First eigenvalue of embedded minimal surfaces in S3S^3

We prove that for an embedded minimal surface $\Sigma$ in $S^3$, the first eigenvalue of the Laplacian operator $\lambda_1$ satisfies $\lambda_1\geq 1+\epsilon_g$, where $\epsilon_g>0$ is a constant depending only on the genus $g$ of $\Sigma$. This improves previous result of Choi-Wang

Optimization Of An Elisa-Based Leptospirosis Diagnostic

Leptospirosis is an infectious zoonotic disease responsible for about 1 million cases annually. Issues in early diagnosis is a major contributor towards poor health outcomes. Current tests for diagnosis, such as whole-cell ELISA-based diagnostics or the microscopic agglutination test (MAT), importantly lack sensitivity during acute-phase disease. Therefore, creating an ELISA-based diagnostic, which is generally cheaper and easier to perform than the MAT, using highly-conserved targets, that has high sensitivity towards acute-phase disease, would be desirable. This study first attempted to optimize protocols for diagnosis, and next applied the optimized protocol against epitopes derived from hamsters vaccinated with a multi-recombinant protein construct. Cutoffs were created by testing against 50 negative sera samples and then adjusted. These cutoffs would then be used when testing against acute- and convalescent-sera, which would determine individual sensitivities. Vaccine-derived epitopes were also paired with specific recombinant proteins using rabbit polyclonal sera against individual proteins. Two vaccine epitopes were identified as strong candidates, both of which also demonstrated comparable or stronger sensitivity during acute-phase disease compared to the MAT, though both also lacked specificity and convalescent phase sensitivity. No epitopes were able to be definitively paired with vaccine recombinant proteins. Overall, this study provided insights regarding the potential for an epitope-based diagnostic assay for leptospirosis, paving the way to further studies that can offer a highly specific and sensitive ELISA-based diagnostic

Genetic regulation of the Type III Secretion System and its potential effect on the lateral flagella system in Aeromonas hydrophila AH-3

Aeromonas species are ubiquitous water-borne bacteria that are able to cause a variety of diseases in poikilothermics and humans. Aeromonas hydrophila is one of the most pathogenic species, responsible for aeromonad septicaemia in fish, gastroenteritis and wound infections in humans. The T3SS is utilized to inject protein effectors directly into host cells. One of the major genetic regulators of the T3SS in several Gram-negative bacterial species is the AraC-like protein ExsA. Lateral flagella are expressed by bacteria upon contact with host cells or a surface and are required for host cell adherence and biofilm formation. However, no direct link between the T3SS and the lateral flagella system has yet been found in A. hydrophila. Moreover, the genetic regulation of the T3SS that involves the master regulator ExsA has not been determined in A. hydrophila AH-3. The aim of this project is to determine the genetic regulation of the T3SS and the potential interaction between the T3SS and the lateral flagella system in A. hydrophila AH-3. The genes encoding the T3SS regulatory components including exsA, exsD, exsC and exsE were mutated and the activities of the T3SS promoters were measured in exs mutant backgrounds. The interactions between each of the Exs proteins were investigated using BACTH assay and Far-Western Blot. Together, the findings suggested a regulatory cascade that the effector protein ExsE was bound to the chaperone protein ExsC, which sequestered the anti-activator ExsD from inhibiting the T3SS master regulator ExsA via direct protein-protein interactions. The T3SS regulatory components were also shown to affect the expression of the lateral flagella system using swarming assays. The activities of the lateral flagella promoters were shown to be repressed by the absence of ExsD and ExsE, suggesting that the T3SS master regulator ExsA was a negative regulator of the lateral flagella system

Investigating the Robustness and Properties of Detection Transformers (DETR) Toward Difficult Images

Transformer-based object detectors (DETR) have shown significant performance across machine vision tasks, ultimately in object detection. This detector is based on a self-attention mechanism along with the transformer encoder-decoder architecture to capture the global context in the image. The critical issue to be addressed is how this model architecture can handle different image nuisances, such as occlusion and adversarial perturbations. We studied this issue by measuring the performance of DETR with different experiments and benchmarking the network with convolutional neural network (CNN) based detectors like YOLO and Faster-RCNN. We found that DETR performs well when it comes to resistance to interference from information loss in occlusion images. Despite that, we found that the adversarial stickers put on the image require the network to produce a new unnecessary set of keys, queries, and values, which in most cases, results in a misdirection of the network. DETR also performed poorer than YOLOv5 in the image corruption benchmark. Furthermore, we found that DETR depends heavily on the main query when making a prediction, which leads to imbalanced contributions between queries since the main query receives most of the gradient flow

Pedestrian Detection with Wearable Cameras for the Blind: A Two-way Perspective

Blind people have limited access to information about their surroundings, which is important for ensuring one's safety, managing social interactions, and identifying approaching pedestrians. With advances in computer vision, wearable cameras can provide equitable access to such information. However, the always-on nature of these assistive technologies poses privacy concerns for parties that may get recorded. We explore this tension from both perspectives, those of sighted passersby and blind users, taking into account camera visibility, in-person versus remote experience, and extracted visual information. We conduct two studies: an online survey with MTurkers (N=206) and an in-person experience study between pairs of blind (N=10) and sighted (N=40) participants, where blind participants wear a working prototype for pedestrian detection and pass by sighted participants. Our results suggest that both of the perspectives of users and bystanders and the several factors mentioned above need to be carefully considered to mitigate potential social tensions.Comment: The 2020 ACM CHI Conference on Human Factors in Computing Systems (CHI 2020

Batch mode sparse active learning

Abstract-Sparse representation, due to its clear and powerful insight deep into the structure of data, has seen a recent surge of interest in the classification community. Based on this, a family of reliable classification methods have been proposed. On the other hand, obtaining sufficiently labeled training data has long been a challenging problem, thus considerable research has been done regarding active selection of instances to be labeled. In our work, we will present a novel unified framework, i.e. BMSAL(Batch Mode Sparse Active Learning). Based on the existing sparse family of classifiers, we define rigorously the corresponding BMSAL family and explore their shared properties, most importantly (approximate) submodularity. We focus on the feasibility and reliability of the BMSAL family: The first one inspires us to optimize the algorithms and conduct experiments comparing with state-of-the-art methods; for reliability, we give error-bounded algorithms, as well as detailed logical deductions and empirical tests for applying sparse in non-linear data sets

Parametric Design of Femoral Implant with Gradient Porous Structure

Patients who has been implanted with hip implant usually undergo revision surgery. The reason is that high stiff implants would cause non-physiological distribution loadings, which is also known as stress shielding, and finally lead to bone loss and aseptic loosening. Titanium implants are widely used in human bone tissues; however, the subsequent elastic modulus mismatch problem has become increasingly serious, and can lead to stress-shielding effects. This study aimed to develop a parametric design methodology of porous titanium alloy hip implant with gradient elastic modulus, and mitigate the stress-shielding effect. Four independent adjustable dimensions of the porous structure were parametrically designed, and the Kriging algorithm was used to establish the mapping relationship between the four adjustable dimensions and the porosity, surface-to-volume ratio, and elastic modulus. Moreover, the equivalent stress on the surface of the femur was optimized by response surface methodology, and the optimal gradient elastic modulus of the implant was obtained. Finally, through the Kriging approximation model and optimization results of the finite element method, the dimensions of each segment of the porous structure that could effectively mitigate the stress-shielding effect were determined. Experimental results demonstrated that the parameterized design method of the porous implant with gradient elastic modulus proposed in this study increased the strain value on the femoral surface by 17.1% on average. Consequently, the stress-shielding effect of the femoral tissue induced by the titanium alloy implant was effectively mitigated