bluuebunny/crossref_metadata_2025_split huggingface dataset:
Profiling of plasma extracellular vesicle transcriptome reveals that circRNAs are prevalent and differ between multiple sclerosis patients and healthy controls
Authors: Iparraguirre Leire , Alberro Ainhoa , Hansen Thomas Birkballe , Castillo-Triviño Tamara , Muñoz-Culla Maider , Otaegui David 
Published: September 2021

Abstract
Background
Extracellular vesicles (EVs) are released by almost all cell types and are implicated in a number of biological and pathological processes including autoimmune diseases such as multiple sclerosis (MS). Differences in the number and cargo of plasma derived EVs have been described in MS. In this work, we attempt to characterise the EV RNA cargo of MS patients with particular attention to a recently discovered non coding RNA type, circular RNAs (circRNAs), which have been shown to play important roles in physiology and disease and hold a great biomarker potential.
Methods
Plasma was collected from 20 MS patients and 8 healthy controls (HC) and total RNA was isolated from plasma-derived extracellular vesicles isolated by differential centrifugation. Samples were pooled in disease status, sex and age paired groups and RNA-Sequenced with Illumina HiSeq X Ten after rRNA depletion. CircRNAs were detected by both find_circ and CIRI2 and their quantification was based on BSJ-spanning reads. Linear transcripts were quantified by HTSEq. Differential expression analysis was performed using DESeq2. RNA type distribution was analyzed based on biomart classification. MiRNA binding site number and density for circRNAs was calculated based on the TargetScan prediction performed by Circinteractome. CircRNA secondary structure prediction was calculated by their length normalized Gibbs free energy. All the statistical analysis were performed in Rstudio.
Results
The EV linear and circular transcriptome of MS patients and controls is characterized and compared to the transcriptome previously described in leucocytes. Results reveal differences in the RNA type distribution, showing that circRNAs are enriched in EVs compared to leucocytes. Nevertheless, highly structured circRNAs are preferentially retained in leukocytes. Additionally, differential expression analysis reports significant differences in circRNA and linear RNA expression between MS patients and controls as well as between different MS types.
Conclusions
The plasma derived EV RNA cargo is not a representation of leukocytes’ cytoplasm but a message that must be studied. Moreover, our results reveal the interest of circRNAs as part of this message highlighting the importance to further understand the RNA regulation in MS.
Recommender System for Telecom Packages Based on the Deep &amp; Cross Network
Authors: Shi Congming , Wang Wen , Wei Shoulin , Lv Feiya 
Published: September 2021

Abstract
With the evolution of 5G technology, the telecom industry has influenced the livelihood of people and impacted the development of the national economy significantly. To increase revenue per customer and secure long-term contracts of users, telecommunications firms and enterprises have launched different types of telecom packages to satisfy the varying requirements of users. Although several recommender systems have been proposed in recent years for telecommunication package recommendation, extracting effective feature information from large and complex consumption data remains challenging. Considering the telecom package recommendation problems, traditional recommendation methods either use complex expert feature engineering or fail to perform end-to-end deep learning training. In this study, a recommender system based on deep & cross network (DCN), deep belief network (DBN), Embedding, and Word2Vec is proposed using the powerful learning abilities of deep learning. The proposed system fits the telecom package recommender system in terms of click-through rate prediction to provide a potential solution for the recommendation challenges faced by telecom enterprises. The proposed model can effectively capture the finite order interactional features and deep hidden features. Additionally, the text information in the data is completely used to further improve the recommendation ability of the model. Moreover, the proposed method does not require feature engineering. We conducted comprehensive experiments using real-world datasets, and the results verify that our method can generate improved recommendation accuracy in comparison with those observed in DBN, DCN, deep factorization machine, and deep neural network models individually.
Programmable Wavelength Filter with Double Ring Loaded MZI
Authors: Wang Mi , Chen Xiangfeng , Khan Umar , Bogaerts Wim 
Published: September 2021

Abstract
We propose a novel filter circuit that incorporates a double ring resonator with a balanced Mach-Zehnder interferometer (MZI).The circuit has a response equivalent to a conventional ring loaded MZI filter, but with added flexibility in terms of configurability. The second-order filter can also be cascaded to realize higher-order filters. The circuit incorporates a two-stage input and output coupler to further reduce the effect of dispersion. A combination of local and global optimization strategies to program the filter, using tailored objective functions, have been tested in simulation and experiments. To our best knowledge, this is the first time a global optimization strategy is directly used in ARMA filter synthesis and simulation without any additional requirement. We further extend the optimization strategy into experiments and demonstrated its use in practical case for programmable filter circuits.
Measured distribution of cloud chamber tracks from radioactive decay
Authors: Schonfeld Jonathan 
Published: September 2021

Abstract
Using publically available video of a cloud chamber with a very small radioactive source, I measure the spatial distribution of where tracks start, and consider possible implications. This is directly relevant to the quantum measurement problem and its possible resolution, and appears never to have been done before. The raw data are relatively uncontrolled, leading to caveats that should guide future, more tailored experiments. Track distributions from decays in cloud chambers represent a previously unappreciated way to probe the foundations of quantum mechanics, and a novel case of wavefunctions with macroscopic signatures.
Climatic rather than edaphic variables determine leaf C, N, P stoichiometry of deciduous Quercus species
Authors: Lin Yutong , Lai Yuan , Tang Songbo , Qin Zhangfen , Liu Jianfeng , Kang Fengfeng , Kuang Yuanwen 
Published: September 2021

Abstract
Purpose

Leaf stoichiometry of carbon (C), nitrogen (N) and phosphorus (P) is indicative of plant nutrient limitation, community composition, ecosystem function. Understanding leaf stoichiometry patterns of C, N, P of eurytopic species at genus-level across large-scale geographic regions and identifying their driven factors are of great importance to assess and to predict species’ distribution range shifts affected by climate change.
Methods

Here, we determined the patterns of leaf C, N, P stoichiometry of five deciduous oaks species (Quercus) across China covering ~ 20 latitude (~ 21–41˚ N) and longitude (~ 99–119˚ E) degrees, and detected their relationships with climatic, edaphic variables.
Results

We found that the concentrations ([ ]) of leaf C, N and the ratios ( / ) of N: P, C: P significantly increased, while leaf [P] and C/N decreased with the increasing latitude. However, leaf stoichiometry had no significant trends along the longitudinal gradient with an exception of leaf [C] which decreased with increasing longitude. The climatic variables, i.e. mean annual temperature precipitation (MAP), the max temperature of the warmest month (Tmax), temperature seasonality (TS), aridity index (AI) were the determinants on the geographic patterns of leaf C, N, P stoichiometry. The studied deciduous Quercus species growing in warm and wet environments tended to increase leaf [C], [N], C/P, and N/P, but to decrease leaf [P] and C/N, which maybe their nutritional strategies to improve adaptability.
Conclusion

The adaptative mechanisms of leaf stoichiometry should be further studied to assess the fate of deciduous Quercus species affected by climate change.
ExtRamp Online: Enabling ramp sequence calculations via an intuitive web interface.
Authors: Miller Justin B. , Hodgman Matthew W. , Miller Kyle N. , Meurs Taylor E. , Ebbert Mark T. W. , Kauwe John S. K. , Ridge Perry G. 
Published: September 2021

Abstract
Motivation: Ramp sequences are an understudied evolutionarily-conserved mechanism for regulating protein translational efficiency. Slowly-translated codons concentrated at the 5' end of genes form ramp sequences that counterintuitively increase overall translational efficiency by evenly spacing ribosomes at initiation, which limits downstream ribosomal collisions. We previously developed ExtRamp, which is the only algorithm to identify translational ramp sequences in single genes. ExtRamp currently lacks a web interface to facilitate wider adoption and application for non-programmers. Additionally, ExtRamp currently identifies ramp sequences using only species-wide codon efficiencies that may lack the specificity of tissue and cell type-specific codon usage biases.Results: We present an online interface for ExtRamp to facilitate wider adoption and application for non-programmers, along with a significant improvement to the underlying algorithm to calculate tissue and cell type-specific ramp sequences (https://ramps.byu.edu/ExtRampOnline). ExtRamp Online contains all options available in the original ExtRamp algorithm with additional pre-set default values to enable researchers to calculate human tissue-specific or genome-wide ramp sequences on any web browser. Human tissue and cell type-specific codon usage biases have been precomputed and can be applied with a simple drop-down menu. Hover-over hints provide users with detailed information on all available options, which will help facilitate future creative analyses using ramp sequences. Availability: ExtRamp Online is publicly available at https://ramps.byu.edu/ExtRampOnline. All associated scripts are publicly available at https://github.com/ridgelab/ExtRampOnline.
Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods
Authors: Khomich Maryia , Måge Ingrid , Rud Ida , Berget Ingunn 
Published: September 2021

Abstract
The diet plays a major role in shaping gut microbiome composition and function in both humans and animals, and dietary intervention trials are often used to investigate and understand these effects. A plethora of statistical methods for analysing the differential abundance of microbial taxa exists, and new methods are constantly being developed, but there is a lack of benchmarking studies and clear consensus on the best multivariate statistical practices. This makes it hard for a biologist to decide which method to use. We compared the outcomes of generic multivariate ANOVA (ASCA and FFMANOVA) against statistical methods commonly used for community analyses (PERMANOVA and SIMPER) and methods designed for analysis of count data from high-throughput sequencing experiments (ALDEx2, ANCOM and DESeq2). The comparison is based on both simulated data and five published dietary intervention trials representing different subjects and study designs. We found that the methods testing differences at the community level were in agreement regarding both effect size and statistical significance. However, the methods that provided ranking and identification of differentially abundant operational taxonomic units (OTUs) gave incongruent results, implying that the choice of method is likely to influence the biological interpretations. The generic multivariate ANOVA tools have the flexibility needed for analysing multifactorial experiments and provide outputs at both the community and OTU levels; good performance in the simulation studies suggests that these statistical tools are also suitable for microbiome data sets.
Improving real-time operational streamflow simulations using discharge data to update state variables of a distributed hydrological model
Authors: Silvestro Francesco , Ercolani Giulia , Gabellani Simone , Giordano Pietro , Falzacappa Marco 
Published: September 2021

AbstractReducing errors in streamflow simulations is one of the main issues for a reliable forecast system aimed to manage floods and water resources. Data assimilation is a powerful tool to reduce model errors. Unfortunately, its use in operational chains with distributed and physically based models is a challenging issue since many methodologies require computational times that are hardly compatible with operational needs. The implemented methodology corrects modelled water level in channels and root-zone soil moisture using real-time water level gauge stations. Model's variables are corrected locally, then the updates are propagated upstream with a simple approach that accounts for sub-basins’ contributions. The overfitting issue, which arises when updating a spatially distributed model with sparse streamflow data, is hence here addressed in the context of a large-scale operational implementation working in real time thanks to the simplicity of the strategy. To test the method, a hindcast of daily simulations covering 18 months was performed on the Italian Tevere basin, and the modelling results with and without assimilation were compared. The setup was that currently in place in the operational framework in both cases. The analysis evidences a clear overall benefit of applying the proposed method even out of the assimilation time window.
Beneficial Effects of Dietary Polyphenols in the Prevention and Treatment
of NAFLD: Cell-Signaling Pathways Underlying Health Effects
Authors: Echeverría Francisca , Bustamante Andrés , Sambra Verónica , Álvarez Daniela , Videla Luis , Valenzuela Rodrigo 
Published: September 2021

Background:
Non-alcoholic fatty liver disease (NAFLD) is characterized by
hepatic accretion of triacylglycerides in the absence of alcohol intake that may progress
to steatohepatitis, fibrosis and cirrhosis, becoming the main cause of chronic liver disease.
This article discusses recent data concerning the use of dietary polyphenols in the
prevention and treatment of NAFLD in vitro, in vivo, and in clinical trials.

Methods:
Study searches were performed using the PubMed database from the National
Library of Medicine-National Institutes of Health.

Results:
Polyphenols exert beneficial effects in NAFLD, with positive outcomes being related
to body weight gain, insulin resistance, liver fat accumulation, oxidative stress, proinflammatory
status, mitochondrial dysfunction and ER stress. Data reported for hydroxytyrosol
suggest that the activation of the hepatic PPAR-α-FGF21-AMPK-PGC-1α signaling
cascade is associated with fatty acid oxidation enhancement, de novo lipogenesis
diminution and recovery of mitochondrial function, a contention that is supported by the
actions of several polyphenols on specific components of this signaling pathway. Besides,
polyphenols downregulate NF-κB, suppressing the pro-inflammatory state developed
in NAFLD and upregulate liver Nrf2, increasing the cellular antioxidant potential.
The latter feature of polyphenols is contributed by chelation of pro-oxidant trace elements,
reduction of free radicals to stable forms and inhibition of free radical generating
systems.

Conclusion:
Polyphenols are relevant bioactive compounds in terms of prevention and
treatment of NAFLD, which exhibit low bioavailability and instability in biological systems
that could limit their health effects. These drawbacks reinforce the necessity of further
studies to improve the efficacy of polyphenol formulations for human interventions.
Recent Advances in the Development of Triose Phosphate Isomerase
Inhibitors as Antiprotozoal Agents
Authors: Vázquez-Jiménez Lenci K. , Moreno-Herrera Antonio , Juárez-Saldivar Alfredo , González-González Alonzo , Ortiz-Pérez Eyra , Paz-González Alma D. , Palos Isidro , Ramírez-Moreno Esther , Rivera Gildardo 
Published: September 2021

Background:
Parasitic diseases caused by protozoa, such as Chagas disease,
leishmaniasis, malaria, African trypanosomiasis, amoebiasis, trichomoniasis, and giardiasis,
are considered serious public health problems in developing countries. Drug resistance
among parasites justifies the search for new therapeutic drugs, and the identification
of new targets becomes a valuable approach. In this scenario, the glycolysis pathway,
which converts glucose into pyruvate, plays an important role in the protozoa energy supply,
and it is therefore considered a promising target. In this pathway, triose phosphate
isomerase (TIM) plays an essential role in efficient energy production. Furthermore, protozoa
TIM shows structural differences with human enzyme counterparts, suggesting the
possibility of obtaining selective inhibitors. Therefore, TIM is considered a valid approach
to develop new antiprotozoal agents, inhibiting the glycolysis in the parasite.

Objective:
In this review, we discuss the drug design strategies, structure-activity relationship,
and binding modes of outstanding TIM inhibitors against Trypanosoma cruzi,
Trypanosoma brucei, Plasmodium falciparum, Giardia lamblia, Leishmania mexicana,
Trichomonas vaginalis, and Entamoeba histolytica.

Results:
TIM inhibitors have mainly shown aromatic systems and symmetrical structure,
where the size and type of heteroatom are important for enzyme inhibition. This inhibition
is mainly based on the interaction with i) the interfacial region of TIM inducing changes
on the quaternary and tertiary structure or ii) with the TIM catalytic region, the main
pathways that disable the catalytic activity of the enzyme.

Conclusion:
Benzothiazole, benzoxazole, benzimidazole, and sulfhydryl derivatives stand
out as TIM inhibitors. In silico and in vitro studies have demonstrated that the inhibitors
bind mainly at the TIM dimer interface. In this review, the development of new TIM inhibitors
as antiprotozoal drugs is demonstrated as an important pharmaceutical strategy
that may lead to new therapies for these ancient parasitic diseases.
Recent Development of Machine Learning Methods in Sumoylation Sites Prediction
Authors: Zhao Yi-Wei , Zhang Shihua , Ding Hui 
Published: September 2021

Abstract:Sumoylation of proteins is an important reversible post-translational modification of proteins and mediates a variety of cellular processes. Sumo-modified proteins can change their subcellular localization, activity, and stability. In addition, it also plays an important role in various cellular processes such as transcriptional regulation and signal transduction. The abnormal sumoylation is involved in many diseases, including neurodegeneration and immune-related diseases, as well as the development of cancer. Therefore, identification of the sumoylation site (SUMO site) is fundamental to understanding their molecular mechanisms and regulatory roles. In contrast to labor-intensive and costly experimental approaches, computational prediction of sumoylation sites in silico has also attracted much attention for its accuracy, convenience, and speed. At present, many computational prediction models have been used to identify SUMO sites, but their contents have not been comprehensively summarized and reviewed. Therefore, the research progress of relevant models is summarized and discussed in this paper. We have briefly summarized the development of bioinformatics methods for sumoylation site prediction by mainly focusing on the benchmark dataset construction, feature extraction, machine learning method, published results, and online tools. We hope that this review will provide more help for wet-experimental scholars.
CRISPR-Cas9 Genome Engineering: Trends in Medicine and Health
Authors: Zaib Sumera , Khan Imtiaz , Saleem Mushtaq A. 
Published: September 2021

:
The ability to engineer biological systems and organisms holds enormous potential for applications
across basic science, medicine, and biotechnology. Over the past few decades, the development
of CRISPR (clustered regularly interspaced short palindromic repeat) has revolutionized the
whole genetic engineering process utilizing the principles of Watson-Crick base pairing. CRISPRCas9
technology offers the simplest, fastest, most versatile, reliable, and precise method of genetic
manipulation, thus enabling geneticists and medical researchers to edit parts of the genome by removing,
adding, or altering sections of the DNA sequence. The current review focuses on the applications
of CRISPR-Cas9 in the field of medical research. Compared with other gene-editing technologies,
CRISPR/Cas9 demonstrates numerous advantages for the treatment of various medical conditions,
including cancer, hepatitis B, cardiovascular diseases, or even high cholesterol. Given its promising
performance, CRISPR/Cas9 gene-editing technology will surely help in the therapy of several disorders
while addressing the issues pertaining to the minimization of the off-target effects of gene editing
and incomplete matches between sgRNA and genomic DNA by Cas9.
Anticancer Potential of Naringenin, Biosynthesis, Molecular Target, and
Structural Perspectives
Authors: Prakash Om , Singh Ruchi , Singh Namrata , Usmani Shazia , Arif Mohd , Kumar Rajesh , Ved Akash 
Published: September 2021

Abstract:
Numerous novel medicinal agents isolated from plant sources were used as indigenous
remedies for the management and treatment of various types of cancer diseases. Naringenin is a naturally
occurring flavanone glycoside, and aglycone (genin) moiety of naringin, predominantly found in
citrus and grapefruits, has emerged as a potential therapeutic agent for the management of a variety of
diseases. A number of scientific papers have been published on naringenin describing its detailed
studies and its therapeutic application in different diseases. The current study highlights a comprehensive
study on naringenin concerning its biosynthesis, molecular targets/pathways involved in carcinogenesis,
mechanism of actions (MOAs), and structure-activity relationships (SARs), and patents
granted have been highlighted. Naringenin and its derivatives have great anti-cancer activity due to
their inhibitory potential against diverse targets, namely ABCG2/P-gp/BCRP, 5a-reductase, 17-
bhydroxysteroid dehydrogenase, aromatase, proteasome, HDAC/Situin-1, VEGF, VEGFR-2 kinase,
MMP-2/9, JAK/STAT signaling pathways, CDC25B, tubulin, topoisomerase-II, cathepsin-K, Wnt,
NF-kB, B-Raf and mTOR, etc. With the in-depth knowledge of molecular targets, structural intuition,
and SARs, the current study may be beneficial to design more potent, safe, effective, and economic
anti-cancer naringenin. This has been concluded that naringenin is a promising natural product for the
management and therapy of cancer. Further evolution for pharmacological importance, clinical research,
and trials are required to manifest its therapeutic action on metabolic syndrome in the human
community.