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The Mojave and Sonoran Deserts, recognized as a global hotspot for bee biodiversity, are experiencing habitat degradation from urbanization, utility‐scale solar energy (USSE) development, and climate change. In this study, we evaluated the current and future distribution of bee diversity, assessed how protected areas safeguard bee species richness, and predicted how global change may affect bees across the region. Using Joint Species Distribution Models (JSDMs) of 148 bee species, we project changes in species distributions, occurrence area, and richness under four global change scenarios between 1971 and 2050. We evaluated the threat posed by USSE development and predicted how climate change will affect the suitability of protected areas for conservation. Our findings indicate that changes in temperature and precipitation do not uniformly affect bee richness. Lower elevation protected areas are projected to experience mean losses of up to 5.8 species, whereas protected areas at higher elevations and transition zones may gain up to 7.8 species. Areas prioritized for future USSE development have an average species richness of 4.2 species higher than the study area average, and lower priority “variance” areas have 8.2 more species. USSE zones are expected to experience declines of up to 8.0 species by 2050 due to climate change alone. Despite the importance of solitary bees for pollination, their diversity is often overlooked in land management decisions. Our results show the utility of JSDMs for leveraging existing collection records to ease the inclusion of data‐limited insect species in land management decision‐making. The American Desert Southwest is home to approximately one‐quarter of North American bee species. However, warming temperatures, shifting precipitation patterns, and development threaten to drive bee species redistribution, highlighting the need to include these data‐limited but ecologically vital species in land management decisions.

- Here we present the first description of nest architecture, immature stages, and brood-parasitism of Andrena (Callandrena s. l.) asteris (Aster Miner Bee) and the first description of the nesting biology of any Callandrena in eastern North America. Brood cells varied from 50 to 91 cm in depth, making this the deepest solitary bee nest recorded in northeastern North America. Additionally, we assembled data on soil texture, phenology, geographic distribution, and host-plant preferences. By modeling publicly available observation data, we find that areas of peak habitat suitability for A. asteris are in proximity to coastal and inland shorelines and major water courses. Our results corroborate a recent assessment of the conservation status of New York pollinators, which ranked A. asteris as \"vulnerable\".

Ascorbic acid 2-glucoside (AA2G), glucosylated ascorbic acid (AA), has superior properties for bioavailability and stability compared to AA. Although AA2G has shown radioprotective properties similar to AA, effects for UV light, especially UVC and UVB, are not studied. AA2G was tested for cytotoxicity and protective effects against ionizing radiation, UVC, and broadband and narrowband UVB in Chinese hamster ovary (CHO) cells and compared to AA and dimethyl sulfoxide (DMSO). Pretreatment with DMSO, AA, and AA2G showed comparative protective effects in CHO wild type and radiosensitive xrs5 cells for cell death against ionizing radiation with reducing the number of radiation-induced DNA damages. Pretreatment with AA and AA2G protected CHO wild type and UV sensitive UV135 cells from UVC and broadband UV, but not from narrowband UVB. DMSO showed no protective effects against tested UV. The UV filtration effects of AA and AA2G were analyzed with a spectrometer and spectroradiometer. AA and AA2G blocked UVC and reduced short wavelengths of UVB, but had no effect on wavelengths above 300 nm. These results suggest that AA2G protects cells from radiation by acting as a radical scavenger to reduce initial DNA damage, as well as protecting cells from certain UVB wavelengths by filtration.

Here we present the first description of nest architecture, immature stages, and brood-parasitism of Andrena (Callandrena s. l.) asteris (Aster Miner Bee) and the first description of the nesting biology of any Callandrena in eastern North America. Brood cells varied from 50 to 91 cm in depth, making this the deepest solitary bee nest recorded in northeastern North America. Additionally, we assembled data on soil texture, phenology, geographic distribution, and host-plant preferences. By modeling publicly available observation data, we find that areas of peak habitat suitability for A. asteris are in proximity to coastal and inland shorelines and major water courses. Our results corroborate a recent assessment of the conservation status of New York pollinators, which ranked A. asteris as “vulnerable”.

The Mojave and Sonoran Deserts, recognized as a global hotspot for bee biodiversity, are experiencing habitat degradation from urbanization, utility-scale solar energy (USSE) development, and climate change. In this study, we evaluated the current and future distribution of bee diversity in the region, assessed how protected areas safeguard bee species richness, and predicted how global change may affect bees across the region. Using Joint Species Distribution Models (JSDMs) of 148 bee species, we project changes in species distributions, occurrence area, and richness across the region under four global change scenarios between 1971 and 2050. We evaluated the threat posed by USSE development and predicted how climate change will affect the suitability of protected areas for conservation. Our findings indicate that changes in temperature and precipitation do not uniformly affect bee richness across the region. Protected areas in the Sonoran and Mojave Deserts are projected to experience mean losses of up to 5.8 species, whereas protected areas at higher elevations and transition zones may gain up to 7.8 species. Outside protected areas, bee diversity is threatened by urbanization and USSE development. Areas prioritized for future USSE development have an average species richness of 4.2 species higher than the study area average, and lower priority areas have 8.2 more species. USSE zones are expected to experience declines of 2.7 to 8.0 species by 2050 due to climate change alone. Despite the importance of solitary bees for pollination, their diversity is often overlooked in land management decisions. Our results show the utility of JSDMs for extending the usability of existing data-limited bee species records, easing the inclusion of these species in conservation and land management decision-making. The multiple threats from global change drivers underscore the importance of including ecologically vital, though often data-limited, species in land-use decisions.

Anthropogenic drivers of global change threaten bee diversity and the ecosystem services bees provide. Despite their importance, the conservation of bee pollinators is complicated by limited and often heavily biased occurrence data. A recent state-wide survey of insect pollinators across New York, United States generated a large spatial dataset of bee species occurrence records from community scientists, historical collections, and survey efforts. Using a combination of the state survey records with occurrence data from across the contiguous United States, we applied an ensemble modeling approach using balanced random forest and small bivariate generalized linear models to predict the distributions of most of the state’s bee species. We predicted the spatial distribution of bee species richness using a stacked species distribution model with climate, land cover, and soil covariates. To inform bee diversity conservation, we predicted spatial variation for each species and groups of species sharing similar life history traits. We also estimated statewide distribution of range-size rarity, ecological uniqueness, and climate exposure. We found that the richness of modeled species is high across the state, with the greatest richness in regions with low soil clay content and intermediate forest cover. The fine spatial scale and extent of our gridded data layers match the scale of conservation action in the state, providing an opportunity to incorporate wild bee diversity into broader statewide conservation planning. Conserving New York State’s bee pollinators is not straightforward, and decisions should be based on broader conservation priorities that incorporate bee biodiversity indicators into decision-making. Here, we encourage the inclusion of these vital pollinators in conservation decisions by leveraging the best available data and methods robust to small sample sizes to provide spatially explicit data products representing the distribution of bee diversity across the state of New York.

Learning from data is fast becoming the rule rather than the exception for many science and engineering research problems, particularly those encountered in nuclear engineering. Problems associated with learning from data fall under the more general category of inverse problems . A data-drive inverse problem involves constructing a predictive model of a target system from a collection of input/output observations. One of the difficulties associated with constructing a model that approximates such unknown causes based solely on observations of their effects is that collinearities in the input data result in the problem being ill-posed. Ill-posed problems cause models obtained by conventional techniques, such as linear regression, neural networks and kernel techniques, to become unstable, producing unreliable results. Methods of regularization using ordinary ridge regression (ORR) and kernel regression (KR) have been proposed as viable solutions to ill-posed problems. Successful application of ORR and KR require the selection of optimal parameter values—ridge parameters for ORR and bandwidth parameters for KR. The common practice for both methods is to select a single parameter based on minimizing an objective function which is an estimate of empirical risk. The single parameter value is then applied to all predictor variables indiscriminately, in a sort of one-size-fits-all fashion. Versions of ORR and KR have been proposed that make use of individual localized ridge and a matrix of localized bandwidth parameters that are optimally selected based on the relevance of their associated predictor variables to reducing empirical risk. While the practical and theoretical value of both localized regression techniques is recognized they have obtained limited use because of the difficulties associated with selecting multiple optimal ridge parameters for localized ridge regression (LRR)—defined as the localized ridge regression problem—and multiple optimal bandwidth parameters for localized kernel regression (LKR)—defined as the localized kernel regression problem—particularly for multivariate predictor data with more than four variables. This dissertation introduces a method of selecting optimal ridge parameters for LRR and a method of selecting a matrix of optimal bandwidth parameters for LKR based on the use of Differential Evolution (DE), a population based direct search global optimization technique. Three different objective functions, selected as prediction risk estimators, were developed and evaluated for LRR: Mallows' CL, an Information Complexity (ICOMP) based method of regression parameter selection (ICOMPRPS), and Generalized Cross-Validation (GCV). Leave-one-out cross-validation (LOO-CV) was used as the objective function for LKR. (Abstract shortened by UMI.)

Intrinsic activity in the brain is organized into networks. Although constrained by their anatomical connections, functional correlations between nodes of these networks reorganize dynamically. Dynamic organization implies that couplings between network nodes can be reconfigured to support processing demands. To explore such reconfigurations, we combined repetitive transcranial magnetic stimulation (rTMS) and functional connectivity MRI (fcMRI) to modulate cortical activity in one node of the default network, and assessed the effect of this upon functional correlations throughout the network. Two different frequencies of rTMS to the same default network node (the left posterior inferior parietal lobule, lpIPL) induced two topographically distinct changes in functional connectivity. High-frequency rTMS to lpIPL decreased functional correlations between cortical default network nodes, but not between these nodes and the hippocampal formation. In contrast, low frequency rTMS to lpIPL did not alter connectivity between cortical default network nodes, but increased functional correlations between lpIPL and the hippocampal formation. These results suggest that the default network is composed of (at least) two subsystems. More broadly, the finding that two rTMS stimulation regimens to the same default network node have distinct effects reveals that this node is embedded within a network that possesses multiple, functionally distinct relationships among its distributed partners.

The immune mechanisms that mediate synovitis and joint destruction in rheumatoid arthritis (RA) remain poorly defined. Although increased levels of CD8 + T cells have been described in RA, their function in pathogenesis remains unclear. Here we perform single cell transcriptome and T cell receptor (TCR) sequencing of CD8 + T cells derived from anti-citrullinated protein antibodies (ACPA)+ RA blood. We identify GZMB + CD8 + subpopulations containing large clonal lineage expansions that express cytotoxic and tissue homing transcriptional programs, while a GZMK + CD8 + memory subpopulation comprises smaller clonal expansions that express effector T cell transcriptional programs. We demonstrate RA citrullinated autoantigens presented by MHC class I activate RA blood-derived GZMB + CD8 + T cells to expand, express cytotoxic mediators, and mediate killing of target cells. We also demonstrate that these clonally expanded GZMB + CD8 + cells are present in RA synovium. These findings suggest that cytotoxic CD8 + T cells targeting citrullinated antigens contribute to synovitis and joint tissue destruction in ACPA+ RA. The immune mechanisms underlying synovitis and joint tissue destruction in rheumatoid arthritis (RA) remain incompletely defined. Here, the authors demonstrate that ACPA+ RA patients have activated clonally expanded cytotoxic GZMB + CD8+ T cells in blood and synovium that target and are activated by citrullinated antigens to mediate cell killing.

Neural correlates of remembering were examined using event-related functional MRI (fMRI) in 20 young adults. A recognition paradigm based on the remember/know (RK) procedure was used to separately classify studied items that were correctly identified and accompanied by a conscious recollection of details about the study episode from studied items that were correctly identified in the absence of conscious recollection. To facilitate exploration of the basis of remember decisions, studied items were paired with pictures and sounds to encourage retrieval of specific content during scanned testing. Analyses using a priori regions of interest indicated that remembering recruited both regions that associate with the perception and/or decision that information is old and regions that associate preferentially with visual content, while knowing recruited regions associated with oldness, but did not recruit visual content regions. Exploratory analyses further indicated a functional dissociation across regions of parietal cortex that may aid to reconcile several divergent results in the literature. Lateral parietal regions responded preferentially to remember decisions, while a slightly medial region responded robustly to both remember and know decisions. Taken collectively, these results suggest that remembering and knowing associate with common processes supporting a perception and/or the decision that information is old. Remembering additionally recruits regions specific to retrieved content, which may participate to convey the vividness typical of recollective experience.