bluuebunny/crossref_metadata_2025_split huggingface dataset:
A new conceptual framework for spatial predictive modelling of land degradation in a semiarid area
Authors: Abolhasani Azam , Zehtabian Gholamreza , Khosravi Hassan , Rahmati Omid , Alamdarloo Esmail Heydari , D'Odorico Paolo 
Published: June 2022

AbstractAlthough land degradation (LD) is known as a severe environmental problem, spatial predictive modelling of this phenomenon remains a challenge. This research aimed to develop a new conceptual framework to predict LD susceptibility based on net primary production (NPP) and machine learning approaches. The annual NPP over the period 2001–2020 were obtained using MOD17A3 and the trend of NPP changes was considered to investigate the occurrence sites of LD within Qazvin Plain, in Qazvin Province, Iran, under a semiarid climate, with an area of about 9500 km2. An inventory map of LD was generated based on the LD study sites. The locations were randomly split‐sampled as training (70%) and testing (30%) datasets to evaluate the efficiency of the built models. Fifteen geo‐environmental factors were considered as LD predictive variables such as altitude, slope, land use, and temperature. Four advanced machine‐learning techniques were performed to model LD susceptibility. Finally, the predictive efficiency of the models was measured utilizing the area under the (ROC) curve Area Under the ROC Curve(AUC) and true skill as statics (TSS). The results indicated that the randomForest (RF), with the AUC = 0.81 and TSS = 0.5, showed the highest efficiency for predicting LD in the Qazvin Plain followed by boosted regression tree (BRT) with AUC = 0.76 and TSS = 0.47, support vector machine (SVM) with AUC = 0.71 and TSS = 0.39, and classification and regression tree (CART) with AUC = 0.63 and TSS = 0.31. The findings illustrated that altitude was the most influential variable within RF, BRT, and SVM while rainfall showed the most important contribution in modelling based on the CART algorithm. This study proposed a new modelling framework that is easily replicable in different contexts for the assessment of LD modelling and analysis.
Dry beans (Phaseolus vulgaris L.) as a vital component of sustainable agriculture and food security—A review
Authors: Uebersax Mark A. , Cichy Karen A. , Gomez Francisco E. , Porch Timothy G. , Heitholt Jim , Osorno Juan M. , Kamfwa Kelvin , Snapp Sieglinde S. , Bales Scott 
Published: June 2022

AbstractThe importance of legumes in sustainable cropping systems has been studied extensively. Among legumes, common beans (Phaseolus vulgaris L.) are a rich world resource of biodiversity with two centers of domestication (Andes and Central America) and over 10 major market classes cultivated globally. Common beans are recognized as a nutrient‐dense, healthy food source due to their high protein, dietary fiber, and minerals content and also being a rich source of resistant and slowly digestible starch, which elicits a lower glycemic response. Some bioactive compounds present in beans are reported to mitigate cardiovascular diseases, hypertension, hyper‐cholesterolemia, and cancer. Dry bean production systems provide unique advantages that support sustainability, including a low carbon footprint and short growth cycle, which facilitates crop diversification and cover crop integration. Symbiotic nitrogen fixation (SNF), a unique characteristic of legumes, promotes environmentally friendly production through modest fertilizer use. Advances to improve the upright plant architecture of beans during the last two decades have enhanced options for direct harvest thereby reducing the number of equipment passes required. Overall, the sustainability implications of diversifying crop rotation using beans result in reduced requirements for environmentally unfriendly inputs and buffering of crop productivity under variable weather conditions. This review article covers common beans' role in agricultural sustainability (biodiversity, SNF, rotational diversity, harvest management) and as a sustainable source of nutrition and food security. Further discussion includes measures to enhance dry beans sustainability through breeding and crop management practices by addressing biotic and abiotic stresses (diseases, drought, high temperature, waterlogging, conservation tillage).
Spatial and temporal variations in atmospheric gas flux from the Hudson River: the estuarine gas exchange maximum
Authors: Scully Malcolm E. , Michel Anna P. M. , Nicholson David P. , Traylor Shawnee 
Published: June 2022

AbstractA unique combination of data collected from fixed instruments, spatial surveys, and a long‐term observing network in the Hudson River demonstrate the importance of spatial and temporal variations in atmospheric gas flux. The atmospheric exchanges of oxygen (O2) and carbon dioxide (CO2) exhibit variability at a range of time scales including pronounced modulation driven by spring‐neap variations in stratification and mixing. During weak neap tides, bottom waters become enriched in pCO2 and depleted in dissolved oxygen because strong stratification limits vertical mixing and isolates sub‐pycnocline water from atmospheric exchange. Estuarine circulation also is enhanced during neap tides so that bottom waters, and their associated dissolved gases, are transported up‐estuary. Strong mixing during spring tides effectively ventilates bottom waters resulting in enhanced CO2 evasion and O2 invasion. The spring‐neap modulation in the estuarine portion of the Hudson River is enhanced because fortnightly variations in mixing have a strong influence on phytoplankton dynamics, allowing strong blooms to occur during weak neap tides. During blooms, periods of CO2 invasion and O2 evasion occur over much of the lower stratified estuary. The along‐estuary distribution of stratification, which decreases up‐estuary, favors enhanced gas exchange near the limit of salt, where vertical stratification is absent. This region, which we call the estuarine gas exchange maximum (EGM), results from the convergence in bottom transport and is analogous to the estuarine turbidity maximum (ETM). Much like the ETM, the EGM is likely to be a common feature in many partially mixed and stratified estuarine systems.
Polymer Vesicles in a Nanochannel under Flow Fields: A DPD Simulation Study
Authors: Wu Fangsheng , Lin Jiaping , Wang Liquan , Lin Shaoliang 
Published: June 2022

AbstractThe general lack of knowledge on the dynamics of polymer vesicles under flow field has confounded their applications in drug delivery and other fields. Therefore, dissipative particle dynamics (DPD) simulations on the dynamic behavior of diblock copolymer vesicles in a nanochannel under Poiseuille flow are conducted. The simulation results show that the block copolymer vesicles with hydrophobic blocks of different rigidities exhibit significantly different mechanical stabilities under the flow fields. With increasing the Reynolds number, the coil–coil block copolymer vesicles undergo a shape transition from spherical to bullet‐like to asymmetric and eventually split into two parts. In contrast, the rod–coil block copolymer vesicles deform from spherical to bullet‐like to parachute‐like structures and ultimately ruptured. The flow‐induced distortion and reorganization of copolymers mainly cause the deformation of vesicles. The distinguished motions of the block copolymers with different rigid blocks lead to the distinct performance of the vesicles. The findings could help understand the flow behavior of polymer vesicles in a nanochannel or a blood vessel and assist the design of bio‐functional polymersomes.
Simultaneously optimizing sampling pattern for joint acceleration of multi‐contrast MRI using model‐based deep learning
Authors: Seo Sunghun , Luu Huan Minh , Choi Seung Hong , Park Sung‐Hong 
Published: June 2022

AbstractBackgroundAcceleration of MR imaging (MRI) is a popular research area, and usage of deep learning for acceleration has become highly widespread in the MR community. Joint acceleration of multiple‐acquisition MRI was proven to be effective over a single‐acquisition approach. Also, optimization in the sampling pattern demonstrated its advantage over conventional undersampling pattern. However, optimizing the sampling patterns for joint acceleration of multiple‐acquisition MRI has not been investigated well.PurposeTo develop a model‐based deep learning scheme to optimize sampling patterns for a joint acceleration of multi‐contrast MRI.MethodsThe proposed scheme combines sampling pattern optimization and multi‐contrast MRI reconstruction. It was extended from the physics‐guided method of the joint model–based deep learning (J‐MoDL) scheme to optimize the separate sampling pattern for each of multiple contrasts simultaneously for their joint reconstruction. Tests were performed with three contrasts of T2‐weighted, FLAIR, and T1‐weighted images. The proposed multi‐contrast method was compared to (i) single‐contrast method with sampling optimization (baseline J‐MoDL), (ii) multi‐contrast method without sampling optimization, and (iii) multi‐contrast method with single common sampling optimization for all contrasts. The optimized sampling patterns were analyzed for sampling location overlap across contrasts. The scheme was also tested in a data‐driven scenario, where the inversion between input and label was learned from the under‐sampled data directly and tested on knee datasets for generalization test.ResultsThe proposed scheme demonstrated a quantitative and qualitative advantage over the single‐contrast scheme with sampling pattern optimization and the multi‐contrast scheme without sampling pattern optimization. Optimizing the separate sampling pattern for each of the multi‐contrasts was superior to optimizing only one common sampling pattern for all contrasts. The proposed scheme showed less overlap in sampling locations than the single‐contrast scheme. The main hypothesis was also held in the data‐driven situation as well. The brain‐trained model worked well on the knee images, demonstrating its generalizability.ConclusionOur study introduced an effective scheme that combines the sampling optimization and the multi‐contrast acceleration. The seamless combination resulted in superior performance over the other existing methods.
Bayesian statistics‐guided label refurbishment mechanism: Mitigating label noise in medical image classification
Authors: Gao Mengdi , Feng Ximeng , Geng Mufeng , Jiang Zhe , Zhu Lei , Meng Xiangxi , Zhou Chuanqing , Ren Qiushi , Lu Yanye 
Published: June 2022

AbstractPurposeDeep neural networks (DNNs) have been widely applied in medical image classification, benefiting from its powerful mapping capability among medical images. However, these existing deep learning‐based methods depend on an enormous amount of carefully labeled images. Meanwhile, noise is inevitably introduced in the labeling process, degrading the performance of models. Hence, it is significant to devise robust training strategies to mitigate label noise in the medical image classification tasks.MethodsIn this work, we propose a novel Bayesian statistics‐guided label refurbishment mechanism (BLRM) for DNNs to prevent overfitting noisy images. BLRM utilizes maximum a posteriori probability in the Bayesian statistics and the exponentially time‐weighted technique to selectively correct the labels of noisy images. The training images are purified gradually with the training epochs when BLRM is activated, further improving classification performance.ResultsComprehensive experiments on both synthetic noisy images (public OCT & Messidor datasets) and real‐world noisy images (ANIMAL‐10N) demonstrate that BLRM refurbishes the noisy labels selectively, curbing the adverse effects of noisy data. Also, the anti‐noise BLRMs integrated with DNNs are effective at different noise ratio and are independent of backbone DNN architectures. In addition, BLRM is superior to state‐of‐the‐art comparative methods of anti‐noise.ConclusionsThese investigations indicate that the proposed BLRM is well capable of mitigating label noise in medical image classification tasks.
Foot self‐care behaviour in type 2 diabetes adults with and without comorbid heart failure
Authors: Aga Fekadu , Dunbar Sandra B. , Kebede Tedla , Guteta Senbeta , Higgins Melinda Kay , Gary Rebecca A. 
Published: June 2022

AbstractAimsTo compare the correlates of foot self‐care behaviours among type 2 diabetes mellitus (T2D) adults with and without comorbid heart failure (HF).DesignCross‐sectional, correlational, comparative design.MethodsA 210 T2D adults (105 with HF and 105 without HF) participated from August–December 2020. Foot self‐care behaviour was measured using the foot care subscale of the Summary of Diabetes Self‐Care Activities (SDSCA) instrument. A stepwise logistic regression analysis was used to explore variables predicting foot self‐care behaviour.ResultsThe participants' mean age was 58.7 ± 10.9 years. Poor foot self‐care behaviour was reported in T2D adults both with (53.3%) and without (54.3%) HF. Participants with HF‐comorbidity were statistically significantly older and had higher total daily medication intake. Household income and the total number of daily medications statistically significantly predicted foot self‐care behaviour in HF‐comorbid T2D adults. Marital status, social support and body mass index predicted foot self‐care behaviour in the non‐HF group.
Frequency and co‐occurrence of porous skeletal lesions in identified non‐adults from Portugal (19th to 20th centuries) and its association with respiratory infections as cause of death
Authors: Gomes Ricardo A. M. P. , Petit Jimmy , Dutour Olivier , Santos Ana Luisa 
Published: June 2022

AbstractPorous skeletal lesions (PSL) have been reported in scientific literature since the 19th century, yet their etiology(ies) remains a complex issue under debate. The aim of this study is twofold: evaluate the frequency, co‐occurrence, and association of PSL with biological sex and age at death and examine possible relationships between the presence/absence of PSL with the known cause of death (COD). Fifty‐six non‐adult skeletons from the Coimbra Identified Skeletal Collection (19th to 20th centuries) were evaluated. Lesion presence/absence was recorded macroscopically without previous knowledge of the biographic data. Forty‐nine individuals (87.5%) presented at least one type of PSL, and cribra femoralis was the most frequent (n = 39), followed by orbitalia (n = 26), humeralis (n = 15), and cranii (n = 6). Porous skeletal lesions affected males and females equally, and younger individuals presented a significantly higher frequency of cribra femoralis. Even without statistical significance, the co‐occurrence of cribra orbitalia, humeralis, and femoralis was common, as described in the “cribrous syndrome” where it is advocated a shared etiology for these lesions, based on macroscopic, microscopic, and radiological features. Individuals with a COD as respiratory infection, particularly pulmonary tuberculosis, exhibited a higher risk of expressing cribra orbitalia. The results of this investigation suggest an influence of age and infectious diseases in the expression of PSL, impacting the bioarchaeological interpretations. There is a need for further research in clinical, autopsy, and identified skeletal collection samples to further understand co‐occurrence, comorbidity, and possible frailty related to PSL.
Mechanical and thermal properties of synergistic modification of bismaleimide polymer composites with nano‐SiO2 and hyperbranched polyimide
Authors: Gao Feng , Shi Jiahao , Zhang Xiaorui , Wang Xuan , Weng Ling , Sun Xue , Yu Lida , Li Chenhao 
Published: June 2022

AbstractBismaleimide resin (BMI) is a thermosetting resin with excellent properties. However, due to its poor toughness as a cured product, this paper uses hyperbranched polyimide (HBPI) and nano‐SiO2 in a synergistic manner to improve the performance of BMI. Isophorone diisocyanated tripolymer (IPDI trimer) and methyl nadic anhydride (MNA) were used to synthesize HBPI, and then we investigated the effect of the synergistic modification on the mechanical and thermal properties and the modification mechanism of HBPI and nano‐SiO2. The experiments showed that the most significant improvements were achieved at the addition of 40 and 3 wt% of HBPI and nano‐SiO2 respectively, with the impact strength of the materials increasing by 97.2%, fracture toughness indexes GIC and KIC increased by 36.7% and 34.6% respectively and glass transition temperature (Tg) was reduced. The SEM results showed that the nanoparticles were uniformly dispersed and further validated the synergistic modification mechanism.
Chinese adolescents' positive relationships with parents: The effects of the sex of their younger sibling
Authors: Chen Bin‐Bin , Shen Mengqian , Jin Shenghua , French Doran C. 
Published: June 2022

AbstractWe investigated how sibling status and sex of younger siblings influence Chinese adolescents' relationships with their fathers and mothers as a function of resource dilution and preference for sons. The sample included 1,093 adolescents from the 8th grade (Mage = 13.96 years,SD = 0.75 years; 47% girls) and the 11th grade (Mage = 17.25 years,SD = 0.74 years; 57% girls) in a longitudinal study. Consistent with expectations pertaining to the hypothesis of resource dilution and son preference in combination, girls with a younger brother reported less positive relationships with mothers than either singleton girls or girls with a younger sister. No significant difference was found between singleton boys and boys with a younger sibling. The current findings show implications that the resource‐dilution theory and son preference culture together may place girls with a younger brother in an unfavorable condition of resource allocation.
Maize resistance to insect herbivory is enhanced by silencing expression of genes for jasmonate‐isoleucine degradation using sugarcane mosaic virus
Authors: Chung Seung Ho , Zhang Shudi , Song Hojun , Whitham Steven A. , Jander Georg 
Published: June 2022

AbstractPreviously, sugarcane mosaic virus (SCMV) was developed as a vector for transient expression of heterologous genes in Zea mays (maize). Here, we show that SCMV can also be applied for virus‐induced gene silencing (VIGS) of endogenous maize genes. Comparison of sense and antisense VIGS constructs targeting maize phytoene desaturase (PDS) showed that antisense constructs resulted in a greater reduction in gene expression. In a time course of gene expression after infection with VIGS constructs targeting PDS, lesion mimic 22 (Les22), and Iodent japonica 1 (Ij1), efficient expression silencing was observed 2, 3, and 4 weeks after infection with SCMV. However, at Week 5, expression of Les22 and Ij1 was no longer significantly reduced compared with control plants. The defense signaling molecule jasmonate‐isoleucine (JA‐Ile) can be inactivated by 12C‐hydroxylation and hydrolysis, and knockout of these genes leads to herbivore resistance. JA‐Ile hydroxylases and hydrolases have been investigated in Arabidopsis, rice, and Nicotiana attenuata. To determine whether the maize homologs of these genes function in plant defense, we silenced expression of ZmCYP94B1 (predicted JA‐Ile hydroxylase) and ZmJIH1 (predicted JA‐Ile hydrolase) by VIGS with SCMV, which resulted in elevated expression of two defense‐related genes, Maize Proteinase Inhibitor (MPI) and Ribosome Inactivating Protein 2 (RIP2). Although ZmCYP94B1 and ZmJIH1 gene expression silencing increased resistance to Spodoptera frugiperda (fall armyworm), Schistocerca americana (American birdwing grasshopper), and Rhopalosiphum maidis (corn leaf aphid), there was no additive effect from silencing the expression of both genes. Further work will be required to determine the more precise functions of these enzymes in regulating maize defenses.
Real‐time biomechanical modelling of the liver using LightGBM model
Authors: Zhu Jiahua , Su Yixian , Liu Ziteng , Liu Bainan , Sun Yu , Gao Wenpeng , Fu Yili 
Published: June 2022

AbstractBackgroundAccurate and real‐time biomechanical modelling of the liver is a major challenge in computer‐assisted surgery. Finite element method is often used to predict the deformation of organs for its high modelling accuracy. However, its high computation cost hinders its application in real time, such as virtual surgery simulations.MethodA liver model with biomechanical properties similar to real one is created using finite element method and a data set of the liver deformation with different forces (whose magnitude ranges from 0.1 to 0.5 N in omni‐direction) acting on different surface points is generated. The mechanical behaviour of liver is simulated in real time by a tree‐based LightGBM regression model trained with the generated data set.ResultsIn comparison with the Random Forest and XGBoost, the LightGBM model achieves the best accuracy with 0.0774 mm, 0.0786 mm, 0.0801 mm in the mean absolute error (MAE) and 0.0591 mm, 0.0609 and 0.0622 mm in the root mean square error (RMSE) along x, y and z axis, respectively. In addition, it only takes 33 ms for the LightGBM model to estimate the deformation of the liver, which is much faster than finite element model (29.91 s).ConclusionThese results lay a foundation for the future development of real‐time virtual surgery systems of simulating liver deformation during minimally invasive surgeries using our method.
Metal‐Organic Frameworks (MOFs) and their Applications in CO2Adsorption and Conversion
Authors: Zulkifli Zuraini I. , Lim Kean L. , Teh Lee P. 
Published: June 2022

AbstractNowadays, sustaining the atmosphere and protecting it from global warming are recognized as the main concerns of the planet. This problem has been addressed through the development of a number of different carbon capture and sequestration approaches. Lately, metal‐organic framework (MOF) has emerged as the most auspicious CO2adsorption and conversion material. This novel class of porous material has demonstrated tremendous potential for gas separation technologies, particularly CO2adsorption and conversion due to their remarkable characteristics. Therefore, the present review attempted to provide current understanding of the CO2adsorption and conversion performance over MOF‐based materials. This review paper discussed the type and properties of the MOF, effect of metal and ligand on MOF's properties, and various applications of MOF. Furthermore, future insights were proposed for better design and development of MOF‐based materials for CO2applications.
H2O2‐Promoted Inter‐ and Intramolecular C−N Bond Formation: Synthesis of Quinazoline Derivatives
Authors: Liu Qingjie , Chen Hong , Li Shigai , Guo Yanchun , Cao Shuxia , Zhao Yufen 
Published: June 2022

AbstractHydrogen peroxide, a cheap and easily available green oxidant, can not only produce oxygen, but also produce hydroxyl radical by homogenization under the catalysis of metal. Toluene and benzyl halide can be oxidized to benzaldehyde by hydrogen peroxide and participate in the reaction with 2‐aminobenzylamine. Through the construction of C−N bond, a series of quinazoline derivatives were synthesized. This reaction allows a broad range of substrates as well as good functional group compatibility.