WMSA detection on simulated preterm T2-weighted brain images.

<p> Images at three mid-axial levels show, from left to right: noise-free images with manually drawn WMSA regions in yellow (ground truth); addition of Rician noise (SNR = 15) and INU (20% level); and WMSA detection by our proposed method marked in yellow. </p

A flowchart of the generation of individual tissue probability maps.

<p>A target individual anatomy was first normalized to the reference space formed by the very preterm probabilistic atlas using LDDMM and the resultant transformation matrix was saved. The inverse transformation matric was applied to the tissue probability maps to create the desired target individual tissue probability maps.</p

Automated WMSA detection at six different axial levels.

<p>Top row: T2-weighted images; bottom row: detected WMSA marked in red. The automated detections closely approximated the visually apparent signal abnormalities. </p

Linear regression and Pearson correlation analyses of automated quantified WMSA within different WM regions and Bayley III cognitive and language scores.

<p><i>R</i>² denotes linear regression’s coefficient of determination and <i>r</i> denotes Pearson correlation coefficient. Larger WMSA volumes correspond with both lower cognitive and language scores. WMSA regional volume within centrum semiovale is a better predictor of Bayley scores than that within the periventricular WM regions.</p

Comparison of automated WMSA detection on simulated infant MR images with ground truth.

<p>Quantitatively, automated detection showed very high Dice similarity index values (left) and low false detection rates (right) at each noise level with ground truth. </p

The results of hierarchical regression analysis.

<p>Note. post-traumatic growth (PTG); social support; coping strategies;</p><p>P<0.05;</p><p>P<0.01;</p><p>P<0.001.</p

Investigation of the Impact of a Pesticide Adjuvant on Dimethoate Persistence, Penetration, and Stability on Apples Using Surface-Enhanced Raman Spectroscopy

Pesticides are often applied with adjuvants to improve their efficacy and stability. However, the knowledge about pesticide behaviors with adjuvants is still lacking. This study investigated the impact of a common adjuvant on the persistence and penetration of dimethoate on and in apples and dimethoate stability in washing solutions using the surface-enhanced Raman spectroscopic (SERS) mapping technique. Results demonstrated that the adjuvant did not significantly affect dimethoate removal after postharvest washing or dimethoate penetration (30 ± 5 μm) in apples. Tap water showed similar effectiveness as baking soda to remove dimethoate with and without the adjuvant in 5 min. Dimethoate with and without the adjuvant was stable in tap water but was degraded to omethoate in the baking soda rapidly in 40 min. Considering the higher toxicity of omethoate, tap water would be recommended to remove dimethoate compared with baking soda. The study provides a better understanding of adjuvant effects on pesticide behaviors

Filter-based surface-enhanced Raman spectroscopy for rapid and sensitive detection of the fungicide ferbam in water

<p>Surface-enhanced Raman spectroscopy (SERS) has been widely applied for rapid and sensitive detection of various chemical and biological targets. Here, we incorporated a filter syringe system into the SERS method to detect the fungicide ferbam in water. Silver nanoparticles (Ag NPs) were aggregated by sodium chloride (NaCl) to form nanoclusters that could be trapped in the pores of the filter membrane to from the SERS-active membrane. Then samples were filtered through the membrane. After capturing the target, the membrane was taken out and air dried before measuring by a Raman instrument. After optimisation of various parameters, the developed filter SERS method was able to detect the fungicide ferbam as low as 2.5 μg/L and had a good quantitative capability. The developed method was successfully applied in three water samples, including double-distilled water, tap water, and pond water. The test can be carried out on site using a portable Raman instrument. This study shows that the filter-based SERS method improves the detection capability in water samples, including the sensitivity and portability, and could be applied in the detection of various toxins in real-world water samples.</p

Scores of PTG-Total by background characteristics.

<p>Note. PTG = posttraumatic growth.</p

Data_Sheet_1_Enhancing Diagnosis of Autism With Optimized Machine Learning Models and Personal Characteristic Data.docx

Autism spectrum disorder (ASD) is a developmental disorder, affecting about 1% of the global population. Currently, the only clinical method for diagnosing ASD are standardized ASD tests which require prolonged diagnostic time and increased medical costs. Our objective was to explore the predictive power of personal characteristic data (PCD) from a large well-characterized dataset to improve upon prior diagnostic models of ASD. We extracted six personal characteristics (age, sex, handedness, and three individual measures of IQ) from 851 subjects in the Autism Brain Imaging Data Exchange (ABIDE) database. ABIDE is an international collaborative project that collected data from a large number of ASD patients and typical non-ASD controls from 17 research and clinical institutes. We employed this publicly available database to test nine supervised machine learning models. We implemented a cross-validation strategy to train and test those machine learning models for classification between typical non-ASD controls and ASD patients. We assessed classification performance using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Of the nine models we tested using six personal characteristics, the neural network model performed the best with a mean AUC (SD) of 0.646 (0.005), followed by k-nearest neighbor with a mean AUC (SD) of 0.641 (0.004). This study established an optimal ASD classification performance with PCD as features. With additional discriminative features (e.g., neuroimaging), machine learning models may ultimately enable automated clinical diagnosis of autism.</p