bluuebunny/crossref_metadata_2025_split huggingface dataset:
Photosynthetic enhancement, lifespan extension, and leaf area enlargement in flag leaves increased the yield of transgenic rice plants overproducing Rubisco under sufficient N fertilization
Authors: Tanaka Marin , Keira Mamoru , Yoon Dong-Kyung , Mae Tadahiko , Ishida Hiroyuki , Makino Amane , Ishiyama Keiki 
Published: December 2021

Abstract
Background: Improvement in photosynthesis is one of the most promising approaches to increase grain yields in crop plants. In our previous research using an isolated experimental paddy field, transgenic rice plants overproducing Rubisco by 30% (RBCS-sense rice plants) showed up to 28% increase in grain yields under sufficient nitrogen (N) fertilization. Furthermore, the plant N contents above-ground sections and Rubisco contents of the flag leaves were higher in the RBCS-sense rice plants than the wild-type rice plants during the ripening period, which may be reasons for the increased yields.Result: In this research, the photosynthetic capacity and canopy architecture were analyzed to explore factors for the increased yields of RBCS-sense rice plants. It was found that N had already been preferentially distributed into the flag leaves at the early ripening stage, contributing to maintaining higher Rubisco content levels in the enlarged flag leaves and extending the lifespan of the flag leaves of RBCS-sense rice plants throughout ripening periods under sufficient N fertilization. The higher amounts of Rubisco also improved the photosynthetic activity in the flag leaves throughout the ripening period. Although the enlarged flag leaves of the RBCS-sense rice plants occupied large spatial areas of the uppermost layer in the canopy, no significant prevention of light penetration to leaves below the flag leaves was observed. Additionally, since the CO2 assimilation rates of lower leaves between wild-type and RBCS-sense rice plants were the same at the early ripening stage, the lower leaves did not contribute to an increase in yields between the two genotypes.Conclusion: It was concluded that improvements in the photosynthetic capacity by higher leaf N and Rubisco contents, enlarged the leaf area, and extended the lifespan of flag leaves, causing an increase in grain yields of RBCS-sense rice plants grown under sufficient N fertilization.
Supervised learning techniques to predict compounds in pathway modules based on molecular properties
Authors: Juan Liu , Ali Hayat , Yang Zihyi , Zhang xiaolie , Feng Jing 
Published: December 2021

Abstract
Machine learning algorithms provide significant indications in metabolomics to predict chemical compounds in metabolic pathways and in their modules. The modules in the metabolic pathway are sub networks of functionally related genes based on rules such as protein-protein interactions, co-regulated expression, coordinated physiological activity, and successive reaction steps. Fully functional modules are helpful to improve the diseases process, drug discover, and prediction of missing reaction. All modules in the metabolic pathway are not functional due to missing reaction steps. The structural mapping of chemical compounds with the pathway module is helpful to understand the mechanism of prediction unknown reaction step. The main purpose of this paper to predict the chemical compounds in pathway modules and their classes. We have constructed binary and multi-label classification data sets to predict pathway module and module classes, respectively. In order to identify the pathway module and its classes, we have built an ensemble Extra trees classifier to learn the molecular and atomic properties of chemical compounds. We have also experimented with different ensemble machine learning algorithm for the prediction of pathway modules. The overall prediction rate of the classifier 98.59%, indicating extra tree classifier features are more interpretable and have a high predictive performance on various tasks.
Machine Learning Methods for Predicting Long-term Mortality in Patients after Cardiac Surgery
Authors: Yu Yue , Peng Chi , Zhang Zhiyuan , Shen Kejia , Zhang Yufeng , Xiao Jian , Xi Wang , Wang Pei , Jin Zhichao , Wang Zhinong 
Published: December 2021

Abstract
Background

Establishing a mortality prediction model of patients undergoing cardiac surgery might be useful for clinicians for alerting, judgment, and intervention, while few predictive tools for long-term mortality have been developed targeting patients post-cardiac surgery.
Objective

We aimed to construct and validate several machine learning (ML) algorithms to predict long-term mortality and identify risk factors in unselected patients after cardiac surgery during a 4-year follow-up.
Methods

The Medical Information Mart for Intensive Care (MIMIC-III) database was used to perform a retrospective administrative database study. Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, prognostic scoring systems, and treatment information on the first day of ICU admission. 4-year mortality was set as the study outcome. We used the ML methods of logistic regression (LR), artificial neural network (NNET), naïve bayes (NB), gradient boosting machine (GBM), adapting boosting (Ada), random forest (RF), bagged trees (BT), and eXtreme Gradient Boosting (XGB). The prognostic capacity and clinical utility of these ML models were compared using the area under the receiver operating characteristic curves (AUC), calibration curves, and decision curve analysis (DCA).
Results

Of 7,368 patients in MIMIC-III included in the final cohort, a total of 1,337 (18.15%) patients died during a 4-year follow-up. Among 65 variables extracted from the database, a total of 25 predictors were selected using recursive feature elimination (RFE) and included in the subsequent analysis. The Ada model performed best among eight models in both discriminatory ability with the highest AUC of 0.801 and goodness of fit (visualized by calibration curve). Moreover, the DCA shows that the net benefit of the RF, Ada, and BT models surpassed that of other ML models for almost all threshold probability values. Additionally, through the Ada technique, we determined that red blood cell distribution width (RDW), blood urea nitrogen (BUN), SAPS II, anion gap (AG), age, urine output, chloride, creatinine, congestive heart failure, and SOFA were the Top 10 predictors in the feature importance rankings.
Conclusions

The Ada model performs best in predicting long-term mortality after cardiac surgery among the eight ML models. The ML-based algorithms might have significant application in the development of early warning systems for patients following operations.
“Taking Care of Your Pregnancy”: A mixed-methods study of group antenatal care in Kakamega County, Kenya
Authors: Somji Aleefia , Ramsey Kate , Dryer Sean , Makokha Fredrick , Ambasa Constance , Aryeh Brittany , Booth Kathleen , Xueref Serge , Moore Seneca , Mwenesi Ralpher , Rashid Shafia 
Published: December 2021

Abstract
Background

Traditional antenatal care (ANC) models often do not meet women’s needs for information, counseling, and support, resulting in gaps in quality and coverage. Group ANC (GANC) provides an alternative, person-centered approach where pregnant women of similar gestational age meet with the same health provider for facilitated discussion. There are few studies that show associations between GANC and various outcomes.
Methods

We used mixed methods to evaluate a contextualized GANC model (Lea Mimba Pregnancy Clubs) and to understand implementation experiences at six health facilities in Kakamega County, Kenya. Between April 2018 and January 2019, we tracked 1,652 women who were assigned to 162 GANC cohorts to assess ANC retention. Using an intention-to-treat approach, we conducted baseline (N = 112) and endline surveys (N = 360) with women attending immunization visits to assess outcomes and used time diaries to assess wait times. At endline, we conducted 29 in-depth interviews (IDIs) and three focus group discussions with women who were currently and previously participating in GANC, and 15 IDIs with stakeholders.
Results

GANC was associated with enhanced social support, with some evidence for improved knowledge, adoption of healthy behaviors, enhanced self-efficacy, and improved experience of care. Quantitatively, we found strong associations between GANC and knowledge of danger signs, women who shared their feelings with other women, knowledge and competence of health workers, respect shown by ANC providers, overall quality of care, and birth preparations; as well as an improvement in ANC retention. No changes were seen in knowledge of positive behaviors, empowerment, several aspects related to women’s experience of care, ANC retention, early initiation of ANC, and other healthy behaviors. Qualitatively, women and stakeholders noted improved interactions between health providers and women, improved counseling, increased feelings of empowerment to ask questions and speak freely, and strengthened social networks and enhanced social cohesion among women. Both wait times and counseling times increased in GANC compared to traditional ANC.
Conclusions

This is one of the few mixed-methods studies evaluating GANC and offers new measures for experience of care, empowerment, and adoption of healthy behaviors. While more research is required, GANC holds promise for enhancing women’s experiences during pregnancy. Modifications are needed for sustainability and scalability.
Circulatory Response to Rapid Volume Expansion and Cardiorespiratory Fitness in Fontan Circulation
Authors: Möller Thomas , Klungerbo Vibeke , Diab Simone , Holmstrøm Henrik , Edvardsen Elisabeth , Grindheim Guro , Brun Henrik , Thaulow Erik , Köhn-Luque Alvaro , Rösner Assami , Døhlen Gaute 
Published: December 2021

Abstract
BackgroundThe role of dysfunction of the single ventricle in Fontan failure is incompletely understood.ObjectivesWe aimed to evaluate haemodynamic responses to preload increase in Fontan circulation, to determine whether circulatory limitations in different locations identified by experimental preload increase are associated with cardiorespiratory fitness (CRF), and to assess the impact of left versus right ventricular morphology.MethodsIn 38 consecutive patients (median age=16.6 years, 16 females), heart catheterisation was supplemented with a rapid 5-mL/kg body weight volume expansion. Central venous pressure (CVP), ventricular end-diastolic pressure (VEDP), and peak systolic pressure were averaged for 15‒30 s, 45‒120 s, and 4‒6 min (steady state), respectively. CRF was assessed by peak oxygen consumption (VO2peak) and ventilatory threshold (VT).ResultsMedian CVP increased from 13 mmHg at baseline to 14.5 mmHg (p<0.001) at steady state. CVP increased by more than 20% in eight patients. Median VEDP increased from 10 mmHg at baseline to 11.5 mmHg (p<0.001). Ten patients had elevated VEDP at steady state, and in 21, VEDP increased more than 20%. The transpulmonary pressure difference (CVP‒VEDP) and CVP were consistently higher in patients with right ventricular morphology across repeated measurements. CVP at any stage was associated with VO2peak and VT. VEDP after volume expansion was associated with VT.ConclusionsPreload challenge demonstrates the limitations beyond baseline measurements. Elevation of both CVP and VEDP are associated with impaired CRF. Transpulmonary flow limitation was more pronounced in right ventricular morphology. Ventricular dysfunction may contribute to functional impairment after Fontan operation in young adulthood.ClinicalTrials.govidentifier: NCT02378857
Kinetic Study on Degradation of Micro-organics by Different UV-based advanced oxidation processes in EfOM Matrix
Authors: Yuan Donghai , Liu Guangyu , Qi Fei , Wang Jinggang , Kou Yingying , Cui Yanqi , Bai Minghui , Li Xinyu 
Published: December 2021

Abstract
Effluent Organic Matter (EfOM) contains a large number of substances that are harmful to both the environment and human health. To avoid the negative effects of organic matter in EfOM, advanced treatment of organic matter is an urgent task. Four typical oxidants (H2O2, PS, PMS, NaClO) and UV-combined treatments were used to treat micro-contaminants in the presence or absence of effluent organic matter (EfOM), because the active radical species produced in these UV-AOPs are highly reactive with organic contaminants. However, the removal efficiency of trace contaminants was greatly affected by the presence of EfOM. The degradation kinetics of two representative micro-contaminants (benzoic acid (BA) and para-chlorobenzoic acid(p-CBA)) was significantly reduced in the presence of EfOM, compared to the degradation kinetics in its absence. Using the method of competitive kinetics, with BA, p-CBA and 1,4-dimethoxybenzene (DMOB) as probes, the radicals (HO·, SO4-·, ClO·) proved to be the key to reaction species in advanced oxidation processes. UV irradiation on EfOM was not primarily responsible for the degradation of micro-contaminants. The second-order rate constants of the EfOM with radicals were determined to be (5.027±0.643)×102(SO4-·), (3.192±0.153)×104 (HO·) and 1.35×106 (ClO·) (mg-C/L)-1·s-1. In addition, this study evaluated the production of three radicals based on the concept of Rct, which can better analyze its reaction mechanism.
CoT-Enabled Robust Surveillance System using Fog Machine Learning
Authors: R Mohanasundaram , Mule Rishikesh Y , Gengavel Gowrison , Ghalib Muhammad Rukunuddin , Shankar Achyut , Singhal Abhishek 
Published: December 2021

Abstract
Surveillance system is a method of securing resources and loss of lives against fire, gas leakage, intruder, earthquake, and weather. In today’s time, people own home, farm, factory, office etc. It has become more crucial to monitor everything for securing resources and loss of lives against fire, gas leakage, intruder, earthquake. As a part of surveillance, monitoring weather is also essential. Climate change and agriculture are interrelated processes, Today's sophisticated commercial farming like weather monitoring, suffers from a lack of precision, which results huge loss in farm. Monitoring residential and commercial arenas throughout is an efficient technique to decrease personal and property losses due to fire, gas leakage, earthquake catastrophes. Internet of Things make it possible and can be implemented separately for each thing or site. But it is very difficult to monitor each site and have centralized access of it across the world. This arises the need of heterogenous system which will monitor all IoTs and perform decision making accordingly. IoT itself a large-scale thing. For single IoT application, sensors used are more in number. These sensors generate thousands of records for an instance of time, some of those are valuable and some requires just analysis. This huge amount of data on servers requires better data processing and analytics. Maintenance is also a critical task. Cloud extends these functionalities but storing all the data on cloud entail users to pay tremendous cost to the cloud service providers. This problem is catered by “CoTsurF” framework. This paper presents novel and cost effective “CoTsurF” framework, CoT-enabled robust Surveillance system using fog machine learning, a Proof-Of-Concept implementation of heterogenous and robust surveillance system based on internet of things and cloud computing by leveraging a groundbreaking concept of Fog machine learning that is Fog Computing and machine learning in Cloud of Things.
Best Practices and Lessons Learned for Action Research in eHealth Design and Implementation: Literature Review
Authors: Oberschmidt Kira , Grünloh Christiane , Nijboer Femke , van Velsen Lex 
Published: December 2021

BackgroundAction research (AR) is an established research framework to introduce change in a community following a cyclical approach and involving stakeholders as coresearchers in the process. In recent years, it has also been used for eHealth development. However, little is known about the best practices and lessons learned from using AR for eHealth development.ObjectiveThis literature review aims to provide more knowledge on the best practices and lessons learned from eHealth AR studies. Additionally, an overview of the context in which AR eHealth studies take place is given.MethodsA semisystematic review of 44 papers reporting on 40 different AR projects was conducted to identify the best practices and lessons learned in the research studies while accounting for the particular contextual setting and used AR approach.ResultsRecommendations include paying attention to the training of stakeholders’ academic skills, as well as the various roles and tasks of action researchers. The studies also highlight the need for constant reflection and accessible dissemination suiting the target group.ConclusionsThis literature review identified room for improvements regarding communicating and specifying the particular AR definition and applied approach.
ESTRATÉGIAS ENSINO METACOGNITIVAS NO ENSINO DE CIÊNCIAS: O CONTEXTO BRASILEIRO EM FOCO
Authors: Maraglia Pedro Henrique , De Assis Márcia Regina , Abreu Pinto Peixoto Mauricio 
Published: December 2021

Acreditamos que a metacognição quando empregada em contextos de ensino pode gerar resultados muito positivos tanto no amadurecimento de processos reflexivos, quanto no autogerenciamento dos processos de aprendizagem. Sabemos que uma das formas de se estimular a metacognição em situações de ensino é por meio de estratégias de ensino metacognitivas. No entanto, pouco se sabe sobre como vem se dando a pesquisa em estratégias metacognitivas no ensino nas áreas das ciências no Brasil. Para responder esta pergunta, realizamos uma revisão sistemática de literatura no Google Scholar entre os anos de 1997 e 2017, abrangendo apenas trabalhos nacionais redigidos em português e inglês, publicados na forma de artigos. Utilizamos como termos de busca as palavras, “estratégias metacognitivas”, “Química”, “Física”, “Matemática”, “Biologia”, “Ensino de Química”, “Ensino de Física”, “Ensino de Matemática” e “Ensino de Biologia”, interligados pelo operador .AND. As buscas geraram 488 artigos catalogados e registrados, que posteriormente passaram por um rigoroso processo de seleção, resultando em apenas 4 artigos que realmente apresentavam pesquisas sobre estratégias de ensino metacognitivas em ensino de ciências. As estratégias podem ser subdivididas em duas categorias: mental e produção de material. A produção na área apresentada teve inicio em 2008 e pode ser considerada pequena em relação aos demais campos de pesquisa em ensino de ciências. Não obstante, a literatura é ampla ao afirmar o seu potencial em colaborar na formação de indivíduos mais críticos e reflexivos. Por isto, acreditamos ser importante fomentar o campo.
Pengaruh Penerapan Metode Demonstrasi Terhadap Kemampuan Motorik Halus Anak Usia 5 – 6 Tahun di PAUD Harapan Kita
Authors: Ningsih Ardianti Surya , Simatupang Dorlince 
Published: December 2021

Permasalahan dalam penelitian ini adalah perkembangan motorik halus anak usia 5-6 tahun masih kurang berkembang khususnya dalam mewarnai dan menempel gambar. Oleh karena itu perlu menggunakan metode yang tepat, salah satunya metode demontrasi. Penelitian ini bertujuan untuk mengetahui pengaruh metode demontrasi terhadap perkembangan motorik halus anak usia 5-6 tahun di Paud Harapan KitaT.A 2016/2017.Metode penelitian ini termasuk penelitian eksperimen yaitu only-post test control grup design. Penelitian ini melibatkan dua kelas sampel yaitu kelas eksperimen dan kelas kontrol dengan jumlah sampel sebanyak 62 anak, yang terdiri dari 31 anak di kelas eksperimen dan 31 anak di kelas kontrol. Instrumen penelitian ini menggunakan lembar observasi.Berdasarkan hasil penelitian dan pembahasan diperoleh rata-rata nilai pada kelas eksperimen 14,16 dengan nilai tertinggi 16 dan nilai terendah 11, sehingga perkembangan motorik halus anak pada kelas eksperimen lebih baik dibanding kelas kontrol yang mempunyai nilai rata-rata 11,19 dengan nilai tertinggi 13 dan nilai terendah 11. Berdasarkan hasil tersebut hipotesis menyatakan bahwa pembelajaran menggunakan metode demontrasi berpengaruh secara signifikan terhadap perkembangan motorik halus anak yaitu dari hasil uji hipotesis diperoleh thitung > ttabel yaitu 8,427 > 1,6749 pada taraf α = 0.05. Dengan demikian hipotesis Ho ditolak dan Ha diterima sehingga dapat dinyatakan : Ada Pengaruh Yang Signifikan Dari Metode Demontrasi Terhadap Perkembangan Motorik Halus Anak Usia 5-6 Tahun Di Paud Harapan Kita Tahun Ajaran 2017/2018.Dengan demikian dapat ditarik kesimpulan bahwa pembelajaran dengan menerapkan metode demontrasi mampu memberikan peningkatan terhadap perkembangan motorik halus anak
Studi Deskriptif Tentang Perilaku Prososial Pada Anak Usia 5-6 Tahun Di TK St. Antonius 2 Mandala Medan
Authors: Tuturop Hermina , Simaremare Aman 
Published: December 2021

Penelitian ini adalah untuk mengetahui gambaran perilaku prososial pada anak di TK St. Antonius 2 Mandala Medan.Jenis penelitian ini adalah penelitian studi kasus dan data dianalisi menggunakan analisis kuantitatif deskriptif. Dengan populasi yang diambil 85 terdiri dari 3 kelas yaitu kelas B1= 28 orang, kelas B2 = 28 orang, kelas B3 = 29 orang anak. Maka sampel ini adalah kelas B3 dengan jumlah 29 anak yang diambil secara acak yaitu terdiri dari 15 orang anak. variabel yang digunakan dalam penelitian ini adalah perilaku prososial Pengumpulan data dilakukan oleh peneliti dengan teknik observasi. Analisisi data menggunakan teknik statistik deskritif berupa terdensi sentral yaitu dengan Mean dengan presentase. Hasil penelitian menunjukkan bahwa perilaku prososial anak di TK St. Antonius 2 Mandala Medan termasuk kategori sedang (skor rata-rata 8,6)
Evaluation of Documentation System in Iraqi Construction Projects
Authors: R.  Majeed Hamsa , A. Hatem Wadhah , A. Jasem Nidal 
Published: December 2021

In Iraq, the construction industry is considered one of the main sectors that contribute strongly to the Iraqi economy. Documentation is a process of collecting documents from past and current works of corporations, then archiving and classification of information to become easy and useful for reuse in the future. The aim of this study is to investigate the reality of documentation in the Iraqi construction industry and how to improve archiving and classification of documents and how documentation can be useful used in contractor and consultant corporations. This research has been conducted through literature reviews followed by a field survey. 80 questionnaires were distributed to contractors and engineering offices. 70 useful questionnaires were received from respondents with an 87% response rate. The findings indicated that the construction projects in the Diyala governorate are applying documentation. The study found that foundations classified and updating the documents as printed and written files and computer files, but foundations lack computer programs and web models for easy archiving and discovery of documents, documentation system currently used doesn't prevent Confusing, conflict in schemas because they still used Auto CAD and paper documentation. The large number and accumulation of paper documents are also considered the most often problem in the documentation system currently used, and construction projects don't have a database for all information related to projects where, it is limited to cabinets full of paper documents, which causes a waste of time and effort when searching for any document or information related to the projects. The study recommended that companies and foundations have to increase cost and effort consumed in the documentation process, improve the owner and staff abilities in computer and web applications, assign a key person for collection and archiving project documents, establishing an internet website for companies, or using cloud applications for storage of documents and sharing information.