bluuebunny/crossref_metadata_2025_split huggingface dataset:
Groundwater Flow Algorithm: A Novel Hydro-geology based Optimization Algorithm
Authors: Guha Ritam , Ghosh Soulib , Ghosh Kushal Kanti , Sarkar Ram 
Published: May 2020

Abstract
A novel meta-heuristic nature-inspired optimization algorithm known as Groundwater Flow Algorithm (GWFA) is proposed in this paper. GWFA is inspired from the movement of groundwater from recharge areas to discharge areas. It follows a position update procedure guided by Darcy's law which provides a mathematical framework of groundwater flow. The proposed optimization algorithm has been evaluated on 23 benchmark functions. The significance of the results is statistically validated using Wilcoxon rank-sum, Friedman and Kruskal Walis tests. To prove the robustness of the algorithm, it has been further applied on several standard engineering problems. From these exhaustive experiments, it has been observed that the proposed GWFA can outperform many state-of-the-art optimization algorithms.
El místico de la nostalgia: Guillermo de Saint-Thierry
Authors: Reyes-Gacitúa Eva 
Published: May 2020

Guillermo de Saint-Thierry, teólogo y místico del siglo XII, despliega en un lenguaje de auténtica belleza, la nostalgia y el deseo del hombre por conocer y amar a Dios. Esta vía de movimiento ascensional –presente en todos sus escritos– le permite desarrollar una sólida teología, en cuanto el hombre puede llegar a comprender que amar a Dios le posibilita entrar en la misma Trinidad.
Por esta razón, para el abad de Saint-Thierry, la nostalgia es una forma del amor que se hace camino y aspira llegar a la meta del día final que en el Comcant corresponde al Amado salutare Dei. De este movimiento, el deseo no solo se reduce a un simple fenómeno psicológico, sino se transforma en destino del hombre fruitio Dei. Por consiguiente, esta línea de pensamiento se ha de verificar a partir del método teológico del autor, el fundamento bíblico y la riqueza de la interpretación espiritual presente en el tejido de esta investigación.
Phytoremediation performance of Acorus calamus and Canna indica for the treatment of primary treated domestic sewage through vertical subsurface flow constructed wetlands: a field-scale study
Authors: Barya Mahesh Prasad , Gupta Deepak , Thakur Tarun Kumar , Shukla Reetika , Singh Gurudatta , Mishra Virendra Kumar 
Published: May 2020

Abstract
Under the present investigation, vertical subsurface flow constructed wetlands (VSSFCWs) planted with macrophytes treated domestic sewage in an environmentally sustainable manner. Treatment of domestic sewage with wetlands is an alternative method that decreases energy consumption and economic costs involved in the treatment of environmental contaminants. This study evaluates the potential efficiency of VSSFCWs using two different macrophytes, Acorus calamus and Canna indica for the treatment of domestic sewage. To perform this study, two chambers of VSSFCWs of dimensions 2.48 m × 1.24 m × 1.54 m were built. The wetland was fed with the primary treated sewage at a hydraulic loading rate (HLR) of 0.67 m3/h (hours) in a batch flow. Treatment of primary sewage was observed from day 1 to day 6; once a day (i.e. 24 h to 144 h). The treatment of sewage was found to be significant up to day 6 (144 h); beyond this time, no significant removal was observed. The results revealed that both the wetland setups performed significant removal of TDS, BOD5, total nitrogen, and phosphate. The wetland planted with Canna indica was a better performer for the removal of TDS (22.31%), BOD5 (81.79%), total nitrogen (60.37%), and phosphate (80%).
Determination of Impurities in Perampanel Bulk Drugs by High- Performance Liquid Chromatography and Gas Chromatography
Authors: Xia Yun-Yan , Zou Qiao-Gen , Yang Yu-Fei , Sun Qian , Han Cheng-Qun 
Published: May 2020

Background:
High-performance liquid chromatography (HPLC) method has been used to detect related impurities of perampanel. However, the detection of impurities is incomplete, and the limits of quantification and detection are high. A sensitive, reliable method is in badly to be developed and applied for impurity detection of perampanel bulk drug.

Objective:
Methodologies utilising HPLC and gas chromatography (GC) were established and validated for quantitative determination
of perampanel and its related impurities (a total of 10 impurities including 2 genotoxic impurities).

Methods:
The separation was achieved on a Dikma Diamonsil C18 column (250 mm × 4.6 mm, 5 μm)
with the mobile phase of 0.01 mol/L potassium dihydrogen phosphate solution (A) and acetonitrile (B)
in gradient elution mode. The compound 2-bromopropane was determined on an Agilent DB-624 column
(0.32 mm × 30 m, 1.8 μm) by electron capture detector (μ-ECD) with split injection ratio of 1:5
and proper gradient temperature program.

Result:
Both HPLC and GC methods were established and validated to be sensitive, accurate and robust
according to the International Council for Harmonization (ICH) guidelines. The methods developed
were linear in the selected concentration range (R2≥0.9944). The average recovery of all impurities
was between 92.6% and 103.3%. The possible production mechanism of impurities during the synthesis
and degradation processes of perampanel bulk drug was also discussed. Five impurities were
analyzed by liquid chromatography–mass spectrometry (LC-MS). Moreover, two of them were simultaneously
characterized by LC-MS, IR and NMR.

Conclusion:
The HPLC and GC methods were developed and optimized, which could be applied for quantitative detection of the
impurities, and further stability study of perampanel.
Development of Sertaconazole Nitrate Loaded Nanostructured Lipid Carriers Gel Using Central Composite Design: In vitro and Ex vivo Evaluation
Authors: Patil Moreshwar , Bhagade Pallavi , Amale Meghana , Sonawane Sandeep , Kshirsagar Sanjay 
Published: May 2020

Aim:
The aim of this study was to develop effective topical antifungal formulation containing
sertaconazole nitrate.

Background:
Sertaconazole nitrate, a topical antifungal, was incorporated in solid-liquid lipid
nanostructures and gelled further for topical application.

Objective:
The objective of this investigation was to develop a topical formulation containing
sertaconazole nitrate which was incorporated in the solid-state of the matrix to prolong the release
in deep skin infection and hence reduce the application frequency.

Methods:
The nanostructured lipid carriers of sertaconazole nitrate were developed by high-speed
homogenization followed by ultrasonication using Estosoft-GTS® (glyceryl tristearate) as a solid
lipid, oleic acid as liquid lipid and Tween 80 as an emulsifier. The central composite design was
used to optimize total lipid concentration and fraction of liquid lipid in the total lipid and its effect
on entrapment efficiency and drug release was determined.

Results:
The carrier particles had an average size of 366.3 nm; entrapment efficiency in between
50.66% to 87.36%; cumulative drug release up to 92.90% and zeta potential of 7.43 mV. Characterization
by FTIR indicated no negative interaction between drug and excipients, XRD showed
the disappearance of crystalline peaks of the encapsulated drug while DSC revealed complete solubilization
of the drug. About 99.6% of the drug was estimated by HPLC method. The drug release
from gel and cream was 25.04% and 72.97% respectively. The lipid and gel excipients did not interfere
with the antifungal activity of the drug.

Conclusion:
The developed nanocarriers loaded gel was stable. It prolonged the drug release (for
24 h) than marketed cream. It could be a promising concept for the topical delivery of antifungal
and anti-inflammatory materials.
Hot Deformation Behavior and Processing Map of a Novel Dilute Extruded Mg-Zn-Gd-Y-Nd Alloy
Authors: Abbasi Zahra , Ebrahimi Ramin 
Published: May 2020

Background:
The hot working characteristic of magnesium alloys have received considerable
attention in order to evaluate the constitutive behavior of these alloys under hot working conditions
with a view to evaluate constitutive conditions to optimize hot workability and control the microstructure
so as to obtain consistent mechanical properties.

Methods:
The hot deformation response of a new dilute Mg alloy was investigated by means of a series
of hot compression tests in the temperature and strain rate range of 375-450°C and 0.001-1 s-1,
respectively. The stress-strain behavior, microstructure evolution and processing parameters optimization
were studied carefully. Micro-structural characterization studies conducted on a series of deformed
samples using optical microscopy revealed that during hot deformation, the main restoration
mechanism was dynamic recrystallization (DRX).

Results:
In the final microstructure of the material, grain boundaries were thoroughly covered by
layers of fine DRXed grains. Moreover, a strong twinning induced necklace structure was the most
significant characteristic at high strain rates which was accompanied by smaller grain size in the domain
material. Based on the measured stress-strain data, constitutive model was conducted on two
regimes of low and high temperatures. Moreover, the processing map of the studied material was obtained
and interpreted using dynamic material model (DMM).

Conclusion:
The processing map was built and divided into a feasible domain at high temperatures
in the whole range of strain rates and two separated instable domains in the temperature range of 375
to 435°C at high and low strain rates of 1 and 0.001 s-1.
Physical Activity Monitoring Using a Fitbit Device in Ischemic Stroke Patients: Prospective Cohort Feasibility Study
Authors: Katzan Irene , Schuster Andrew , Kinzy Tyler 
Published: May 2020

Background
Continuous tracking of ambulatory activity in real-world settings using step activity monitors has many potential uses. However, feasibility, accuracy, and correlation with performance measures in stroke patients have not been well-established.

Objective
The primary study objective was to determine adherence with wearing a consumer-grade step activity monitor, the Fitbit Charge HR, in home-going ischemic stroke patients during the first 90 days after hospital discharge. Secondary objectives were to (1) determine accuracy of step counts of the Fitbit Charge HR compared with a manual tally; (2) calculate correlations between the Fitbit step counts and the mobility performance scores at discharge and 30 days after stroke; (3) determine variability and change in weekly step counts over 90 days; and (4) evaluate patient experience with using the Fitbit Charge HR poststroke.

Methods
A total of 15 participants with recent mild ischemic stroke wore a Fitbit Charge HR for 90 days after discharge and completed 3 mobility performance tests from the National Institutes of Health Toolbox at discharge and Day 30: (1) Standing Balance Test, (2) 2-Minute Walk Endurance Test, and (3) 4-Meter Walk Gait Speed Test. Accuracy of step activity monitors was assessed by calculating differences in steps recorded on the step activity monitor and a manual tally during 2-minute walk tests.

Results
Participants had a mean age of 54 years and a median modified Rankin scale score of 1. Mean daily adherence with step activity monitor use was 83.6%. Mean daily step count in the first week after discharge was 4376. Daily step counts increased slightly during the first 30 days after discharge (average increase of 52.5 steps/day; 95% CI 32.2-71.8) and remained stable during the 30-90 day period after discharge. Mean step count difference between step activity monitor and manual tally was –4.8 steps (–1.8%). Intraclass correlation coefficients for step counts and 2-minute walk, standing balance, and 4-meter gait speed at discharge were 0.41 (95% CI –0.14 to 0.75), –0.12 (95% CI –0.67 to 0.64), and 0.17 (95% CI –0.46 to 0.66), respectively. Values were similarly poor at 30 days.

Conclusions
The use of consumer-grade Fitbit Charge HR in patients with recent mild stroke is feasible with reasonable adherence and accuracy. There was poor correlation between step counts and gait speed, balance, and endurance. Further research is needed to evaluate the association between step counts and other outcomes relevant to patients, including patient-reported outcomes and measures of physical function.
Circadian Rhythm Analysis Using Wearable Device Data: Novel Penalized Machine Learning Approach
Authors: Li Xinyue , Kane Michael , Zhang Yunting , Sun Wanqi , Song Yuanjin , Dong Shumei , Lin Qingmin , Zhu Qi , Jiang Fan , Zhao Hongyu 
Published: May 2020

Background
Wearable devices have been widely used in clinical studies to study daily activity patterns, but the analysis remains a major obstacle for researchers.

Objective
This study proposes a novel method to characterize sleep-activity rhythms using actigraphy and further use it to describe early childhood daily rhythm formation and examine its association with physical development.

Methods
We developed a machine learning–based Penalized Multiband Learning (PML) algorithm to sequentially infer dominant periodicities based on the Fast Fourier Transform (FFT) algorithm and further characterize daily rhythms. We implemented and applied the algorithm to Actiwatch data collected from a cohort of 262 healthy infants at ages 6, 12, 18, and 24 months, with 159, 101, 111, and 141 participants at each time point, respectively. Autocorrelation analysis and Fisher test in harmonic analysis with Bonferroni correction were applied for comparison with the PML. The association between activity rhythm features and early childhood motor development, assessed using the Peabody Developmental Motor Scales-Second Edition (PDMS-2), was studied through linear regression analysis.

Results
The PML results showed that 1-day periodicity was most dominant at 6 and 12 months, whereas one-day, one-third–day, and half-day periodicities were most dominant at 18 and 24 months. These periodicities were all significant in the Fisher test, with one-fourth–day periodicity also significant at 12 months. Autocorrelation effectively detected 1-day periodicity but not the other periodicities. At 6 months, PDMS-2 was associated with the assessment seasons. At 12 months, PDMS-2 was associated with the assessment seasons and FFT signals at one-third–day periodicity (P<.001) and half-day periodicity (P=.04), respectively. In particular, the subcategories of stationary, locomotion, and gross motor were associated with the FFT signals at one-third–day periodicity (P<.001).

Conclusions
The proposed PML algorithm can effectively conduct circadian rhythm analysis using time-series wearable device data. The application of the method effectively characterized sleep-wake rhythm development and identified the association between daily rhythm formation and motor development during early childhood.
Mining the Characteristics of COVID-19 Patients in China: Analysis of Social Media Posts
Authors: Huang Chunmei , Xu Xinjie , Cai Yuyang , Ge Qinmin , Zeng Guangwang , Li Xiaopan , Zhang Weide , Ji Chen , Yang Ling 
Published: May 2020

Background
In December 2019, pneumonia cases of unknown origin were reported in Wuhan City, Hubei Province, China. Identified as the coronavirus disease (COVID-19), the number of cases grew rapidly by human-to-human transmission in Wuhan. Social media, especially Sina Weibo (a major Chinese microblogging social media site), has become an important platform for the public to obtain information and seek help.

Objective
This study aims to analyze the characteristics of suspected or laboratory-confirmed COVID-19 patients who asked for help on Sina Weibo.

Methods
We conducted data mining on Sina Weibo and extracted the data of 485 patients who presented with clinical symptoms and imaging descriptions of suspected or laboratory-confirmed cases of COVID-19. In total, 9878 posts seeking help on Sina Weibo from February 3 to 20, 2020 were analyzed. We used a descriptive research methodology to describe the distribution and other epidemiological characteristics of patients with suspected or laboratory-confirmed SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection. The distance between patients’ home and the nearest designated hospital was calculated using the geographic information system ArcGIS.

Results
All patients included in this study who sought help on Sina Weibo lived in Wuhan, with a median age of 63.0 years (IQR 55.0-71.0). Fever (408/485, 84.12%) was the most common symptom. Ground-glass opacity (237/314, 75.48%) was the most common pattern on chest computed tomography; 39.67% (167/421) of families had suspected and/or laboratory-confirmed family members; 36.58% (154/421) of families had 1 or 2 suspected and/or laboratory-confirmed members; and 70.52% (232/329) of patients needed to rely on their relatives for help. The median time from illness onset to real-time reverse transcription-polymerase chain reaction (RT-PCR) testing was 8 days (IQR 5.0-10.0), and the median time from illness onset to online help was 10 days (IQR 6.0-12.0). Of 481 patients, 32.22% (n=155) lived more than 3 kilometers away from the nearest designated hospital.

Conclusions
Our findings show that patients seeking help on Sina Weibo lived in Wuhan and most were elderly. Most patients had fever symptoms, and ground-glass opacities were noted in chest computed tomography. The onset of the disease was characterized by family clustering and most families lived far from the designated hospital. Therefore, we recommend the following: (1) the most stringent centralized medical observation measures should be taken to avoid transmission in family clusters; and (2) social media can help these patients get early attention during Wuhan’s lockdown. These findings can help the government and the health department identify high-risk patients and accelerate emergency responses following public demands for help.
Use of a Machine Learning Program to Correctly Triage Incoming Text Messaging Replies From a Cardiovascular Text–Based Secondary Prevention Program: Feasibility Study
Authors: Lowres Nicole , Duckworth Andrew , Redfern Julie , Thiagalingam Aravinda , Chow Clara K 
Published: May 2020

Background
SMS text messaging programs are increasingly being used for secondary prevention, and have been shown to be effective in a number of health conditions including cardiovascular disease. SMS text messaging programs have the potential to increase the reach of an intervention, at a reduced cost, to larger numbers of people who may not access traditional programs. However, patients regularly reply to the SMS text messages, leading to additional staffing requirements to monitor and moderate the patients’ SMS text messaging replies. This additional staff requirement directly impacts the cost-effectiveness and scalability of SMS text messaging interventions.

Objective
This study aimed to test the feasibility and accuracy of developing a machine learning (ML) program to triage SMS text messaging replies (ie, identify which SMS text messaging replies require a health professional review).

Methods
SMS text messaging replies received from 2 clinical trials were manually coded (1) into “Is staff review required?” (binary response of yes/no); and then (2) into 12 general categories. Five ML models (Naïve Bayes, OneVsRest, Random Forest Decision Trees, Gradient Boosted Trees, and Multilayer Perceptron) and an ensemble model were tested. For each model run, data were randomly allocated into training set (2183/3118, 70.01%) and test set (935/3118, 29.98%). Accuracy for the yes/no classification was calculated using area under the receiver operating characteristics curve (AUC), false positives, and false negatives. Accuracy for classification into 12 categories was compared using multiclass classification evaluators.

Results
A manual review of 3118 SMS text messaging replies showed that 22.00% (686/3118) required staff review. For determining need for staff review, the Multilayer Perceptron model had highest accuracy (AUC 0.86; 4.85% false negatives; and 4.63% false positives); with addition of heuristics (specified keywords) fewer false negatives were identified (3.19%), with small increase in false positives (7.66%) and AUC 0.79. Application of this model would result in 26.7% of SMS text messaging replies requiring review (true + false positives). The ensemble model produced the lowest false negatives (1.43%) at the expense of higher false positives (16.19%). OneVsRest was the most accurate (72.3%) for the 12-category classification.

Conclusions
The ML program has high sensitivity for identifying the SMS text messaging replies requiring staff input; however, future research is required to validate the models against larger data sets. Incorporation of an ML program to review SMS text messaging replies could significantly reduce staff workload, as staff would not have to review all incoming SMS text messages. This could lead to substantial improvements in cost-effectiveness, scalability, and capacity of SMS text messaging–based interventions.
Donor Age, Cold Ischemia Time, and Delayed Graft Function
Authors: Helanterä Ilkka , Ibrahim Hassan N. , Lempinen Marko , Finne Patrik 
Published: May 2020

Background and objectives
Increased donor age is one of the most important risk factors for delayed graft function (DGF), and previous studies suggest that the harmful effect of cold ischemia time is increased in kidneys from older donors. Our aim was to study the association of increased donor age and cold ischemia time with the risk of delayed graft function in a large cohort kidney transplants from the current era.

Design, setting, participants, & measurements
The Scientific Registry of Transplant Recipients was used for this observational, retrospective registry analysis to identify all deceased donor kidney transplantations in the United States between 2010 and September 2018, who were on dialysis pretransplantation (n=90,810). The association of donor age and cold ischemia time with the risk of DGF was analyzed in multivariable models adjusted for recipient characteristics (age, race, sex, diabetes, calculated panel-reactive antibodies, pretransplant dialysis duration) and donor characteristics (cause of death, sex, race, body mass index, creatinine, donation after circulatory death status, history of hypertension, and HLA mismatch).

Results
Cold ischemia time and donor age were independently associated with the risk of DGF, but the risk of DGF was not statistically significantly lower in donor age categories between 50 and 64 years, compared with donors ≥65 years. The harmful association of cold ischemia time was not higher in kidneys from older donors in any age category, not even among donation after circulatory death donors. When donor risk was assessed with kidney donor profile index, although a statistically significant interaction with cold ischemia time was found, no practically meaningful increase in cold-ischemia susceptibility of kidneys with a high kidney donor profile index was found.

Conclusions
We were unable to demonstrate an association between donor age and DGF. The association of longer cold ischemia time with the risk of DGF was not magnified in older or more marginal donors.