Developing the Kiwanis Environmental Education Preserve

In 2017, the Kiwanis Club of Ann Arbor Foundation purchased a 17-acre property to the west of Ann Arbor at 100 N. Staebler Road, Scio Township. This property has 7.6-acre vegetated area that includes a small pocket of wetlands including two ponds. It was historically used for agriculture followed by a book manufacturing facility, and was poorly managed. Upon acquiring the property, the Kiwanis sponsors, Margaret Krasnoff and Dan Devers visioned this parcel as an opportunity to pursue and practice Environmental Education. Ever since, they have been pushing their vision to bring the Kiwanis Environmental Education Preserve (KEEP) into existence. During Phase I (2019 – 2020), a SEAS Masters Project conducted a census and inventory of vegetation and wildlife, a baseline characterization of existing ecosystem types, and modelled stormwater runoff from the Kiwanis warehouse and parking lot. Phase II continued the vision and expanded the project by completing the site inventory, refining preliminary designs for the KEEP parcel, developed educational materials and displays, and initiated the restoration and educational process. It resulted in a package of landscape design that will be built mostly by volunteers in the future and will help visitors from all ages to better experience the KEEP. Management options and education module suggestions were also provided as reference to initiate the next phase of work.Master of Landscape ArchitectureSchool for Environment and SustainabilityUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/167304/3/Gao_Yanning_Practicumpdf.pd

S3C: Semi-Supervised VQA Natural Language Explanation via Self-Critical Learning

VQA Natural Language Explanation (VQA-NLE) task aims to explain the decision-making process of VQA models in natural language. Unlike traditional attention or gradient analysis, free-text rationales can be easier to understand and gain users' trust. Existing methods mostly use post-hoc or self-rationalization models to obtain a plausible explanation. However, these frameworks are bottlenecked by the following challenges: 1) the reasoning process cannot be faithfully responded to and suffer from the problem of logical inconsistency. 2) Human-annotated explanations are expensive and time-consuming to collect. In this paper, we propose a new Semi-Supervised VQA-NLE via Self-Critical Learning (S3C), which evaluates the candidate explanations by answering rewards to improve the logical consistency between answers and rationales. With a semi-supervised learning framework, the S3C can benefit from a tremendous amount of samples without human-annotated explanations. A large number of automatic measures and human evaluations all show the effectiveness of our method. Meanwhile, the framework achieves a new state-of-the-art performance on the two VQA-NLE datasets.Comment: CVPR202

Truck model recognition for an automatic overload detection system based on the improved MMAL-Net

Efficient and reliable transportation of goods through trucks is crucial for road logistics. However, the overloading of trucks poses serious challenges to road infrastructure and traffic safety. Detecting and preventing truck overloading is of utmost importance for maintaining road conditions and ensuring the safety of both road users and goods transported. This paper introduces a novel method for detecting truck overloading. The method utilizes the improved MMAL-Net for truck model recognition. Vehicle identification involves using frontal and side truck images, while APPM is applied for local segmentation of the side image to recognize individual parts. The proposed method analyzes the captured images to precisely identify the models of trucks passing through automatic weighing stations on the highway. The improved MMAL-Net achieved an accuracy of 95.03% on the competitive benchmark dataset, Stanford Cars, demonstrating its superiority over other established methods. Furthermore, our method also demonstrated outstanding performance on a small-scale dataset. In our experimental evaluation, our method achieved a recognition accuracy of 85% when the training set consisted of 20 sets of photos, and it reached 100% as the training set gradually increased to 50 sets of samples. Through the integration of this recognition system with weight data obtained from weighing stations and license plates information, the method enables real-time assessment of truck overloading. The implementation of the proposed method is of vital importance for multiple aspects related to road traffic safety

Study on the Molecular Mechanisms of dlk1 Stimulated Lung Cancer Cell Proliferation

Background and objective The imprinted gene dlk1 has been recognized as a cancer related gene since it aberrantly expressed in a series of cancer tissues, but its role in lung cancer is still unknown. The aim of this study is to examine dlk1’s expression in non-small cell lung cancers (NSCLCs) and investigate the molecular mechanism by which dlk1 could accelerate the proliferation of the cells in lung cancer cell lines (H520). Methods The relative expression of dlk1 among 30 NSCLC specimens and their adjacent normal lung tissues were analyzed by RT-PCR. A cell model that stably expressed exogenous dlk1 was established following that the dlk1 gene was cloned into a eukaryotic expression vector and then transfected into the lung cancer cells H520. CCK8 analysis and colony forming assay were employed to investigate the effect of dlk1 on cell proliferation. The expression of CyclinB1 was detected by Western blot. Results dlk1 aberrantly expressed in 36.7% (11/30) of the tumor tissues of NSCLC compared with their adjacent cancer lung tissues. CCK8 analysis showed that overexpression of dlk1 could promote the proliferation of H520 cells (P < 0.05) and the results was further confirmed by colony forming assay. Western blot analysis found that over expression of dlk1 could up-regulate the expression of CyclinB1 (P < 0.05). Conclusion dlk1 aberrantly expressed in NSCLCs. The Overexpression of dlk1 could accelerate the proliferation of lung cancer cells H520 in vitro, probably through up-regulating the expression of cell cycle protein CyclinB1

The Protective Antibodies Induced by a Novel Epitope of Human TNF-α Could Suppress the Development of Collagen-Induced Arthritis

Tumor necrosis factor alpha (TNF-α) is a major inflammatory mediator that exhibits actions leading to tissue destruction and hampering recovery from damage. At present, two antibodies against human TNF-α (hTNF-α) are available, which are widely used for the clinic treatment of certain inflammatory diseases. This work was undertaken to identify a novel functional epitope of hTNF-α. We performed screening peptide library against anti-hTNF-α antibodies, ELISA and competitive ELISA to obtain the epitope of hTNF-α. The key residues of the epitope were identified by means of combinatorial alanine scanning and site-specific mutagenesis. The N terminus (80–91 aa) of hTNF-α proved to be a novel epitope (YG1). The two amino acids of YG1, proline and valine, were identified as the key residues, which were important for hTNF-α biological function. Furthermore, the function of the epitope was addressed on an animal model of collagen-induced arthritis (CIA). CIA could be suppressed in an animal model by prevaccination with the derivative peptides of YG1. The antibodies of YG1 could also inhibit the cytotoxicity of hTNF-α. These results demonstrate that YG1 is a novel epitope associated with the biological function of hTNF-α and the antibodies against YG1 can inhibit the development of CIA in animal model, so it would be a potential target of new therapeutic antibodies

Genomic heterogeneity of multiple synchronous lung cancer

Multiple synchronous lung cancers (MSLCs) present a clinical dilemma as to whether individual tumours represent intrapulmonary metastases or independent tumours. In this study we analyse genomic profiles of 15 lung adenocarcinomas and one regional lymph node metastasis from 6 patients with MSLC. All 15 lung tumours demonstrate distinct genomic profiles, suggesting all are independent primary tumours, which are consistent with comprehensive histopathological assessment in 5 of the 6 patients. Lung tumours of the same individuals are no more similar to each other than are lung adenocarcinomas of different patients from TCGA cohort matched for tumour size and smoking status. Several known cancer-associated genes have different mutations in different tumours from the same patients. These findings suggest that in the context of identical constitutional genetic background and environmental exposure, different lung cancers in the same individual may have distinct genomic profiles and can be driven by distinct molecular events

Evaluation of a computer-aided diagnostic model for corneal diseases by analyzing in vivo confocal microscopy images

ObjectiveIn order to automatically and rapidly recognize the layers of corneal images using in vivo confocal microscopy (IVCM) and classify them into normal and abnormal images, a computer-aided diagnostic model was developed and tested based on deep learning to reduce physicians’ workload.MethodsA total of 19,612 corneal images were retrospectively collected from 423 patients who underwent IVCM between January 2021 and August 2022 from Renmin Hospital of Wuhan University (Wuhan, China) and Zhongnan Hospital of Wuhan University (Wuhan, China). Images were then reviewed and categorized by three corneal specialists before training and testing the models, including the layer recognition model (epithelium, bowman’s membrane, stroma, and endothelium) and diagnostic model, to identify the layers of corneal images and distinguish normal images from abnormal images. Totally, 580 database-independent IVCM images were used in a human-machine competition to assess the speed and accuracy of image recognition by 4 ophthalmologists and artificial intelligence (AI). To evaluate the efficacy of the model, 8 trainees were employed to recognize these 580 images both with and without model assistance, and the results of the two evaluations were analyzed to explore the effects of model assistance.ResultsThe accuracy of the model reached 0.914, 0.957, 0.967, and 0.950 for the recognition of 4 layers of epithelium, bowman’s membrane, stroma, and endothelium in the internal test dataset, respectively, and it was 0.961, 0.932, 0.945, and 0.959 for the recognition of normal/abnormal images at each layer, respectively. In the external test dataset, the accuracy of the recognition of corneal layers was 0.960, 0.965, 0.966, and 0.964, respectively, and the accuracy of normal/abnormal image recognition was 0.983, 0.972, 0.940, and 0.982, respectively. In the human-machine competition, the model achieved an accuracy of 0.929, which was similar to that of specialists and higher than that of senior physicians, and the recognition speed was 237 times faster than that of specialists. With model assistance, the accuracy of trainees increased from 0.712 to 0.886.ConclusionA computer-aided diagnostic model was developed for IVCM images based on deep learning, which rapidly recognized the layers of corneal images and classified them as normal and abnormal. This model can increase the efficacy of clinical diagnosis and assist physicians in training and learning for clinical purposes