bluuebunny/crossref_metadata_2025_split huggingface dataset:
Signal integration and integral feedback control with biochemical reaction networks
Authors: Andrews Steven S. , Kochen Michael , Smith Lucian , Feng Song , Wiley H. Steven , Sauro Herbert M. 
Published: April 2024

AbstractBiochemical reaction networks perform a variety of signal processing functions, one of which is computing the integrals of signal values. This is often used in integral feedback control, where it enables a system’s output to respond to changing inputs, but to then return exactly back to some pre-determined setpoint value afterward. To gain a deeper understanding of how biochemical networks are able to both integrate signals and perform integral feedback control, we investigated these abilities for several simple reaction networks. We found imperfect overlap between these categories, with some networks able to perform both tasks, some able to perform integration but not integral feedback control, and some the other way around. Nevertheless, networks that could either integrate or perform integral feedback control shared key elements. In particular, they included a chemical species that was neutrally stable in the open loop system (no feedback), meaning that this species does not have a unique stable steady-state concentration. Neutral stability could arise from zeroth order decay reactions, binding to a partner that was produced at a constant rate (which occurs in antithetic control), or through a long chain of covalent cycles. Mathematically, it arose from rate equations for the reaction network that were underdetermined when evaluated at steady-state.
MooViE – Engine for single-view visual analysis of multivariate data
Authors: Stratmann Anton , Beyß Martin , Jadebeck Johann F. , Nöh Katharina 
Published: April 2024

AbstractSummaryUnderstanding input-output relationships within multivariate datasets is an ubiquitous task in the life and data sciences. For this, visual analysis is indispensable for providing expressive summaries and preparing decision-making. We present the visual analysis approach and softwareMooViE, which is designed to strike the balance between being tailored to the specific data semantic and while being broadly applicable.MooViEsupports the data exploration process for extracting important information from the data and captures the result in a fresh single-view visualization.MooViEis implemented in C++ to facilitate fast access and effective interaction with comprehensive multivariate datasets. We showcase the engine for various application fields, relevant to the life sciences.Availability and ImplementationThe source code is available under MIT license athttps://jugit.fz-juelich.de/IBG-1/ModSim/MooViEandhttps://github.com/JuBiotechMooViE, with detailed documentation and usage instructions (https://moovie.readthedocs.io), as well as zenodo-archived releases (https://doi.org/10.5281/zenodo.10997388). Platform independent Docker images are also available (jugit-registry.fz-juelich.de/ibg-1/modsim/moovie/moovie).ContactKatharina Nöhk.noeh@fz-juelich.de
Enhancing cellular production of fucoxanthin through machine learning assisted predictive approach inIsochrysis galbana
Authors: Manochkumar Janani , Jonnalagadda Annapurna , Cherukuri Aswani Kumar , Vannier Brigitte , Janjaroen Dao , Ramamoorthy Siva 
Published: April 2024

AbstractThe marine microalgae,Isochrysis galbanais a prolific producer of fucoxanthin which is a xanthophyll carotenoid with substantial global market value boasting extensive applications in the food, nutraceutical, pharmaceutical, and cosmetic industries. Although supplementation of different phytohormones to medium enhances fucoxanthin production, the quantification of pigment by conventional means is time-consuming and labor-intensive. This study addressed the multiple methodological limitations of HPLC-based fucoxanthin quantification and emphasized the need to develop a Machine Learning (ML) model as optimization and precise prediction remain a challenging task. Hence, an integrated experimental approach coupled with ML models was employed to predict fucoxanthin yield by supplementation of various phytohormones. The accuracy of fucoxanthin prediction excluding and including hormone descriptors was compared and evaluated using the ML models namely Random Forest (RF), Linear Regression (LR), Artificial Neural Network (ANN), and Support Vector Machine (SVM). RF model provided the most accurate prediction excluding hormone descriptors with coefficient of determination (R2= 0.809) and root-mean-square error (RMSE= 0.776) followed by the ANN model with (R2= 0.722) and (RMSE= 0.937). The inclusion of hormone descriptors for training and pre-processing of data further improved the fucoxanthin prediction accuracy of the RF model to (R2= 0.839) and (RMSE= 0.712) and ANN model to (R2= 0.738) and (RMSE= 0.909). These results indicated that the combination of low-cost, Ultraviolet (UV) spectrometric-based fucoxanthin quantification coupled with ML algorithms can be efficiently used for reliable prediction and enhanced fucoxanthin production, therefore highlighting a promising approach and furnishing invaluable insights towards the commercialization of microalgal fucoxanthin production.
DMT1 contributes to MF- 438 - mediated inhibition of glioma cells
Authors: Zhang Laurence , Yamasaki Tomohiro , Dowdy Tyrone , Larion Mioara 
Published: April 2024

AbstractElevated SCD1 expression has been associated with enhanced cancer cell survival, proliferation, and resistance to therapy in many cancer types including gliomas. Hereby, we investigate the impact of MF-438 on SCD1-mediated lipid metabolism and its consequences on glioma growth and survival. Our data reveals an IDHmut-specific inhibitory effect of MF438 on gliomas. Also, we delineate a dual mechanism of action: while SCD1-mediated lipid metabolism is hindered by MF-438 treatment, MF-438 also exerts an SCD1-independent inhibition on DMT1 expression. Supporting data from the DMT1 blocker underscores its significance in MF-438’s anti-glioma efficacy.
Loss of spontaneous vasomotion precedes impaired cerebrovascular reactivity and microbleeds in a mouse model of cerebral amyloid angiopathy
Authors: Kozberg Mariel G , Munting Leon P , Maresco Lee H , Auger Corinne A , van den Berg Maarten L , Denis de Senneville Baudouin , Hirschler Lydiane , Warnking Jan M , Barbier Emmanuel L , Farrar Christian T , Greenberg Steven M , Bacskai Brian J , van Veluw Susanne J 
Published: April 2024

ABSTRACTBackgroundCerebral amyloid angiopathy (CAA) is a cerebral small vessel disease in which amyloid-β accumulates in vessel walls. CAA is a leading cause of symptomatic lobar intracerebral hemorrhage and an important contributor to age-related cognitive decline. Recent work has suggested that vascular dysfunction may precede symptomatic stages of CAA, and that spontaneous slow oscillations in arteriolar diameter (termed vasomotion), important for amyloid-β clearance, may be impaired in CAA.MethodsTo systematically study the progression of vascular dysfunction in CAA, we used the APP23 mouse model of amyloidosis, which is known to develop spontaneous cerebral microbleeds mimicking human CAA. Usingin vivo2-photon microscopy, we longitudinally imaged unanesthetized APP23 transgenic mice and wildtype littermates from 7 to 14 months of age, tracking amyloid-β accumulation and vasomotion in individual pial arterioles over time. MRI was used in separate groups of 12-, 18-, and 24-month-old APP23 transgenic mice and wildtype littermates to detect microbleeds and to assess cerebral blood flow and cerebrovascular reactivity with pseudo-continuous arterial spin labeling.ResultsWe observed a significant decline in vasomotion with age in APP23 mice, while vasomotion remained unchanged in wildtype mice with age. This decline corresponded in timing to initial vascular amyloid-β deposition (∼8-10 months of age), although was more strongly correlated with age than with vascular amyloid-β burden in individual arterioles. Declines in vasomotion preceded the development of MRI-visible microbleeds and the loss of smooth muscle actin in arterioles, both of which were observed in APP23 mice by 18 months of age. Additionally, evoked cerebrovascular reactivity was intact in APP23 mice at 12 months of age, but significantly lower in APP23 mice by 24 months of age.ConclusionsOur findings suggest that a decline in spontaneous vasomotion is an early, potentially pre-symptomatic, manifestation of CAA and vascular dysfunction, and a possible future treatment target.
Targeting oncogenic K-Ras mutants with a small-molecule degrader through Nedd4-1
Authors: Zeng Taoling , Jiang Tingting , Zhang Baoding , Zhang Ting , Dai Wanjun , Yin Xun , Li Yunzhan , Wu Caiming , Wu Yaying , Chi Ximin , Deng Xianming , Wang Hong-Rui 
Published: April 2024

AbstractK-Ras mutations represent a most prevalent oncogenic alteration in human cancers. Despite of tremendous efforts, it remains a big challenge to develop inhibitors that can target the oncogenic K-Ras mutants, especially mutants without specific active or charged side chains such as K-RasG12V. Here, taking advantage of our previous finding that Nedd4-1 is a bona fide E3 ubiquitin ligase for wild-type Ras proteins, we developed a compound XMU-MP-9 that can promote ubiquitination and degradation of various K-Ras mutants including K-RasG12V, and significantly inhibit proliferation and tumor development of K-Ras mutant harboring cells. Mechanistically, XMU-MP-9 acts as a bifunctional compound to bind the C2 domain of Nedd4-1 and an allosteric site of K-Ras to enhance Nedd4-1 and K-Ras interaction, and to induce a conformational change of Nedd4-1/K-Ras complex to allow Nedd4-1 targeting K128 of K-Ras for ubiquitination. Hence, our study presents a robust strategy to develop small-molecule degrader of K-Ras mutants, and also sheds light on the development of small-molecule degraders for H-Ras and N-Ras mutants.
Comparative metagenomic analysis provides novel insights into the ecophysiology of Myxococcota in activated sludge systems
Authors: Kurashita Hazuki , Hatamoto Masashi , Tomita Shun , Yamaguchi Takashi , Narihiro Takashi , Kuroda Kyohei 
Published: April 2024

AbstractMyxobacteria, belonging to the phylum Myxococcota, are ubiquitous in soil, marine, and other environments. Recent metagenomic sequencing analysis has shown that Myxococcota are predominant in activated sludge systems; however, their ecophysiological traits remain unclear. In this study, we evaluated the potential biological functions of 46 metagenomic bins of Myxococcota reconstructed from activated sludge samples from four municipal sewage treatment plants. The results showed that most Myxococcota bins had an almost complete set of genes associated with glycolysis and the TCA cycle. Palsa-1104 and Polyangiales bins contain glycoside hydrolase GH13 and peptidase M23, which are presumably involved in the lysis of the cell wall and cellular cytoplasm, indicating that part of the Myxococcota from activated sludge can prey on other microorganisms. The previously known cell contact-dependent predatory functions ofMyxococcus xanthusare conserved in the family Myxococcaceae, though not in other families. Two bins belonging to Palsa-1104 had phototrophic gene clusters, suggesting that these bins may have both heterotrophic and autotrophic metabolisms. Assessment of the social behavior of the activated sludge Myxococcota, a FruA gene, and a C-signal gene involved in the regulation of fruiting body formation were lacking in Myxococcota bins, suggesting that they are incapable of fruiting body formation. In addition, multiple bins of Myxococcota had novel secondary metabolite biosynthesis gene clusters that may be used for the predation of other bacteria in the activated sludge. Our metagenome-based analyses provide novel insights into the microbial interactions associated with Myxococcota in activated sludge ecosystems.
Translational modelling of low and medium intensity transcranial magnetic stimulation from rodents to humans
Authors: Bolland Samuel J. , Goryachev Maxim , Opitz Alexander , Tang Alexander D , Rodger Jennifer 
Published: April 2024

AbstractBackgroundRodent models using subthreshold intensities of transcranial magnetic stimulation (TMS) have provided insight into the biological mechanisms of TMS but often differ from human studies in the intensity of the electric field (E-field) induced in the brain.ObjectiveTo develop a finite element method model as a guide for translation between low and medium intensity TMS rodent studies and high intensity TMS studies in humans.MethodsFEM models using three head models (mouse, rat, and human), and eight TMS coils were developed to simulate the magnetic flux density (B-field) and E-field values induced by three intensities.ResultsIn the mouse brain, maximum B-fields ranged from 0.00675 T to 0.936 T and maximum E-field of 0.231 V/m to 60.40 V/m E-field. In the rat brains maximum B-fields ranged from of 0.00696 T to 0.567 T and maximum E-fields of 0.144 V/m to 97.2 V/m. In the human brain, the S90 Standard coil could be used to induce a maximum B-field of 0.643 T and E-field of 241 V/m, while the MC-B70 coil induced 0.564 T B-field and 220 V/m E-field.ConclusionsWe have developed a novel FEM modelling tool that can help guide the replication of rodent studies using low intensity E-fields to human studies using commercial TMS coils. Modelling limitations include lack of data on dielectric values and CSF volumes for rodents and simplification of tissue geometry impacting E-field distribution, methods for mitigating these issues are discussed. A range of additional cross-species factors affecting the translation of E-fields were identified that will aid TMS E-field modelling in both humans and rodents. We present data that describes to what extent translation of brain region-specific E-field values from rodents to humans is possible and detail requirements for future improvement. A graphical abstract of the translational modelling pipeline from this study is provided below (Figure A.1).HighlightsClinical translation of rodent TMS studies is challenging due to the differences in coil and brain size and shape between rodents and humans.We have built a FEM model for the accurate replication of TMS-derived E-fields validated in rodent models in multiple brain regions in humans.This model is useful in designing stimulation parameters for humans based on rodent studies.This model is a critical part of a translational pipeline for evidence based TMS.
Deciphering the Transcription Factor Landscape in Neuroendocrine Prostate Cancer Progression: A Novel Approach to Understand NE Transdifferentiation
Authors: Wang Yu , Xue Hui , Zhu Xiaohui , Lin Dong , Dong Xin , Chen Zheng , Chen Junru , Shi Mingchen , Ni Yuchao , Cao Jonathan , Wu Rebecca , Kang Ning , Pang Xinyao , Crea Francesco , Lin Yen-Yi , Collins Colin C. , Gleave Martin E. , Parolia Abhijit , Chinnaiyan Arul , Ong Christopher J. , Wang Yuzhuo 
Published: April 2024

AbstractBackground and ObjectiveProstate cancer (PCa) is a leading cause of cancer mortality in men, with neuroendocrine prostate cancer (NEPC) representing a particularly resistant subtype. The role of transcription factors (TFs) in the progression from prostatic adenocarcinoma (PRAD) to NEPC is poorly understood. This study aims to identify and analyze lineage-specific TF profiles in PRAD and NEPC and illustrate their dynamic shifts during NE transdifferentiation.MethodsA novel algorithmic approach was developed to evaluate the weighted expression of TFs within patient samples, enabling a nuanced understanding of TF landscapes in PCa progression and TF dynamic shifts during NE transdifferentiation.Resultsunveiled TF profiles for PRAD and NEPC, identifying 126 shared TFs, 46 adenocarcinoma-TFs, and 56 NEPC-TFs. Enrichment analysis across multiple clinical cohorts confirmed the lineage specificity and clinical relevance of these lineage-TFs signatures. Functional analysis revealed that lineage-TFs are implicated in pathways critical to cell development, differentiation, and lineage determination. Novel lineage-TF candidates were identified, offering potential targets for therapeutic intervention. Furthermore, our longitudinal study on NE transdifferentiation highlighted dynamic TF expression shifts and delineated a three-phase hypothesis for the process comprised of de-differentiation, dormancy, and re-differentiation. and proposing novel insights into the mechanisms of PCa progression.ConclusionThe lineage-specific TF profiles in PRAD and NEPC reveal a dynamic shift in the TF landscape during PCa progression, highlighting three distinct phases of NE transdifferentiation.
Spinal cord perfusion impairments in the M83 mouse model of Parkinson’s disease
Authors: Combes Benjamin F. , Kalva Sandeep Kumar , Benveniste Pierre-Louis , Tournant Agathe , Law Man Hoi , Newton Joshua , Krüger Maik , Weber Rebecca Z. , Dias Inês , Noain Daniela , Dean-Ben Xose Luis , Konietzko Uwe , Baumann Christian R. , Gillberg Per-Göran , Hock Christoph , Nitsch Roger M. , Cohen-Adad Julien , Razansky Daniel , Ni Ruiqing 
Published: April 2024

AbstractMetabolism and bioenergetics in the central nervous system play important roles in the pathophysiology of Parkinson’s disease (PD). Here, we employed a multimodal imaging approach to assess oxygenation changes in the spinal cord of a transgenic M83 murine model of PD in comparison to non-transgenic littermates at 9-12 months-of-age. A lower oxygen saturation (SO2)SVOTwas detectedin vivowith spiral volumetric optoacoustic tomography (SVOT) in the spinal cord of M83 mice compared to non-transgenic littermate mice.Ex-vivohigh-field T1-weighted magnetic resonance imaging (MRI) and immunostaining for alpha-synuclein (phospho-S129) and vascular organisation (CD31 and GLUT1) were used to investigate the nature of the abnormalities detected viain vivoimaging.Ex-vivoanalysis showed that the vascular network in the spinal cord was not impaired in the spinal cord of M83 mice.Ex-vivoMRI assisted with deep learning-based automatic segmentation showed no volumetric atrophy in the spinal cord of M83 mice compared to non-transgenic littermates, whereas nuclear alpha-synuclein phosphorylated at Ser129 site could be linked to early pathology and metabolic dysfunction. The proposed and validated non-invasive high-resolution imaging tool to study oxygen saturation in the spinal cord of PD mice holds promise for assessing early changes preceding motor deficits in PD mice.
Dendritic cell targeting in lymph nodes with engineered modular adapters improves HAdV5 and HC-HAdV5 tumor vaccination by co-secretion of IL-2v and IL-21
Authors: Weiss Fabian , Kolibius Jonas , Freitag Patrick C. , Gantenbein Felix , Kipar Anja , Plückthun Andreas 
Published: April 2024

AbstractAdenoviral vectors demonstrate encouraging clinical outcomes for B- and T-cell vaccines. With such approaches, multiple payloads can be delivered, beyond the antigen itself. Nevertheless, the human adenoviral vector serotype C5 (HAdV5) exhibits limited transduction efficiency to dendritic cells (DC), therefore necessitating very high viral loads. Targeting antigen-presenting cells (APC) has remained challenging. To solve this problem, we developed a versatile platform that employs modular retargeting adapters to enhance transduction of specific cell types, including challenging host cells. By rational design, we constructed a dual-adapter for DC-SIGN and CD11c and demonstrate successful targeting of HAdV5 to human and murine DCs. Ourin vivocharacterization highlights improved and specific transduction of DCs in draining lymph nodes. Moreover, a tumor vaccination study showcases the advantageous co-expression of T cell stimulatory cytokines (IL-2v or IL-21) locally in lymph nodes alongside a potent tumor antigen. Lymph node-directed gene therapy at significantly reduced vector loads circumvents potential systemic toxicity of stimulating payloads. Our proposed low-dosage DC-targeted vaccine offers an effective solution for patients and also minimizes potential adenovirus-related side-effects. The robust immunogenicity of HC-HAdV5, with its large coding capacity (37 kbp DNA), opens up exciting possibilities for future therapeutic combination strategies.
Genetic Dissection of Heat Stress Tolerance in Soybean through Genome-Wide Association Studies and the Use of Genomic Prediction to Enhance Breeding Applications
Authors: Van der Laan Liza , Peixoto Leonardo de Azevedo , Singh Asheesh K. 
Published: April 2024

ABSTRACTRising temperatures and associated heat stress pose an increasing threat to soybean [Glycine maxL. (Merr.)] productivity. Due to a limited choice of mitigation strategies, the primary arsenal in crop protection comes from improved genetic stress tolerance. Despite this current and looming threat to soybean production, limited studies have examined the genetics of heat stress tolerance. There is a need to conduct large-scale germplasm screening and genetic studies, including genome-wide association mapping and genomic prediction, to identify genomic regions and useful markers associated with heat tolerance traits that can be utilized in soybean breeding programs. We screened a diverse panel of 450 soybean accessions from MG 0-IV to dissect the genetic architecture of physiological and growth-related traits under optimal and heat stress temperatures and study trait relationships and predictive ability. The genetic architecture information of the response to heat revealed in this study provides insights into the genetics of heat stress tolerance. Thirty-seven significant SNPs were detected, with 20 unique SNPs detected in optimal, 16 detected in heat stress, and a single SNP detected for a heat tolerance index. Only one significant SNP was identified across temperature treatments indicating a genetic divergence in soybean responses to temperature. The genomic prediction worked well for biomass traits, but physiological traits associated with heat stress had poor model accuracy. Through our phenotyping efforts, we identified heat tolerant soybean accessions. The identification of heat tolerant accessions and significant SNPs are useful in heat tolerant variety development through marker-assisted and genomic selection.Core ideasSoybean exhibit phenotypic diversity in response to heat stress.Large scale phenotypic screening identified heat tolerant accessions.Previously unreported QTL and SNP associated with biomass and physiological parameters under heat stress are reported.Genomic prediction shows promise in abiotic stress breeding applications.
Autophagy is suppressed in peripheral blood mononuclear cells during chronic obstructive pulmonary disease
Authors: Cooper James M , Chen Shiye , Lester Susan E , Kim Julia , Gummow Jason , Crowhurst Tom , Lawton Emily , Badiei Arash , Nguyen Phan T , Jersmann Hubertus PA , Roscioli Eugene 
Published: April 2024

AbstractAssessing autophagy promises to provide valuable information regarding the pathogenesis of chronic obstructive pulmonary disease (COPD). However, measuring the dynamic aspect of autophagy is challenging, and sample manipulation can cause signal fluctuations that deviate from the situationin vivo. Our aim was to assess an organotypic method to quantify autophagy in the context of COPD, where autophagy has demonstrated disease-related modulation. Blood from control and COPD participants was treated with/out chloroquine. Protein from peripheral blood mononuclear cells (PBMC) was then isolated and compared for LC3B-II abundance. Our observations show that while basal level LC3B-II abundance was similar between each group (P = 0.60), autophagic flux was significantly lower in the COPD cohort, suggesting disruption in autophagy (P = 0.004). We aim to extend this inquiry and compare pulmonary vs blood samples, to identify the utility of measuring autophagy in blood as a diagnostic outcome predictive of early COPD.