Cornell University

Although numerous studies have projected changes in freezing rain under future climate conditions, the internal variability of freezing rain remains poorly quantified. Here, we introduce a framework utilizing a novel machine‐learning algorithm to diagnose freezing rain in reanalysis and climate model simulations. By employing multivariate quantile mapping, we decompose the projected freezing rain trend into contributions from changes in temperature, relative humidity, and precipitation, which helps separate the forced response from internal climate variability. Our finding reveals a notable decrease in freezing rain occurrence in most areas. Despite a substantial temperature increase, internal variability overshadows climate forcing across a large portion of the eastern United States until about 2050. This insight has implications for practitioners, suggesting that the observed freezing rain frequency climatology continues to provide a relevant baseline for decision‐making in the near term. However, longer‐term design and adaptation plans should consider the projected changes in these regions.

The article discusses the relation between skills (or competences), creditability, and aptness. The positive suggestion is that we might make progress understanding the relation between creditability and aptness by inquiring more generally about how different kinds of competences and their exercise might underwrite allocation of credit. Whether or not a competence is acquired and whether or not a competence is actively exercised might matter for the credit that the agent deserves for the exercise of that competence. A fine-grained taxonomy of competences opens up the possibility of instinctual knowledge (knowledge by mere instincts) as well as the possibility of habitual knowledge (knowledge by mere habits), alongside knowledge by skills (or alongside knowledge by yet other sorts of competences). If instinctual knowledge were possible, it is suggested that it might not be of the sort that deserves credit at all. By piggybacking from the literature in evolutionary psychology, I suggest that, as inborn social learners, merely instinctual—and so not fully creditable—knowledge might be a reality for us.

A bstract We examine the phase structure of a QCD-like theory at $\overline{\theta}$ θ ¯ = π obtained from supersymmetric SU( N ) QCD perturbed by a small amount of supersymmetry breaking via anomaly mediation (AMSB QCD). The spectrum of this theory matches that of QCD at the massless level, though the superpartners are not decoupled. In this theory it is possible to nail down the phase structure at $\overline{\theta}$ θ ¯ = π as a function of the quark masses and the number of flavors F . For one flavor we find that there is a critical quark mass, below which CP is unbroken, while above the critical mass CP is spontaneously broken. At the critical mass there is a second-order phase transition along with a massless η ′ . We are able to analytically solve for the minima and the critical mass for N = 2, 3 as well as for the large N limit, while for other N one can find numerical results. For two flavors, we find that CP is always broken as long as the quark masses are equal and non-zero, however there is a non-trivial phase boundary for unequal quark masses, which we find numerically. For F ≥ 3 we obtain an intricate phase boundary which reproduces the various quark mass limits. All our results are in agreement with the predictions of refs. [1–5] for ordinary QCD that were based on anomaly matching arguments for generalized symmetries and the effective chiral Lagrangian. We also briefly comment on the domain wall solutions first discussed by Draper [6], and are able to present analytic results for the simplest case of SU(2) with one flavor.

Prof. Gabriel “Gabi” Popescu was a faculty member and the director of the Quantitative Light Imaging (QLI) Laboratory at the University of Illinois at Urbana-Champaign. He was a pioneer in quantitative phase imaging (QPI), having developed several common-path QPI methods at MIT and Illinois, and promoted the applications of QPI across different domains. Tragically, Prof. Popescu passed away on June 16, 2022, in Prundu, Romania. His untimely loss is deeply felt by the scientific community and his colleagues, students, and friends around the world. As former members of his group and close friends, we recount our academic journeys with Gabi in QPI from different perspectives.

Agriculture is a vital industry in New York State, which ranks among the top‐producing states for dairy, fruits, and several other commodities. As agriculture depends on the weather and specific climatic conditions, this sector faces extraordinary challenges as New York's climate changes. This chapter explores the many impacts of a changing climate on agriculture, the ways these impacts interact with other challenges that New York farmers and farmworkers face, and opportunities for the agriculture industry to adapt and build resilience.

Objective The purpose of this study was to determine the M1/M2 macrophage ratio in concentrated bone marrow aspirate (cBMA) in patients undergoing surgical intervention augmented with cBMA for osteochondral lesions of the talus (OLTs). Design Samples of peripheral blood (PB), bone marrow aspirate (BMA), and cBMA were collected during the procedure. The samples were analyzed by automated cell counting and multicolor fluorescence-activated cell sorting with specific antibodies recognizing monocytes (CD14+ CD16+) and the M1 (CD86+) and M2 (CD163+CD206+) populations within that monocyte population. Cytokine concentrations within the samples were evaluated with enzyme-linked immunosorbent assay (ELISA). The composition of cBMA was compared between 2 commercially available BMA concentration systems. Results Thirty-eight patients with a mean age of 43.2 ± 10.1 years old undergoing a surgical procedure for the treatment of OLTs involving the use of cBMA were included. cBMA had a mean fold increase of 4.7 for all white blood cells, 6.1 for monocytes, 7.9 for lymphocytes, 2.4 for neutrophils, and 9.6 for platelets when compared to BMA. The mean M1/M2 ratio for PB, BMA, and cBMA was 15.2 ± 12.0, 20.8 ± 13.3, and 22.1 ± 16.0, respectively. There was a statistically significant higher concentration of interleukin-1 receptor antagonist (IL-1Ra) in the cBMA sample (8243.3 ± 14,837.4 pg/mL) compared to both BMA (3143.0 ± 2218.5 pg/mL) and PB (1847.5 ± 1520.4 pg/mL) samples. The IL-1Ra/IL-1β ratio for PB, BMA, and cBMA was 790.6 ± 581.9, 764.7 ± 675.2, and 235.7 ± 192.1, respectively. There was no difference in the cBMA M1/M2 ratio (19.0 ± 11.1 vs 24.0 ± 18.3) between the Magellan (Isto Biologics, Hopkinton, Massachusetts) and Angel systems (Arthrex Inc, Naples, Florida). Conclusion This prospective study found that the M1/M2 ratio in cBMA was 22.1 ± 16.0, with significant patient to patient variation observed. Overall, there was no statistically significant difference in the M1/M2 ratio across PB, BMA, and cBMA samples. This is the first study to characterize the macrophage subpopulation within cBMA, which may have significant clinical implications in future studies.

Aberrant activation of Notch signaling, mediated by the Notch intracellular domain (NICD), is linked to certain types of cancer. The NICD is released through γ-secretase-mediated cleavage of the Notch receptor. Therefore, development of a γ-secretase inhibitor (GSI) represents an anticancer strategy. Here we report the cryo-electron microscopy structures of human γ-secretase bound individually to five clinically tested GSIs (RO4929097, crenigacestat, BMS906024, nirogacestat and MK-0752) at overall resolutions of 2.4–3.0 Å. Three of the five GSIs are in active anticancer clinical trials, while nirogacestat was recently approved. Each of these GSIs similarly occupies the substrate-binding site of presenilin 1 but shows characteristic differences in detailed recognition pattern. The size and shape of the binding pocket are induced by the bound GSI. Analysis of these structural features suggest strategies for modification of the GSI with improved inhibition potency.

Situating in the scholarly debate on whether Fintech serves as a development catalyst for African leapfrogging or a new mechanism for capitalist exploitation, the chapter asks how Fintech—as a socio-technical infrastructure—reshapes urban economy and experiences of urban living. The empirical case focuses on the Nigerian ride-hailing sector, examining the collaboration between Fintech companies and ride-hailing platforms. Using a case study of Moove, a Fintech company, and Uber, a global giant of ride-hailing platform, we explore how such partnerships transform the urban mobility landscape in Lagos on the one hand, and tremendously influence drivers’ livelihoods and wellbeing, on the other. Specifically, we argue that Fintech expands vehicle financing solutions, enabling more Nigerians to participate in the digital economy. However, the collaboration between Fintech companies and ride-hailing platforms also exacerbates employment vulnerability in times of economic hardships and policy change in Nigeria. The case sheds light on the complexities and socio-economic implications of Fintech-platform collaborations in Nigeria’s ride-hailing sector and inform future scholarly conversation about and policy intervention towards urban mobility and financial inclusion.

The 15-minute City, an innovative urban model, emerged prominently during the recent COVID-19 pandemic, capturing the attention of urban planners and policymakers. Rooted in principles of proximity, sustainability, and health, this model envisions a post-pandemic world where all essential services are accessible within a 15-minute travel distance (walking or biking) from every household. These services encompass groceries, medical facilities, childcare, education, employment, and other daily necessities, contributing to the creation of smaller neighbourhood cities within cities. While the concept of a well serviced dense urban lifestyle is not novel and has been integral to various cities globally, the 15-minute City framework offers a conceptual shift to enhance city planning and elevate overall quality of life. Serving as a guiding tool, it holds the potential for widespread applicability across diverse geographical landscapes. This research employs a Systems Thinking lens, using an Agent-Based Approach, to comprehensively analyse the ‘Conceptual and Global 15-minute City Framework’ and assess its effectiveness, opportunities, and limitations, as a model for sustainable urban development. Furthermore, it envisions a future system of a ‘Comprehensive Proximity-Urbanism Framework’ to support the real-world implementation of 15-minute Cities across varied socio-cultural, economic, and political contexts. The analytical tools used include a DSRP Analysis (Distinctions, Systems, Relationships, Perspectives), a POSIWID Analysis (Purpose of the System is What it Does), and a CAS Analysis (Complex Adaptive System) of the system. Adopting a systems thinking approach allows for a methodical identification of blind spots inherent in this conceptual and global framework. This chapter reflects on the recent scholarly discussions surrounding the 15-minute City framework to critically analyse its real-world feasibility. In addition, to ground the research and analysis in context the research includes two case study cities. The first case study analyses Dubai’s proposed 20-minute City initiative, while the second case study examines Barcelona’s 15-minute City analysis of its existing urban morphology. By adopting this holistic approach, the chapter aims to contribute to the ongoing discourse surrounding urban planning paradigms, shedding light on the viability and adaptability of the 15-minute City framework in shaping the cities of the future.

The effectiveness of governments in addressing the Covid-19 pandemic has been analyzed through numerous perspectives, including state authority, capacity, and legitimacy. In Hong Kong, incumbent political tensions and lack of trust in government potentially weakened public support for official Covid-19 mitigation measures, which included a government-mandated mobile application to monitor personal movements and contacts. This episode invites academic inquiry about public trust in policies and associated technology. Based on a 2022 survey of more than 5,000 residents, this study finds that support for Hong Kong’s LeaveHomeSafe application is associated with, among other factors, a predisposition to cooperate with government in sharing personal data. Several control variables, including age, social media use, and vaccination status, are also associated with support for the application. Trust in policies that use technology and data, and agreement that the government was effective in managing the spread of Covid-19, do not significantly associate with public support for the application. This study deepens scholarly understandings about the distinction between public trust in government and public trust in government technology, a topic of recent interest in the context of smart cities and rapid advancements in artificial intelligence.

Premise Although previous studies have reported a positive correlation between leaf dry mass per unit area (LMA) and mean leaf thickness (LT), the LMA versus LT scaling relationship has not been determined due to limited sample sizes, despite its importance in estimating leaf bulk tissue density (mass per unit volume). Methods This issue was addressed using between 174 and 185 leaves from each of nine phylogenetically diverse species to investigate the LMA vs. LT scaling relationship. For each leaf, lamina thickness was measured at 12 positions (avoiding midribs and major veins) to calculate LT, and LMA was measured based on leaf area and dry mass measurements. Reduced major axis regression protocols were used to determine the LMA vs. LT scaling exponent (i.e., the slope). Bootstrap percentile methods were used to calculate the 95% confidence intervals of slopes. Results A statistically significant LMA vs. LT relationship was found for each species; seven of the nine scaling exponents were significantly greater than unity indicating that LMA (and thus leaf bulk tissue density) disproportionately increased with increasing LT. In addition, the conspecific variation in LMA exceeded the interspecific variation in LMA as a consequence of differences in LT. Conclusions These results indicate that empirical measurements of LMA and LT can be used to accurately estimate leaf bulk tissue density, which provides insights into adaptive life‐history strategies, conspecific variation, and (with sufficiently large data sets) phylogenetic trends.

Compound coastal flooding due to astronomic, atmospheric, oceanographic, and hydrologic drivers poses severe threats to coastal communities. While physics-driven approaches are able to dynamically simulate temporally and spatially varying compound flooding generated by multiple drivers with correlations between some of them, computational burdens limit their capability to explore the full range of conditions that contribute to compound coastal hazards. Data-driven statistical approaches address some of these computational challenges; however, they are also unable to explore all possible forcing combinations due to short observational records, and projections are typically limited to a few locations. This study proposes a hybrid statistical–dynamical framework for compound coastal flooding analysis that integrates a stochastic generator of compound flooding drivers, a hydrodynamic model, and machine learning-based surrogate models. The framework was demonstrated in San Francisco Bay (SF) over the past 500 years with accuracy similar to the physics-driven approach but with much higher computational efficiency. The stochastic generator of compound flooding drivers is developed by coupling a sea surface temperature (SST) reconstruction model with a climate emulator, weather generator, and model of the hydrological and reservoir system. Using reconstructed SSTs as input, the generator of compound flooding drivers is employed to simulate time series of the forcing factors contributing to compound flooding (e.g. surge, waves, river discharge, etc) in SF Bay. A process-based hydrodynamic model is built to predict total water levels varying in time and space throughout SF Bay based on stochastically generated drivers. The machine learning-based surrogate models are then developed from a relatively small library (several hundred) of hydrodynamic model simulations to efficiently predict water levels for compound flooding analysis under the full range of stochastic drivers. This study contributes a hybrid statistical–dynamical framework to better understand the spatial distribution and temporal evolution of compound coastal-fluvial flooding, along with the relative contributions of drivers in complex nearshore, estuarine, and river environments for centennial timescales under past, present, and future climates.

INTRODUCTION High microglial heterogeneities hinder the development of microglia‐targeted treatment for Alzheimer's disease (AD). METHODS We integrated 0.7 million single‐nuclei RNA‐sequencing transcriptomes from human brains using a variational autoencoder. We predicted AD‐relevant microglial subtype‐specific transition networks for disease‐associated microglia (DAM), tau microglia, and neuroinflammation‐like microglia (NIM). We prioritized drugs by specifically targeting microglia‐specific transition networks and validated drugs using two independent real‐world patient databases. RESULTS We identified putative AD molecular drivers (e.g., SYK, CTSB, and INPP5D) in transition networks of DAM and NIM. Via specifically targeting NIM, we identified that usage of ketorolac was associated with reduced AD incidence in both MarketScan (hazard ratio [HR] = 0.89) and INSIGHT (HR = 0.83) Clinical Research Network databases, mechanistically supported by ketorolac‐treated transcriptomic data from AD patient induced pluripotent stem cell–derived microglia. DISCUSSION This study offers insights into the pathobiology of AD‐relevant microglial subtypes and identifies ketorolac as a potential anti‐inflammatory treatment for AD. Highlights An integrative analysis of ≈ 0.7 million single‐nuclei RNA‐sequencing transcriptomes from human brains identified Alzheimer's disease (AD)–relevant microglia subtypes. Network‐based analysis identified putative molecular drivers (e.g., SYK, CTSB, INPP5D) of transition networks between disease‐associated microglia (DAM) and neuroinflammation‐like microglia (NIM). Via network‐based prediction and population‐based validation, we identified that usage of ketorolac (a US Food and Drug Administration–approved anti‐inflammatory medicine) was associated with reduced AD incidence in two independent patient databases. Mechanistic observation showed that ketorolac treatment downregulated the Type‐I interferon signaling in patient induced pluripotent stem cell–derived microglia, mechanistically supporting its protective effects in real‐world patient databases.

Purpose To develop a breath‐hold cardiac quantitative susceptibility mapping (QSM) sequence for noninvasive measurement of differential cardiac chamber blood oxygen saturation (ΔSO2). Methods A non‐gated three‐dimensional stack‐of‐spirals QSM sequence was implemented to continuously sample the data throughout the cardiac cycle. Measurements of ΔSO2 between the right and left heart chamber obtained by the proposed sequence and a previously validated navigator Cartesian QSM sequence were compared in three cohorts consisting of healthy volunteers, coronavirus disease 2019 survivors, and patients with pulmonary hypertension. In the pulmonary‐hypertension cohort, Bland–Altman plots were used to assess the agreement of ΔSO2 values obtained by QSM and those obtained by invasive right heart catheterization (RHC). Results Compared with navigator QSM (average acquisition time 419 ± 158 s), spiral QSM reduced the scan time on average by over 20‐fold to a 20‐s breath‐hold. In all three cohorts, spiral QSM and navigator QSM yielded similar ΔSO2. Among healthy volunteers and coronavirus disease 2019 survivors, ΔSO2 was 17.41 ± 4.35% versus 17.67 ± 4.09% for spiral and navigator QSM, respectively. In pulmonary‐hypertension patients, spiral QSM showed a slightly smaller ΔSO2 bias and narrower 95% limits of agreement than that obtained by navigator QSM (1.09% ± 6.47% vs. 2.79% ± 6.99%) when compared with right heart catheterization. Conclusion Breath‐hold three‐dimensional spiral cardiac QSM for measuring differential cardiac chamber blood oxygenation is feasible and provides values in good agreement with navigator cardiac QSM and with reference right heart catheterization.

China's agricultural development has historically depended on substantial resource inputs, raising sustainability concerns. While previous studies have examined agricultural efficiency, the spatial and temporal dynamics of decoupling agricultural resource inputs (ARIs) from agricultural economic development (AED) in China's 31 states remain underexplored. This study used Tapio Model to investigate how have the ARIs decoupled from AED across different regions over 15 years. We assessed the decoupling scenarios every five years from 2006 to 2021, categorizing 15 years into three periods. We measured AED through agricultural yield and production value, while ARIs were assessed based on the consumption of machinery gross power, water, fertilizer, pesticides, electricity, and plastic film. Using provincial data from 2006 to 2021, we calculated the decoupling status (weak decoupling, strong decoupling, recessive decoupling, expansive coupling, recessive coupling, expansive negative decoupling, strong negative decoupling, and weak negative decoupling) of each indicator for each time period in each region in Excel based on Tapio Model and created data visualization in ArcGIS that demonstrated the spatial and temporal decoupling trends. We found that (1) the decoupling statuses between AED and ARIs shows different times of progress and deterioration due to government actions and agricultural technology innovations. (2) Some decoupling status between AED and ARIs was spatially different due to the degree of technology inputs, weather conditions, government initiatives and urbanization. This study provides policy recommendations grounded in increasing AED and mitigating ARIs to help China achieve its sustainable development goals by enhancing yield and resource utilization efficiency, increasing technological investment and promoting agricultural modernization, and strengthening environmental protection and advancing clean energy development.