bluuebunny/crossref_metadata_2025_split huggingface dataset:
Life expectancy, long-term care demand and dynamic financing mechanism simulation: an empirical study of Zhejiang Pilot, China
Authors: Xu Xueying , Li Yichao , Mi Hong 
Published: April 2024

Abstract
Background
China has piloted Long-Term Care Insurance (LTCI) to address increasing care demand. However, many cities neglected adjusting LTCI premiums since the pilot, risking the long-term sustainability of LTCI. Therefore, using Zhejiang Province as a case, this study simulated mortality-adjusted long-term care demand and the balance of LTCI funds through dynamic financing mechanism under diverse life expectancy and disability scenarios.

Methods
Three-parameter log-quadratic model was used to estimate the mortality from 1990 to 2020. Mortality with predicted interval from 2020 to 2080 was projected by Lee-Carter method extended with rotation. Cohort-component projection model was used to simulate the number of older population with different degrees of disability. Disability data of the older people is sourced from China Health and Retirement Longitudinal Study 2018. The balance of LTCI fund was simulated by dynamic financing actuarial model.

Results
Life expectancy of Zhejiang for male (female) is from 80.46 (84.66) years in 2020 to 89.39 [86.61, 91.74] (91.24 [88.90, 93.25]) years in 2080. The number of long-term care demand with severe disability in Zhejiang demonstrates an increasing trend from 285 [276, 295] thousand in 2023 to 1027 [634, 1657] thousand in 2080 under predicted mean of life expectancy. LTCI fund in Zhejiang will become accumulated surplus from 2024 to 2080 when annual premium growth rate is 5.25% [4.20%, 6.25%] under various disability scenarios, which is much higher than the annual growth of unit cost of long-term care services (2.25%). The accumulated balance of LTCI fund is sensitive with life expectancy.

Conclusions
Dynamic growth of LTCI premium is essential in dealing with current deficit around 2050 and realizing Zhejiang’s LTCI sustainability in the long-run. The importance of dynamic monitoring disability and mortality information is emphasized to respond immediately to the increase of premiums. LTCI should strike a balance between expanding coverage and controlling financing scale. This study provides implications for developing countries to establish or pilot LTCI schemes.
Systemic IL-26 correlates with improved asthma control in children sensitized to dog allergen
Authors: Kovach Melissa A. , Käck Ulrika , Che Karlhans F , Brundin Bettina , Konradsen Jon R. , Lindén Anders 
Published: April 2024

Abstract
Background
Interleukin (IL)-26 is produced by T helper type 17 (Type 17) cells and exerts immunomodulatory plus antimicrobial effects. Previous studies show that local IL-26 concentrations in the airways are higher in patients with uncontrolled than in those with controlled asthma, and that this intriguing cytokine bears biomarker potential. Here, we determined how systemic IL-26 relates to allergen sensitization, asthma severity, and to IL-17 A in children.

Methods
Serum samples were obtained from children with (n = 60) and without (n = 17) sensitization to dog allergen, and IL-26 and IL-17 A protein concentrations were measured using ELISA. Self-reported history, including medication use and validated symptom-based questionnaire scores, was recorded.

Results
The serum concentrations of IL-26 were enhanced in allergen-sensitized subjects and correlated with those of IL-17 A in a positive manner. However, the IL-26 concentrations did not markedly differ between allergen-sensitized subjects with and without asthma, eczema, allergic rhinitis, or a history of food allergy. Notably, IL-26 concentrations correlated with increasing Asthma Control Test (ACT) scores in a positive manner and with inhaled corticosteroid in a negative manner, amongst sensitized subjects with asthma. Moreover, subjects with asthma requiring ≥ 1 course of oral corticosteroids in the preceding 12 months had decreased IL-26 concentrations.

Conclusion
This study forwards evidence that systemic IL-26, just like IL-17 A, is involved in allergen sensitization among children. The association of systemic IL-26 with improved asthma control is compatible with the cellular sources being recruited into the airways in severe asthma, which supports that this kinocidin bears potential as a biomarker and therapeutic target.
Optimizing microbioreactor cultivation strategies for Trichoderma reesei: from batch to fed-batch operations
Authors: Rohr Katja , Gremm Lisa , Geinitz Bertram , Jourdier Etienne , Wiechert Wolfgang , Ben Chaabane Fadhel , Oldiges Marco 
Published: April 2024

Abstract
Background
Filamentous fungi have long been recognized for their exceptional enzyme production capabilities. Among these, Trichoderma reesei has emerged as a key producer of various industrially relevant enzymes and is particularly known for the production of cellulases. Despite the availability of advanced gene editing techniques for T. reesei, the cultivation and characterization of resulting strain libraries remain challenging, necessitating well-defined and controlled conditions with higher throughput. Small-scale cultivation devices are popular for screening bacterial strain libraries. However, their current use for filamentous fungi is limited due to their complex morphology.

Results
This study addresses this research gap through the development of a batch cultivation protocol using a microbioreactor for cellulase-producing T. reesei strains (wild type, RutC30 and RutC30 TR3158) with offline cellulase activity analysis. Additionally, the feasibility of a microscale fed-batch cultivation workflow is explored, crucial for mimicking industrial cellulase production conditions. A batch cultivation protocol was developed and validated using the BioLector microbioreactor, a Round Well Plate, adapted medium and a shaking frequency of 1000 rpm. A strong correlation between scattered light intensity and cell dry weight underscores the reliability of this method in reflecting fungal biomass formation, even in the context of complex fungal morphology. Building on the batch results, a fed-batch strategy was established for T. reesei RutC30. Starting with a glucose concentration of 2.5 g l$$^{-1}$$

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1

in the batch phase, we introduced a dual-purpose lactose feed to induce cellulase production and prevent carbon catabolite repression. Investigating lactose feeding rates from 0.3 to 0.75 g (l h)$$^{-1}$$

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1

, the lowest rate of 0.3 g (l h)$$^{-1}$$

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1

revealed a threefold increase in cellobiohydrolase and a fivefold increase in $$\upbeta$$
β
-glucosidase activity compared to batch processes using the same type and amount of carbon sources.

Conclusion
We successfully established a robust microbioreactor batch cultivation protocol for T. reesei wild type, RutC30 and RutC30 TR3158, overcoming challenges associated with complex fungal morphologies. The study highlights the effectiveness of microbioreactor workflows in optimizing cellulase production with T. reesei, providing a valuable tool for simultaneous assessment of critical bioprocess parameters and facilitating efficient strain screening. The findings underscore the potential of microscale fed-batch strategies for enhancing enzyme production capabilities, revealing insights for future industrial applications in biotechnology.
Unlocking the genome of the non-sourdough Kazachstania humilis MAW1: insights into inhibitory factors and phenotypic properties
Authors: Mielecki Damian , Detman Anna , Aleksandrzak-Piekarczyk Tamara , Widomska Małgorzata , Chojnacka Aleksandra , Stachurska-Skrodzka Anna , Walczak Paulina , Grzesiuk Elżbieta , Sikora Anna 
Published: April 2024

Abstract
Background
Ascomycetous budding yeasts are ubiquitous environmental microorganisms important in food production and medicine. Due to recent intensive genomic research, the taxonomy of yeast is becoming more organized based on the identification of monophyletic taxa. This includes genera important to humans, such as Kazachstania. Until now, Kazachstania humilis (previously Candida humilis) was regarded as a sourdough-specific yeast. In addition, any antibacterial activity has not been associated with this species.

Results
Previously, we isolated a yeast strain that impaired bio-hydrogen production in a dark fermentation bioreactor and inhibited the growth of Gram-positive and Gram-negative bacteria. Here, using next generation sequencing technologies, we sequenced the genome of this strain named K. humilis MAW1. This is the first genome of a K. humilis isolate not originating from a fermented food. We used novel phylogenetic approach employing the 18 S-ITS-D1-D2 region to show the placement of the K. humilis MAW1 among other members of the Kazachstania genus. This strain was examined by global phenotypic profiling, including carbon sources utilized and the influence of stress conditions on growth. Using the well-recognized bacterial model Escherichia coli AB1157, we show that K. humilis MAW1 cultivated in an acidic medium inhibits bacterial growth by the disturbance of cell division, manifested by filament formation. To gain a greater understanding of the inhibitory effect of K. humilis MAW1, we selected 23 yeast proteins with recognized toxic activity against bacteria and used them for Blast searches of the K. humilis MAW1 genome assembly. The resulting panel of genes present in the K. humilis MAW1 genome included those encoding the 1,3-β-glucan glycosidase and the 1,3-β-glucan synthesis inhibitor that might disturb the bacterial cell envelope structures.

Conclusions
We characterized a non-sourdough-derived strain of K. humilis, including its genome sequence and physiological aspects. The MAW1, together with other K. humilis strains, shows the new organization of the mating-type locus. The revealed here pH-dependent ability to inhibit bacterial growth has not been previously recognized in this species. Our study contributes to the building of genome sequence-based classification systems; better understanding of K.humilis as a cell factory in fermentation processes and exploring bacteria-yeast interactions in microbial communities.
Micropeptides: potential treatment strategies for cancer
Authors: Zhou He , Wu Yan , Cai Ji , Zhang Dan , Lan Dongfeng , Dai Xiaofang , Liu Songpo , Song Tao , Wang Xianyao , Kong Qinghong , He Zhixu , Tan Jun , Zhang Jidong 
Published: April 2024

AbstractSome noncoding RNAs (ncRNAs) carry open reading frames (ORFs) that can be translated into micropeptides, although noncoding RNAs (ncRNAs) have been previously assumed to constitute a class of RNA transcripts without coding capacity. Furthermore, recent studies have revealed that ncRNA-derived micropeptides exhibit regulatory functions in the development of many tumours. Although some of these micropeptides inhibit tumour growth, others promote it. Understanding the role of ncRNA-encoded micropeptides in cancer poses new challenges for cancer research, but also offers promising prospects for cancer therapy. In this review, we summarize the types of ncRNAs that can encode micropeptides, highlighting recent technical developments that have made it easier to research micropeptides, such as ribosome analysis, mass spectrometry, bioinformatics methods, and CRISPR/Cas9. Furthermore, based on the distribution of micropeptides in different subcellular locations, we explain the biological functions of micropeptides in different human cancers and discuss their underestimated potential as diagnostic biomarkers and anticancer therapeutic targets in clinical applications, information that may contribute to the discovery and development of new micropeptide-based tools for early diagnosis and anticancer drug development.
Machine-learning analysis reveals an important role for negative selection in shaping cancer aneuploidy landscapes
Authors: Jubran Juman , Slutsky Rachel , Rozenblum Nir , Rokach Lior , Ben-David Uri , Yeger-Lotem Esti 
Published: April 2024

AbstractBackgroundAneuploidy, an abnormal number of chromosomes within a cell, is a hallmark of cancer. Patterns of aneuploidy differ across cancers, yet are similar in cancers affecting closely related tissues. The selection pressures underlying aneuploidy patterns are not fully understood, hindering our understanding of cancer development and progression.ResultsHere, we apply interpretable machine learning methods to study tissue-selective aneuploidy patterns. We define 20 types of features corresponding to genomic attributes of chromosome-arms, normal tissues, primary tumors, and cancer cell lines (CCLs), and use them to model gains and losses of chromosome arms in 24 cancer types. To reveal the factors that shape the tissue-specific cancer aneuploidy landscapes, we interpret the machine learning models by estimating the relative contribution of each feature to the models. While confirming known drivers of positive selection, our quantitative analysis highlights the importance of negative selection for shaping aneuploidy landscapes. This is exemplified by tumor suppressor gene density being a better predictor of gain patterns than oncogene density, and vice versa for loss patterns. We also identify the importance of tissue-selective features and demonstrate them experimentally, revealingKLF5as an important driver for chr13q gain in colon cancer. Further supporting an important role for negative selection in shaping the aneuploidy landscapes, we find compensation by paralogs to be among the top predictors of chromosome arm loss prevalence and demonstrate this relationship for one paralog interaction. Similar factors shape aneuploidy patterns in human CCLs, demonstrating their relevance for aneuploidy research.ConclusionsOur quantitative, interpretable machine learning models improve the understanding of the genomic properties that shape cancer aneuploidy landscapes.
Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq
Authors: Fan Yue , Li Lei , Sun Shiquan 
Published: April 2024

AbstractWe present a non-parametric statistical method called TDEseq that takes full advantage of smoothing splines basis functions to account for the dependence of multiple time points in scRNA-seq studies, and uses hierarchical structure linear additive mixed models to model the correlated cells within an individual. As a result, TDEseq demonstrates powerful performance in identifying four potential temporal expression patterns within a specific cell type. Extensive simulation studies and the analysis of four published scRNA-seq datasets show that TDEseq can produce well-calibrated p-values and up to 20% power gain over the existing methods for detecting temporal gene expression patterns.
Overflow metabolism provides a selective advantage to Escherichia coli in mixed cultures
Authors: Yasir Muhammad , Thomson Nicholas M. , Turner A. Keith , Webber Mark A. , Charles Ian G. 
Published: April 2024

Abstract
Purpose
It has previously been shown that organic acids produced by Escherichia coli suppress the growth of Pseudomonas aeruginosa in co-cultures under conditions of glucose excess, due to overflow metabolism. Inactivation of genes involved in central carbon metabolism favours fermentation of glucose over respiration and therefore increases production of organic acid by-products such as acetate and lactate. We sought to extend and refine the list of genes known to contribute to the metabolic balance between respiration and fermentation, to better understand the role of overflow metabolism in competitive survival of E. coli.

Methods
We confirmed the previous finding that E. coli excludes P. aeruginosa from co-cultures by producing organic acids in the presence of glucose. Using a genome-wide transposon screen we identified E. coli genes that are important for survival in co-cultures with P. aeruginosa, both with and without glucose supplementation.

Results
Central carbon metabolism was the dominant gene function under selection in our experimental conditions, indicating that the observed inhibition is a side-effect of overflow metabolism adopted by E. coli as a response to high glucose concentrations. The presence of a competing species increased the selective pressure for central carbon metabolism genes, with 31 important for growth in the presence of P. aeruginosa and glucose, while only 9 were significant for pure E. coli cultures grown with glucose. In our experiments, each transposon mutant was competed against all others in the pool, suggesting that overflow metabolism provides benefits to individual E. coli cells in addition to competitive inhibition derived from acidification of the growth medium.

Conclusion
Co-culture assays using transposon mutant libraries can provide insight into the selective pressures present in mixed species competition. This work demonstrates central carbon metabolism is the dominant gene function under selection in E. coli for aerobic growth in glucose and a side-effect of this is overflow metabolism which can inhibit growth of bystander species.
Zombie cheminformatics: extraction and conversion of Wiswesser Line Notation (WLN) from chemical documents
Authors: Blakey Michael , Pearman-Kanza Samantha , Frey Jeremy G. 
Published: April 2024

Abstract
Purpose
Wiswesser Line Notation (WLN) is a old line notation for encoding chemical compounds for storage and processing by computers. Whilst the notation itself has long since been surpassed by SMILES and InChI, distribution of WLN during its active years was extensive. In the context of modernising chemical data, we present a comprehensive WLN parser developed using the OpenBabel toolkit, capable of translating WLN strings into various formats supported by the library. Furthermore, we have devised a specialised Finite State Machine l, constructed from the rules of WLN, enabling the recognition and extraction of chemical strings out of large bodies of text. Available open-access WLN data with corresponding SMILES or InChI notation is rare, however ChEMBL, ChemSpider and PubChem all contain WLN records which were used for conversion scoring. Our investigation revealed a notable proportion of inaccuracies within the database entries, and we have taken steps to rectify these errors whenever feasible.

Scientific contribution
Tools for both the extraction and conversion of WLN from chemical documents have been successfully developed. Both the Deterministic Finite Automaton (DFA) and parser handle the majority of WLN rules officially endorsed in the three major WLN manuals, with the parser showing a clear jump in accuracy and chemical coverage over previous submissions. The GitHub repository can be found here: https://github.com/Mblakey/wiswesser.
Mind your prevalence!
Authors: Guesné Sébastien J. J. , Hanser Thierry , Werner Stéphane , Boobier Samuel , Scott Shaylyn 
Published: April 2024

Abstract
Multiple metrics are used when assessing and validating the performance of quantitative structure–activity relationship (QSAR) models. In the case of binary classification, balanced accuracy is a metric to assess the global performance of such models. In contrast to accuracy, balanced accuracy does not depend on the respective prevalence of the two categories in the test set that is used to validate a QSAR classifier. As such, balanced accuracy is used to overcome the effect of imbalanced test sets on the model’s perceived accuracy. Matthews' correlation coefficient (MCC), an alternative global performance metric, is also known to mitigate the imbalance of the test set. However, in contrast to the balanced accuracy, MCC remains dependent on the respective prevalence of the predicted categories. For simplicity, the rest of this work is based on the positive prevalence. The MCC value may be underestimated at high or extremely low positive prevalence. It contributes to more challenging comparisons between experiments using test sets with different positive prevalences and may lead to incorrect interpretations. The concept of balanced metrics beyond balanced accuracy is, to the best of our knowledge, not yet described in the cheminformatic literature. Therefore, after describing the relevant literature, this manuscript will first formally define a confusion matrix, sensitivity and specificity and then present, with synthetic data, the danger of comparing performance metrics under nonconstant prevalence. Second, it will demonstrate that balanced accuracy is the performance metric accuracy calibrated to a test set with a positive prevalence of 50% (i.e., balanced test set). This concept of balanced accuracy will then be extended to the MCC after showing its dependency on the positive prevalence. Applying the same concept to any other performance metric and widening it to the concept of calibrated metrics will then be briefly discussed. We will show that, like balanced accuracy, any balanced performance metric may be expressed as a function of the well-known values of sensitivity and specificity. Finally, a tale of two MCCs will exemplify the use of this concept of balanced MCC versus MCC with four use cases using synthetic data.

Scientific contribution
This work provides a formal, unified framework for understanding prevalence dependence in model validation metrics, deriving balanced metric expressions beyond balanced accuracy, and demonstrating their practical utility for common use cases. In contrast to prior literature, it introduces the derived confusion matrix to express metrics as functions of sensitivity, specificity and prevalence without needing additional coefficients. The manuscript extends the concept of balanced metrics to Matthews' correlation coefficient and other widely used performance indicators, enabling robust comparisons under prevalence shifts.
Effects of yeast culture on in vitro ruminal fermentation and microbial community of high concentrate diet in sheep
Authors: Wang Hongze , Liu Guiqiong , Zhou Aimin , Yang Huiguo , Kang Kun , Ahmed Sohail , Li Biao , Farooq Umar , Hou Fuqing , Wang Chaoli , Bai Xue , Chen Yan , Ding Yi , Jiang Xunping 
Published: April 2024

AbstractThis research aimed to investigate effects of different yeast culture (YC) levels on in vitro fermentation characteristics and bacterial and fungal community under high concentrate diet. A total of 5 groups were included in the experiment: control group without YC (CON), YC1 (0.5% YC proportion of substrate dry matter), YC2 (1%), YC3 (1.5%) and YC4 (2%). After 48 h of fermentation, the incubation fluids and residues were collected to analyze the ruminal fermentation parameters and bacterial and fungal community. Results showed that the ruminal fluid pH of YC2 and YC4 groups was higher (P < 0.05) than that of CON group. Compared with CON group, the microbial protein, propionate and butyrate concentrations and cumulative gas production at 48 h of YC2 group were significantly increased (P < 0.05), whereas an opposite trend of ammonia nitrogen and lactate was observed between two groups. Microbial analysis showed that the Chao1 and Shannon indexes of YC2 group were higher (P < 0.05) than those of CON group. Additionally, YC supplementation significantly decreased (P < 0.05) Succinivibrionaceae_UCG-001, Streptococcus bovis and Neosetophoma relative abundances. An opposite tendency of Aspergillus abundance was found between CON and YC treatments. Compared with CON group, the relative abundances of Prevotella, Succiniclasticum, Butyrivibrio and Megasphaera elsdenii were significantly increased (P < 0.05) in YC2 group, while Apiotrichum and unclassified Clostridiales relative abundances were decreased (P < 0.05). In conclusion, high concentrate substrate supplemented with appropriate YC (1%) can improve ruminal fermentation and regulate bacterial and fungal composition.