Fruit tree identification that is quick and precise lays the groundwork for scientifically evaluating orchard yields and dynamically monitoring planting areas. This study aims to evaluate the applicability of time series Sentinel-1/2 satellite data for fruit tree classification and to provide a new method for accurately extracting fruit tree species. Therefore, the study area selected is the Tarim Basin, the most important fruit-growing region in northwest China. The main focus is on identifying several major fruit tree species in this region. Time series Sentinel-1/2 satellite images acquired from the Google Earth Engine (GEE) platform are used for the study. A multi-scale segmentation approach is applied, and six categories of features including spectral, phenological, texture, polarization, vegetation index, and red edge index features are constructed. A total of forth-four features are extracted and optimized using the Vi feature importance index to determine the best time phase. Based on this, an object-oriented (OO) segmentation combined with the Random Forest (RF) method is used to identify fruit tree species. To find the best method for fruit tree identification, the results are compared with three other widely used traditional machine learning algorithms: Support Vector Machine (SVM), Gradient Boosting Decision Tree (GBDT), and Classification and Regression Tree (CART). The results show that: (1) the object-oriented segmentation method helps to improve the accuracy of fruit tree identification features, and September satellite images provide the best time window for fruit tree identification, with spectral, phenological, and texture features contributing the most to fruit tree species identification. (2) The RF model has higher accuracy in identifying fruit tree species than other machine learning models, with an overall accuracy (OA) and a kappa coefficient (KC) of 94.60% and 93.74% respectively, indicating that the combination of object-oriented segmentation and RF algorithm has great value and potential for fruit tree identification and classification. This method can be applied to large-scale fruit tree remote sensing classification and provides an effective technical means for monitoring fruit tree planting areas using medium-to-high-resolution remote sensing images.

UNASSIGNED: Despite holding promise, reports of using MIGS in severe glaucoma are scarce, and none has described combining multiple MIGS in this population. To the best of our knowledge, this is the largest study to report outcomes of phacoemulsification and MIGS (Phaco/MIGS) in patients with severe glaucoma.
UNASSIGNED: This retrospective review comprised 327 clinical visits of 71 patients with severe glaucoma who underwent Phaco/MIGS with iStent, endocyclodestruction, Kahook Dual Blade, Hydrus Microstent, or a combination of these MIGS (cMIGS) performed between 2016 and 2021. Primary outcomes included intraocular pressure (IOP) and medication burden evaluated by Generalized Estimating Equations, as well as Kaplan-Meier Estimates. Further analyses compared the efficacy of cMIGS and single Phaco/MIGS (sMIGS), procedure duration, visual acuity, and complications.
UNASSIGNED: Mean preoperative IOP was 16.7 mmHg ± 5.8 (SD) on 2.3 ± 1.9 medications overall (N = 71), 16.9 ± 6.3 mmHg on 1.7 ± 1.9 medications in the sMIGS group (N = 37), and 16.4 ± 5.3 mmHg on 2.9 ± 1.6 medications in the cMIGS group (N = 34). Throughout 12 months, Phaco/MIGS led to significant reduction patterns in IOP (p < 0.001) and medications (p = 0.03). At 12 months, 47.5%, 87.5%, and 64.7% of the patients achieved IOP ≤ 12 mmHg, 17 mmHg, or predetermined goal IOP, respectively, without additional medication or procedure. Mean 12-month IOP was 13.5 ± 3.1 mmHg on 1.8 ± 1.7 medications. After adjusting for baseline medication burden, the reduction pattern in IOP (p < 0.05) was different between cMIGS and sMIGS, favoring cMIGS, and the groups had similar reduction patterns in medications (p = 0.75).
UNASSIGNED: The use of Phaco/MIGS in patients with cataract and severe glaucoma may significantly reduce IOP and medication burden throughout 12 months and, thus, may serve as a stepping stone in severe glaucoma patients with visually significant cataract before proceeding with more invasive glaucoma surgery. This effect may be potentiated by the combination effect of cMIGS.
Many patients with cataract and mild or moderate glaucoma who undergo cataract surgery also benefit from microinvasive glaucoma surgery (MIGS) performed at the same time, but the role of MIGS in patients with severe glaucoma and cataract is not clear. We report that combined cataract surgery and MIGS were associated with significant reductions in eye pressure in patients with severe glaucoma for more than 12 months.

Chronic diseases contribute to 68% of global mortality, highlighting the importance of early detection and management of conditions such as metabolic syndrome. Effective lifestyle interventions, particularly through mobile health (mHealth), have shown potential in promoting health and reducing cardiometabolic risk. This study utilized mHealth data from public health centers in South Korea, targeting adults with risk factors for metabolic syndrome. The Intervention-Motivation-Behavioral skills (IMB) theoretical model was applied to categorize participants\' practice patterns over time using the Group-Based Trend Model (GBTM). And the Generalized Estimating Equations (GEE) methodology was applied to confirm the effective practice patterns for improving metabolic syndrome. Data were collected over 24 weeks. The dataset encompasses life-log data capable of capturing changes in intervention, self-report surveys, and clinical measurements, all linked to personal identification keys and thereby integrated. Participants demonstrated improved health behaviors, with the healthy eating score increasing from 5.0 to 6.4 and physical activity rates rising from 41.5% to 59%. Health risk factors decreased significantly, with the mean number of risk factors dropping from 2.4 to 1.4. The percentage of subjects with three or more metabolic syndrome components decreased from 42.3% in the initial period to 19.2% in the final period. Practice patterns by IMB components were classified into three categories: continuous type, late decline type, and early decline type. Improvements in health behavior and metabolic syndrome were observed in the continuous type of each IMB component. The mHealth interventions were confirmed to be positively associated with improved health behavior and management of metabolic syndrome in the continuous practice patterns of IMB.

BACKGROUND: The optimal timing of performing ICSI on immature oocytes for POSEIDON patients is still unknown to get better early embryonic development outcomes. The purpose of this study was to implore the most appropriate time to carry out ICSI on in vitro maturation GV and MI oocytes for POSEIDON patients.
METHODS: Two hundred thirty-nine immature oocytes from 163 POSEIDON patients were prospectively performed ICSI at different timings: P-ICSI (ICSI was performed on in vitro matured oocytes 4-6 h after the first polar body extrusion, N = 81), R-ICSI (ICSI was performed on in vitro matured oocytes less than 4 h after the first polar body extrusion, N = 80), and E-ICSI (ICSI was performed on in vitro matured oocytes the next day after oocytes retrieval, N = 78). Fertilization and embryonic development outcomes were collected and statistically analyzed. Mitochondria distribution of cytoplasm of in vitro matured oocytes with different time cultures after the first polar body (PB1) extrusion was stained.
RESULTS: Compared to the E-ICSI group, more day 3 embryos from P-ICSI became blastocysts after sequential culture though without statistical significance (OR = 3.71, 95% CI: 0.94-14.63, P = 0.061). Compared to the E-ICSI group, more embryos from both P-ICSI and R-ICSI groups were clinically used with statistical significance (OR = 5.67, 95% CI: 2.24-14.35, P = 0.000 for P-ICSI embryos; OR = 3.23, 95% CI: 1.23-8.45, P = 0.017 for R-ICSI embryos). Compared to the E-ICSI group, transferred embryos from P-ICSI and R-ICSI had a higher implantation rate though without statistical significance (35.3% for P-ICSI embryos; 9.1% or R-ICSI embryos and 0% for E-ICSI embryos, P = 0.050). Among the three group, there were most healthy babies delivered from the P-ICSI group (5, 1 and 0 for P-ICSI, R-ICSI and E-ICSI respectively). The mitochondria in the cytoplasm of in vitro matured oocytes with a less than 4 h and 4-6 h culture after PB1 extrusion presented semiperipheral and diffused distribution patterns, respectively.
CONCLUSIONS: Our results revealed P-ICSI (ICSI was performed on in vitro matured oocytes 4-6 h after the first polar body extrusion) provided the most efficient method to utilize the immaturation oocytes basing on embryos utilization and live birth outcome for low prognosis patients under the POSEIDON classification. The mitochondria distribution of the in vitro matured oocytes\' cytoplasm from P-ICSI varied that from R-ICSI.

Cinnamomumparthenoxylon (Jack) Meisn. is a tree in genus Cinnamomum that has been facing global threats due to forest degradation and habitat fragmentation. Many recent studies aim to describe habitats and assess population and species genetic diversity for species conservation by expanding afforestation models for this species. Understanding their current and future potential distribution plays a major role in guiding conservation efforts. Using five modern machine-learning algorithms available on Google Earth Engine helped us evaluate suitable habitats for the species. The results revealed that Random Forest (RF) had the highest accuracy for model comparison, outperforming Support Vector Machine (SVM), Classification and Regression Trees (CART), Gradient Boosting Decision Tree (GBDT) and Maximum Entropy (MaxEnt). The results also showed that the extremely suitable ecological areas for the species are mostly distributed in northern Vietnam, followed by the North Central Coast and the Central Highlands. Elevation, Temperature Annual Range and Mean Diurnal Range were the three most important parameters affecting the potential distribution of C.parthenoxylon. Evaluation of the impact of climate on its distribution under different climate scenarios in the past (Last Glacial Maximum and Mid-Holocene), in the present (Worldclim) and in the future (using four climate change scenarios: ACCESS, MIROC6, EC-Earth3-Veg and MRI-ESM2-0) revealed that of C.parthenoxylon would likely expand to the northeast, while a large area of central Vietnam will gradually lose its adaptive capacity by 2100.

In Tunisia, urban air pollution is becoming a bigger problem. This study used a combined strategy of biomonitoring with lichens and satellite mapping with Sentinel-5 satellite data processed in Google Earth Engine (GEE) to assess the air quality over metropolitan Tunis. Lichen diversity was surveyed across the green spaces of the Faculty of Science of Tunisia sites, revealing 15 species with a predominance of pollution-tolerant genera. The Index of Atmospheric Purity (IAP) calculated from the lichen data indicated poor air quality. Spatial patterns of pollutants sulfur dioxide (SO2), ozone (O3), nitrogen dioxide (NO2), carbon monoxide (CO), and aerosol index across Greater Tunis were analyzed from Sentinel-5 datasets on the GEE platform. The higher values of these indices in the research area indicate that it may be impacted by industrial activity and highlight the considerable role that vehicle traffic plays in air pollution. The results of the IAP, IBL, and the combined ground-based biomonitoring and satellite mapping techniques confirm poor air quality and an environment affected by atmospheric pollutants which will enable proactive air quality management strategies to be put in place in Tunisia\'s rapidly expanding cities.

The national-level land cover database is essential to sustainable landscape management, environmental protection, and food security. In Afghanistan, the existing national-level land cover data from 1972, 1993, and 2010 relied on satellite data from diverse sensors adopted three different land cover classification systems. This inconsistent land cover map across the various years leads to the challenge of assessing landscape changes that are crucial for management efforts. To address this challenge, a 19-year national-level land cover dataset from 2000 to 2018 was developed for the first time to aid policy development, settlement planning, and the monitoring of forests and agriculture across time. In the development of the 19 year span of land cover data products, a state-of-the-art remote sensing approach, employing a harmonized classification scheme was implemented through the utilization of Google Earth Engine (GEE). Publicly accessible Landsat imagery and additional geospatial covariates were integrated to produce an annual land cover database for Afghanistan. The generated dataset bridges historical data gaps and facilitates robust land cover change information. The annual land cover database is now accessible through https://rds.icimod.org/. This repository ensures that the annual land cover data is readily available to all users interested in comprehending the dynamic land cover changes happening in Afghanistan.

The generalized estimating equations method (GEE) is commonly applied to analyze data obtained from family studies. GEE is well known for its robustness on misspecification of correlation structure. However, the unbalanced distribution of family sizes and complicated genetic relatedness structure within each family may challenge GEE performance. We focused our research on binary outcomes. To evaluate the performance of GEE, we conducted a series of simulations, on data generated adopting the kinship matrix (correlation structure within each family) from the Strong Heart Family Study (SHFS). We performed a fivefold cross-validation to further evaluate the GEE predictive power on data from the SHFS. A Bayesian modeling approach, with direct integration of the kinship matrix, was also included to contrast with GEE. Our simulation studies revealed that GEE performs well on a binary outcome from families having a relatively simple kinship structure. However, data with a binary outcome generated from families with complex kinship structures, especially with a large genetic variance, can challenge the performance of GEE.

Istanbul is a megacity with a population of 15.5 million and is one of the fastest-growing cities in Europe. Due to the rapidly increasing population and urbanization, Istanbul\'s daily water needs are constantly increasing. In this study, eight drinking water basins that supply water to Istanbul were comprehensively examined using remote sensing observations and techniques. Water surface area changes were determined monthly, and their relationships with meteorological parameters and climate change were investigated. Monthly water surface areas of natural lakes and dams were determined with the Normalized Difference Water Index (NDWI) applied to Sentinel-2 satellite images. Sentinel-1 Synthetic Aperture Radar (SAR) images were used in months when optical images were unavailable. The study was carried out using 3705 optical and 1167 SAR images on the Google Earth Engine (GEE) platform. Additionally, to determine which areas of water resources are shrinking, water frequency maps of the major drinking water resources were produced. Land use/land cover (LULC) changes that occurred over time were determined, and the effects of the increase in urbanization, especially on drinking water surface areas, were investigated. ESRI LULC data was used to determine LULC changes in watersheds, and the increase in urbanization areas from 2017 to 2022 ranged from 1 to 91.43%. While the basin with the least change was in Istranca, the highest increase in the artificial surface was determined to be in the Büyükçekmece basin with 1833.03 ha (2.89%). While there was a 1-12.35% decrease in the surface areas of seven water resources from 2016 to 2022, an increase of 2.65-93% was observed in three water resources (Büyükçekmece, Sazlıdere, and Elmalı), each in different categories depending on their size. In the overall analysis, total WSA decreased by 62.33 ha from 2016 to 2022, a percentage change of 0.70%. Besides the areal change analysis, the algae contents of the drinking water resources over the years were examined for the major water basins using the Normalized Difference Chlorophyll Index (NDCI) and revealed their relationship with meteorological factors and urbanization.

BACKGROUND: The stepped-wedge cluster randomized trial (SW-CRT) design has become popular in healthcare research. It is an appealing alternative to traditional cluster randomized trials (CRTs) since the burden of logistical issues and ethical problems can be reduced. Several approaches for sample size determination for the overall treatment effect in the SW-CRT have been proposed. However, in certain situations we are interested in examining the heterogeneity in treatment effect (HTE) between groups instead. This is equivalent to testing the interaction effect. An important example includes the aim to reduce racial disparities through healthcare delivery interventions, where the focus is the interaction between the intervention and race. Sample size determination and power calculation for detecting an interaction effect between the intervention status variable and a key covariate in the SW-CRT study has not been proposed yet for binary outcomes.
METHODS: We utilize the generalized estimating equation (GEE) method for detecting the heterogeneity in treatment effect (HTE). The variance of the estimated interaction effect is approximated based on the GEE method for the marginal models. The power is calculated based on the two-sided Wald test. The Kauermann and Carroll (KC) and the Mancl and DeRouen (MD) methods along with GEE (GEE-KC and GEE-MD) are considered as bias-correction methods.
RESULTS: Among three approaches, GEE has the largest simulated power and GEE-MD has the smallest simulated power. Given cluster size of 120, GEE has over 80% statistical power. When we have a balanced binary covariate (50%), simulated power increases compared to an unbalanced binary covariate (30%). With intermediate effect size of HTE, only cluster sizes of 100 and 120 have more than 80% power using GEE for both correlation structures. With large effect size of HTE, when cluster size is at least 60, all three approaches have more than 80% power. When we compare an increase in cluster size and increase in the number of clusters based on simulated power, the latter has a slight gain in power. When the cluster size changes from 20 to 40 with 20 clusters, power increases from 53.1% to 82.1% for GEE; 50.6% to 79.7% for GEE-KC; and 48.1% to 77.1% for GEE-MD. When the number of clusters changes from 20 to 40 with cluster size of 20, power increases from 53.1% to 82.1% for GEE; 50.6% to 81% for GEE-KC; and 48.1% to 79.8% for GEE-MD.
CONCLUSIONS: We propose three approaches for cluster size determination given the number of clusters for detecting the interaction effect in SW-CRT. GEE and GEE-KC have reasonable operating characteristics for both intermediate and large effect size of HTE.