bluuebunny/crossref_metadata_2025_split huggingface dataset:
Climate and Aridity Measures Relationships with Spectral Vegetation Indices across Desert Fringe Shrublands in the South-Eastern Mediterranean Basin
Authors: Shoshany Maxim , Mozhaeva Sofia 
Published: September 2022

Abstract
Mediterranean regions are hot spots of climate change, where the expected decrease in water resources threatens the sustainability of shrublands at their arid margins. Studying spectral vegetation indices relationships with rainfall and Potential Evapotranspiration (PET) changes across Mediterranean to arid transition zones is instrumental for developing methods for mapping and monitoring the effects of climate change on desert fringe shrublands. Here we examined relationships between 17 spectral vegetation indices (VIs) and four climate and aridity measures: rainfall, PET, Aridity Index (AI) and Water Deficit (WD) calculated at accumulation lags between 1 and 6 months. For this purpose, VIs for 38 sites (100x100 meters each) representing less disturbed areas were extracted from Sentinel 2A images for 3 years with high (2016), low (2017), and average (2018) annual rainfall. Most of the VIs had shown the highest correlation with the four climate and aridity measures at 2 months accumulation interval. While NDVI relationships with climate measures gained the widest use, our data suggest that indices combining NIR and SWIR bands better correlate climate parameters. AI is one of the leading annual measures of dryness worldwide, when calculating it monthly, WD was found to better represent the balance between precipitation and PET across the climate transition zone, and to be better correlated with VI’s. Relationships between NIR and SWIR VIs and Water Deficit may thus facilitate improvements in monitoring and mapping desert fringe shrublands responses to climate change if supported by similar results from wider areas.
Association of KIR2DL5, KIR2DS5, and KIR2DS1 allelic variation and Atopic Dermatitis
Authors: Margolis David J. , Mitra Nandita , Hoffstad Ole J , BA Ronald Berna , Kim Brian S , Chopra Abha , Phillips Elizabeth J 
Published: September 2022

Abstract
Natural killer cells (NK) have been associated with the pathophysiology of atopic dermatitis (AD). NK function is regulated by killer cell Ig-like receptor family (KIR) receptors that interact with HLA ligands. The goal of this study was to focus on allelic variation in genes KIR2DL5, KIR2DS5, and KIR2DS1 with respect to AD. This was a case-control study of individuals with (n = 313) and without (n = 176) AD. Associations were estimated using logistic regression. Evaluations included interactions between KIR and known HLA ligand pairs. The prevalence of KIR2DL5 was 52.5% (95% CI: 48.0,57.0), KIR2DS5 was 33.0% (28.8,37.3), and KIR2DS1 was 33.6% (29.4,38.0). When compared to those who did not have KIR2DL5, homozygote individuals for KIR2DL5*001:01 were more likely to have AD (OR: 2.16 (95% CI:1.31,3.53) p = 0.0023). The effect of KIR2DL5*001:01 was similar in Whites and Blacks. The alleles from the other KIR genes of interest were not associated with AD. There is no known HLA ligand for KIR2DL5. However, the effect of KIR2DL5*001:01 increases in the presence of HLA-B *-21TT leader sequence (2.46(1.37,4.41) p = 0.0025) and HLA-C2 ligand (2.07 (1.37,4.41, p = 0.000002). This is the first study to explore KIR allelic variation in AD. KIR2DL5*001:01 allele is independently associated with an increased risk of AD.
Comparing G-Computation, Propensity Score-Based Weighting, and Targeted Maximum Likelihood Estimation for Analyzing Externally Controlled Trials with Both Measured and Unmeasured Confounders: A Simulation Study
Authors: Ren Jinma , Cislo Paul , Cappelleri Joseph C. , Hlavacek P , DiBonaventura M 
Published: September 2022

Abstract
Objectives
To have confidence in one's interpretation of treatment effects assessed by comparing trial results to external controls, minimizing bias is a critical step. We sought to investigate different methods for causal inference in simulated data sets with measured and unmeasured confounders.
Methods
The simulated data included three types of outcomes (continuous, binary, and time-to-event), treatment assignment, two measured baseline confounders, and one unmeasured confounding factor. Three scenarios were set to create different intensities of confounding effect (e.g., small, medium and large for scenario 1 to 3, respectively) caused by the unmeasured confounder. The methods of g-computation (GC), inverse probability of treatment weighting (IPTW), overlap weighting (OW), standardized mortality/morbidity ratio (SMR), and targeted maximum likelihood estimation (TMLE) were used to estimate average treatment effects and reduce potential biases.
Results
The results with the greatest extent of biases were from the raw model that ignored all the potential confounders. In scenario 2, the unmeasured factor indirectly influenced the treatment assignment through a measured controlling factor and led to medium confounding. The methods of GC, IPTW, OW, SMR, and TMLE removed most of bias observed in average treatment effects for all three types of outcomes from the raw model. Similar results were found in scenario 1, but the results tended to be biased in scenario 3. GC had the best performance followed by OW.
Conclusions
The aforesaid methods can be used for causal inference in externally controlled studies when the unmeasured confounding is not large. GC and OW are the preferable approaches.
Ecological Response to Industrialisation Drivers in Africa
Authors: Aladejare Samson Adeniyi , Nyiputen Ishaku Rimantanung 
Published: September 2022

Abstract

More than ever, ecological conditions are fast deteriorating due to rising greenhouse gas emissions. These emissions are significantly propelled into the atmosphere by growth in specific human activities that also advance industrialisation. It is opined that these activities will further rise significantly in developing countries since industrialisation can lead to rapid economic modernisation. Consequently, this study examined Africa's long-term ecological response to these rising cogent industrialisation drivers, including manufacturing activities, resource wealth, urbanisation, income growth, globalisation, and human capital development. A panel analyses of 32 African countries was conducted between 1991 and 2019. Empirical inferences were derived through a battery of advance estimation techniques. They include second-generation panel unit root and cointegration tests, generalised estimating equation, generalised least squares mixed effect model, dynamic common correlated effect, Driscoll-Kraay, and the panel corrected standard error methods. These robust econometric techniques prevented methodological challenges in prior studies, such as heterogeneity, cross-sectional dependence, and heteroscedasticity. The study observed that an increase in manufacturing value-added, natural resources wealth, income growth, globalisation, and human capital diminishes ecological quality in Africa. However, urbanisation was found to be environmentally-enhancing. The study proposed relevant policy measures to mitigate environmental pollution from industrialisation drivers.
JEL Classification:N57, O13, O14.
Effect of co-treatment with mesenchymal stem cells and polyvinyl alcohol- coated selenium nanoparticles on rats with streptozotocin-induced Alzheimer's disease
Authors: Shahidi Siamak , Asl Sara Soleimani , Gholamigeravand Bahareh , Afshar Simin , Hashemi-Firouzi Nasrin , Samzadeh-Kermani Alireza , Majidi Mahsa , Amiri Kimia 
Published: September 2022

Abstract
Mesenchymal stem cell (MSC) transplantation is a potential strategy for the treatment of Alzheimer’s disease (AD). The stem cells have a short lifespan after transplantation in the recipient animals. Selenium nanoparticles facilitate drug treatment in brain diseases due to their size. The aim of this study was to investigate the possible therapeutic effect of co-treatment of MSCs with polyvinyl alcohol (PVA)-coated selenium nanoparticles (SeNPs) against streptozotocin (STZ)-mediated AD in rats.
Rats were assigned to five groups: control, Alz (STZ; 3 mg/kg, 10 μL, ICV), Alz+stem cell (ICV transplantation), Alz+SeNP (0.4 mg/kg, orally), and Alz+stem cell+SeNPs. ICV administration of STZ mimicked some aspects of AD in Alz groups. SeNPs were administrated for 30 days after STZ administration. The novel object recognition (NOR) and passive avoidance learning (PAL) tests were used to measure cognition and memory. Oxidative stress biomarkers, brain-derived neurotrophic factor (BDNF), and amyloid-beta plaques were assessed by biochemical analysis, ELISA kit, and Congo red staining, respectively.
The combined treatment of PVA-coated SeNPs and MSCs transplantation was more effective in increasing the memory reacquisition than the administration of SeNPs or MSCs alone. The stem cells and PVA-coated SeNPs together significantly increased the antioxidant capacity and reduced the amyloid-beta plaques.
It seems that co-treatment with PVA-coated SeNP with MSCs has a great neuroprotection effect against AD in animal models.
Prevalence, feeding practices, and factors associated with undernutrition among HIV-exposed uninfected children aged 6 to 18 months in Bushenyi district, western Uganda: A cross-sectional study
Authors: Kumbakulu Patrick Kumbowi , Ndeezi Grace , Egesa Walufu Ivan , Nakalema Gloria , Odoch Simon , Kambele Rachel Lokengama , Twesigemukama Sabinah , Daniel Asiimwe , Waibi William M. , Elmi Said Abdirashid , Nduwimana Martin 
Published: September 2022

Abstract
Background
HIV-exposed children are vulnerable to undernutrition. Understanding the prevalence and factors with undernutrition remains essential for developing effective strategies to reduce the impact of malnutrition among this vulnerable population.
Methods
This was a cross sectional, descriptive and analytical study to determine the prevalence, feeding practices, and factors associated with undernutrition among HIV-exposed uninfected children aged 6 to 18 months in Bushenyi District. The study involved 245 mother-child pairs attending four highly active antiretroviral treatment (HAART) clinics. Data was collected from April to June 2021. Questionnaire interviews were used to obtain factors associated with undernutrition. A digital weighing scale, stadiometer and mid-upper-arm circumference (MUAC) tape were used for anthropometry. Undernutrition was defined by either a length-for-age or a weight-for-length or a weight-for-age below − 2 SD based on the 2006 World Health Organization growth standards. Data were entered and analysed using IBM SPSS 27.0 statistics for windows. The prevalence of undernutrition and indicators for feeding practices were determined as proportion, whereas binary logistic regression was used to determine factors associated with undernutrition. A p-value < 0.05 was considered for statistical significance at multivariable level.
Results
Of the 245 participants, 48(19.6%) had undernutrition. The prevalence of stunting, wasting and underweight was 11%, 5.3% and 6.5% respectively. Most study participants 176(71.8%) were initiated on breastfeeding during their first hour of life, and 185(75.5%) were exclusively breastfed for 6 months. However, 93(38%) of them did not receive an acceptable meal frequency and 188 (76.7%) did not have an acceptable dietary diversity score during the last 24 hours. Only 15(6.1%) continued breastfeeding after 1 year of age. The majority of mothers 162(66.1%) were advised on feeding practices. Low birthweight [p = 0,03; aOR 2.94(1.23–7.03)], history of illness in the past two weeks [p = 0,02; aOR 2.35(1.13–4.91)], maternal viral load of ≥ 1000 copies/µL [p = 0,03; aOR 3.12(1.10–8.89)], maternal undernutrition [p = 0,003; aOR 4.05(1.61–10.20)], and mixed feeding [p = 0,04; aOR 1.35(0.06–2.73)] were factors significantly associated with undernutrition.
Conclusion
The prevalence of undernutrition among HIV-exposed uninfected children was relatively high in this study. It demonstrates the need for routine nutritional assessment of HIV-exposed uninfected children.
Does Conversion of Sleeve Gastrectomy to One-Anastomosis Gastric Bypass induce Gastro-Esophageal Reflux Disease?
Authors: Rayman Shlomi , Staierman Maor , Assaf Dan , Rachmuth Jacob , Carmeli Idan , Keidar Andrei 
Published: September 2022

Abstract
Purpose
Assess the subjective impact of GERD symptoms on patients undergoing revision from LSG to OAGB using the Diagnostic Questionnaire for GERD (DRQ) and the GERD-health related quality of life score (GERD-HRQL), prior to- and following conversion.
Methods
Patients undergoing revision from LSG to OAGB were prospectively followed between May 2015 and December 2020. Data retrieved included demographics, anthropometrics, previous bariatric history, time interval between LSG and OAGB, weight loss and co-morbidities. Pre- and post-OAGB DRQ and GERD-HRQL questionnaires were obtained. In case of sleeve dilatation, a partial gastrectomy was performed.
Results
During the study period, 37 patients underwent revision from LSG to OAGB. Mean age at LSG and pre-OAGB were 38 ± 11.74 and 46 ± 12.75, respectively. Median follow-up time was 21.5 months (range 3–65). DRQ and GERD-HRQL scores were obtained at a median of 14 months (range 3–51) between pre- and post-OAGB. Median RDQ score pre-OAGB vs post-OAGB was significantly reduced [30 (range 12–72) vs 14 (range 12–60), p = 0.007]. All 3 parts of the GERD- HRQL questionnaires were significantly reduced between pre-OAGB and post-OAGB: Symptoms (20; 62.5% vs 10; 31.3%, p = 0.012), overall score [15(0–39) vs 7(0–28), p = 0.04] and subjective improvement [10; 31% vs 20; 62.5%, p = 0.025).
Conclusion
Conversion of LSG to OAGB does not induce subjective GERD symptoms in a short-term follow up.
A low power injection-locked CDR using 28 nm FDSOI technology for burst-mode applications
Authors: Mao Yuqing , Charlon Yoann , Jacquemod Gilles , Leduc Yves 
Published: September 2022

Abstract
In this paper, a low-power Injection-Locked Clock and Data Recovery (ILCDR) using 28 nm FDSOI technology is presented. The back-gate auto-biasing of UTBB-FDSOI transistors allows us to create a QRO (Quadrature Ring Oscillator) reducing both size and power consumption. By injecting a digital signal into this circuit, we realize an Injection-Locked Oscillator (ILO) with low jitter. Thanks to the good performance of this oscillator, we can propose a low power ILCDR with low jitter and fast locking time for burst-mode applications. The main novelty consists in the implementation of a complementary QRO based on back-gate control using FDSOI technology in order to realize a simple and efficient ILCDR circuit. With a Pseudo-Random Binary Sequence (27 PRBS) at 868Mbps, the recovered clock jitter is 18.5 ps (1.6%UIp-p) and the recovered data jitter is 18.7 ps (1.6%UIp-p). With a 0.6 V power supply, the power consumption is 4.67 mA (2.8 mW). The estimated Chip size is around 6600 µm².
Unexpected self-lofting and dynamical confinement of volcanic plumes: the Raikoke 2019 case
Authors: Khaykin Sergey M. , de Laat A. T. Jos , Godin-Beekmann Sophie , Hauchecorne Alain , Ratynski Mathieu 
Published: September 2022

Abstract
Recent research has put in evidence the self-lofting capacity of smoke aerosols in the stratosphere and their self-confinement by persistent anticyclones, which prolongs their atmospheric residence time and radiative effects. By contrast, the volcanic aerosols - composed mostly of non-absorptive sulphuric acid droplets – were never reported to be subject of self-lofting nor of dynamical confinement. Here we use high-resolution satellite observations to show that the eruption of Raikoke volcano in June 2019 produced a long-lived stratospheric anticyclone containing 24% of the total erupted mass of sulphur dioxide. The anticyclone persisted for more than 3 months, circumnavigated the globe three times, and ascended diabatically to 27 km altitude throughradiative heating of volcanic ash contained by the plume. The mechanism of dynamical confinement has important implications for the planetary-scale transport of volcanic emissions, their stratospheric residence time, and atmospheric radiation balance. It also provides a challenge or “out of sample test” for weather and climate models that should be capable of reproducing similar structures.
Production of low-protein cocoa powder with enzyme-assisted hydrolysis
Authors: Cerit İnci , Mehdizade Könül , Avcı Ayşe , Demirkol Omca 
Published: September 2022

Abstract

Amino acid-related disorders are caused by a defect in the metabolic pathways of amino acid groups. These patients must follow a lifelong protein diet. The objective of this study was to produce a low-protein cocoa powder with enzymatic hydrolysis and precipitation method. Firstly, the solubility of cocoa powder was increased by heat and enzyme treatments (Amylase, Viscozyme and Alcalase). Then, protein level was decreased by isoelectric precipitation. According to obtained results, the solubility of cocoa powder rose from 28.61% to 50.69%. Protein content decreased by almost 40.00% and significant reductions in the amino acid profile were also provided; the highest ones were detected in methionine (100%), lysine (73.65%), leucine (53.64%), alanine (46.17%), and isoleucine (44.73%) levels. Low-protein cocoa powder had high phenolic content (25.10 mg/g GAE) and the changes in the antioxidant activities were not significant (p>0.05). Chocolate production has also been carried out successfully with the obtained cocoa powder.
Deep Learning performance in predicting dairy cows’ behaviour from a tri-axial accelerometer data
Authors: Balasso Paolo , Taccioli Cristian , Serva Lorenzo , Magrin Luisa , Andrighetto Igino , Marchesini Giorgio 
Published: September 2022

Abstract
The accurate detection of behavioural changes represents a promising method to early reveal the onset of diseases in dairy cows. This study assessed the performance of deep learning (DL) in classifying dairy cows’ behaviour from accelerometery data and compared the results with those of classical machine learning (ML). Twelve cows with a tri-axial accelerometer were observed for 136 ± 29 min each to detect 5 main behaviours. For each 8s time-interval 15 metrics were calculated obtaining a dataset of 211,720 observation units and 15 columns. The whole dataset was randomly split into training (80%) and testing (20%). An 8-layer Convolutional Neural Network (CNN) was made of 3 convolution, 1 dropout, 1 max-pooling, 1 flattening and 2 dense layers. The CNN accuracy, precision and sensitivity/recall were calculated and compared with the performance of classical ML. The CNN overall accuracy and F1-score were equal to 0.94. The precision, sensitivity/recall, and F1-score of single behaviours had the following ranges 0.88–0.99, 0.88–0.99 and 0.89–0.99, respectively. The CNN outperformed all classical ML algorithms. The CNN in our specific raising conditions showed an overall high performance in successfully predicting multiple behaviours using a single accelerometer. Further studies considering different breeds, housing conditions and sensors are warranted.
Discrimination of Loss of Excitation Fault in Synchronous Generators from Power Swing Using Machine Learning Approach
Authors: Hemavathi R. , Geethanjali M. 
Published: September 2022

Abstract

Amidst several faults in Synchronous generators, Loss of Excitation (LOE) is the most considerable fault since it affects both the generators and power network. The traditional protection method for LOE is based on impedance trajectory of the machine with negative offset mho relay. Meanwhile the traditional method experiences malfunctions and speed dip in LOE detection. This paper presents machine learning approach to detect LOE fault as well as classification logic to discriminate LOE fault from normal operating conditions and power swing conditions due to Line fault. This paper utilizes Hotelling’s-T2 statistical method to calculate Hotelling’s-T2 based Fault Indices (HT2 -FI) for LOE detection and Support Vector Machine (SVM) for classification. The time series data of electrical quantities such as Terminal voltage and Reactive Power of the generator are extracted from simulated Single Machine Infinite Bus test system and used as input data. This data is involved in calculation of HT2 –FI and in development of classification logic. The proposed method is simulated and verified for complete, partial LOE conditions and power swing conditions. Simulation outcomes depict the notable signs of the proposed method in LOE identification from power swing. Comparative assessment also reports that the method is capable of saving time in detecting LOE.
Bursting phenomenon created by bridge piers group in open channel flow
Authors: Ikani Nima , Pu Jaan H. , Taha Tarek , Hanmaiahgari Prashanth Reddy , Penna Nadia 
Published: September 2022

AbstractBridge pier is a common feature in hydraulic structure. Its impact to the river usually occurs in group form rather than a single pier, so this challenging pier-group influence towards river hydraulics and turbulence needs to be explored. In this paper, the measurements were conducted using an Acoustic Doppler Velocimeter (ADV) to study velocities in three dimensions (longitudinal, transversal, and vertical). Based on the experimental data, we have observed reversed depth-averaged velocity vector after each pier in the group of three-pier. The analysis has been conducted on the contribution of each bursting event to Reynolds shear stress (RSS) generation, in order to identify the critical bursting events and turbulence structures around the piers. In the upstream near-wake flow in the bed-wall layer, strong sweep and ejection events have been observed; while at downstream, sweeps were more dominant. The pattern of bursting changed in the outer layer of flow, where ejections were more dominant. Furthermore, the contribution fractional ratio to RSS variation at hole size H = 0 indicates that sweeps and ejections were significantly generated at the near wake-flow in upstream.