently interactive, surveillance is of sufficient sensitivity and specificity, and setup costs are substantial. In our value of information (VOI) assessment, the net value of surveillance information for both patches is non-monotonic with respect to the degree of patch interaction. Increased mixing between the patches allows for quicker surveillance detection but is worse for overall infection burden. Overall, optimizing the value of surveillance information for all patches being surveilled requires coordination and deliberate selection of surveillance sites and sampling frequencies. This paper provides a VOI assessment of WES to determine the optimal number of sites and sampling frequency at a high level of abstraction, leaving opportunity to adapt the model to specific pathogens and populations as needed. Our findings can inform the cost-efficient implementation of WES for infectious diseases, particularly in resource-constrained settings.

The modern marketing approach highly relies on behavioural segmentation to allow businesses the capability of reaching customers through their observed behaviours and preferences. The combination of traditional segmentation tools and AI-powered marketing platforms proves inaccessible to small businesses because of their high implementation expenses and technical complexity together with their requirement for specialized expertise. The article introduces an extensive system to implement affordable AI technologies specifically designed for behavioural segmentation operations within small business domains. An approach that uses lightweight machine learning models alongside open-source platforms and cloud-based infrastructures allows us to perform precise customer clustering and personalized marketing while avoiding requirements for large-scale data and computational resources. The research team establishes and tests a program framework that utilizes K-Means clustering with decision trees alongside sentiment analysis APIs on genuine dataset samples extracted from local storefronts and service firms. Our method proves successful in detecting separate behavioural patterns while maintaining below 15% error rate using minimal financial resources and limited training sessions. The paper presents implementation strategies along with recommendations for ethical AI usage in small-scale marketing fields and scales to process requirements.