bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of Defatted Rice Bran Supplementation on Metabolic Parameters and Inflammatory Status in Overweight/obese Adults with Hypercholesterolemia, a Randomised, Placebo-controlled Intervention
Authors: Saphyakhajorn Weeraya , Sirirat Rawiwan , sapwarobol suwimol 
Published: March 2022

Abstract
Background: Defatted rice bran (DRB), a by-product of rice bran oil production is a rich source of fiber, protein and antioxidant compounds that may pose beneficial effects on metabolic profiles. The current study aimed to investigate the effects of DRB supplementation on anthropometric, blood biochemical indices, dietary intake and inflammatory status in overweight/obese subjects with hypercholesterolemia.Methods: In a 12-week-randomized placebo-controlled trial, 61 overweight/obese participants with a total cholesterol (TC) level >200 mg/dL were randomly assigned either to 30 g/d DRB (n = 30) or to 10 g/d maltodextrin (n = 31).Results: DRB intervention significantly reduced systolic and diastolic blood pressure by 4.27% and 4.50 %, respectively (126.20±13.63 to 120.60±13.72 mmHg, p = 0.0003 and 80.87±7.38 to 77.17±9.83 mmHg, p = 0.0035). HbA1c also decreased significantly by 3.59% (5.89±0.76 to 5.66±0.62%, p = 0.0001) after DRB supplementation. Total cholesterol, TG and LDL-c levels also decreased insignificantly by 3.12, 1.32 and 1.53% after DRB supplementation. Insignificant differences of FBG, insulin, HOMA-IR, QUICKI, hs-CRP and homocysteine levels after DRB intervention were also observed. Reduction in caloric and fat intake were reported in DRB groups.Conclusions: DRB supplementation improved blood pressure and HbA1C levels. It also lowers blood cholesterol, albeit insignificantly. Caloric and fat intake were also significantly lower after DRB supplementation. Further study is needed to evaluate the mechanisms by which DRB improve these metabolic indices.Trial registration: Thai Clinical Trial Registration number is TCTR20191020003. Registered 20 October 2019, https://www.clinicaltrials.in.th/.
How variation in risk allele output and gene interactions shape the genetic architecture of schizophrenia
Authors: Kasap Merve , Dwyer Donard S. 
Published: March 2022

Abstract

Schizophrenia is a highly heritable polygenic psychiatric disorder. Characterization of its genetic architecture may lead to a better understanding of the overall burden of risk variants and how they determine susceptibility to disease. A major goal of this project was to develop a modeling approach to compare and quantify the relative effects of single nucleotide polymorphisms (SNPs), copy number variants (CNVs) and other factors. We derived a mathematical model for the various genetic contributions based on the probability of expressing a combination of risk variants at a frequency that matched disease prevalence. The model included estimated risk variant allele outputs (VAOs) adjusted for population allele frequency. We hypothesized that schizophrenia risk genes would be more interactive than random genes and we confirmed this relationship. Gene-gene interactions may cause network ripple effects that spread and amplify small individual effects of risk variants. The modeling revealed that the number of risk alleles required to achieve threshold for susceptibility will be determined by the average functional locus output (FLO) associated with a risk allele, the risk allele frequency (RAF), the number of protective variants present and the extent of gene interactions within and between risk loci. The model can account for the quantitative impact of protective variants as well as CNVs on disease susceptibility. The fact that non-affected individuals must carry a non-trivial burden of risk alleles suggests that genetic susceptibility will inevitably reach threshold for schizophrenia at a recurring frequency in the population.
Proteomic analysis of low- and high-grade human colon adenocarcinoma tissues and tissue-derived primary cell lines reveals unique biological functions of tumours and new protein biomarker candidates
Authors: Munro Matthew J , Wickremesekera Susrutha K , Tan Swee T , Peng Lifeng 
Published: March 2022

Abstract
Background: Colon cancer is the third most common cancer and second highest cause of cancer deaths worldwide. The aim of the study was to find new biomarkers for diagnosis, prognosis and therapeutic drug targets for this disease.Methods: Four low-grade and four high-grade human colon adenocarcinoma tumours with patient-matched normal colon tissues were analysed. Additionally, tissue-derived primary cell lines were established from each tumour tissue. The cell lines were validated using DNA sequencing to confirm that they are a suitable in vitro model for colon adenocarcinoma based on conserved gene mutations. Label-free quantitation proteomics was performed to compare the proteomes of colon adenocarcinoma samples to normal colon samples, and of colon adenocarcinoma tissues to tissue-derived cell lines to find significantly differentially abundant proteins. The functions enriched within the differentially expressed proteins were assessed using STRING. Proteomics data was validated by Western blotting. Results: A total of 4767 proteins were identified across all tissues, and 4711 across primary tissue-derived cell lines. Of these, 3302 proteins were detected in both the tissues and the cell lines. On average, primary cell lines shared about 70% of proteins with their parent tissue, and they retained mutations to key colon adenocarcinoma-related genes and did not diverge far genetically from their parent tissues. Colon adenocarcinoma tissues displayed upregulation of RNA processing, steroid biosynthesis during tumour initiation and immune system responses and detoxification during tumour progression, downregulation of cytoskeletal organisation and loss of normal muscle function. Tissue-derived cell lines exhibited increased interferon-gamma signalling and aberrant ferroptosis. Overall, 318 proteins were significantly up-regulated and 362 proteins down-regulated by comparisons of high-grade with low-grade tumours and low-grade tumour with normal colon tissues from both sample types. Conclusions: The differences exhibited between tissues and cell lines highlight the additional information that can be obtained from patient-derived primary cell lines. DNA sequencing and proteomics confirmed that these cell lines can be considered suitable in vitro models of the parent tumours. Various potential biomarkers for colon adenocarcinoma initiation and progression and drug targets were identified and discussed, including ten novel markers ACSL4, ANK2, AMER3, CAV3, EXOSC6, EXOSC6, GCLM, LDB3, MUC5B and TRFC.
Dynamic walking of multi-humanoid robots using BFGS Quasi-Newton method aided Artificial potential field approach for uneven terrain
Authors: Kashyap Abhishek kumar , Parhi Dayal R 
Published: March 2022

Abstract
The humanoid robot garners paramount interest because of its ability to mimic human-like behavior in real-time environments. In this paper, a hybridized Artificial Potential Field (APF)-Broyden Fletcher Goldfarb Shanno (BFGS) Quasi-Newton technique is being proposed for the trajectory planning of the humanoid robot. The proposed methodology combines the faster local search BFGS Quasi-Newton method with the global search APF method for an efficient and faster-hybridized trajectory planning technique. The humanoid robot herein is being tested in a multi-humanoid working space having uneven surfaces. The data obtained from the sensors concerning the location of obstacles and the target are at first imparted to the APF method, which supplies an intermediate turning angle dependent on the predesigned training model of the APF method. The obtained turning angle is then fed into the BFGS quasi-newton method to produce an optimum turning angle, which inevitably guides the humanoid robots to the target by keeping a minimum safe distance from the obstacles. Multi-humanoid robots are employed in an environment having random static obstacles, with unique targets for them to reach. The proposal of using a multi-humanoid system arises the chance of inter-collision among the humanoids. To get rid of this situation, the dining philosophers controller is being implemented. The simulations and experiments are carried using the proposed technique to ratify the efficacy of the proposed technique. The experimental and simulation results yield a suitable acceptance rate under 5 %. In comparison with the previously used navigational technique, it proves to be comfortably skillful and robust for the trajectory planning of the humanoid robot.
A hybrid model integrating Elman neural network with variational mode decomposition and Box-Cox transformation for monthly runoff time series prediction
Authors: Kang Yan , Zhang Fangqin , Cheng Xiao , Chen Peiru , Song Songbai 
Published: March 2022

Abstract
Precise and reliable monthly runoff prediction plays a vital role in optimal management of water resources but non-stationarity and skewness of monthly runoff time series can pose major challenges for developing appropriate prediction models. To address these issues, this paper proposes a novel hybrid prediction model based on Elman neural network (Elman), variational mode decomposition (VMD) and Box-Cox transformation (BC), named VMD-BC-Elman model. Firstly, the observed runoff is decomposed into sub-time series using VMD for the better frequency resolution. Secondly, the input datasets were transformed into normal distribution using Box-Cox, and as a result, skewedness in the data was removed and the correlation between the input and output variables enhanced. Finally, Elman is used to simulate the respective sub-time series. The proposed model is evaluated using monthly runoff time series at Zhangjiashan, Zhuangtou and Huaxian hydrological stations in Wei River Basin in China. The model performances are compared with those of single models (SVM, Elman), decomposition-based (VMD-SVM, VMD-Elman et.al) and BC-based models (BC-SVM and BC-Elman) by employing four metrics. The results show that the hybrid models outperform single models, and VMD-BC-Elman model performs best in all considered hybrid models with NSE greater than 0.95, R greater than 0.98, NMSE less than 4.73%, and PBIAS less than 0.39% in both training and testing periods. The study indicates that VMD-BC-Elman model is a satisfactory data-driven approach to predict the non-stationary and skewed monthly runoff time series, representing an effective tool for predicting monthly runoff series.
Development of a Simple, Rapid, and Economical Method for Extraction
and Isolation of 3-O-acetyl-11-keto-β-boswellic Acid from the Resins of
Boswellia serrata
Authors: Beldar Vishal Gokul , Jadhao Manojkumar , Laddha Kirti 
Published: March 2022

Background:
Boswellia serrata is an important species from the Boswellia genus,
which contains a variety of significant phytoconstituents. Numerous applications of the B. Serrata
have been mentioned in the literature of the Indian ayurvedic system. Researchers reported abundant
pharmacological activities of B. Serrata resins extract. The extract produces synergistic
pharmacological activity due to the presence of the Boswellia acids and their derivatives. Among
all Boswellia acids, 3-O-acetyl-11-keto-β-boswellic acid (AKBA) was found to be more potent.
Traditionally, column chromatography was used for the isolation of AKBA from raw material as
well as extracts. However, the column chromatography method was monotonous and timeconsuming.

Objective:
The main goal of the research was to develop a new, simple, rapid, and reproducible
method for the isolation of AKBA from the resin extract of B. Serrata.

Methods:
The extraction and isolation of AKBA involved extraction of resins using hydroalcoholic
solution followed by alkali treatment. The alkali solution was further treated with acid to
precipitate the crude AKBA.

Results:
The obtained crude AKBA was subjected to the dry column vacuum chromatography to
separate and yield the high purity of the AKBA. The purity of the isolated AKBA was established
by TLC & UHPLC. Spectral characterization of the isolated compound was performed by employing
IR, MS, and NMR.

Conclusion:
The proposed method can be used to isolate AKBA from resin extract of B. Serrata.
Some modifications in this method lead to the large-scale production of highly pure AKBA for
various pharmaceutical applications.
Tangeretin: A Biologically Potential Citrus Flavone
Authors: Murti Yogesh , Agrawal Krishn Kumar 
Published: March 2022

Background:
Flavonoids are plant-derived chemicals found naturally in various parts of
plants. They are an important component in a broad range of nutraceuticals because of their antimutagenic,
antibacterial, anti-inflammatory, and antioxidative properties. Tangeretin, an example of the
flavone class of flavonoid compounds, is found in tangerine and other citrus fruit peels. It is a natural
constituent with vast pharmacological activities and is extensively found in numerous fruits or
fruit juices.

Objective:
The goal of the study was to gather information on tangeretin as well as its pharmacological
characteristics.

Method:
Electronic databases like Google Scholar, Scopus, Science Direct, PubMed, and Web of
Science were thoroughly searched for tangeretin, properties, and uses.

Result:
A total of 80 articles were reviewed in the present study covering current trends of research
and development on tangeretin. Tangeretin's chemistry along with its source, extraction methods,
and pharmaceutical importance, are exhaustively compiled here.

Conclusion:
On the basis of the literature survey, it can be concluded that tangeretin has a great potential
to become an active drug molecule in various ailments.
Advances in Magnetic Polymeric Styrene-divinylbenzene Nanocomposites
between Magnetite and Maghemite Nanoparticles: An Overview
Authors: Lima Mendes Matheus de Souza , Araujo Ayla Bastos , Neves Márcia Angelica Fernandes e Silva , Pedrosa Marcelo Sierpe 
Published: March 2022

Abstract:
Superparamagnetic nanoparticles, such as magnetite (Fe3O4) and maghemite (γ-Fe2O3), have been used to produce magnetic nanocomposites with several polymeric matrices including mag-netic styrene-divinylbenzene nanocomposites. Through the incorporation of these nanoparticles, the nanocomposite presents superparamagnetism, low coercivity, and high magnetic susceptibility. Due to these features, magnetic nanomaterials can be removed from the site where they are inserted through an external magnetic field, thus distinguishing them from conventional systems such as those used to treat oily water, which require expensive chemical agents for removal. These properties de-pend directly on the size distribution of the nanoparticles and the presence or absence of interactions between the surface of the polymeric matrix and the contaminants. These materials have many appli-cations. The objective of this article is to present a bibliographic review of the state-of-the-art evolu-tion of magnetic styrene-divinylbenzene nanocomposites over the years. According to the reports in the literature, these systems are superior to those applied conventionally in the sectors of biotechnol-ogy, agriculture, oil/gas, and nuclear chemistry, mainly for the removal of toxic metals from aqueous media.
Predicting Sepsis Mortality in a Population-Based National Database: Machine Learning Approach
Authors: Park James Yeongjun , Hsu Tzu-Chun , Hu Jiun-Ruey , Chen Chun-Yuan , Hsu Wan-Ting , Lee Matthew , Ho Joshua , Lee Chien-Chang 
Published: March 2022

Background
Although machine learning (ML) algorithms have been applied to point-of-care sepsis prognostication, ML has not been used to predict sepsis mortality in an administrative database. Therefore, we examined the performance of common ML algorithms in predicting sepsis mortality in adult patients with sepsis and compared it with that of the conventional context knowledge–based logistic regression approach.

Objective
The aim of this study is to examine the performance of common ML algorithms in predicting sepsis mortality in adult patients with sepsis and compare it with that of the conventional context knowledge–based logistic regression approach.

Methods
We examined inpatient admissions for sepsis in the US National Inpatient Sample using hospitalizations in 2010-2013 as the training data set. We developed four ML models to predict in-hospital mortality: logistic regression with least absolute shrinkage and selection operator regularization, random forest, gradient-boosted decision tree, and deep neural network. To estimate their performance, we compared our models with the Super Learner model. Using hospitalizations in 2014 as the testing data set, we examined the models’ area under the receiver operating characteristic curve (AUC), confusion matrix results, and net reclassification improvement.

Results
Hospitalizations of 923,759 adults were included in the analysis. Compared with the reference logistic regression (AUC: 0.786, 95% CI 0.783-0.788), all ML models showed superior discriminative ability (P<.001), including logistic regression with least absolute shrinkage and selection operator regularization (AUC: 0.878, 95% CI 0.876-0.879), random forest (AUC: 0.878, 95% CI 0.877-0.880), xgboost (AUC: 0.888, 95% CI 0.886-0.889), and neural network (AUC: 0.893, 95% CI 0.891-0.895). All 4 ML models showed higher sensitivity, specificity, positive predictive value, and negative predictive value compared with the reference logistic regression model (P<.001). We obtained similar results from the Super Learner model (AUC: 0.883, 95% CI 0.881-0.885).

Conclusions
ML approaches can improve sensitivity, specificity, positive predictive value, negative predictive value, discrimination, and calibration in predicting in-hospital mortality in patients hospitalized with sepsis in the United States. These models need further validation and could be applied to develop more accurate models to compare risk-standardized mortality rates across hospitals and geographic regions, paving the way for research and policy initiatives studying disparities in sepsis care.
Blockchain-Based Architecture Design for Personal Health Record: Development and Usability Study
Authors: Costa Thiago Bulhões da Silva , Shinoda Lucas , Moreno Ramon Alfredo , Krieger Jose E , Gutierrez Marco 
Published: March 2022

Background
The importance of blockchain-based architectures for personal health record (PHR) lies in the fact that they are thought and developed to allow patients to control and at least partly collect their health data. Ideally, these systems should provide the full control of such data to the respective owner. In spite of this importance, most of the works focus more on describing how blockchain models can be used in a PHR scenario rather than whether these models are in fact feasible and robust enough to support a large number of users.

Objective
To achieve a consistent, reproducible, and comparable PHR system, we build a novel ledger-oriented architecture out of a permissioned distributed network, providing patients with a manner to securely collect, store, share, and manage their health data. We also emphasize the importance of suitable ledgers and smart contracts to operate the blockchain network as well as discuss the necessity of standardizing evaluation metrics to compare related (net)works.

Methods
We adopted the Hyperledger Fabric platform to implement our blockchain-based architecture design and the Hyperledger Caliper framework to provide a detailed assessment of our system: first, under workload, ranging from 100 to 2500 simultaneous record submissions, and second, increasing the network size from 3 to 13 peers. In both experiments, we used throughput and average latency as the primary metrics. We also created a health database, a cryptographic unit, and a server to complement the blockchain network.

Results
With a 3-peer network, smart contracts that write on the ledger have throughputs, measured in transactions per second (tps) in an order of magnitude close to 102 tps, while those contracts that only read have rates close to 103 tps. Smart contracts that write also have latencies, measured in seconds, in an order of magnitude close to 101 seconds, while that only read have delays close to 100 seconds. In particular, smart contracts that retrieve, list, and view history have throughputs varying, respectively, from 1100 tps to 1300 tps, 650 tps to 750 tps, and 850 tps to 950 tps, impacting the overall system response if they are equally requested under the same workload. Varying the network size and applying an equal fixed load, in turn, writing throughputs go from 102 tps to 101 tps and latencies go from 101 seconds to 102 seconds, while reading ones maintain similar values.

Conclusions
To the best of our knowledge, we are the first to evaluate, using Hyperledger Caliper, the performance of a PHR blockchain architecture and the first to evaluate each smart contract separately. Nevertheless, blockchain systems achieve performances far below what the traditional distributed databases achieve, indicating that the assessment of blockchain solutions for PHR is a major concern to be addressed before putting them into a real production.
"La vuelta al texto en un índice". Circunnavegar el texto. Los índices en la Edad Moderna
Authors: Solano Agustín 
Published: March 2022

Reseña de:Circunnavegar el texto. Los índices en la Edad Moderna. Maria Gioia Tavoni. Trad. de Noelia López Souto. Asturias, España: Trea Ediciones, 2020, 327 pp. ISBN: 978-84-17987-99-2.
ANALISIS KEPUASAN DAN LOYALITAS PELANGGAN PENGARUHNYA TERHADAP KEPUTUSAN’MENGGUNAKAN JASA LAYANAN ONLINE (GRAB) DI WILAYAH KOTA MEDAN
Authors: Parhusip Austin Alexander 
Published: March 2022

<p>Dalam studi ini bermaksud untuk menganalisis kepuasan pelanggan dan loyalitas atas keputusan untuk menggunakan layanan online (grab) dan bagaimana pengaruh kepuasan’pelanggan’dan loyalitas terhadap keputusan untuk menggunakan layanan layanan online (grab) secara partial, serta pengaruh kepuasan’dan’loyalitas pelanggan secara simultan terhadap’keputusan menggunakan’jasa layanan online (grab). Jenis’penelitian yang’digunakan’dalam riset ini adalah survei. Populasi pada’penelitian ini’adalah seluruh’masyarakat yang berada di kota Medan yang pernah melakukan pembelian atau menggunakan aplikasi grab. Metode purposive sampling yang digunakan dalam pengambilan’sampel sebanyak’100 orang. Teknik pengumpulan’data menggunakan kuesioner’yang’telah diuji’validitas dan reliabilitasnya. Teknik analisis’data yang’digunakan’untuk menjawab’hipotesis adalah regresi berganda. Hasil’penelitian’ini’menunjukkan’bahwa: terdapat’pengaruh positif kepuasan yang berubah-ubah terhadap keputusan’pembelian’online (grab), terdapat pengaruh positif variabel loyalitas terhadap’keputusan pembelian’online (grab. Secara simultan kepuasan, dan loyalitas berpengaruh positif terhadap minat beli secara online.</p>