bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Olefin‐based, Fluorescent Covalent Organic Framework for Selective Sensing of Aromatic Amines
Authors: Li Lihua , Ma Yinghu , Yang Haohao , Niu Jing , Yang Haoran , Wang Faqiang , Hu Chengxian , Zhang Yubao , Guan Xiaolin , Peng Hui , Ma Guofu 
Published: April 2022

AbstractOwing to various chemical structures and different basicity of amines, a fluorescent probe that could distinguish aromatic and aliphatic amines is highly desirable for practical applications. Herein, an olefin‐based, fluorescent COF (COF−Py−AN) with unique π‐electron deficient groups was synthesized, which was a functionalized platform to realize sensing of π electron‐rich aromatic amines. COF−Py−AN displayed fluorescence quenching in the presence of aromatic amines like aniline, with excellent selectivity against a variety of aromatic molecules, and the detection limit of COF−Py−AN toward aniline was as low as 1.29 μM. More importantly, COF−Py−AN could realize discriminating between aromatic and aliphatic amines. Furthermore, density functional theory calculations showed that the photoinduced electron transfer mechanism contributes to fluorescence sensing. This is the first example of olefin‐based fluorescent COF materials for selective sensing of aromatic amines.
Construction of Branched DNA‐based Nanostructures for Diagnosis, Therapeutics and Protein Engineering
Authors: Hu Pin , Dong Yuhang , Yao Chi , Yang Dayong 
Published: April 2022

AbstractBranched DNA with multibranch‐like anisotropic topology serves as a promising and powerful building block in constructing multifunctional‐integrated nanomaterials in a programmable and controllable manner. Recently, a series of branched DNA‐based functional nanomaterials were developed by elaborate molecular design. In this review, we focused on the construction of branched DNA‐based nanostructures for biological and biomedical applications. First, the molecular design and synthesis method of branched DNA monomer were briefly described. Then, the construction strategies of branched DNA‐based nanostructures were categorially discussed, including target‐triggered polymerization, enzymatic extension and hybrid assembly. Finally, the biological and biomedical applications including diagnosis, therapeutics and protein engineering were summarized. We envision that the review will contribute to the further development of branched DNA‐based nanomaterials with great application potential in the field of biomedicine, thus building a new bridge between material chemistry and biomedicine.
Comparative effectiveness and toxicity of radiotherapy regimens in limited stage small cell lung cancer: A network meta‐analysis
Authors: Zhou Jiupeng , Guo Hui , Zhang Yongfeng , Liu Heng , Dou Quanli 
Published: April 2022

AbstractPurposeThe aim of this Network Meta‐analysis was to compare the current radiotherapy regimens of limited‐stage small cell lung cancer (LS‐SCLC), in terms of overall survival (OS), progression‐free survival (PFS), and the incidence of acute radioactive esophagitis and radioactive pneumonia.MethodsPubMed, Embase, Web of Science, and the Cochrane Library were comprehensively searched until January 2022. The studies were included, comparing radiotherapy regimens in LS‐SCLC patients. We compared hypofractionated radiotherapy (HypoTRT), hyperfractionated radiotherapy (HyperTRT), and conventionally fractionated radiotherapy (ConvTRT1(<60 Gy), ConvTRT2(≥60 Gy)).ResultsThere was similar efficacy among the contemporary radiotherapy regimens for PFS of LS‐SCLC. HypoTRT and HyperTRT significantly improved the OS of LS‐SCLC compared with ConvTRT1 (<60 Gy), while not improving the OS of LS‐SCLC compared with ConvTRT2 (≥60 Gy). There was no significant difference between HypoTRT and HyperTRT, between ConvTRT1(<60 Gy) and ConvTRT2(≥60 Gy), respectively. HyperTRT developed the highest odds of acute radioactive esophagitis compared to ConvTRT1(<60 Gy) and ConvTRT2(≥60 Gy). There was no significant difference in the incidence of acute radioactive esophagitis between HypoTRT and HyperTRT, ConvTRT1(<60 Gy), ConvTRT2(≥60 Gy), respectively and between ConvTRT1 and ConvTRT2. There was no statistically significant difference among radiotherapy regimens for the incidence of acute radioactive pneumonia.ConclusionThe current radiotherapy regimens are similar in efficacy and toxicity for LS‐SCLC, except for ConvTRT1(<60 Gy). Given the lower costs and convenient logistics management of HypoTRT comparatively, it is an acceptable alternative for LS‐SCLC.
Reduced Graphene Oxide Overlayer on Copper Nanocube Electrodes Steers the Selectivity Towards Ethanol in Electrochemical Reduction of Carbon Dioxide
Authors: Mardones‐Herrera Elías , Castro‐Castillo Carmen , Nanda Kamala Kanta , Veloso Nicolás , Leyton Felipe , Martínez Francisco , Sáez‐Pizarro Natalia , Ruiz‐León Domingo , Aguirre María Jesús , Armijo Francisco , Isaacs Mauricio 
Published: April 2022

AbstractDeveloping copper‐based electrocatalysts that favor high‐value multi‐carbon oxygenates is desired, given their use as platform chemicals and as a direct fuel for transportation. Combining a CO‐selective catalyst with copper shifts the selectivity of CO2 electroreduction toward C2 products. Herein, we developed a reduced graphene oxide (rGO)‐modified copper nanocube electrocatalyst that could shift the selectivity of CO2 electroreduction towards ethanol (Faradaic efficiency 76. 84 % at −0.9 V vs. reversible hydrogen electrode (RHE)). Spectroelectrochemical Raman analysis reveals a higher population of *C2HxOy intermediates at −0.9 V vs. RHE on the rGO‐modified copper nanocube electrocatalyst surface, which coincides with the highest faradaic efficiency of ethanol upon CO2 electroreduction at the same potential. Our results demonstrate that the rGO modification can enhance ethanol selectivity through a probable tandem electrocatalysis mechanism and provide insights into controlling electrocatalytic activity and product selectivity in the CO2 electroreduction reaction.
Transfer Mechanism of Anthracycline Antibiotics and Their Ion Association with PAMAM Dendrimer at Liquid|Liquid Interfaces**
Authors: Takami Toshinari , Kanai Shohei , Nishiyama Yoshio , Lee Hye Jin , Nagatani Hirohisa 
Published: April 2022

AbstractThe transfer reaction of anthracycline antibiotics, daunorubicin (DNR) and doxorubicin (DOX), was investigated at the water|1,2‐dichloroethane (DCE) interface. Spectroelectrochemical analysis demonstrated significant changes in the transfer mechanism of anthracycline cations with a slight structural difference across the bare water|DCE interface and the biomimetic interface with a phospholipid layer. The facilitated ion transfer feature was found for both HDNR+ and HDOX+ at the biomimetic interface, where the adsorption process of HDOX+ was observed over a wide potential range at the aqueous side of the interface, that is, the polar headgroup region of the phospholipid layer. The molecular association between the carboxylate‐terminated generation 3.5 polyamidoamine (G3.5 PAMAM) dendrimer and anthracycline derivatives occurred preferentially under neutral pH conditions. The negatively charged G3.5 PAMAM dendrimer induced positive shifts of the transfer potential of anthracycline cations at the water|DCE interface, indicating its ability as potential‐ and pH‐responsive drug carriers.
A comparative performance of machine learning algorithms on laser‐induced breakdown spectroscopy data of minerals
Authors: Alix Gian , Lymer Elizabeth , Zhang Guanlin , Daly Michael , Gao Xin 
Published: April 2022

AbstractThe exploration and analyses of chemical components in (extra‐)terrestrial geological materials (such as asteroids and meteorites) are insightful in modern research. Laser‐induced breakdown spectroscopy (LiBS) is a popular method for analyzing the chemical attributes of geologic samples—which scientists use to study and understand planetary bodies and their complex histories. In the literature, several machine learning models that produce high‐accuracy predictions have been proposed. In our work, we compared the performances of such models in predicting elemental abundances on a certain spectroscopic dataset. Models included partial least squares (PLS), extreme gradient boost machines (XGB), neural networks, and linear models. In our results, we showed how PLS and XGB are superior in terms of high predictive power, their ability to generalize, and their reasonably efficient runtimes. In addition, we proposed Ensemble models that aggregate predictions of top‐tier models and observed that they can be desirable. We intend to gain better understanding of how these models perform in predicting elemental compositions on specific spectrum (LIBS) datasets.
Two‐Step Dielectric Responsive Organic‐Inorganic Hybrid Material with Mid‐Band Light Emission
Authors: Jia Qiang‐Qiang , Tong Liang , Zhang Wan‐Ying , Fu Da‐Wei , Lu Hai‐Feng 
Published: April 2022

AbstractHybrid organic‐inorganic perovskite (HOIP) have received tremendous scientific attention because of the phase transition and photovoltaic properties. However, achieving the special perovskite structure with both two‐step dielectric response and luminescence characteristics is rarely reported. Herein, we report an organic‐inorganic hybrid perovskite, [(BA)2 ⋅ PbI4] (Compound 1, BA=n‐butylamine) by introducing flexible organic cations (HBA+), with direct mid‐band gap as 2.28 eV. Interestingly, this material exhibits two‐step reversible dielectric response at 350 K and 460 K (in heating process), respectively. Besides, the photoluminescence was found: it emits charming green light under 365 nm lamp (Photoluminescence quantum yield is 9.52 %). The outstanding two‐step dielectric response and luminescence characteristics of this compound might pave the way for the application of dielectric and ferroelectric functional materials in temperature sensors and mechanical switches.
Memetic swarm clustering with deep belief network model for e‐learning recommendation system to improve learning performance
Authors: Venkatesh Maganti , Sathyalaksmi S. 
Published: April 2022

AbstractIn this article, an efficient clustering and classification model using memetic swarm clustering and deep belief network called MSC‐DBN model, to derive a recommendation system for web‐based learning. In the proposed method, the three main phases are considered: (i) clustering, (ii) classification, and (iii) recommendation. In first phase, MSC uses ant bee colony, Particle swarm optimization and k‐MEANS clustering. In the second phase, DBN is used for classification in the clustered based users. Clustered based user's has three categories of learners. Finally, MSC‐DBN system is automatically recommends the learning materials to the learners based on the complexity level of the material and learner's capability. A detailed experimental analysis is conducted to ensure the effective recommendation performance. The proposed method is activated in Java. The proposed method attains higher precision 93.26%, 96.12%, and 91.11%, higher recall 89.56%, 92.36%, and 95.66% and higher F‐score 93.12%, 97.14%, and 92.11% compared with the existing approaches, like generalized sequential pattern (GSP) model with context aware (CA)‐collaborative filtering (CF) called (GSP‐CA‐CF), context aware (CA)‐collaborative filtering (CF) called (CF‐CA), and generalized sequential pattern (GSP). Finally, the simulation outcomes demonstrate that the proposed method can be able to find the optimal solutions efficiently and accurately.
A novel approach for cardiovascular disease prediction using machine learning algorithms
Authors: Arunachalam Saran Kumar , Rekha Rajagopal 
Published: April 2022

AbstractFor the past few decades, cardiovascular disease has shown a binding impact on the country's mortality rate. The prediction of cardiovascular disease is more challenging during the process of clinical data analysis. The emergence of Machine Learning approaches paved the way to predict the disease and determining the consequences of the disease in the earlier stage to help the physicians during complex decision‐making. This work adopts k‐Nearest Neighbor as baseline classifier and ensemble X‐boost, Adaboost, and Random subspace classifier model to predict heart disease and predict the features of cardiovascular disease using Linear Support Vector Feature Measure (). This model considers the diverse combination of features to make the better classification process. The model shows superior performance with precision via Clinical Decision Support System. The factors that influence the cardiovascular disease need to predict, and better decision is taken during the critical condition. Here, the online available University of California Irvine (UCI) Machine Learning dataset is used for training and testing where 80% data is considered for training and 20% considered for testing purpose. The simulation is done in MATLAB 2020b simulation environment, and the outcomes are compared with various existing approaches. Here, performance metrics like accuracy, precision, F‐measure, stability rate, region of curve, and recall is measured to show the model efficiency. The prediction accuracy of the proposed model is 96% which is higher than existing approaches. The overall performance of proposed ensemble model is 96% accuracy, 97% precision, 95% sensitivity, 95% F‐measure, 93% Matthew's correlation coefficients, 4.53% False Positive Rate, 3.10% False Negative Rate, and 96% True Positive Rate, respectively.
Durability of treatment effects following internet‐delivered cognitive behavioural therapy for depression and anxiety delivered within a routine care setting
Authors: Palacios Jorge E. , Enrique Angel , Mooney Olwyn , Farrell Simon , Earley Caroline , Duffy Daniel , Eilert Nora , Harty Siobhan , Timulak Ladislav , Richards Derek 
Published: April 2022

AbstractObjectiveTo investigate post‐treatment relapse and remission rates 3, 6 and 9 months after completion of an acute phase of a clinician‐supported internet‐delivered cognitive‐behavioural therapy (iCBT) for anxiety and depressive symptoms, within a routine care setting.MethodSecondary analysis from a 12‐month pragmatic randomized‐controlled trial delivered within the Improving Access to Psychological Therapies (IAPT) programme in England. Participants in the intervention arm were included if they met criteria for reliable recovery from depression (PHQ‐9) and anxiety (GAD‐7) at post‐treatment assessment. Survival analysis was used to assess durability of treatment effects and determine predictors to relapse at 3‐, 6‐ and 9‐month follow‐up. Hazard ratios predicting time‐to‐relapse were estimated with semi‐parametric Cox proportional hazards model.ResultsOf the 241 participants in the intervention arm, 89 participants met the criteria for reliable recovery from depression and anxiety at the post‐treatment assessment. Of these 89 eligible cases, 29.2% relapsed within the 9‐month period, with 70.8% remaining in remission at 9 months post‐treatment. Of those who relapsed, 53.8% experienced a relapse of depression and anxiety; 7.7% experienced a relapse of depression only; and 38.4% experienced a relapse of anxiety only. Younger age, having a long‐term condition, and residual symptoms of anxiety at end‐of‐treatment were all significant predictors of relapse.ConclusionsThis study is the first to explore the remission and relapse rates after an acute phase of iCBT treatment, within a routine, stepped‐care setting. The results add to the scarce literature on the durability of the effects of iCBT treatment in routine care settings, where patients are not typically followed up after receiving a completed course of treatment.
A Mouse Model of Acute and Chronic Pancreatitis
Authors: Minaga Kosuke , Watanabe Tomohiro , Kamata Ken , Kudo Masatoshi , Strober Warren 
Published: April 2022

AbstractPancreatitis occurs in two forms defined by its chronicity. Acute pancreatitis (AP) occurs suddenly and only lasts for several days. Consequently, most patients with AP recover without permanent damage to the pancreas, and about 20% of patients with AP have severe disease. In contrast, chronic pancreatitis (CP) is a long‐lasting inflammation that causes permanent damage to pancreatic tissue; consequently, this form is marked by the emergence of persistent endocrine and exocrine pancreatic insufficiency. Despite these differences, AP and CP share central mechanisms of disease: in both forms, inflammation is initiated and/or sustained by the intrapancreatic activation of pancreatic digestive enzymes followed by the autodigestion of pancreatic tissues. In addition, in both forms enzymatic damage is accompanied by changes in intestinal permeability and entry of commensal organisms into the pancreas where they elicit innate immune responses that ultimately dominate and define pancreatic inflammation. In the murine models of AP and CP described here, both of these elements of pancreatitis pathogenesis are taken into account. Thus, in one approach mice are administered high doses of cerulein, a cholecystokinin analog with the ability at this dose to induce excessive activation of the cholecystokinin receptor expressed in pancreatic acinar cells and the release of active trypsin that causes both direct and indirect acinar damages due to entry of commensal organisms and stimulation of innate immune responses. In a second approach mice are administered low doses of cerulein, which causes little or no damage to the pancreas unless given along with nucleotide‐binding oligomerization domain 1 (NOD1) ligand, which in the presence of low‐dose cerulein administration induces a pathologic innate immune response mediated by NOD1. These approaches are adopted to produce AP when cerulein or cerulein plus NOD1 ligand is applied only once or to produce CP when a similar regimen is applied multiple times. © 2022 Wiley Periodicals LLC.Basic Protocol 1: Cerulein‐induced acute pancreatitisAlternate Protocol 1: Acute pancreatitis induced by cerulein and NOD1 ligandBasic Protocol 2: Cerulein‐induced chronic pancreatitisAlternate Protocol 2: Chronic pancreatitis induced by cerulein and NOD1 ligandSupport Protocol: Isolation of pancreatic mononuclear cells
Solvent‐Free Strategy for Direct Access to Versatile Quaternary Ammonium Salts with Complete Atom Economy
Authors: Fan Weibin , Zhang Yuan , Li Yinghua , Zhang Wei , Huang Deguang 
Published: April 2022

AbstractA solvent‐free method for the synthesis of quaternary ammonium salts (QAS) by iodoquaternization of alkenes with N‐heteroarenes was reported. Its advantages lie in energy‐saving and clean production by using iodine as the oxidant and manual grinding the starting materials, together with the complete atom economy and low process mass intensity (PMI) value. Demonstrated by 50 examples, the generated QAS was proved to be able to produce valuable chemicals, such as biological protease inhibitors, anti‐cancer agents, and organic fluorescent materials.
Single‐parentage assignments reveal negative‐assortative mating in an endangered salmonid
Authors: Steele Craig A. , Delomas Thomas A. , Campbell Matthew R. , Powell John H. 
Published: April 2022

AbstractUnderstanding reproductive patterns in endangered species is critical for supporting their recovery efforts. In this study we use a combination of paired‐parent and single‐parent assignments to examine the reproductive patterns in an endangered population of sockeye salmon (Oncorhynchus nerka) that uses Redfish Lake in central Idaho as a spawning and nursery lake. Recovery efforts include the release of maturing adults into the lake for volitional spawning. The lake is also inhabited by a population of residentO.nerkathat is genetically indistinguishable, but phenotypically smaller, to the maturing adults released into the lake. The resident population is difficult to sample and the reproductive patterns between the two groups are unknown. We used results of paired‐ and single‐parentage assignments to specifically examine the reproductive patterns of male fish released into the lake under an equal sex ratio and a male‐biased sex ratio. Assignment results of offspring leaving the lake indicated a reproductive shift by males under the two scenarios. Males displayed an assortative mating pattern under an equal sex ratio and spawned almost exclusively with the released females. Under a male‐biased sex ratio most males shifted to a negative‐assortative mating pattern and spawned with smaller females from the resident population. These males were younger and smaller than males that spawned with released females suggesting they were unable to compete with larger males for spawning opportunities with the larger, released females. The results provided insights into the reproductive behavior of this endangered population and has implications for recovery efforts.
The influence of predator community composition on photoprotective traits of copepods
Authors: Oester Rebecca , Greenway Ryan , Moosmann Marvin , Sommaruga Ruben , Tartarotti Barbara , Brodersen Jakob , Matthews Blake 
Published: April 2022

AbstractTrait expression of natural populations often jointly depends on prevailing abiotic environmental conditions and predation risk. Copepods, for example, can vary their expression of compounds that confer protection against ultraviolet radiation (UVR), such as astaxanthin and mycosporine‐like amino acids (MAAs), in relation to predation risk. Despite ample evidence that copepods accumulate less astaxanthin in the presence of predators, little is known about how the community composition of planktivorous fish can affect the overall expression of photoprotective compounds. Here, we investigate how the (co‐)occurrence of Arctic charr (Salvelinus alpinus) and threespine stickleback (Gasterosteus aculeatus) affects the photoprotective phenotype of the copepod Leptodiaptomus minutus in lake ecosystems in southern Greenland. We found that average astaxanthin and MAA contents were lowest in lakes with stickleback, but we found no evidence that these photoprotective compounds were affected by the presence of charr. Furthermore, variance in astaxanthin among individual copepods was greatest in the presence of stickleback and the astaxanthin content of copepods was negatively correlated with increasing stickleback density. Overall, we show that the presence and density of stickleback jointly affect the content of photoprotective compounds by copepods, illustrating how the community composition of predators in an ecosystem can determine the expression of prey traits that are also influenced by abiotic stressors.
Biodiversity, climate change, and adaptation in the Mediterranean
Authors: Aurelle Didier , Thomas Séverine , Albert Cécile , Bally Marc , Bondeau Alberte , Boudouresque Charles‐François , Cahill Abigail E. , Carlotti François , Chenuil Anne , Cramer Wolfgang , Davi Hendrik , De Jode Aurélien , Ereskovsky Alexander , Farnet Anne‐Marie , Fernandez Catherine , Gauquelin Thierry , Mirleau Pascal , Monnet Anne‐Christine , Prévosto Bernard , Rossi Vincent , Sartoretto Stéphane , Van Wambeke France , Fady Bruno 
Published: April 2022

AbstractPotential for, and limits to, adaptation to environmental changes are critical for resilience and risk mitigation. The Mediterranean basin is a mosaic of biodiversity‐rich ecosystems long affected by human influence, whose resilience is now questioned by climate change. After reviewing the different components of biological adaptation, we present the main characteristics of marine and terrestrial biodiversity in the Mediterranean basin and of the pressures they face. Taking climatic trends into consideration, we discuss the adaptive potential of a range of ecosystems dominated by species without active dispersal. We argue that the high heterogeneity of Mediterranean landscapes and seascapes constitutes a laboratory for the study of adaptation when environmental conditions change rapidly and may provide opportunities for adaptation and adaptability of species and ecosystems. Adaptive management in the Mediterranean can and should harness the nature‐based solutions offered by both ecological and evolutionary processes for increasing the resilience of ecosystems to climate change.