Distinct Categories Research Articles

B Chromosomes in Wheat: Evolution, Functions and Breeding Potential

Supernumerary B chromosomes (Bs) have been documented in over 2000 species across the plant, animal, and fungal kingdoms, representing one of the most intriguing components of the genome that have consistently garnered significant attention in the past. These Bs form a distinct category of chromosomes that are not deemed essential. While coexisting with the standard set of chromosomes in an organism, the copy numbers of Bs can vary among individuals within a population or even among cells within a single organism. Multiple processes, including chromosomal rearrangement, uneven segregation, chromatid non-disjunction, and deletion in specific regions, lead to the formation of the Bs from the A chromosome. These dispensable Bs possess their own genetic content and do not undergo recombination with A chromosomes. Although various roles of Bs in breeding have been explored in the literature, their specific role in wheat improvement remains unclear. The understanding of their origins, composition, and evolutionary history in wheat is still incomplete. Therefore, this review discusses the current knowledge of the genetic makeup of Bs, their functions, their relationship with A chromosomes, and the morphogenetic consequences in the context of wheat.

Diagnosis of TP53-mutated myeloid disease by the ICC and WHO 5th Edition Classifications

The International Consensus (ICC) and World Health Organization 5th edition (WHO5) classifications introduced new but differing categories of myeloid disease defined by TP53 mutations. We reviewed a cohort of 188 cases of TP53-mutated myeloid disease to determine how diagnoses and outcomes differ between the two classifications. Overall, 120 (64%) cases were classified differently by the ICC and WHO5, including 24/80 (30%) cases with <20% blasts. These cases were discrepantly categorized primarily due to inclusion of complex karyotype (CK) as a surrogate for biallelic TP53 inactivation only in the ICC. However, there were no significant differences in clinicopathologic characteristics or overall survival between cases categorized as TP53-mutated disease by both classifications and those with a single TP53 mutation and CK, suggesting that CK reliably identifies TP53-mutated cases with biallelic TP53 inactivation. The majority of cases of AML (96/102; 94%) were discrepantly diagnosed between the ICC and WHO5 due to the introduction of AML with mutated TP53 as a distinct category only in the ICC. Nearly all of these were instead diagnosed as AML, myelodysplasia-related (AML, MR) by WHO5. However, when compared to a separate cohort of patients with AML, MR without TP53 mutations, patients with TP53-mutated AML showed a distinct genetic profile and significantly worse overall survival, supporting the inclusion of AML with mutated TP53 as a distinct disease category. Overall, our results show that a significant percentage of TP53-mutated myeloid disease is classified differently by the ICC and WHO5 and highlight areas to address in future classification systems.

Integrated cerebellar radiomic‐network model for predicting mild cognitive impairment in Alzheimer's disease

AbstractINTRODUCTIONPathological and neuroimaging alterations in the cerebellum of Alzheimer's disease (AD) patients have been documented. However, the role of cerebellum‐derived radiomic and structural connectome modeling in the prediction of AD progression remains unclear.METHODSRadiomic features were extracted from magnetic resonance imaging (MRI) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 1319) and an in‐house dataset (n = 308). Integrated machine learning models were developed to predict the conversion risk of normal cognition (NC) to mild cognitive impairment (MCI) over a 6‐year follow‐up.RESULTSThe cerebellar models outperformed hippocampal models in distinguishing MCI from NC and in predicting transitions from NC to MCI across both cohorts. Key predictors included textural features in the right III and left I and II lobules, and network properties in Vermis I and II, which were associated with cognitive decline in AD.DISCUSSIONCerebellum‐derived radiomic‐network modeling shows promise as a tool for early identification and prediction of disease progression during the preclinical stage of AD.Highlights Altered cerebellar radiomic features and topological networks were identified in the subjects with mild cognitive impairment (MCI). The cerebellar radiomic‐network integrated models outperformed hippocampal models in distinguishing MCI from normal cognition. The cerebellar radiomic model effectively predicts MCI risk and can stratify individuals into distinct risk categories. Specific cerebellar radiomic features are associated with cognitive impairment across various stages of amyloid beta and tau pathology.

Towards harmonization of SO(3)-equivariance and expressiveness: a hybrid deep learning framework for electronic-structure Hamiltonian prediction

Abstract Deep learning for predicting the electronic-structure Hamiltonian of quantum systems necessitates satisfying the covariance laws, among which achieving SO(3)-equivariance without sacrificing the non-linear expressive capability of networks remains unsolved. To navigate the harmonization between SO(3)-equivariance and expressiveness, we propose HarmoSE, a deep learning method synergizing two distinct categories of neural mechanisms as a two-stage encoding and regression framework. The first stage corresponds to group theory-based neural mechanisms with inherent SO(3)-equivariant properties prior to the parameter learning process, while the second stage is characterized by a non-linear 3D graph Transformer network we propose, featuring high capability on non-linear expressiveness. Their combination lies in the point that, the first stage predicts baseline Hamiltonians with abundant SO(3)-equivariant features extracted, assisting the second stage in empirical learning of equivariance; and in turn, the second stage refines the first stage’s output as a fine-grained prediction of Hamiltonians using powerful non-linear neural mappings, compensating for the intrinsic weakness on non-linear expressiveness capability of mechanisms in the first stage. Our method enables precise, generalizable predictions while capturing SO(3)-equivariance under rotational transformations, and achieves state-of-the-art performance in Hamiltonian prediction tasks under multiple mean absolute error (MAE) metrics, such as the average MAE across all samples and matrix elements, the MAE for challenging samples, the MAE for different Hamiltonian blocks, and the MAE for the challenging blocks. It also demonstrates significant improvements in accuracy for downstream quantities, such as occupied orbital energy and the electronic wavefunction, as measured by MAE and cosine similarity, respectively.

Aggregate morphing of self-aligning soft active disks in semi-confined geometry

AbstractWe study the dependence of alignment and confinement on the aggregate morphology of self-aligning soft disks(particles) in a planer box (two dimensional) geometry confined along y direction using Langevin dynamics simulations. We show that when the box width decreases, the aggregate wall accumulation becomes non-uniform and displays non-monotonic behaviour in terms of phase behavior and height of these aggregates with an increase in alignment strength. Additionally, we identify two distinct categories of wall aggregates: layered and non-layered structures each exhibiting distinct local structural properties. For non-layered structures, local speed of the particles stay nearly constant as we move away from the boundary, while for layered structures, it increases with distance from the boundary. Our analysis shows that active pressure difference is a useful indicator for different aggregate morphologies and the peaks in the pressure curve are indicative of the average and minimum height of the structure.

Students' perceptions of using artificial intelligence in tertiary education

Artificial Intelligence (AI) is increasingly influencing various aspects of teaching and learning in tertiary educational institutes. This concise paper presents the preliminary findings of an ongoing research that aims at exploring university students' perceptions of the use of AI in higher education settings. Phenomenographic framework has been adapted as the methodological guide for this research. Semi-structured interviews were conducted among students of an international university to capture students' perceptions. Findings revealed that university students possess diverse perspectives on the use of AI in higher education settings. Four qualitatively distinct categories of perceptions of AI emerged from the interview data. It is found that university students perceive the use of AI as a) an essential academic aid, b) a facilitator of personalized learning, c) an inhibitor to critical thinking, and d) an ethical challenger. Findings have pedagogical, administrative, and ethical implications for developing AI-driven technology-focused policy that facilitates diverse student needs.

Cell Death by Metals: Diversity of Phenomena vs. Distinctive Categories

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COVID-19 pandemic microplastics environmental impacts predicted by deep random forest (DRF) predictive model

BackgroundMicroplastic pollution is a pressing issue with far-reaching environmental and public health consequences. This study delves into the intricacies of predicting microplastic pollution during the COVID-19 pandemic in Tehran, Iran.MethodsThe research introduces a rigorous comparative analysis that evaluates the predictive prowess of the Deep Random Forest algorithm and established benchmarks, such as Random Forest, Decision Trees, Gradient Boosting, AdaBoost, and Support Vector Machine. The evaluation process encompasses a meticulous 70–30 training–testing split of the main data set. Performance is assessed by analysis metrics, including ROC and statistical errors. The primary data set encompasses distinct categories, including household wastes, hospital wastes, clinics wastes, and unknown-originated susceptible waste which is categorized in Infected items, PPEs, SUPs, Test kits, Medical packages, Unknown-originated pandemic mircoplastic waste. Deliberately, this data set was partitioned into training and testing subsets, ensuring the robustness and reliability of subsequent analyses. Approximately 70% of the main database was allocated to the training data set, with the remaining 30% constituting the testing data set.ResultsThe findings underscore the proposed algorithm’s supremacy, boasting an impressive AUC = 0.941. This exceptional score reflects the model’s precision in categorizing microplastics. These results have profound implications for environmental management and public health during pandemics.ConclusionsThe study positions the proposed model as a potent tool for microplastic pollution prediction, encouraging further research to refine predictive models and tap into new data sources for a more comprehensive understanding of microplastic dynamics in urban settings.

Heterogeneity in Reported Side Effects Following Initiation of Elexacaftor-Tezacaftor-Ivacaftor: Experiences at a Quaternary CF Care Center.

The benefits of Elexecaftor-Tezacaftor-Ivacaftor (ETI) therapy on the health and wellbeing of people with CF (pwCF) are well documented. Since approval, however, a growing number of potential side effects have emerged in reports from clinical practice. With current understanding of ETI tolerability limited to data from clinical trials, the prevalence of side effects and their impact on care decision making remains poorly categorized. A 10-question survey was developed and distributed to patients 18 years or older who were treated at the Massachusetts General Hospital CF centers. Reports of side effects were measured across 12 distinct categories, and dose adjustments and discontinuation due to side effects were collected. If a patient reported no side effects, they did not have to complete the entire survey. Among 92 respondents initiated on ETI, 51 respondents (55.4%) reported potential side effects and 41 (44.5%) respondents reported no adverse events. The most commonly reported side effects were mental health, changes in appearance, and gastrointestinal complaints, which were reported by 22.8%, 30.4%, and 21.7% of patients, respectively. Eighteen (19.6%) respondents modified their dosing in response to side effects, and six discontinued treatment permanently (6.52%) due to persistent side effects. Responses demonstrated marked heterogeneity, with most respondents reporting at least one side effect following initiation. Dose modification was commonly utilized to mitigate adverse effects, however few respondents had to discontinue treatment. These findings demonstrate the importance of monitoring for potential drug-related side effects of ETI in clinical settings.

Forecasting Suitable Habitats of the Clouded Leopard (Neofelis nebulosa) in Asia: Insights into the Present and Future Climate Projections Within and Beyond Extant Boundaries

Terrestrial carnivores, such as the clouded leopard (Neofelis nebulosa), are ‘vulnerable’ and experiencing significant population declines in mainland Asia. Considering the assessed threats, the IUCN has repetitively revised the range of this felid and now characterized it into four distinct categories (extant, possibly extant, possibly extinct, and extinct). Although several ecological works have been accomplished on this enigmatic carnivore, the overall view of its habitat suitability, fragmentation, and corridor connectivity in both present and historical ranges is lacking. Thus, achieving this ecological information under present and future climate scenarios is crucial. The model identified merely 44,033 sq. km falling within the extant range (representing 31.66%) and 20,034 sq. km (8.13%) in the possibly extant range. Fascinatingly, within the historical ranges, an additional 15,264 sq. km (6.58%) has been identified as suitable habitat in the possibly extinct range and 14,022 sq. km (2.38%) in the extinct range. Notably, a total of 25,614 sq. km of suitable habitat is found within designated protected areas across the entire range. Nevertheless, climate change is expected to drive habitat loss of up to 41% (overall IUCN range) for N. nebulosa in both present and historical extent, with habitat patches becoming increasingly fragmented. This is reflected in a projected decline in the number of viable habitat patches (NP) by up to 23.29% in the future. This study also identified 18 transboundary biological corridors for N. nebulosa, with Southeast Asian countries expected to experience the most significant declines in corridor connectivity. In contrast, the South Asian countries (Bhutan, Nepal, and India) are projected to maintain relatively higher connectivity in the future. Nevertheless, a substantial decline in overall mean corridor connectivity is projected in the near future due to the impacts of climate change. This study underscores the urgent need for a coordinated and multifaceted conservation strategy for N. nebulosa, focusing on mitigating habitat loss and fragmentation. Practical measures must be implemented to protect the species’ shrinking range, considering its declining corridor networks and heightened vulnerability to inbreeding depression. Moreover, the assessment of habitat suitability both within and beyond the extant range, alongside corridor connectivity measures, provides valuable insights into potential translocation and reintroduction sites for this species. These findings provide a critical foundation for developing a strategic conservation plan tailored to the specific needs of this felid species across South and Southeast Asia, ensuring enhanced climate resilience and mitigating associated threats.

The use of poll aggregators in political journalism during Brazilian 2022 presidential campaign

The present research aims to understand how Brazilian media outlets use electoral forecasting methods in journalistic articles within the scope of coverage of the 2022 presidential elections. The year 2022 was remarkable due to the launch of several poll aggregation tools by the press, which have been used as an alternative to traditional polls to reinforce the demanding electoral coverage. In this context, we collected data from journalistic articles of selected media outlets that cite their own poll aggregators from January 1, 2022, to July 31, 2022. The research maps the utilization of aggregators in newspapers and presents an analysis of the articles that use the poll aggregators. To do so, we classified the articles into three distinct categories, identified according to patterns found in the way they use the aggregator. Finally, it underlines the innovative character of the research on the use of poll aggregators by media outlets.

Whole-Genome Sequencing Reveals the Population Structure and Genetic Diversity of Salmonella Typhimurium ST34 and ST19 Lineages.

Salmonella Typhimurium is an invasive gastrointestinal pathogen for both humans and animals. To investigate the genetic framework and diversity of S. Typhimurium, a total of 194 S. Typhimurium isolates were collected from patients in a tertiary hospital between 2020 and 2021. Antimicrobial susceptibility testing was used to confirm the resistance phenotype. Whole-genome sequencing and bioinformatics analysis were performed to determine the sequence type, phylogenetic relationships, resistance gene profiles, Salmonella pathogenicity island (SPI) and the diversity of the core and pan genome. The result showed that 57.22% of S. Typhimurium isolates were multidrug resistant and resistance of total isolates to the first-line drug ciprofloxacin was identified in 60.82%. The population structure of S. Typhimurium was categorized into three lineages: ST19 (20.10%, 39/194), ST34-1 (47.42%, 92/194) and ST34-2 (40.65%, 63/194), with the population size exhibiting increasing trends. All lineages harbored variety of fimbrial operons, prophages, SPIs and effectors that contributed to the virulence and long-term infections of S. Typhimurium. Importantly, ST34-1 lineage might potentially be more invasive due to the possession of SPI1-effector gene sopE which was essential for the proliferation, internalization and intracellular presence of S. Typhimurium in hosts. Multiple antimicrobial resistance genes were characteristically distributed across three lineages, especially carbapenem genes only detected in ST34-1&2 lineages. The distinct functional categories of pan genome among three lineages were observed in metabolism, signaling and gene information processing. This study provides a theoretical foundation for the evolved adaptation and genetic diversity of S. Typhimurium ST19 and ST34, among which ST34 lineages with multidrug resistance and potential hypervirulence need to pay more attention to epidemiological surveillance.

Mapping schistosomiasis risk landscapes and implications for disease control: A case study for low endemic areas in the Middle Paranapanema river basin, São Paulo, Brazil.

Schistosomiasis, a chronic parasitic disease, remains a public health issue in tropical and subtropical regions, especially in low and moderate-income countries lacking assured access to safe water and proper sanitation. A national prevalence survey carried out by the Brazilian Ministry of Health from 2011 to 2015 found a decrease in human infection rates to 1%, with 19 out of 26 states still classified as endemic areas. There is a risk of schistosomiasis reemerging as a public health concern in low-endemic regions. This study proposes an integrated landscape-based approach to aid surveillance and control strategies for schistosomiasis in low-endemic areas. In the Middle Paranapanema river basin, specific landscapes linked to schistosomiasis were identified using a comprehensive methodology. This approach merged remote sensing, environmental, socioeconomic, epidemiological, and malacological data. A team of experts identified ten distinct landscape categories associated with varying levels of schistosomiasis transmission potential. These categories were used to train a supervised classification machine learning algorithm, resulting in a 92.5% overall accuracy and a 6.5% classification error. Evaluation revealed that 74.6% of collected snails from water collections in five key municipalities within the basin belonged to landscape types with higher potential for S. mansoni infection. Landscape connectivity metrics were also analysed. This study highlights the role of integrated landscape-based analyses in informing strategies for eliminating schistosomiasis. The methodology has produced new schistosomiasis risk maps covering the entire basin. The region's low endemicity can be partly explained by the limited connectivity among grouped landscape-units more prone to triggering schistosomiasis transmission. Nevertheless, changes in social, economic, and environmental landscapes, especially those linked to the rising pace of incomplete urbanization processes in the region, have the potential to increase risk of schistosomiasis transmission. This study will help target interventions to bring the region closer to schistosomiasis elimination.

Margaret: Streamlining Research Productivity Analysis in Colombia with an R Package for GrupLAC Integration

Margaret is an advanced R package designed to systematically extract and consolidate data pertaining to research outputs (such as publications, books, book chapters, and conference presentations) of scientific groups from the GrupLAC platform, an online application managed by Minciencias in Colombia for the registration and updating of researcher and research group information in the fields of science, technology, and innovation. The challenge of monitoring and evaluating scientific production across various web platforms presents a substantial barrier to universities in tracking their contributions effectively. To address this challenge, Margaret accepts a designated list of links corresponding to university-affiliated research groups within GrupLAC. Utilizing web-scraping techniques, the package retrieves and compiles this data into a comprehensive XLSX file. This file encompasses information across 51 distinct categories of research products, enabling research directors to meticulously assess, monitor, and enhance various strategies that aim to augment the production, quality, and impact of scientific outputs. The Shiny application is publicly accessible and can be accessed via the following link: https://ucatolicaluisamigo-investigaciones.shinyapps.io/margaret/

Decoding viewer emotions in video ads

Understanding and predicting viewers’ emotional responses to videos has emerged as a pivotal challenge due to its multifaceted applications in video indexing, summarization, personalized content recommendation, and effective advertisement design. A major roadblock in this domain has been the lack of expansive datasets with videos paired with viewer-reported emotional annotations. We address this challenge by employing a deep learning methodology trained on a dataset derived from the application of System1’s proprietary methodologies on over 30,000 real video advertisements, each annotated by an average of 75 viewers. This equates to over 2.3 million emotional annotations across eight distinct categories: anger, contempt, disgust, fear, happiness, sadness, surprise, and neutral, coupled with the temporal onset of these emotions. Leveraging 5-second video clips, our approach aims to capture pronounced emotional responses. Our convolutional neural network, which integrates both video and audio data, predicts salient 5-second emotional clips with an average balanced accuracy of 43.6%, and shows particularly high performance for detecting happiness (55.8%) and sadness (60.2%). When applied to full advertisements, our model achieves a strong average AUC of 75% in determining emotional undertones. To facilitate further research, our trained networks are freely available upon request for research purposes. This work not only overcomes previous data limitations but also provides an accurate deep learning solution for video emotion understanding.

In vivo lacrimal gland imaging artefact assessment based on swept-source optical coherence tomography for dry eye disease.

This study aimed to characterise imaging artefacts in the lacrimal gland using swept-source optical coherence tomography (SS-OCT) in patients with dry eye disease (DED) and healthy participants and identify risk factors for these artefacts. In total, 151 eyes, including 104 from patients with DED and 47 from non-DED participants, were analysed. Demographic data collection, comprehensive ocular examinations and SS-OCT imaging of the palpebral lobe of the lacrimal gland were performed. Artefacts were classified into distinct categories with different severities. Univariate and multivariate logistic regression analyses were performed to evaluate the association of age, gender, best-corrected visual acuity, intraocular pressure (IOP) and the presence of DED with the presence of artefacts. Eight artefact types and severity grading were defined by analysing 1208 lacrimal SS-OCT images. The three most prevalent artefacts were defocus (75.83%), cliff (67.47%) and Z-off (58.44%). The presence of artefacts was significantly associated with the presence of DED (OR=9.13; 95% CI, 2.39 to 34.88; p=0.001) and higher IOP (OR=1.34; 95% CI, 1.14 to 1.58; p<0.001). Furthermore, multivariate logistic analyses showed that lower tear film breakup time (OR=0.71; 95% CI, 0.55 to 0.92; p=0.009) and higher meibum quality score (OR=2.86; 95% CI, 1.49 to 5.48; p=0.002) were significantly associated with higher odds for the presence of artefacts. DED eyes had more SS-OCT image artefacts than normal eyes. Stringent standardised image quality control should be implemented before further image analysis when using SS-OCT to assess lacrimal gland image.

Practical approach for sand-shale mixtures classification based on rocks multi-physical properties

Sandstones are the most common reservoir rocks, providing reservoirs for oil and gas and serving as reservoirs for groundwater. The Gulf of Mexico is known for its sand-shale mixtures and potential for its oil and hydrate gas resources in sandstone units. Understanding these variations is essential for assessing hydrocarbon potential and unconventional prospectivity. In this study, we utilized the Elastic, Electrical, and Radioactive (EER) properties of rocks for lithological categorization of well logging data, leading to the development of a novel rock physics template. The electrical and radioactive properties of the rocks facilitated a broad lithological classification, while their elastic characteristics helped distinguish between porous and low-porosity zones. Electrical and radioactive properties are utilized for well data classification because in sandstone formations, there is a decrease in log gamma and an increase in log resistivity. As a result, these opposing shifts in the two geophysical logs enhance the spread of data points on the lithological resistivity-gamma ray scatter plot, thereby simplifying the process of lithological categorization. Ultimately, the well logging data was sorted into three distinct categories: low shale sands (shale volume < 30 %), sand-shale mixtures (shale volume = 30 to 80 %), and shale-dominated areas. Subsequently, the Thomas Stieber model was employed to identify the types of clay minerals present in both sandstones and sand-shale mixtures. The model's findings revealed that dispersed type clay minerals are predominantly found in sandstones, with laminar and structured types being relatively rare. However, in sand-shale mixtures, both dispersed and laminar clays observed.

Maximizing Steel Slice Defect Detection: Integrating ResNet101 Deep Features with SVM via Bayesian Optimization

Accurate detection of defects on steel surfaces is crucial for maintaining quality standards in steel production. This paper addresses the challenge of classifying steel sheets into distinct defect categories by presenting a robust method that leverages deep learning and advanced optimization techniques. We propose a novel approach that utilizes the ResNet101 model to extract deep features, which are then classified using a support vector machine (SVM). To enhance the SVM's performance, Bayesian optimization is employed for hyperparameter tuning. Our method is validated using the "Severstal: Steel Defect Detection" dataset from Kaggle, achieving a validation accuracy of 89.1% and a test accuracy of 90.6%, with a classification error of 0.10934. Additionally, the area under the curve (AUC) for each class exceeds 0.95 in both the validation and test sets, demonstrating excellent discriminatory power. Further evaluation on the DAGM dataset achieved flawless results, with an accuracy of 100%, AUC of 1, sensitivity of 100%, specificity of 100%, precision of 100%, MCC of 100%, F1 score of 1, and kappa of 100%. On the NEU dataset, our method achieved an accuracy of 97.92%, sensitivity of 97.92%, specificity of 99.58%, precision of 98.06%, F1 score of 0.9791, MCC of 97.55%, and kappa of 92.50%. These results demonstrate the robustness and adaptability of the proposed method, offering an efficient and reliable solution for automating steel defect detection and surface defect classification in industrial applications.

Spatiotemporal changes and management measure to enhance ecosystem services in the Mongolian Plateau

The eco-environment in many underdeveloped areas is vulnerable. Elucidating the changes and relationships in ecosystem services (ESs) is a fundamental basis for achieving sustainable development in these regions. Utilizing multiple data sources and models, we quantified water provision (WP), habitat quality (HQ), carbon fixation (CF), soil conservation (SC), and wind erosion prevention (WEP) on the Mongolian plateau during 1992-2018. We then analyzed the spatial-temporal changes, trade-offs/synergies, and bundles relationship of these ESs. The results indicated there was high spatial variability in five ESs across the plateau, with overall higher ESs in forested areas, and grassland and cropland areas had higher WEP values. The annual mean values of WP, HQ, CF, SC, and WEP in the plateau during 1992-2018 were 27.15 mm, 0.57, 176.17 gC·m2, 48.83 t·hm2, and 58.31 t·hm2, respectively. Due to fluctuations in precipitation, vegetation cover and land use, the five ESs decreased from 1992 to 2000s, but increased from 2000s to 2018, Notably, the HQ changed most significantly (p<0.01), while the WP changed insignificantly (p>0.05). The different ESs on the plateau overall exhibited synergistic effects, trade-offs between several pairs of ESs related to HQ, were observed in cropland areas. We further identified five distinct categories of ES bundles and proposed targeted recommendations for spatial planning and management of these ESs, tailored to their characteristics. The findings offer a valuable reference for high-quality environmental management, as well as the restoration and enhancement of ESs in arid inland areas in a warming climate.

Perovskite Solar Cells: Challenges Facing Polymeric Hole Selective Materials in p–i–n Configuration

AbstractPolymeric hole‐selective materials (P‐HSMs) offer advantages like solution processability, tunable energy levels, and improved mechanical stability, making them suitable for large‐scale and flexible substrates. Poly[bis(4‐phenyl) (2,4,6‐trimethylphenyl) amine] (PTAA) based p–i–n perovskite solar cells exhibit promising power conversion efficiency (PCE), but wettability, dopant, and cost challenges necessitate the development of advanced next‐generation P‐HSMs. To provide a clear understanding of the structural property with photovoltaic performance, this review classifies such newly developed P‐HSMs into five distinct structural categories. Specifically, this review discusses the current status, advancements, challenges, and prospects in structural design and synthetic variations, focusing on enhancing photovoltaic performance, wettability, mitigating surface defects, and stability. Notably, incorporating polar units into P‐HSMs enhances wettability and mitigates ion instabilities and uncoordinated lead defects. Promising structural designs like polymeric self‐assembled monolayers and in situ polymerized hole‐selective materials are examined. Despite performance advancements, emerging, P‐HSMs face significant challenges such as limited thermal stress analysis (55–85 °C) and scalability restricted to small‐scale devices. To bridge this gap, this review emphasizes the urgent need for prioritizing thermal stability testing and large‐scale device fabrication in future research, paving the way for commercial viability of P‐HSMs in p–i–n perovskite photovoltaics.