bluuebunny/crossref_metadata_2025_split huggingface dataset:
Linnean and Wallacean shortfalls of Leptophlebiidae (Ephemeroptera: Insecta) in the Brazilian Amazon
Authors: Lima Myllena , Salles Frederico F. , de Andrade André Felipe A. , Dias‐Silva Karina , Juen Leandro , Brasil Leandro Schlemmer 
Published: May 2024

Abstract
One of the primary challenges in biodiversity conservation is the limited knowledge of the taxonomy and geographical distribution of species, particularly in megadiverse regions such as the Amazon
In this context, the present study aimed to mitigate the Linnean and Wallacean shortfalls within the Family Leptophlebiidae (Ephemeroptera: Insecta) in the Brazilian Amazon, identify priority areas for conducting faunal inventories and enhance taxonomic knowledge of this group.
Utilizing both primary Leptophlebiidae data collected in the state of Pará, and secondary data compiled from the literature on species occurring in the Amazon, we employed Species Distribution Modelling to identify areas with higher potential species richness and priority areas for faunal inventories in the Brazilian Amazon of the family.
For the primary data, 2,120 specimens were identified, representing 16 genera and 28 species. Among the 28 species analysed, 7 constitute new records for the state of Pará. Additionally, two new genera, five new species and six morphospecies were identified, significantly expanding the taxonomic landscape of the family in the state. Maps depicting potential species richness and priority areas for faunal inventories in the Brazilian Amazon generated from the Modelling indicated that the states of Pará and the western region of Maranhão exhibit high potential species richness, with overlapping areas identified as priorities for Leptophlebiidae faunal inventories.
In this context, this study represents a substantial advancement in the taxonomic knowledge of Leptophlebiidae in the state of Pará and the Amazon, underscoring the urgency of research and biodiversity conservation efforts for the group in the region. Furthermore, the identification of new taxa emphasizes the need for ongoing collaborative efforts to mitigate Linnean and Wallacean shortfalls.
Greening the path to disruptive innovation: The roles of CEOs characteristics, green organizational identity, and green product competitiveness
Authors: Kiani Ataullah , Bullini Orlandi Ludovico , Chen Qian 
Published: May 2024

AbstractThis study explores the link between sustainability orientation and disruptive innovation in small and medium‐sized enterprises (SMEs) by embracing both the natural resource‐based view (NRBV) and upper echelons theory. By drawing on these theories and extending them, it sheds light on the executives' characteristics and organizational contingencies leading to disruptive innovation. Starting from the CEOs' sustainability orientation, it investigates if and how it leads to disruptive innovation through the mediating role of green organizational identity and the moderating influence of both green product competitiveness and CEOs' gender‐related characteristics. Two wave time‐lagged survey data from 257 Chinese SMEs are analyzed to empirically test these relationships. Results suggest that SMEs with CEOs characterized by sustainability focus foster a green organizational identity, enabling them to generate disruptive innovations. Additionally, the presence of high green product competitiveness strengthens the connection between sustainability orientation and green organizational identity. Lastly, the relationship between this latter and disruptive innovation can be further enhanced by female CEOs by leveraging their specific skills and abilities. Implications for managers, organizations, and public policy are discussed.
Multi‐scale edge aggregation mesh‐graph‐network for character secondary motion
Authors: Wang Tianyi , Liu Shiguang 
Published: May 2024

AbstractAs an enhancement to skinning‐based animations, light‐weight secondary motion method for 3D characters are widely demanded in many application scenarios. To address the dependence of data‐driven methods on ground truth data, we propose a self‐supervised training strategy that is free of ground truth data for the first time in this domain. Specifically, we construct a self‐supervised training framework by modeling the implicit integration problem with steps as an optimization problem based on physical energy terms. Furthermore, we introduce a multi‐scale edge aggregation mesh‐graph block (MSEA‐MG Block), which significantly enhances the network performance. This enables our model to make vivid predictions of secondary motion for 3D characters with arbitrary structures. Empirical experiments indicate that our method, without requiring ground truth data for model training, achieves comparable or even superior performance quantitatively and qualitatively compared to state‐of‐the‐art data‐driven approaches in the field.
AG‐SDM: Aquascape generation based on stable diffusion model with low‐rank adaptation
Authors: Zhang Muyang , Yang Jinming , Xian Yuewei , Li Wei , Gu Jiaming , Meng Weiliang , Zhang Jiguang , Zhang Xiaopeng 
Published: May 2024

AbstractAs an amalgamation of landscape design and ichthyology, aquascape endeavors to create visually captivating aquatic environments imbued with artistic allure. Traditional methodologies in aquascape, governed by rigid principles such as composition and color coordination, may inadvertently curtail the aesthetic potential of the landscapes. In this paper, we propose Aquascape Generation based on Stable Diffusion Models (AG‐SDM), prioritizing aesthetic principles and color coordination to offer guiding principles for real artists in Aquascape creation. We meticulously curated and annotated three aquascape datasets with varying aspect ratios to accommodate diverse landscape design requirements regarding dimensions and proportions. Leveraging the Fréchet Inception Distance (FID) metric, we trained AGFID for quality assessment. Extensive experiments validate that our AG‐SDM excels in generating hyper‐realistic underwater landscape images, closely resembling real flora, and achieves state‐of‐the‐art performance in aquascape image generation.
Microfacet rendering with diffraction compensation
Authors: Yang Xudong , Lyu Aoran , Xian Chuhua , Cai Hongmin 
Published: May 2024

AbstractThe traditional microfacet rendering models usually only consider the straight propagation of light and do not take into account the diffraction effect when calculating the radiance of outgoing light. However, ignoring the energy generated by diffraction can lead to darker rendering results when the object's surface has many small details. To address this issue, we introduce a diffraction energy term in the microfacet model to compensate for the energy loss caused by diffraction. Starting from the Fresnel‐Kirchhoff diffraction theorem, we combine it with the Cook‐Torrance model. By incorporating the computed diffraction radiance into the outgoing radiance of the microfacet, we obtain a diffraction‐compensated BRDF (Bidirectional Reflectance Distribution Function) model. Experimental results demonstrate that our proposed method has a significant effect in compensating for outgoing light and produces more realistic rendering results.
A novel transformer‐based graph generation model for vectorized road design
Authors: Zhou Peichi , Li Chen , Zhang Jian , Wang Changbo , Qin Hong , Liu Long 
Published: May 2024

AbstractRoad network design, as an important part of landscape modeling, shows a great significance in automatic driving, video game development, and disaster simulation. To date, this task remains labor‐intensive, tedious and time‐consuming. Many improved techniques have been proposed during the last two decades. Nevertheless, most of the state‐of‐the‐art methods still encounter problems of intuitiveness, usefulness and/or interactivity. As a rapid deviation from the conventional road design, this paper advocates an improved road modeling framework for automatic and interactive road production driven by geographical maps (including elevation, water, vegetation maps). Our method integrates the capability of flexible image generation models with powerful transformer architecture to afford a vectorized road network. We firstly construct a dataset that includes road graphs, density map and their corresponding geographical maps. Secondly, we develop a density map generation network based on image translation model with an attention mechanism to predict a road density map. The usage of density map facilitates faster convergence and better performance, which also serves as the input for road graph generation. Thirdly, we employ the transformer architecture to evolve density maps to road graphs. Our comprehensive experimental results have verified the efficiency, robustness and applicability of our newly‐proposed framework for road design.
Exploring the Recent Pioneering Developments of Small Molecules in Antimalarial Drug Armamentarium: A Chemistry Prospective Appraisal
Authors: Bagratee Tameika , Prawlall Ritika , Ndlovu Thabani , Sibisi Sinqobile , Ndadane Sisa , Shaik Baji Baba , Palkar Mahesh B. , Gampa Raghavachary , Karpoormath Rajshekhar 
Published: May 2024

AbstractMalaria is a very destructive and lethal parasitic disease that causes significant mortality worldwide, resulting in the loss of millions of lives annually. It is an infectious disease transmitted by mosquitoes, which is caused by different species of the parasite protozoan belonging to the genus Plasmodium. The uncontrolled intake of antimalarial drugs often employed in clinical settings has resulted in the emergence of numerous strains of plasmodium that are resistant to these drugs, including multidrug‐resistant strains. This resistance significantly diminishes the effectiveness of many primary drugs used in the treatment of malaria. Hence, there is an urgent need for developing unique classes of antimalarial drugs that function with distinct mechanisms of action. In this context, the design and development of hybrid compounds that combine pharmacophoric properties from different lead molecules into a single unit gives a unique perspective towards further development of malaria drugs in the next generation. In recent years, the field of medicinal chemistry has made significant efforts resulting in the discovery and synthesis of numerous small novel compounds that exhibit potent antimalarial properties, while also demonstrating reduced toxicity and desirable efficacy. In light of this, we have reviewed the progress of hybrid antimalarial agents from 2021 up to the present. This manuscript presents a comprehensive overview of the latest advancements in the medicinal chemistry pertaining to small molecules, with a specific focus on their potential as antimalarial agents. As possible antimalarial drugs that might target both the dual stage and multi‐stage stages of the parasite life cycle, these small hybrid molecules have been studied. This review explores a variety of physiologically active compounds that have been described in the literature in order to lay a strong foundation for the logical design and eventual identification of antimalarial drugs based on lead frameworks.
Synthesis of Styrylbenzazole Photoswitches and Evaluation of their Photochemical Properties
Authors: Stindt Charlotte N. , Crespi Stefano , Feringa Ben L. 
Published: May 2024

AbstractStyrylbenzazoles form a promising yet under‐represented class of photoswitches that can perform a light‐driven E‐Z isomerization of the central alkene double bond without undergoing irreversible photocyclization, typical of the parent stilbene. In this work, we report the synthesis and photochemical study of 23 styrylbenzazole photoswitches. Their thermal stabilities, quantum yields, maximum absorption wavelengths and photostationary state (PSS) distributions can be tuned by changing the benzazole heterocycle and the substitution pattern on the aryl ring. In particular, we found that push‐pull systems show large redshifts of the maximum absorption wavelengths and the highest quantum yields, whereas ortho‐substituted styrylbenzazole photoswitches exhibit the most favorable PSS ratios. Taking advantage of both design principles, we produced 2,6‐dimethyl‐4‐(dimethylamino)‐styrylbenzothiazole, a thermally stable and efficient P‐type photoswitch which displays negative photochromism upon irradiation with visible light up to 470 nm to obtain a near‐quantitative isomerization with a very high quantum yield of 59 %. Furthermore, 4‐hydroxystyrylbenzoxazole was demonstrated to be a pH‐sensitive switch which exhibits a 100 nm redshift upon deprotonation. Ortho‐methylation of its benzothiazole analogue improved the obtained PSS ratio in its deprotonated state from E : Z=53 : 47 to E : Z=18 : 82. We anticipate that this relatively unexplored class of photoswitches will form a valuable expansion of the current family of photoswitches.
Cleaving Folded RNA by Multifunctional DNAzyme Nanomachines
Authors: Nedorezova Daria D. , Dubovichenko Mikhail V. , Eldeeb Ahmed A. , Nur Moustapha A. Y. , Bobkov Gleb A. , Ashmarova Anna I. , Kalnin Arsenij J. , Kolpashchikov Dmitry M. 
Published: May 2024

AbstractBoth tight and specific binding of folded biological mRNA is required for gene silencing by oligonucleotide gene therapy agents. However, this is fundamentally impossible using the conventional oligonucleotide probes according to the affinity/specificity dilemma. This study addresses this problem for cleaving folded RNA by using multicomponent agents (dubbed ‘DNA nanomachine’ or DNM). DNMs bind RNA by four short RNA binding arms, which ensure tight and highly selective RNA binding. Along with the improved affinity, DNM maintain the high sequence selectivity of the conventional DNAzymes. DNM enabled up to 3‐fold improvement in DNAzymes catalytic efficiency (kcat/Km) by facilitating both RNA substrate binding and product release steps of the catalytic cycle. This study demonstrates that multicomponent probes organized in sophisticated structures can help to achieve the balance between affinity and selectivity in recognizing folded RNA and thus creates a foundation for applying complex DNA nanostructures derived by DNA nanotechnology in gene therapy.
Synthesis of Multi‐Functional Graphene Monolayers via Bipolar Electrochemistry
Authors: Gao Ruchao , Beladi‐Mousavi Mohsen , Salinas Gerardo , Zhang Lin , Kuhn Alexander 
Published: May 2024

AbstractGraphene has gained substantial research interest in many fields due to its remarkable properties among many other two‐dimensional materials. In this study, we propose a wireless electrochemical approach, bipolar electrochemistry, for the precise modification of single layers of graphene at predefined locations, such as distinct edges or corners, with a variety of metals or polymers, thus enabling the elaboration of multi‐functional monolayer graphene sheets. We illustrate the concept e. g. by depositing multiple metals, or platinum and a catalyst‐containing porous polymer on the same graphene sheet, but at separate corners. This configuration allows activating chemiluminescence on the polymer spot, and simultaneously generates the driving force for autonomous motion on the Pt side through the catalytic decomposition of hydrogen peroxide into oxygen bubbles. This integration of different chemical features on the same object, exemplified by these proof‐of‐principle experiments, enhances the functionality of two‐dimensional materials, paving the way for the use of these hybrid materials for a variety of applications, ranging from sensing and catalysis to targeted delivery.
A Brief Cognitive Analytic Therapy–Informed Approach for Young People That Have Self‐Injured (CATCH‐Y): A Case Series
Authors: Haw R. , Marsden M. , Hartley S. , Turpin C. , Taylor P. J. 
Published: May 2024

ABSTRACTBackgroundNon‐suicidal self‐injury (NSSI) presents an increasingly prevalent problem for young people; however, there remains a scarce evidence base for effective, scalable treatments for adolescents. This study aimed to assess the feasibility and acceptability of a brief, cognitive analytic therapy (CAT)–informed intervention for young people who engage in NSSI (CATCH‐Y).MethodsA case series design recruited 13 young people who met the inclusion and exclusion criteria to participate in the five‐session intervention. Eligible participants were aged 13–17 years (M = 15.15, SD = 1.28) and had engaged in NSSI at least once in the previous 6 months. Feasibility and acceptability were measured via recruitment, retention, qualitative feedback and missing data. The secondary outcome measures of personal recovery and motivation were administered pre‐ and post‐assessment, with measures of depressive symptoms and urges to self‐injure.ResultsThe intervention was found to be largely feasible and acceptable with high rates of recruitment, retention and pre‐/post‐assessment data completeness. Measures showed preliminary support for positive change in rates of NSSI, urges to self‐harm, low mood and personal recovery, although results were mixed. Completion rates for remote assessments were low.ConclusionsThe findings of this study support further evaluation of the CATCH‐Y intervention on a larger scale. In‐person assessments may be preferable to remote to ensure good completion rates.
Supporting Nano Catalysts for the Selective Hydrogenation of Biomass‐derived Compounds
Authors: Su Chunjing , Zou Sibei , Li Jiaquan , Wang Lizhuo , Huang Jun 
Published: May 2024

AbstractThe selective hydrogenation of biomass derivatives presents a promising pathway for the production of high‐value chemicals and fuels, thereby reducing reliance on traditional petrochemical industries. Recent strides in catalyst nanostructure engineering, achieved through tailored support properties, have significantly enhanced the hydrogenation performance in biomass upgrading. A comprehensive understanding of biomass selective upgrading reactions and the current advancement in supported catalysts is crucial for guiding future processes in renewable biomass. This review aims to summarize the development of supported nanocatalysts for the selective hydrogenation of the US DOE′s biomass platform compounds derivatives into valuable upgraded molecules. The discussion includes an exploration of the reaction mechanisms and conditions in catalytic transfer hydrogenation (CTH) and high‐pressure hydrogenation. By thoroughly examining the tailoring of supports, such as metal oxide catalysts and porous materials, in nano‐supported catalysts, we elucidate the promoting role of nanostructure engineering in biomass hydrogenation. This endeavor seeks to establish a robust theoretical foundation for the fabrication of highly efficient catalysts. Furthermore, the review proposes prospects in the field of biomass utilization and address application bottlenecks and industrial challenges associated with the large‐scale utilization of biomass.
MAFLD and glomerular hyperfiltration in subjects with normoglycemia, prediabetes and type 2 diabetes: A cross‐sectional population study
Authors: Abbate Manuela , Parvanova Aneliya , López‐González Ángel Arturo , Yañez Aina M. , Bennasar‐Veny Miquel , Ramírez‐Manent José Ignacio , Reseghetti Elia , Ruggenenti Piero 
Published: May 2024

AbstractBackgroundMetabolic dysfunction‐associated fatty liver disease (MAFLD, 2020 diagnostic criteria) and glomerular hyperfiltration share common risk factors, including obesity, insulin resistance, impaired glucose tolerance, diabetes, dyslipidemia, and hypertension.AimsTo assess the prevalence of MAFLD and its association with glomerular hyperfiltration and age‐related worsening of kidney function in subjects with normoglycemia, prediabetes and type 2 diabetes mellitus (T2DM).MethodsWe analysed data recorded during occupational health visits of 125,070 Spanish civil servants aged 18–65 years with a de‐indexed glomerular filtration rate (GFR) estimated with the chronic‐kidney‐disease‐epidemiological (CKD‐EPI) equation (estimated glomerular filtration rate [eGFR]) ≥60 mL/min. Subjects were categorised according to fasting plasma glucose levels <100 mg/dL (normoglycemia), ≥100 and ≤ 125 mg/dL (prediabetes), or ≥126 mg/dL and/or antidiabetic treatment (T2DM). The association between MAFLD and glomerular hyperfiltration, defined as a de‐indexed eGFR above the age‐ and gender‐specific 95th percentile, was assessed by multivariable logistic regression.ResultsIn the whole study group, MAFLD prevalence averaged 19.3%. The prevalence progressively increased from 14.7% to 33.2% and to 48.9% in subjects with normoglycemia, prediabetes and T2DM, respectively (p < 0.001 for trend). Adjusted odds ratio (95% CI) for the association between MAFLD and hyperfiltration was 9.06 (8.53–9.62) in the study group considered as a whole, and 8.60 (8.03–9.21), 9.52 (8.11–11.18) and 8.31 (6.70–10.30) in subjects with normoglycemia, prediabetes and T2DM considered separately. In stratified analyses, MAFLD amplified age‐dependent eGFR decline in all groups (p < 0.001).ConclusionsMAFLD prevalence increases across the glycaemic spectrum. MAFLD is significantly associated with hyperfiltration and amplifies the age‐related eGFR decline.
The favourable alliance between CardioMEMS and levosimendan in patients with advanced heart failure
Authors: Visco Valeria , Esposito Cristina , Rispoli Antonella , Di Pietro Paola , Izzo Carmine , Loria Francesco , Di Napoli Daniele , Virtuoso Nicola , Bramanti Alessia , Manzo Michele , Vecchione Carmine , Ciccarelli Michele 
Published: May 2024

AbstractAimsWe report the results of a real‐world study based on heart failure (HF) patients' continuous remote monitoring strategy using the CardioMEMS system to assess the impact of this device on healthcare outcomes, costs, and patients' management and quality of life.Methods and resultsWe enrolled seven patients (69.00 ± 4.88 years; 71.43% men) with HF, implanted with CardioMEMS, and daily remote monitored to optimize both tailored adjustments of home therapy and/or hospital infusions of levosimendan. We recorded clinical, pharmacological, biochemical, and echocardiographic parameters and data on hospitalizations, emergency room access, visits, and costs. Following the implantation of CardioMEMS, we observed a 50% reduction in the total number of hospitalizations and a 68.7% reduction in the number of days in the hospital. Accordingly, improved patient quality of life was recorded with EQ‐5D (pre 58.57 ± 10.29 vs. 1 year post 84.29 ± 19.02, P = 0.008). Echocardiographic data show a statistically significant improvement in both systolic pulmonary artery pressure (47.86 ± 8.67 vs. 35.14 ± 9.34, P = 0.022) and E/e′ (19.33 ± 5.04 vs. 12.58 ± 3.53, P = 0.023). The Quantikine® HS High‐Sensitivity Kit determined elevated interleukin‐6 values at enrolment in all patients, with a statistically significant reduction after 6 months (P = 0.0211). From an economic point of view, the net savings, including the cost of CardioMEMS, were on average €1580 per patient during the entire period of observation, while the analysis performed 12 months after the implant vs. 12 months before showed a net saving of €860 per patient. The ad hoc analysis performed on the levosimendan infusions resulted in 315 days of hospital avoidance and a saving of €205 158 for the seven patients enrolled during the observation period.ConclusionsThis innovative strategy prevents unplanned access to the hospital and contributes to the efficient use of healthcare facilities, human resources, and costs.