Accurate aphasia diagnosis is important in stroke care. A wide range of language tests are available and include informal assessments, tests developed by healthcare institutions and commercially published tests available for purchase in pre-packaged kits. The psychometrics of these tests are often reported online or within the purchased test manuals, not the peer-reviewed literature, therefore the diagnostic capabilities of these measures have not been systematically evaluated. This review aimed to identify both commercial and non-commercial language tests and tests used in stroke care and to examine the diagnostic capabilities of all identified measures in diagnosing aphasia in stroke populations.

Language has been extensively investigated by functional neuroimaging studies. However, only a limited number of structural neuroimaging studies have examined the relationship between language performance and brain structure in healthy adults, and the number is even less in older adults. The present study sought to investigate correlations between grey matter volumes and three standardized language tests in late life. The participants were 344 non-demented, community-dwelling adults aged 70-90 years, who were drawn from the population-based Sydney Memory and Ageing Study. The three language tests included the Controlled Oral Word Association Task (COWAT), Category Fluency (CF), and Boston Naming Test (BNT). Correlation analyses between voxel-wise GM volumes and language tests showed distinctive GM correlation patterns for each language test. The GM correlates were located in the right frontal and left temporal lobes for COWAT, in the left frontal and temporal lobes for CF, and in bilateral temporal lobes for BNT. Our findings largely corresponded to the neural substrates of language tasks revealed in fMRI studies, and we also observed a less hemispheric asymmetry in the GM correlates of the language tests. Furthermore, we divided the participants into two age groups (70-79 and 80-90 years old), and then examined the correlations between structural laterality indices and language performance for each group. A trend toward significant difference in the correlations was found between the two age groups, with stronger correlations in the group of 70-79 years old than those in the group of 80-90 years old. This difference might suggest a further decline of language lateralization in different stages of late life.

The objective of this study was to assess the classification capability of Breast Imaging Reporting and Data System (BI-RADS) ultrasound feature descriptors targeting established commercial transcriptomic gene signatures that guide management of breast cancer.

Primary visual cortex (V1) is generally thought of as a low-level sensory area that primarily processes basic visual features. Although there is evidence for multisensory effects on its activity, these are typically found for the processing of simple sounds and their properties, for example spatially or temporally-congruent simple sounds. However, in congenitally blind individuals, V1 is involved in language processing, with no evidence of major changes in anatomical connectivity that could explain this seemingly drastic functional change. This is at odds with current accounts of neural plasticity, which emphasize the role of connectivity and conserved function in determining a neural tissue's role even after atypical early experiences. To reconcile what appears to be unprecedented functional reorganization with known accounts of plasticity limitations, we tested whether V1's multisensory roles include responses to spoken language in sighted individuals. Using fMRI, we found that V1 in normally sighted individuals was indeed activated by comprehensible spoken sentences as compared to an incomprehensible reversed speech control condition, and more strongly so in the left compared to the right hemisphere. Activation in V1 for language was also significant and comparable for abstract and concrete words, suggesting it was not driven by visual imagery. Last, this activation did not stem from increased attention to the auditory onset of words, nor was it correlated with attentional arousal ratings, making general attention accounts an unlikely explanation. Together these findings suggest that V1 responds to spoken language even in sighted individuals, reflecting the binding of multisensory high-level signals, potentially to predict visual input. This capability might be the basis for the strong V1 language activation observed in people born blind, re-affirming the notion that plasticity is guided by pre-existing connectivity and abilities in the typically developed brain.

As COVID-19 vaccines become available, screening individuals for prior COVID-19 infection and vaccine response in point-of-care (POC) settings has renewed interest. We prospectively screened at-risk individuals for SARS-CoV-2 spike and nucleocapsid protein antibodies in a POC setting to determine if it was a feasible method to identify antibody from prior infection.

Lexical borrowing, the transfer of words from one language to another, is one of the most frequent processes in language evolution. In order to detect borrowings, linguists make use of various strategies, combining evidence from various sources. Despite the increasing popularity of computational approaches in comparative linguistics, automated approaches to lexical borrowing detection are still in their infancy, disregarding many aspects of the evidence that is routinely considered by human experts. One example for this kind of evidence are phonological and phonotactic clues that are especially useful for the detection of recent borrowings that have not yet been adapted to the structure of their recipient languages. In this study, we test how these clues can be exploited in automated frameworks for borrowing detection. By modeling phonology and phonotactics with the support of Support Vector Machines, Markov models, and recurrent neural networks, we propose a framework for the supervised detection of borrowings in mono-lingual wordlists. Based on a substantially revised dataset in which lexical borrowings have been thoroughly annotated for 41 different languages from different families, featuring a large typological diversity, we use these models to conduct a series of experiments to investigate their performance in mono-lingual borrowing detection. While the general results appear largely unsatisfying at a first glance, further tests show that the performance of our models improves with increasing amounts of attested borrowings and in those cases where most borrowings were introduced by one donor language alone. Our results show that phonological and phonotactic clues derived from monolingual language data alone are often not sufficient to detect borrowings when using them in isolation. Based on our detailed findings, however, we express hope that they could prove to be useful in integrated approaches that take multi-lingual information into account.

Learning new content and vocabulary in a foreign language can be particularly difficult. Yet, there are educational programs that require people to study in a language they are not native speakers of. For this reason, it is important to understand how these learning processes work and possibly differ from native language learning, as well as to develop strategies to ease this process. The current study takes advantage of emotionality-operationally defined as positive valence and high arousal-to improve memory. In two experiments, the present paper addresses whether participants have more difficulty learning the names of objects they have never seen before in their foreign language and whether embedding them in a positive semantic context can help make learning easier. With this in mind, we had participants (with a minimum of a B2 level of English) in two experiments (43 participants in Experiment 1 and 54 in Experiment 2) read descriptions of made-up objects-either positive or neutral and either in their native or a foreign language. The effects of language varied with the difficulty of the task and measure used. In both cases, learning the words in a positive context improved learning. Importantly, the effect of emotionality was not modulated by language, suggesting that the effects of emotionality are independent of language and could potentially be a useful tool for improving foreign language vocabulary learning.

Language tests developed and validated in one country may lose their desired properties when translated for use in another, possibly resulting in misleading estimates of ability. Using Item Response Theory (IRT) methodology, we assess the performance of a test of receptive vocabulary, the U.S.-validated Peabody Picture Vocabulary Test-Third Edition (PPVT-III), when translated, adapted, and administered to children 3 to 10 years of age in Madagascar (N = 1372), in the local language (Malagasy). Though Malagasy is considered a single language, there are numerous dialects spoken in Madagascar. Our findings were that test scores were positively correlated with age and indicators of socio-economic status. However, over half (57/96) of items evidenced unexpected response variation and/or bias by local dialect spoken. We also encountered measurement error and reduced differentiation among person abilities when we used the publishers' recommended stopping rules, largely because we lost the original item ordering by difficulty when we translated test items into Malagasy. Our results suggest that bias and testing inefficiency introduced from the translation of the PPVT can be significantly reduced with the use of methods based on IRT at both the pre-testing and analysis stages. We explore and discuss implications for cross-cultural comparisons of internationally recognized tests, such as the PPVT.

Different aspects of attention can be assessed through psychological tests to identify stable individual or group differences as well as alterations after interventions. Aiming for a wide applicability of attentional assessments, Psychology Experiment Building Language (PEBL) is an open-source software system for designing and running computerized tasks that tax various attentional functions. Here, we evaluated the reliability and validity of computerized attention tasks as provided with the PEBL package: Continuous Performance Task (CPT), Switcher task, Psychomotor Vigilance Task (PVT), Mental Rotation task, and Attentional Network Test. For all tasks, we evaluated test-retest reliability using the intraclass correlation coefficient (ICC), as well as internal consistency through within-test correlations and split-half ICC. Across tasks, response time scores showed adequate reliability, whereas scores of performance accuracy, variability, and deterioration over time did not. Stability across application sites was observed for the CPT and Switcher task, but practice effects were observed for all tasks except the PVT. We substantiate convergent and discriminant validity for several task scores using between-task correlations and provide further evidence for construct validity via associations of task scores with attentional and motivational assessments. Taken together, our results provide necessary information to help design and interpret studies involving attention assessments.

The evolutionary origin of human language and its neurobiological foundations has long been the object of intense scientific debate. Although a number of theories have been proposed, one particularly contentious model suggests that human language evolved from a manual gestural communication system in a common ape-human ancestor. Consistent with a gestural origins theory are data indicating that chimpanzees intentionally and referentially communicate via manual gestures, and the production of manual gestures, in conjunction with vocalizations, activates the chimpanzee Broca's area homologue--a region in the human brain that is critical for the planning and execution of language. However, it is not known if this activity observed in the chimpanzee Broca's area is the result of the chimpanzees producing manual communicative gestures, communicative sounds, or both. This information is critical for evaluating the theory that human language evolved from a strictly manual gestural system. To this end, we used positron emission tomography (PET) to examine the neural metabolic activity in the chimpanzee brain. We collected PET data in 4 subjects, all of whom produced manual communicative gestures. However, 2 of these subjects also produced so-called attention-getting vocalizations directed towards a human experimenter. Interestingly, only the two subjects that produced these attention-getting sounds showed greater mean metabolic activity in the Broca's area homologue as compared to a baseline scan. The two subjects that did not produce attention-getting sounds did not. These data contradict an exclusive "gestural origins" theory for they suggest that it is vocal signaling that selectively activates the Broca's area homologue in chimpanzees. In other words, the activity observed in the Broca's area homologue reflects the production of vocal signals by the chimpanzees, suggesting that this critical human language region was involved in vocal signaling in the common ancestor of both modern humans and chimpanzees.

"Faster, higher, stronger" is the motto of any professional athlete. Does that apply to brain dynamics as well? In our paper, we performed a series of EEG experiments on Visually Evoked Potentials and a series of cognitive tests-reaction time and visual search, with professional eSport players in Counter-Strike: Global Offensive (CS:GO) and novices (control group) in order to find important differences between them. EEG data were studied in a temporal domain by Event-Related Potentials (ERPs) and in a frequency domain by Variational Mode Decomposition. The EEG analysis showed that the brain reaction of eSport players is faster (P300 latency is earlier on average by 20-70 ms, p < 0.005) and stronger (P300 peak amplitude is higher on average by 7-9 mkV, p < 0.01). Professional eSport players also exhibit stronger stimulus-locked alpha-band power. Besides, the Spearman correlation analysis showed a significant correlation between hours spend in CS:GO and mean amplitude of P200 and N200 for the professional players. The comparison of cognitive test results showed the superiority of the professional players to the novices in reaction time (faster) and choice reaction time-faster reaction, but similar correctness, while a significant difference in visual search skills was not detected. Thus, significant differences in EEG signals (in spectrograms and ERPs) and cognitive test results (reaction time) were detected between the professional players and the control group. Cognitive tests could be used to separate skilled players from novices, while EEG testing can help to understand the skilled player's level. The results can contribute to understanding the impact of eSport on a player's cognitive state and associating eSport with a real sport. Moreover, the presented results can be useful for evaluating eSport team members and making training plans.

The existing frameworks for the evaluation of genetic and genomic applications clearly address the technical and clinical value of a test, but are less concerned with the way genetic services are delivered and organized. We therefore aimed to develop a comprehensive new framework that includes an assessment of service delivery.

Numerous studies in the field of embodied cognition have shown a crosstalk between language and sensorimotor processes. In particular, it has been demonstrated that perceiving an action influences subsequent language processing. However, when studying the effect of action observation on language processing it has not been considered whether the context of action presentation could modulate this influence. To test this assumption, the participants in our study observed a prime, specifically a cartoon picture of a person performing an action in either a usual or an unusual context, and then had to perform a semantic decision task involving action verbs that could be congruent or incongruent with the action in the prime. Data analyses showed a significant difference on response times for congruent action verbs compared with incongruent action verbs in the usual context, whereas no difference was observed in the unusual context. This finding indicates that the influence of action observation on language appears only with usual actions, suggesting that the context of action presentation is crucial to enable the influence of action observation on action verbs processing.

Recently, there have been many case-control studies proposed to test for association between haplotypes and disease, which require the Hardy-Weinberg equilibrium (HWE) assumption of haplotype frequencies. As such, haplotype inference of unphased genotypes and development of haplotype-based HWE tests are crucial prior to fine mapping. The goodness-of-fit test is a frequently-used method to test for HWE for multiple tightly-linked loci. However, its degrees of freedom dramatically increase with the increase of the number of loci, which may lack the test power. Therefore, in this paper, to improve the test power for haplotype-based HWE, we first write out two likelihood functions of the observed data based on the Niu's model (NM) and inbreeding model (IM), respectively, which can cause the departure from HWE. Then, we use two expectation-maximization algorithms and one expectation-conditional-maximization algorithm to estimate the model parameters under the HWE, IM and NM models, respectively. Finally, we propose the likelihood ratio tests LRT[Formula: see text] and LRT[Formula: see text] for haplotype-based HWE under the NM and IM models, respectively. We simulate the HWE, Niu's, inbreeding and population stratification models to assess the validity and compare the performance of these two LRT tests. The simulation results show that both of the tests control the type I error rates well in testing for haplotype-based HWE. If the NM model is true, then LRT[Formula: see text] is more powerful. While, if the true model is the IM model, then LRT[Formula: see text] has better performance in power. Under the population stratification model, LRT[Formula: see text] is still more powerful. To this end, LRT[Formula: see text] is generally recommended. Application of the proposed methods to a rheumatoid arthritis data set further illustrates their utility for real data analysis.

Preterm children often have language problems. This atypical language development is probably due to atypical brain development. We conducted a systematic review to provide an overview of the extensive and diverse scientific literature on the relations between language outcome and underlying brain structures in school-aged preterm-born children. Embase, Medline Ovid, Web of Science, Cochrane central and Google scholar were searched for relevant studies. Inclusion criteria were: cases are school-aged preterm children; structural MRI (T1- and T2-weighted sequences) or DTI used in combination with a neurocognitive language test; publication in an English-language peer-reviewed journal. Correlational measures between language scores and brain volume or fractional anisotropy of a brain structure were extracted. 23 studies were included. The relations between oral language, verbal fluency and/or written language and MRI/DTI measurements of white matter, gray matter, cerebellum, corpus callosum and/or the fasciculi are presented. Oral language skills and verbal fluency appear to be related to the corpus callosum. Oral language skills are also related to the uncinate fasciculus. There seems to be no clear relation between cerebellar development and verbal fluency skills. Not one single brain area is responsible for atypical language development, but several brain areas and their connections are essential. For future research it is recommended to relate brain areas to oral language skills on a microstructural level in preterm children. We also recommend to use language tests in which it is possible to distinguish between several language domains, such as perceptive and expressive language.

There is an urgent requirement to identify which clinical settings are in most need of COVID-19 tests and the priority role(s) for tests in these settings to accelerate the development of tests fit for purpose in health and social care across the UK. This study sought to identify and prioritize unmet clinical needs for COVID-19 tests across different settings within the UK health and social care sector via an online survey of health and social care professionals and policymakers. Four hundred and forty-seven responses were received between 22nd May and 15th June 2020. Hospitals and care homes were recognized as the settings with the greatest unmet clinical need for COVID-19 diagnostics, despite reporting more access to laboratory molecular testing than other settings. Hospital staff identified a need for diagnostic tests for symptomatic workers and patients. In contrast, care home staff expressed an urgency for screening at the front door to protect high-risk residents and limit transmission. The length of time to test result was considered a widespread problem with current testing across all settings. Rapid tests for staff were regarded as an area of need across general practice and dental settings alongside tests to limit antibiotics use.

Copy number variation (CNV) plays a role in pathogenesis of many human diseases, especially cancer. Several whole genome CNV association studies have been performed for the purpose of identifying cancer associated CNVs. Here we undertook a novel approach to whole genome CNV analysis, with the goal being identification of associations between CNV of different genes (CNV-CNV) across 60 human cancer cell lines. We hypothesize that these associations point to the roles of the associated genes in cancer, and can be indicators of their position in gene networks of cancer-driving processes. Recent studies show that gene associations are often non-linear and non-monotone. In order to obtain a more complete picture of all CNV associations, we performed omnibus univariate analysis by utilizing dCov, MIC, and HHG association tests, which are capable of detecting any type of association, including non-monotone relationships. For comparison we used Spearman and Pearson association tests, which detect only linear or monotone relationships. Application of dCov, MIC and HHG tests resulted in identification of twice as many associations compared to those found by Spearman and Pearson alone. Interestingly, most of the new associations were detected by the HHG test. Next, we utilized dCov's and HHG's ability to perform multivariate analysis. We tested for association between genes of unknown function and known cancer-related pathways. Our results indicate that multivariate analysis is much more effective than univariate analysis for the purpose of ascribing biological roles to genes of unknown function. We conclude that a combination of multivariate and univariate omnibus association tests can reveal significant information about gene networks of disease-driving processes. These methods can be applied to any large gene or pathway dataset, allowing more comprehensive analysis of biological processes.

We evaluated therapeutic TAT for a tertiary hospital in Western Kenya, using a time-motion study focusing specifically on common hematology and biochemistry orders. The aim was to determine significant bottlenecks in diagnostic testing processes at the institution.

Much of our current knowledge regarding the association of FOXP2 with speech and language development comes from singleton and small family studies where a small number of rare variants have been identified. However, neither genome-wide nor gene-specific studies have provided evidence that common polymorphisms in the gene contribute to individual differences in language development in the general population. One explanation for this inconsistency is that previous studies have been limited to relatively small samples of individuals with low language abilities, using low density gene coverage. The current study examined the association between common variants in FOXP2 and a quantitative measure of language ability in a population-based cohort of European decent (n = 812). No significant associations were found for a panel of 13 SNPs that covered the coding region of FOXP2 and extended into the promoter region. Power analyses indicated we should have been able to detect a QTL variance of 0.02 for an associated allele with MAF of 0.2 or greater with 80% power. This suggests that, if a common variant associated with language ability in this gene does exist, it is likely of small effect. Our findings lead us to conclude that while genetic variants in FOXP2 may be significant for rare forms of language impairment, they do not contribute appreciably to individual variation in the normal range as found in the general population.

Pulmonary function tests (PFTs) are usually interpreted by clinicians using rule-based strategies and pattern recognition. The interpretation, however, has variabilities due to patient and interpreter errors. Most PFTs have recognizable patterns that can be categorized into specific physiological defects. In this study, we developed a computerized algorithm using the python package (pdfplumber) and validated against clinicians' interpretation. We downloaded PFT reports in the electronic medical record system that were in PDF format. We digitized the flow volume loop (FVL) and extracted numeric values from the reports. The algorithm used FEV1/FVC<0.7 for obstruction, TLC<80%pred for restriction and <80% or >120%pred for abnormal DLCO. The algorithm also used a small airway disease index (SADI) to quantify late expiratory flattening of the FVL to assess small airway dysfunction. We devised keywords for the python Natural Language Processing (NLP) package (spaCy) to identify obstruction, restriction, abnormal DLCO and small airway dysfunction in the reports. The algorithm was compared to clinicians' interpretation in 6,889 PFTs done between March 1st, 2018, and September 30th, 2020. The agreement rates (Cohen's kappa) for obstruction, restriction and abnormal DLCO were 94.4% (0.868), 99.0% (0.979) and 87.9% (0.750) respectively. In 4,711 PFTs with FEV1/FVC≥0.7, the algorithm identified 190 tests with SADI < lower limit of normal (LLN), suggesting small airway dysfunction. Of these, the clinicians (67.9%) also flagged 129 tests. When SADI was ≥ LLN, no clinician's reports indicated small airway dysfunction. Our results showed the computerized algorithm agreed with clinicians' interpretation in approximately 90% of the tests and provided a sensitive objective measure for assessing small airway dysfunction. The algorithm can improve efficiency and consistency and decrease human errors in PFT interpretation. The computerized algorithm works directly on PFT reports in PDF format and can be adapted to incorporate a different interpretation strategy and platform.