bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genomic and population characterization of a diversity panel of dwarf and giant coconut accessions from the International Coconut Genebank for Latin America and Caribbean
Authors: Silva Allison Vieira da , Costa Emiliano Fernandes Nassau , Diniz Leandro Eugenio Cardamone , Ramos Semíramis Rabelo Ramalho , Fritsche-Neto Roberto 
Published: April 2023

Abstract

The characterization and construction of genetic diversity panels provide valuable information for developing germplasm conservation strategies and applying advanced breeding techniques. Thus, we performed analysis of diversity and genetic structure in Cocos nucifera L. with a collection of dwarf and giant accessions belonging to the International Coconut Genebank for Latin America in the Caribbean. The collection comprises six dwarf accessions (represented by 36 individuals) and six giants (represented by 48 individuals). The analysis of clustering and DAPC performed from a set of 4,044 SNP markers showed the existence of three clusters, one of which was formed exclusively by all dwarf coconut accessions. The giant accessions were separated into two distinct clusters, one formed by accessions from regions bathed by the Pacific Ocean (Southeast Asia and Oceania) and another formed by accessions from regions bathed by the Atlantic Ocean (Brazilian coastline and West Africa). The analysis allowed us to observe that the cluster formed by the accessions of dwarf coconut is closer genetically to the cluster formed by the accessions of giant coconut from the Pacific than the others from the Atlantic. Both groups presented similar genetic diversity (GD) values, 0.25 and 0.26, respectively. The inbreeding coefficient (F) revealed the presence of greater heterozygosity than expected in the dwarf coconut accessions and the inbreeding in the giant accessions. Consequently, we provide important information for maintaining those accessions in the germplasm bank and for future implementation of genomic-wide association studies (GWAS) and genomic selection (GS) with the evaluated accessions.
A Giant Recurrent Upper Thoracic Hydatid Cyst: A Case Report
Authors: Gok Haydar , Yangi Kivanc , Gulhan Ahmet Ali 
Published: April 2023

Abstract
Background
Hydatid cyst is a zoonotic disease with an annual prevalence of more than one million people. It can survive for one week in water, at room temperature, and for one year in the soil. Parasites deported into the systemic circulation through hepatic sinusoids or porto-venous shunts can cause lung, heart, eye, pancreas, brain, muscle, and bone pathologies. Spinal hydatid cyst is a disease with high mortality and morbidity, and the poor prognosis is equivalent to any malignancies. It is rarely seen in the spinal cord, especially in the upper thoracic region. Surgery should be planned carefully to achieve successful functional outcomes. The surgeon should be cautious to prevent further dissemination and remove the cyst entirely.
Case presentation:
Hereby, we present a 45-year-old male patient, who underwent surgery for a hydatid cyst in the thoracic spine, which recurred in the same location (T4-T8) 4 years later. Complete resection has been achieved at the second surgery and the patient’s neurological complaints healed dramatically after the surgery with physiotherapy.
Conclusions
For the giant upper thoracic hydatid cysts, surgical planning should be done carefully; we believe that the critical point of the surgery is irrigation of the operation lodge with scolocidal agents after the excision of the germinative membrane. On the other hand, before and after the surgical resection, the treatment should be supported with antihelminthic drugs.
Comparison of urine glycosaminoglycan excretion between children with autism spectrum disorder and typically developed children
Authors: Indika Neluwa-Liyanage Ruwan , Jasinge Eresha , Wijetunge Swarna , Peiris Hemantha , Engelen Marielle P.K.J. , Deutz Nicolaas E.P. , Perera Rasika 
Published: April 2023

Abstract

Abnormalities pertaining to glycoconjugates (glycoproteins, proteoglycans, and glycolipids) of the neural extracellular matrix have been identified as a susceptibility factor in autism spectrum disorder. We studied the urine excretion of sulfated glycosaminoglycans in children with autism spectrum disorder (n=61), in comparison to age- and sex-matched neurotypical controls (n=61). Urine glycosaminoglycan levels were determined in random urine samples by the dimethylmethylene blue (DMMB) dye-binding assay, using a microplate reader. Urine GAG levels are significantly higher (p = 0.026) in the ASD group when covariates such as age, weight, urinary creatinine, and height are taken into consideration by ANCOVA. In neurotypical subjects, the urine glycosaminoglycan levels appear to decline with age, height, and weight while this trend was not apparent in subjects with autism spectrum disorder. Glycosaminoglycan excretion did not correlate with the presence of co-occurring symptoms of autism spectrum disorder; frequent gastrointestinal symptoms, self-injurious behaviors, food aversions, or parent-reported sleep problems. Children with autism spectrum disorder exhibit higher urine glycosaminoglycan excretion. Further research is needed to identify its correlation with severity and genetic factors and other metabotypes.
Metabolomics analysis of peony root using NMR spectroscopy and impact of the preprocessing method for NMR data in multivariate analysis
Authors: Dong Yuzhuo , Toume Kazufumi , Zhu Shu , Shi Yanhong , Tamura Takayuki , Yoshimatsu Kayo , Komatsu Katsuko 
Published: April 2023

Abstract
Peony root is an important herbal drug used as an antispasmodic analgesic. To evaluate peony roots with different botanical origin, producing areas, and post-harvest processing, 1H NMR-based metabolomics analysis was employed. Five types of monoterpenoids, including albiflorin (4), paeoniflorin (6), and sulfonated paeoniflorin (25), and six other compounds, including 1,2,3,4,6-penta-O-galloyl-β-d-glucose (18), benzoic acid (21), gallic acid (22), and sucrose (26) were detected in the extracts of peony root samples. Among them, compounds 4, 6, 18, and total monoterpenoids including 21 were quantified by quantitative 1H NMR (qHNMR). Compound 25 was detected in 1H NMR spectra of sulfur-fumigated white peony root (WPR) extracts indicating that 1H NMR was a fast and effective method for identifying sulfur-fumigated WPR. The content of 26, the main factor affecting extract yield, increased significantly in peony root after low-temperature storage for one month, whereas that in WPR did not increase due to the boiling treatment after harvesting. We investigated the impact of preprocessing methods to such analysis for NMR data from commercial samples, resulting that the data matrix transformed from qHNMR spectra and normalized to internal standard were optimum for multivariate analysis. The multivariate analysis demonstrated that among commercial samples derived from P. lactiflora, peony root samples in Japanese market (PR) had high contents of 18 and 22, and red peony root (RPR) samples had high content of monoterpenoids represented by 6; and among RPR samples, those derived from P. veitchii showed higher contents of 18 and 22 than those from P. lactiflora. The 1H NMR-based metabolomics method coupled with qHNMR was useful for evaluation of peony root and would be applicable for other crude drugs.
Coinfections in Covid-19 patients in India: A Systematic Review
Authors: Patel Divya , Chhaya Vatsal , Khambholja Kapil , Sekhar Sonal 
Published: April 2023

Abstract
Objectives
To determine the rate of coinfections and its subsequent impact on hospitalization and mortality rate in Indian COVID-19 patients.
Method
Systematic literature search was performed on PubMed, Cochrane, WHO-COVID-19 database, and Google Scholar. The studies were retrieved and included based on JBI’s CoCoPop framework. Meta-analysis was not performed due to limited number of studies and high heterogeneity. Hence, descriptive statistics was summarized based on the retrieved coinfections data. The protocol was registered with PROSPERO – CRD42021275644.
Results
Eight studies included 2418 patients. The prevalence of coinfections ranged from 4%-46%. Pathogen-specific data showed the highest prevalence of bacterial (57.3%) coinfections, followed by parasitic (21.1.%), viral (14.6%), and fungal coinfection (6.9%). About 60–80% of the patients with coinfections required ICU admissions with an average length of stay of 13.67 ± 3.51 days. The mortality rate of COVID-19 patients with coinfections ranged from 9%-65%.
Conclusion
The prevalence of bacterial coinfections was highest among COVID-19 patients, consistent with previous literature. A causal relationship between coinfections and mortality rate in COVID-19 patients remained unexplored. This brings up the need for comprehensive data recording practices and meticulous reporting. Further, large-scale epidemiologic studies are needed to determine the nationwide burden of coinfections in the COVID-19 pandemic.
An equation for determining the beta Euler shape exponents of plant growth data – a programming toolbox for plant physiologists and ecologists
Authors: Kosek Jerzy , Pietruszka Mariusz 
Published: April 2023

Abstract
Since the almost simultaneous appearance of the acid growth homeostasis approach in plant physiology and the increased availability of mainframe computers in the mid-20th century, there has been an amplified need to predict the plant cell critical parameters during growth quantitatively. Here, we provide a ready-to-use computer program using an original numerical method, solving a highly nonlinear equation based on the formalism published in [J. Plant Growth Regul. (2020) 39: 841–866]. In this method article, the standalone program is written in a Python-based open-source scientific software environment (CoCalc) to determine these cardinal traits and facilitate its use without any barriers to readers. It can be used to determine the growth of an elongating individual plant cell or multicellular organ (coleoptile or hypocotyl segment) at the non-meristematic limit. The low number of relevant parameters (two) and the inclusion of the quantities that are decisive for growth, such as pH and temperature, make this program a practical set of tools for comparing growth-related experimental data in various areas of plant biology. It can also be helpful to predict plant growth during assisted migration when the Earth faces climate change.
Modern climate agenda: what changes are relevant in Russian forestry?
Authors: Птичников А.В. , Шварц Е.А. 
Published: April 2023

В стратегии социально-экономического развития РФ с низким уровнем выбросов ПГ до 2050 г. (СНУР) сделана ставка на повышение поглощения парниковых газов лесами с текущих 535 млн т до 1200 млн т СО2 экв. к 2050 г. Исследование показало наличие существенных нормативных и управленческих барьеров на этом пути. К их числу отнесены отсутствие наработок в области эффективных климатических проектов для их тиражирования; отсутствие приемлемых для инвесторов институциональных условий для привлечения инвестиций в природно-климатические проекты, соответственно низкая экономическая мотивация компаний инвестировать в такие проекты; отсутствие«углеродных» показателей при планировании федеральных программ и проектов в области лесного хозяйства; отсутствие привязки лесохозяйственного планирования к целям декарбонизации и др. Для исправления ситуации предложено принять значительные корректирующие меры по каждому из перечисленных направлений. Сделан вывод о том, что для реализации всегопотенциала снижения эмиссий и повышения поглощения в лесах России потребуется масштабная управленческая и политическая работа по трансформации и модернизации
In the strategy of socio-economic development of the Russian Federation with low GHG emissions until 2050 (SNSD), a stake is placed on increasing the absorption of greenhouse gases by forests from the current 535 million tons to 1200 million tons of CO2 eq. by 2050. Our study showed that there are significant regulatory and managerial barriers to this path. We included among them: the lack of developments in the field of effective climate projects for their replication; lack of institutional conditions acceptable to investors for attracting investments in natural and climatic projects. Accordingly, low economic motivation of companies to invest in such projects; lack of "carbon" indicators in the planning of federal programs and projects in the field of forestry; lack of linkage of forestry planning to the goals of decarbonization, etc. To correct the situation, it is proposed to take significant corrective measures in each of the listed areas. It is concluded that in order to realize the full potential of reducing emissions and increasing absorption in the forests of Russia, large-scale managerial and political work will be required to transform and modernize forest management.
Analysis of the landscape of the Pervomaisky park of Ufa using satellite images and GIS
Authors: Байтурина Р.Р. , Ишбирдина Л.М. , Габделхаков А.К. , Рахматуллин З.З. , Одинцов Г.Е. 
Published: April 2023

Активное жилищное строительство приводит к частичному сокращению площади, занятой зелеными насаждениями. При этом темпы реконструкции зеленых зон становятся интенсивнее. Для изучения изменений на территории парковых зон после проведения реконструкции в качестве объекта исследования выбран парк культуры и отдыха (ПКиО) «Первомайский». Сравнительный анализ данных 2017 и 2022 гг. позволил оценить изменения, произошедшие в насаждениях: динамику количественного и видового состава, состояние деревьев и кустарников. Также изучена динамика баланса площадей парка и его пространственной структуры. Целью исследования является сравнение состояния территории парка культуры и отдыха (ПКиО) «Первомайский» г. Уфа до реконструкции и после нее с использованием доступных спутниковых снимков и ГИС и натурного ландшафтного анализа.
Active housing construction leads to a partial reduction in the area occupied by green spaces. At the same time, the pace of reconstruction of green areas is becoming more intense. To study the changes in the territory of park zones after the reconstruction, the Pervomaisky Park of Culture and Recreation (PCaR) was chosen as the object of research. A comparative analysis of the 2017 and 2022 data allowed us to assess the changes that occurred in the plantings: the dynamics of quantitative and species composition, the condition of trees and shrubs. The dynamics of the balance ofthe park's areas and its spatial structure have also been studied. The purpose of the study is to compare the state of the territory of the Park of Culture and Recreation (PCaR) «Pervomaisky» Ufa before and after reconstruction using available satellite images and GIS and full-scale landscape analysis.
Predicting olivine formation environments using machine learning and implications for magmatic sulfide prospecting
Authors: Xue Shengchao , Niu Yunyun , Yao Zhuosen , Wang Luyang , Zhang Xiaohao , Wang Qingfei 
Published: April 2023

Abstract
Global volcanic and plutonic olivines record the compositional characteristics and physicochemical conditions of the parental magmas. Thus, they have significant potential for use as petrogenetic discriminators of the olivine formation environment and prospecting indicators for potential host rocks of magmatic sulfide deposits. Several data visualization approaches have been proposed by researchers to determine olivine origins. However, they can only discriminate specific olivine populations and require the incorporation of trace elements for which data are lacking globally. In this study, a machine-learning method consisting of the random forest algorithm and the synthetic minority oversampling technique (SMOTE) is used to discriminate the crystallization environments of olivine and predict the sulfide potential of olivine-bearing mafic-ultramafic intrusions. We employ a global data set of 24 341 olivine samples from 12 environments to determine the contents of MgO, FeO, Ni, Ca, Mn, and Cr and the Fo number [100 × Mg/(Mg+Fe)]. The results indicate that the proposed method can classify olivine into genetically distinct populations and distinguish olivine derived from mineralized intrusions from that derived from sulfide-barren intrusions with high accuracies (higher than 99% on average). We develop a dimensionality reduction algorithm to visualize the olivine classifications using low-dimensional vectors and an olivine classifier (accessible at http://101.33.204.62:8080/olivine_web/main.html, China University of Geosciences, Beijing). The model is used successfully to identify the contributions of distinct sources to regional magmatism using olivines from the late Permian picrite and basalt along the western margin of the Yangtze block (SW China) and to predict the sulfide potential of the newly discovered Qixin mafic-ultramafic complex in the southern Central Asian Orogenic Belt (NW China). The findings suggest that the proposed approach enables the accurate identification of olivine origins in different formation environments and is a reliable indicator suitable for global Ni-Cu-platinum group element (PGE) exploration.
Raman spectroscopic investigation of selected natural uranyl sulfate minerals
Authors: Spano Tyler L. , Olds Travis A. , McDonnell Marshall , Smith Robert , Shields Ashley E. 
Published: April 2023

Abstract
Uranyl sulfates are important constituents of uranium ores and represent a significant fraction of U(VI) minerals discovered in recent years owing to their propensity to form in mine tailings and legacy sites related to uranium exploration. Recently, we surveyed all published Raman spectra for uranium minerals and found significantly less easily accessible data available for uranyl sulfates relative to other groups of uranium minerals (Spano et al. 2023). In that work, we described average spectra for groups of uranyl minerals to understand common vibrational spectroscopic features attributable to similarities in oxyanion chemistry among U(VI) minerals, but only data for three uranyl sulfate minerals were included in the study. The present work reports on Raman spectra collected for 18 additional uranyl sulfate minerals. To better understand underlying structural and chemical features that give rise to spectroscopic observables, we relate differences in structural topology, charge-balancing cations, and locality of origin to features observed in the Raman spectra of selected natural uranyl sulfates.
Autism Spectrum Disorders: Advances in Proteomics
Authors: Zhou Hui , Feng Xiao-Li 
Published: April 2023

Abstract:
Autism is a class of developmental disorders with extremely high rates of disability,
affecting patients throughout their lives. There is no cure to date clinically, and early
rehabilitation interventions can improve some of the behavioral problems of autistic patients, but
these are limited by age and often have minimal effects in older adults with autism. Early
diagnosis is also necessary while developing effective autism therapies. At present, the early
diagnosis of autism is dependent on the search for effective markers in an attempt to screen
differentially expressed proteins in autistic patients using high-throughput assays, such as
synaptic scaffolding proteins, microtubule-associated proteins, apolipoproteins, immunoglobulin
G complement factor-related proteins, etc. It would also be a big step forward for mechanistic
studies of autism if a valid biomarker for autism could be found.
A Review on Deep Learning-driven Drug Discovery: Strategies, Tools and
Applications
Authors: Sumathi Sundaravadivelu , Suganya Kanagaraj , Swathi Kandasamy , Sudha Balraj , Poornima Arumugam , Varghese Chalos Angel , Aswathy Raghu 
Published: April 2023

Abstract:
It takes an average of 10-15 years to uncover and develop a new drug, and the process is incredibly
time-consuming, expensive, difficult, and ineffective. In recent years the dramatic changes in the field of artificial
intelligence (AI) have helped to overcome the challenges in the drug discovery pipeline. Artificial intelligence
(AI) has taken root in various pharmaceutical sectors, from lead compound identification to clinical trials.
Deep learning (DL) is a component of artificial intelligence (AI) that has excelled in many fields of Artificial
intelligence (AI) research over the past decades. Its numerous applications in the realms of science and technology,
especially in biomedicine and bioinformatics, are witnessed deep learning (DL) applications significantly
accelerate drug discovery and pharmaceutical research in recent years, and their usefulness has exceeded expectations
and shown good promise in tackling a range of issues with drug discovery. Deep learning (DL) holds
great potential for drug development since it allows for sophisticated image interpretation, molecular structure
and function prediction, and the automated creation of novel chemical entities with specific features. In the process
of drug discovery, deep learning (DL) can be incorporated at all stages like identification of targets, prognostic
biomarkers, drug designing and development, synergism and antagonism prediction, etc. This review
summarizes various approaches of deep learning (DL) in drug discovery like deep generative models for drug
discovery, deep learning (DL) tools for drug discovery, synergy prediction, and precision medicine.
Artificial Intelligence: An Emerging Intellectual Sword for Battling
Carcinomas
Authors: Arfi Sadaf , Srivastava Nimisha , Sharma Nisha 
Published: April 2023

Abstract:
Artificial Intelligence (AI) is a branch of computer science that deals with mathematical
algorithms to mimic the abilities and intellectual work performed by the human brain. Nowadays,
AI is being effectively utilized in addressing difficult healthcare challenges, including complex
biological abnormalities, diagnosis, treatment, and clinical prognosis of various life-threatening
diseases, like cancer. Deep neural networking (DNN), a subset of AI, is prominently being applied
in clinical research programs on cancer. AI acts as a promising tool in radiotherapy, mammography,
imaging, cancer prognosis, cancer genomics and molecular signaling, pathology, drug discovery,
chemotherapy, immunotherapy, and clinical decision support system. This article provides
an elaborative view concerning the application of AI in cancer, an explorative review that how AI
has been used as a trenchant tool in the past, present and future of cancer. This review article provides
a new prospective that how the mimic of human intellectual (AI technology) has put forward
an unprecedented accuracy in the field of clinical research of cancer.