Fearless: Yaou Liu

Humbly and passionately serving the campus community as a true “servant leader” for the past three-and-a-half years, actively engaging in dialogues and initiatives to promote awareness about social injustices, and constantly striving to learn more, act more, and teach more, Yaou Liu ’14, is a fearless role model for the campus community, showing in everything she does a restless passion to see the injustices in the world righted, awareness increased, and the future changed for the better. She is an inspiring, courageous student who has enriched the lives of many both on campus and in the greater Gettysburg community, using her leadership skills to express what she believes, and lead others to understanding. Her time here at Gettysburg has changed her, but she, too, has changed Gettysburg. [excerpt

Positive-unlabeled learning for binary and multi-class cell detection in histopathology images with incomplete annotations

Cell detection in histopathology images is of great interest to clinical practice and research, and convolutional neural networks (CNNs) have achieved remarkable cell detection results. Typically, to train CNN-based cell detection models, every positive instance in the training images needs to be annotated, and instances that are not labeled as positive are considered negative samples. However, manual cell annotation is complicated due to the large number and diversity of cells, and it can be difficult to ensure the annotation of every positive instance. In many cases, only incomplete annotations are available, where some of the positive instances are annotated and the others are not, and the classification loss term for negative samples in typical network training becomes incorrect. In this work, to address this problem of incomplete annotations, we propose to reformulate the training of the detection network as a positive-unlabeled learning problem. Since the instances in unannotated regions can be either positive or negative, they have unknown labels. Using the samples with unknown labels and the positively labeled samples, we first derive an approximation of the classification loss term corresponding to negative samples for binary cell detection, and based on this approximation we further extend the proposed framework to multi-class cell detection. For evaluation, experiments were performed on four publicly available datasets. The experimental results show that our method improves the performance of cell detection in histopathology images given incomplete annotations for network training.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2022:027. arXiv admin note: text overlap with arXiv:2106.1591

Architectural Color Conservation and Renewal Strategies in Historic Urban Areas: An Analysis Based on the Historic City of Macao

As a city with a long history, Macao has valuable resources rich in historical and cultural heritage, which has many historical buildings with unique colors. The color of the architectural environment plays an important role in its conservation and renewal. This study aims to explore strategies for the color conservation and color renewal in the architectural environment of the Historic Centre of Macao. Through the field investigation and analysis of the historic center of Macao, the environmental principles of architectural color conservation were determined, including the conservation and restoration of the original architectural appearance of the historic district, the integrity and richness of the main color of the building, and the control principles of color and other environmental elements. Aiming at the architectural color conservation in Macao\u27s historic urban area, this paper puts forward some strategies, such as determining the architectural color tone, establishing a color database, and giving some suggestions on conservation and renewal, and provides some references and guidance for the renovation and construction of Macao\u27s historic urban area

Amide proton transfer-weighted imaging of pediatric brainstem glioma and its predicted value for H3 K27 alteration

BACKGROUND: Non-invasive determination of H3 K27 alteration of pediatric brainstem glioma (pedBSG) remains a clinical challenge. PURPOSE: To predict H3 K27-altered pedBSG using amide proton transfer-weighted (APTw) imaging. MATERIAL AND METHODS: This retrospective study included patients with pedBSG who underwent APTw imaging and had the H3 K27 alteration status determined by immunohistochemical staining. The presence or absence of foci of markedly increased APTw signal in the lesion was visually assessed. Quantitative APTw histogram parameters within the entire solid portion of tumors were extracted and compared between H3 K27-altered and wild-type groups using Student's t-test. The ability of APTw for differential diagnosis was evaluated using logistic regression. RESULTS: Sixty pedBSG patients included 48 patients with H3 K27-altered tumor (aged 2-48 years) and 12 patients with wild-type tumor (aged 3-53 years). Visual assessment showed that the foci of markedly increased APTw signal intensity were more common in the H3 K27-altered group than in wild-type group (60% vs. 16%, P = 0.007). Histogram parameters of APTw signal intensity in the H3 K27-altered group were significantly higher than those in the wild-type group (median, 2.74% vs. 2.22%, P = 0.02). The maximum (area under the receiver operating characteristic curve [AUC] = 0.72, P = 0.01) showed the highest diagnostic performance among histogram analysis. A combination of age, median and maximum APTw signal intensity could predict H3 K27 alteration with a sensitivity of 81%, specificity of 75% and AUC of 0.80. CONCLUSION: APTw imaging may serve as an imaging biomarker for H3 K27 alteration of pedBSGs

Difference between Pb and Cd Accumulation in 19 Elite Maize Inbred Lines and Application Prospects

In the last two decades, the accumulation of heavy metal in crop grains has become the study hotspot. In this study, 19 representative elite maize inbred lines and 3 hybrid varieties were investigated at the seedling stage, which can accumulate Pb and Cd in the stems and leaves, respectively. The results demonstrated that significant differences are among inbred lines for accumulation of heavy metals, implying that the Cd accumulation is significant correlation between the male parents and their hybrids and some inbred lines have been selected for cross-breeding with low Pb or Cd accumulation, such as S37, 9782, and ES40; Moreover, some inbred lines could be suitable for phytoremediation species for soil bioremediation with high levels of Pb and Cd accumulation, including 178, R08, 48-2, and Mo17ht

Genome-wide comparative analysis of digital gene expression tag profiles during maize ear development

Background: Development of the maize (Zea mays L.) female inflorescence (ear) has an important impact on corn yield. However, the molecular mechanisms underlying maize ear development are poorly understood. Results: We profiled and analyzed gene expression of the maize ear at four developmental stages: elongation phase (I), spikelet differentiation phase (II), floret primordium differentiation phase (III), and floret organ differentiation phase (IV). Based on genome-wide profile analysis, we detected differential mRNA of maize genes. Among the ~6,800 differentially expressed genes (DEGs), 3,325 genes were differentially expressed in stage II, 3,765 genes in III, and 1,698 genes in IV, compared to its previous adjacent stages, respectively. Furthermore, some of DEGs were predicted to be potential candidates in maize ear development, such as AGAMOUS (GRMZM2G052890) and ATFP3 (GRMZM2G155281). Meanwhile, some genes were well-known annotated to the mutants during maize inflorescence development such as compact plant2 (ct2), zea AGAMOUS homolog1 (zag1), bearded ear (bde), and silky1 (si1). Some DEGs were predicted targets of microRNAs such as microRNA156. K-means clustering revealed that the DEGs showed 18 major expression patterns. Thirteen transcriptional factors from 10 families were differentially expressed across three comparisons of adjacent stages (II vs. I, III vs. II, IV vs. III). Antisense transcripts were widespread during all four stages, and might play important roles in maize ear development. Finally, we randomly selected 32 DEGs to validate their expression patterns using quantitative reverse-transcription polymerase chain reaction (qRT-PCR). The results were consistent with those from Solexa sequencing. Conclusions: DEGs technique had shown an advantage in detecting candidates, and some transcription factors during maize ear development. RT-PCR data were consistent with our sequencing data and supplied additional information on ear developmental processes. These results provide a molecular foundation for future research on maize ear development

Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning

Spinal cord tumors lead to neurological morbidity and mortality. Being able to obtain morphometric quantification (size, location, growth rate) of the tumor, edema, and cavity can result in improved monitoring and treatment planning. Such quantification requires the segmentation of these structures into three separate classes. However, manual segmentation of three-dimensional structures is time consuming, tedious and prone to intra- and inter-rater variability, motivating the development of automated methods. Here, we tailor a model adapted to the spinal cord tumor segmentation task. Data were obtained from 343 patients using gadolinium-enhanced T1-weighted and T2-weighted MRI scans with cervical, thoracic, and/or lumbar coverage. The dataset includes the three most common intramedullary spinal cord tumor types: astrocytomas, ependymomas, and hemangioblastomas. The proposed approach is a cascaded architecture with U-Net-based models that segments tumors in a two-stage process: locate and label. The model first finds the spinal cord and generates bounding box coordinates. The images are cropped according to this output, leading to a reduced field of view, which mitigates class imbalance. The tumor is then segmented. The segmentation of the tumor, cavity, and edema (as a single class) reached 76.7 ± 1.5% of Dice score and the segmentation of tumors alone reached 61.8 ± 4.0% Dice score. The true positive detection rate was above 87% for tumor, edema, and cavity. To the best of our knowledge, this is the first fully automatic deep learning model for spinal cord tumor segmentation. The multiclass segmentation pipeline is available in the Spinal Cord Toolbox (https://spinalcordtoolbox.com/). It can be run with custom data on a regular computer within seconds

The Impact of Genetic Relationship and Linkage Disequilibrium on Genomic Selection

Genomic selection is a promising research area due to its practical application in breeding. In this study, impact of realized genetic relationship and linkage disequilibrium (LD) on marker density and training population size required was investigated and their impact on practical application was further discussed. This study is based on experimental data of two populations derived from the same two founder lines (B73, Mo17). Two populations were genotyped with different marker sets at different density: IBM Syn4 and IBM Syn10. A high-density marker set in Syn10 was imputed into the Syn4 population with low marker density. Seven different prediction scenarios were carried out with a random regression best linear unbiased prediction (RR-BLUP) model. The result showed that the closer the real genetic relationship between training and validation population, the fewer markers were required to reach a good prediction accuracy. Taken the short-term cost for consideration, relationship information is more valuable than LD information. Meanwhile, the result indicated that accuracies based on high LD between QTL and markers were more stable over generations, thus LD information would provide more robust prediction capacity in practical applications

Heterosis in Early Maize Ear Inflorescence Development: A Genome-Wide Transcription Analysis for Two Maize Inbred Lines and Their Hybrid

Heterosis, or hybrid vigor, contributes to superior agronomic performance of hybrids compared to their inbred parents. Despite its importance, little is known about the genetic and molecular basis of heterosis. Early maize ear inflorescences formation affects grain yield, and are thus an excellent model for molecular mechanisms involved in heterosis. To determine the parental contributions and their regulation during maize ear-development-genesis, we analyzed genome-wide digital gene expression profiles in two maize elite inbred lines (B73 and Mo17) and their F1hybrid using deep sequencing technology. Our analysis revealed 17,128 genes expressed in these three genotypes and 22,789 genes expressed collectively in the present study. Approximately 38% of the genes were differentially expressed in early maize ear inflorescences from heterotic cross, including many transcription factor genes and some presence/absence variations (PAVs) genes, and exhibited multiple modes of gene action. These different genes showing differential expression patterns were mainly enriched in five cellular component categories (organelle, cell, cell part, organelle part and macromolecular complex), five molecular function categories (structural molecule activity, binding, transporter activity, nucleic acid binding transcription factor activity and catalytic activity), and eight biological process categories (cellular process, metabolic process, biological regulation, regulation of biological process, establishment of localization, cellular component organization or biogenesis, response to stimulus and localization). Additionally, a significant number of genes were expressed in only one inbred line or absent in both inbred lines. Comparison of the differences of modes of gene action between previous studies and the present study revealed only a small number of different genes had the same modes of gene action in both maize seedlings and ear inflorescences. This might be an indication that in different tissues or developmental stages, different global expression patterns prevail, which might nevertheless be related to heterosis. Our results support the hypotheses that multiple molecular mechanisms (dominance and overdominance modes) contribute to heterosis