Age-related differences in affective behaviors in mice: possible role of prefrontal cortical-hippocampal functional connectivity and metabolomic profiles.

Introduction: The differential expression of emotional reactivity from early to late adulthood may involve maturation of prefrontal cortical responses to negative valence stimuli. In mice, age-related changes in affective behaviors have been reported, but the functional neural circuitry warrants further investigation. Methods: We assessed age variations in affective behaviors and functional connectivity in male and female C57BL6/J mice. Mice aged 10, 30 and 60 weeks (wo) were tested over 8 weeks for open field activity, sucrose preference, social interactions, fear conditioning, and functional neuroimaging. Prefrontal cortical and hippocampal tissues were excised for metabolomics. Results: Our results indicate that young and old mice differ significantly in affective behavioral, functional connectome and prefrontal cortical-hippocampal metabolome. Young mice show a greater responsivity to novel environmental and social stimuli compared to older mice. Conversely, late middle-aged mice (60wo group) display variable patterns of fear conditioning and during re-testing in a modified context. Functional connectivity between a temporal cortical/auditory cortex network and subregions of the anterior cingulate cortex and ventral hippocampus, and a greater network modularity and assortative mixing of nodes was stronger in young versus older adult mice. Metabolome analyses identified differences in several essential amino acids between 10wo mice and the other age groups. Discussion: The results support differential expression of 'emotionality' across distinct stages of the mouse lifespan involving greater prefrontal-hippocampal connectivity and neurochemistry.

Age-Related Differences in Affective Behaviors in Mice: Possible Role of Prefrontal Cortical-Hippocampal Functional Connectivity and Metabolomic Profiles.

The differential expression of emotional reactivity from early to late adulthood may involve maturation of prefrontal cortical responses to negative valence stimuli. In mice, age-related changes in affective behaviors have been reported, but the functional neural circuitry warrants further investigation. We assessed age variations in affective behaviors and functional connectivity in male and female C57BL6/J mice. Mice aged 10, 30 and 60 weeks (wo) were tested over 8 weeks for open field activity, sucrose preference, social interactions, fear conditioning, and functional neuroimaging. Prefrontal cortical and hippocampal tissues were excised for metabolomics. Our results indicate that young and old mice differ significantly in affective behavioral, functional connectome and prefrontal cortical-hippocampal metabolome. Young mice show a greater responsivity to novel environmental and social stimuli compared to older mice. Conversely, late middle-aged mice (60wo group) display variable patterns of fear conditioning and with re-testing with a modified context. Functional connectivity between a temporal cortical/auditory cortex network and subregions of the anterior cingulate cortex and ventral hippocampus, and a greater network modularity and assortative mixing of nodes was stronger in young versus older adult mice. Metabolome analyses identified differences in several essential amino acids between 10wo mice and the other age groups. The results support differential expression of 'emotionality' across distinct stages of the mouse lifespan involving greater prefrontal-hippocampal connectivity and neurochemistry.

Discovering Content through Text Mining for a Synthetic Biology Knowledge System.

Scientific articles contain a wealth of information about experimental methods and results describing biological designs. Due to its unstructured nature and multiple sources of ambiguity and variability, extracting this information from text is a difficult task. In this paper, we describe the development of the synthetic biology knowledge system (SBKS) text processing pipeline. The pipeline uses natural language processing techniques to extract and correlate information from the literature for synthetic biology researchers. Specifically, we apply named entity recognition, relation extraction, concept grounding, and topic modeling to extract information from published literature to link articles to elements within our knowledge system. Our results show the efficacy of each of the components on synthetic biology literature and provide future directions for further advancement of the pipeline.

Effects of data and entity ablation on multitask learning models for biomedical entity recognition.

MOTIVATION: Training domain-specific named entity recognition (NER) models requires high quality hand curated gold standard datasets which are time-consuming and expensive to create. Furthermore, the storage and memory required to deploy NLP models can be prohibitive when the number of tasks is large. In this work, we explore utilizing multi-task learning to reduce the amount of training data needed to train new domain-specific models. We evaluate our system across 22 distinct biomedical NER datasets and evaluate the extent to which transfer learning helps task performance using two forms of ablation. RESULTS: We found that multitasking models generally do not improve performance, but in many cases perform on par compared to single-task models. However, we show that in some cases, new unseen tasks can be trained as a single model using less data by starting with weights from a multitask model and improve performance. AVAILABILITY: The software underlying this article are available in: https://github.com/NLPatVCU/multitasking_bert-1.

Synthetic Biology Knowledge System.

The Synthetic Biology Knowledge System (SBKS) is an instance of the SynBioHub repository that includes text and data information that has been mined from papers published in ACS Synthetic Biology. This paper describes the SBKS curation framework that is being developed to construct the knowledge stored in this repository. The text mining pipeline performs automatic annotation of the articles using natural language processing techniques to identify salient content such as key terms, relationships between terms, and main topics. The data mining pipeline performs automatic annotation of the sequences extracted from the supplemental documents with the genetic parts used in them. Together these two pipelines link genetic parts to papers describing the context in which they are used. Ultimately, SBKS will reduce the time necessary for synthetic biologists to find the information necessary to complete their designs.

3D Printing of High Viscosity Reinforced Silicone Elastomers.

Recent advances in additive manufacturing, specifically direct ink writing (DIW) and ink-jetting, have enabled the production of elastomeric silicone parts with deterministic control over the structure, shape, and mechanical properties. These new technologies offer rapid prototyping advantages and find applications in various fields, including biomedical devices, prosthetics, metamaterials, and soft robotics. Stereolithography (SLA) is a complementary approach with the ability to print with finer features and potentially higher throughput. However, all high-performance silicone elastomers are composites of polysiloxane networks reinforced with particulate filler, and consequently, silicone resins tend to have high viscosities (gel- or paste-like), which complicates or completely inhibits the layer-by-layer recoating process central to most SLA technologies. Herein, the design and build of a digital light projection SLA printer suitable for handling high-viscosity resins is demonstrated. Further, a series of UV-curable silicone resins with thiol-ene crosslinking and reinforced by a combination of fumed silica and MQ resins are also described. The resulting silicone elastomers are shown to have tunable mechanical properties, with 100-350% elongation and ultimate tensile strength from 1 to 2.5 MPa. Three-dimensional printed features of 0.4 mm were achieved, and complexity is demonstrated by octet-truss lattices that display negative stiffness.

Working memory, cortical dopamine tone, and frontoparietal brain recruitment in post-traumatic stress disorder: a randomized controlled trial.

Post-traumatic stress disorder (PTSD) leads to impairments in both cognitive and affective functioning. Animal work suggests that chronic stress reduces dopamine tone, and both animal and human studies argue that changes in dopamine tone influence working memory, a core executive function. These findings give rise to the hypothesis that increasing cortical dopamine tone in individuals with greater PTSD symptomatology should improve working memory performance. In this pharmacological functional magnetic resonance imaging (fMRI) study, 30 US military veterans exhibiting a range of PTSD severity completed an emotional working memory task. Each subject received both placebo and the catechol-O-methyl transferase inhibitor tolcapone, which increases cortical dopamine tone, in randomized, double-blind, counterbalanced fashion. Mnemonic discriminability (calculated with d', an index of the detectability of working memory signals) and response bias were evaluated in the context of task-related brain activations. Subjects with more severe PTSD showed both greater tolcapone-mediated improvements in d' and larger tolcapone-mediated reductions in liberally-biased responding for fearful stimuli. FMRI revealed that tolcapone augmented activity within bilateral frontoparietal control regions during the decision phase of the task. Specifically, tolcapone increased cortical responses to fearful relative to neutral stimuli in higher severity PTSD subjects, and reduced cortical responses to fearful stimuli for lower severity PTSD subjects. Moreover, tolcapone modulated prefrontal connectivity with areas overlapping the default mode network. These findings suggest that enhancing cortical dopamine tone may represent an approach to remediating cognitive and affective dysfunction in individuals with more severe PTSD symptoms.

BRCA1 and BRCA2 gene variants and nonsyndromic cleft lip/palate.

BACKGROUND: Nonsyndromic cleft lip with or without cleft palate (NSCL/P) is a debilitating condition that not only affects the individual, but the entire family. The purpose of this study was to investigate the association of BRCA1 and BRCA2 genes with NSCL/P. METHODS: Twelve polymorphisms in/nearby BRCA1 and BRCA2 were genotyped using Taqman chemistry. Our data set consisted of 3,473 individuals including 2,191 nonHispanic white (NHW) individuals (from 151 multiplex and 348 simplex families) and 1,282 Hispanic individuals (from 92 multiplex and 216 simplex families). Data analysis was performed using Family-Based Association Test (FBAT), stratified by ethnicity and family history of NSCL/P. RESULTS: Nominal associations were found between NSCL/P and BRCA1 in Hispanics and BRCA2 in NHW and Hispanics (p < .05). Significant haplotype associations were found between NSCL/P and both BRCA1 and BRCA2 (p ≤ .004). CONCLUSIONS: Our results suggest a modest association between BRCA1 and BRCA2 and NSCL/P. Further studies in additional populations and functional studies are needed to elucidate the role of these genes in developmental processes and signaling pathways contributing to NSCL/P.

CVLT-II Forced Choice Recognition Trial as an Embedded Validity Indicator: A Systematic Review of the Evidence.

OBJECTIVES: The Forced Choice Recognition (FCR) trial of the California Verbal Learning Test, 2nd edition, was designed as an embedded performance validity test (PVT). To our knowledge, this is the first systematic review of classification accuracy against reference PVTs. METHODS: Results from peer-reviewed studies with FCR data published since 2002 encompassing a variety of clinical, research, and forensic samples were summarized, including 37 studies with FCR failure rates (N=7575) and 17 with concordance rates with established PVTs (N=4432). RESULTS: All healthy controls scored >14 on FCR. On average, 16.9% of the entire sample scored ≤14, while 25.9% failed reference PVTs. Presence or absence of external incentives to appear impaired (as identified by researchers) resulted in different failure rates (13.6% vs. 3.5%), as did failing or passing reference PVTs (49.0% vs. 6.4%). FCR ≤14 produced an overall classification accuracy of 72%, demonstrating higher specificity (.93) than sensitivity (.50) to invalid performance. Failure rates increased with the severity of cognitive impairment. CONCLUSIONS: In the absence of serious neurocognitive disorder, FCR ≤14 is highly specific, but only moderately sensitive to invalid responding. Passing FCR does not rule out a non-credible presentation, but failing FCR rules it in with high accuracy. The heterogeneity in sample characteristics and reference PVTs, as well as the quality of the criterion measure across studies, is a major limitation of this review and the basic methodology of PVT research in general. (JINS, 2016, 22, 851-858).

Multiscale metallic metamaterials.

Materials with three-dimensional micro- and nanoarchitectures exhibit many beneficial mechanical, energy conversion and optical properties. However, these three-dimensional microarchitectures are significantly limited by their scalability. Efforts have only been successful only in demonstrating overall structure sizes of hundreds of micrometres, or contain size-scale gaps of several orders of magnitude. This results in degraded mechanical properties at the macroscale. Here we demonstrate hierarchical metamaterials with disparate three-dimensional features spanning seven orders of magnitude, from nanometres to centimetres. At the macroscale they achieve high tensile elasticity (>20%) not found in their brittle-like metallic constituents, and a near-constant specific strength. Creation of these materials is enabled by a high-resolution, large-area additive manufacturing technique with scalability not achievable by two-photon polymerization or traditional stereolithography. With overall part sizes approaching tens of centimetres, these unique nanostructured metamaterials might find use in a broad array of applications.

Selective enrichment of microRNAs in extracellular matrix vesicles produced by growth plate chondrocytes.

Matrix vesicles (MVs) are membrane organelles found in the extracellular matrix of calcifying cells, which contain matrix processing enzymes and regulate the extracellular environment via action of these enzymes. It is unknown whether MVs are also exosomic mediators of cell-cell communication via transfer of RNA material, and specifically, microRNA (miRNA). We investigated the presence of RNA in MVs isolated from cultures of costochondral growth zone chondrocytes. Our results showed that the average yield of MV RNA was 1.93±0.78ng RNA/10(4) cells, which was approximately 0.1% of the parent cell's total RNA. MV RNA was well-protected from RNase by the lipid membrane and was highly enriched in small RNA molecules compared to cells. Moreover, coding and non-coding small RNAs in MVs were in proportions that differed from parent cells. Enrichment of specific miRNAs was consistently observed in all three miRNA detection platforms that we used, suggesting that miRNAs are selectively packaged into MVs. MV-enriched miRNAs were related to different signaling pathways associated with bone formation. This study suggests a significant role for MVs as "matrisomes" in cell-cell communication in cartilage and bone development via transfer of specific miRNAs.