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A general theoretical framework is proposed to quantify the thermodynamic properties of multicomponent hard colloidal mixtures. This framework is used to predict the phase behavior of mixtures of rods with spheres and rods with plates taking into account (liquid) crystal phases of both components. We demonstrate a rich and complex range of phase behaviors featuring a large variety of different multiphase coexistence regions, including two five-phase coexistence regions for hard rod/sphere mixtures, and even a six-phase equilibrium for hard rod/plate dispersions. The various multiphase coexistences featured in a particular mixture are in line with a recently proposed generalized phase rule and can be tuned through subtle variations of the particle shape and size ratio. Our approach qualitatively accounts for certain multiphase equilibria observed in rod/plate mixtures of clay colloids and will be a useful guide in tuning the phase behavior of shape-disperse mixtures in general.

Deficits in inhibitory control are common with advancing age and may underlie declines in other complex cognitive functions. The inhibitory P300 event-related potential (ERP) generally decreases in amplitude with age, reflecting deficits in inhibitory performance evaluation and adaptation, with possible generators including precentral and inferior frontal gyri and midcingulate and parietal cortex. Exposure to early-life stress (ELS) is also associated with deficits in inhibitory control, smaller P300 amplitudes, and dysfunction in regions associated with P300 generation. Although biopsychosocial effects of ELS are evident in older adulthood, the influence of ELS on neural processes in later life is unknown. In the current study, 13 young adults and 21 healthy older adults completed a high-accuracy go/no-go task and the Juvenile Victimization Questionnaire (JVQ), an indicator of ELS. Regression analyses revealed significant central-parietal models, with smaller P300 amplitudes predicted by both older age and greater exposure to ELS. Age group*ELS interactions moderated P300 prediction at central and centro-parietal electrodes, such that older age predicted smaller P300 amplitudes only in those with lower to moderate ELS. Amplitudes did not significantly differ by age in those with higher ELS. Post-hoc within-age group correlations showed that greater ELS was associated with smaller P300 amplitudes in young adults. However, greater ELS was modestly associated with larger central amplitudes in older adults, potentially suggestive of anterior age-related compensatory recruitment to maintain high task performance. These findings suggest long-lasting neural implications of ELS that interact with normative neuro-cognitive aging processes, such that ELS may be an important risk factor for age-related cognitive decline.

The Network for Pancreatic Organ donors with Diabetes (nPOD) is the largest biorepository of human pancreata and associated immune organs from donors with type 1 diabetes (T1D), maturity-onset diabetes of the young (MODY), cystic fibrosis-related diabetes (CFRD), type 2 diabetes (T2D), gestational diabetes, islet autoantibody positivity (AAb+), and without diabetes. nPOD recovers, processes, analyzes, and distributes high-quality biospecimens, collected using optimized standard operating procedures, and associated de-identified data/metadata to researchers around the world. Herein describes the release of high-parameter genotyping data from this collection. 372 donors were genotyped using a custom precision medicine single nucleotide polymorphism (SNP) microarray. Data were technically validated using published algorithms to evaluate donor relatedness, ancestry, imputed HLA, and T1D genetic risk score. Additionally, 207 donors were assessed for rare known and novel coding region variants via whole exome sequencing (WES). These data are publicly-available to enable genotype-specific sample requests and the study of novel genotype:phenotype associations, aiding in the mission of nPOD to enhance understanding of diabetes pathogenesis to promote the development of novel therapies.

This paper provides an active learning strategy with 34 interactive Excel spreadsheets incorporating 8 core calculative principles for use in management accounting courses. Central to this “interactive learning strategy” is how the dynamic formula functionality of Excel enables highly complex calculative exercises to be parsimoniously structured in templates with color-coded instant feedback. Careful design and selection of solution cells focus learning on core concepts of calculative principles. The strategy involves three forms of interactive learning: students interact with the exercises via the instant feedback functionality, students can interact personally with the instructor, and students can collaboratively interact with other students. With the exercises, the learning strategy is easily implementable into in-person and online classes as complements to traditional lecture segments and textbook exercises. It offers a more engaging and more productive learning experience as perceived by students compared to traditional “pen and paper” exercises (for online classes too). Data Availability: Data are available upon request.

This paper describes the design, fabrication, and record performance of a new class of ultra-wideband wavelength tuning, ultra-low noise semiconductor laser, the Integrated Coherent Tunable Laser (ICTL). The ICTL device is designed for, and fabricated in, a CMOS foundry based Silicon Photonics platform, utilizing heterogeneous integration of III-V material to create the integrated gain section of the laser - enabling high-volume mass-market manufacturing at low cost and with high reliability. The ICTL incorporates three or more ultra-low loss micro-ring resonators, with large ring size, in a Sagnac loop reflector geometry, creating exceptional laser reflector performance, plus an extended laser cavity length that enables highly-coherent output; ultra-low linewidth and phase noise. This paper describes record integrated laser performance; 118 nm wavelength tuning, covering S-, C- and L-bands, with Lorentzian linewidth

This case instructs students through the preparation of a real-world business case with a sustainability theme. The case involves capital expenditure analysis and communication for installing solar panels on roofs of two warehouses in the Super-Savers Supermarkets supply chain. Installing solar panels would save electricity costs from the public grid and save carbon dioxide greenhouse gas emissions. There are insource and outsource scenarios. The business case has three parts: (1) use Excel (including Goal Seek) to prepare a financial model with a discounted cash flow method—to analyze incremental capital expenditures, costs, profit impacts, and carbon dioxide emission savings; (2) prepare a Word report that concisely communicates the financial model and business case; and (3) prepare a PowerPoint presentation that concisely communicates the financial model and business case. A practice financial modeling exercise is included. The case suits undergraduate and graduate management accounting courses.

Hypothesis Mixtures of colloids and supramolecular polymers may exhibit stimuli-responsive phase behaviour. However, in theoretical descriptions of such systems, the polymers are commonly described either as flexible chains or as rigid rods, while in experimental systems supramolecular polymers usually fall in between these two limits. We expect the flexibility of the polymers to have a profound effect on the stimuli-responsive phase behaviour. Theory We propose a general approach to predict the phase behaviour of colloidal hard spheres mixed with covalent or supramolecular polymers of arbitrary persistence length using free volume theory and an interpolation between flexible and rigid chains. Findings The binodals are predicted to shift to lower monomer concentrations as the persistence length is increased, making the polymers more efficient depletants. The persistence length is therefore an additional degree of freedom for manipulating the phase behaviour of colloid–polymer mixtures. We show that by manipulating the persistence length of temperature responsive supramolecular polymers, a wide range of phase diagrams with various topologies can be obtained. For example, we find phase diagrams with a critical point but no triple point or displaying two triple points for temperature-sensitive supramolecular polymers mixed with hard spheres.

As human exposure to hydroclimatic extremes increase and the number of in situ precipitation observations declines, precipitation estimates, such as those provided by the Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) mission, provide a critical source of information. Here, we present a new gauge-enhanced dataset [the Climate Hazards Center IMERG with Stations (CHIMES)] designed to support global crop and hydrologic modeling and monitoring. CHIMES enhances the IMERG Late Run product using an updated Climate Hazards Center (CHC) high-resolution climatology (CHPclim) and low-latency rain gauge observations. CHPclim differs from other products because it incorporates long-term averages of satellite precipitation, which increases CHPclim’s fidelity in data-sparse areas with complex terrain. This fidelity translates into performance increases in unbiased IMERGlate data, which we refer to as CHIME. This is augmented with gauge observations to produce CHIMES. The CHC’s curated rain gauge archive contains valuable contributions from many countries. There are two versions of CHIMES: preliminary and final. The final product has more copious and better-curated station data. Every pentad and month, bias-adjusted IMERGlate fields are combined with gauge observations to create pentadal and monthly CHIMESprelim and CHIMESfinal. Comparisons with pentadal, high-quality gridded station data show that IMERGlate performs well (r = 0.75), but has some systematic biases which can be reduced. Monthly cross-validation results indicate that unbiasing increases the variance explained from 50% to 63% and decreases the mean absolute error from 48 to 39 mm month−1. Gauge enhancement then increases the variance explained to 75%, reducing the mean absolute error to 27 mm month−1.

The Modern-Era Retrospective Analysis for Research and Application version 2 (MERRA-2) is a well-established reanalysis dataset and is widely used for driving global-scale hydrological models. However, owing to its relatively coarse spatial resolution (0.5°), the capability of MERRA-2 is repeatedly challenged in regional-scale studies, especially for smaller areas of interest. In addition, the availability of in situ observation data is a pressing issue for generating meteorological forcing. We developed a grid-based high spatial (0.125°) and temporal (hourly) resolution meteorological forcing dataset, which can evaluate hydrological processes in South Korea using state-of-the-art meteorological observations from 1980 to 2020. The forcing dataset was created by combining Automated Synoptic Observing System (ASOS) in situ measurement data from the Korean Meteorological Administration and MERRA-2 reanalysis datasets. Five meteorological variables were provided in the ASOS-MERRA2 (precipitation, air temperature, surface pressure, specific humidity, and wind speed). The study demonstrates that the region-based and high spatial resolution of ASOS-MERRA2 is superior to the existing MERRA-2 with improvements of all five weather variables, for example, from 5.6 to 2.8 mm root mean square error of precipitation. The ASOS-MERRA2 was more capable of reducing the biases and root mean squared error by improving the coefficient of determination compared with MERRA-2 for all five variables. The newly developed ASOS-MERRA2 provides an opportunity to drive land surface models to evaluate the hydroclimatic conditions in South Korea.

Background Leaf surface phenotypes can indicate plant health and relate to a plant’s adaptations to environmental stresses. Identifying these phenotypes using non-invasive techniques can assist in high-throughput phenotyping and can improve decision making in plant breeding. Identification of these surface phenotypes can also assist in stress identification. Incorporating surface phenotypes into leaf optical modelling can lead to improved biochemical parameter retrieval and species identification. Results In this paper, leaf surface phenotypes are characterized for 349 leaf samples based on polarized light reflectance measured at Brewster’s Angle, and microscopic observation. Four main leaf surface phenotypes (glossy wax, glaucous wax, high trichome density, and glabrous) were identified for the leaf samples. The microscopic and visual observations of the phenotypes were used as ground truth for comparison with the spectral classification. In addition to surface classification, the microscope images were used to assess cell size, shape, and cell cap aspect ratios; these surface attributes were not found to correlate significantly with spectral measurements obtained in this study. Using a quadratic discriminant analysis function, a series of 10,000 classifications were run with the data randomly split between training and testing datasets, with 150 and 199 samples, respectively. The average correct classification rate was 72.9% with a worst-case classification of 60.3%. Conclusions Leaf surface phenotypes were successfully correlated with spectral measurements that can be obtained remotely. Remote identification of these surface phenotypes will improve leaf optical modelling and biochemical parameter estimations. Phenotyping of leaf surfaces can inform plant breeding decisions and assist with plant health monitoring.

Silicon nitride (SiN) waveguides with ultra-low optical loss enable integrated photonic applications including low noise, narrow linewidth lasers, chip-scale nonlinear photonics, and microwave photonics. Lasers are key components to SiN photonic integrated circuits (PICs), but are difficult to fully integrate with low-index SiN waveguides due to their large mismatch with the high-index III-V gain materials. The recent demonstration of multilayer heterogeneous integration provides a practical solution and enabled the first-generation of lasers fully integrated with SiN waveguides. However, a laser with high device yield and high output power at telecommunication wavelengths, where photonics applications are clustered, is still missing, hindered by large mode transition loss, non-optimized cavity design, and a complicated fabrication process. Here, we report high-performance lasers on SiN with tens of milliwatts output power through the SiN waveguide and sub-kHz fundamental linewidth, addressing all the aforementioned issues. We also show Hertz-level fundamental linewidth lasers are achievable with the developed integration techniques. These lasers, together with high-Q SiN resonators, mark a milestone towards a fully integrated low-noise silicon nitride photonics platform. This laser should find potential applications in LIDAR, microwave photonics and coherent optical communications., Achieving high output power and low noise integrated lasers is a major challenge. Here the authors experimentally demonstrate integrated lasers from a Si/SiN heterogeneous platform that shows Hertz-level linewidth, paving the way toward fully integrating low-noise silicon nitride photonics in volume using real devices for lasing.

Late blight caused by Phytophthora infestans is a serious threat to potato production. Currently, solutions to manage late blight in organic systems are scarce. This study was undertaken to evaluat...

A missense mutation (R620W) of protein tyrosine phosphatase nonreceptor type 22 (PTPN22), which encodes lymphoid-tyrosine phosphatase (LYP), confers genetic risk for multiple autoimmune diseases including type 1 diabetes. LYP has been putatively demonstrated to attenuate proximal T and BCR signaling. However, limited data exist regarding PTPN22 expression within primary T cell subsets and the impact of the type 1 diabetes risk variant on human T cell activity. In this study, we demonstrate endogenous PTPN22 is differentially expressed and dynamically controlled following activation. From control subjects homozygous for the nonrisk allele, we observed 2.1- (p < 0.05) and 3.6-fold (p < 0.001) more PTPN22 transcripts in resting CD4+ memory and regulatory T cells (Tregs), respectively, over naive CD4+ T cells, with expression peaking 24 h postactivation. When LYP was overexpressed in conventional CD4+ T cells, TCR signaling and activation were blunted by LYP-620R (p < 0.001) but only modestly affected by the LYP-620W risk variant versus mock-transfected control, with similar results observed in Tregs. LYP overexpression only impacted proliferation following activation by APCs but not anti-CD3– and anti-CD28–coated microbeads, suggesting LYP modulation of pathways other than TCR. Notably, proliferation was significantly lower with LYP-620R than with LYP-620W overexpression in conventional CD4+ T cells but was similar in Treg. These data indicate that the LYP-620W variant is hypomorphic in the context of human CD4+ T cell activation and may have important implications for therapies seeking to restore immunological tolerance in autoimmune disorders.

Silicon photonics enables wafer-scale integration of optical functionalities on chip. Silicon-based laser frequency combs can provide integrated sources of mutually coherent laser lines for terabit-per-second transceivers, parallel coherent light detection and ranging, or photonics-assisted signal processing. We report heterogeneously integrated laser soliton microcombs combining both indium phospide/silicon (InP/Si) semiconductor lasers and ultralow-loss silicon nitride (Si3N4) microresonators on a monolithic silicon substrate. Thousands of devices can be produced from a single wafer by using complementary metal-oxide-semiconductor-compatible techniques. With on-chip electrical control of the laser-microresonator relative optical phase, these devices can output single-soliton microcombs with a 100-gigahertz repetition rate. Furthermore, we observe laser frequency noise reduction due to self-injection locking of the InP/Si laser to the Si3N4 microresonator. Our approach provides a route for large-volume, low-cost manufacturing of narrow-linewidth, chip-based frequency combs for next-generation high-capacity transceivers, data centers, space and mobile platforms.

We examine the drought variability over the conterminous United States (CONUS) for 1915–2018 using the Noah-MP land surface model. We examine different model options on drought reconstruction, including optional representation of groundwater and dynamic vegetation phenology. Over our 104-yr reconstruction period, we identify 12 great droughts that each covered at least 36% of CONUS and lasted for at least 5 months. The great droughts tend to have smaller areas when groundwater and/or dynamic vegetation are included in the model configuration. We detect a small decreasing trend in dry area coverage over CONUS in all configurations. We identify 45 major droughts in the baseline (with a dry area coverage greater than 23.6% of CONUS) that are, on average, somewhat less severe than great droughts. We find that representation of groundwater tends to increase drought duration for both great and major droughts, primarily by leading to earlier drought onset (some due to short-lived recovery from a previous drought) or later demise (groundwater anomalies lag precipitation anomalies). In contrast, representation of dynamic vegetation tends to shorten major droughts duration, primarily due to earlier drought demise (closed stoma or dead vegetation reduces ET loss during droughts). On a regional basis, the U.S. Southwest (Southeast) has the longest (shortest) major drought durations. Consistent with earlier work, dry area coverage in all subregions except the Southwest has decreased. The effects of groundwater and dynamic vegetation vary regionally due to differences in groundwater depths (hence connectivity with the surface) and vegetation types.

This study presents an evaluation of the impact of vegetation conditions on a land surface model (LSM) simulation of agricultural drought. The Noah-MP LSM is used to simulate water and energy fluxes and states, which are transformed into drought categories using percentiles over the continental United States from 1979 to 2017. Leaf area index (LAI) observations are assimilated into the dynamic vegetation scheme of Noah-MP. A weekly operational drought monitor (the U.S. Drought Monitor) is used for the evaluation. The results show that LAI assimilation into Noah-MP’s dynamic vegetation scheme improves the model’s ability to represent drought, particularly over cropland areas. LAI assimilation improves the simulation of the drought category, detection of drought conditions, and reduces the instances of drought false alarms. The assimilation of LAI in these locations not only corrects model errors in the simulation of vegetation, but also can help to represent unmodeled physical processes such as irrigation toward improved simulation of agricultural drought.

Millions of people across the globe are affected by droughts every year, and recent droughts have highlighted the considerable agricultural impacts and economic costs of these events. Monitoring the state of droughts depends on integrating multiple indicators that each capture particular aspects of hydrologic impact and various types and phases of drought. As the capabilities of land surface models and remote sensing have improved, important physical processes such as dynamic, interactive vegetation phenology, groundwater, and snowpack evolution now support a range of drought indicators that better reflect coupled water, energy, and carbon cycle processes. In this work, we discuss these advances, including newer classes of indicators that can be applied to improve the characterization of drought onset, severity, and duration. We utilize a new model-based drought reconstruction to illustrate the role of dynamic phenology and groundwater in drought assessment. Further, through case studies on flash droughts, snow droughts, and drought recovery, we illustrate the potential advantages of advanced model physics and observational capabilities, especially from remote sensing, in characterizing droughts.

Rainfall retrieval algorithms for passive microwave radiometers often exploit the brightness temperature depression due to ice scattering at high-frequency channels (≥85 GHz) over land. This study presents an alternate method to estimate the daily rainfall amount using the emissivity temporal variation (i.e., Δe) under rain-free conditions at low-frequency channels (19, 24, and 37 GHz). Emissivity is derived from 10 passive microwave radiometers, including the Global Precipitation Measurement (GPM) Microwave Imager (GMI), the Advanced Microwave Scanning Radiometer 2 (AMSR2), three Special Sensor Microwave Imager/Sounders (SSMIS), the Advanced Technology Microwave Sounder (ATMS), and four Advanced Microwave Sounding Units-A (AMSU-A). Four different satellite combination schemes are used to derive the Δe for daily rainfall estimates. They are all 10 satellites, 5 imagers, 6 satellites with very different equator crossing times, and GMI only. Results show that Δe from all 10 satellites has the best performance with a correlation of 0.60 and RMSE of 6.52 mm, compared with the Integrated Multisatellite Retrievals for GPM (IMERG) Final run product. The 6-satellites scheme has comparable performance with the all-10-satellites scheme. The 5-imagers scheme performs noticeably worse with a correlation of 0.49 and RMSE of 7.28 mm, while the GMI-only scheme performs the worst with a correlation of 0.25 and RMSE of 11.36 mm. The inferior performance from the 5-imagers and GMI-only schemes can be explained by the much longer revisit time, which cannot accurately capture the emissivity temporal variation.

Alterations in polyunsaturated fatty acids (PUFAs), including omega-3 and omega-6, have been implicated in the pathophysiology of psychotic disorders, but little is known about their associations with neuropsychological functioning. The present study includes 46 recent-onset psychosis patients who participated in a larger (n = 50) double blind, placebo-controlled randomized clinical trial comparing 16 weeks of treatment with either risperidone + fish oil (FO) (EPA 740 mg and DHA 400 mg daily) or risperidone + placebo and completed neuropsychological assessments at the baseline timepoint. We investigated the relationship between baseline omega-3 (i.e., eicosapentaenoic acid, EPA; docosapentaenoic acid, DPA and docosahexaenoic acid, DHA) and omega-6 (i.e., arachidonic acid, AA) PUFA with baseline MATRICS Consensus Cognitive Battery (MCCB) and Brief Psychiatric Rating Scale (BPRS) scores. Twenty-five patients had neuropsychological data available at 16 weeks following participation in the clinical trial, which included 12 patients assigned to risperidone + FO and 13 patients assigned to risperidone + placebo. At baseline both higher DHA and EPA correlated significantly with better social cognition after controlling for functioning on other neuropsychological domains, total BPRS score, AA level and substance use. Also, at baseline higher AA correlated significantly with hostility/uncooperativeness after controlling for DHA + EPA + DPA, overall neuropsychological functioning and substance use. Patients treated with risperidone + FO demonstrated a significant longitudinal increase in social cognition that was significantly higher at 16 weeks compared to patients treated with risperidone + placebo. DHA also correlated significantly with social cognition at the 16-week timepoint. This study provides novel evidence for a differential role of omega-3 vs. omega-6 PUFA in neuropsychological deficits and symptoms in recent-onset psychosis and its treatment.

Estimating diffuse recharge of precipitation is fundamental to assessing groundwater sustainability. Diffuse recharge is also the process through which climate and climate change directly affect groundwater. In this study, we evaluated diffuse recharge over the conterminous United States simulated by a suite of land surface models (LSMs) that were forced using a common set of meteorological input data. Simulated annual recharge exhibited spatial patterns that were similar among the LSMs, with the highest values in the eastern United States and Pacific Northwest. However, the magnitudes of annual recharge varied significantly among the models and were associated with differences in simulated ET, runoff, and snow. Evaluation against two independent datasets did not answer the question of whether the ensemble mean performs the best, due to inconsistency between those datasets. The amplitude and timing of seasonal maximum recharge differed among the models, influenced strongly by model physics governing deep soil moisture drainage rates and, in cold regions, snowmelt. Evaluation using in situ soil moisture observations suggested that true recharge peaks 1–3 months later than simulated recharge, indicating systematic biases in simulating deep soil moisture. However, recharge from lateral flows and through preferential flows cannot be inferred from soil moisture data, and the seasonal cycle of simulated groundwater storage actually compared well with in situ groundwater observations. Long-term trends in recharge were not consistently correlated with either precipitation trends or temperature trends. This study highlights the need to employ dynamic flow models in LSMs, among other improvements, to enable more accurate simulation of recharge.

Accurate, physically based precipitation retrieval over global land surfaces is an important goal of the NASA/JAXA Global Precipitation Measurement Mission (GPM). This is a difficult problem for the passive microwave constellation, as the signal over radiometrically warm land surfaces in the microwave frequencies means that the measurements used are indirect and typically require inferring some type of relationship between an observed scattering signal and precipitation at the surface. GPM, with collocated radiometer and dual-frequency radar, is an excellent tool for tackling this problem and improving global retrievals. In the years following the launch of the GPM Core Observatory satellite, physically based passive microwave retrieval of precipitation over land continues to be challenging. Validation efforts suggest that the operational GPM passive microwave algorithm, the Goddard profiling algorithm (GPROF), tends to overestimate precipitation at the low (−1) end of the distribution over land. In this work, retrieval sensitivities to dynamic surface conditions are explored through enhancement of the algorithm with dynamic, retrieved information from a GPM-derived optimal estimation scheme. The retrieved parameters describing surface and background characteristics replace current static or ancillary GPROF information including emissivity, water vapor, and snow cover. Results show that adding this information decreases probability of false detection by 50% and, most importantly, the enhancements with retrieved parameters move the retrieval away from dependence on ancillary datasets and lead to improved physical consistency.

Atomic force microscopy is used to study the ordering dynamics of symmetric diblock copolymer films. The films order to form a lamellar structure which results in a frustration when the film thickness is incommensurate with the lamellae. By probing the morphology of incommensurate films in the early ordering stages, we discover an intermediate phase of lamellae arranged perpendicular to the film surface. This morphology is accompanied by a continuous growth in amplitude of the film surface topography with a characteristic wavelength, indicative of a spinodal process. Using self-consistent field theory, we show that the observation of perpendicular lamellae suggests an intermediate state with parallel lamellae at the substrate and perpendicular lamellae at the free surface. The calculations confirm that the intermediate state is unstable to thickness fluctuations, thereby driving the spinodal growth of surface structures.