bluuebunny/crossref_metadata_2025_split huggingface dataset:
GoFish: A low-cost, open-source platform for closed-loop behavioural experiments on fish
Authors: Ajuwon Victor , Cruz Bruno F. , Carriço Paulo ,   Champalimaud Foundation Scientific Hardware Platform, Kacelnik Alex , Monteiro Tiago 
Published: April 2022

AbstractFish are the most species-rich vertebrate group, displaying vast ecological, anatomical and behavioural diversity, and therefore are of major interest for the study of behaviour and its evolution. Despite this, with respect to other vertebrates, fish are relatively underrepresented in behavioural research. This is partly due to the difficulty of implementing stimuli, manipulanda, and data recording underwater, meaning that this is frequently done with gates to control subjects, physical displays as stimuli, and visual annotation of videos to record data. To overcome these restrictions we developed GoFish, a fully-automated platform for behavioural experiments. GoFish includes real-time video tracking of subjects, presentation of stimuli in a computer screen, an automatic feeder device, and closed-loop control of task contingencies and data acquisition. The design and software components of the platform are freely available, while the hardware is widely available and relatively inexpensive. The control software, Bonsai, is user-friendly and supported by a growing community of users. As an illustration and test of its use, we present the results of 2 experiments on discrimination learning, reversal, and choice in goldfish (Carassius auratus). GoFish enables the relatively easy implementation of high-throughput tasks and the acquisition of rich behavioural data. Our platform has the potential to become a widely used tool that facilitates complex behavioural experiments in aquatic species.
A computational modeling of primary-microRNA expression
Authors: Zheng Hansi , Wang Saidi , Li Xiaoman , Hu Haiyan 
Published: April 2022

AbstractMicroRNAs (miRNAs) play crucial roles in gene regulation. Most studies so far focus on mature miRNAs, which leaves many gaps in our knowledge in primary miRNAs (pri-miRNA). To fill these gaps, we attempted to model the expression of pri-miRNAs in 1829 primary cell types and tissues in this study. We demonstrated that the expression of their associated mRNAs could model the expression of the pri-miRNAs well. These associated mRNAs are different from their corresponding target mRNAs and are enriched with specific functions. The majority of the associated mRNAs of a miRNA are shared across conditions, although a fraction of the associated mRNAs are condition-specific. Our study shed new light on the understanding of miRNA biogenesis and general gene transcriptional regulation.
Differential pathogenesis of SARS-CoV-2 variants of concern in human ACE2-expressing mice
Authors: Natekar Janhavi Prasad , Pathak Heather , Stone Shannon , Kumari Pratima , Sharma Shaligram , Arora Komal , Rothan Hussin Alwan , Kumar Mukesh 
Published: April 2022

AbstractSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic resulting in millions of deaths worldwide. Increasingly contagious variants of concern (VoC) have fueled recurring global infection waves. A major question is the relative severity of disease caused by the previous and currently circulating variants of SARS-CoV-2. In this study, we evaluated the pathogenesis of SARS-CoV-2 variants in human ACE-2-expressing (K18-hACE2) mice. Eight-week-old K18-hACE2 mice were inoculated intranasally with a representative virus from the original B.1 lineage, or the emerging B.1.1.7 (alpha), B.1.351 (beta), B.1.617.2 (delta) or B.1.1.529 (omicron) lineages. We also infected a group of mice with the mouse-adapted SARS-CoV-2 (MA10). Our results demonstrate that B.1.1.7, B.1.351 and B.1.617.2 viruses are significantly more lethal than B.1 strain in K18-hACE2 mice. Infection with B.1.1.7, B.1.351 and B.1.617.2 variants resulted in significantly higher virus titers in the lungs and brain of mice compared to the B.1 virus. Interestingly, mice infected with the B.1.1.529 variant exhibited less severe clinical signs and high survival rate. We found that B.1.1.529 replication was significantly lower in the lungs and brain of infected mice in comparison to other VoC. Transcription levels of cytokines and chemokines in the lungs of the B.1.1.529-infected mice were significantly less when compared to those challenged with the B.1.1.7 virus. Together, our data provide insights into the pathogenesis of the previous and circulating SARS-CoV-2 VoC in mice.
Drug addiction mutations unveil a methylation ceiling in EZH2-mutant lymphoma
Authors: Kwok Hui Si , Freedy Allyson M. , Siegenfeld Allison P. , Morriss Julia W. , Waterbury Amanda L. , Kissler Stephen M. , Liau Brian B. 
Published: April 2022

AbstractCancer mutations in Polycomb Repressive Complex 2 (PRC2) drive aberrant epigenetic states. Although therapies inhibiting the PRC2 enzymatic component EZH2 are FDA-approved, oncogene-specific dependencies remain to be discovered. Here, we identify mutations that confer both resistance and drug addiction to PRC2 inhibitors in EZH2-mutant lymphoma, resulting in cancer cells that paradoxically depend on drug for survival. Drug addiction is mediated by hypermorphic mutations in the CXC domain of EZH2, which maintain H3K27me3 levels even in the presence of PRC2 inhibitors. Drug removal leads to overspreading of H3K27me3, surpassing a repressive methylation ceiling compatible with lymphoma cell survival. Activating EZH2 cancer mutations establish an epigenetic state precariously close to this ceiling, which we show can be breached by inhibition of SETD2, a PRC2 antagonist, to block lymphoma growth. More broadly, we highlight how approaches to identify drug addiction mutations can be leveraged to discover cancer vulnerabilities.
Molecular-Dynamics Simulation Methods for Macromolecular Crystallography
Authors: Wych David C. , Aoto Phillip C. , Vu Lily , Wolff Alexander M. , Mobley David L. , Fraser James S. , Taylor Susan S. , Wall Michael E. 
Published: April 2022

AbstractTo assess the potential benefits of molecular-dynamics (MD) simulations for macromolecular crystallography (MX), we performed room-temperature X-ray diffraction studies of the catalytic subunit of mouse protein kinase A (PKA-C). We then performed crystalline MD simulations of PKA-C, computed simulated electron densities from the water, protein, and ion components of the MD simulations, and carefully compared them to the initial crystal structure. The results led to the development of an MD-MX analysis procedure and several associated methods: 1)density comparisonto evaluate consistency between the MD and the initial crystal structure model; 2)water buildingto generate alternative solvent models; and 3)protein remodelingto improve the crystal structure where interpretation of density is unclear. This procedure produced a revised structure of PKA with a new ordered water model and a modified protein structure. The revisions yield new insights into PKA mechanisms, including: a sensitivity of the His294 conformation to protonation state, with potential consequences for regulation of substrate binding; a remodeling of the Lys217 side chain along with a bound phosphate; an alternative conformation for Lys213 associated with binding to the regulatory subunit; and an alternative conformation for catalytic base Asp166 and nearby waters, suggesting a mechanism of progression of the phosphotransfer reaction via changes in Mg2+coordination. Based on the benefits seen applying these methods to PKA, we recommend incorporating our MD-MX procedure into MX studies, to decide among ambiguous interpretations of electron density that occur, inevitably, as part of standard model refinement.
Climate change will redefine taxonomic, functional, and phylogenetic diversity patterns of Odonata in space and time
Authors: Cancellario Tommaso , Miranda Rafael , Baquero Enrique , Fontaneto Diego , Martínez Alejandro , Mammola Stefano 
Published: April 2022

AbstractClimate change is rearranging the mosaic of biodiversity on our planet. These broad-scale species re-distributions will affect the structure of communities across multiple biodiversity facets (taxonomic, phylogenetic, and functional diversity). The current challenges to understand such effects involve focusing on organisms other than vertebrates and considering the signature of species redistribution on phylogenetic and functional diversity in addition to species composition. Using European dragonflies and damselflies (Odonata), we asked: i) how climate change will redefine taxonomic, phylogenetic, and functional diversity at continental scales; ii) which traits will mediate species’ response to global change; and iii) whether this response will be conserved across the phylogeny. First, we constructed stacked species distribution models for 107 species of Odonata under current and future climate conditions. Then, we quantified the temporal variation of taxonomic, functional and phylogenetic components, forecasting alpha and beta diversity changes through our geographical grid. Lastly, we used phylogenetic comparative models to test the influence of phylogeny and traits on range shifts. We observed broad latitudinal and altitudinal rearrangements in community composition driven by climate change. Given the high dispersal ability of Odonata, changes are predicted to be rapid, especially in areas experiencing faster climate change rates. According to our predictions, changes in species composition cascade to affect functional and phylogenetic diversity, determining broad turnovers in traits and evolutionary lineages. There was no clear phylogenetic signal in the range-shift response of European Odonata to climate change. According to our phylogenetic regression models, only body size and flight period can be partly correlated with observed range shifts. By considering all three primary facets of biodiversity, our results support the design of inclusive management and conservation strategies, accounting not only for the diversity of species, but also the services they provide and the phylogenetic heritage they carry in a targeted ecosystem.
Mechanism-based crosslinking probes capture E. coli ketosynthase FabB in conformationally-distinct catalytic states
Authors: Chen Aochiu , Mindrebo Jeffrey T. , Davis Tony D. , Kim Woojoo E. , Katsuyama Yohei , Jiang Ziran , Ohnishi Yasuo , Noel Joseph P. , Burkart Michael D. 
Published: April 2022

AbstractKetosynthases (KS) catalyse essential carbon-carbon bond forming reactions in fatty acid biosynthesis using a two-step, ping-pong reaction mechanism. In E. coli, there are two homodimeric elongating KSs, FabB and FabF, both of which possess overlapping substrate selectivity. However, FabB is essential for the biosynthesis of unsaturated fatty acids (UFAs) required for cell survival in the absence of exogenous UFAs. Additionally, FabB has reduced activity towards substrates longer than 14 carbons, whereas FabF efficiently catalyses the elongation of saturated C14 and unsaturated C16:1 acyl-acyl carrier protein (ACP) complexes. In this study, we solved two crosslinked crystal structures of FabB in complex with ACPs functionalized with long-chain fatty acid crosslinking probes that approximate catalytic steps. Both homodimeric structures possess asymmetric substrate binding pockets, suggestive of cooperative relationships between the two FabB monomers when engaged with C14 and C16 acyl chains. In addition, these structures capture an unusual rotamer of the active site gating residue, F392, potentially representative of the catalytic state prior to substrate release. These structures demonstrate the utility of mechanism-based crosslinking methods to capture and elucidate at near atomic resolution conformational transitions accompanying KS-mediated catalysis.SynopsisCrystal structures of KS-ACP crosslinked complex elucidate chain length preference and substrate processing mechanism of E. coli FabB.
Characterizing the extracellular matrix transcriptome of cervical, endometrial, and uterine cancers
Authors: Cook Carson J. , Miller Andrew E. , Barker Thomas H. , Di Yanming , Fogg Kaitlin C. 
Published: April 2022

ABSTRACTIncreasingly, the matrisome, a set of proteins that form the core of the extracellular matrix (ECM) or are closely associated with it, has been demonstrated to play a key role in tumor progression. However, in the context of gynecological cancers, the matrisome has not been well characterized. A holistic, yet targeted, exploration of the tumor microenvironment is critical for better understanding the progression of gynecological cancers, identifying key biomarkers for cancer progression, establishing the role of gene expression in patient survival, and for assisting in the development of new targeted therapies. In this work, we explored the matrisome gene expression profiles of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), uterine corpus endometrial carcinoma (UCEC), and uterine carcinosarcoma (UCS) using publicly available RNA-seq data from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) portal. We hypothesized that the matrisomal expression patterns of CESC, UCEC, and UCS would be highly distinct with respect to genes which are differentially expressed and hold inferential significance with respect to tumor progression, patient survival, or both. Through a combination of statistical and machine learning analysis techniques, we identified sets of genes and gene networks which characterized each of the gynecological cancer cohorts. Our findings demonstrate that the matrisome is critical for characterizing gynecological cancers and transcriptomic mechanisms of cancer progression and outcome. Furthermore, while the goal of pan-cancer transcriptional analyses is often to highlight the shared attributes of these cancer types, we demonstrate that they are highly distinct diseases which require separate analysis, modeling, and treatment approaches. In future studies, matrisome genes and gene ontology terms that were identified as critical for predicting patient survival or cancer stage can be evaluated as potential drug targets and incorporated intoin vitromodels of disease.
The left posterior angular gyrus is engaged by autobiographical recall not object-semantics, or event-semantics: Evidence from contrastive propositional speech production
Authors: Humphreys Gina F. , Ajay D. Halai , Francesca M. Branzi , Lambon Ralph Matthew A. 
Published: April 2022

AbstractThe angular gyrus (AG) has been implicated in a myriad of cognitive functions. Using the previously under-studied naturalistic task of propositional speech production, we investigated the engagement of the left posterior AG (pAG) by three forms of memory: 1) episodic/autobiographical memory, 2) object semantic-memory, and 3) event-semantic knowledge. We conducted an ALE meta-analysis, followed by an fMRI study. The meta-analysis showed that pAG is only engaged as part of the propositional speech network when the task carries an autobiographical component. This finding was supported by the fMRI results, which also showed that: 1) pAG was positively engaged during autobiographical memory retrieval; 2) pAG was strongly deactivated for definitions of object semantics and non-propositional speech; 3) pAG activation increased with the degree to which the event descriptions relied on autobiographical information; 4) critically, the pAG showed a different pattern to known semantic representation regions (e.g., ventral anterior temporal lobe (vATL)) thereby providing clear evidence that the pAG is not acting as a semantic hub. Instead, the pAG activation profile directly mirrored that found in the wider autobiographical retrieval network. We propose that information stored elsewhere in the episodic system is temporally buffered online in the pAG during autobiographical retrieval/memory construction.
Sub-type selective muscarinic acetylcholine receptors modulation for the treatment of parkinsonian tremor
Authors: Bickham Kinsey , Withers C. Price , Diedrich Augusto , Moehle Mark Stephen 
Published: April 2022

AbstractParkinson’s Disease is characterized by hallmark motor symptoms including resting tremor, akinesia, rigidity, and postural instability. In patient surveys of Parkinson’s Disease symptoms and quality of life, tremor consistently ranks among the top concerns of patients with disease. However, the gold standard of treatment, levodopa, has inconsistent or incomplete anti-tremor effects in patients, necessitating new therapeutic strategies to help relieve this burden. Non-selective anti-muscarinic acetylcholine receptor therapeutic agents which target each of the 5 muscarinic receptor subtypes have been used as an adjunct therapy in this disease, as well as other movement disorders, and have been shown to have anti-tremor efficacy. Despite this, anti-muscarinic therapy is poorly tolerated due to adverse effects. Recent pharmacological advances have led to the discovery of muscarinic subtype selective antagonists that may keep the anti-tremor efficacy of non-selective compounds, while reducing or eliminating adverse effects. Here, we directly test this hypothesis using pharmacological models of parkinsonian tremor combined with recently discovered selective positive allosteric modulators and antagonists of the predominant brain expressed muscarinic receptors M1, M4, and M5. Surprisingly, we find that selective modulation of M1, M4, or M5 does not reduce tremor in these pre-clinical models, suggesting that central or peripheral M2 or M3 receptors may be responsible for the anti-tremor efficacy of non-selective anti-muscarinic therapies currently used in the clinic.
Learning in chunks: A model of hippocampal representations for processing temporal regularities in statistical learning
Authors: Tang Wei , Christiansen Morten H. , Qi Zhenghan 
Published: April 2022

AbstractWe investigated the neural basis of chunking during statistical learning (SL). Behavioral evidence suggests that a common mechanism in learning and memory can serve to combine smaller units into larger ones to facilitate sensory and higher-level processing. And yet, the neural underpinnings of this mechanism remain unclear. Drawing insights from previous findings of neural codes in the hippocampus, we propose a computational model to account for the temporal chunking process in SL for sequential inputs. We operationalize chunking into a hidden Markov model (HMM) that incorporates two core principles: (1) the hidden states represent serial order rather than specific visual features, and (2) the formation of temporal chunks leads to autocorrelated brain activity. We show with numeric simulations that the HMM can decode embedded triplet representations when both assumptions hold. Applying the HMM to functional neuroimaging data from subjects performing a visual SL task, we show that decoding was successful (1) for triplet sequences but not random sequences, (2) at the later stage but not earlier stage of learning, and (3) in the hippocampus but not in the early visual cortex. These results provide evidence for a hippocampal representation of generalized temporal structure emerged from sequential visual input, shedding light on the chunking mechanism for SL.SignificanceIn statistical learning (SL), individuals develop internal representations of patterns after brief exposure to structured stimuli. People tend to recognize frequently co-occurring items as a single unit. This process, known as “chunking”, is understood to play an important role in facilitating sensory processing for learning. However, its neural underpinnings remain unclear. In this study we draw insights from hippocampal coding theories and introduce a chunking model focusing on generalized presentations for SL. With functional neuroimaging data from human subjects performing a visual learning task, the chunking model successfully decoded the temporal regularities embedded in the sequential inputs. This model and related findings provide critical evidence for a chunking process underlying SL as well as its representation in the human hippocampus.
Neural systems underlying RDoC social constructs: An activation likelihood estimation meta-analysis
Authors: Lobo Rosario Pintos , Bottenhorn Katherine L. , Riedel Michael C. , Toma Afra I. , Hare Megan M. , Smith Donisha D. , Moor Alexandra C. , Cowan Isis K. , Valdes Javier A. , Bartley Jessica E. , Salo Taylor , Boeving Emily R. , Pankey Brianna , Sutherland Matthew T. , Musser Erica D. , Laird Angela R. 
Published: April 2022

AbstractNeuroscientists have sought to identify the underlying neural systems supporting social processing that allow interaction and communication, forming social relationships, and navigating the social world. Through the use of NIMH’s Research Domain Criteria (RDoC) framework, we evaluated consensus among studies that examined brain activity during social tasks to elucidate regions comprising the “social brain”. We examined convergence across tasks corresponding to the four RDoC social constructs, including Affiliation and Attachment, Social Communication, Perception and Understanding of Self, and Perception and Understanding of Others. We performed a series of coordinate-based meta-analyses using the activation likelihood estimate (ALE) method. Meta-analysis was performed on whole-brain coordinates reported from 864 fMRI contrasts using the NiMARE Python package, revealing convergence in medial prefrontal cortex, anterior cingulate cortex, posterior cingulate cortex, temporoparietal junction, bilateral insula, amygdala, fusiform gyrus, precuneus, and thalamus. Additionally, four separate RDoC-based meta-analyses revealed differential convergence associated with the four social constructs. These outcomes highlight the neural support underlying these social constructs and inform future research on alterations among neurotypical and atypical populations.
Chronic hM3Dq-DREADD mediated chemogenetic activation of parvalbumin-positive inhibitory interneurons in postnatal life alters anxiety and despair-like behavior in adulthood in a task and sex-dependent manner
Authors: Banerjee Toshali , Pati Sthitapranjya , Tiwari Praachi , Vaidya Vidita A. 
Published: April 2022

AbstractAnimal models of early adversity or neurodevelopmental disorders are associated with altered parvalbumin (PV)-positive inhibitory interneuron number and function, correlated with a dysregulated excitation-inhibition (E/I) balance that is implicated in the pathophysiology of neuropsychiatric disorders. We sought to address whether altering neuronal activity of PV-positive interneurons during the postnatal developmental window influences the emergence of anxio-depressive behaviors in adulthood, which are known to be perturbed in models of early adversity and neurodevelopmental disorders. We used a PV-Cre::hM3Dq-DREADD bigenic mouse line that selectively expresses the hM3Dq-DREADD receptor in PV-positive interneurons, and chemogenetically enhanced Gq signaling in PV-positive interneurons during the postnatal window via administration of the DREADD agonist, clozapine-N-oxide. Immunofluorescence studies indicated the selective expression of hM3Dq-DREADD in PV-positive interneurons in limbic circuits, and revealed a reduction in expression of the neuronal activity marker, c-Fos, in these circuits, following chemogenetic hM3Dq-DREADD mediated activation of PV-positive inhibitory interneurons. We noted no change in either growth or sensorimotor reflex milestones following chronic hM3Dq-DREADD mediated chemogenetic activation of PV-positive inhibitory interneurons in postnatal life. Adult male and female PV-Cre::hM3Dq-DREADD bigenic mice with a history of postnatal chemogenetic activation of PV-positive interneurons exhibited a reduction in anxiety and despair-like behavior in adulthood, which was noted in both a behavioral task and sex-dependent manner. These results indicate that altering neuronal activity within PV-positive interneurons during the critical postnatal developmental window can shape the emergence of anxio-depressive behaviors in adulthood, and suggest that interactions with sex as a variable play a key role in determining behavioral outcomes.