The language of paranoia: linguistic analysis of SMI speech with considerations of race and sex.

Background: Linguistic analysis, notably using conceptually derived linguistic categories, has been used to quantify various aspects of serious mental illness. It has the potential for understanding paranoia, defined in terms of perceived and intentional threats from others. However, paranoia and the language expressing it potentially varies due to demographic factors, notably race and sex.

Network Analysis of Autistic Language Learners Along the Small World Spectrum.

Recent network analyses of vocabulary growth revealed important relationships between the structure of the semantic environment and early vocabulary acquisition in non-autistic children. However, autistic children may be less likely to encode associated features of novel objects, suggesting divergent processes for acquiring semantic information about words. We examined the expressive vocabularies of 815 non-autistic and 163 autistic children (words produced: M = 183.

The impact of threat of shock on memory for threat-irrelevant information: Evidence for a role of reduced semantic organization.

Research into the effects of acute anxiety on episodic memory has produced inconsistent findings, particularly for threat-neutral information. In two experiments, we tested the hypothesis that anxiety induced by threat of shock can interfere with the use of semantic-organizational processes that benefit memory. In Experiment 1, participants viewed and freely recalled two lists of semantically unrelated neutral words, one encoded in a threatening context (threat blocks) and one encoded without threat (safe blocks).

A longitudinal evaluation of personalized intrinsic network topography and cognitive decline in Parkinson's disease.

Resting state functional magnetic resonance imaging (R-fMRI) offers insight into how synchrony within and between brain networks is altered in disease states. Individual and disease-related variability in intrinsic connectivity networks may influence our interpretation of R-fMRI data. We used a personalized approach designed to account for individual variation in the spatial location of correlation maxima to evaluate R-fMRI differences between Parkinson's disease (PD) patients who showed cognitive decline, those who remained cognitively stable and cognitively stable controls.

Representational similarity learning reveals a graded multidimensional semantic space in the human anterior temporal cortex.

Neurocognitive models of semantic memory have proposed that the ventral anterior temporal lobes (vATLs) encode a graded and multidimensional semantic space-yet neuroimaging studies seeking brain regions that encode semantic structure rarely identify these areas. In simulations, we show that this discrepancy may arise from a crucial mismatch between theory and analysis approach. Utilizing an analysis recently formulated to investigate graded multidimensional representations, (RSL), we decoded semantic structure from ECoG data collected from the vATL cortical surface while participants named line drawings of common items.

Interpretations of meaningful and ambiguous hand gestures in autistic and non-autistic adults: A norming study.

Gestures are ubiquitous in human communication, and a growing but inconsistent body of research suggests that people with autism spectrum disorder (ASD) may process co-speech gestures differently from neurotypical individuals. To facilitate research on this topic, we created a database of 162 gesture videos that have been normed for comprehensibility by both autistic and non-autistic raters. These videos portray an actor performing silent gestures that range from highly meaningful (e.

Decoding semantic representations in mind and brain.

A key goal for cognitive neuroscience is to understand the neurocognitive systems that support semantic memory. Recent multivariate analyses of neuroimaging data have contributed greatly to this effort, but the rapid development of these novel approaches has made it difficult to track the diversity of findings and to understand how and why they sometimes lead to contradictory conclusions. We address this challenge by reviewing cognitive theories of semantic representation and their neural instantiation.

Using in vivo functional and structural connectivity to predict chronic stroke aphasia deficits.

Focal brain damage caused by stroke can result in aphasia and advances in cognitive neuroscience suggest that impairment may be associated with network-level disorder rather than just circumscribed cortical damage. Several studies have shown meaningful relationships between brain-behaviour using lesions; however, only a handful of studies have incorporated in vivo structural and functional connectivity. Patients with chronic post-stroke aphasia were assessed with structural (n = 68) and functional (n = 39) MRI to assess whether predicting performance can be improved with multiple modalities and if additional variance can be explained compared to lesion models alone.

Bipartite functional fractionation within the neural system for social cognition supports the psychological continuity of self versus other.

Research of social neuroscience establishes that regions in the brain's default-mode network (DN) and semantic network (SN) are engaged by socio-cognitive tasks. Research of the human connectome shows that DN and SN regions are both situated at the transmodal end of a cortical gradient but differ in their loci along this gradient. Here we integrated these 2 bodies of research, used the psychological continuity of self versus other as a "test-case," and used functional magnetic resonance imaging to investigate whether these 2 networks would encode social concepts differently.

Child-oriented word associations improve models of early word learning.

How words are associated within the linguistic environment conveys semantic content; however, different contexts induce different linguistic patterns. For instance, it is well known that adults speak differently to children than to other adults. We present results from a new word association study in which adult participants were instructed to produce either unconstrained or child-oriented responses to each cue, where cues included 672 nouns, verbs, adjectives, and other word forms from the McArthur-Bates Communicative Development Inventory (CDI; Fenson et al.

High Predictive Accuracy of Negative Schizotypy With Acoustic Measures.

Negative schizotypal traits potentially can be digitally phenotyped using objective vocal analysis. Prior attempts have shown mixed success in this regard, potentially because acoustic analysis has relied on small, constrained feature sets. We employed machine learning to (a) optimize and cross-validate predictive models of self-reported negative schizotypy using a large acoustic feature set, (b) evaluate model performance as a function of sex and speaking task, (c) understand potential mechanisms underlying negative schizotypal traits by evaluating the key acoustic features within these models, and (d) examine model performance in its convergence with clinical symptoms and cognitive functioning.

Evidence for a deep, distributed and dynamic code for animacy in human ventral anterior temporal cortex.

How does the human brain encode semantic information about objects? This paper reconciles two seemingly contradictory views. The first proposes that local neural populations independently encode semantic features; the second, that semantic representations arise as a dynamic distributed code that changes radically with stimulus processing. Combining simulations with a well-known neural network model of semantic memory, multivariate pattern classification, and human electrocorticography, we find that both views are partially correct: information about the animacy of a depicted stimulus is distributed across ventral temporal cortex in a dynamic code possessing feature-like elements posteriorly but with elements that change rapidly and nonlinearly in anterior regions.

Validating Biobehavioral Technologies for Use in Clinical Psychiatry.

The last decade has witnessed the development of sophisticated biobehavioral and genetic, ambulatory, and other measures that promise unprecedented insight into psychiatric disorders. As yet, clinical sciences have struggled with implementing these objective measures and they have yet to move beyond "proof of concept." In part, this struggle reflects a traditional, and conceptually flawed, application of traditional psychometrics (i.

Mass Spectrometry and Microbial Diagnostics in the Clinical Laboratory.

Over the past decade, matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry has revolutionized the practice of clinical microbiology and infectious disease diagnostics. Rapid advancement has occurred through the development and implementation of mass spectrometric protein profiling technologies that are widely available. Ease of sample preparation, rapid turnaround times, and high throughput accuracy have accelerated acceptance within the clinical laboratory.

Finding Distributed Needles in Neural Haystacks.

The human cortex encodes information in complex networks that can be anatomically dispersed and variable in their microstructure across individuals. Using simulations with neural network models, we show that contemporary statistical methods for functional brain imaging-including univariate contrast, searchlight multivariate pattern classification, and whole-brain decoding with L1 or L2 regularization-each have critical and complementary blind spots under these conditions. We then introduce the sparse-overlapping-sets (SOS) LASSO-a whole-brain multivariate approach that exploits structured sparsity to find network-distributed information-and show in simulation that it captures the advantages of other approaches while avoiding their limitations.

Current Perspective: 3D Spheroid Models Utilizing Human-Based Cells for Investigating Metabolism-Dependent Drug-Induced Liver Injury.

Drug-induced liver injury (DILI) remains a leading cause for the withdrawal of approved drugs. This has significant financial implications for pharmaceutical companies, places increasing strain on global health services, and causes harm to patients. For these reasons, it is essential that liver models are capable of detecting DILI-positive compounds and their underlying mechanisms, prior to their approval and administration to patients or volunteers in clinical trials.

Characterizing the early vocabulary profiles of preverbal and minimally verbal children with autism spectrum disorder.

Although preverbal and minimally verbal children with autism spectrum disorder represent a significant portion of the autism spectrum disorder population, we have a limited understanding of and characterization of them. Although it is a given that their lexical profiles contain fewer words, it is important to determine whether (a) the words preverbal and minimally verbal children with autism spectrum disorder produce are similar to the first words typically developing children produce or (b) there are unique features of the limited words that preverbal and minimally verbal children with autism spectrum disorder produce. The current study compared the early word profiles of preverbal and minimally verbal children with autism spectrum disorder to vocabulary-matched typically developing toddlers.

Using machine learning of computerized vocal expression to measure blunted vocal affect and alogia.

Negative symptoms are a transdiagnostic feature of serious mental illness (SMI) that can be potentially "digitally phenotyped" using objective vocal analysis. In prior studies, vocal measures show low convergence with clinical ratings, potentially because analysis has used small, constrained acoustic feature sets. We sought to evaluate (1) whether clinically rated blunted vocal affect (BvA)/alogia could be accurately modelled using machine learning (ML) with a large feature set from two separate tasks (i.

Preparation of Primary Rat Hepatocyte Spheroids Utilizing the Liquid-Overlay Technique.

Herein, we describe a protocol for the preparation and analysis of primary isolated rat hepatocytes in a 3D cell culture format described as spheroids. The hepatocyte cells spontaneously self-aggregate into spheroids without the need for synthetic extracellular matrices or hydrogels. Primary rat hepatocytes (PRHs) are a readily available source of primary differentiated liver cells and therefore conserve many of the required liver-specific functional markers, and elicit the natural in vivo phenotype when compared with common hepatic cells lines.

Metal Oxide Laser Ionization Mass Spectrometry Imaging (MOLI MSI) Using Cerium(IV) Oxide.

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is a powerful technique for spatially resolved metabolomics. A variation on MALDI, termed metal oxide laser ionization (MOLI), capitalizes on the unique property of cerium(IV) oxide (CeO) to induce laser-catalyzed fatty acyl cleavage from lipids and has been utilized for bacterial identification. In this study, we present the development and utilization of CeO as an MSI catalyst.

Matrix metalloprotease-1 inhibits and disrupts Enterococcus faecalis biofilms.

Enterococcus faecalis is a major opportunistic pathogen that readily forms protective biofilms leading to chronic infections. Biofilms protect bacteria from detergent solutions, antimicrobial agents, environmental stress, and effectively make bacteria 10 to 1000-fold more resistant to antibiotic treatment. Extracellular proteins and polysaccharides are primary components of biofilms and play a key role in cell survival, microbial persistence, cellular interaction, and maturation of E.

Efficient protease based purification of recombinant matrix metalloprotease-1 in E. coli.

MMP1 is an essential enzyme for tissue remodeling both in normal and pathological states. We report a method of purifying activated human MMP1 in E. coli without using urea or 4-Aminophenylmercuric acetate (APMA).

Perceptual Learning Generalization from Sequential Perceptual Training as a Change in Learning Rate.

With practice, humans tend to improve their performance on most tasks. But do such improvements then generalize to new tasks? Although early work documented primarily task-specific learning outcomes in the domain of perceptual learning [1-3], an emerging body of research has shown that significant learning generalization is possible under some training conditions [4-9]. Interestingly, however, research in this vein has focused nearly exclusively on just one possible manifestation of learning generalization, wherein training on one task produces an immediate boost to performance on the new task.

Transfer in Rule-Based Category Learning Depends on the Training Task.

While learning is often highly specific to the exact stimuli and tasks used during training, there are cases where training results in learning that generalizes more broadly. It has been previously argued that the degree of specificity can be predicted based upon the learning solution(s) dictated by the particular demands of the training task. Here we applied this logic in the domain of rule-based categorization learning.