University of California, Santa Barbara

The institutionalization of occupations tends to assume homogenization of occupational values. This study addresses the question of how members of an occupation with dissenting preferences reach consensus on a code of ethics. We build on prior theorization of occupational institutionalization and institutional discourse to theorize ethical codification as a dynamic discursive process of internal dissent and consensus culminating in a professional code of ethics. We use email data from the IEEE-ACM Software Engineering Ethics and Professional Practice Committee tasked with producing the 1997 Software Engineering Code of Ethics to show how ethical codification follows a process of initial competition followed by semantic convergence. This study demonstrates how natural language processing and semantic network analysis can contribute to discourse analyses of institutional processes.

Microchimerism is defined as the presence of a small population of genetically distinct cells within a host that is derived from another individual. Throughout pregnancy, maternal and fetal cells are known to traffic across the feto-maternal interface and result in maternal and fetal microchimerism, respectively. However, the routes of cell transfer, the molecular signaling as well as the timing in which trafficking takes place are still not completely understood. Recently, the presence of inflammation at the feto-maternal interface has been linked with maternal microchimeric cells modulating organ development in the fetus. Here, we review the current literature and suggest that inflammatory processes at the feto-maternal interface tissues are a physiological prerequisite for the establishment of microchimerism. We further propose a spatio-temporal corridor of microchimeric cell migration to potentially explain some biological effects of microchimerism. Additionally, we elaborate on the possible consequences of a shift in this spatio-temporal corridor, potentially responsible for the development of pathologies in the neonate.

The introduction of degradable units into the backbone of commodity vinyl polymers represents a major opportunity to address the societal challenge of plastic waste and polymer recycling. Previously, we reported the facile copolymerization of α ‐lipoic acid derivatives containing 1,2‐dithiolane rings with vinyl monomers leading to the incorporation of degradable S–S disulfide bonds along the backbone at relatively high dithiolane monomer feed ratios. To further enhance the recyclability of these systems, here we describe a facile and user‐friendly strategy for backbone degradation at significantly lower dithiolane loading levels through cleavage of both SS and SC backbone units. Copolymers of n ‐butyl acrylate ( n BA) or styrene (St) with small amounts of either α ‐lipoic acid (LA) or ethyl lipoate (ELp) dissolved in DMF were observed to undergo efficient degradation when heated at 100°C under air. For example, at only 5 mol% ELp, a high molecular weight poly(ELp‐ co ‐ n BA) ( M n = 62 kg mol ⁻¹ ) degraded to low molecular weight oligomers ( M n = 3.2 kg mol ⁻¹ ) by simple heating in DMF. In contrast, extended heating of either poly( n BA) or poly(St) homopolymers under the same conditions did not lead to any change in molecular weight or cleavage of the C–C backbone. This novel approach allows for the effective degradation of vinyl‐based polymers with negligible impact on properties and performance due to the low levels of dithiolane incorporation.

This study investigated the underlying mechanism between bullying victimization and depressive symptoms via social trust cross-sectionally and longitudinally among 4,548 early adolescents at T1 and 4,484 adolescents at T2 from rural areas in Guizhou, China. Correlational data showed that all forms of bullying victimization at T1 showed negative correlations with in-group trust at T1 and T2 and generalized trust and depressive symptoms at T1. Both forms of trust were negatively associated with depressive symptoms at both time points. Results of structural equation modeling revealed a significant cross-sectional relationship between T1 bullying victimization and T1 depressive symptoms and that T1 bullying victimization was indirectly related to T1 depressive symptoms through both in-group and generalized social trust at T1. Thus, among students who reported experiencing more bullying, there was a lower level of trust in familiar people and authorities, which mediated their reports of depression. However, social trust did not explain the nonsignificant longitudinal relationship between bullying victimization at T1 and depressive symptoms at T2, potentially due to the lack of control of confounding variables. One implication is the importance of immediate intervention to counteract the tendency to overgeneralize bullying victimization to overall social trust. This study contributes to an empirical understanding of the underlying mechanism between bullying victimization and psychopathology symptoms among early adolescents in rural China.

Familial Platelet Disorder with associated Myeloid Malignancy (FPDMM, FPD/AML, RUNX1‐FPD), caused by monoallelic deleterious germline RUNX1 variants, is characterized by bleeding diathesis and predisposition for hematologic malignancies, particularly myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). Clinical data on FPDMM‐associated AML (FPDMM‐AML) are limited, complicating evidence‐based clinical decision‐making. Here, we present retrospective genetic and clinical data of the largest cohort of FPDMM patients reported to date. We describe 159 European patients (from 94 families) of whom 134 were evaluable for the development of malignant disease. Sixty developed a hematologic malignancy (44.8%), most frequently AML (36/134, 26.9%) or MDS (18/134, 13.4%). Somatic alterations of RUNX1 by gene mutation (48%) and chromosome 21 aberrations (14.3%) were the most common somatic genetic aberrations in FPDMM‐AML, followed by FLT3‐ITD mutations (24.1%). Somatic RUNX1 and FLT3‐ITD mutations were not detected in FPDMM‐associated MDS, suggesting important contributions to leukemic transformation. Remission‐induction chemotherapy resulted in complete remission in 80% of FPDMM‐AML patients with a 5‐year overall survival (OS) of 50.4%. Survival outcome was non‐inferior compared to a large cohort of newly diagnosed adult RUNX1‐mutated AML (5‐year OS 36.6%, p = 0.5), with relatively infrequent concurrent adverse risk somatic aberrations (ASXL1 mutation, monosomal karyotype, monosomy 5/del 5q) in FPDMM‐AML. Collectively, data support the notion that step‐wise leukemic evolution in FPDMM is associated with distinct genetic events and indicate that a substantial subset of FPDMM‐AML patients achieves prolonged survival with conventional AML treatment, including allogeneic stem cell transplant. These findings are anticipated to inform personalized clinical decision‐making in this rare disorder.

Decentralized Finance (DeFi) is reshaping traditional finance by enabling direct transactions without intermediaries, creating a rich source of open financial data. Layer 2 (L2) solutions are emerging to enhance the scalability and efficiency of the DeFi ecosystem, surpassing Layer 1 (L1) systems. However, the impact of L2 solutions is still underexplored, mainly due to the lack of comprehensive transaction data indices for economic analysis. This study bridges that gap by analyzing over 50 million transactions from Uniswap, a major decentralized exchange, across both L1 and L2 networks. We created a set of daily indices from blockchain data on Ethereum, Optimism, Arbitrum, and Polygon, offering insights into DeFi adoption, scalability, decentralization, and wealth distribution. Additionally, we developed an open-source Python framework for calculating decentralization indices, making this dataset highly useful for advanced machine learning research. Our work provides valuable resources for data scientists and contributes to the growth of the intelligent Web3 ecosystem.

Successful emotion regulation (ER) requires effective strategy selection. Research suggests that disengagement strategies (e.g., distraction) are more often selected than engagement strategies (e.g., reappraisal) as emotional experiences intensify. However, the extent to which ER strategy choice in controlled circumstances reflects strategy usage during complex, multimodal events is not well understood. The present research uses dynamic, multimodal stimuli (i.e., a haunted house, horror movies) to examine the association between affective intensity and regulatory strategy usage among untrained participants—individuals given no prior regulation instructions or direction. Both a preliminary study (n = 54) and Study 1 (n = 118) failed to find relationships between emotional intensity and strategy usage to downregulate emotions as participants navigated a haunted house. Distraction was self-reported to be less successful than reappraisal at high intensities, contrary to expectations. Participants in Study 2 (n = 152) forecasted regulation strategy usage based upon descriptions of emotionally regulated experiences from the preliminary haunted house study. Affective intensity predicted which strategies forecasters predicted they would use; though, forecasters overpredicted how often distraction was used in practice. Study 3 (n = 242) incorporated strategy usage and forecasting within the same design by showing untrained participants video stimuli of varying intensity and capturing their regulatory responses. Forecasters again predicted using distraction more often than strategy users did in practice. Forecasters also overpredicted how effectively distraction reduced negative affective intensity relative to what strategy users reported. These results may highlight a disconnect between strategy fittedness when self-regulation occurs in uncontrolled, highly intense, or complex circumstances.

Plain Language Summary River corridors are fertile landscapes hosting nearly 3 billion people worldwide and supporting agriculture and commerce. However, rivers are not static; they shift their positions over time through a variety of processes. Understanding the controls on river movement is crucial, especially given that climate change and humans are altering water and sediment flows through rivers globally. Here, we compile daily water discharge records and river mobility measurements spanning multiple years for 48 rivers worldwide that cover a wide range of climates and planform shapes. We find that river mobility is correlated with water discharge variability across daily, seasonal, and yearly timescales. Data show that, for similar average water flow rates, rivers with higher discharge variability move faster across their floodplain. A machine learning model suggests that water discharge variability best explains the typical pace of river movement on a multi‐decadal time frame. These results provide, for the first time, widespread evidence that faster river mobility is linked with higher water discharge variability. Our findings can inform how rivers will respond to future climate change‐driven shifts in the distribution of hydro‐climatic extremes.

Block copolymers play a vital role in materials science due to their diverse self-assembly behavior. Traditionally, exploring the phase space of block copolymer self-assembly and associated structure-property re- lationships involves iterative synthesis, characterization, and theory, which is labor-intensive both experimentally and computationally. Here, we intro- duce a versatile, high-throughput workflow towards materials discovery that integrates controlled polymerization and automated chromatographic separa- tion with a novel physics-informed machine learning algorithm for the rapid analysis of small-angle X-ray scattering data. Leveraging the expansive and high-quality experimental datasets generated by fractionating polymers using automated chromatography, this machine learning method effectively reduces data dimensionality by extracting chemical-independent features from SAXS data. This new approach allows for the rapid and accurate prediction of mor- phologies without repetitive and time-consuming manual analysis, achieving out-of-sample predictive accuracy of around 95% for both novel and existing materials in the training dataset. By focusing on a subset of samples with large predictive uncertainty, only a small fraction of the samples needs to be inspected to further improve accuracy. Collectively, the synergistic combi- nation of controlled synthesis, automated chromatography, and data-driven analysis creates a powerful workflow that markedly expedites the discovery of structure-property relationships in advanced soft materials.

Presented is an O-band silicon photonics dual-polarization coherent/IMDD modulator integrated with semiconductor optical amplifiers and tunable laser to enhance the short-reach link budget. The laser demonstrated output power >6 dBm and a <250 kHz linewidth over a 14 nm tuning range. Modulators paired with custom 64 Gbaud QPSK drivers exhibited improved analog link sensitivity compared to similar devices without integrated gain sections. They also demonstrated 53 Gbaud dual-polarization PAM4 operation when characterized with a linear driver and MaxLinear 100G/lane DSP board. Both optical links achieved BERs at the KP4-FEC threshold and overall transmitter assembly energy consumption <6.9 pJ/bit without any thermal control when at steady room temperatures.

Quantifying ecosystem services provided by mobile species like insectivorous bats remains a challenge, particularly in understanding where and how these services vary over space and time. Bats are known to offer valuable ecosystem services, such as mitigating insect pest damage to crops, reducing pesticide use, and reducing nuisance pest populations. However, determining where bats forage is difficult to monitor. In this study, we use a weather‐radar‐based bat‐monitoring algorithm to estimate bat foraging distributions during the peak season of 2019 in California's Northern Central Valley. This region is characterized by valuable agricultural crops and significant populations of both crop and nuisance pests, including midges, moths, mosquitos, and flies. Our results show that bat activity is high but unevenly distributed, with rice fields experiencing significantly elevated activity compared to other land cover types. Specifically, bat activity over rice fields is 1.5 times higher than over any other land cover class and nearly double that of any other agricultural land cover. While irrigated rice fields may provide abundant prey, wetland and water areas showed less than half the bat activity per hectare compared to rice fields. Controlling for land cover type, we found bat activity significantly associated with higher flying insect abundance, indicating that bats forage in areas where crop and nuisance pests are likely to be found. This study demonstrates the effectiveness of radar‐based bat monitoring in identifying where and when bats provide ecosystem services.

The rapid acceleration of global warming is leading to an increased rate of glacier melt at the Earth's poles, which exacerbates the threats posed by the climate crisis and contributes to rising ocean levels. The Greenland Ice Sheet, recognized as the second-largest ice sheet globally, has housed its extensive glaciers and ice caps for at least 18 million years [1]. The melting and potential collapse of this ice sheet could significantly affect Worldwide climate and sea height. This research aims to develop a coupled model to assess rising sea levels caused by the thawing of the Greenland Ice Sheet, incorporating factors such as global warming, glacier volume, hydrothermal expansion, isostatic rebound, ice dynamics, changes in the gravitational field, and the absorption and release of methane gas. Additionally, the paper investigates the hazards connected to melting glaciers exacerbating global warming. This research provides a crucial foundation for forecasting the long-term impacts of the Greenland Ice Sheet's melting on global climate change.