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The study is made to predict the amount of fish consumption in Indonesia throughout the years 1960 to current year. The amount of fish production and catches will be used as supplementary information to help validate the fish consumption rate. This study is conducted using the Go programming language to prove that even though Go is a general programming language that is rarely being used for data science, it can still be used to perform analytics and machine learning while out-performing other languages that are usually used to do data science like Python and R. There are two primary datasets that are being used in this study, them being the fish captures dataset and the fish consumption dataset. These two datasets will later be parsed and processed to a single file before being fed to the linear regression and decision tree models to achieve the objective of predicting Indonesia’s fish consumption. The Linear Regression model created from our Go Program has predicted a successful model that has a very low R2 score of the predicted regression value vs the true value. Additionally using Go a Decision Tree model has also been created to further strengthen the results of our models given they agree with each other. Both models actually show very high correlation with their final predictions which is 92%. The result of this study solidifies 3 points and that is that Go is a very capable language to be used for data science, linear regression performs better than decision tree in this given scenario that is being used, and finally the fish consumption rate of Indonesia is rising at a much greater rate the world has seen in 1900s.

Abstract: Although leprosy is a disease with a wide range of clinical manifestations, there is a lack of knowledge about the presence of carriers of Mycobacterium leprae (M leprae), as well as its transmission and the immunological phenomena that cause the onset of the illness. The glycolipid phenolic-1 antigen (PGL-1) is a M. leprae specific virulence factor, that protects the bacteria against attack by macrophages. Anti PGL-1 antibodies were detected in health center workers (physicians and paramedics) in close contact with leprosy patients to assess their degree of exposition to the bacilli and to determine their usefulness as a risk factor for developing clinical leprosy. Blood samples from 52 leprosy patients contacts and 60 healthy individuals without leprosy patients contact (control group) were collected. The presence of IgM anti-PGL-1 antibodies was determined using the ELISA technique. It was found that 42.3% (22) of the contacts and 5.0% (3) of the healthy individuals without leprosy patient contact were positive for anti PGL-1 antibodies (p

Abstract: Introduction: Vertebral osteomyelitis (VO) by non-albicans Candida species is an infrequent type of osteomyelitis, however, its incidence has increased due to the increasing number of immunosuppressed patients and invasive procedures. Candida spp is more frequently resistant to azole antifungal drugs, such as fluconazole, than Candida albicans. Methods: We report one case of a 75-year-old diabetic male, with low back pain radiated to both legs, tenderness, and fever 3 weeks after an epidural anesthesia for prostatectomy. Results: VO diagnosis was supported by lumbosacral magnetic resonance imaging (RMI), which showed osteolitic lesions in the L4-L5 spine and the intervertebral disk. Surgical drainage was performed, with a sample for culture yielding Candida spp. The patient was treated with on I.V fluconazole 400 mg per day. Symptoms persisted 15 days later; the erythrocyte sedimentation rate (ESR) was elevated, the C-reactive protein was positive and there was purulent secretion in the surgical wound. A new lumbosacral spine MRI still showed vertebral osteomyelitis and a paravertebral abscess. The patient underwent a new surgical debridement and the sample for culture yielded a heavy growth of Candida spp. In view of the clinical and microbiologic treatment failure, the patient was placed on iv caspofungin 50 mg daily. The patient became asymptomatic after 10 days of treatment; the ESR decreased to 5 mm and the C-reactive protein was negative; therefore, treatment was switched to 200 mg of itraconazole for 3 months. A follow-up MRI showed a L4-L5 spine fusion with no evidence of osteomyelitis. Conclusions: Few cases of epidural blockage related to vertebral osteomyelitis was found in the reviewed literature and this is the only case due to Candida. The percentage of fluconazole-resistant C. tropicalis isolates is low but when no-albicans Candida fluconazole resistance emerges, alternatives such as amphotericin B and caspofungin have to be used. Caspofungin was chosen in this case due to the patient□s underlying renal injury. This case demonstrates that caspofungin can be an alternative for the treatment of Candida spp vertebral osteomyelitis. Resumen : Introducción: La osteomielitis Vertebral (OV) por especies de Candida no albicans es una forma infrecuente de osteomielitis, sin embargo, el aumento de los pacientes inmunosuprimidos y las técnicas invasivas han aumentado su incidencia. La resistencia de estos Hongos a los antifungicos del tipo de los azoles como el Fluconazol es mayor que en Candida albicans. Método: Se reporta el caso de un paciente masculino de 75 años, Diabético con antecedente de prostatectomia con anestesia por bloqueo epidural, que 3 semanas después presenta fiebre y dolor lumbar irradiado a piernas. Resultado: El diagnóstico de OV fue realizado por Resonancia Magnética (R.M) de columna lumbosacra que reportó lesiones osteolíticas en L4 y L5 y en el disco intervertebral. Se practicó drenaje quirúrgico, obteniéndose muestra para cultivo que reportó Candida spp., se inició tratamiento con FLUCONAZOL 400 mg. E.V. día. Después de 15 días, el paciente persistía con sintomatología, Velocidad de Sedimentación Globular (VSG) elevada, Proteína C Reactiva positiva y secreción purulenta por la herida quirúrgica. Se reporta nuevamente OV por R.M. de columna lumbosacra y absceso paravertebral. Se somete el paciente a nueva limpieza quirúrgica donde se aísla nuevamente Candida spp. en cantidad abundante. En vista del fracaso clínico y microbiológico se inicia terapia con Caspofungina 50 mg. E.V día. Luego de 10 días de tratamiento, el paciente se vuelve asintomático con VSG de 5 mm y Proteína C reactiva negativa, por lo que se cambio el tratamiento a Itraconazole 200 mg. V.O. día por 3 meses; se practicó R.M de control que reportó fusión de L4-L5, sin evidencias de osteomielitis. Conclusiones: En la literatura revisada existen pocos casos de OV debido a Candida spp. posterior a bloqueo epidural y este es el único en nuestro país. El porcentaje de resistencia de especies Candida no albicans al FLUCONAZOL es bajo pero cuando aparece se deben utilizar alternativas como la Anfotericina B y la Caspofungina. En este caso se decidió utilizar la Caspofungina por el daño renal subyacente al paciente diabético. Este caso demuestra que la Caspofungina puede ser una alternativa valida en el tratamiento de la OV por Candida no albicans.

Traumatic brain injury (TBI) can drastically affect an individual's cognition, physical, emotional wellbeing, and behavior. Even patients with mild TBI (mTBI) may suffer from a variety of long-lasting symptoms, which motivates researchers to find better biomarkers. Machine learning algorithms have shown promising results in detecting mTBI from resting-state functional network connectivity (rsFNC) data. However, data collected at multiple sites introduces additional noise called site-effects, resulting in erroneous conclusions. Site errors are controlled through a process called harmonization, but its use in classifying neuroimaging data has been addressed lightly. With the ongoing need to improve mTBI detection, this study shows that harmonization should be integrated into the machine learning process when working with multi-site neuroimaging datasets.

Alcohol use disorder (AUD) is a burden to society creating social and health problems. Detection of AUD and its effects on the brain are difficult to assess. This problem is enhanced by the comorbid use of other substances such as nicotine that has been present in previous studies. Recent machine learning algorithms have raised the attention of researchers as a useful tool in studying and detecting AUD. This work uses AUD and controls samples free of any other substance use to assess the performance of a set of commonly used machine learning classifiers detecting AUD from resting state functional network connectivity (rsFNC) derived from independent component analysis. The cohort used included 51 alcohol dependent subjects and 51 control subjects. Despite alcohol, none of the 102 subjects reported use of nicotine, cannabis or any other dependence or habit formation substance. Classification features consisted of whole brain rsFNC estimates undergoing a feature selection process using a random forest approach. Features were then fed to 10 different machine learning classifiers to be evaluated based on their classification performance. A neural network classifier showed the highest performance with an area under the curve (AUC) of 0.79. Other good performers with similar AUC scores were logistic regression, nearest neighbor, and support vector machine classifiers. The worst results were obtained with Gaussian process and quadratic discriminant analysis. The feature selection outcome pointed to functional connections between visual, sensorimotor, executive control, reward, and salience networks as the most relevant for classification. We conclude that AUD can be identified using machine learning classifiers in the absence of nicotine comorbidity., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Vergara, Espinoza and Calhoun.)

Udder measures have been used to assess milk yield of sheep through classical methods of estimation. Artificial neural networks (ANN) can deal with complex non-linear relationships between input and output variables. In the current study, ANN were applied to udder measures from Pelibuey ewes to estimate their milk yield and this was compared with linear regression. A total of 357 milk yield records with its corresponding udder measures were used. A supervised learning was used to train and teach the network using a two-layer ANN with seven hidden structures. The globally convergent algorithm based on the resilient backpropagation was used to calculate ANN. Goodness of fit was evaluated using the mean square prediction error (MSPE), root MSPE (RMSPE), correlation coefficient (r), Bayesian's Information Criterion (BIC), Akaike's Information Criterion (AIC) and accuracy. The 15-15 ANN architecture showed that the best predictive milk yield performance achieved an accuracy of 97.9% and the highest values of r 2 (0.93), and the lowest values of MSPE (0.0023), RMSPE (0.04), AIC (- 2088.81) and BIC (- 2069.56). The study revealed that ANN is a powerful tool to estimate milk yield when udder measures are used as input variables and showed better goodness of fit in comparison with classical regression methods., (© 2022. The Author(s).)

Thorough assessment of cerebral dysfunction after acute lesions is paramount to optimize predicting clinical outcomes. We here built random forest classifier-based prediction models of acute motor impairment and recovery post-stroke. Predictions relied on structural and resting-state fMRI data from 54 stroke patients scanned within the first days of symptom onset. Functional connectivity was estimated via static and dynamic approaches. Motor performance was phenotyped in the acute phase and 6 months later. A model based on the time spent in specific dynamic connectivity configurations achieved the best discrimination between patients with and without motor impairments (out-of-sample area under the curve, 95% confidence interval: 0.67 ± 0.01). In contrast, patients with moderate-to-severe impairments could be differentiated from patients with mild deficits using a model based on the variability of dynamic connectivity (0.83 ± 0.01). Here, the variability of the connectivity between ipsilesional sensorimotor cortex and putamen discriminated the most between patients. Finally, motor recovery was best predicted by the time spent in specific connectivity configurations (0.89 ± 0.01) in combination with the initial impairment. Here, better recovery was linked to a shorter time spent in a functionally integrated configuration. Dynamic connectivity-derived parameters constitute potent predictors of acute impairment and recovery, which, in the future, might inform personalized therapy regimens to promote stroke recovery., (© The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain.)

Acute ischaemic stroke disturbs healthy brain organization, prompting subsequent plasticity and reorganization to compensate for the loss of specialized neural tissue and function. Static resting state functional MRI studies have already furthered our understanding of cerebral reorganization by estimating stroke-induced changes in network connectivity aggregated over the duration of several minutes. In this study, we used dynamic resting state functional MRI analyses to increase temporal resolution to seconds and explore transient configurations of motor network connectivity in acute stroke. To this end, we collected resting state functional MRI data of 31 patients with acute ischaemic stroke and 17 age-matched healthy control subjects. Stroke patients presented with moderate to severe hand motor deficits. By estimating dynamic functional connectivity within a sliding window framework, we identified three distinct connectivity configurations of motor-related networks. Motor networks were organized into three regional domains, i.e. a cortical, subcortical and cerebellar domain. The dynamic connectivity patterns of stroke patients diverged from those of healthy controls depending on the severity of the initial motor impairment. Moderately affected patients (n = 18) spent significantly more time in a weakly connected configuration that was characterized by low levels of connectivity, both locally as well as between distant regions. In contrast, severely affected patients (n = 13) showed a significant preference for transitions into a spatially segregated connectivity configuration. This configuration featured particularly high levels of local connectivity within the three regional domains as well as anti-correlated connectivity between distant networks across domains. A third connectivity configuration represented an intermediate connectivity pattern compared to the preceding two, and predominantly encompassed decreased interhemispheric connectivity between cortical motor networks independent of individual deficit severity. Alterations within this third configuration thus closely resembled previously reported ones originating from static resting state functional MRI studies post-stroke. In summary, acute ischaemic stroke not only prompted changes in connectivity between distinct networks, but it also caused characteristic changes in temporal properties of large-scale network interactions depending on the severity of the individual deficit. These findings offer new vistas on the dynamic neural mechanisms underlying acute neurological symptoms, cortical reorganization and treatment effects in stroke patients., (© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain.)

Background: Clustering analysis is employed in brain dynamic functional connectivity (dFC) to cluster the data into a set of dynamic states. These states correspond to different patterns of functional connectivity that iterate through time. Although several cluster validity index (CVI) methods to determine the best clustering partition exists, the appropriateness of methods to apply in the case of dynamic connectivity analysis has not been determined., New Method: Currently employed indexes do not provide a crisp answer on what is the best number of clusters. In addition, there is a lack of CVI testing in the context of dFC data. This work tests a comprehensive set of twenty four cluster validity indexes applied to addiction data and suggest the best ones for clustering dynamic functional connectivity., Results: Out of the twenty four considered CVIs, Davies-Bouldin and Ray-Turi were the most suitable methods to find the number of clusters in both simulation and real data. The solution for these two CVIs is to find a local minimum critical point, which can be automated using computational algorithms., Comparison With Existing Methods: Elbow-Criterion, Silhouette and GAP-Statistic methods have been widely used in dFC studies. These methods are included among the tested CVIs where the performances of all twenty four CVIs are compared., Conclusions: Davies-Bouldin and Ray-Turi CVIs showed better performance among a group of twenty four CVIs in determining the number of clusters to use in dFC analysis., Competing Interests: Declaration of Competing Interest No competing financial interests exist., (Copyright © 2020 Elsevier B.V. All rights reserved.)

The current study set out to investigate the dynamic functional connectome in relation to long-term recovery after mild to moderate traumatic brain injury (TBI). Longitudinal resting-state functional MRI data were collected (at 1 and 3 months postinjury) from a prospectively enrolled cohort consisting of 68 patients with TBI (92% mild TBI) and 20 healthy subjects. Patients underwent a neuropsychological assessment at 3 months postinjury. Outcome was measured using the Glasgow Outcome Scale Extended (GOS-E) at 6 months postinjury. The 57 patients who completed the GOS-E were classified as recovered completely (GOS-E = 8; n = 37) or incompletely (GOS-E < 8; n = 20). Neuropsychological test scores were similar for all groups. Patients with incomplete recovery spent less time in a segregated brain state compared to recovered patients during the second visit. Also, these patients moved less frequently from one meta-state to another as compared to healthy controls and recovered patients. Furthermore, incomplete recovery was associated with disruptions in cyclic state transition patterns, called attractors, during both visits. This study demonstrates that poor long-term functional recovery is associated with alterations in dynamics between brain networks, which becomes more marked as a function of time. These results could be related to psychological processes rather than injury-effects, which is an interesting area for further work. Another natural progression of the current study is to examine whether these dynamic measures can be used to monitor treatment effects., (© 2019 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc.)

Clustering analysis is employed in brain dynamic functional connectivity to cluster the data into a set of dynamic states. These states correspond to different patterns of functional connectivity that iterate through time. Although several methods to determine the best clustering partition exists, the appropriateness of methods to apply in the case of dynamic connectivity analysis has not been determined. In this work we examine the use of the Davies-Bouldin clustering validity index via simulation and real data analysis. Currently employed indexes, such as the Silhouette index, do not provide an effective estimation requiring the use of an elbow criterion. All elbow criteria rely on users experience and introduce uncertainty into the estimation. We demonstrate the feasibility of using the Davies-Bouldin index as a method delivering a unique discrete response to provide automated selection of the number of clusters.

Brain functional connectivity has been shown to change over time during resting state fMRI experiments. Close examination of temporal changes have revealed a small set of whole-brain connectivity patterns called dynamic states. Dynamic functional network connectivity (dFNC) studies have demonstrated that it is possible to replicate the dynamic states across several resting state experiments. However, estimation of states and their temporal dynamicity still suffers from noisy and imperfect estimations. In regular dFNC implementations, states are estimated by comparing connectivity patterns through the data without considering time, in other words only zero order changes are examined. In this work we propose a method that includes first order variations of dFNC in the searching scheme of dynamic connectivity patterns. Our approach, referred to as temporal variation of functional network connectivity (tvFNC), estimates the derivative of dFNC, and then searches for reoccurring patterns of concurrent dFNC states and their derivatives. The tvFNC method is first validated using a simulated dataset and then applied to a resting-state fMRI sample including healthy controls (HC) and schizophrenia (SZ) patients and compared to the standard dFNC approach. Our dynamic approach reveals extra patterns in the connectivity derivatives complementing the already reported state patterns. State derivatives consist of additional information about increment and decrement of connectivity among brain networks not observed by the original dFNC method. The tvFNC shows more sensitivity than regular dFNC by uncovering additional FNC differences between the HC and SZ groups in each state. In summary, the tvFNC method provides a new and enhanced approach to examine time-varying functional connectivity.

Dynamic functional network connectivity (dFNC) is an expansion of traditional, static FNC that measures connectivity variation among brain networks throughout scan duration. We used a large resting-state fMRI (rs-fMRI) sample from the PREDICT-HD study (N = 183 Huntington disease gene mutation carriers [HDgmc] and N = 78 healthy control [HC] participants) to examine whole-brain dFNC and its associations with CAG repeat length as well as the product of scaled CAG length and age, a variable representing disease burden. We also tested for relationships between functional connectivity and motor and cognitive measurements. Group independent component analysis was applied to rs-fMRI data to obtain whole-brain resting state networks. FNC was defined as the correlation between RSN time-courses. Dynamic FNC behavior was captured using a sliding time window approach, and FNC results from each window were assigned to four clusters representing FNC states, using a k-means clustering algorithm. HDgmc individuals spent significantly more time in State-1 (the state with the weakest FNC pattern) compared to HC. However, overall HC individuals showed more FNC dynamism than HDgmc. Significant associations between FNC states and genetic and clinical variables were also identified. In FNC State-4 (the one that most resembled static FNC), HDgmc exhibited significantly decreased connectivity between the putamen and medial prefrontal cortex compared to HC, and this was significantly associated with cognitive performance. In FNC State-1, disease burden in HDgmc participants was significantly associated with connectivity between the postcentral gyrus and posterior cingulate cortex, as well as between the inferior occipital gyrus and posterior parietal cortex., (© 2019 Wiley Periodicals, Inc.)

Background: Gray matter (GM) atrophy in the striatum and across the brain is a consistently reported feature of the Huntington Disease (HD) prodrome. More recently, widespread prodromal white matter (WM) degradation has also been detected. However, longitudinal WM studies are limited and conflicting, and most analyses comparing WM and clinical functioning have also been cross-sectional., Objective: We simultaneously assessed changes in WM and cognitive and motor functioning at various prodromal HD stages., Methods: Data from 1,336 (1,047 prodromal, 289 control) PREDICT-HD participants were analyzed (3,700 sessions). MRI images were used to create GM, WM, and cerebrospinal fluid probability maps. Using source-based morphometry, independent component analysis was applied to WM probability maps to extract covarying spatial patterns and their subject profiles. WM profiles were analyzed in two sets of linear mixed model (LMM) analyses: one to compare WM profiles across groups cross-sectionally and longitudinally, and one to concurrently compare WM profiles and clinical variables cross-sectionally and longitudinally within each group., Results: Findings illustrate widespread prodromal changes in GM-adjacent-WM, with premotor, supplementary motor, middle frontal and striatal changes early in the prodrome that subsequently extend sub-gyrally with progression. Motor functioning agreed most with WM until the near-onset prodromal stage, when Stroop interference was the best WM indicator. Across groups, Trail-Making Test part A outperformed other cognitive variables in its similarity to WM, particularly cross-sectionally., Conclusions: Results suggest that distinct regions coincide with cognitive compared to motor functioning. Furthermore, at different prodromal stages, distinct regions appear to align best with clinical functioning. Thus, the informativeness of clinical measures may vary according to the type of data available (cross-sectional or longitudinal) as well as age and CAG-number.

Studies have used resting-state functional magnetic resonance imaging (rs-fMRI) to examine associations between psychopathy and brain connectivity in selected regions of interest as well as networks covering the whole-brain. One of the limitations of these approaches is that brain connectivity is modeled as a constant state through the scan duration. To address this limitation, we apply group independent component analysis (GICA) and dynamic functional network connectivity (dFNC) analysis to uncover whole-brain, time-varying functional network connectivity (FNC) states in a large forensic sample. We then examined relationships between psychopathic traits (PCL-R total scores, Factor 1 and Factor 2 scores) and FNC states obtained from dFNC analysis. FNC over the scan duration was better represented by five states rather than one state previously shown in static FNC analysis. Consistent with prior findings, psychopathy was associated with networks from paralimbic regions (amygdala and insula). In addition, whole-brain FNC identified 15 networks from nine functional domains (subcortical, auditory, sensorimotor, cerebellar, visual, salience, default mode network, executive control and attentional) related to psychopathy traits (Factor 1 and PCL-R scores). Results also showed that individuals with higher Factor 1 scores (affective and interpersonal traits) spend more time in a state with weaker connectivity overall, and changed states less frequently compared to those with lower Factor 1 scores. On the other hand, individuals with higher Factor 2 scores (impulsive and antisocial behaviors) showed more dynamism (changes to and from different states) than those with lower scores., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

This study assessed how BDNF (brain-derived neurotrophic factor) and other genes involved in its signaling influence brain structure and clinical functioning in pre-diagnosis Huntington's disease (HD). Parallel independent component analysis (pICA), a multivariate method for identifying correlated patterns in multimodal datasets, was applied to gray matter concentration (GMC) and genomic data from a sizeable PREDICT-HD prodromal cohort ( N = 715). pICA identified a genetic component highlighting NTRK2 , which encodes BDNF's TrkB receptor, that correlated with a GMC component including supplementary motor, precentral/premotor cortex, and other frontal areas ( p < 0.001); this association appeared to be driven by participants with high or low levels of the genetic profile. The frontal GMC profile correlated with cognitive and motor variables (Trail Making Test A ( p = 0.03); Stroop Color ( p = 0.017); Stroop Interference ( p = 0.04); Symbol Digit Modalities Test ( p = 0.031); Total Motor Score ( p = 0.01)). A top-weighted NTRK2 variant (rs2277193) was protectively associated with Trail Making Test B ( p = 0.007); greater minor allele numbers were linked to a better performance. These results support the idea of a protective role of NTRK2 in prodromal HD, particularly in individuals with certain genotypes, and suggest that this gene may influence the preservation of frontal gray matter that is important for clinical functioning.

Psychopathy is a personality disorder characterized by antisocial behavior, lack of remorse and empathy, and impaired decision making. The disproportionate amount of crime committed by psychopaths has severe emotional and economic impacts on society. Here we examine the neural correlates associated with psychopathy to improve early assessment and perhaps inform treatments for this condition. Previous resting-state functional magnetic resonance imaging (fMRI) studies in psychopathy have primarily focused on regions of interest. This study examines whole-brain functional connectivity and its association to psychopathic traits. Psychopathy was hypothesized to be characterized by aberrant functional network connectivity (FNC) in several limbic/paralimbic networks. Group-independent component and regression analyses were applied to a data set of resting-state fMRI from 985 incarcerated adult males. We identified resting-state networks (RSNs), estimated FNC between RSNs, and tested their association to psychopathy factors and total summary scores (Factor 1, interpersonal/affective; Factor 2, lifestyle/antisocial). Factor 1 scores showed both increased and reduced functional connectivity between RSNs from seven brain domains (sensorimotor, cerebellar, visual, salience, default mode, executive control, and attentional). Consistent with hypotheses, RSNs from the paralimbic system-insula, anterior and posterior cingulate cortex, amygdala, orbital frontal cortex, and superior temporal gyrus-were related to Factor 1 scores. No significant FNC associations were found with Factor 2 and total PCL-R scores. In summary, results suggest that the affective and interpersonal symptoms of psychopathy (Factor 1) are associated with aberrant connectivity in multiple brain networks, including paralimbic regions., (© 2018 Wiley Periodicals, Inc.)

Huntington's disease (HD) is an inherited brain disorder characterized by progressive motor, cognitive, and behavioral dysfunctions. It is caused by abnormally large trinucleotide cytosine-adenine-guanine (CAG) repeat expansions on exon 1 of the Huntingtin gene. CAG repeat length (CAG-RL) inversely correlates with an earlier age of onset. Region-based studies have shown that HD gene mutation carrier (HDgmc) individuals (CAG-RL ≥36) present functional connectivity alterations in subcortical (SC) and default mode networks. In this analysis, we expand on previous HD studies by investigating associations between CAG-RL and connectivity in the whole brain, as well as between CAG-dependent connectivity and motor and cognitive performances. We used group-independent component analysis on resting-state functional magnetic resonance imaging scans of 261 individuals (183 HDgmc and 78 healthy controls) from the PREDICT-HD study, to obtain whole-brain resting state networks (RSNs). Regression analysis was applied within and between RSNs connectivity (functional network connectivity [FNC]) to identify CAG-RL associations. Connectivity within the putamen RSN is negatively correlated with CAG-RL. The FNC between putamen and insula decreases with increasing CAG-RL, and also shows significant associations with motor and cognitive measures. The FNC between calcarine and middle frontal gyri increased with CAG-RL. In contrast, FNC in other visual (VIS) networks declined with increasing CAG-RL. In addition to observed effects in SC areas known to be related to HD, our study identifies a strong presence of alterations in VIS regions less commonly observed in previous reports and provides a step forward in understanding FNC dysfunction in HDgmc.

Huntington's disease (HD) is a neurodegenerative disorder caused by an expansion mutation of the cytosine-adenine-guanine (CAG) trinucleotide in the HTT gene. Decline in cognitive and motor functioning during the prodromal phase has been reported, and understanding genetic influences on prodromal disease progression beyond CAG will benefit intervention therapies. From a prodromal HD cohort ( N  = 715), we extracted gray matter (GM) components through independent component analysis and tested them for associations with cognitive and motor functioning that cannot be accounted for by CAG-induced disease burden (cumulative effects of CAG expansion and age). Furthermore, we examined genetic associations (at the genomic, HD pathway, and candidate region levels) with the GM components that were related to functional decline. After accounting for disease burden, GM in a component containing cuneus, lingual, and middle occipital regions was positively associated with attention and working memory performance, and the effect size was about a tenth of that of disease burden. Prodromal participants with at least one dystonia sign also had significantly lower GM volume in a bilateral inferior parietal component than participants without dystonia, after controlling for the disease burden. Two single-nucleotide polymorphisms (SNPs: rs71358386 in NCOR1 and rs71358386 in ADORA2B) in the HD pathway were significantly associated with GM volume in the cuneus component, with minor alleles being linked to reduced GM volume. Additionally, homozygous minor allele carriers of SNPs in a candidate region of ch15q13.3 had significantly higher GM volume in the inferior parietal component, and one minor allele copy was associated with a total motor score decrease of 0.14 U. Our findings depict an early genetical GM reduction in prodromal HD that occurs irrespective of disease burden and affects regions important for cognitive and motor functioning.

Purpose: This study was performed to evaluate the potential of a collagen-based membrane, collagen vitrigel (CV), for reconstructing corneal epithelium in the stromal wound and limbal stem cell deficiency (LSCD) models., Methods: Three groups of rabbits were used in the stromal wound model: CV affixed using fibrin glue (CV + FG group, n = 9), fibrin glue only (FG group, n = 3) and an untreated control group (n = 3). In the LSCD model, one group received CV containing human limbal epithelial cells (CV + hLEC group, n = 2) and the other was an untreated control (n = 1). Gross observation, including fluorescent staining, pathological examination, immunohistochemistry and electron microscopy, was used to evaluate the effect of CV on the corneal epithelium., Results: In the stromal wound model, fluorescent staining showed that epithelial reconstruction occurred as rapidly in the CV + FG group as it did in the control group. The pathological examination proved that the CV supported a healthy corneal epithelium in the CV + FG group, whereas FG led to hypertrophy and inappropriate differentiation of corneal epithelium in the FG group. In the LSCD model, the corneas in the CV + hLEC group showed sustained tissue transparency with good epithelialization, low inflammatory response and reduced neovascularization. However, the control cornea was translucent and showed high amounts of inflammation and neovascularization., Conclusion: We have demonstrated that CV supports corneal epithelial differentiation and prevents epithelial hypertrophy, in addition to serving as a scaffold for hLEC transplantation, without complications., (© 2014 Acta Ophthalmologica Scandinavica Foundation. Published by John Wiley & Sons Ltd.)

To investigate why responses of mast cells to antigen-induced IgE receptor (FcεRI) aggregation depend nonlinearly on antigen dose, we characterized a new artificial ligand, DF3, through complementary modeling and experimentation. This ligand is a stable trimer of peptides derived from bacteriophage T4 fibritin, each conjugated to a hapten (DNP). We found low and high doses of DF3 at which degranulation of mast cells sensitized with DNP-specific IgE is minimal, but ligand-induced receptor aggregation is comparable to aggregation at an intermediate dose, optimal for degranulation. This finding makes DF3 an ideal reagent for studying the balance of negative and positive signaling in the FcεRI pathway. We find that the lipid phosphatase SHIP and the protein tyrosine phosphatase SHP-1 negatively regulate mast cell degranulation over all doses considered. In contrast, SHP-2 promotes degranulation. With high DF3 doses, relatively rapid recruitment of SHIP to the plasma membrane may explain the reduced degranulation response. Our results demonstrate that optimal secretory responses of mast cells depend on the formation of receptor aggregates that promote sufficient positive signaling by Syk to override phosphatase-mediated negative regulatory signals.

This study was performed to evaluate the potential of a chondroitin sulfate-polyethylene glycol (CS-PEG) adhesive and collagen-based membrane (collagen vitrigel, CV) combination as a method to treat penetrating ocular injuries on the battlefield and to improve this method with two technologies: an antibiotic releasing CS-PEG adhesive and a corneal shaped CV. Burst testing using porcine cadaveric eyes, high-performance liquid chromatography, the Kirby-Bauer bacterial inhibition test, and CV implantations on the live and cadaveric rabbit eyes were performed. The ocular burst test showed CS-PEG adhesive could successfully repair 5-mm to 6-mm length wounds in the corneal and corneoscleral regions but would require CS-PEG + CV to treat larger wounds similar to those seen on the battlefield. In addition, high performance liquid chromatography and the Kirby-Bauer bacterial inhibition test presented evidence suggesting the vancomycin incorporated CS-PEG could inhibit Staphylococcus infection for 9 days. Furthermore, the curved CV showed an advantage by matching the corneal contour without any wrinkle formation. Although this pilot study showed a limited range of possible applications, we demonstrated that the combination of CS-PEG adhesive + CV is a promising method and the 2 technologies improve their applicability to the special demands of the battlefield., (Reprint & Copyright © 2014 Association of Military Surgeons of the U.S.)

Calcium-regulated exocytosis in neuroendocrine cells and neurons is accompanied by the redistribution of phosphatidylserine (PS) to the extracellular space, leading to a disruption of plasma membrane asymmetry. How and why outward translocation of PS occurs during secretion are currently unknown. Immunogold labeling on plasma membrane sheets coupled with hierarchical clustering analysis demonstrate that PS translocation occurs at the vicinity of the secretory granule fusion sites. We found that altering the function of the phospholipid scramblase-1 (PLSCR-1) by expressing a PLSCR-1 calcium-insensitive mutant or by using chromaffin cells from PLSCR-1⁻/⁻ mice prevents outward translocation of PS in cells stimulated for exocytosis. Remarkably, whereas transmitter release was not affected, secretory granule membrane recapture after exocytosis was impaired, indicating that PLSCR-1 is required for compensatory endocytosis but not for exocytosis. Our results provide the first evidence for a role of specific lipid reorganization and calcium-dependent PLSCR-1 activity in neuroendocrine compensatory endocytosis.

This report provides a synopsis of the esterase processing of short chain fatty acid (SCFA)-derivatized hexosamine analogs used in metabolic glycoengineering by demonstrating that the extracellular hydrolysis of these compounds is comparatively slow (e.g., with a t(1/2) of ∼4 h to several days) in normal cell culture as well as in high serum concentrations intended to mimic in vivo conditions. Structure-activity relationship (SAR) analysis of common sugar analogs revealed that O-acetylated and N-azido ManNAc derivatives were more refractory against extracellular inactivation by FBS than their butanoylated counterparts consistent with in silico docking simulations of Ac(4)ManNAc and Bu(4)ManNAc to human carboxylesterase 1 (hCE1). By contrast, all analogs tested supported increased intracellular sialic acid production within 2h establishing that esterase processing once the analogs are taken up by cells is not rate limiting., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Current models propose that the plasma membrane of animal cells is composed of heterogeneous and dynamic microdomains known variously as cytoskeletal corrals, lipid rafts and protein islands. Much of the experimental evidence for these membrane compartments is indirect. Recently, live cell single particle tracking studies using quantum dot-labeled IgE bound to its high affinity receptor FcϵRI, provided direct evidence for the confinement of receptors within micrometer-scale cytoskeletal corrals. In this study, we show that an innovative time-series analysis of single particle tracking data for the high affinity IgE receptor, FcϵRI, on mast cells provides substantial quantitative information about the submicrometer organization of the membrane. The analysis focuses on the probability distribution function of the lengths of the jumps in the positions of the quantum dots labeling individual IgE FcϵRI complexes between frames in movies of their motion. Our results demonstrate the presence, within the micrometer-scale cytoskeletal corrals, of smaller subdomains that provide an additional level of receptor confinement. There is no characteristic size for these subdomains; their size varies smoothly from a few tens of nanometers to a over a hundred nanometers. In QD-IGE labeled unstimulated cells, jumps of less than 70 nm predominate over longer jumps. Addition of multivalent antigen to crosslink the QD-IgE-FcϵRI complexes causes a rapid slowing of receptor motion followed by a long tail of mostly jumps less than 70 nm. The reduced receptor mobility likely reflects both the membrane heterogeneity revealed by the confined motion of the monomeric receptor complexes and the antigen-induced cross linking of these complexes into dimers and higher oligomers. In both cases, the probability distribution of the jump lengths is well fit, from 10 nm to over 100 nm, by a novel power law. The fit for short jumps suggests that the motion of the quantum dots can be modeled as diffusion in a fractal space of dimension less than two.

Cell biologists have developed methods to label membrane proteins with gold nanoparticles and then extract spatial point patterns of the gold particles from transmission electron microscopy images using image processing software. Previously, the resulting patterns were analyzed using the Hopkins statistic, which distinguishes nonclustered from modestly and highly clustered distributions, but is not designed to quantify the number or sizes of the clusters. Clusters were defined by the partitional clustering approach which required the choice of a distance. Two points from a pattern were put in the same cluster if they were closer than this distance. In this study, we present a new methodology based on hierarchical clustering to quantify clustering. An intrinsic distance is computed, which is the distance that produces the maximum number of clusters in the biological data, eliminating the need to choose a distance. To quantify the extent of clustering, we compare the clustering distance between the experimental data being analyzed with that from simulated random data. Results are then expressed as a dimensionless number, the clustering ratio that facilitates the comparison of clustering between experiments. Replacing the chosen cluster distance by the intrinsic clustering distance emphasizes densely packed clusters that are likely more important to downstream signaling events.We test our new clustering analysis approach against electron microscopy images from an experiment in which mast cells were exposed for 1 or 2 minutes to increasing concentrations of antigen that crosslink IgE bound to its high affinity receptor, FcϵRI, then fixed and the FcϵRI β subunit labeled with 5 nm gold particles. The clustering ratio analysis confirms the increase in clustering with increasing antigen dose predicted from visual analysis and from the Hopkins statistic. Access to a robust and sensitive tool to both observe and quantify clustering is a key step toward understanding the detailed fine scale structure of the membrane, and ultimately to determining the role of spatial organization in the regulation of transmembrane signaling.

Inverse metabolic engineering (IME) is a combinatorial approach for identifying genotypes associated with a particular phenotype of interest. In this study, gene disruptions that increase the biosynthesis of poly-3-hydroxybutyrate (PHB) in the photosynthetic bacterium Synechocystis PCC6803 were identified. A Synechocystis mutant library was constructed by homologous recombination between the Synechocystis genome and a mutagenized genomic plasmid library generated through transposon insertion. Using a fluorescence-activated cell sorting-based high throughput screen, high PHB accumulating mutants from the library grown in different nutrient conditions were isolated and characterized. While several mutants isolated from the screen had increased PHB accumulation, transposon insertions in only two ORFs could be linked to increased PHB production. Disruptions of sll0461, coding for gamma-glutamyl phosphate reductase (proA), and sll0565, a hypothetical protein, resulted in increased accumulation in standard growth media and acetate supplemented media. These genetic perturbations have increased PHB accumulation in Synechocystis and serve as markers for engineering increased polymer production in higher photosynthetic organisms., (2009 American Institute of Chemical Engineers Biotechnol.)