bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design, Synthesis and Photophysical Studies of Luminescent Rhodium(III) Complexes in Near‐Infrared Region
Authors: Lu Jingyi , Zhao Shunan , Wei Fangfang , Wong Keith Man‐Chung 
Published: February 2023

AbstractA series of near‐infrared (NIR)‐emitting cyclometalated rhodium(III) complexes have been designed, synthesized and characterized. The NIR luminescence has been realized by rational design of strong σ‐donor cyclometalating (C^N) ligand with extended π‐conjugation structure for decreasing the energy level of intraligand (IL) state. In addition, the investigation of substituents on the benzo[g]quinoxaline moiety as the carbon‐donor demonstrated that the luminescence can be further shifted to the red by the introduction of electron‐donating thienyl groups. The luminescence maxima of these complexes are ranging from 763 nm to 792 nm with the luminescence quantum yield (Φlum) of 0.41 %–0.66 % in dichloromethane solution. This work demonstrates the first example of NIR‐phosphorescent rhodium(III) complexes and provides an alternative for diversifying the development of NIR materials.
The effects of climate change adaptation strategies on the welfare of rural farm households in Ethiopia
Authors: Mogess Yohannes Kefale , Ayen Dereje Degu 
Published: February 2023

AbstractWe estimate the effect of multiple climate change adaptation strategies (CCAS) on farm household welfare using a new three‐wave panel data derived from the World Bank's geo‐referenced household socioeconomic survey and the National Aeronautics and Space Administration's (NASA) historical weather data. Using the multinomial panel endogenous switching regression model, we discovered that combining climate change adaptation strategies such as crop rotation and improved seed varieties is the most effective way to improve the welfare of smallholder farmers. The average treatment effect (ATT) results show that all CCAS have a positive and significant effect on the pattern of welfare improvement among smallholder farmers, but combinations of CCAS provide the highest payoffs. As a result, the combination of crop rotation and improved seed is the most appealing CCAS package that farmers could adopt as an ex‐ante strategy to improve farm household welfare. We also discovered that climate change and technology adoption are the most important factors influencing the welfare of rural farm households. Furthermore, we discover that the effectiveness of climate change adaptation strategies varies by farm households. Thus, we argued that targeted extension and advisory services, as well as other policies that support the synchronization of adaptation strategies, are critical for improving the welfare of rural farm households.
Modeling and Thermodynamic Studies of γ‐Valerolactone Production from Bio‐derived Methyl Levulinate
Authors: Montejano‐Nares Elena , Ivars‐Barceló Francisco , Osman Sameh M. , Luque Rafael 
Published: February 2023

AbstractThe exploitation of biomass to reduce the dependency on fossil fuels represents a challenge that needs to be solved as soon as possible. Nowadays, one of the most fashionable processes is γ‐valerolactone (GVL) production from bio‐derived methyl levulinate (ML). Deep understanding of the thermodynamic aspects involved in this process is key for a successful outcome, but detailed studies are missing in the existing literature. A thermodynamic study of the reaction of γ‐valerolactone (GVL) production from bio‐derived methyl levulinate (ML) is performed by the Gibbs free energy minimization method. The effect of various reaction conditions (temperature, concentration, flow rate) and the implication of possible intermediates and byproducts are assessed. Conversion and selectivity are calculated from the simulation of the ML hydrogenation using isopropanol as the hydrogen donor under continuous flow conditions. Significant increases in GVL selectivity can be achieved under dry conditions, keeping the high conversion. Comparison between theoretical and experimental results from a previous article discloses the effect of using 5%RuTiO2 catalysts, which increases the selectivity from 3–40% to 41–98%. Enthalpy and Gibbs free energy of the reactions at issue are also calculated from models using Barin equations according to Aspen Physical Property System parameters.
The use of the second thoracic vertebral landmark for skeletal muscle assessment and computed tomography‐defined sarcopenia evaluation in patients with head and neck cancer
Authors: Vangelov Belinda , Bauer Judith , Moses Daniel , Smee Robert 
Published: February 2023

AbstractBackgroundThe cross‐sectional area (CSA) of skeletal muscle (SM) at the third lumbar vertebra (L3) is used to determine computed tomography (CT)‐defined sarcopenia. We investigated the feasibility of SM assessment at the second thoracic vertebra (T2) in patients with head and neck cancer (HNC).MethodsDiagnostic PET‐CT scans were used to develop a prediction model for L3‐CSA using T2‐CSA. Effectiveness of the model and cancer‐specific survival (CSS) were investigated.ResultsScans of 111 patients (85% male) were evaluated. The predictive formula: L3‐CSA (cm2) = 174.15 + [0.212 × T2‐CSA (cm2)] – [40.032 × sex] – [0.928 × age (years)] + [0.285 × weight (kg)] had good correlation r = 0.796, ICC = 0.882 (p < 0.001). SM index (SMI) mean difference (bias) was −3.6% (SD 10.2, 95% CI −8.7% to 1.3%). Sensitivity (82.8%), specificity (78.2%), with moderate agreement (ƙ = 0.540, p < 0.001). Worse 5‐year CSS with lower quartile T2‐SMI (51%, p = 0.003).ConclusionsSM at T2 can be effectively used for CT‐defined sarcopenia evaluation in HNC.
A data‐driven approach for flood prediction using grid‐based meteorological data
Authors: Wang Yizhi , Liu Jia , Li Chuanzhe , Liu Yuchen , Xu Lin , Yu Fuliang 
Published: February 2023

AbstractEstablishing a physically‐based hydrological model for flood prediction in ungauged or data‐limited catchments has always been a difficult problem. In this study, a data‐driven approach based on the NASA Global Land Data Assimilation System (GLDAS) data is proposed for flood prediction with the assistance of the Gamma Test. A runoff generation model together with a routing model based on the most advanced deep learning model, i.e. Long Short‐Term Memory (LSTM) network is established. By calculating the noise of the input data, Gamma Test can effectively avoid the overfitting phenomenon with the LSTM network. Taking the a small‐scale mountainous catchment from northern China as an example, Gamma Test in this study is used to help select the optimal combination of the GLDAS inputs for the runoff generation model, and meanwhile the input grids involved in the routing model. The established models are then verified based on the Nash‐Sutcliffe Efficiency coefficient (NSE), and it is found that the surface runoff generation model shows a good performance, with an average NSE value of 0.8708, while the baseflow‐groundwater runoff generation model also results in an acceptable performance with an average NSE value of 0.6320. After the involvement of the observed rainfall data, the model performance (NSE) of the routing model has increased from 0.5738 to 0.7144. Finally, the runoff generation models and the routing model are integrated, and the grid‐based GLDAS meteorological data are used directly to simulate the streamflow at the catchment outlet. The integrated model performs well with an NSE value of 0.7909, which indicates the feasibility of this data‐driven approach for flood prediction using the gird‐based meteorological data. The methodology adopted in this study provides a reference for flood prediction using data‐driven models in ungauged or data‐limited catchments.
A joint segmentation and classification framework for COVID‐19 infection segmentation and detection from chest CT images
Authors: Jeevitha S. , Valarmathi K. 
Published: February 2023

AbstractIn COVID19 management, CT images are used as noninvasive diagnostic tools for screening and disease monitoring. Segmentation of infections provides valuable visual interpretations in the process of prognosis and decision making. Segmentation of COVID19 infection from chest CT images is challenging due to the presence of multiple infection types and complex morphological patterns. This paper presents a novel multi task learning framework for COVID19 infection segmentation and detection. The proposed model called DB‐YNet, is built on YNet architecture with a dense bottleneck and attention based UNet backbone. This model is trained and tested with standard datasets, demonstrating superior segmentation and classification metrics. It achieves a Dice score of 0.9923 for segmentation, and classification accuracies of 0. 0.9875 and 0.9961 under binary and multi class classifications respectively. In COVID19 triage, DB‐YNet is a promising tool to assist physicians in the early identification of COVID19 infected patients for quick clinical interventions.
Automated diagnosis of COVID stages using texture‐based Gabor features in variational mode decomposition from CT images
Authors: Patel Rajneesh Kumar , Kashyap Manish 
Published: February 2023

AbstractCOVID‐19 is a deadly and fast‐spreading disease that makes early death by affecting human organs, primarily the lungs. The detection of COVID in the early stages is crucial as it may help restrict the spread of the progress. The traditional and trending tools are manual, time‐inefficient, and less accurate. Hence, an automated diagnosis of COVID is needed to detect COVID in the early stages. Recently, several methods for exploiting computed tomography (CT) scan pictures to detect COVID have been developed; however, none are effective in detecting COVID at the preliminary phase. We propose a method based on two‐dimensional variational mode decomposition in this work. This proposed approach decomposes pre‐processed CT scan pictures into sub‐bands. The texture‐based Gabor filter bank extracts the relevant features, and the student's t‐value is used to recognize robust traits. After that, linear discriminative analysis (LDA) reduces the dimensionality of features and provides ranks for robust features. Only the first 14 LDA features are qualified for classification. Finally, the least square‐ support vector machine (SVM) (radial basis function) classifier distinguishes between COVID and non‐COVID CT lung images. The results of the trial showed that our model outperformed cutting‐edge methods for COVID classification. Using tenfold cross‐validation, this model achieved an improved classification accuracy of 93.96%, a specificity of 95.59%, and an F1 score of 93%. To validate our proposed methodology, we conducted different relative experiments with deep learning and traditional machine learning‐based models like random forest, K‐nearest neighbor, SVM, convolutional neural network, and recurrent neural network. The proposed model is ready to help radiologists identify diseases daily.
An updated review of the association of host genetic factors with susceptibility and resistance to COVID‐19
Authors: Mohammadpour Somayeh , Torshizi Esfahani Amir , Halaji Mehrdad , Lak Mina , Ranjbar Reza 
Published: February 2023

AbstractThe emergence of the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) in human populations sparked a global pandemic of the coronavirus disease 2019 (COVID‐19). According to preliminary data, about 14% of cases are considered severe and 5% of cases result in critical illness and, reported case fatality rates vary from 1% to more than 7%. However, the symptoms of the disease and the clinical outcome are very different in infected people. In view of these differences, it is clearly apparent that to gain insight into the biology of the SARS‐CoV‐2, it is important to study not just the infectious particle in itself but also to investigate the virus‐host cell interactions that occur during infection. This review seeks to consider the various aspects of genetic factors in determining the susceptibility and host resistance to SARS‐CoV‐2 throughout the recently published literature.
The vaginal microbiota among the different status of human papillomavirus infection and bacterial vaginosis
Authors: Liu Yujing , Li Tingyuan , Guo Ruochun , Chen Tingting , Wang Sumeng , Wu Dingkun , Li Jing , Liu Zhihua , Zhao Yuqian , Yin Jian , Qin Junjie , Sun Li , Chen Wen 
Published: February 2023

AbstractAlthough human papillomavirus (HPV) infection plays a decisive role in causing tumors, its infection is insufficient for independently promoting cancer development and other co‐factors facilitate the carcinogenic process. The objective of this study was to demonstrate the association between vaginal microbiota and high‐risk human papillomavirus (HR‐HPV) infection in women with and without bacterial vaginosis (BV). The study included 1015 women aged 21–64 who participated in cervical cancer screening in two areas of China from 2018 to 2019. Women were collected cervical exfoliated cell specimens and reproductive tract secretions samples for HR‐HPV, BV and microbial composition testing. From the non‐BV & HPV− group (414 HPV‐negative women without BV) to the non‐BV & HPV+ group (108 HPV‐positive women without BV), to the BV & HPV−group (330 HPV‐negative women with BV) and then to the BV & HPV+ group (163 HPV positive‐women with BV), microbial diversity increased. The relative abundance of 12 genera, including Gardnerella, Prevotella, and Sneathia increased, while Lactobacillus declined. Correlation networks of these genera and host characteristics were disrupted in the non‐BV & HPV+ group, and the network trended more disordered in the BV & HPV+ group. Besides, multiple HPV infection, certain HPV genotype infection and cervical intraepithelial neoplasia (CIN) status were associated with some microbes and higher microbial diversity. HPV shifted the composition and diversity of vaginal microbiota, and BV further reinforced the trend. The relative abundance of 12 genera increased and 1 genus decreased on account of BV and HPV infection, and some genera including Lactobacillus, Prevotella, and Sneathia were associated with some specific HPV genotypes infection and CIN.
Biomechanical evaluation of a novel biomimetic artificial intervertebral disc in canine cervical cadaveric spines
Authors: Jacobs Celien A. M. , Doodkorte Remco J. P. , Kamali S. Amir , Abdelgawad Abdelrahman M. , Ghazanfari Samaneh , Jockenhoevel Stefan , Arts J. J. Chris , Tryfonidou Marianna A. , Meij Björn P. , Ito Keita 
Published: February 2023

AbstractBackground ContextCervical disc replacement (CDR) aims to restore motion of the treated level to reduce the risk of adjacent segment disease (ASD) compared with spinal fusion. However, first‐generation articulating devices are unable to mimic the complex deformation kinematics of a natural disc. Thus, a biomimetic artificial intervertebral CDR (bioAID), containing a hydroxyethylmethacrylate (HEMA)—sodium methacrylate (NaMA) hydrogel core representing the nucleus pulposus, an ultra‐high‐molecular‐weight‐polyethylene fiber jacket as annulus fibrosus, and titanium endplates with pins for primary mechanical fixation, was developed.PurposeTo assess the initial biomechanical effect of the bioAID on the kinematic behavior of the canine spine, an ex vivo biomechanical study in 6‐degrees‐of‐freedom was performed.Study DesignA canine cadaveric biomechanical study.MethodsSix cadaveric canine specimens (C3‐C6) were tested in flexion‐extension (FE), lateral bending (LB) axial rotation (AR) using a spine tester in three conditions: intact, after C4‐C5 disc replacement with bioAID, and after C4‐C5 interbody fusion. A hybrid protocol was used where first the intact spines were subjected to a pure moment of ±1 Nm, whereafter the treated spines were subjected to the full range of motion (ROM) of the intact condition. 3D segmental motions at all levels were measured while recording the reaction torsion. Biomechanical parameters studied included ROM, neutral zone (NZ), and intradiscal pressure (IDP) at the adjacent cranial level (C3‐C4).ResultsThe bioAID retained the sigmoid shape of the moment‐rotation curves with a NZ similar to the intact condition in LB and FE. Additionally, the normalized ROMs at the bioAID‐treated level were statistically equivalent to intact during FE and AR while slightly decreased in LB. At the two adjacent levels, ROMs showed similar values for the intact compared to the bioAID for FE and AR and an increase in LB. In contrast, levels adjacent to the fused segment showed an increased motion in FE and LB as compensation for the loss of motion at the treated level. The IDP at the adjacent C3‐C4 level after implantation of bioAID was close to intact values. After fusion, increased IDP was found compared with intact but did not reach statistical significance.ConclusionThis study indicates that the bioAID can mimic the kinematic behavior of the replaced intervertebral disc and preserves that for the adjacent levels better than fusion. As a result, CDR using the novel bioAID is a promising alternative treatment for replacing severely degenerated intervertebral discs.
Effect of skull porosity on ultrasound transmission and wave mode conversion at large incidence angles
Authors: Jing Bowen , Strassle Rojas Stephan , Lindsey Brooks D. 
Published: February 2023

AbstractBackgroundTranscranial ultrasound imaging and therapy depend on the efficient transmission of acoustic energy through the skull. Multiple previous studies have concluded that a large incidence angle should be avoided during transcranial‐focused ultrasound therapy to ensure transmission through the skull. Alternatively, some other studies have shown that longitudinal‐to‐shear wave mode conversion might improve transmission through the skull when the incidence angle is increased above the critical angle (i.e., 25° to 30°).PurposeThe effect of skull porosity on the transmission of ultrasound through the skull at varying incidence angles was investigated for the first time to elucidate why transmission through the skull at large angles of incidence is decreased in some cases but improved in other cases.MethodsTranscranial ultrasound transmission at varying incidence angles (0°–50°) was investigated in phantoms and ex vivo skull samples with varying bone porosity (0% to 28.54% ± 3.36%) using both numerical and experimental methods. First, the elastic acoustic wave transmission through the skull was simulated using micro‐computed tomography data of ex vivo skull samples. The trans‐skull pressure was compared between skull segments having three levels of porosity, that is, low porosity (2.65% ± 0.03%), medium porosity (13.41% ± 0.12%), and high porosity (26.9%). Next, transmission through two 3D‐printed resin skull phantoms (compact vs. porous phantoms) was experimentally measured to test the effect of porous microstructure alone on ultrasound transmission through flat plates. Finally, the effect of skull porosity on ultrasound transmission was investigated experimentally by comparing transmission through two ex vivo human skull segments having similar thicknesses but different porosities (13.78% ± 2.05% vs. 28.54% ± 3.36%).ResultsNumerical simulations indicated that an increase in transmission pressure occurs at large incidence angles for skull segments having low porosities but not for those with high porosity. In experimental studies, a similar phenomenon was observed. Specifically, for the low porosity skull sample (13.78% ± 2.05%), the normalized pressure was 0.25 when the incidence angle increased to 35°. However, for the high porosity sample (28.54% ± 3.36%), the pressure was no more than 0.1 at large incidence angles.ConclusionsThese results indicate that the skull porosity has an evident effect on the transmission of ultrasound at large incidence angles. The wave mode conversion at large, oblique incidence angles could enhance the transmission of ultrasound through parts of the skull having lower porosity in the trabecular layer. However, for transcranial ultrasound therapy in the presence of highly porous trabecular bone, transmission at a normal incidence angle is preferable relative to oblique incidence angles due to the higher transmission efficiency.
Validation of AND/ASPEN pediatric malnutrition diagnosis in children admitted to medical wards in two tertiary hospitals in Malaysia
Authors: Ong Shu Hwa , Chen Seong Ting , Chee Winnie Siew Swee 
Published: February 2023

AbstractBackgroundIdentification and management of malnutrition among pediatric hospitalized patients is critical for improved clinical outcomes and recovery. This study investigated the use of the Academy of Nutrition and Dietetics and the American Society for Parenteral and Enteral Nutrition (AND/ASPEN) pediatric malnutrition diagnosis in comparison with the Subjective Global Nutritional Assessment (SGNA) tool and single anthropometric indicators (weight, height, body mass index, and mid‐upper arm circumference) among hospitalized children.MethodsA cross‐sectional study was conducted among 260 children admitted to general medical wards. SGNA and anthropometric measurements were used as references. Kappa agreement, diagnostic values, and area under the curve (AUC) were analyzed to evaluate the diagnostic ability of the AND/ASPEN malnutrition diagnosis tool. Logistic binary regression was performed to determine the predictive ability of each malnutrition diagnosis tool on the length of hospital stay.ResultsThe AND/ASPEN diagnosis tool detected the highest malnutrition rate (41%) among the hospitalized children in comparison with the reference methods. This tool demonstrated fair specificity of 74% and sensitivity of 70% compared with the SGNA. It obtained a weak agreement in determining the presence of malnutrition by kappa (0.06–0.42) and receiver operating characteristic curve analysis (AUC = 0.54–0.72). The use of the AND/ASPEN tool obtained an odds ratio of 0.84 (95% CI, 0.44–1.61; P = 0.59) in predicting the length of hospital stay.ConclusionsThe AND/ASPEN malnutrition tool is an acceptable nutrition assessment tool for hospitalized children in general medical wards.
Life satisfaction and difficulties experienced by the family members of individuals with thalassemia
Authors: Sevinç Sibel 
Published: February 2023

AbstractAimThis study aimed to examine the life satisfaction and difficulties experienced by the family members of individuals with thalassemia.DesignThis study design is mix‐method research. This research adheres to the COREQ guidelines and checklist.MethodsThe research was conducted in the Blood Diseases Polyclinic of a state hospital in a Mediterranean city in Turkey between February 2022 and April 2022.ResultsThe mean life satisfaction scale score was 11.18 ± 5.13, and a negative correlation was found between the mother's age and life satisfaction score (r = −0.438; p = 0.042, p < 0.05). Qualitative analysis of the experiences of the family members of individuals with thalassemia yielded 10 themes.