Tumour-draining axillary lymph nodes in patients with large and locally advanced breast cancers undergoing neoadjuvant chemotherapy (NAC): the crucial contribution of immune cells (effector, regulatory) and cytokines (TH1, TH2) to immune-mediated tumour cell death induced by NAC

Background The tumour microenvironment consists of malignant cells, stroma and immune cells. In women with large and locally advanced breast cancers (LLABCs) undergoing neoadjuvant chemotherapy (NAC), tumour-infiltrating lymphocytes (TILs), various subsets (effector, regulatory) and cytokines in the primary tumour play a key role in the induction of tumour cell death and a pathological complete response (pCR) with NAC. Their contribution to a pCR in nodal metastases, however, is poorly studied and was investigated. Methods Axillary lymph nodes (ALNs) (24 with and 9 without metastases) from women with LLABCs undergoing NAC were immunohistochemically assessed for TILs, T effector and regulatory cell subsets, NK cells and cytokine expression using labelled antibodies, employing established semi-quantitative methods. IBM SPSS statistical package (21v) was used. Non-parametric (paired and unpaired) statistical analyses were performed. Univariate and multivariate regression analyses were carried out to establish the prediction of a pCR and Spearman’s Correlation Coefficient was used to determine the correlation of immune cell infiltrates in ALN metastatic and primary breast tumours. Results In ALN metastases high levels of TILs, CD4+ and CD8+ T and CD56+ NK cells were significantly associated with pCRs.. Significantly higher levels of Tregs (FOXP3+, CTLA-4+) and CD56+ NK cells were documented in ALN metastases than in the corresponding primary breast tumours. CD8+ T and CD56+ NK cells showed a positive correlation between metastatic and primary tumours. A high % CD8+ and low % FOXP3+ T cells and high CD8+: FOXP3+ ratio in metastatic ALNs (tumour-free para-cortex) were associated with pCRs. Metastatic ALNs expressed high IL-10, low IL-2 and IFN-ϒ. Conclusions Our study has provided new data characterising the possible contribution of T effector and regulatory cells and NK cells and T helper1 and 2 cytokines to tumour cell death associated with NAC in ALNs

Uniform etching for failure analysis of integrated circuits

Following the trend towards transistor miniaturization and power efficiency in Integrated Circuits (ICs), the concurrent miniaturization of possible defects challenges Failure Analysis (FA) engineers to accurately deprocess ICs with great precision before accessing the defects. As such, etch uniformity is crucial to avoid accidental removal of defects. However, in the context of this research, thickness differences between the passivation layer, consisting of silicon nitride stacked above silicon dioxide, above and between metal lines inhibit the pursuit for uniform etch profiles. Hence, this research aims to develop a SF6/O2 etch recipe, using the Inductively Coupled Plasma Reactive Ion Etching (ICP RIE), with a high silicon nitride to silicon dioxide selectivity. By maximizing the etch selectivity, the objective is to overcome the thickness difference of the passivation layer (~400 nm) and minimize the extent of over etch as much as possible, whilst ensuring global uniformity. Upon formulation of the SF6/O2 recipe, an investigation regarding the presence of microloading effects as well as development of techniques to overcome edge effects was carried out to address possible sources of etch non-uniformities. Following that, three different etching techniques were being evaluated using the Field Emission Scanning Electron Microscope (FESEM) and Focused Ion Beam-Scanning Electron Microscope (FIB-SEM). By analysing the captured SEM images, the extent of over etch was measured and the technique involving a transition from low bias (silicon nitride removal) to high bias (silicon dioxide removal) yielded the best results due to an increase in etch selectivity from 3.5 to 8.7. The over etch was measured to be ~210 nm compared to a likely over etch of ~400 nm if an etch recipe with minimal selectivity was utilized. However, microtrenching effects have led to a pronounced etch rate at the corners of trenches, leading to those regions experiencing an over etch of ~340 nm. As such, future works may look into promoting more isotropic etch profiles to reduce the presence of slanted etch profiles which often results in microtrenching. Furthermore, despite improvements in the extent of over etch, ~210 nm is still not ideal for Physical FA (PFA) studies and future works may explore adding N2 gas into the recipe to further promote the nitride to oxide selectivity.Bachelor of Engineering (Materials Engineering

A quantitative, high-throughput urease activity assay for comparison and rapid screening of ureolytic bacteria

Urease is a dinickel enzyme commonly found in numerous organisms that catalyses the hydrolysis of urea into ammonia and carbon dioxide. The microbially induced carbonate precipitation (MICP) process mediated by urease-producing bacteria (UPB) can be used for many applications including, environmental bioremediation, soil improvement, healing of cracks in concrete, and sealing of rock joints. Despite the importance of urease and UPB in various applications, a quantitative, high-throughput assay for the comparison of urease activity in UPB and rapid screening of UPB from diverse environments is lacking. Herein, we reported a quantitative, 96-well plate assay for urease activity based on the Christensen's urea agar test. Using this assay, we compared urease activity of six bacterial strains (E. coli BL21, P. putida KT2440, P. aeruginosa PAO1, S. oneidensis MR-1, S. pasteurii DSM 33, and B. megaterium DSM 319) and showed that S. pasteurii DSM 33 exhibited the highest urease activity. We then applied this assay to quantify the inhibitory effect of calcium on urease activity of S. pasteurii DSM 33. No significant inhibition was observed in the presence of calcium at concentrations below 10 mM, while the urease activity decreased rapidly at higher concentrations. At a concentration higher than 200 mM, calcium completely inhibited urease activity under the tested conditions. We further applied this assay to screen for highly active UPB from a wastewater enrichment and identified a strain of S. pasteurii exhibiting a substantially higher urease activity than DSM 33. Taken together, we established a 96-well plate-based quantitative, high-throughput urease activity assay that can be used for comparison and rapid screening of UPB. As UPB and urease activity are of interest to environmental, civil, and medical researchers and practitioners, we envisage wide applications of the assay reported in this study.Ministry of Education (MOE)Nanyang Technological UniversityNational Research Foundation (NRF)This research was supported by Grant No MOE2015-T2-2-142 provided by the Ministry of Education (MOE), Singapore, the Centre for Urban Solutions, Nanyang Technological University, Singapore, and the National Research Foundation and MOE Singapore, under its Research Centre of Excellence Programme, Singapore Centre for Environmental Life Sciences Engineering (SCELSE) (M4330005.C70 to B.C.), Nanyang Technological University, Singapore

Assessment of Soybean Lodging Using UAV Imagery and Machine Learning

Plant lodging is one of the most essential phenotypes for soybean breeding programs. Soybean lodging is conventionally evaluated visually by breeders, which is time-consuming and subject to human errors. This study aimed to investigate the potential of unmanned aerial vehicle (UAV)-based imagery and machine learning in assessing the lodging conditions of soybean breeding lines. A UAV imaging system equipped with an RGB (red-green-blue) camera was used to collect the imagery data of 1266 four-row plots in a soybean breeding field at the reproductive stage. Soybean lodging scores were visually assessed by experienced breeders, and the scores were grouped into four classes, i.e., non-lodging, moderate lodging, high lodging, and severe lodging. UAV images were stitched to build orthomosaics, and soybean plots were segmented using a grid method. Twelve image features were extracted from the collected images to assess the lodging scores of each breeding line. Four models, i.e., extreme gradient boosting (XGBoost), random forest (RF), K-nearest neighbor (KNN) and artificial neural network (ANN), were evaluated to classify soybean lodging classes. Five data preprocessing methods were used to treat the imbalanced dataset to improve classification accuracy. Results indicate that the preprocessing method SMOTE-ENN consistently performs well for all four (XGBoost, RF, KNN, and ANN) classifiers, achieving the highest overall accuracy (OA), lowest misclassification, higher F1-score, and higher Kappa coefficient. This suggests that Synthetic Minority Oversampling-Edited Nearest Neighbor (SMOTE-ENN) may be a good preprocessing method for using unbalanced datasets and the classification task. Furthermore, an overall accuracy of 96% was obtained using the SMOTE-ENN dataset and ANN classifier. The study indicated that an imagery-based classification model could be implemented in a breeding program to differentiate soybean lodging phenotype and classify lodging scores effectively