Operation of membrane bioreactors (MBRs) for wastewater treatment is hampered by the membrane biofouling resulting from microbial activities. However, the knowledge of the microbial ecology of both ...biofilm and activated sludge in MBRs has not been sufficient. In this study, we scrutinized microbial communities of biofilm and activated sludge from 10 full-scale MBR plants. Overall, Flavobacterium, Dechloromonas and Nitrospira were abundant in order of abundance in biofilm, whereas Dechloromonas, Flavobacterium and Haliscomenobacter in activated sludge. Community structure was analyzed in either biofilm or activated sludge. Among MBRs, as expected, not only diversity of microbial community but also its composition was different from one another (p < 0.05). Between the biofilm and activated sludge, community composition made significant difference, but its diversity measures (i.e., alpha diversity, e.g., richness, diversity and evenness) did not (p > 0.05). Effects of ten environmental factors on community change were investigated using Spearman correlation. MLSS, HRT, F/M ratio and SADm explained the variation of microbial composition in the biofilm, whereas only MLSS did in the activated sludge. Microbial networks were constructed with the 10 environmental factors. The network results revealed that there were different topological characteristics between the biofilm and activated sludge networks, in which each of the 4 factors had different associations with microbial nodes. These results indicated that the different microbial associations were responsible for the variation of community composition between the biofilm and activated sludge.
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•Microbial communities of ten actual MBRs were scrutinized using Miseq sequencing.•Both microbial composition and structure differed among the MBRs.•Some environmental factors could explain the compositional variation among the MBRs.•Both microbial composition and networks were different between the biofilm and activated sludge.•It was confirmed that the variation in microbial association resulted in the compositional difference.
Background
Determination of preoperative soft tissue sarcoma (STS) margin is crucial for patient prognosis.
Purpose
To evaluate diagnostic performance of radiomics model using T2‐weighted Dixon ...sequence for infiltration degree of STS margin.
Study Type
Retrospective.
Population
Seventy‐two STS patients consisted of training (n = 58) and test (n = 14) sets.
Field Strength/Sequence
A 3.0 T; T2‐weighted Dixon images.
Assessment
Pathologic result of marginal infiltration in STS (circumscribed margin; n = 27, group 1, focally infiltrative margin; n = 31, group 2‐A, diffusely infiltrative margin; n = 14, group 2‐B) was the reference standard. Radiomic volume and shape (VS) and other (T2) features were extracted from entire tumor volume and margin, respectively. Twelve radiomics models were generated using four combinations of classifier algorithms (R, SR, LR, LSR) and three different inputs (VS, T2, VS + T2 VST2 features) to differentiate the three groups. Three radiologists (reader 1, 2, 3) analyzed the marginal infiltration with 6–scale confidence score.
Statistical Tests
Area under the receiver operating characteristic curve (AUC) and concordance rate.
Results
Averaged AUCs of R, SR, LR, LSR models were 0.438, 0.466, 0.438, 0.466 using VS features, 0.596, 0.584, 0.814, 0.815 using T2 features, and 0.581, 0.587, 0.821, 0.821 using VST2 features, respectively. The LR and LSR models constructed with T2 or VST2 features showed higher AUC and concordance rate compared to radiologists' analysis (AUC; 0.730, 0.675, 0.706, concordance rate; 0.46, 0.43, 0.47 in reader 1, 2, 3).
Data Conclusion
Radiomics model constructed with features from tumor margin on T2‐weighted Dixon sequence is a promising method for differentiating infiltration degree of STS margin.
Evidence Level
4
Technical Efficacy
Stage 2
Many previous studies focused on differentiating between benign and malignant soft tissue tumors using radiomics model based on various magnetic resonance imaging (MRI) sequences, but it is still ...unclear how to set up the input radiomic features from multiple MRI sequences. Here, we evaluated two types of radiomics models generated using different feature incorporation strategies. In order to differentiate between benign and malignant soft tissue tumors (STTs), we compared the diagnostic performance of an ensemble of random forest (R) models with single-sequence MRI inputs to R models with pooled multi-sequence MRI inputs. One-hundred twenty-five STT patients with preoperative MRI were retrospectively included and consisted of training (n = 100) and test (n = 25) sets. MRI included T1-weighted (T1-WI), T2-weighted (T2-WI), contrast-enhanced (CE)-T1-WI, diffusion-weighted images (DWIs, b = 800 sec/mm2) and apparent diffusion coefficient (ADC) maps. After tumor segmentation on each sequence, 100 original radiomic features were extracted from each sequence image and divided into three-feature sets: T features from T1- and T2-WI, CE features from CE-T1-WI, and D features from DWI and ADC maps. Four radiomics models were built using Lasso and R with four combinations of three-feature sets as inputs: T features (R-T), T+CE features (R-C), T+D features (R-D), and T+CE+D features (R-A) (Type-1 model). An ensemble model was built by soft voting of five, single-sequence-based R models (Type-2 model). AUC, sensitivity, specificity, and accuracy of each model was calculated with five-fold cross validation. In Type-1 model, AUC, sensitivity, specificity, and accuracy were 0.752, 71.8%, 61.1%, and 67.2% in R-T; 0.756, 76.1%, 70.4%, and 73.6% in R-C; 0.750, 77.5%, 63.0%, and 71.2% in R-D; and 0.749, 74.6%, 61.1%, and 68.8% R-A models, respectively. AUC, sensitivity, specificity, and accuracy of Type-2 model were 0.774, 76.1%, 68.5%, and 72.8%. In conclusion, an ensemble method is beneficial to incorporate features from multi-sequence MRI and showed diagnostic robustness for differentiating malignant STTs.
Recently, it has become evident that cognitive abilities such as the approximate number system (ANS), number knowledge, and intelligence affect individuals' fundamental mathematical ability. However, ...it is unclear which of these cognitive abilities have the greatest impact on the non-symbolic division ability in preschoolers. Therefore, in the present study, we included 4- to 6-year-old Korean preschoolers without prior formal education of division in order to test their ability to solve non-symbolic division problems, ANS acuity, and intelligence, and to determine the interrelationships among those functions (
= 38). We used the Panamath Dot Comparison Paradigm to measure the ANS acuity, employed non-symbolic division tasks to measure the ability to solve non-symbolic division problems, and measured the intelligence using the Korean version of the WPPSI-IV (Wechsler Preschool Primary Scale of Intelligence-IV). Our results showed that, in all conditions of the non-symbolic division tasks, the 4- to 6-years old children were able to perform better than chance level. Additionally, in a relatively easy condition, the children's performance showed a significant positive correlation with full-scale intelligence quotient (FSIQ) and ANS acuity; however, in a more complex condition, only FSIQ was significantly correlated with their performance. Overall, we found significant relationships between the children's performance in the non-symbolic division tasks and verbal comprehension, fluid reasoning, and processing speed index. Taken together, our findings demonstrate that preschoolers without formal education on the arithmetic problem solving can solve non-symbolic division problems. Moreover, we suggest that both FSIQ and ANS ability play essential roles in children's ability to solve non-symbolic division problems, highlighting the significance of intelligence on children's fundamental mathematical ability.
The outbreak of coronavirus disease 2019 (COVID-19), which began in December 2019, is still ongoing in Korea, with >9,000 confirmed cases as of March 25, 2020. COVID-19 is a severe acute respiratory ...syndrome Coronavirus 2 (SARS-CoV-2) infection, and real-time reverse transcription-PCR is currently the most reliable diagnostic method for COVID-19 around the world. Korean Society for Laboratory Medicine and the Korea Centers for Disease Prevention and Control propose guidelines for diagnosing COVID-19 in clinical laboratories in Korea. These guidelines are based on other related domestic and international guidelines, as well as expert opinions and include the selection of test subjects, selection of specimens, diagnostic methods, interpretation of test results, and biosafety.
Although cesarean delivery and prenatal exposure to antibiotics are likely to affect the gut microbiome in infancy, their effect on the development of atopic dermatitis (AD) in infancy is unclear. ...The influence of individual genotypes on these relationships is also unclear. To evaluate with a prospective birth cohort study whether cesarean section, prenatal exposure to antibiotics, and susceptible genotypes act additively to promote the development of AD in infancy.
The Cohort for Childhood of Asthma and Allergic Diseases (COCOA) was selected from the general Korean population. A pediatric allergist assessed 412 infants for the presence of AD at 1 year of age. Their cord blood DNA was subjected to interleukin (IL)-13 (rs20541) and cluster-of-differentiation (CD)14 (rs2569190) genotype analysis.
The combination of cesarean delivery and prenatal exposure to antibiotics associated significantly and positively with AD (adjusted odds ratio, 5.70; 95% CI, 1.19-27.3). The association between cesarean delivery and AD was significantly modified by parental history of allergic diseases or risk-associated IL-13 (rs20541) and CD14 (rs2569190) genotypes. There was a trend of interaction between IL-13 (rs20541) and delivery mode with respect to the subsequent risk of AD. (P for interaction = 0.039) Infants who were exposed prenatally to antibiotics and were born by cesarean delivery had a lower total microbiota diversity in stool samples at 6 months of age than the control group. As the number of these risk factors increased, the AD risk rose (trend p<0.05).
Cesarean delivery and prenatal antibiotic exposure may affect the gut microbiota, which may in turn influence the risk of AD in infants. These relationships may be shaped by the genetic predisposition.
Abstract
Diffusion-weighted imaging (DWI) is proven useful to differentiate benign and malignant soft tissue tumors (STTs). Radiomics utilizing a vast array of extracted imaging features has a ...potential to uncover disease characteristics. We aim to assess radiomics using DWI can outperform the conventional DWI for STT differentiation. In 151 patients with 80 benign and 71 malignant tumors, ADC
mean
and ADC
min
were measured on solid portion within the mass by two different readers. For radiomics approach, tumors were segmented and 100 original radiomic features were extracted on ADC map. Eight radiomics models were built with training set (n = 105), using combinations of 2 different algorithms—multivariate logistic regression (MLR) and random forest (RF)—and 4 different inputs: radiomics features (R), R + ADC
min
(I), R + ADC
mean
(E), R + ADC
min
and ADC
mean
(A). All models were validated with test set (n = 46), and AUCs of ADC
mean
, ADC
min
, MLR-R, RF-R, MLR-I, RF-I, MLR-E, RF-E, MLR-A and RF-A models were 0.729, 0.753 0.698, 0.700, 0.773, 0.807, 0.762, 0.744, 0.773 and 0.807, respectively, without statistically significant difference. In conclusion, radiomics approach did not add diagnostic value to conventional ADC measurement for differentiating benign and malignant STTs.
This study investigates the temperature coefficient of resistance (TCR) of a-Si:H according to process conditions to find out the temperature-dependent mechanism for microbolometer applications. Four ...types of a-Si:H films were prepared using PECVD by adjusting the SCCM of the doping gases: high B 2 H 6 (B1), low B 2 H 6 (B2), high PH 3 (P1), and low PH 3 (P2). Secondary ion mass spectroscopy (SIMS) analysis is performed to confirm the doping concentration. N-type (P1 or P2) seems to be less favorable for improving the channel conductance, but it has a large TCR meaning that P2 has the highest temperature dependency as well as the largest absolute TCR value. After pre-annealing in D 2 atmospheres, P1 and P2 show a significant reduction in the TCR. In addition, R C is observed to be reduced with TCR after post-metal annealing. The 1/ f noise is also correlated with TCR and is found to have a trade-off relation. These results imply that the TCR is primarily influenced by the trap states in a-Si:H film. Based on the experimental results, thermal-assisted transport through the local potential barrier is suggested for the temperature dependency of TCR in a-Si:H.
Most research on perovskite solar cells has focused on improving power-conversion efficiency and stability. However, if one could refurbish perovskite solar cells, their stability might not be a ...critical issue. From the perspective of cost effectiveness, if failed, perovskite solar cells could be collected and recycled; reuse of their gold electrodes and transparent conducting glasses could reduce the price per watt of perovskite photovoltaic modules. Herein, we present a simple and effective method for removing the perovskite layer and reusing the mesoporous TiO2-coated transparent conducting glass substrate via selective dissolution. We find that the perovskite layer can be easily decomposed in polar aprotic solvents because of the reaction between polar aprotic solvents and Pb(2+) cations. After 10 cycles of recycling, a mesoporous TiO2-coated transparent conducting glass substrate-based perovskite solar cell still shows a constant power-conversion efficiency, thereby demonstrating the possibility of recycling perovskite solar cells.
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