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Lakes in arid regions play an important role in regional water cycles and are a vital economic resource, but can fluctuate widely in area and volume. This study demonstrates the use of a multisensor satellite remote sensing method for the comprehensive monitoring of lake surface areas, water levels, and volume for the Toshka Lakes in southern Egypt, from lake formation in 1998 to mid-2017. Two spectral water indices were used to construct a daily time-series of surface area from the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), validated by higher-resolution Landsat images. Water levels were obtained from analysis of digital elevation models from the Shuttle Radar Topography Mission (SRTM) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), validated with ICESat Geoscience Laser Altimeter System (GLAS) laser altimetry. Total lake volume peaked at 26.54 × 109 m3 in December 2001, and declined to 0.76 × 109 m3 by August 2017. Evaporation accounted for approximately 86% of the loss, and groundwater recharge accounted for 14%. Without additional inflows, the last remaining lake will likely disappear between 2020 and 2022. The Enhanced Lake Index, a water index equivalent to the Enhanced Vegetation Index, was found to have lower noise levels than the Normalized Difference Lake Index. The results show that multi-platform satellite remote sensing provides an efficient method for monitoring the hydrology of lakes.

Background Randomization is the foundation of any clinical trial involving treatment comparison. It helps mitigate selection bias, promotes similarity of treatment groups with respect to important known and unknown confounders, and contributes to the validity of statistical tests. Various restricted randomization procedures with different probabilistic structures and different statistical properties are available. The goal of this paper is to present a systematic roadmap for the choice and application of a restricted randomization procedure in a clinical trial. Methods We survey available restricted randomization procedures for sequential allocation of subjects in a randomized, comparative, parallel group clinical trial with equal (1:1) allocation. We explore statistical properties of these procedures, including balance/randomness tradeoff, type I error rate and power. We perform head-to-head comparisons of different procedures through simulation under various experimental scenarios, including cases when common model assumptions are violated. We also provide some real-life clinical trial examples to illustrate the thinking process for selecting a randomization procedure for implementation in practice. Results Restricted randomization procedures targeting 1:1 allocation vary in the degree of balance/randomness they induce, and more importantly, they vary in terms of validity and efficiency of statistical inference when common model assumptions are violated (e.g. when outcomes are affected by a linear time trend; measurement error distribution is misspecified; or selection bias is introduced in the experiment). Some procedures are more robust than others. Covariate-adjusted analysis may be essential to ensure validity of the results. Special considerations are required when selecting a randomization procedure for a clinical trial with very small sample size. Conclusions The choice of randomization design, data analytic technique (parametric or nonparametric), and analysis strategy (randomization-based or population model-based) are all very important considerations. Randomization-based tests are robust and valid alternatives to likelihood-based tests and should be considered more frequently by clinical investigators.

Digital health interventions could be a scalable approach to delivering at-home COVID-19 testing. SCALE-UP II aimed to investigate the effectiveness of three digital health interventions on the delivery of mailed at-home COVID-19 testing: text messaging (TM), automated chatbot (CA), and patient navigation upon request (PN). Pragmatic randomized controlled trial. Participants who self-reported that they had a smartphone were randomized in a 2x2x2 factorial design (Smartphone study) to receive (i) chatbot or TM; (ii) option to request PN; and (iii) intervention frequency every 10 or 30 days. All other participants were randomized in a 2x2 factorial design (Non-Smartphone study) to receive (i) option to request PN; and (ii) intervention frequency every 10 or 30 days. Study settings were safety net community health centers (CHCs) located across the state of Utah, USA. Eligible patients were >18 years old, with a primary care visit in the last three years, and a valid cellphone in the CHC electronic health record. The primary outcome was proportion of participants requesting at-home COVID-19 tests. The trial enrolled 2,117 in the Smartphone study and 31,439 in the Non-Smartphone study. In the Smartphone study, the proportion of participants who requested test kits in the Chatbot arm was lower than in TM (16.6% vs. 52.1%, aRR=0.317 [98.33% CI 0.27-0.38], P<.0001). In the Non-Smartphone study, the proportion of participants who requested test kits was higher if they were messaged every 10 days rather than every 30 days (5.5% vs 4.8%, aRR=1.144 [97.5% CI 1.03-1.28], P=.005). Yet, participants in the 10-day vs. 30-day condition were more likely to opt out of receiving study interventions (12.6% vs 7.3%, aRR=1.72 [97.5% CI 1.59-1.86], P<.0001). In the Non-Smartphone study, the proportion of participants who requested test kits was lower for those in the PN condition compared to No PN (4.3% vs 5.9%, aRR=0.729 [97.5% CI 0.65-0.81], P<.0001). Simple bidirectional TM was more effective than an interactive Web-based chatbot on the delivery of COVID-19 testing. Although messaging every 10 days was more effective than every 30 days, it also led to a larger opt-out rate. Digital health interventions based on automated bidirectional text messaging is a simple, scalable, and low-cost strategy to offer access to at-home COVID-19 testing. Similar approaches may be used to support public health response and other forms of at-home testing. Clinicaltrials.gov (NCT05533918 and NCT05533359). RR2-doi: 10.1136/bmjopen-2023-081455.

Ephemeral rivers in arid regions act as linear oases, where corridors of vegetation supported by accessible groundwater and intermittent surface flows provide biological refugia in water-limited landscapes. The ecological and hydrological dynamics of these systems are poorly understood compared to perennial systems and subject to wide variation over space and time. This study used imagery obtained from an unmanned aerial vehicle (UAV) to enhance satellite data, which were then used to quantify change in woody vegetation cover along the ephemeral Kuiseb River in the Namib Desert over a 35-year period. Ultra-high resolution UAV imagery collected in 2016 was used to derive a model of fractional vegetation cover from five spectral vegetation indices, calculated from a contemporaneous Landsat 8 Operational Land Imager (OLI) image. The Normalized Difference Vegetation Index (NDVI) provided the linear best-fit relationship for calculating fractional cover; the model derived from the two 2016 datasets was subsequently applied to 24 intercalibrated Landsat images to calculate fractional vegetation cover for the Kuiseb extending back to 1984. Overall vegetation cover increased by 33% between 1984 and 2019, with the most highly vegetated reach of the river exhibiting the greatest positive change. This reach corresponds with the terminal alluvial zone, where most flood deposition occurs. The spatial and temporal trends discovered highlight the need for long-term monitoring of ephemeral ecosystems and demonstrate the efficacy of a multi-sensor approach to time series analysis using a UAV platform.

Background The 2023 FDA's guidance on covariate adjustment encourages the judicious use of baseline covariates to enhance efficiency. However, when performing covariate adjustment in non-linear models, care must be taken on preserving estimation of the target estimand as introduced by the ICH E9(R1) addendum. To understand the current practices of covariate adjustment within the context of the estimands framework across various sectors and associated challenges, the conditional and marginal effect task force within the ASA Oncology Estimand working group conducted a survey. Methods The target participants of the survey were biostatisticians who support study designs and analyses in clinical trials in the drug development industry or in academia. A total of 19 questions were included in an online survey that was distributed between June and July 2023. The survey was disseminated via a shared online link to contacts from more than 50 organisations. The survey response and experience from the working group on challenges of covariate adjustment and stratified analysis are summarized and discussed in detail. Results A total of 122 responses were received from 12 countries. The survey results suggest that there remain gaps in the understanding of different statistical analysis models which may target different estimands for non-collapsable measures, highlighting the need for further clarification and training on this topic. In terms of general practice, when performing the analysis under stratified randomization, additional covariates may be added in the analysis model beyond those used for stratifying randomization, and small strata may be pooled to avoid the estimation challenges. Conclusions This paper summarises the results from this survey and based on our findings, we provide some recommendations to establish consistency and clarifications on any widely misunderstood practices.

Geodetic surface mass balance calculations rely on satellite radar altimeters such as CryoSat-2 to understand elevation and volumetric changes of the Greenland Ice Sheet (GrIS). However, the impact of varying GrIS shallow subsurface stratigraphic conditions on level 2 CryoSat-2 elevation products is poorly understood. We investigate the reliability of the Offset Center Of Gravity (OCOG) and University College London Land-Ice (ULI) elevation retracking algorithms through the analysis of (and comparison with) level 1B waveform-derived leading-edge width (LeW). We generate a 2010 to 2021 LeW time series using temporal clustering and Bayesian model averaging, and we compare them with level 2 OCOG and ULI elevation time series. We perform this workflow at Summit Station, North Greenland Eemian Ice Drilling (NEEM) Camp, and Raven Camp, chosen to represent the upper and lower bounds of the dry-snow zone and percolation zone. We note that melting event, snowpack recovery, and potentially anomalous snow accumulation and high-speed wind signatures are evident in Summit Station's LeW time series. We find that level 1B LeW has a significant inverse relationship with the ULI level 2 elevations at NEEM Camp and Summit Station and likely with the entire dry-snow zone. The ULI retracked level 2 elevations at Raven Camp (and likely the entire percolation zone) have no clear elevation bias associated with significant melt events. The OCOG retracked level 2 elevations showed no significant association with LeW at any site. Future work is needed to understand the impacts of GrIS high-speed wind events and snow accumulation on elevation products.

The Red Sea was witness to important events during human history, including the first long steps in a trade network (the spice route) that would drive maritime technology and shape geopolitical fortunes for thousands of years. Punt was a pivotal early node in the rise of this enterprise, serving as an important emporium for luxury goods, including sacred baboons ( Papio hamadryas ), but its location is disputed. Here, we use geospatial variation in the oxygen and strontium isotope ratios of 155 baboons from 77 locations to estimate the geoprovenance of mummified baboons recovered from ancient Egyptian temples and tombs. Five Ptolemaic specimens of P. anubis (404–40 BC) showed evidence of long-term residency in Egypt prior to mummification, consistent with a captive breeding program. Two New Kingdom specimens of P. hamadryas were sourced to a region that encompasses much of present-day Ethiopia, Eritrea, and Djibouti, and portions of Somalia and Yemen. This result is a testament to the tremendous reach of Egyptian seafaring during the 2nd millennium BC. It also corroborates the balance of scholarly conjecture on the location of Punt. Strontium is a chemical element that can act as a geographic fingerprint: its composition differs between locations, and as it enters the food chain, it can help to retrace the life history of extant or past animals. In particular, strontium in teeth – which stop to develop early – can reveal where an individual was born; strontium in bone and hair, on the other hand, can show where it lived just before death. Together, these analyses may hold the key to archaeological mysteries, such as the location of a long-lost kingdom revered by ancient Egyptians. For hundreds of years, the Land of Punt was one of Egypt’s strongest trading partners, and a place from which to import premium incense and prized monkeys. Travellers could reach Punt by venturing south and east of Egypt, suggesting that the kingdom occupied the southern Red Sea region. Yet its exact location is still highly debated. To investigate, Dominy et al. examined the mummies of baboons present in ancient Egyptian tombs, and compared the strontium compositions of the bones, hair and teeth of these remains with the ones found in baboons living in various regions across Africa. This shed a light on the origins of the ancient baboons: while some were probably raised in captivity in Egypt, others were born in modern Ethiopia, Eritrea, Djibouti, Somalia and Yemen – areas already highlighted as potential locations for the Land of Punt. The work by Dominy et al. helps to better understand the ancient trade routes that shaped geopolitical fortunes for millennia. It also highlights the need for further archaeological research in Eritrea and Somalia, two areas which are currently understudied.

A key challenge for schools throughout the COVID-19 pandemic was finding ways to monitor and prevent COVID-19 cases. While diagnostic testing and connecting students and their families to appropriate resources to mitigate the spread of COVID-19 were recommended, few schools had scalable infrastructure, including information technology systems, to implement these types of measures. This study tested a new approach to COVID-19 testing (SCALE-UP Counts) in school settings that used automated bidirectional text messages provided to the school community that alerted parents of students to COVID-19 testing options and guidance on when to test. The SCALE-UP Counts trial was designed as a Sequential Multiple Assignment Randomized Trial and final analyses compared results from parents who received intensive, fully automated, bidirectional text messaging about COVID-19 testing or usual care (control; fully automated unidirectional text messaging about COVID-19 testing), unblinded interventions. From the 16 selected schools, we enrolled all eligible participants who did not opt out of the study. The study provided schools from both arms of the trial with free at-home COVID-19 test kits. The primary outcome was the proportion of parents whose households tested for COVID-19, and the secondary outcome was the number of missed school days. The study asked parents to respond to self-report measures on testing outcomes and missed school days through web-based questionnaires. The study included 7122 parents of students from 16 schools, half of which were title 1 schools; 2588 were randomized to usual care or control and 4534 to bidirectional text messaging. The SCALE-UP Counts intervention led to increased self-reported testing when compared with the control condition (22.8% vs 13.5%, relative testing rate=1.64, 95% CI 1.31-2.02; P<.001). There was no observed difference in missed school days between the study arms (0.43 per month vs 0.28 in usual care, relative missed days rate=1.55, 95% CI 0.98-2.45; P=.06). SCALE-UP Counts worked closely with schools and the state's public health system to implement and test a scalable health information technology approach that delivered automated text messages to students' parents around COVID-19 testing and provided access to free at-home test kits. Such an approach can help facilitate COVID-19 testing among school communities, including those that provide education and resources to students and their families from racial or ethnic minorities and with low socioeconomic status. Similar health information technology approaches could be used to increase ease of access to testing, reduce testing burden, and provide tailored information on health measures in school communities for a variety of illnesses or public health concerns. ClinicalTrials.gov NCT05112900; http://clinicaltrials.gov/ct2/show/NCT05112900.

There has been little rigorous investigation of the transferability of existing empirical water clarity models developed at one location or time to other lakes and dates of imagery with differing conditions. Machine learning methods have not been widely adopted for analysis of lake optical properties such as water clarity, despite their successful use in many other applications of environmental remote sensing. This study compares model performance for a random forest (RF) machine learning algorithm and a simple 4-band linear model with 13 previously published empirical non-machine learning algorithms. We use Landsat surface reflectance product data aligned with spatially and temporally co-located in situ Secchi depth observations from northeastern USA lakes over a 34-year period in this analysis. To evaluate the transferability of models across space and time, we compare model fit using the complete dataset (all images and samples) to a single-date approach, in which separate models are developed for each date of Landsat imagery with more than 75 field samples. On average, the single-date models for all algorithms had lower mean absolute errors (MAE) and root mean squared errors (RMSE) than the models fit to the complete dataset. The RF model had the highest pseudo-R2 for the single-date approach as well as the complete dataset, suggesting that an RF approach outperforms traditional linear regression-based algorithms when modeling lake water clarity using satellite imagery.

Background Survivors of non‐Hodgkin lymphoma (NHL) have increased secondary malignancy (SM) risk. We quantified this risk by patient and treatment factors. Methods Standardized incidence ratios (SIR, observed‐to‐expected [O/E] ratio) were assessed in 142,637 NHL patients diagnosed from 1975 to 2016 in the National Cancer Institute's Surveillance, Epidemiology, and End Results Program. Comparisons were made between subgroups in terms of their SIRs relative to respective endemic populations. Results In total, 15,979 patients developed SM, more than the endemic rate (O/E 1.29; p < 0.05). Compared with white patients, relative to respective endemic populations, ethnic minorities had a higher risk of SM (white O/E 1.27, 95% CI 1.25–1.29; black O/E 1.40, 95% CI 1.31–1.48; other O/E 1.59, 95% CI 1.49–1.70). Relative to respective endemic populations, patients who received radiotherapy had similar SM rates to those who did not (O/E 1.29 each), but irradiated patients had increased breast cancer (p < 0.05). Patients who received chemotherapy had higher SM rates than those who did not (O/E 1.33 vs. 1.24, p < 0.05) including more leukemia, Kaposi sarcoma, kidney, pancreas, rectal, head and neck, and colon cancers (p < 0.05). Conclusions This is the largest study to examine SM risk in NHL patients with the longest follow‐up. Treatment with radiotherapy did not increase overall SM risk, while chemotherapy was associated with a higher overall risk. However, certain subsites were associated with a higher risk of SM, and they varied by treatment, age group, race and time since treatment. These findings are helpful for informing screening and long‐term follow‐up in NHL survivors. Survivors of non‐Hodgkin lymphoma (NHL) have increased secondary malignancy (SM) risk. Treatment with radiotherapy did not increase overall SM risk, while chemotherapy was associated with a higher overall risk. However, certain sub‐sites were associated with higher risk of SM, and they varied by treatment, age group, race and time since treatment.\" cd_value_code=\"text