Visual Rating Method Research Articles

A comparison of visual assessment and semi-quantification for the diagnostic and prognostic use of [18F]flortaucipir PET in a memory clinic cohort

Purpose[18F]Flortaucipir PET is a powerful diagnostic and prognostic tool for Alzheimer’s disease (AD). Tau status definition is mainly based in the literature on semi-quantitative measures while in clinical settings visual assessment is usually preferred. We compared visual assessment with established semi-quantitative measures to classify subjects and predict the risk of cognitive decline in a memory clinic population.MethodsWe included 245 individuals from the Geneva Memory Clinic who underwent [18F]flortaucipir PET. Amyloid status was available for 207 individuals and clinical follow-up for 135. All scans were blindly evaluated by three independent raters who visually classified the scans according to Braak stages.Standardized uptake value ratio (SUVR) values were obtained from a global meta-ROI to define tau positivity, and the Simplified Temporo-Occipital Classification (STOC) was applied to obtain semi-quantitatively tau stages. The agreement between measures was tested using Cohen’s kappa (k). ROC analysis and linear mixed-effects models were applied to test the diagnostic and prognostic values of tau status and stages obtained with the visual and semi-quantitative approaches.ResultsWe found good inter-rater reliability in the visual interpretation of tau Braak stages, independently from the rater’s expertise (k>0.68, p<0.01). A good agreement was equally found between visual and SUVR-based classifications for tau status (k=0.67, p<0.01). All tau-assessment modalities significantly discriminated amyloid-positive MCI and demented subjects from others (AUC>0.80) and amyloid-positive from negative subjects (AUC>0.85). Linear mixed-effect models showed that tau-positive individuals presented a significantly faster cognitive decline than the tau-negative group (p<0.01), independently from the classification method.ConclusionOur results show that visual assessment is reliable for defining tau status and stages in a memory clinic population. The high inter-rater reliability, the substantial agreement, and the similar diagnostic and prognostic performance of visual rating and semi-quantitative methods demonstrate that [18F]flortaucipir PET can be robustly assessed visually in clinical practice.

Differential diagnosis of Lewy body dementias using multivariate EEG classifiers

AbstractBackgroundLewy body dementia (LBD) results from pathology in ɑ‐synuclein and is the second most common cause of dementia. LBD has two manifestations: Parkinson’s disease dementia (PDD) and dementia with Lewy bodies (DLB). There is currently no non‐invasive, fast, reliable way to distinguish between early‐stage PDD, DLB, and Alzheimer’s disease (AD). Although previous traditional EEG visual rating methods have not been useful in delineating dementia subtypes, resting‐state electroencephalography (rsEEG) is a promising method for differential diagnosis of dementia, because it is inexpensive, non‐invasive, and widely available for clinical use. Here, we demonstrate how a multivariate classifier using rsEEG as input can differentiate PDD, DLB, and Alzheimer’s disease.MethodWe analyzed rsEEG data from older adults (N = 133, age 59‐89) with AD (n = 33), DLB (n = 27) or PDD (n = 45). We computed the aperiodic and periodic parameterization of brain activity, along with metrics of variation in fractal dimension within specific frequency bands. We then used these features, along with age and sex, to develop XGBoost machine‐learning models to predict patient diagnoses. Models were evaluated using cross‐validation with balanced accuracy, weighted sensitivity, and weighted specificity.ResultThe first task was a two‐way classification: Control vs Dementia (accuracy = 86%, sensitivity = 93%, specificity = 79%, p &lt; .001). The second task was a three‐way classification: AD vs DLB vs PDD (accuracy = 66%, sensitivity = 70%, specificity = 82%, p &lt; .001). We also evaluated two‐way models distinguishing between AD vs DLB (accuracy = 86%, sensitivity = 87%, specificity = 86%, p &lt; .001), AD vs PDD (accuracy = 84%, sensitivity = 85%, specificity = 83%, p &lt; .001), AD vs LBD (combined DLB and PDD; accuracy = 82%, sensitivity = 85%, specificity = 78%, p &lt; .01), and DLB vs PDD (accuracy = 68%, sensitivity = 71%, specificity = 65%, p &lt; .05).ConclusionMachine‐learning and rsEEG are capable of distinguishing between AD and LBD. Moreover, machine‐learning combined with spectral and fractal features can distinguish between DLB and PDD, despite both forms of dementia arising from ɑ‐synuclein pathology.

Integrating Non-photochemical Quenching (NPQ) Measurements for Identifying Flood-Tolerant Soybean Genotypes in the Era of Climate Change

Climate change has negatively affected agriculture worldwide, including soybean production. Studies have shown that rising temperatures and extreme weather events like droughts and floods significantly reduce soybean yields. Developing flood-tolerant soybean genotypes is crucial for ensuring food security. Conventional breeding programs are limited by laborious and imprecise visual rating methods for flooding tolerance identification. High-throughput platforms for plant phenotyping using imaging techniques offer potential solutions, but they lack information on underlying physiological mechanisms. Non-photochemical quenching (NPQ) is a molecular adaptation in photosynthesis that dissipates excess light energy, protecting plants from damage. This study aimed to integrate NPQ measurements into high-throughput phenotyping procedures to identify flooding-tolerant soybean genotypes. The study evaluated 160 soybean genotypes for flooding tolerance, identifying those with higher grain yield potential. Subsequently, ten genotypes were selected for monitoring NPQ responses under flooded conditions. Results showed that genotypes with higher grain yields also exhibited superior NPQ performance, suggesting a positive correlation between flooding tolerance and energy dissipation capacity. Among these genotypes, 58I60 RSF IPRO, 64HO130 I2X and BRS 525 displayed superior potential and could be further exploited in breeding efforts, considering their grain yield capacity, plant leaf area, and photoprotective capacity under flooding conditions. These findings suggest that integrating NPQ measurements into high-throughput phenotyping platforms can aid in identifying flood-tolerant soybean genotypes for breeding programs, leading to more resilient crops in the face of climate change. Further field studies are warranted to validate these hypotheses and improve crop models for future climate scenarios.

Influence of Ultrasound Settings on Laboratory Vertical Artifacts.

The aim of the work described here was to analyze the relationship between the change in ultrasound (US) settings and the vertical artifacts' number, visual rating and signal intensity METHODS: An in vitro phantom consisting of a damp sponge and gelatin mix was created to simulate vertical artifacts. Furthermore, several US parameters were changed sequentially (i.e., frequency, dynamic range, line density, gain, power and image enhancement) and after image acquisition. Five US experts rated the artifacts for number and quality. In addition, a vertical artifact visual score was created to determine the higher artifact rating ("optimal") and the lower artifact rating ("suboptimal"). Comparisons were made between the tested US parameters and baseline recordings. The expert intraclass correlation coefficient for the number of vertical artifacts was 0.694. The parameters had little effect on the "optimal" vertical artifacts but changed their number. Dynamic range increased the number of discernible vertical artifacts to 3 from 36 to 102 dB. The intensity did not correlate with the visual rating score. Most of the available US parameters did not influence vertical artifacts.

Measuring soybean iron deficiency chlorosis progression and yield prediction with unmanned aerial vehicle

Iron deficiency chlorosis (IDC), a symptom of reduction in chlorophyll and stunted growth, causes a great yield loss in soybean every year in the Midwest, USA and the most efficient method to manage IDC is to plant tolerant cultivars. The assessment of cultivars' tolerance is traditionally performed by visually rating the IDC symptoms based on leaves discoloration twice during the growing season. However, the visual rating method is time-consuming, subjective, not suitable at large scales, labor-intensive, and unaffordable for frequent observation. Therefore, in this study, we used an unmanned aerial vehicle (UAV) as a tool to monitor the soybean cultivars more frequently and more efficiently through image processing approach of the whole field. Images were taken with a DJI Phantom 4 and orthomosaicked in Agisoft Photoscan. A 40-cultivar soybean experimental plots (3000 m2; ▪) at 5 locations in North Dakota, USA (Amenia, Colfax, Leonard (2), and Hunter) for 2 years (2016 and 2017) were used in the study. The orthomosaicked images were processed in MATLAB to calculate the dark green color index (DGCI), which is a good indicator of chlorophyll in soybean leaves. The grayscale DGCI images were then processed in ArcGIS to extract the average DGCI and canopy size (CS) for each plot for each flight. The area under the curve (AUC) was calculated for DGCI, CS, and CS × DGCI product (CDP) to aggregate the values of all flights within each year. The correlation of AUC of CDP and the yield was more consistent among both years and was the better predictor of yield (R2=0.74 and R2=0.79). The latest growth stage (more representative of yield) values of both years were combined to build models for yield prediction and the CDP produced the lowest error (11.72%). Future studies should look into IDC progress measurement involving more cultivars, geographical locations, frequent imaging, as well as methods applied to regular soybean production sites to evaluate various image-based parameters and their interaction for yield predictions.

Enlarged Perivascular Spaces Are Negatively Associated With Montreal Cognitive Assessment Scores in Older Adults.

Emerging evidence suggests that enlarged perivascular spaces (ePVS) may be a clinically significant neuroimaging marker of global cognitive function related to cerebral small vessel disease (cSVD). We tested this possibility by assessing the relationship between ePVS and both a standardized measure of global cognitive function, the Montreal Cognitive Assessment (MoCA), and an established marker of cSVD, white matter hyperintensity volume (WMH) volume. One hundred and eleven community-dwelling older adults (56–86) underwent neuroimaging and MoCA testing. Quantification of region-specific ePVS burden was performed using a previously validated visual rating method and WMH volumes were computed using the standard ADNI pipeline. Separate linear regression models were run with ePVS as a predictor of MoCA scores and whole brain WMH volume. Results indicated a negative association between MoCA scores and both total ePVS counts (P ≤ 0.001) and centrum semiovale ePVS counts (P ≤ 0.001), after controlling for other relevant cSVD variables. Further, WMH volumes were positively associated with total ePVS (P = 0.010), basal ganglia ePVS (P ≤ 0.001), and centrum semiovale ePVS (P = 0.027). Our results suggest that ePVS burden, particularly in the centrum semiovale, may be a clinically significant neuroimaging marker of global cognitive dysfunction related to cSVD.

Sensitivity to sublethal rates of dicamba for selected mid-Atlantic vegetable crops

AbstractDicamba is a synthetic auxin herbicide that may be applied over the top of transgenic dicamba-tolerant crops. The increasing prevalence of herbicide-resistant weeds has resulted in increased reliance on dicamba-based herbicides in soybean production systems. Because of the high volatility of dicamba it is prone to off-target movement, and therefore concern exists regarding its drift onto nearby specialty crops. The present study evaluates 12 mid-Atlantic vegetable crops species for sensitivity to sublethal rates of dicamba. Soybean, snap bean, lima bean, tomato, eggplant, bell pepper, cucumber, summer squash, watermelon, pumpkin, sweet basil, lettuce, and kale were grown in a greenhouse and exposed to dicamba at 0, 0.056, 0.11, 0.28, 0.56, 1.12, 2.24 g ae ha−1, which is, respectively, 0, 1/10,000, 1/5,000, 1/2,000, 1/1,000, 1/500, and 1/250 of the maximum recommended label rate for soybean application (560 g ae ha−1). Vegetable crop injury was evaluated 4 wk after treatment using visual rating methods and leaf deformation index measurements. Overall, snap bean was the most sensitive crop, with dicamba rates as low as 0.11 g ae ha−1 resulting in significantly higher leaf deformation levels compared with the nontreated control. Other Fabaceae and Solanaceae species also demonstrated high sensitivity to sublethal rates of dicamba with rates ranging 0.28 to 0.56 g ae ha−1 causing higher leaf deformation compared with the nontreated control. While cucumber, pumpkin, and summer squash were no or moderately sensitive to dicamba, watermelon showed greater sensitivity with unique symptoms at rates as low as 0.056 g ae ha−1 based on visual evaluation. Within the range of tested dicamba rates, sweet basil, lettuce, and kale demonstrated tolerance to dicamba with no injury observed at the maximum rate of 2.24 g ae ha−1.

Zero-Energy Cool Chamber in Anthracnose Disease Management of Postharvest Capsicum frutescens

Anthracnose caused by Colletotrichum gloeosporioides is one of important diseases causing postharvest losses on chili. This study aimed to determine the effectiveness of the zero energy cool chamber (ZECC) in reducing the severity of anthracnose in Capsicum frutescens L. and extending the shelf life of chilies. The research was started by designing and building ZECC as a storage area for chilies. Chilies with and without fungal inoculation were stored in ZECC and storage chamber at room temperature. C. gloeosporioides experienced a growth inhibition of 40.48% after being stored in ZECC compared to its growth at room temperature. Storage of chilies in ZECC was able to reduce the severity of anthracnose by 56.2% on the 15th day, did not affect vitamin C content and total dissolved solids (TSS) and was able to reduce chilies weight loss during storage. Based on the organovisual test using the visual quality rating method and the Hedonic sensory test, consumers prefer chilies stored in the ZECC. The results showed that storage in ZECC could extend the shelf life of chilies. This storage model does not use electricity or is zero energy so it can be used by small scale farmers. This is the first report on the use of ZECC for the management of anthracnose in postharvest chilies in Indonesia.

Rating Iron Deficiency in Soybean Using Image Processing and Decision-Tree Based Models

The most efficient way of soybean (Glycine max (L.) Merrill) iron deficiency chlorosis (IDC) management is to select a tolerant cultivar suitable for the specific growing condition. These cultivars are selected by field experts based on IDC visual ratings. However, this visual rating method is laborious, expensive, time-consuming, subjective, and impractical on larger scales. Therefore, a modern digital image-based method using tree-based machine learning classifier models for rating soybean IDC at plot-scale was developed. Data were collected from soybean IDC cultivar trial plots. Images were processed with MATLAB and corrected for light intensity by using a standard color board in the image. The three machine learning models used in this study were decision tree (DT), random forest (RF), and adaptive boosting (AdaBoost). Calculated indices from images, such as dark green color index (DGCI), canopy size, and pixel counts into DGCI ranges and IDC visual scoring were used as input and target variables to train these models. Metrics such as precision, recall, and f1-score were used to assess the performance of the classifier models. Among all three models, AdaBoost had the best performance (average f1-score = 0.75) followed by RF and DT the least. Therefore, a ready-to-use methodology of image processing with AdaBoost model for soybean IDC rating was recommended. The developed method can be easily adapted to smartphone applications or scaled-up using images from aerial platforms.

A Novel Chicken Meat Quality Evaluation Method Based on Color Card Localization and Color Correction

Among all the chicken meat quality evaluation metrics, color is one of the most significant factors directly related to the freshness of meat inducing the purchase desire. Biochemical tests for evaluating meat quality may contaminate or damage the test samples. Visual rating method is subjectively inefficient and difficult to realize online detection. Colorimeter has the disadvantages of complicated time-consuming operations, high technical requirements and expensive instruments. This paper proposes a low-cost, contactless chicken meat quality evaluation method by examining the color image of chicken meat. Specifically, the meat image is acquired by the camera of a smartphone. To eliminate the chromatic aberration, a pre-defined color card is put beside meat and automatically localized to extract the captured color information for color correction. Finally, the corrected colors of all the experimental meat samples are analyzed by hierarchical clustering to achieve 3 different quality levels.

A proposal of Visual Rating method to set the priority of detailed evaluation for masonry infilled RC building

In developing countries, an enormous stock of vulnerable buildings is to be considered for seismic evaluation, which requires a rigorous analysis with much expertise, cost, and time. Identification of most vulnerable buildings beforehand would be helpful in prioritizing the detailed seismic evaluation. This study aims to develop a quick seismic evaluation method, designated as Visual Rating method, in order to set the priority of vulnerable building to be gone through detailed evaluation. The proposed method approximately estimates the seismic capacity of a building, in terms of Visual Rating Index, considering cross-sectional areas and shear strength of RC columns and walls. This is followed by a modification of seismic capacity to reflect the effect of different building attributes such as structural configuration, buildings deterioration, and year of construction etc. Applicability of the proposed Visual Rating method has been investigated using post-earthquake damage database of the 2016 Taiwan earthquake. The obtained results show that the lower Visual Rating Index corresponds to severely damaged buildings in the 2016 Taiwan earthquake and the proposed method could be applied for identifying the most vulnerable buildings.

Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective

The detection and identification of plant diseases is crucial for an appropriate and targeted application of plant protection measures in crop production. Recently, intensive research has been conducted to develop innovative and technology-based optical methods for plant disease detection. In contrast to common visual rating and detection methods, optical sensors are able to measure pathogen-induced changes in the plant physiology non-invasively and objectively. Several studies showed that especially hyperspectral sensors are valuable tools for disease detection, identification and quantification on different scales from the tissue to the canopy level. This review describes the basic principles of hyperspectral measurements and different types of available hyperspectral sensors. Possible applications of hyperspectral sensors on different scales for disease detection and plant protection are discussed and evaluated. The advantages and disadvantages on each particular scale, as well as the impact of external factors, such as: light, wind, viewing angle, for measurements in laboratories, greenhouses and fields, are critically assessed in order to support researchers and agriculture technicians. Additionally, a comprehensive literature review about the use of hyperspectral sensors on these different scales for plant disease detection reflects the possibilities of non-invasive measurement systems. This highlights advantages of hyperspectral sensors when investigating plant–pathogen interactions through multiple examples. By some approaches, detection before visible symptoms appear is feasible. The potential of hyperspectral sensors as a tool for disease identification and quantification, based on disease characteristic changes in the plants spectral signature, is discussed as well. The review is concluded with an overview on different data analysis methods, which are required to extract key information from gathered hyperspectral datasets.

Longitudinal changes in the brain in mild cognitive impairment: a magnetic resonance imaging study using the visual rating method and tensor-based morphometry.

Background Brain atrophy is associated with mild cognitive impairment (MCI), and by using volumetric and visual analyzing methods, it is possible to differentiate between individuals with progressive MCI (MCIp) and stable MCI (MCIs). Automated analysis methods detect degenerative changes in the brain earlier and more reliably than visual methods. Purpose To detect and evaluate structural brain changes between and within the MCIs, MCIp, and control groups during a two-year follow-up period. Material and Methods Brain magnetic resonance imaging (MRI) scans of 11 participants with MCIs, 18 participants with MCIp, and 84 controls were analyzed by the visual rating method (VRM) and tensor-based morphometry (TBM). Results At baseline, both VRM and TBM differentiated the whole MCI group (combined MCIs and MCIp) and the MCIp group from the control group, but they did not differentiate the MCIs group from the control group. At follow-up, both methods differentiated the MCIp group from the control group, but minor differences between the MCIs and control groups were only seen by TBM. Neuropsychological tests did not find differences between the MCIs and control groups at follow-up. Neither method revealed relevant signs of brain atrophy progression within or between MCI subgroups during the follow-up time. Conclusion Both methods are equally good in the evaluation of structural brain changes in MCI if the groups are sufficiently large and the disease progresses to AD. Only TBM disclosed minor atrophic changes in the MCIs group compared to controls at follow-up. The results need to be confirmed with a large patient group and longer follow-up time.

Correlation between hippocampal volumes and medial temporal lobe atrophy in patients with Alzheimer's disease.

Introduction:Hippocampus undergoes atrophy in patients with Alzheimer's disease (AD). Calculation of hippocampal volumes can be done by a variety of methods using T1-weighted images of magnetic resonance imaging (MRI) of the brain. Medial temporal lobes atrophy (MTL) can be rated visually using T1-weighted MRI brain images. The present study was done to see if any correlation existed between hippocampal volumes and visual rating scores of the MTL using Scheltens Visual Rating Method.Materials and Methods:We screened 84 subjects presented to the Department of Neurology of a Tertiary Care Hospital and enrolled forty subjects meeting the National Institute of Neurological and Communicative Disorders and Stroke, AD related Disease Association criteria. Selected patients underwent MRI brain and T1-weighted images in a plane perpendicular to long axis of hippocampus were obtained. Hippocampal volumes were calculated manually using a standard protocol. The calculated hippocampal volumes were correlated with Scheltens Visual Rating Method for Rating MTL. A total of 32 cognitively normal age-matched subjects were selected to see the same correlation in the healthy subjects as well. Sensitivity and specificity of both methods was calculated and compared.Results:There was an insignificant correlation between the hippocampal volumes and MTL rating scores in cognitively normal elderly (n = 32; Pearson Correlation coefficient = 0.16, P > 0.05). In the AD Group, there was a moderately strong correlation between measured hippocampal volumes and MTL Rating (Pearson's correlation coefficient = −0.54; P < 0.05. There was a moderately strong correlation between hippocampal volume and Mini-Mental Status Examination in the AD group. Manual delineation was superior compared to the visual method (P < 0.05).Conclusions:Good correlation was present between manual hippocampal volume measurements and MTL scores. Sensitivity and specificity of manual measurement of hippocampus was higher compared to visual rating scores for MTL in patients with AD.

Evaluation of Resistance to Leaf Scald by Quantitative PCR of Xanthomonas albilineans in Sugarcane.

Leaf scald, caused by Xanthomonas albilineans, is a major sugarcane disease controlled primarily with host resistance. Because visual evaluation can be uncertain due to erratic symptom expression, a reliable resistance screening method is needed. A quantitative polymerase chain reaction (qPCR) with potential for resistance screening was used to compare bacterial populations in 31 clones at different times after inoculation, and the correlation with the visual symptom rating method was determined. Comparisons of bacterial populations quantified by qPCR and visual symptom severity ratings in systemically infected leaves showed variable results, with the highest correlation at 8 weeks after inoculation. To measure consistency, the correlation was determined among three different field experiments for data obtained with the same method at different times after inoculation. The qPCR assay was more consistent among experiments compared with visual symptom rating at 8 weeks after inoculation. Susceptible check cultivars always had high bacterial populations but the severe inoculation resulted in moderate to high bacterial populations in two of three resistant checks in some experiments. The results suggest that qPCR can provide an improved method to evaluate resistance to leaf scald in sugarcane; however, multiple experiments will be needed to accurately determine clone resistance levels.

Effect of brain atrophy on the cognition in patients with subcortical ischemic vascular disease

To explore the effect of brain atrophy on the cognition in patients with subcortical ischemic vascular disease (SIVD). A total of 116 SIVD patients were enrolled from the Department of Neurology, First Affiliated Hospital, Anhui Medical University between September 2013 and December 2014. Lobar atrophy, leukoaraiosis (LA), lacunar infarcts (LI) and vascular risk factors were analyzed in the 116 SIVD patients who were divided into three groups according to the diagnostic criteria: non-cognitive impairment group (SIVD-NCI) , mild cognitive impairment group (SIVD-MCI) and dement group (SIVD-VaD). All patients underwent magnetic resonance imaging (MRI) with a 3.0-T system. The cognitive functions were evaluated by mini-metal state examination (MMSE), the Cambridge cognitive examination-Chinese version (CAMCOG-C), etc. A widely used visual atrophy rating method (0 to 3) was adopted to rate the severity of frontal, parietal and temporal lobe atrophy. The degree of LA and the numbers of LI in 4 brain regions (frontal, parieto-occipital, temporal, and basal ganglia) were evaluated meanwhile. Firstly, both the SIVD-MCI and SIVD-VaD groups showed significantly higher total scores of atrophy, higher frontal lobe atrophy scores, higher LA scores and larger LI numbers than SIVD-NCI (H=6.138, P=0.013; H=45.845, P=0.000; H=36.818, P=0.000; H=37.46, P=0.000). There were no significant differences in temporal lobe atrophy scores between SIVD-NCI group and SIVD-MCI group. Parietal lobe atrophy scores also showed no differences among the three groups. Secondly, as well as total numbers of LI, total scores of atrophy and LA were negatively correlated with SIVD cognition, especially frontal lobe atrophy scores, parieto-occipital LA scores and basal ganglia LI numbers had a remarkable negative correlation with MMSE scores, CAMCOG-C scores and partial subitems in CAMCOG-C scores (P<0.005). However temporal LI numbers was absence of correlation with MMSE scores, CAMCOG-C scores and subitems in CAMCOG-C scores (P>0.005). Thirdly, education (OR= 0.787, P=0.009), total scores of LA (OR= 1.201, P=0.036) and total numbers of LI (OR= 1.221, P=0.011) remained to be associated with cognition after controlling for sex, age, education, hypertension, diabetes, smoking. While, after adjusting atrophy scores, total scores of LA had no significant association with cognition. Atrophy scores, total numbers of LI and education still showed significant association with cognition, particularly frontal lobe atrophy scores (OR= 16.082, P=0.000), but not temporal and parietal lobe atrophy scores. Brain atrophy may be a new and independent predictive index of cognitive impairment in SIVD. Further, the effect of brain atrophy, the numbers of LI and degree of LA on cognitive decline is independent and decreases in turn.

Visual analog rating of mood by people with aphasia

Background:Considerable attention has been given to the identification of depression in stroke survivors with aphasia, but there is more limited information about other mood states. Visual analog scales are often used to collect subjective information from people with aphasia. However, the validity of these methods for communicating about mood has not been established in people with moderately to severely impaired language.Objective:The dual purposes of this study were to characterize the relative endorsement of negative and positive mood states in people with chronic aphasia after stroke and to examine congruent validity for visual analog rating methods for people with a range of aphasia severity.Methods:Twenty-three left-hemisphere stroke survivors with aphasia were asked to indicate their present mood by using two published visual analog rating methods. The congruence between the methods was estimated through correlation analysis, and scores for different moods were compared.Results:Endorsement was significantly stronger for “happy” than for mood states with negative valence. At the same time, several participants displayed pronounced negative mood compared to previously published norms for neurologically healthy adults. Results from the two rating methods were moderately and positively correlated.Conclusions:Positive mood is prominent in people with aphasia who are in the chronic stage of recovery after stroke, but negative moods can also be salient and individual presentations are diverse. Visual analog rating methods are valid methods for discussing mood with people with aphasia; however, design optimization should be explored.

Genetic mapping of quantitative trait loci conditioning salt tolerance in wild soybean (Glycine soja) PI 483463

Salt tolerance in soybean [Glycine max (L.) Merr.] is controlled by major quantitative trait loci (QTL) or single gene(s). Among soybean germplasm, wild soybean plant introduction PI 483463 was reported to have a single dominant gene for salt tolerance. The objective of this study was to genetically map the QTL in a recombinant inbred line (RIL) population derived from a cross between PI 483463 and Hutcheson. Simple sequence repeat (SSR) markers and universal soybean single nucleotide polymorphism (SNP) panel (the USLP 1.0) were utilized for molecular genotyping. The RILs were phenotyped in two independent tests in a greenhouse using a 1–5 scale visual rating method. The results showed that the salt tolerant QTL in PI 483463 was mapped to chromosome 3 in a genomic region between the Satt255 and BARC-038333-10036 markers. The favorable allele inherited from PI 483463 conferred tolerance to salinity and had an additive effect on reducing leaf scorch. A subset of 66 iso-lines was developed from the F3 families of the same cross and was used for genetic confirmation of the QTL. The integration of recombination events and the salt reaction data indicate that the QTL is located in the region of approximately a 658 kb segment between SSR03_1335 at nucleotide 40,505,992 and SSR03_1359 at nucleotide 41,164,735 on chromosome 3. This narrow region can facilitate further genomic research for salt tolerance in soybean including cloning salt tolerance genes.

Association of Ultrasonographic Parameters with Subclinical White-Matter Hyperintensities in Hypertensive Patients

Background and Purpose. Cerebral white matter hyperintensities (WMHs) are regarded as typical MRI expressions of small-vessel disease (SVD) and are common in hypertensive patients. Hypertension induces pathologic changes in macrocirculation and in microcirculation. Changes in microcirculation may lead to SVD of brain and consequently to hypertensive end-organ damage. This damage is regarded the result of interactions between the macrovascular and microvascular levels. We sought to investigate the association of cerebral WMHs with ultrasonographic parameters of cerebral macrocirculation evaluated by carotid duplex ultrasound (CDU) and transcranial doppler (TCD). Subjects and Methods. The study was prospective, cross-sectional and consecutive and included hypertensive patients with brain MRI with WMHs. Patients underwent CDU and TCD. The clinical variables recorded were demographic characteristics (age, gender, race) and vascular risk factors (hypertension, diabetic mellitus, hypercholesterolemia, current smoking, and body mass index). Excluded from the study were patients with history of clinical stroke (including lacunar stroke and hemorrhagic) or transient ischemic attack (either hemispheric or ocular), hemodynamically significant (>50%) extra- or intracranial stenosis, potential sources of cardioembolism, and absent transtemporal windows. WMHs were quantified with the use of a semiquantitative visual rating method. Ultrasound parameters investigated were (1) common carotid artery (CCA) diameter and intima-media thickness, (2) blood flow velocity in the CCA and internal carotid artery (ICA), and (3) blood flow velocity and pulsatility index of middle cerebral artery (MCA). Results. A total of 52 patients fulfilled the study inclusion criteria (mean age 71.4 ± 4.5 years, 54% men, median WMH-score: 20). The only two ultrasound parameters that were independently associated with WMH score in multivariate linear regression models adjusting for demographic characteristics and vascular risk factors were increased mean common carotid artery (CCA) diameter (beta = 0.784, SE = 0.272, P = 0.006, R 2 = 23.9%) and increased middle cerebral artery pulsatility index (MCA-PI; beta = 0.262, SE = 0.110, P = 0.025, R 2 = 9.0%). Among all ultrasound parameters the highest AUC (areas under the receiver operating characteristic curve) were documented for MCA-PI (AUC = 0.82, 95% CI = 0.68−0.95, P < 0.001) and mean CCA diameter (AUC = 0.80, 95% CI = 0.67−0.92, P < 0.001). Conclusions. Our study showed that in hypertensive individuals with brain SVD the extent of structural changes in cerebral microcirculation as reflected by WMHs burden is associated with the following ultrasound parameters of cerebral macrocirculation: CCA diameter and MCA-PI.

Peripheral endogenous opioids in low level laser therapy analgesia, 820 nm, in Wistar rats with acute knee synovitis

To investigate physiotherapist agreement in rating movement quality during lower extremity functional tests using two visual rating methods and physiotherapists with differing clinical experience.Clinical measurement.Six healthy individuals were rated by 44 physiotherapists. These raters were in three groups (inexperienced, novice, experienced).Video recordings of all six individuals performing four lower extremity functional tests were visually rated (dichotomous or ordinal scale) using two rating methods (overall or segment) on two occasions separated by 3–4 weeks. Intra and inter-rater agreement for physiotherapists was determined using overall percentage agreement (OPA) and the first order agreement coefficient (AC1).Intra-rater agreement for overall and segment methods ranged from slight to almost perfect (OPA: 29–96%, AC1: 0.01 to 0.96). AC1 agreement was better in the experienced group (84–99% likelihood) and for dichotomous rating (97–100% likelihood). Inter-rater agreement ranged from fair to good (OPA: 45–79%; AC1: 0.22–0.71). AC1 agreement was not influenced by clinical experience but was again better using dichotomous rating.Physiotherapists’ visual rating of movement quality during lower extremity functional tests resulted in slight to almost perfect intra-rater agreement and fair to good inter-rater agreement. Agreement improved with increased level of clinical experience and use of dichotomous rating.