bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of central carbon metabolism bypasses on the production of beta-carotene inYarrowia lipolytica
Authors: Markuš Tadej , Soldat Mladen , Magdevska Vasilka , Horvat Jaka , Kavšček Martin , Kosec Gregor , Fujs Štefan , Petrovič Uroš 
Published: November 2023

AbstractYarrowia lipolyticais an oleaginous yeast with ever growing popularity in the metabolic engineering circles. It is well known for its ability to accommodate a high carbon flux through acetyl-CoA and is being extensively studied for production of chemicals derived from it.We investigated the effects of modifying the upstream metabolism leading to acetyl-CoA on beta-carotene production, including its titer, yield, and content. We examined the pyruvate and the phosphoketolase bypass, both of which are stoichiometrically favorable for the production of acetyl-CoA and beta-carotene. Additionally, we examined a set of genes involved in the carnitine shuttle. We constructed a set of parental strains derived from theY. lipolyticaYB-392 wild-type strain, each with a different capacity for beta-carotene production, and introduced genes for the metabolic bypasses in each of the constructed parental strains. Subsequently, we subjected these constructed strains to a series of fermentation experiments.We discovered that altering the upstream metabolism in most cases led to a decrease in performance for production of beta-carotene. Most notably, a set of genes used for the pyruvate bypass (YlPDC2,YlALD5, andYlACS1) and the phosphoketolase bypass (LmXPKandCkPTA) resulted in the reduction of more than 30%.Our findings contribute to our understanding ofY. lipolytica’s metabolic capacity and suggest that production of beta-carotene is most likely not limited solely by the acetyl-CoA supply. We also highlight a complex nature of engineeringY. lipolytica, as most of the results from studies using a different strain background did not align with our findings.
Flexible Perception of Tactile Motion in Multiple Reference Frames
Authors: Ahuja Himanshu , Shivkumar Sabyasachi K. , Feistritzer Catalina , Haefner Ralf , DeAngelis Gregory C. , Gomez-Ramirez Manuel 
Published: November 2023

AbstractPerceptual representations of tactile motion are thought to emerge from computations that integrate cutaneous cues such as direction, speed, and saliency of the object moving on the skin. However, this knowledge was accrued from studies that presented stimuli with the hand in a fixed proprioceptive state. We studied how perception of tactile motion is modulated by proprioception, and how interactions between proprioceptive and tactile motion inputs are controlled by the reference frame of motion judgements. Participants judged the direction of motion of stimuli presented to their index finger in two reference frames (relative to the hand or their sternum) while the stimulated hand was placed in different positions. Our data show that tactile motion can be flexibly perceived in different reference frames, with proprioceptive modulations occurring only for motion judgements made in a Sternum-centric reference frame. We developed a Bayesian model that robustly accounts for participants’ perceptual decisions. Taken together, our data highlight the flexibility of the brain to represent tactile motion information in different coordinate systems. Our study also provides a computational framework for how task-dependent reference frame signals regulate the integration of proprioceptive and tactile inputs to generate flexible perception of tactile motion.
Speeding up iterative applications of the Build supertree algorithm
Authors: Redelings Benjamin D. , Holder Mark T. 
Published: November 2023

AbstractThe Open Tree of Life (OToL) project produces a supertree that summarizes phylogenetic knowledge from tree estimates published in the primary literature. The supetree construction algorithm iteratively calls Aho’s Build algorithm thousands of times in order to assess the compatability of different phylogenetic groupings. We describe an incrementalized version of the Build algorithm that is able to share work between successive calls to Build. We provide details that allow a programmer to implement the incremental algorithm BuildInc, including pseudo-code and a description of data structures. We assess the effect of BuildIncon our supertree algorithm by analyzing simulated data and by analyzing a supertree problem taken from the OToL 13.4 synthesis tree. We find that BuildIncprovides up to 550-fold speedup for our supertree algorithm.
A Spatio-temporal Investigation of Dynamics of a Two-dimensional Multi-scale Fitz-Hugh Nagumo Neuronal Network
Authors: Gharahi Alireza , Mohajerani Majid 
Published: November 2023

AbstractThe multi scale architecture by Breakspear and Stam [2] introduces a framework to consider the dynamical processes specific to a nested hierarchy of spatial scales, from neuronal masses to cortical columns and functional brain regions. They hypothesize that the neural dynamics is a function of the structural properties of the neural system at a certain scale as well as the emergent behaviour of the smaller scale activities. In this paper, we adopt the multi scale framework to investigate a generalized version of the stochastic Fitz-Hugh Nagumo (FHN) neuronal system within the small scale process and their emergent large scale synchronization effects leading to the formation of travelling waves in the large scale system. We extend the multi scale framework to incorporate the nonlinear biological synaptic connectivity at the neuronal mass scale. The modified multi scale scheme utilizes the two-dimensional wavelet decomposition in the plane of dynamical interconnected neurons. In addition, we consider the large-scale spatio-temporal system of FHN reaction-diffusion partial differential equations and evaluate the formation of travelling waves in the simplified context of a cellular neural network (CNN) model. Numerical examples are given to illustrate the response and the isolated influence of the strength of neural connectivity on the travelling wave formation modes.
Complexity Measures of Psychotic Brain Activity in the Fmri Signal
Authors: Li Qiang , Seraji Masoud , Calhoun Vince D , Iraji Armin 
Published: November 2023

ABSTRACTWhen viewing the brain as a sophisticated, nonlinear dynamic system, employing complexity measures offers a valuable way to measure the intricate and dynamic aspects of spontaneous psychotic brain activity. These measures can help us identify irregularities and patterns in complex systems. In our study, we utilized fuzzy recurrence plots and sample entropy to evaluate the dynamic characteristics of psychiatric disorders. This assessment focused on understanding the temporal and spatial neural activity patterns, and more specifically, we applied complexity measures to investigate the functional connectivity within the psychotic brain. This involves understanding how different brain regions synchronize their activity, and complexity measures can reveal the patterns of these connections. It provides a means to understand how different brain regions interact and communicate under resting-state abnormal conditions. This study offers evidence demonstrating that fuzzy recurrence plots can serve as descriptors for functional connectivity and discusses their relevance to sample entropy in the context of the psychotic brain. In summary, complexity measures offer valuable insights that enrich our comprehension of atypical brain activity and the complexities present in the psychotic brain1.
Therapeutic targeting of immunometabolism in Alzheimer’s disease reveals a critical reliance on Hexokinase 2 dosage on microglial activation and disease progression
Authors: Codocedo Juan F. , Mera-Reina Claudia , Lin Peter Bor-Chian , Puntambekar Shweta S. , Casali Brad T. , Jury Nur , Martinez Pablo , Lasagna-Reeves Cristian A. , Landreth Gary E. 
Published: November 2023

SummaryMicrogliosis and neuroinflammation are prominent features of Alzheimer’s disease (AD). Disease-responsive microglia meet their increased energy demand by reprogramming metabolism, specifically, switching to favor glycolysis over oxidative phosphorylation. Thus, targeting of microglial immunometabolism might be of therapeutic benefit for treating AD, providing novel and often well understood immune pathways and their newly recognized actions in AD. We report that in the brains of 5xFAD mice and postmortem brains of AD patients, we found a significant increase in the levels of Hexokinase 2 (HK2), an enzyme that supports inflammatory responses by rapidly increasing glycolysis. Moreover, binding of HK2 to mitochondria has been reported to regulate inflammation by preventing mitochondrial dysfunction and NLRP3 inflammasome activation, suggesting that its inflammatory role extends beyond its glycolytic activity. Here we report, that HK2 antagonism selectively affects microglial phenotypes and disease progression in a gene-dose dependent manner. Paradoxically, complete loss of HK2 fails to improve AD progression by exacerbating inflammasome activity while its haploinsufficiency results in reduced pathology and improved cognition in the 5XFAD mice. We propose that the partial antagonism of HK2, is effective in slowed disease progression and inflammation through a non-metabolic mechanism associated with the modulation of NFKβ signaling, through its cytosolic target IKBα. The complete loss of HK2 affects additional inflammatory mechanisms associated to mitochondrial dysfunction.HighlightsHexokinase 2, the first and rate-limiting enzyme of glycolysis, is specifically upregulated in plaque-associated microglia of AD mice models and in the postmortem cortex of human AD patients.Microglia haploinsufficient in HK2 exhibit reduced amyloid burden and inflammation as well as improved cognition in a mouse model of AD. Paradoxically, the complete loss of HK2 results in opposite effects, by exacerbating inflammation.Lonidamine, an anticancer drug that inhibits HK2, mimics the salutary effects of HK2 haploinsufficiency in the 5xFAD mice, but only in males during the early stages of disease.HK2 deletion induced mitochondrial dysfunction associated to increased expression of inflammasome elements and IL-1β.HK2 partial antagonism exerts beneficial effects independent of its energetic or mitochondrial role, likely through cytosolic stabilization of IκBα and inhibition of the NF-κB pathway, leading to reduced proinflammatory gene expression.
Loss of TMEM106B exacerbates Tau pathology and neurodegeneration in PS19 mice
Authors: Feng Tuancheng , Du Huan , Hu Fenghua 
Published: November 2023

AbstractTMEM106B, a gene encoding a lysosome membrane protein, is tightly associated with brain aging, hypomyelinating leukodystrophy, and multiple neurodegenerative diseases, including frontotemporal lobar degeneration with TDP-43 aggregates (FTLD-TDP). Recently,TMEM106Bpolymorphisms have been associated with tauopathy in chronic traumatic encephalopathy (CTE) and FTLD-TDP patients. However, how TMEM106B influences Tau pathology and its associated neurodegeneration, is unclear. Here we show that loss of TMEM106B enhances the accumulation of pathological Tau, especially in the neuronal soma in the hippocampus, resulting in severe neuronal loss in the PS19 Tau transgenic mice. Moreover,Tmem106b-/-PS19 mice develop significantly increased disruption of the neuronal cytoskeleton, autophagy-lysosomal function, and lysosomal trafficking along the axon as well as enhanced gliosis compared with PS19 andTmem106b-/-mice. Together, our findings demonstrate that loss of TMEM106B drastically exacerbates Tau pathology and its associated disease phenotypes, and provide new insights into the roles of TMEM106B in neurodegenerative diseases.
Genome assembly and genomic architecture of a prominent cold-resistant rapeseed germplasm
Authors: Wu Zefeng , Zheng Guoqiang , Sun Yali , Dong Xiaoyun , Wang Ying , Li Hui , Wei Jiaping , Cui Junmei , Fang Yan , Niu Yinin , Huang Zhen , Hu Jihong , Liu Zigang 
Published: November 2023

Graphical AbstractThe genomic landscape of cold-tolerant winter rapeseed (Brassica napus, L) has been poorly characterized. We assembled a high-quality reference genome of a prominent cold-tolerant winter rapeseed cultivar, NTS57, and performed phylogenetic and pan-genomic analyses by integrating other reportedBrassica napusaccessions. The transcriptome analysis revealed that microtubule-associated biological pathways were much more active in NTS57 under cold stress than in the cold-sensitive variety. Whole genome methylation data analysis revealed that DNA demethylation on protein-coding genes and repetitive elements, especially at CHH sites, is essential for cold response in winter rapeseed.
Dual therapy with corticosteroid ablates the beneficial effect of DP2 antagonism in chronic experimental asthma
Authors: Ullah Md Ashik , Rittchen Sonja , Li Jia , Curren Bodie F. , Rahman Muhammed Mahfuzur , Sikder Md Al Amin , Rashid Ridwan B. , Collinson Natasha , Lor Mary , Smythe Mark L. , Phipps Simon 
Published: November 2023

ABSTRACTBackgroundProstaglandin D2 (PGD2) signals via the DP1 and DP2 receptors. In Phase II trials, DP2 antagonism decreased airway inflammation and airway smooth muscle (ASM) area in patients with moderate-to-severe asthma, but in the Phase III clinical trials, DP2 antagonism failed to significantly lower the rate of exacerbations. Here, we hypothesised that DP2 antagonism resolves established ASM remodeling via endogenous PGD2/DP1 activation and that this beneficial effect is ablated by dual corticosteroid therapy.MethodsNeonatal mice were co-exposed to pneumonia virus of mice (PVM) and cockroach extract in early life to induce severe bronchiolitis, then re-infected with PVM and challenged to cockroach extract in adulthood to progress disease to chronic experimental asthma (CEA). The efficacy of DP2 antagonism monotherapy or various dual therapies was assessed in the setting of a rhinovirus (RV)-induced exacerbation.ResultsRV inoculation increased PGD2 release, mucus production, collagen deposition, transforming growth factor (TGF)-β1 expression and type-2 inflammation. Treatment with a DP2 antagonist or DP1 agonist ablated the aforementioned phenotypes, increased type-1 immunity, and decreased ASM area. Dual DP1-DP2 antagonism or dual corticosteroid/DP2 antagonism, which attenuated endogenous PGD2 levels, prevented the resolution of ASM area induced by DP2 antagonism alone. The resolution of ASM remodelling following DP2 antagonism was mediated by IFN-γ and associated with decreased TGF-β1 expression.ConclusionDP2 antagonism resolved ASM remodelling via PGD2/DP1-mediated upregulation of interferon-γ expression. Dual DP2 antagonism/corticosteroid therapy, as occurred in many of the human trials, suppressed PGD2 and IFN-γ production, impairing the efficacy of DP2 antagonism.
Self-supervised contrastive learning improves machine learning discrimination of full thickness macular holes from epiretinal membranes in retinal OCT scans
Authors: Wheeler Tim , Hunter Kaitlyn , Garcia Patricia , Li Henry , Thomson Andrew , Hunter Allan , Mehanian Courosh 
Published: November 2023

AbstractThere is a growing interest in using computer-assisted models for the detection of macular conditions using optical coherence tomography (OCT) data. As the quantity of clinical scan data of specific conditions is limited, these models are typically developed by fine-tuning a generalized network to classify specific macular conditions of interest. Full thickness macular holes (FTMH) present a condition requiring timely surgical intervention to prevent permanent vision loss. Other works on automated FTMH classification have tended to use supervised ImageNet pre-trained networks with good results but leave room for improvement. In this paper, we develop a model for FTMH classification using OCT slices around the central foveal region to pre-train a naïve network using contrastive self-supervised learning. We found that self-supervised pre-trained networks outperform ImageNet pre-trained networks despite a small training set size (284 eyes total, 51 FTMH+ eyes, 3 slices from each eye). 3D spatial contrast pre-training yields a model with an F1-score of 1.0 on holdout data (50 eyes total, 10 FTMH+), compared ImageNet pre-trained models, respectively. These results demonstrate that even limited data may be applied toward self-supervised pre-training to substantially improve performance for FTMH classification, indicating applicability toward other OCT-based problems.Author SummaryFull thickness macular holes (FTMH) are a sight-threatening condition that involves the fovea, the area of the eye involved in central vision. Timely diagnosis is paramount because of the risk of permanent vision loss. In clinical practice, full thickness macular holes are commonly diagnosed with the aid of optical coherence tomography (OCT) images of the fovea. However, certain conditions such as pseudoholes and epiretinal membranes may complicate the diagnosis of full thickness macular holes on imaging. Here, we employ the use of artificial intelligence and present a machine-learning model for full thickness macular hole classification and distinction from conditions that may present similarly upon image review. Despite training our model with a smaller data set, it outperformed traditional models previously seen in other works. We provide a strong framework for a self-supervised pre-trained model that can accurately distinguish full thickness macular holes from epiretinal membranes and pseudoholes. Overall, our study provides evidence of the benefit and efficacy with the introduction of artificial intelligence for image classification.
The Cognitive Connectome in Dementia with Lewy Bodies
Authors: Yánez-Pérez Roraima , García-Cabello Eloy , Habich Annegret , Cedres Nira , Diaz-Galvan Patricia , Abdelnour Carla , Toledo Jon B. , Barroso José , Ferreira Daniel 
Published: November 2023

ABSTRACTObjectivesCognition plays a central role for diagnosing and characterizing dementia with Lewy bodies (DLB). However, the complex associations among cognitive functions are largely unknown in DLB. To fill this gap, we compared the cognitive connectome of DLB patients, healthy controls (HC) and patients with Alzheimer’s disease (AD).MethodsWe obtained data from the National Alzheimer’s Coordinating Center (NIA/NIH Grant U24-AG072122). We built separate cognitive connectomes for DLB (n=104), HC (n= 3703), and AD (n=1985) groups using pairwise correlations between 24 cognitive variables mapping multiple cognitive functions. The cognitive connectomes in DLB, HC, and AD groups were compared using standard global and nodal graph measures of centrality, integration, and segregation.ResultsIn global connectome measures, DLB patients showed a higher global efficiency (integration) and lower transitivity (segregation) than HCs and AD. Nodal connectome measures showed a higher global efficiency in most cognitive functions in DLB compared to HCs. Additionally, we found a lower local efficiency (segregation) and nodal strength (centrality) in memory variables and a higher participation coefficient in executive variables (centrality) in DLB compared with both HCs and AD.ConclusionsThe cognitive connectome in DLB showed a signature dedifferentiation pattern of aberrant correlations. Executive, processing speed and attention functions played a central role in the cognitive connectome of DLB patients. Furthermore, the role of executive and memory functions in the cognitive connectome distinguished DLB and AD patients. These findings may help advance our understanding of the clinical phenotype in DLB, and continue to improve the challenging differential diagnosis between DLB and AD.
Improving Interprofessional Collaboration: Building Confidence Using A Novel HIV Curriculum For Healthcare Workers Across Sub-Saharan Africa
Authors: Khanyola Judy , Reid Mike , Dadasovich Rand , Derbew Miliard , Couper Ian , Dassah Edward T. , Forster Maeve , Gachuno Onesmus , Haruzivishe Clara , Kazembe Abigail , Martin Shayanne , Moltwantwa Mmoloki , Motlhatlhedi Keneilwe , Mteta Kien Alfred , Nadesan-Reddy Nisha , Suleman Fatima , Ngoma Catherine , Odaibo Georgina N. , Mubuuke Roy , von Zinkernagel Deborah , Kiguli-Malwadde Elsie , Sears David 
Published: November 2023

AbstractThe 21st century presents significant global health challenges that necessitate an integrated health workforce capable of delivering person-centered and integrated healthcare services. Interprofessional collaboration (IPC) plays a vital role in achieving integration and training an IPC-capable workforce in Sub-Saharan Africa (SSA) has become imperative. This study aimed to assess changes in IPC confidence among learners participating in a team-based, case-based HIV training program across diverse settings in SSA. Additionally, it sought to examine the impact of different course formats (in-person, synchronous virtual, or blended learning) on IPC confidence. Data from 20 institutions across 18 SSA countries were collected between May 1, 2021, and December 31, 2021. Logistic regression analysis was conducted to estimate associations between variables of interest and the gain in IPC confidence. The analysis included 3,842 learners; nurses comprised 37.9% (n=1,172) and physicians 26.7% (n=825). The majority of learners (67.2%, n=2,072) were pre-service learners, while 13.0% (n=401) had graduated within the past year. Factors significantly associated with increased IPC confidence included female gender, physician cadre, completion of graduate training over 12 months ago, and participation in virtual or in-person synchronous workshops (p<0.05). The insights gained from this analysis can inform future curriculum development to strengthen interprofessional healthcare delivery across SSA.