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Increasing population and the consequent rise in the demand for food and water resources pose significant challenges for the future of agriculture in Egypt. Rapid large-scale agricultural expansion has occurred in the country to meet the growing demand, but agricultural loss from urban infringement and field abandonment remains prevalent. Documenting the full spectrum of changes within Egypt’s agricultural systems is crucial for developing effective land-use policies that improve food security. Here we map and estimate the areal extent of multiple types of agricultural change in Egypt (i.e., agricultural gain, agricultural abandonment, and agricultural loss from urban growth) by applying the Landsat-based detection of trends in disturbance and recovery (LandTrendr) algorithm, a widely used time series temporal segmentation algorithm. First, we used LandTrendr to identify areas of agricultural gain and loss throughout Egypt from 1987 to 2019. Second, we combined land-cover maps and the LandTrendr results to create a comprehensive land-cover change map. Lastly, we evaluated the accuracy of our findings and estimated per-class areas with quantified uncertainty using high-quality reference data. Our results reveal a notable expansion in Egypt’s agricultural land area. However, this growth is accompanied by the widespread loss of prime agricultural land, a consequence of urban development and agricultural abandonment. This study emphasizes the pressing need for the implementation of sustainable land-use policies in Egypt, particularly as climate change will exacerbate pressures on the agricultural sector in the future.

Abstract Despite the importance of informed consent in healthcare, the readability and specificity of consent forms often impede patients’ comprehension. This study investigates the use of GPT-4 to simplify surgical consent forms and introduces an AI-human expert collaborative approach to validate content appropriateness. Consent forms from multiple institutions were assessed for readability and simplified using GPT-4, with pre- and post-simplification readability metrics compared using nonparametric tests. Independent reviews by medical authors and a malpractice defense attorney were conducted. Finally, GPT-4’s potential for generating de novo procedure-specific consent forms was assessed, with forms evaluated using a validated 8-item rubric and expert subspecialty surgeon review. Analysis of 15 academic medical centers’ consent forms revealed significant reductions in average reading time, word rarity, and passive sentence frequency (all P

New construction activities can alter surface albedo and structure, which then affect surface temperature and roughness, and hence have a significant impact on urban climate. Construction activity is also an important indicator of human development and movement and is of high interest to the intelligence community. A new approach for Broad-Area-Search of New Construction activities (BASC) by combining time series analysis and rule-based filters using Landsat data was developed and tested in five selected cities (Boston, Shanghai, São Paulo, Dubai, and Ho Chi Minh City). The algorithm transforms Landsat images into fractions of a set of four endmembers using Linear Spectral Mixture Analysis (LSMA) and then applies the Continuous Change Detection and Classification (CCDC) algorithm for change detection. A set of rule-based filters and spatial processing was then applied to narrow the search to changes related to construction activities. Overall, BASC reached a recall of 0.83, a precision of 0.58, and an F1-Score of 0.68. Among the five cities, Dubai had the highest recall of 1.0 and the highest F1-score of 0.75, while Boston had the highest precision of 0.63. BASC performed worst in Shanghai with an F1-Score of 0.6, mainly due to it having the lowest recall of 0.62, while São Paulo has the lowest precision of 0.5. Common sources of omission errors include low-density, redevelopment, and small sites, while common commission errors include roofing, land clearing, water level changes, and re-surfacing projects. For comparison, BASC using Sentinel-2 Top-of-Atmosphere (TOA) Reflectance data recorded an overall F1-Score of 0.63, but with higher recall and lower precision. Integration of Sentinel-2 Surface Reflectance and Sentinel-1 SAR data has the potential to further improve the performance of BASC. The new algorithm provided a method for routine monitoring of construction activities over large areas. The result of such monitoring can be used as a baseline to narrow down the candidate targets of construction activities, where very high-resolution imagery can then be requested to perform further examination.

Abstract State-of-the-art cloud computing platforms such as Google Earth Engine (GEE) enable regional-to-global land cover and land cover change mapping with machine learning algorithms. However, collection of high-quality training data, which is necessary for accurate land cover mapping, remains costly and labor-intensive. To address this need, we created a global database of nearly 2 million training units spanning the period from 1984 to 2020 for seven primary and nine secondary land cover classes. Our training data collection approach leveraged GEE and machine learning algorithms to ensure data quality and biogeographic representation. We sampled the spectral-temporal feature space from Landsat imagery to efficiently allocate training data across global ecoregions and incorporated publicly available and collaborator-provided datasets to our database. To reflect the underlying regional class distribution and post-disturbance landscapes, we strategically augmented the database. We used a machine learning-based cross-validation procedure to remove potentially mis-labeled training units. Our training database is relevant for a wide array of studies such as land cover change, agriculture, forestry, hydrology, urban development, among many others.

ObjectiveTo establish whether or not a natural language processing technique could identify two common inpatient neurosurgical comorbidities using only text reports of inpatient head imaging.Materials and methodsA training and testing dataset of reports of 979 CT or MRI scans of the brain for patients admitted to the neurosurgery service of a single hospital in June 2021 or to the Emergency Department between July 1-8, 2021, was identified. A variety of machine learning and deep learning algorithms utilizing natural language processing were trained on the training set (84% of the total cohort) and tested on the remaining images. A subset comparison cohort (n = 76) was then assessed to compare output of the best algorithm against real-life inpatient documentation.ResultsFor "brain compression", a random forest classifier outperformed other candidate algorithms with an accuracy of 0.81 and area under the curve of 0.90 in the testing dataset. For "brain edema", a random forest classifier again outperformed other candidate algorithms with an accuracy of 0.92 and AUC of 0.94 in the testing dataset. In the provider comparison dataset, for "brain compression," the random forest algorithm demonstrated better accuracy (0.76 vs 0.70) and sensitivity (0.73 vs 0.43) than provider documentation. For "brain edema," the algorithm again demonstrated better accuracy (0.92 vs 0.84) and AUC (0.45 vs 0.09) than provider documentation.DiscussionA natural language processing-based machine learning algorithm can reliably and reproducibly identify selected common neurosurgical comorbidities from radiology reports.ConclusionThis result may justify the use of machine learning-based decision support to augment provider documentation.

Background: Dozens of paraneoplastic syndromes affect the visual system ranging from conjunctival pemphigoid to encephalopathy of the occipital cortex. The most profiled ocular syndromes are bilateral diffuse uveal melanocytic proliferation (BDUMP) and the autoimmune retinopathies. Purpose: To review the critical features of these 2 entities then concentrate on advancements in treatment made within the last 10 years. Study Design: Literature review with structured data abstraction. Results: Major insights into pathogenesis have been wanting. Plasmapheresis appears to improve vision in a substantial proportion of patients with BDUMP. The number of clinical variables that influence visual outcome in paraneoplastic retinopathies combined with the variety of local and systemic treatment options makes interpretation of clinical effectiveness difficult. Conclusions: The rarity of these disorders makes randomized clinical trials unlikely. It may be time for a clinical professional organization to use a modified Delphi method to establish a consensus algorithm for the diagnosis and management of retinal paraneoplastic syndromes to augment clinical communications and clinical trials.

The solid-state structure of di-μ-iodido-bis{[(1,2,5,6-η)-cycloocta-1,4-diene]rhodium(I)}, [Rh2I2(C8H12)2] or [Rh(μ-I)(COD)]2, was determined from two crystals with different morphologies, which were found to correspond to two polymorphs containing Rh dimers with significantly different molecular structures. Both polymorphs are monoclinic and the [Rh(μ-I)(COD)]2 molecules in each case possess C2v symmetry. However, the core geometry of the butterfly-shaped Rh2I2 core differs substantially. In the C2/c polymorph, the core geometry of [Rh(μ-I)(COD)]2B is bent, with a hinge angle of 96.13 (8)° and a Rh...Rh distance of 2.9612 (11) Å. The P21/c polymorph features a more planar [Rh(μ-I)(COD)]2P core geometry, with a hinge angle of 145.69 (9)° and a Rh...Rh distance of 3.7646 (5) Å.

Land cover maps are essential for characterizing the biophysical properties of the Earth’s land areas. Because land cover information synthesizes a rich array of information related to both the ecological condition of land areas and their exploitation by humans, they are widely used for basic and applied research that requires information related to land surface properties (e.g., terrestrial carbon models, water balance models, weather, and climate models) and are core inputs to models and analyses used by natural resource scientists and land managers. As the Earth’s global population has grown over the last several decades rates of land cover change have increased dramatically, with enormous impacts on ecosystem services (e.g., biodiversity, water supply, carbon sequestration, etc.). Hence, accurate information related to land cover is essential for both managing natural resources and for understanding society’s ecological, biophysical, and resource management footprint. To address the need for high-quality land cover information we are using the global record of Landsat observations to compile annual maps of global land cover from 2001 to 2020 at 30 m spatial resolution. To create these maps we use features derived from time series of Landsat imagery in combination with ancillary geospatial data and a large database of training sites to classify land cover at annual time step. The algorithm that we apply uses temporal segmentation to identify periods with stable land cover that are separated by breakpoints in the time series. Here we provide an overview of the methods and data sets we are using to create global maps of land cover. We describe the algorithms used to create these maps and the core land cover data sets that we are creating through this effort, and we summarize our approach to accuracy assessment. We also present a synthesis of early results and discuss the strengths and weaknesses of our early map products and the challenges that we have encountered in creating global land cover data sets from Landsat. Initial accuracy assessment for North America shows good overall accuracy (77.0 ± 2.0% correctly classified) and 79.8% agreement with the European Space Agency (ESA) WorldCover product. The land cover mapping results we report provide the foundation for robust, repeatable, and accurate mapping of global land cover and land cover change across multiple decades at 30 m spatial resolution from Landsat.

The Landsat program has the longest collection of moderate-resolution satellite imagery, and the data are free to everyone. With the improvements of standardized image products, the flexibility of cloud computing platforms, and the development of time series approaches, it is now possible to conduct global-scale analyses of time series using Landsat data over multiple decades. Efforts in this regard are limited by the density of usable observations. The availability of usable Landsat Tier 1 observations at the scale of individual pixels from the perspective of time series analysis for land change monitoring is remarkably variable both in space (globally) and time (1985–2020), depending most immediately on which sensors were in operation, the technical capabilities of the mission, and the acquisition strategies and objectives of the satellite operators (e.g., USGS, commercial company) and the international ground receiving stations. Additionally, analysis of data density at the pixel scale allows for the integration of quality control data on clouds, cloud shadows, and snow as well as other properties returned from the atmospheric correction process. Maps for different time periods show the effect of excluding observations based on the presence of clouds, cloud shadows, snow, sensor saturation, hazy observations (based on atmospheric opacity), and lack of aerosol optical depth information. Two major discoveries are: 1) that filtering saturated and hazy pixels is helpful to reduce noise in the time series, although the impact may vary across different continents; 2) the atmospheric opacity band needs to be used with caution because many images are removed when no value is given in this band, when many of those observations are usable. The results provide guidance on when and where time series analysis is feasible, which will benefit many users of Landsat data.

Although shifting cultivation is the major land use type in Laos, the spatial-temporal patterns and the associated carbon emissions of shifting cultivation in Laos are largely unknown. This study provides a nationwide analysis of the spatial-temporal patterns of shifting cultivation and estimations of the associated carbon emissions in Laos over the last three decades. This study found that shifting cultivation has been expanding and intensifying in Laos, especially in the last 5 years. The newly cultivated land from 2016 to 2020 accounted for 4.5% (±1.2%) of the total land area of Laos. Furthermore, the length of fallow periods has been continuously declining, indicating that shifting cultivation is becoming increasingly intensive. Combining biomass derived from Global Ecosystem Dynamics Investigation and shifting cultivation maps and area estimates, we found that the net carbon emissions from shifting cultivation declined in 2001–2015 but increased in 2016–2020. The largest carbon source is conversion from intact forests to shifting cultivation, which contributed to 89% of the total emissions from 2001 to 2020. In addition, there were increased emissions from intensified use of fallow lands. This research provides useful information for policymakers in Laos to understand the changes in shifting cultivation and improve land use management. This study not only supports Reducing Emissions from Deforestation and Forest Degradation reporting for Laos but also provides a methodology for tracking carbon emissions and removals of shifting cultivation.

Abstract Protected areas in Guatemala provide habitat for diverse tropical ecosystems, contain ancient archeological sites, sequester carbon, and support economic activity through eco‐tourism. However, many of the forests in these protected areas have been converted to other uses or degraded by human activity, and therefore are considered “paper parks”. In this study, we analyzed time series of satellite data to monitor deforestation, degradation, and natural disturbance throughout Guatemala from 2000 to 2017. A recently developed methodology, Continuous Degradation Detection (CODED), was used to detect forest disturbances of varying size and magnitude. Through sample‐based statistical inference, we estimated that 854 137 ha (± 83 133 ha) were deforested and 1 012 947 ha (±139 512 ha) of forest was disturbed but not converted during our study period. Forest disturbance in protected areas ranged from under 1% of a park's area to over 95%. Our estimate of the extent of deforestation is similar to previous studies, however, degradation and natural disturbance affect a larger area. These results suggest that the total amount of forest disturbance can be significantly underestimated if degradation and natural disturbance are not taken into account. As a consequence, we found that the protected areas of Guatemala are more affected by disturbance than previously realized.

Abstract Introduction Social determinants of health (SDH) may influence inpatient utilization rates and outcomes but have yet to be associated with ocular diagnoses. The purpose of this paper was to determine whether the SDH are associated with ocular hospitalizations. Methods Patients from the national Medicare 100% Inpatient Limited Dataset were examined and linked to SDH measures from the Robert Wood Johnson Foundation (RWJF) County Health Rankings. Patients were included in the study group with either an admitting or primary diagnosis of an ophthalmic condition. All other hospitalized Medicare patients served in the comparison group. Nested logistic regression of these Medicare patients was conducted in their respective communities at the county level. SDH measures were benchmarked above or below the national median. Results Positively associated SDH factors included communities with air pollution exceeding 11.62 micro grams per cubic meter (OR 1.05; 95% CI 1.01–1.08), communities where severe housing problems exceeding 14.38% (OR 1.13; 95% CI 1.09–1.18), children in single parent households exceeding 32.13% (OR 1.06; 95% CI 1.02–1.11), violent crime rate exceeding 250.54 per 100,000 (OR 1.07; 95% CI 1.03–1.12), diabetes exceeding 10.95% (OR 1.09: 95% CI 1.04–1.14), and drug poisoning deaths including opioids exceeding 14.17 per 100,000 (OR 1.04; 95% CI 1.01–1.08). Conclusion When compared to an all-condition, hospitalized population, ocular hospitalizations tended to have small, yet statistically significant associations with health behaviors, socioeconomic, and physical environment factors. Further research will be needed on how the physical environment, social, and community variables affect ocular health relative to all-cause hospitalizations.

Purpose: To report a case of severe stromal microsporidal keratitis successfully treated with oral albendazole and topical voriconazole. Observations: A 71-year-old man presented with progressive vision loss and corneal opacification for one year. Initial visual acuity was counting fingers attributed to a dense subepithelial opacification. Confocal microscopy and subsequent corneal biopsy lead to the diagnosis of microsporidial keratitis. The patient completed a four-week course of oral albendazole and topical voriconazole which resulted in resolution of the corneal opacification and improvement in visual acuity to 20/250. Conclusions and Importance: A four-week course of oral albendazole and topical voriconazole was an effective treatment for severe stromal microsporidial keratitis.

Recurrent pyogenic cholangitis (RPC) is a condition found almost exclusively in individuals who lived in Southeast Asia. We report a case of a Caucasian veteran diagnosed with RPC after presenting with a 5-year history of recurrent fevers and abdominal pain 20 years after serving in Japan, South Korea, and Guam. Extensive evaluation led to the diagnosis of RPC with improvement after biliary decompression and antibiotics. Although rare, RPC should be considered in individuals who present with recurrent bouts of abdominal pain and fevers regardless of race.

Nile tilapia (Oreochromis niloticus) is among the most important finfish in aquaculture, particularly in Asia. Numerous genetically improved strains of Nile tilapia have been developed and disseminated through formal and informal channels to hatcheries, many of which operate at a relatively small scale in developing countries. The primary objective of this study was to assess the extent to which molecular genetic tools can identify different and interrelated strains of Nile tilapia in Bangladesh and the Philippines, two globally significant producers. A tool was developed using a low-density panel of single-nucleotide polymorphisms (SNPs), genotyping-by-sequencing and discriminant analysis of principal components (DAPC). When applied to 2,057 samples from 205 hatcheries in Bangladesh and the Philippines, for hatcheries where the hatchery-identified strain was one of the sampled core populations used to develop the tool, hatchery-identified and DAPC-assigned hatchery-level strains were in agreement in 74.1% of cases in Bangladesh and 80.6% of cases in the Philippines. The dominant hatchery-identified and DAPC-assigned strains were GIFT, in Bangladesh, and GET-ExCEL—a composite strain partially derived from GIFT—in the Philippines.

Land cover has been designated by the Global Climate Observing System (GCOS) as an Essential Climate Variable due to its integral role in many climate and environmental processes. Land cover and change affect regional precipitation patterns, surface energy balance, the carbon cycle and biodiversity. Accurate information on land cover and change is essential for climate change mitigation programs such as UN-REDD+. Still, uncertainties related to land change are large, in part due to the use of traditional land cover and change mapping techniques that use one or a few remotely sensed images, preventing a comprehensive analysis of ecosystem change processes. The opening of the Landsat archive and the initiation of the Copernicus Program have enabled analyses based on time series data, allowing the scientific community to explore global land cover dynamics in ways that were previously limited by data availability. One such method is the Continuous Change Detection and Classification algorithm (CCDC), which uses all available Landsat data to model temporal-spectral features that include seasonality, trends, and spectral variability. Until recently, the CCDC algorithm was restricted to academic environments due to computational requirements and complexity, preventing its use by local practitioners. The situation has changed with the recent implementation of CCDC in the Google Earth Engine, which enables analyses at global scales. What is still missing are tools that allow users to explore, analyze and process CCDC outputs in a simplified way. In this paper, we present a suite of free tools that facilitate interaction with CCDC outputs, including: (1) time series viewers of CCDC-generated time segments; (2) a spatial data viewer to explore CCDC model coefficients and derivatives, and visualize change information; (3) tools to create land cover and land cover change maps from CCDC outputs; (4) a tool for unbiased area estimation of key climate-related variables like deforestation extent; and (5) an API for accessing the functionality underlying these tools. We illustrate the usage of these tools at different locations with examples that explore Landsat time series and CCDC coefficients, and a land cover change mapping example in the Southeastern USA that includes area and accuracy estimates.

Accurate, automated cloud and cloud shadow detection is a key component of the processing needed to prepare optical satellite imagery for scientific analysis. Many existing cloud detection algorithms rely on temperature information to identify clouds, making detection difficult for imagers that lack a thermal band, like Sentinel-2. To get maximum benefit from Sentinel-2 products it is critical to understand which algorithms best identify clouds and their shadows in images. We examined the relative performance of five different cloud-masking algorithms (Sen2Cor, MAJA, LaSRC, Fmask and Tmask) in 6 Sentinel-2 scenes (28 total images) distributed across the Eastern Hemisphere. Expanding on these comparisons, we tested ensemble approaches to improve results. We tested three ensemble approaches to cloud and shadow classification based on the outputs of the five initial algorithms using the cloud masks in: (1) a majority prediction model; (2) a random forests model; and (3) a conditional logic model. Accuracy assessments show a trade-off between omission and commission errors in cloud detection for individual algorithms across all sites, and some algorithms are better at detecting either clouds or cloud shadows. No single algorithm outperforms the others for both clouds and shadows. Aggregating the results from multiple algorithms produces fewer undetected clouds and higher overall accuracy than any single algorithm, with as high as 2.7% improvement over the top-performing algorithm, suggesting an ensemble approach may be the most useful for processing of Sentinel-2 data.

Purpose: To familiarize clinicians with the clinical and magnetic resonance imaging (MRI) features of a small orbital apex lymphaticovenous malformation that resulted in blindness and evaded timely clinical diagnosis. Observations: A 68-year-old man presented with severe vision loss due to a 9 mm mass at the apex of the orbit above the optic nerve. When surgically removed 4 years later, the lesion was characterized by vascular spaces of varying size. Larger ones were filled with fibrin and organized thrombi. Stromal septa of endothelial-lined cavernous spaces were partially necrotic and there was evidence of remote hemorrhage. Some endothelial cells expressed D2-40, a marker of lymphatic channels. Conclusions and importance: Unless a high index of suspicion is maintained for a lymphaticovenous malformation the clinical diagnosis of a small but vision-threatening lesion can be overlooked.

Objective: The Pediatric and Neonatal Working group developed new ventilator associated events (VAE) definitions for children and neonates. VAE includes ventilator-associated condition (VAC), infection-related ventilator-associated complication (IVAC), and ventilator-associated pneumonia (VAP). Acute kidney injury (AKI) and fluid overload (FO) have been associated with worse clinical outcomes of ventilated children. Fluid Overload and Kidney Injury Score (FOKIS) is an automatically calculated score that combines AKI and FO in one numeric quantifiable metric. This study analyzed the association between FOKIS and VAE.Design: Retrospective matched case control study.Setting: A freestanding children's hospital.Patients: A total of 168 who were ventilated > 2 days.Interventions: None.Measurements and Main Results: We identified 42 VAC cases (18 IVAC and 24 non-infection-related VAC cases). Controls were matched to cases for age, immunocompromised status and ventilator days prior to VAC. VAC cases had longer ICU days, median (IQR), 28.5 (15, 47) vs. controls 11 (6, 16), p < 0.001; longer ventilation days, 19.5 (13, 32) vs. 9 (4,13), p < 0.001; and higher hospital mortality, 45.2 vs. 18%, p < 0.001. VACs had a higher incidence of AKI, 85.7 vs. 47.3%, p < 0.001; higher peak daily FO% within 3 days preceding VAC, mean (SD), 8.1(7.8) vs. 4.1 (3.4), p < 0.005; and higher peak FOKIS, 6.4(3.8) vs. 3.7(2.8), (p < 0.001). Multivariate regression model adjusted for severity of illness identified peak FOKIS (odds ratio [OR] 1.29, 95%CI: 1.14–1.48, p < 0.001) and peak inspiratory pressure (OR 1.08, 95%CI: 1.02–1.15, p = 0.007) as risk factors for VAC.Conclusions: The FOKIS and its clinical variables were associated risk factors for ventilator-associated events. Further studies will determine the utility of FOKIS as a predictor for VAEs.

Bis-protic N-heterocyclic carbene complexes of platinum and palladium (4) yield dimeric structures 6 when treated with sodium tert-butoxide in CH2Cl2. The use of a more polar solvent (THF) and a strong base (LiN(iPr)2) gave the lithium chloride adducts monobasic complex 7 or analogous dibasic complex 8.

A new ferrocene complex, 16-ferrocenylmethyl-3β-hydroxyestra-1,3,5(10)-trien-17-one dimethyl sulfoxide monosolvate, [Fe(C5H5)(C24H27O2)]·C2H6OS, has been synthesized and structurally characterized by single-crystal X-ray diffraction techniques. The molecule crystallizes in the space group P21 with one molecule of dimethyl sulfoxide. A hydrogen bond links the phenol group and the dimethyl sulfoxide O atom, with an O...O distance of 2.655 (5) Å. The ferrocene group is positioned in the β face of the estrone moiety, with an O—C—C—C torsion angle of 44.1 (5)°, and the carbonyl bond length of the hormone moiety is 1.216 (5) Å, typical of a C=O double bond. The average Fe—C bond length of the substituted Cp ring [Fe—C(Cp*)] is similar to that of the unsubstituted one [Fe—C(Cp)], i.e. 2.048 (3) versus 2.040 (12) Å. The structure of the complex is compared with those of estrone and ethoxymethylestrone.

Four new platinum(II) complexes, namely tetraethylammonium tribromido(2-methyl-1,3-benzothiazole-κN)platinate(II), [NEt4][PtBr3(C8H7NS)] (1), tetraethylammonium tribromido(6-methoxy-2-methyl-1,3-benzothiazole-κN)platinate(II), [NEt4][PtBr3(C9H9NOS)] (2), tetraethylammonium tribromido(2,5,6-trimethyl-1,3-benzothiazole-κN)platinate(II), [NEt4][PtBr3(C10H11NS)] (3), and tetraethylammonium tribromido(2-methyl-5-nitro-1,3-benzothiazole-κN)platinate(II), [NEt4][PtBr3(C8H6N2O2S)] (4), have been synthesized and structurally characterized by single-crystal X-ray diffraction techniques. These species are precursors of compounds with potential application in cancer chemotherapy. All four platinum(II) complexes adopt the expected square-planar coordination geometry, and the benzothiazole ligand is engaged in bonding to the metal atom through the imine N atom (Pt—N). The Pt—N bond lengths are normal: 2.035 (5), 2.025 (4), 2.027 (5) and 2.041 (4) Å for complexes 1, 2, 3 and 4, respectively. The benzothiazole ligands are positioned out of the square plane, with dihedral angles ranging from 76.4 (4) to 88.1 (4)°. The NEt4 cation in 3 is disordered with 0.57/0.43 occupancies.

To date, individual addressing of ion qubits has relied primarily on local Rabi or transition frequency differences between ions created via electromagnetic field spatial gradients or via ion transport operations. Alternatively, it is possible to synthesize arbitrary local one-qubit gates by leveraging local phase differences in a global driving field. Here we report individual addressing of ^40 Ca ^+ ions in a two-ion crystal using axial potential modulation in a global gate laser field. We characterize the resulting gate performance via one-qubit randomized benchmarking, applying different random sequences to each co-trapped ion. We identify the primary error sources and compare the results with single-ion experiments to better understand our experimental limitations. These experiments form a foundation for the universal control of two ions, confined in the same potential well, with a single gate laser beam.