bluuebunny/crossref_metadata_2025_split huggingface dataset:
Preparation and application of lignin nanoparticles: a review
Authors: Zhu Baoping , Xu Yang , Xu Huanfei 
Published: July 2022

Abstract
Lignin is a natural renewable biopolymer with abundant reserves and great potential. As a by-product of the pulp and paper industry, the world can produce 150 billion tons of it every year, but it has not been effectively utilized. It was found that disordered and complex lignin can be converted into ordered and homogeneous nanoparticles by self-assembly, solvent exchange and acid precipitation. Lignin nanoparticles (LNPs) have the advantages of high stability, high activity, good biocompatibility and biodegradability, as well as improved structural and size control, antioxidant activity and other properties. LNPs have great potential for application not only as a natural alternative to traditional petroleum derivatives, biopharmaceutical carriers, but also in hydrogels. In recent years, the research of LNPs has received a lot of attention. It is hoped that more economical, environmentally friendly and high yielding methods for the synthesis of LNPs will be investigated in the future. This paper reviews the preparation methods of LNPs and their applications in various fields.
General formulation of Coulomb explosion dynamics of highly symmetric charge distributions
Authors: Zandi Omid , van der Veen Renske M 
Published: July 2022

Abstract
We present a theoretical approach to study the dynamics of spherical, cylindrical and ellipsoidal charge distributions under their self-Coulomb field and a stochastic force due to collisions and random motions of charged particles. The approach is based on finding the current density of the charge distribution from the charge-current continuity equation and determining the drift velocities of the particles. The latter can be used either to derive the Lagrangian of the system, or to write Newton’s equation of motion with the Lorentz force. We develop a kinetic theory to include the stochastic force due to random motions of electrons in our model. To demonstrate the efficacy of our method, we apply it to various charge distributions and compare our results to N-body simulations. We show that our method reproduces the well-known emittance term in the envelope equation of uniform spherical and cylindrical charge distributions with correct coefficients. We use our model for the gravitational collapse of an ideal gas as well as the cyclotron dynamics of a cylindrical charge distribution in a uniform magnetic field and propose a method to measure the emittance of electron beams.
Coordinately regulated transcription factors EIN3/EIL1 and MYCs in ethylene and jasmonate signaling interact with the same domain of MED25
Authors: Liu Ruiqi , Niimi Hikaru , Ueda Minoru , Takaoka Yousuke 
Published: July 2022

ABSTRACT
Ethylene (ET) and jasmonate (JA) are plant hormones that act synergistically to regulate plant development and defense against necrotrophic fungi infections, and antagonistically in response to wounds and apical hook formation. Previous studies revealed that the coordination of these responses is due to dynamic protein-protein interactions (PPI) between their master transcription factors (TFs) EIN3/EIL1 and MYC in ET and JA signaling, respectively. In addition, both TFs are activated via interactions with the same transcriptional mediator MED25, which upregulates downstream gene expression. Herein, we analyzed the PPI between EIN3/EIL1 and MED25, and as with the PPI between MYC3 and MED25, found that the short binding domain of MED25 (CMIDM) is also responsible for the interaction with EIN3/EIL1 − a finding which suggests that both TFs compete for binding with MED25. These results further inform our understanding of the coordination between the ET and JA regulatory systems.
Incorporating family disease history and controlling case–control imbalance for population-based genetic association studies
Authors: Zhuang Yongwen , Wolford Brooke N , Nam Kisung , Bi Wenjian , Zhou Wei , Willer Cristen J , Mukherjee Bhramar , Lee Seunggeun 
Published: July 2022

ABSTRACTMotivationIn the genome-wide association analysis of population-based biobanks, most diseases have low prevalence, which results in low detection power. One approach to tackle the problem is using family disease history, yet existing methods are unable to address type I error inflation induced by increased correlation of phenotypes among closely related samples, as well as unbalanced phenotypic distribution.ResultsWe propose a new method for genetic association test with family disease history, mixed-model-based Test with Adjusted Phenotype and Empirical saddlepoint approximation, which controls for increased phenotype correlation by adopting a two-variance-component mixed model, accounts for case–control imbalance by using empirical saddlepoint approximation, and is flexible to incorporate any existing adjusted phenotypes, such as phenotypes from the LT-FH method. We show through simulation studies and analysis of UK Biobank data of white British samples and the Korean Genome and Epidemiology Study of Korean samples that the proposed method is robust and yields better calibration compared to existing methods while gaining power for detection of variant–phenotype associations.Availability and implementationThe summary statistics and code generated in this study are available at https://github.com/styvon/TAPE.Supplementary informationSupplementary data are available at Bioinformatics online.
MOCHI: a comprehensive cross-platform tool for amplicon-based microbiota analysis
Authors: Zheng Jun-Jie , Wang Po-Wen , Huang Tzu-Wen , Yang Yao-Jong , Chiu Hua-Sheng , Sumazin Pavel , Chen Ting-Wen 
Published: July 2022

AbstractMotivationMicrobiota analyses have important implications for health and science. These analyses make use of 16S/18S rRNA gene sequencing to identify taxa and predict species diversity. However, most available tools for analyzing microbiota data require adept programming skills and in-depth statistical knowledge for proper implementation. While long-read amplicon sequencing can lead to more accurate taxa predictions and is quickly becoming more common, practitioners have no easily accessible tools with which to perform their analyses.ResultsWe present MOCHI, a GUI tool for microbiota amplicon sequencing analysis. MOCHI preprocesses sequences, assigns taxonomy, identifies different abundant species and predicts species diversity and function. It takes either taxonomic count table or FASTQ of partial 16S/18S rRNA or full-length 16S rRNA gene as input. It performs analyses in real time and visualizes data in both tabular and graphical formats.Availability and implementationMOCHI can be installed to run locally or accessed as a web tool at https://mochi.life.nctu.edu.tw.Supplementary informationSupplementary data are available at Bioinformatics online.
BioADAPT-MRC: adversarial learning-based domain adaptation improves biomedical machine reading comprehension task
Authors: Mahbub Maria , Srinivasan Sudarshan , Begoli Edmon , Peterson Gregory D 
Published: July 2022

ABSTRACTMotivationBiomedical machine reading comprehension (biomedical-MRC) aims to comprehend complex biomedical narratives and assist healthcare professionals in retrieving information from them. The high performance of modern neural network-based MRC systems depends on high-quality, large-scale, human-annotated training datasets. In the biomedical domain, a crucial challenge in creating such datasets is the requirement for domain knowledge, inducing the scarcity of labeled data and the need for transfer learning from the labeled general-purpose (source) domain to the biomedical (target) domain. However, there is a discrepancy in marginal distributions between the general-purpose and biomedical domains due to the variances in topics. Therefore, direct-transferring of learned representations from a model trained on a general-purpose domain to the biomedical domain can hurt the model’s performance.ResultsWe present an adversarial learning-based domain adaptation framework for the biomedical machine reading comprehension task (BioADAPT-MRC), a neural network-based method to address the discrepancies in the marginal distributions between the general and biomedical domain datasets. BioADAPT-MRC relaxes the need for generating pseudo labels for training a well-performing biomedical-MRC model. We extensively evaluate the performance of BioADAPT-MRC by comparing it with the best existing methods on three widely used benchmark biomedical-MRC datasets—BioASQ-7b, BioASQ-8b and BioASQ-9b. Our results suggest that without using any synthetic or human-annotated data from the biomedical domain, BioADAPT-MRC can achieve state-of-the-art performance on these datasets.Availability and implementationBioADAPT-MRC is freely available as an open-source project at https://github.com/mmahbub/BioADAPT-MRC.Supplementary informationSupplementary data are available at Bioinformatics online.
Mocafe: a comprehensive Python library for simulating cancer development with Phase Field Models
Authors: Pradelli Franco , Minervini Giovanni , Tosatto Silvio C E 
Published: July 2022

AbstractSummaryMathematical models are effective in studying cancer development at different scales from metabolism to tissue. Phase Field Models (PFMs) have been shown to reproduce accurately cancer growth and other related phenomena, including expression of relevant molecules, extracellular matrix remodeling and angiogenesis. However, implementations of such models are rarely published, reducing access to these techniques. To reduce this gap, we developed Mocafe, a modular open-source Python package that implements some of the most important PFMs reported in the literature. Mocafe is designed to handle both PFMs purely based on differential equations and hybrid agent-based PFMs. Moreover, Mocafe is meant to be extensible, allowing the inclusion of new models in future releases.Availability and implementationMocafe is a Python package based on FEniCS, a popular computing platform for solving partial differential equations. The source code, extensive documentation and demos are provided on GitHub at URL: https://github.com/BioComputingUP/mocafe. Moreover, we uploaded on Zenodo an archive of the package, which is available at https://doi.org/10.5281/zenodo.6366052.
Overcoming selection bias in synthetic lethality prediction
Authors: Seale Colm , Tepeli Yasin , Gonçalves Joana P 
Published: July 2022

AbstractMotivationSynthetic lethality (SL) between two genes occurs when simultaneous loss of function leads to cell death. This holds great promise for developing anti-cancer therapeutics that target synthetic lethal pairs of endogenously disrupted genes. Identifying novel SL relationships through exhaustive experimental screens is challenging, due to the vast number of candidate pairs. Computational SL prediction is therefore sought to identify promising SL gene pairs for further experimentation. However, current SL prediction methods lack consideration for generalizability in the presence of selection bias in SL data.ResultsWe show that SL data exhibit considerable gene selection bias. Our experiments designed to assess the robustness of SL prediction reveal that models driven by the topology of known SL interactions (e.g. graph, matrix factorization) are especially sensitive to selection bias. We introduce selection bias-resilient synthetic lethality (SBSL) prediction using regularized logistic regression or random forests. Each gene pair is described by 27 molecular features derived from cancer cell line, cancer patient tissue and healthy donor tissue samples. SBSL models are built and tested using approximately 8000 experimentally derived SL pairs across breast, colon, lung and ovarian cancers. Compared to other SL prediction methods, SBSL showed higher predictive performance, better generalizability and robustness to selection bias. Gene dependency, quantifying the essentiality of a gene for cell survival, contributed most to SBSL predictions. Random forests were superior to linear models in the absence of dependency features, highlighting the relevance of mutual exclusivity of somatic mutations, co-expression in healthy tissue and differential expression in tumour samples.Availability and implementationhttps://github.com/joanagoncalveslab/sbslSupplementary informationSupplementary data are available at Bioinformatics online.
TEspeX: consensus-specific quantification of transposable element expression preventing biases from exonized fragments
Authors: Ansaloni Federico , Gualandi Nicolò , Esposito Mauro , Gustincich Stefano , Sanges Remo 
Published: July 2022

AbstractSummaryTransposable elements (TEs) play key roles in crucial biological pathways. Therefore, several tools enabling the quantification of their expression were recently developed. However, many of the existing tools lack the capability to distinguish between the transcription of autonomously expressed TEs and TE fragments embedded in canonical coding/non-coding non-TE transcripts. Consequently, an apparent change in the expression of a given TE may simply reflect the variation in the expression of the transcripts containing TE-derived sequences. To overcome this issue, we have developed TEspeX, a pipeline for the quantification of TE expression at the consensus level. TEspeX uses Illumina RNA-seq short reads to quantify TE expression avoiding counting reads deriving from inactive TE fragments embedded in canonical transcripts.Availability and implementationThe tool is implemented in python3, distributed under the GNU General Public License (GPL) and available on Github at https://github.com/fansalon/TEspeX (Zenodo URL: https://doi.org/10.5281/zenodo.6800331).Supplementary informationSupplementary data are available at Bioinformatics online.
Prediction of HIV sensitivity to monoclonal antibodies using aminoacid sequences and deep learning
Authors: Dănăilă Vlad-Rareş , Buiu Cătălin 
Published: July 2022

AbstractMotivationKnowing the sensitivity of a viral strain versus a monoclonal antibody is of interest for HIV vaccine development and therapy. The HIV strains vary in their resistance to antibodies, and the accurate prediction of virus-antibody sensitivity can be used to find potent antibody combinations that broadly neutralize multiple and diverse HIV strains. Sensitivity prediction can be combined with other methods such as generative algorithms to design novel antibodies in silico or with feature selection to uncover the sites of interest in the sequence. However, these tools are limited in the absence of in silico accurate prediction methods.ResultsOur method leverages the CATNAP dataset, probably the most comprehensive collection of HIV-antibodies assays, and predicts the antibody-virus sensitivity in the form of binary classification. The methods proposed by others focus primarily on analyzing the virus sequences. However, our article demonstrates the advantages gained by modeling the antibody-virus sensitivity as a function of both virus and antibody sequences. The input is formed by the virus envelope and the antibody variable region aminoacid sequences. No structural features are required, which makes our system very practical, given that sequence data is more common than structures. We compare with two other state-of-the-art methods that leverage the same dataset and use sequence data only. Our approach, based on neuronal networks and transfer learning, measures increased predictive performance as measured on a set of 31 specific broadly neutralizing antibodies.Availability and implementationhttps://github.com/vlad-danaila/deep_hiv_ab_pred/tree/fc-att-fix
Decreased junctional adhesion molecule 3 expression induces reactive oxygen species production and apoptosis in trophoblasts
Authors: Chen Chie-Pein , Wang Liang-Kai , Chen Chen-Yu , Chen Chia-Yu , Kuo Yi-Hsiu , Wu Yi-Hsin 
Published: July 2022

Abstract
Junctional adhesion molecule 3 (JAM3) is involved in epithelial cell junction, cell polarity, and motility. The molecular mechanisms underlying the role of JAM3 in placental dysfunction remain unclear. We hypothesized that JAM3 expression regulates trophoblast fusion, differentiation, proliferation, and apoptosis. Our results revealed that JAM3 was expressed in the cytotrophoblasts and syncytiotrophoblasts of first-trimester and term placental villi. JAM3 expression in cell–cell junctions decreased with the formation of syncytiotrophoblasts. Using trophoblasts as an in vitro model, we observed that forskolin and JAM3 knockdown significantly reduced JAM3 expression and increased syncytium formation. JAM3 knockdown additionally inhibited trophoblast proliferation and increased the number of trophoblasts in the sub-G1 and G2/M phases, indicating cell-cycle disturbance and apoptosis. Cell-cycle arrest was associated with the engagement of checkpoint kinase 2-cell division cycle 25C-cyclin-dependent kinase 1/cyclin B1 signaling. Increased expression of BIM, NOXA, XAF1, cytochrome c, and cleaved caspase-3 further indicated trophoblast apoptosis. Overexpression of JAM3 or recombinant JAM3 protein enhanced trophoblast adhesion and migration, which were inhibited by JAM3 knockdown. JAM3 knockdown induced reactive oxygen species and syncytin 2 expression in trophoblasts. Furthermore, H2O2-induced oxidative stress reduced JAM3 expression in trophoblasts and cell culture supernatants. H2O2 simultaneously induced trophoblast apoptosis. JAM3 expression was significantly decreased in the plasmas and placentas of patients with early-onset severe preeclampsia. Thus, our results show that JAM3 may not only be a structural component of trophoblast cell junctions but also regulates trophoblast fusion, differentiation, proliferation, apoptosis, and motility. Dysregulated trophoblast JAM3 expression is crucial in preeclampsia development.
Lithium treatment promotes the activation of primordial follicles through PI3K/Akt signaling
Authors: Li Biao , Wang Weiyong , Huang Yingying , Han Lincheng , Li Jia , Zheng Nana , Wu Zhanying , Zhang Xiaodan , Li Xuelan , Deng Ling , Lin Min , Chen Xin , Zhang Meijia 
Published: July 2022

Abstract
In mammals, dormant primordial follicles represent the ovarian reserve throughout reproductive life. In vitro activation of dormant primordial follicles has been used to treat patients with premature ovarian insufficiency (POI). However, there remains a lack of effective strategies to stimulate follicle activation in vivo. In this study, we used an in vitro ovarian culture system and intraperitoneal injection to study the effect of lithium treatment on primordial follicle activation. Lithium increased the number of growing follicles in cultured mouse ovaries and promoted pre-granulosa cell proliferation. Furthermore, lithium significantly increased the levels of phosphorylated protein kinase B (Akt) and the number of oocytes with forkhead Box O3a (FOXO3a) nuclear export. Inhibition of the phosphatidylinositol 3 kinase (PI3K)/Akt pathway by LY294002 reversed lithium-promoted mouse primordial follicle activation. These results suggest that lithium promotes mouse primordial follicle activation by the PI3K/Akt signaling. Lithium also promoted primordial follicle activation and increased the levels of p-Akt in mouse ovaries in vivo and in human ovarian tissue cultured in vitro. Taken together, lithium promotes primordial follicle activation in mice and humans by the PI3K/Akt signaling. Lithium might be a potential oral drug for treating infertility in POI patients with residual dormant primordial follicles.
Inhibition of SHMT2 mRNA translation increases embryonic mortality in sheep
Authors: Sah Nirvay , Stenhouse Claire , Halloran Katherine M , Moses Robyn M , Seo Heewon , Burghardt Robert C , Johnson Gregory A , Wu Guoyao , Bazer Fuller W 
Published: July 2022

Abstract
The one-carbon metabolism (OCM) pathway provides purines and thymidine for synthesis of nucleic acids required for cell division, and S-adenosyl methionine for polyamine and creatine syntheses and the epigenetic regulation of gene expression. This study aimed to determine if serine hydroxymethyltransferase 2 (SHMT2), a key enzyme in the OCM pathway, is critical for ovine trophectoderm (oTr) cell function and conceptus development by inhibiting translation of SHMT2 mRNA using a morpholino antisense oligonucleotide (MAO). In vitro treatment of oTr cells with MAO-SHMT2 decreased expression of SHMT2 protein, which was accompanied by reduced proliferation (P = 0.053) and migration (P &lt; 0.05) of those cells. Intrauterine injection of MAO-SHMT2 in ewes on Day 11 post-breeding tended to decrease the overall pregnancy rate (on Days 16 and 18) compared with MAO-control (3/10 vs. 7/10, P = 0.07). The three viable conceptuses (n = 2 on Day 16 and n = 1 on Day 18) recovered from MAO-SHMT2 ewes had only partial inhibition of SHMT2 mRNA translation. Conceptuses from the three pregnant MAO-SHMT2 ewes had similar levels of expression of mRNAs and proteins involved in OCM as compared with conceptuses from MAO-control ewes. These results indicate that knockdown of SHMT2 protein reduces proliferation and migration of oTr cells (in vitro) to decrease elongation of blastocysts from spherical to elongated forms. These in vitro effects suggest that increased embryonic deaths in ewes treated with MAO-SHMT2 are the result of decreased SHMT2-mediated trophectoderm cell proliferation and migration supporting a role for the OCM pathway in survival and development of ovine conceptuses.
Use of solar PV inverters during night-time for voltage regulation and stability of the utility grid
Authors: Tharuka Lulbadda Kushan , Hemapala Udayanga 
Published: July 2022

Abstract
Photovoltaic (PV) inverters are vital components for future smart grids. Although the popularity of PV-generator installations is high, their effective performance remains low. Certain inverters are designed to operate in volt-ampere reactive (VAR) mode during the night. Yet, this approach is ineffective due to the consumption of active power from the grid (as internal losses) and the regulation necessity of the direct-current (DC) bus. This paper will demonstrate the operation of a PV inverter in reactive power-injection mode when solar energy is unavailable. The primary focus is on the design of the inverter controller with respect to the synchronous rotating frame control method. The proposed novel method enables an inverter to inject the required level of reactive power to regulate the voltage levels of the utility grid within specified limits. In the process, the inverter does not absorb active power from the grid for its internal operation. The presented model has the ability to inject ≤2 kVAR of reactive power at zero power factor without absorbing active power from the grid. Simulation and hardware models of the inverter were developed and tested for different reactive loads in which the hardware model represented the real-world application. The reactive power injection of the two models ran at zero power factor and produced the expected outcomes for their corresponding independent reactive loads. Henceforth, it was evident that the proposed method can enhance the efficiency of an inverter and ensure the stability of the utility grid to which it is connected.