Interpretable Classification of Myositis from Muscle Ultrasound Images

This study is dedicated to designing and advancing machine learning (ML) algorithms for classifying normal and abnormal muscular tissues, thereby aiding neurologists in diagnosing inclusion body myositis (IBM). Our work mainly aims to leverage machine learning and recent state-of-the-art (SOTA) algorithms to recognize and diagnose myositis from muscle ultrasound images in the preliminary stage and support the traditional diagnostic methodology. Initially, we used an open-source ultrasound image dataset to construct and refine initial models using VGG-16. We employed the Grad-CAM method to annotate muscle ultrasound images and delineate regions of interest (ROI). Subsequent experiments enhanced the VGG16 architecture through extensive layer modifications and parameter adjustments. Our research offers valuable perspectives on utilizing ML to assist neurologists in the early diagnosis of IBM

AutoML-GPT: Large Language Model for AutoML

With the emerging trend of GPT models, we have established a framework called AutoML-GPT that integrates a comprehensive set of tools and libraries. This framework grants users access to a wide range of data preprocessing techniques, feature engineering methods, and model selection algorithms. Through a conversational interface, users can specify their requirements, constraints, and evaluation metrics. Throughout the process, AutoML-GPT employs advanced techniques for hyperparameter optimization and model selection, ensuring that the resulting model achieves optimal performance. The system effectively manages the complexity of the machine learning pipeline, guiding users towards the best choices without requiring deep domain knowledge. Through our experimental results on diverse datasets, we have demonstrated that AutoML-GPT significantly reduces the time and effort required for machine learning tasks. Its ability to leverage the vast knowledge encoded in large language models enables it to provide valuable insights, identify potential pitfalls, and suggest effective solutions to common challenges faced during model training

Electrochemical impedimetric biosensor based on a nanostructured polycarbonate substrate

This study integrates the techniques of nanoelectroforming, hot-embossing, and electrochemical deposition to develop a disposable, low-cost, and high sensitivity nanostructure biosensor. A modified anodic aluminum oxide barrier-layer surface was used as the template for thin nickel film deposition. After etching the anodic aluminum oxide template off, a three-dimensional mold of the concave nanostructure array was created. The fabricated three-dimensional nickel mold was further used for replica molding of a nanostructure polycarbonate substrate by hot-embossing. A thin gold film was then sputtered onto the polycarbonate substrate to form the electrode, followed by deposition of an orderly and uniform gold nanoparticle layer on the three-dimensional gold electrode using electrochemical deposition. Finally, silver nanoparticles were deposited on the uniformly deposited gold nanoparticles to enhance the conductivity of the sensor. Electrochemical impedance spectroscopy analysis was then used to detect the concentration of the target element. The sensitivity of the proposed scheme on the detection of the dust mite antigen, Der p2, reached 0.1 pg/mL

GraphFC: Customs Fraud Detection with Label Scarcity

Custom officials across the world encounter huge volumes of transactions. With increased connectivity and globalization, the customs transactions continue to grow every year. Associated with customs transactions is the customs fraud - the intentional manipulation of goods declarations to avoid the taxes and duties. With limited manpower, the custom offices can only undertake manual inspection of a limited number of declarations. This necessitates the need for automating the customs fraud detection by machine learning (ML) techniques. Due the limited manual inspection for labeling the new-incoming declarations, the ML approach should have robust performance subject to the scarcity of labeled data. However, current approaches for customs fraud detection are not well suited and designed for this real-world setting. In this work, we propose $\textbf{GraphFC}$ ($\textbf{Graph}$ neural networks for $\textbf{C}$ustoms $\textbf{F}$raud), a model-agnostic, domain-specific, semi-supervised graph neural network based customs fraud detection algorithm that has strong semi-supervised and inductive capabilities. With upto 252% relative increase in recall over the present state-of-the-art, extensive experimentation on real customs data from customs administrations of three different countries demonstrate that GraphFC consistently outperforms various baselines and the present state-of-art by a large margin

Common-mode noise reduction schemes for weakly coupled differential serpentine delay microstrip lines

This paper proposes design schemes to reduce the common mode noise from weakly coupled differential serpentine delay microstrip lines (DSDMLs). The proposed approach is twofold: we leverage strongly coupled vertical-turn-coupled traces (VTCTs) instead of weakly coupled VTCTs (conventional pattern) and add guard traces. Time- and frequency-domain analyses of the proposed schemes for reducing the common-mode noise are performed by studying the transmission waveform and the differential-to-common mode conversion using the circuit solver HSPICE and the 3-D full-wave simulator HFSS, respectively. Compared to the conventional design of the weakly coupled DSDMLs, the proposed solutions yield a reduction of about 54% of the peak-to-peak amplitude of the common-mode noise, while the differential impedance remains matched along the complete length of the DSDML. Moreover, the range of frequencies, over which the magnitude of the differential-to-common mode conversion is now significantly reduced, is very wide, i.e. about 0.3-10 GHz. Furthermore, the differential insertion and reflection loss introduced by the newly proposed designs are almost the same as the ones achieved by using the conventional design. Finally, a favorable comparison between simulated and measured results confirms the excellent common-mode noise reduction performance of the proposed schemes

Co-Design Smart Disaster Management Systems with Indigenous Communities

Tribal governments bear an uneven burden in the face of escalating disaster risks, climate change, and environmental degradation, primarily due to their deeply entrenched ties to the environment and its resources. Regrettably, accessing vital information and evidence to secure adequate funding or support poses difficulties for enrolled tribal members and their lands. In response to these challenges, this article collaborates with tribal nations to co-design intelligent disaster management systems using AI chatbots and drone technologies. The primary objective is to explore how tribal governments perceive and experience these emerging technologies in the realm of disaster reporting practices. This article presents participatory design studies. First, we interviewed seasoned first-line emergency managers and hosted an in-person design workshop to introduce the Emergency Reporter chatbot. Second, we organized a follow-up design workshop on tribal land to deliberate the utilization of drones within their community. Through qualitative analysis, we unveiled key themes surrounding integrating these emergency technologies within the jurisdiction of tribal governments. The findings disclosed substantial backing from tribal governments and their tribal members for the proposed technologies. Moreover, we delved into the potential of chatbots and drones to empower tribal governments in disaster management, safeguard their sovereignty, and facilitate collaboration with other agencies

Potential Osteoporosis Recovery by Deep Sea Water through Bone Regeneration in SAMP8 Mice

The aim of this study is to examine the therapeutic potential of deep sea water (DSW) on osteoporosis. Previously, we have established the ovariectomized senescence-accelerated mice (OVX-SAMP8) and demonstrated strong recovery of osteoporosis by stem cell and platelet-rich plasma (PRP). Deep sea water at hardness (HD) 1000 showed significant increase in proliferation of osteoblastic cell (MC3T3) by MTT assay. For in vivo animal study, bone mineral density (BMD) was strongly enhanced followed by the significantly increased trabecular numbers through micro-CT examination after a 4-month deep sea water treatment, and biochemistry analysis showed that serum alkaline phosphatase (ALP) activity was decreased. For stage-specific osteogenesis, bone marrow-derived stromal cells (BMSCs) were harvested and examined. Deep sea water-treated BMSCs showed stronger osteogenic differentiation such as BMP2, RUNX2, OPN, and OCN, and enhanced colony forming abilities, compared to the control group. Interestingly, most untreated OVX-SAMP8 mice died around 10 months; however, approximately 57% of DSW-treated groups lived up to 16.6 months, a life expectancy similar to the previously reported life expectancy for SAMR1 24 months. The results demonstrated the regenerative potentials of deep sea water on osteogenesis, showing that deep sea water could potentially be applied in osteoporosis therapy as a complementary and alternative medicine (CAM)