bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genetic aspects of Wood's lactation curve parameters in Jersey crossbred cattle using Bayesian approach
Authors: Rahman Mokidur , Baneh Hasan , Gayari Indrajit , Karunakaran Muthupalani , Raja Thiruvothur Venkatesan , Deb Sitangsu Mohan , Mandal Ajoy 
Published: January 2024

AbstractThe study was undertaken to estimate the genetic parameters of lactation curve parameters of Wood's function in Jersey crossbred cattle using the Bayesian approach. Data on 33,906 fortnightly test day milk yields of 1,718 lactation records of Jersey crossbred cows, maintained at the ICAR-National Dairy Research Institute in West Bengal, were collected over a period of 40 years. The lactation curve parameters including ‘a’ (initial milk yield after calving), ‘b’ (ascending slope up to peak yield) and ‘c’ (descending slope after peak yield) and lactation curve traits, peak yield (ymax), time of peak yield (tmax) and persistency of milk yield (P) of individual cow for each lactation were estimated using the incomplete gamma function (Wood's model) by fitting the Gauss–Newton algorithm as an iteration method using PROC NLIN procedure of SAS 9.3. Variance components and genetic parameters of lactation curve parameters/traits were estimated by a repeatability animal model using the Bayesian approach. Estimates of heritabilities were found to be 0.18 ± 0.05, 0.09 ± 0.03 and 0.11 ± 0.04 for parameters ‘a’, ‘b’ and ‘c’, respectively and 0.24 ± 0.05, 0.12 ± 0.04, and 0.15 ± 0.05 for ymax, tmax and P, respectively. Repeatability estimates were 0.31 ± 0.03, 0.21 ± 0.04 and 0.30 ± 0.04 for parameters ‘a’, ‘b’ and ‘c’ respectively and 0.39 ± 0.03, 0.24 ± 0.03 and 0.37 ± 0.03 for ymax, tmax and p, respectively. Genetic correlations among lactation curve parameters/traits ranged from −0.75 to 0.95. Existence of genetic correlations among lactation curve parameters/traits indicated substantial genetic and physiological relationships among lactation curve parameters/traits of Jersey crossbred cattle.
Lactoferrin in breast milk-based powders
Authors: Tsakali Efstathia , Aggarwal Rakesh , Houhoula Dimitra , Konteles Spiridon , Batrinou Athimia , Verheyen Davy , Van Impe Jan FM , Chatzilazarou Arhontoula 
Published: January 2024

AbstractThis study aimed to determine lactoferrin (LF) in breast milk-based powders and formulas. Lactoferrin is an important whey protein in all mammalian milks and is responsible in large part for the known antimicrobial effects of human milk in particular. As breast feeding is not always possible, formulas based on cows milk have been developed in order to meet the nutritional needs of the newborn, while more recently human breast milk-based powders have been introduced to offer the biological functionality of human milk to pre-term and critically ill babies. In the present work, the amount of LF in commercial breast milk-based powders was tested by a validated RF-HPLC method for the determination of LF in breast milk in order to examine both the applicability of the method but at a second level the amount of LF in these commercial products. The detection of LF was possible but the complexity of the matrix lead us to the use the standard addition methodology in order to achieve quantification. The results indicated that breast milk-based powders had higher amount of LF than cows milk-based formulas, both non-fortified and fortified.
Lightweight transformers for clinical natural language processing
Authors: Rohanian Omid , Nouriborji Mohammadmahdi , Jauncey Hannah , Kouchaki Samaneh , Nooralahzadeh Farhad ,   ISARIC Clinical Characterisation Group, Clifton Lei , Merson Laura , Clifton David A. 
Published: January 2024

AbstractSpecialised pre-trained language models are becoming more frequent in Natural language Processing (NLP) since they can potentially outperform models trained on generic texts. BioBERT (Sanh et al., Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv: 1910.01108, 2019) and BioClinicalBERT (Alsentzer et al., Publicly available clinical bert embeddings. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, pp. 72–78, 2019) are two examples of such models that have shown promise in medical NLP tasks. Many of these models are overparametrised and resource-intensive, but thanks to techniques like knowledge distillation, it is possible to create smaller versions that perform almost as well as their larger counterparts. In this work, we specifically focus on development of compact language models for processing clinical texts (i.e. progress notes, discharge summaries, etc). We developed a number of efficient lightweight clinical transformers using knowledge distillation and continual learning, with the number of parameters ranging from
$15$
million to
$65$
million. These models performed comparably to larger models such as BioBERT and ClinicalBioBERT and significantly outperformed other compact models trained on general or biomedical data. Our extensive evaluation was done across several standard datasets and covered a wide range of clinical text-mining tasks, including natural language inference, relation extraction, named entity recognition and sequence classification. To our knowledge, this is the first comprehensive study specifically focused on creating efficient and compact transformers for clinical NLP tasks. The models and code used in this study can be found on our Huggingface profile at https://huggingface.co/nlpie and Github page at https://github.com/nlpie-research/Lightweight-Clinical-Transformers, respectively, promoting reproducibility of our results.
Detection and Classification of Sporadic E Using Convolutional Neural Networks
Authors: Ellis J. A. , Emmons D. J. , Cohen M. B. 
Published: January 2024

AbstractIn this work, convolutional neural networks (CNN) are developed to detect and characterize sporadic E (Es), demonstrating an improvement over current methods. This includes a binary classification model to determine if Es is present, followed by a regression model to estimate the Es ordinary mode critical frequency (foEs), a proxy for the intensity, along with the height at which the Es layer occurs (hEs). Signal‐to‐noise ratio (SNR) and excess phase profiles from six Global Navigation Satellite System (GNSS) radio occultation (RO) missions during the years 2008–2022 are used as the inputs of the model. Intensity (foEs) and the height (hEs) values are obtained from the global network of ground‐based Digisonde ionosondes and are used as the “ground truth,” or target variables, during training. After corresponding the two data sets, a total of 36,521 samples are available for training and testing the models. The foEs CNN binary classification model achieved an accuracy of 74% and F1‐score of 0.70. Mean absolute errors (MAE) of 0.63 MHz and 5.81 km along with root‐mean squared errors (RMSE) of 0.95 MHz and 7.89 km were attained for estimating foEs and hEs, respectively, when it was known that Es was present. When combining the classification and regression models together for use in practical applications where it is unknown if Es is present, an foEs MAE and RMSE of 0.97 and 1.65 MHz, respectively, were realized. We implemented three other techniques for sporadic E characterization, and found that the CNN model appears to perform better.
Reducing the Environmental Impact of Sewer Network Overflows Using Model Predictive Control Strategy
Authors: Vasiliev I. , Luca L. , Barbu M. , Vilanova R. , Caraman S. 
Published: January 2024

AbstractThis paper proposes a method for reducing the environmental impact of sewer network (SN) overflows. The main objective of the paper is to minimize the wastewater quantity and the pollutant loads that overflow from the SN. The proposed algorithm to achieve this goal is Model Predictive Control using Particle Swarm Optimization as optimization method. It was tested in simulation using a simplified model of the network based on Benchmark Simulation Modelsewer as prediction model, and a forecasted influent. Three cases have been considered: (a) the fitness function is defined as the global yearly overflow volume calculated using equal weights for each tank; (b) the fitness function uses different weights for each tank depending on the medium loads and (c) integrating a penalty term related to the system state at the end of the prediction horizon in the previous fitness function. The simplified model determined a significant reduction of the integration time minimizing the optimization time.
SA‐RelayGANs: A Novel Framework for the Characterization of Complex Hydrological Structures Based on GANs and Self‐Attention Mechanism
Authors: Cui Zhesi , Chen Qiyu , Liu Gang , Xun Lei 
Published: January 2024

AbstractIncreased availability and quality of surface and subsurface observations provide the chance to improve the perception of hydrological phenomena. An important factor that hinders our understanding of hydrological structures is the characterization of heterogeneous patterns with continuous attributes inside the structures (e.g., porosity, permeability, fluid saturation, etc.). Unlike categorical attributes, continuous attributes convey more realistic characteristics but require more computational resources to characterize such complex earth systems. In this work, we propose a novel deep learning approach for the characterization of complex hydrological realism with continuous attributes based on generative adversarial networks (GANs) and self‐attention mechanism, named SA‐RelayGANs. To address the complexity of heterogeneous hydrological structures, we divide the modeling process into two stages: facies construction stage and property reconstruction stage. In the first stage, we employ an improved GAN with self‐attention mechanism to construct the heterogeneous structures while adhering to hard conditioning constraints. In the second stage, we utilize another GAN with an attribute enhancement term to reconstruct realizations based on the constructed structures and observations. SA‐RelayGANs can successfully predict the statistical distributions of heterogeneous structures with continuous attributes by using limited observations. This study highlights the effectiveness of using GANs to characterize the heterogeneous patterns of hydrological realism and the application over large geoscience fields.
Observation of a single protein by ultrafast X-ray diffraction
Authors: Ekeberg Tomas , Assalauova Dameli , Bielecki Johan , Boll Rebecca , Daurer Benedikt J. , Eichacker Lutz A. , Franken Linda E. , Galli Davide E. , Gelisio Luca , Gumprecht Lars , Gunn Laura H. , Hajdu Janos , Hartmann Robert , Hasse Dirk , Ignatenko Alexandr , Koliyadu Jayanath , Kulyk Olena , Kurta Ruslan , Kuster Markus , Lugmayr Wolfgang , Lübke Jannik , Mancuso Adrian P. , Mazza Tommaso , Nettelblad Carl , Ovcharenko Yevheniy , Rivas Daniel E. , Rose Max , Samanta Amit K. , Schmidt Philipp , Sobolev Egor , Timneanu Nicusor , Usenko Sergey , Westphal Daniel , Wollweber Tamme , Worbs Lena , Xavier Paul Lourdu , Yousef Hazem , Ayyer Kartik , Chapman Henry N. , Sellberg Jonas A. , Seuring Carolin , Vartanyants Ivan A. , Küpper Jochen , Meyer Michael , Maia Filipe R. N. C. 
Published: January 2024

AbstractThe idea of using ultrashort X-ray pulses to obtain images of single proteins frozen in time has fascinated and inspired many. It was one of the arguments for building X-ray free-electron lasers. According to theory, the extremely intense pulses provide sufficient signal to dispense with using crystals as an amplifier, and the ultrashort pulse duration permits capturing the diffraction data before the sample inevitably explodes. This was first demonstrated on biological samples a decade ago on the giant mimivirus. Since then, a large collaboration has been pushing the limit of the smallest sample that can be imaged. The ability to capture snapshots on the timescale of atomic vibrations, while keeping the sample at room temperature, may allow probing the entire conformational phase space of macromolecules. Here we show the first observation of an X-ray diffraction pattern from a single protein, that of Escherichia coli GroEL which at 14 nm in diameter is the smallest biological sample ever imaged by X-rays, and demonstrate that the concept of diffraction before destruction extends to single proteins. From the pattern, it is possible to determine the approximate orientation of the protein. Our experiment demonstrates the feasibility of ultrafast imaging of single proteins, opening the way to single-molecule time-resolved studies on the femtosecond timescale.
Fc receptor-like 5 (FCRL5)-directed CAR-T cells exhibit antitumor activity against multiple myeloma
Authors: Yu Zhengyu , Li Hexian , Lu Qizhong , Zhang Zongliang , Tong Aiping , Niu Ting 
Published: January 2024

AbstractMultiple myeloma (MM) remains a challenging hematologic malignancy despite advancements in chimeric antigen receptor T-cell (CAR-T) therapy. Current targets of CAR-T cells used in MM immunotherapy have limitations, with a subset of patients experiencing antigen loss resulting in relapse. Therefore, novel targets for enhancing CAR-T cell therapy in MM remain needed. Fc receptor-like 5 (FCRL5) is a protein marker with considerably upregulated expression in MM and has emerged as a promising target for CAR-T cell therapeutic interventions, offering an alternative treatment for MM. To further explore this option, we designed FCRL5-directed CAR-T cells and assessed their cytotoxicity in vitro using a co-culture system and in vivo using MM cell-derived xenograft models, specifically focusing on MM with gain of chromosome 1q21. Given the challenges in CAR-T therapies arising from limited T cell persistence, our approach incorporates interleukin-15 (IL-15), which enhances the functionality of central memory T (TCM) cells, into the design of FCRL5-directed CAR-T cells, to improve cytotoxicity and reduce T-cell dysfunction, thereby promoting greater CAR-T cell survival and efficacy. Both in vitro and xenograft models displayed that FCRL5 CAR-T cells incorporating IL-15 exhibited potent antitumor efficacy, effectively inhibiting the proliferation of MM cells and leading to remarkable tumor suppression. Our results highlight the capacity of FCRL5-specific CAR-T cells with the integration of IL-15 to improve the therapeutic potency, suggesting a potential novel immunotherapeutic strategy for MM treatment.
Clinical applications of stem cell-derived exosomes
Authors: Tan Fei , Li Xuran , Wang Zhao , Li Jiaojiao , Shahzad Khawar , Zheng Jialin 
Published: January 2024

AbstractAlthough stem cell-based therapy has demonstrated considerable potential to manage certain diseases more successfully than conventional surgery, it nevertheless comes with inescapable drawbacks that might limit its clinical translation. Compared to stem cells, stem cell-derived exosomes possess numerous advantages, such as non-immunogenicity, non-infusion toxicity, easy access, effortless preservation, and freedom from tumorigenic potential and ethical issues. Exosomes can inherit similar therapeutic effects from their parental cells such as embryonic stem cells and adult stem cells through vertical delivery of their pluripotency or multipotency. After a thorough search and meticulous dissection of relevant literature from the last five years, we present this comprehensive, up-to-date, specialty-specific and disease-oriented review to highlight the surgical application and potential of stem cell-derived exosomes. Exosomes derived from stem cells (e.g., embryonic, induced pluripotent, hematopoietic, mesenchymal, neural, and endothelial stem cells) are capable of treating numerous diseases encountered in orthopedic surgery, neurosurgery, plastic surgery, general surgery, cardiothoracic surgery, urology, head and neck surgery, ophthalmology, and obstetrics and gynecology. The diverse therapeutic effects of stem cells-derived exosomes are a hierarchical translation through tissue-specific responses, and cell-specific molecular signaling pathways. In this review, we highlight stem cell-derived exosomes as a viable and potent alternative to stem cell-based therapy in managing various surgical conditions. We recommend that future research combines wisdoms from surgeons, nanomedicine practitioners, and stem cell researchers in this relevant and intriguing research area.
Cortical microinfarcts potentiate recurrent ischemic injury through NLRP3-dependent trained immunity
Authors: Feng Yiwei , Lin Lishan , Wu Tengteng , Feng Yukun , Liang Fengyin , Li Ge , Li Yongchao , Guan Yalun , Liu Shuhua , Zhang Yu , Xu Guangqing , Pei Zhong 
Published: January 2024

AbstractMicroinfarcts are common among the elderly and patients with microinfarcts are more vulnerable to another stroke. However, the impact of microinfarcts on recurrent stroke has yet to be fully understood. The purpose of this study was to explore the negative effects of microinfarcts on recurrent stroke. To achieve this, two-photon laser was used to induce microinfarcts, while photothrombotic stroke was induced on the opposite side. The results showed that microinfarcts led to trained immunity in microglia, which worsened the pro-inflammatory response and ischemic injury in the secondary photothrombotic stroke. Additionally, the study clarified the role of NLRP3 in microglial nuclei, indicating that it interacts with the MLL1 complex through NACHT domain and increases H3K4 methylation, which suggests that NLRP3 is critical in the formation of innate immune memory caused by microinfarcts. Furthermore, the knockout of NLRP3 in microglia alleviated the trained immunity and reduced the harmful effects of microinfarcts on recurrent stroke. This study emphasizes the detrimental effect of trained immunity on recurrent stroke and highlights the critical role of NLRP3 in mediating the formation of this memory, which may offer a potential therapeutic target for mitigating recurrent strokes.
TRIM22 induces cellular senescence by targeting PHLPP2 in hepatocellular carcinoma
Authors: Kang Donghee , Hwang Hyun Jung , Baek Yurim , Sung Jee Young , Kim KyeongJin , Park Heon Joo , Ko Young-Gyu , Kim Yong-Nyun , Lee Jae-Seon 
Published: January 2024

AbstractThe ubiquitin-proteasome system is a vital protein degradation system that is involved in various cellular processes, such as cell cycle progression, apoptosis, and differentiation. Dysregulation of this system has been implicated in numerous diseases, including cancer, vascular disease, and neurodegenerative disorders. Induction of cellular senescence in hepatocellular carcinoma (HCC) is a potential anticancer strategy, but the precise role of the ubiquitin-proteasome system in cellular senescence remains unclear. In this study, we show that the E3 ubiquitin ligase, TRIM22, plays a critical role in the cellular senescence of HCC cells. TRIM22 expression is transcriptionally upregulated by p53 in HCC cells experiencing ionizing radiation (IR)-induced senescence. Overexpression of TRIM22 triggers cellular senescence by targeting the AKT phosphatase, PHLPP2. Mechanistically, the SPRY domain of TRIM22 directly associates with the C-terminal domain of PHLPP2, which contains phosphorylation sites that are subject to IKKβ-mediated phosphorylation. The TRIM22-mediated PHLPP2 degradation leads to activation of AKT-p53-p21 signaling, ultimately resulting in cellular senescence. In both human HCC databases and patient specimens, the levels of TRIM22 and PHLPP2 show inverse correlations at the mRNA and protein levels. Collectively, our findings reveal that TRIM22 regulates cancer cell senescence by modulating the proteasomal degradation of PHLPP2 in HCC cells, suggesting that TRIM22 could potentially serve as a therapeutic target for treating cancer.
Carcinoembryonic antigen-expressing oncolytic measles virus derivative in recurrent glioblastoma: a phase 1 trial
Authors: Galanis Evanthia , Dooley Katharine E. , Keith Anderson S. , Kurokawa Cheyne B. , Carrero Xiomara W. , Uhm Joon H. , Federspiel Mark J. , Leontovich Alexey A. , Aderca Ileana , Viker Kimberly B. , Hammack Julie E. , Marks Randolph S. , Robinson Steven I. , Johnson Derek R. , Kaufmann Timothy J. , Buckner Jan C. , Lachance Daniel H. , Burns Terry C. , Giannini Caterina , Raghunathan Aditya , Iankov Ianko D. , Parney Ian F. 
Published: January 2024

AbstractMeasles virus (MV) vaccine strains have shown significant preclinical antitumor activity against glioblastoma (GBM), the most lethal glioma histology. In this first in human trial (NCT00390299), a carcinoembryonic antigen-expressing oncolytic measles virus derivative (MV-CEA), was administered in recurrent GBM patients either at the resection cavity (Group A), or, intratumorally on day 1, followed by a second dose administered in the resection cavity after tumor resection on day 5 (Group B). A total of 22 patients received study treatment, 9 in Group A and 13 in Group B. Primary endpoint was safety and toxicity: treatment was well tolerated with no dose-limiting toxicity being observed up to the maximum feasible dose (2×107 TCID50). Median OS, a secondary endpoint, was 11.6 mo and one year survival was 45.5% comparing favorably with contemporary controls. Other secondary endpoints included assessment of viremia, MV replication and shedding, humoral and cellular immune response to the injected virus. A 22 interferon stimulated gene (ISG) diagonal linear discriminate analysis (DLDA) classification algorithm in a post-hoc analysis was found to be inversely (R = −0.6, p = 0.04) correlated with viral replication and tumor microenvironment remodeling including proinflammatory changes and CD8 + T cell infiltration in post treatment samples. This data supports that oncolytic MV derivatives warrant further clinical investigation and that an ISG-based DLDA algorithm can provide the basis for treatment personalization.
Vibronic effects on the quantum tunnelling of magnetisation in Kramers single-molecule magnets
Authors: Mattioni Andrea , Staab Jakob K. , Blackmore William J. A. , Reta Daniel , Iles-Smith Jake , Nazir Ahsan , Chilton Nicholas F. 
Published: January 2024

AbstractSingle-molecule magnets are among the most promising platforms for achieving molecular-scale data storage and processing. Their magnetisation dynamics are determined by the interplay between electronic and vibrational degrees of freedom, which can couple coherently, leading to complex vibronic dynamics. Building on an ab initio description of the electronic and vibrational Hamiltonians, we formulate a non-perturbative vibronic model of the low-energy magnetic degrees of freedom in monometallic single-molecule magnets. Describing their low-temperature magnetism in terms of magnetic polarons, we are able to quantify the vibronic contribution to the quantum tunnelling of the magnetisation, a process that is commonly assumed to be independent of spin-phonon coupling. We find that the formation of magnetic polarons lowers the tunnelling probability in both amorphous and crystalline systems by stabilising the low-lying spin states. This work, thus, shows that spin-phonon coupling subtly influences magnetic relaxation in single-molecule magnets even at extremely low temperatures where no vibrational excitations are present.