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The five-paragraph essay is highly controversial, and yet it has also been a useful format for composition. In this essay we explain why, despite its limits, students need to go along with the format to make what use and get what advantage of it. We then demonstrate that valuing the philosophical, historical, cultural, and educational backgrounds of our students can help navigate away from the restrictive nature of the format and lead to equitable learning for all students. Finally, we introduce a few curriculum designs and instructional practices to expand the epistemological and pedagogical frontiers of the format. In short, we conduct a culturally responsive discussion of issues students with non-Western backgrounds--Asian American students in particular--may encounter when writing the five-paragraph essay. By sharing student learning-focused approaches, we aim to improve Asian American students' learning experiences.

Targeted alpha therapy (TAT) pairs the specificity of antigen targeting with the lethality of alpha particles to eradicate cancerous cells. Actinium-225 [225Ac; t1/2 = 9.920(3) days] is an alpha-emitting radioisotope driving the next generation of TAT radiopharmaceuticals. Despite promising clinical results, a fundamental understanding of Ac coordination chemistry lags behind the rest of the Periodic Table due to its limited availability, lack of stable isotopes, and inadequate systems poised to probe the chemical behavior of this radionuclide. In this work, we demonstrate a platform that combines an 8-coordinate synthetic ligand and a mammalian protein to characterize the solution and solid-state behavior of the longest-lived Ac isotope, 227Ac [t1/2 = 21.772(3) years]. We expect these results to direct renewed efforts for 225Ac-TAT development, aid in understanding Ac coordination behavior relative to other +3 lanthanides and actinides, and more broadly inform this element's position on the Periodic Table.

Abstract Groundwater is a primary source of drinking water for billions worldwide. It plays a crucial role in irrigation, domestic, and industrial uses, and significantly contributes to drought resilience in various regions. However, excessive groundwater discharge has left many areas vulnerable to potable water shortages. Therefore, assessing groundwater potential zones (GWPZ) is essential for implementing sustainable management practices to ensure the availability of groundwater for present and future generations. This study aims to delineate areas with high groundwater potential in the Bankura district of West Bengal using four machine learning methods: Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Voting Ensemble (VE). The models used 161 data points, comprising 70% of the training dataset, to identify significant correlations between the presence and absence of groundwater in the region. Among the methods, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) proved to be the most effective in mapping groundwater potential, suggesting their applicability in other regions with similar hydrogeological conditions. The performance metrics for RF are very good with a precision of 0.919, recall of 0.971, F1-score of 0.944, and accuracy of 0.943. This indicates a strong capability to accurately predict groundwater zones with minimal false positives and negatives. Adaptive Boosting (AdaBoost) demonstrated comparable performance across all metrics (precision: 0.919, recall: 0.971, F1-score: 0.944, accuracy: 0.943), highlighting its effectiveness in predicting groundwater potential areas accurately; whereas, Extreme Gradient Boosting (XGBoost) outperformed the other models slightly, with higher values in all metrics: precision (0.944), recall (0.971), F1-score (0.958), and accuracy (0.957), suggesting a more refined model performance. The Voting Ensemble (VE) approach also showed enhanced performance, mirroring XGBoost's metrics (precision: 0.944, recall: 0.971, F1-score: 0.958, accuracy: 0.957). This indicates that combining the strengths of individual models leads to better predictions. The groundwater potentiality zoning across the Bankura district varied significantly, with areas of very low potentiality accounting for 41.81% and very high potentiality at 24.35%. The uncertainty in predictions ranged from 0.0 to 0.75 across the study area, reflecting the variability in groundwater availability and the need for targeted management strategies. In summary, this study highlights the critical need for assessing and managing groundwater resources effectively using advanced machine learning techniques. The findings provide a foundation for better groundwater management practices, ensuring sustainable use and conservation in Bankura district and beyond.

Drawing on interviews with staff from Language Supplementary Schools (LSS) in Manchester (UK), we discuss the emergence of makeshift ideologies whereby actors seek to legitimise choices and policies of heritage language transmission in the diaspora setting. Actors discuss the use of regional and vernacular varieties, the consideration given to pupils' multilingual repertoires, and the use of the majority language (English) as 'scaffold'. Conscious that such practices potentially clash with the schools' expected mission statements and prevailing ideological dispositions, actors seek to justify them. They do this with reference to changes in setting and attitudes, which they position along time and place axes, comparing origin countries with the diaspora reality, thereby forging new language narratives as a 'diasporic stance'. We consider the discursive tools and actions of talks that constitute descriptions, explanations, justifications, and their interplay in actors' efforts to share their perspective on practices and the attitudes that accompany them. The study shows how ideologies regarding the particular diasporic situation are shifting, and the relevance of pluralistic language repertoires within it. Methodologically, the study suggests that ideologies are not just discursive dimensions, rather they are constructed and communicated discursively, allowing us to trace their emergence using semiotic and discourse-analytical tools.

We describe a novel, university-level, introductory biology course that uses evolution as a narrative framework. Our course conveys the content in an introductory biology course by telling the story of the evolution of life on Earth. We begin with early Earth environments in which biological molecules and processes evolved and led to the first RNA-based living entities, then to DNA-based cells, and then to all of the life around us. We use this framework to describe the evolution of the physiological processes important for beginning biology students. This structure contrasts with the widespread "levels of organization" approach in which the order of topics treats the molecular level first, then cells and organisms, and finally evolutionary and ecological processes. This traditional approach is limited in at least two ways: (1) The material taught at each level is not explicitly connected to that at other levels, making the concepts more difficult to assimilate and retain; and (2) Evolution is taught as a discrete section of the course, rather than as the integrating principle of life itself. We find that our narrative evolutionary approach enables students to more effectively assimilate, retain, and connect the vast amount of information presented in an introductory biology course.

Abstract Targeted alpha therapy (TAT) pairs the specificity of antigen targeting with the lethality of alpha particles to eradicate cancerous cells. Actinium-225 [225Ac; t1/2 = 9.920(3) days] is an alpha-emitting radioisotope driving the next generation of TAT radiopharmaceuticals. Despite promising clinical results, a fundamental understanding of Ac coordination chemistry lags behind the rest of the Periodic Table due to its limited availability, lack of stable isotopes, and inadequate systems poised to probe the chemical behavior of this radionuclide. In this work, we demonstrate a platform that combines an 8-coordinate synthetic ligand and a mammalian protein to characterize the solution and solid-state behavior of the longest-lived Ac isotope, 227Ac [t1/2 = 21.772(3) years]. We expect these results to direct renewed efforts for 225Ac-TAT development, aid in understanding Ac coordination behavior relative to other +3 lanthanides and actinides, and more broadly inform this element’s position on the Periodic Table.

Abstract In response to the COVID-19 pandemic, the American Board of Anesthesiology transitioned from in-person to virtual administration of its APPLIED Examination, assessing more than 3000 candidates for certification purposes remotely in 2021. Four hundred examiners were involved in delivering and scoring Standardized Oral Examinations (SOEs) and Objective Structured Clinical Examinations (OSCEs). More than 80% of candidates started their exams on time and stayed connected throughout the exam without any problems. Only 74 (2.5%) SOE and 45 (1.5%) OSCE candidates required rescheduling due to technical difficulties. Of those who experienced “significant issues”, concerns with OSCE technical stations (interpretation of monitors and interpretation of echocardiograms) were reported most frequently (6% of candidates). In contrast, 23% of examiners “sometimes” lost connectivity during their multiple exam sessions, on a continuum from minor inconvenience to inability to continue. 84% of SOE candidates and 89% of OSCE candidates described “smooth” interactions with examiners and standardized patients/standardized clinicians, respectively. However, only 71% of SOE candidates and 75% of OSCE candidates considered themselves to be able to demonstrate their knowledge and skills without obstacles. When compared with their in-person experiences, approximately 40% of SOE examiners considered virtual evaluation to be more difficult than in-person evaluation and believed the remote format negatively affected their development as an examiner. The virtual format was considered to be less secure by 56% and 40% of SOE and OSCE examiners, respectively. The retirement of exam materials used virtually due to concern for compromise had implications for subsequent exam development. The return to in-person exams in 2022 was prompted by multiple factors, especially concerns regarding standardization and security. The technology is not yet perfect, especially for testing in-person communication skills and displaying dynamic exam materials. Nevertheless, the American Board of Anesthesiology’s experience demonstrated the feasibility of conducting large-scale, high-stakes oral and performance exams in a virtual format and highlighted the adaptability and dedication of candidates, examiners, and administering board staff.

Abstract Improved understanding of crops’ response to soil water stress is important to advance soil-plant system models and to support crop breeding, crop and varietal selection, and management decisions to minimize negative impacts. Studies on eco-physiological crop characteristics from leaf to canopy for different soil water conditions and crops are often carried out at controlled conditions. In-field measurements under realistic field conditions and data of plant water potential, its links with CO2 and H2O gas fluxes, and crop growth processes are rare. Here, we presented a comprehensive data set collected from leaf to canopy using sophisticated and comprehensive sensing techniques (leaf chlorophyll, stomatal conductance and photosynthesis, canopy CO2 exchange, sap flow, and canopy temperature) including detailed crop growth characteristics based on destructive methods (crop height, leaf area index, aboveground biomass, and yield). Data were acquired under field conditions with contrasting soil types, water treatments, and different cultivars of wheat and maize. The data from 2016 up to now will be made available for studying soil/water-plant relations and improving soil-plant-atmospheric continuum models.

The mammalian protein siderocalin binds bacterial siderophores and their iron complexes through cation-π and electrostatic interactions, but also displays high affinity for hydroxypyridinone complexes of trivalent lanthanides and actinides. In order to circumvent synthetic challenges, the use of siderocalin-antibody fusion proteins is explored herein as an alternative targeting approach for precision delivery of trivalent radiometals. We demonstrate the viability of this approach in vivo, using the theranostic pair 90Y (β-, t1/2 = 64 h)/86Y (β+, t1/2 = 14.7 h) in a SKOV-3 xenograft mouse model. Ligand radiolabeling with octadentate hydroxypyridinonate 3,4,3-LI(1,2-HOPO) and subsequent protein binding were achieved at room temperature. The results reported here suggest that the rapid non-covalent binding interaction between siderocalin fusion proteins and the negatively charged Y(iii)-3,4,3-LI(1,2-HOPO) complexes could enable purification-free, cold-kit labeling strategies for the application of therapeutically relevant radiometals in the clinic.

Wet grasslands are crucial components of terrestrial ecosystems, known for their biodiversity and provision of ecosystem services such as flood attenuation and carbon sequestration. Given their ecological significance, monitoring biodiversity within these landscapes is of utmost importance for effective conservation and management strategies. This study, conducted in a wet grassland of Brandenburg, Germany, utilized unmanned aerial vehicles (UAVs) to facilitate the estimation of species richness by the integration of remotely sensed canopy features such as canopy height (CH), spectral data (Vegetation Indices, VI), and texture features (Gray-Level Co-occurrence Matrix, GLCM) using two machine learning methods (Partial Least Square regression (PLS) and Random Forest (RF)). Data was collected over two growing seasons under three different grass cutting regimes, employing multispectral sensors to capture detailed vegetation characteristics. The analysis revealed that the performance of the machine learning methods varied with the feature combinations. Models combining VI and GLCM features demonstrated the highest predictive accuracy, particularly in frequently cut grasslands, as indicated by higher R2 values (up to 0.52) and lower root mean square errors (rRMSE as low as 34.9 %). RF models generally outperformed PLS models across different feature sets, with the CH + VI + GLCM combination yielding the best results. These findings underscore the potential of spectral and textural data to effectively capture the ecological dynamics of wet grasslands, providing valuable insights into biodiversity patterns.

Sugarcane cultivation covers a large area of agricultural land in southwestern Iran. Considering the high water demand of sugarcane and Iran's limited water resources, optimizing efficient Subsurface Drip Irrigation (SDI) systems is key. To support the design of SDI systems, we conducted an on-farm split-plot experiment in southwestern Iran, investigating the effect of dripper installation depths and mulch on crop and soil properties (medium soil texture). The main factor was mulch management comprising M1: the incineration of crop residues, and M2: leaving sugarcane residues on the soil surface after harvest. The sub-factor was SDI at three installation depth (ID) of the laterals: ID1: 15 cm, ID2: 25 cm, and ID3: 35 cm. The main and interaction effects of treatments were investigated on the vertical and horizontal distribution of soil moisture, salinity, and nitrate, and on the quantity and quality of sugarcane yield. The results illustrated that with increasing the dripper installation depth from 25 to 35 cm in average, soil moisture increased (4.4 %) and soil salinity and nitrate decreased by 17.5 and 11 % respectively, which also resulted in 11.2 % non-significant decrease in yield. Using mulch saved moisture by 18.4, 5.7 and 5.3 %, but decreased salinity by 27.2, 22.9 and 21.9 %, and nitrate by 25.6, 18.2 and 14.1 %, at depths of 0–30, 30–60 and 60–90 cm, respectively. While ID3 showed higher moisture levels, ID2 provided moisture at FC over growing season. We identified ID2 under M1 with a higher yield (132.6 t ha–1), total biomass (231.1 t ha–1), and irrigation water productivity (6.4 kg m–3) compared to the other IDs as a promising on-farm dripper ID to promote moisture conservation and salinity avoidance. This research also requires studying sugarcane growth under ID2 treatments in different soil textures and irrigation water qualities.

Abstract To predict outcome to combination bevacizumab (BVZ) therapy, we employed cell-free DNA (cfDNA) to determine chromosomal instability (CIN), nucleosome footprints (NF) and methylation profiles in metastatic colorectal cancer (mCRC) patients. Low-coverage whole-genome sequencing (LC-WGS) was performed on matched tumor and plasma samples, collected from 74 mCRC patients from the AC-ANGIOPREDICT Phase II trial (NCT01822444), and analysed for CIN and NFs. A validation cohort of plasma samples from the University Medical Center Mannheim (UMM) was similarly profiled. 61 AC-ANGIOPREDICT plasma samples collected before and following BVZ treatment were selected for targeted methylation sequencing. Using cfDNA CIN profiles, AC-ANGIOPREDICT samples were subtyped with 92.3% accuracy into low and high CIN clusters, with good concordance observed between matched plasma and tumor. Improved survival was observed in CIN-high patients. Plasma-based CIN clustering was validated in the UMM cohort. Methylation profiling identified differences in CIN-low vs. CIN high (AUC = 0.87). Moreover, significant methylation score decreases following BVZ was associated with improved outcome (p = 0.013). Analysis of CIN, NFs and methylation profiles from cfDNA in plasma samples facilitates stratification into CIN clusters which inform patient response to treatment.

Abstract Background Panel gene sequencing is an established diagnostic tool for precision oncology of solid tumors, but its utility for the treatment of cancers of the digestive system in clinical routine is less well documented. Methods We retrospectively identified patients with advanced or metastatic gastrointestinal, pancreaticobiliary or hepatic cancers who received panel gene sequencing at a tertiary university hospital from 2015 to 2022. For these cases, we determined the spectrum of genetic alterations, clinicopathological parameters and treatment courses. Assessment of actionability of genetic alterations was based on the OncoKB database, cancer-specific ESMO treatment guidelines, and recommendations of the local molecular tumor board. Results In total, 155 patients received panel gene sequencing using either the Oncomine Focus (62 cases), Comprehensive (91 cases) or Childhood Cancer Research Assay (2 cases). The mean age of patients was 61 years (range 24–90) and 37% were female. Most patients suffered from either colorectal cancer (53%) or cholangiocellular carcinoma (19%). 327 genetic alterations were discovered in 123 tumor samples, with an average number of 2.1 alterations per tumor. The most frequently altered genes were TP53, KRAS and PIK3CA. Actionable gene alterations were detected in 13.5–56.8% of tumors, according to ESMO guidelines or the OncoKB database, respectively. Thirteen patients were treated with targeted therapies based on identified molecular alterations, with a median progression-free survival of 8.8 months. Conclusions Actionable genetic alterations are frequently detected by panel gene sequencing in patients with advanced cancers of the digestive tract, providing clinical benefit in selected cases. However, for the majority of identified actionable alterations, sufficient clinical evidence for targeted treatments is still lacking.

Abstract In the past, the cerebellum has been best known for its crucial role in motor function. However, increasingly more findings highlight the importance of cerebellar contributions in cognitive functions and neurodevelopment. Using a total of 7240 neuroimaging scans from 4862 individuals, we describe and provide detailed, openly available models of cerebellar development in childhood and adolescence (age range: 6–17 years), an important time period for brain development and onset of neuropsychiatric disorders. Next to a traditionally used anatomical parcellation of the cerebellum, we generated growth models based on a recently proposed functional parcellation. In both, we find an anterior-posterior growth gradient mirroring the age-related improvements of underlying behavior and function, which is analogous to cerebral maturation patterns and offers evidence for directly related cerebello-cortical developmental trajectories. Finally, we illustrate how the current approach can be used to detect cerebellar abnormalities in clinical samples.

Climate change has substantially increased both the occurrence and intensity of flood events, particularly in the Indian subcontinent, exacerbating threats to human populations and economic infrastructure. The present research employed novel ML models—LR, SVM, RF, XGBoost, DNN, and Stacking Ensemble—developed in the Python environment and leveraged 18 flood-influencing factors to delineate flood-prone areas with precision. A comprehensive flood inventory, obtained from Sentinel-1 Synthetic Aperture Radar (SAR) data using the Google Earth Engine (GEE) platform, provided empirical data for entire model training and validation. Model performance was assessed using precision, recall, F1-score, accuracy, and ROC-AUC metrics. The results highlighted Stacking Ensemble’s superior predictive ability (0.965), followed closely by, XGBoost (0.934), DNN (0.929), RF (0.925), LR (0.921), and SVM (0.920) respectively, establishing the feasibility of ML applications in disaster management. The maps depicting susceptibility to flooding generated by the current research provide actionable insights for decision-makers, city planners, and authorities responsible for disaster management, guiding infrastructural and community resilience enhancements against flood risks.

Process-based crop models have faced rapid development over the last years, and many modelling platforms are now available and can be used in a wide range of conditions. Whilst the selection of a model should be suited to the purpose of its application, very few studies focused on the impact of choosing different model structures and data details on the simulation outputs. One important aspect is the soil water dynamics, which can be simulated at different levels of details in terms of data and approaches. In this study, we investigated the impact of model structure and data detail on simulations of sugarcane growth and irrigation scheduling. Three different soil water routines (Standalone, Tipping-Bucket, SWAP) were coupled with the SAMUCA model and calibrated with a comprehensive field experiment dataset. We also tested the influence of using simplified homogeneous (SL) and detailed (DL) soil profile information in model performance. The model framework was evaluated against independent field experiments across Brazil and used to simulate long-term sugarcane growth and irrigation scheduling. After calibration, the SWAP-DL showed the highest accuracy in soil moisture predictions, with a 6 % error (RRMSE), but the difference from TippingBucket-DL was small (8 %). While the performance of stalk dry mass, LAI and water-use efficiency simulations were within the range found in literature, comprehensive field experiments showing significant impacts of drought on sugarcane growth are still lacking for a more rigorous evaluation. Both SWAP and tipping-bucket approaches showed higher robustness to soil data detail as compared to the Standalone method, which should be avoided when soil water is critical for sugarcane growth. The use of tipping-bucket method may still be preferred when the research goal is focused on crop growth and soil parameters are limited. SWAP-SAMUCA may provide an extended ability to represent agrohydrological processes in sugarcane plantations and process understanding.