Spectroscopic study of wemple-didomenico empirical formula and taucs model to determine the optical band gap of dye-doped polymer based on chitosan: Common poppy dye as a novel approach to reduce the optical band gap of biopolymer.

This study investigates the effect of a natural dye extracted from common poppy (Papaver rhoeas) waste flowers on the optical properties of chitosan (CS) films. The extraction of natural dyes from waste flowers can be considered a new field for research in green chemistry. CS films are flexible and biodegradable but have low optical activity and band gap, limiting their applications in optical devices. The doped CS polymer with different concentrations of Papaver rhoeas dye exhibited enhanced optical properties. Also, 30 % glycerol was added as a plasticizer to omit film brittleness. The FTIR examinations is helpful to propose a mechanism that explains the interaction of the dye with the host polymer. The UV-vis spectroscopic examination establish that the optical characteristics of the films can be modified by adjusting the dye concentration. Furthermore, optical absorption properties are described using the Tauc non-direct transition model, revealing an approximate optical band gap of 1.64 eV. This band gap defines the energy required for electron transitions, elucidating the material's electronic characteristics. The extinction coefficient (k) and refractive index (n) of the CS-doped films' shows a dispersion behavior at visible regions of EM radiation. The Wemple-DiDomenico single oscillator model was used to investigate the n dispersion and determine the oscillator energy equivalent to the optical band gap. Additionally, calculations have been performed on optical dielectric properties and optical conductivity. The Urbach energy was measured and used to detect the structure of the films. The findings underscore the potential applications of these natural dye-doped CS films in eco-friendly materials and optical devices.

Reporting checklists in neuroimaging: promoting transparency, replicability, and reproducibility.

Neuroimaging plays a crucial role in understanding brain structure and function, but the lack of transparency, reproducibility, and reliability of findings is a significant obstacle for the field. To address these challenges, there are ongoing efforts to develop reporting checklists for neuroimaging studies to improve the reporting of fundamental aspects of study design and execution. In this review, we first define what we mean by a neuroimaging reporting checklist and then discuss how a reporting checklist can be developed and implemented. We consider the core values that should inform checklist design, including transparency, repeatability, data sharing, diversity, and supporting innovations. We then share experiences with currently available neuroimaging checklists. We review the motivation for creating checklists and whether checklists achieve their intended objectives, before proposing a development cycle for neuroimaging reporting checklists and describing each implementation step. We emphasize the importance of reporting checklists in enhancing the quality of data repositories and consortia, how they can support education and best practices, and how emerging computational methods, like artificial intelligence, can help checklist development and adherence. We also highlight the role that funding agencies and global collaborations can play in supporting the adoption of neuroimaging reporting checklists. We hope this review will encourage better adherence to available checklists and promote the development of new ones, and ultimately increase the quality, transparency, and reproducibility of neuroimaging research.

Zinc metal complexes synthesized by a green method as a new approach to alter the structural and optical characteristics of PVA: new field for polymer composite fabrication with controlled optical band gap.

The current study employed a novel approach to design polymer composites with modified structural and declined optical band gaps. The results obtained in the present work for polymer composites can be considered an original method to make a new field for research based on green chemistry. Natural dyes extracted from green tea were mixed with hydrated zinc acetate (Zn(CH3COO)2·2H2O) to produce a metal complex. FTIR results comprehensively established the formation of the Zn-metal complex. The interaction among various components of PVA : Zn-metal complex composite was investigated using FTIR spectroscopy. The non-existence of anion bands of acetate in the Zn-metal complex spectrum confirms the formation of the Zn-metal complex. XRD analysis reveals that the Zn-metal complex improves the amorphous phase of the PVA-based composites. The absorption edge of the doped films shifted towards the lower photon energies. Optical dielectric properties were used to determine N/m*, ε ∞, τ, µ opt, ω p, and ρ opt; the W-D model was used to calculate E d, E o and n o parameters. The optical dielectric loss parameter was used to determine the optical band gap while the Tauc model was employed to identify various types of electron transitions. The optical energy band gap was 6.05 eV for clean PVA while it decreased to 1 eV for PVA inserted with the Zn-metal complex. The increase in Urbach energy from 0.26 eV to 0.45 eV is an evidence of the boost of amorphous phases in PVA : Zn-metal complex composites. The nonlinear refractive index and the first-order and second-order nonlinear optical susceptibilities were determined. The value of E o obtained from the W-D model closely matches the optical energy band gap obtained from the Tauc model, which indicates the precision of the analysis in the present study. The increase in SELF and VELF in the composite films establishes that new energy states assigned to the added Zn-metal complex amplify the probability of light-matter interaction.

Brain-age prediction: Systematic evaluation of site effects, and sample age range and size.

Structural neuroimaging data have been used to compute an estimate of the biological age of the brain (brain-age) which has been associated with other biologically and behaviorally meaningful measures of brain development and aging. The ongoing research interest in brain-age has highlighted the need for robust and publicly available brain-age models pre-trained on data from large samples of healthy individuals. To address this need we have previously released a developmental brain-age model. Here we expand this work to develop, empirically validate, and disseminate a pre-trained brain-age model to cover most of the human lifespan. To achieve this, we selected the best-performing model after systematically examining the impact of seven site harmonization strategies, age range, and sample size on brain-age prediction in a discovery sample of brain morphometric measures from 35,683 healthy individuals (age range: 5-90 years; 53.59% female). The pre-trained models were tested for cross-dataset generalizability in an independent sample comprising 2101 healthy individuals (age range: 8-80 years; 55.35% female) and for longitudinal consistency in a further sample comprising 377 healthy individuals (age range: 9-25 years; 49.87% female). This empirical examination yielded the following findings: (1) the accuracy of age prediction from morphometry data was higher when no site harmonization was applied; (2) dividing the discovery sample into two age-bins (5-40 and 40-90 years) provided a better balance between model accuracy and explained age variance than other alternatives; (3) model accuracy for brain-age prediction plateaued at a sample size exceeding 1600 participants. These findings have been incorporated into CentileBrain (https://centilebrain.org/#/brainAGE2), an open-science, web-based platform for individualized neuroimaging metrics.

Plasticised chitosan: Dextran polymer blend electrolyte for energy harvesting application: Tuning the ion transport and EDLC charge storage capacity through TiO2 dispersion.

This study investigates the performance of biopolymer electrolytes based on chitosan and dextran for energy storage applications. The optimization of ion transport and performance of electric double-layer capacitors EDCL using these electrolytes, incorporating different concentrations of glycerol as a plasticizer and TiO2 as nanoparticles, is explored. Impedance measurements indicate a notable reduction in charge transfer resistance with the addition of TiO2. DC conductivity estimates from AC spectra plateau regions reach up to 5.6 × 10-4 S/cm. The electric bulk resistance Rb obtained from the Nyquist plots exhibits a substantial decrease with increasing plasticizer concentration, further enhanced by the addition of the nanoparticles. Specifically, Rb decreases from ∼20 kΩ to 287 Ω when glycerol concentration increases from 10 % to 40 % and further drops to 30 Ω with the introduction of TiO2. Specific capacitance obtained from cyclic voltammetry shows a notable increase as the scan rate decreases, indicating improved efficiency and stability of ion transport. The TiO2-enriched EDCL achieves 12.3 F/g specific capacitance at 20 mV/s scan rate, with high ion conductivity and extended electrochemical stability. These results suggest the great potential of plasticizer and TiO2 with biopolymers in improving the performance of energy storage systems.

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders-ENIGMA study in people with bipolar disorders and obesity.

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

Global warming-related response after bacterial challenge in Astroides calycularis, a Mediterranean thermophilic coral.

A worldwide increase in the prevalence of coral diseases and mortality has been linked to ocean warming due to changes in coral-associated bacterial communities, pathogen virulence, and immune system function. In the Mediterranean basin, the worrying upward temperature trend has already caused recurrent mass mortality events in recent decades. To evaluate how elevated seawater temperatures affect the immune response of a thermophilic coral species, colonies of Astroides calycularis were exposed to environmental (23 °C) or elevated (28 °C) temperatures, and subsequently challenged with bacterial lipopolysaccharides (LPS). Using immunolabeling with specific antibodies, we detected the production of Toll-like receptor 4 (TLR4) and nuclear factor kappa B (NF-kB), molecules involved in coral immune responses, and heat shock protein 70 (HSP70) activity, involved in general responses to thermal stress. A histological approach allowed us to characterize the tissue sites of activation (epithelium and/or gastroderm) under different experimental conditions. The activity patterns of the examined markers after 6 h of LPS stimulation revealed an up-modulation at environmental temperature. Under warmer conditions plus LPS-challenge, TLR4-NF-kB activation was almost completely suppressed, while constituent elevated values were recorded under thermal stress only. An HSP70 up-regulation appeared in both treatments at elevated temperature, with a significantly higher activation in LPS-challenge colonies. Such an approach is useful for further understanding the molecular pathogen-defense mechanisms in corals in order to disentangle the complex interactive effects on the health of these ecologically relevant organisms related to global climate change.

Sea anemones, methylmercury, and bacterial infection: A closer look at multiple stressors.

Specimens of the Mediterranean sea anemone Anemonia viridis were exposed to methylmercury (MeHg) and bacterial infection to study their immune responses to a well-known toxic pollutant. Anemones were housed in laboratory conditions and divided into five experimental groups: 1. control (no microinjection); 2. filtered seawater + buffer injection; 3. filtered seawater + Escherichia coli injection; 4. MeHg + buffer injection; 5. MeHg + E. coli injection. Data showed an increase in antioxidant enzyme production compared to the constitutive condition, while methylmercury inhibited lysozyme production. The buffer inoculation had no statistically significant effects on the animals. In addition, electrophoretic and protease analyses revealed differences in the type of proteins produced, as well as a modulation of proteases depending on the treatment. The study demonstrated the immunomodulatory effect of the organic pollutant on A. viridis, validating its use as a model organism for marine coastal biomonitoring programmes and multiple stress studies.

Corpus callosum microstructural organization mediates the effects of physical neglect on social cognition in schizophrenia.

Exposure to early life adversity is associated with both increased risk of developing schizophrenia and poorer performance on measures of social cognitive functioning. In this study, we examined whether interleukin-6 (IL-6) and Corpus Callosum (CC) microstructure mediated the association between childhood physical neglect and social cognition. Fifty-eight patients with a diagnosis of schizophrenia were included. The CANTAB emotion recognition task (unbiased hit rate) was used to assess social cognition. We found that the microstructural organization of the CC significantly mediated the association between physical neglect and emotion recognition. Furthermore, in a sequential mediation analysis that also considered the role of inflammatory response, the association between physical neglect, and lower emotion recognition performance was sequentially mediated by higher IL-6 and lower fractional anisotropy of the CC. This mediating effect of IL-6 was only present when simultaneously considering the effects of CC microstructural organization and remained significant while controlling for the effects of sex, BMI and medication dosage (but not age). Overall, the findings suggest that the association between physical neglect and poorer emotion recognition in schizophrenia occurs, at least in part, via its association with white matter microstructure.

Along-Tract Statistical Mapping of Microstructural Abnormalities in Bipolar Disorder: A Pilot Study.

Investigating brain circuitry involved in bipolar disorder (BD) is key to discovering brain biomarkers for genetic and interventional studies of the disorder. Even so, prior research has not provided a fine-scale spatial mapping of brain microstructural differences in BD. In this pilot diffusion MRI dataset, we used BUndle ANalytics (BUAN)-a recently developed analytic approach for tractography-to extract, map, and visualize the profile of microstructural abnormalities on a 3D model of fiber tracts in people with BD (N=38) and healthy controls (N=49), and investigate along-tract white matter (WM) microstructural differences between these groups. Using the BUAN pipeline, BD was associated with lower mean fractional anisotropy (FA) in fronto-limbic and interhemispheric pathways and higher mean FA in posterior bundles relative to controls.Clinical Relevance- BUAN combines tractography and anatomical information to capture distinct along-tract effects on WM microstructure that may aid in classifying diseases based on anatomical differences.

Normative Modeling of Brain Morphometry Across the Lifespan Using CentileBrain: Algorithm Benchmarking and Model Optimization.

We present an empirically benchmarked framework for sex-specific normative modeling of brain morphometry that can inform about the biological and behavioral significance of deviations from typical age-related neuroanatomical changes and support future study designs. This framework was developed using regional morphometric data from 37,407 healthy individuals (53% female; aged 3-90 years) following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The Multivariate Factorial Polynomial Regression (MFPR) emerged as the preferred algorithm optimized using nonlinear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins, and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3,000 study participants. The model and scripts described here are freely available through CentileBrain (https://centilebrain.org/).

Postmortem toxicology findings from the Camden Opioid Research Initiative.

The United States continues to be impacted by decades of an opioid misuse epidemic, worsened by the COVID-19 pandemic and by the growing prevalence of highly potent synthetic opioids (HPSO) such as fentanyl. In instances of a toxicity event, first-response administration of reversal medications such as naloxone can be insufficient to fully counteract the effects of HPSO, particularly when there is co-occurring substance use. In an effort to characterize and study this multi-faceted problem, the Camden Opioid Research Initiative (CORI) has been formed. The CORI study has collected and analyzed post-mortem toxicology data from 42 cases of decedents who expired from opioid-related toxicity in the South New Jersey region to characterize substance use profiles. Co-occurring substance use, whether by intent or through possible contamination of the illicit opioid supply, is pervasive among deaths due to opioid toxicity, and evidence of medication-assisted treatment is scarce. Nearly all (98%) of the toxicology cases show the presence of the HPSO, fentanyl, and very few (7%) results detected evidence of medication-assisted treatment for opioid use disorder, such as buprenorphine or methadone, at the time of death. The opioid toxicity reversal drug, naloxone, was detected in 19% of cases, but 100% of cases expressed one or more stimulants, and sedatives including xylazine were detected in 48% of cases. These results showing complex substance use profiles indicate that efforts at mitigating the opioid misuse epidemic must address the complications presented by co-occurring stimulant and other substance use, and reduce barriers to and stigmas of seeking effective medication-assisted treatments.

Predictive modelling of brain disorders with magnetic resonance imaging: A systematic review of modelling practices, transparency, and interpretability in the use of convolutional neural networks.

Brain disorders comprise several psychiatric and neurological disorders which can be characterized by impaired cognition, mood alteration, psychosis, depressive episodes, and neurodegeneration. Clinical diagnoses primarily rely on a combination of life history information and questionnaires, with a distinct lack of discriminative biomarkers in use for psychiatric disorders. Symptoms across brain conditions are associated with functional alterations of cognitive and emotional processes, which can correlate with anatomical variation; structural magnetic resonance imaging (MRI) data of the brain are therefore an important focus of research, particularly for predictive modelling. With the advent of large MRI data consortia (such as the Alzheimer's Disease Neuroimaging Initiative) facilitating a greater number of MRI-based classification studies, convolutional neural networks (CNNs)-deep learning models well suited to image processing tasks-have become increasingly popular for research into brain conditions. This has resulted in a myriad of studies reporting impressive predictive performances, demonstrating the potential clinical value of deep learning systems. However, methodologies can vary widely across studies, making them difficult to compare and/or reproduce, potentially limiting their clinical application. Here, we conduct a qualitative systematic literature review of 55 studies carrying out CNN-based predictive modelling of brain disorders using MRI data and evaluate them based on three principles-modelling practices, transparency, and interpretability. We propose several recommendations to enhance the potential for the integration of CNNs into clinical care.

A labeled data set of underwater images of fish and crab species from five mesohabitats in Puget Sound WA USA.

The sustainable management of fisheries and aquaculture requires an understanding of how these activities interact with natural fish populations. GoPro cameras were used to collect an underwater video data set on and around shellfish aquaculture farms in an estuary in the NE Pacific from June to August 2017 and June to August 2018 to better understand habitat use by the local fish and crab communities. Images extracted from these videos were labeled to produce a data set that is suitable for use in training computer vision models. The labeled data set contains 77,739 images sampled from the collected video; 67,990 objects (fishes and crustaceans) have been annotated in 30,384 images (the remainder have been annotated as "empty"). The metadata of the data set also indicates whether a physical magenta filter was used during video collection to counteract reduced visibility. These data have the potential to help researchers address system-level and in-depth regional shellfish aquaculture questions related to ecosystem services and shellfish aquaculture interactions.

Bilateral anterior corona radiata microstructure organisation relates to impaired social cognition in schizophrenia.

OBJECTIVE: The Corona Radiata (CR) is a large white matter tract in the brain comprising of the anterior CR (aCR), superior CR (sCR), and posterior CR (pCR), which have associations with cognition, self-regulation, and, in schizophrenia, positive symptom severity. This study tested the hypothesis that the microstructural organisation of the aCR, as measured by Fractional Anisotropy (FA) using Diffusion Tensor Imaging (DTI), would relate to poorer social cognitive outcomes and higher positive symptom severity for people with schizophrenia, when compared to healthy participants. We further hypothesised that increased positive symptoms would relate to poorer social cognitive outcomes. METHODS: Data were derived from n = 178 healthy participants (41 % females; 36.11 ± 12.36 years) and 58 people with schizophrenia (30 % females; 42.4 ± 11.1 years). The Positive and Negative Symptom Severity Scale measured clinical symptom severity. Social Cognition was measured using the Reading the Mind in the Eyes Test (RMET) Total Score, as well as the Positive, Neutral, and Negative stimuli valence. The ENIGMA-DTI protocol tract-based spatial statistics (TBSS) was used. RESULTS: There was a significant difference in FA for the CR, in individuals with schizophrenia compared to healthy participants. On stratification, both the aCR and pCR were significantly different between groups, with patients showing reduced white matter tract microstructural organisation. Significant negative correlations were observed between positive symptomatology and reduced microstructural organisation of the aCR. Performance for RMET negative valence items was significantly correlated bilaterally with the aCR, but not the sCR or pCR, and no relationship to positive symptoms was observed. CONCLUSIONS: These data highlight specific and significant microstructural white-matter differences for people with schizophrenia, which relates to positive clinical symptomology and poorer performance on social cognition stimuli. While reduced FA is associated with higher positive symptomatology in schizophrenia, this study shows the specific associated with anterior frontal white matter tracts and reduced social cognitive performance. The aCR may have a specific role to play in frontal-disconnection syndromes, psychosis, and social cognitive profile within schizophrenia, though further research requires more sensitive, specific, and detailed consideration of social cognition outcomes.

Multi-Site Statistical Mapping of Along-Tract Microstructural Abnormalities in Bipolar Disorder with Diffusion MRI Tractometry.

Investigating alterations in brain circuitry associated with bipolar disorder (BD) may offer a valuable approach to discover brain biomarkers for genetic and interventional studies of the disorder and related mental illnesses. Some diffusion MRI studies report evidence of microstructural abnormalities in white matter regions of interest, but we lack a fine-scale spatial mapping of brain microstructural differences along tracts in BD. We also lack large-scale studies that integrate tractometry data from multiple sites, as larger datasets can greatly enhance power to detect subtle effects and assess whether effects replicate across larger international datasets. In this multisite diffusion MRI study, we used BUndle ANalytics (BUAN, Chandio 2020), a recently developed analytic approach for tractography, to extract, map, and visualize profiles of microstructural abnormalities on 3D models of fiber tracts in 148 participants with BD and 259 healthy controls from 6 independent scan sites. Modeling site differences as random effects, we investigated along-tract white matter (WM) microstructural differences between diagnostic groups. QQ plots showed that group differences were gradually enhanced as more sites were added. Using the BUAN pipeline, BD was associated with lower mean fractional anisotropy (FA) in fronto-limbic, interhemispheric, and posterior pathways; higher FA was also noted in posterior bundles, relative to controls. By integrating tractography and anatomical information, BUAN effectively captures unique effects along white matter (WM) tracts, providing valuable insights into anatomical variations that may assist in the classification of diseases.

The Development of a Multidisciplinary Support Network to Support Hospitalized Pediatric Patients, Their Families, and Hospital Staff During the COVID-19 Pandemic.

Background: The COVID-19 pandemic led to emotional and behavioral challenges for hospitalized pediatric patients, their families, and staff. Visitor restrictions, closure of patient lounges and playrooms, masking requirements, and enhanced isolation rules resulted in limited access to typical sources of psychosocial support during this traumatic event. Complementary and integrative health therapies such as acupuncture and related therapies are well suited to provide the humanitarian support patients and families need during times of crisis. Objective: The Multidisciplinary Support Network (i.e., Network) was formed to redesign the delivery of acupuncture and other integrative therapies alongside psychosocial support for hospitalized children, their families, and staff. Intervention: Network members represented a broad range of previously siloed disciplines including integrative therapies, art therapy, child life, nursing, pastoral care, adolescent medicine, pediatric hospital medicine, psychology, and child and adolescent psychiatry. The Network aimed to identify gaps in service and create resources to support children and families during this challenging time. Results: The Network compiled existing complementary and integrative services, provided training on integrative therapies to staff, pediatric trainees, and faculty, developed the Comfort Box containing items to provide symptom relief including pain, anxiety and difficulty sleeping, as well as closed-circuit programming, a pediatric companionship program connecting medical student volunteer companions with pediatric patients, and a well-being workbook. Conclusion: Collaborative teamwork across disciplines using integrative therapies was key to humanitarian efforts to support hospitalized children and their families during this crisis.

Concussion assessment and management - What do community-level cricket participants know?

OBJECTIVES: To explore Australian cricket participants' knowledge of concussion assessment and management, and awareness of current concussion guidelines. DESIGN: Cross-sectional survey. METHODS: Novel and validated surveys were disseminated online, among over 16 year Australian cricket players and officials at the end of the 2018/19 cricket season. Data were collected on knowledge and awareness of concussion and analysed using descriptive statistics and crosstabulations. Further comparisons were made for the players between injured and non-injured, and helmet wearers and non-helmet wearers using Fisher's exact statistical test. RESULTS: Both players (n = 224, 93 %) and officials (n = 36, 100 %) demonstrated strong knowledge of the importance of immediately evaluating suspected concussions. In comparison with players without helmets (n = 11), those using helmets (n = 135) considered replacing their helmets after a concussion to be vital to concussion assessment (p = 0.02). Overall, 80-97 % of players and 81-97 % of officials understood the importance of many factors regarding concussion management. When concussion management knowledge was compared by injury status, injured players (n = 17, 94 %) believed someone with a concussion should be hospitalised immediately, in contrast to non-injured players (n = 154, 69 %) (p = 0.04). Players (63 %) were less aware of concussion guidelines than officials (81 %). CONCLUSIONS: Overall, the knowledge of concussion assessment and management was satisfactory. However, there were discrepancies among players on some aspects of awareness of concussion guidelines. Increasing players' familiarity and experience in using the concussion guidelines is warranted. Targeted campaigns are needed to further improve concussion recognition and treatment at community-level cricket, so all participants play a role in making cricket a safe sport.

Along-Tract Statistical Mapping of Microstructural Abnormalities in Bipolar Disorder: A Pilot Study.

Investigating brain circuitry involved in bipolar disorder (BD) is key to discovering brain biomarkers for genetic and interventional studies of the disorder. Even so, prior research has not provided a fine-scale spatial mapping of brain microstructural differences in BD. In this pilot diffusion MRI dataset, we used BUndle ANalytics (BUAN), a recently developed analytic approach for tractography, to extract, map, and visualize the profile of microstructural abnormalities on a 3D model of fiber tracts in people with BD (N=38) and healthy controls (N=49), and investigate along-tract white matter (WM) microstructural differences between these groups. Using the BUAN pipeline, BD was associated with lower mean Fractional Anisotropy (FA) in fronto-limbic and interhemispheric pathways and higher mean FA in posterior bundles relative to controls. BUAN combines tractography and anatomical information to capture distinct along-tract effects on WM microstructure that may aid in classifying diseases based on anatomical differences.