Building the Path to Early Alzheimer\u27s Prediction Using Machine Learning

Alzheimer’s disease (AD) is the most common form of dementia and one of the most prominent challenges of precision healthcare is early identification of AD. To combat this latency in diagnosis, integration of machine learning has been exercised for more cost efficient and powerful diagnostic tools. Specifically, we have developed a workflow for identifying AD within a given sample. Utilizing cerebral cortex proteomic data as a baseline, we were able to test two different forms of feature selection and 6 different machine learning methods. The best performing of these combinations was using a Support Vector Machine (SVM) method with features selected from Maximum Relevance Minimum Redundancy (MRMR) . This method had an average accuracy of 93.25% across and had yielded relatively good precision across 100 iterations. Furthering these types of predictions methods could allow a better quality and longevity of life for those at risk of Alzheimer\u27s Disease. Funding: Funding for this project was supplied by ND EPSCoR STEM (UND0025726), the American Society for Pharmacology & Experimental Therapeutics (ASPET) SURF Program, the Chair of the Department of Biomedical Sciences, the Division of Research & Economic Development at the University of North Dakota, an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under grant number P20GM103442, and the Dean of the University of North Dakota School of Medicine & Health Sciences.https://commons.und.edu/as-showcase/1004/thumbnail.jp

Querying Streaming System Monitoring Data for Enterprise System Anomaly Detection

The need for countering Advanced Persistent Threat (APT) attacks has led to the solutions that ubiquitously monitor system activities in each enterprise host, and perform timely abnormal system behavior detection over the stream of monitoring data. However, existing stream-based solutions lack explicit language constructs for expressing anomaly models that capture abnormal system behaviors, thus facing challenges in incorporating expert knowledge to perform timely anomaly detection over the large-scale monitoring data. To address these limitations, we build SAQL, a novel stream-based query system that takes as input, a real-time event feed aggregated from multiple hosts in an enterprise, and provides an anomaly query engine that queries the event feed to identify abnormal behaviors based on the specified anomaly models. SAQL provides a domain-specific query language, Stream-based Anomaly Query Language (SAQL), that uniquely integrates critical primitives for expressing major types of anomaly models. In the demo, we aim to show the complete usage scenario of SAQL by (1) performing an APT attack in a controlled environment, and (2) using SAQL to detect the abnormal behaviors in real time by querying the collected stream of system monitoring data that contains the attack traces. The audience will have the option to interact with the system and detect the attack footprints in real time via issuing queries and checking the query results through a command-line UI.Comment: Accepted paper at ICDE 2020 demonstrations track. arXiv admin note: text overlap with arXiv:1806.0933

Deep Cascade Multi-task Learning for Slot Filling in Online Shopping Assistant

Slot filling is a critical task in natural language understanding (NLU) for dialog systems. State-of-the-art approaches treat it as a sequence labeling problem and adopt such models as BiLSTM-CRF. While these models work relatively well on standard benchmark datasets, they face challenges in the context of E-commerce where the slot labels are more informative and carry richer expressions. In this work, inspired by the unique structure of E-commerce knowledge base, we propose a novel multi-task model with cascade and residual connections, which jointly learns segment tagging, named entity tagging and slot filling. Experiments show the effectiveness of the proposed cascade and residual structures. Our model has a 14.6% advantage in F1 score over the strong baseline methods on a new Chinese E-commerce shopping assistant dataset, while achieving competitive accuracies on a standard dataset. Furthermore, online test deployed on such dominant E-commerce platform shows 130% improvement on accuracy of understanding user utterances. Our model has already gone into production in the E-commerce platform.Comment: AAAI 201

Systematic optimization for production of the anti-MRSA antibiotics WAP-8294A in an engineered strain of Lysobacter enzymogenes

WAP-8294A is a group of cyclic lipodepsipeptides and considered as the first-in-class new chemical entity with potent activity against methicillin-resistant Staphylococcus aureus. One of the roadblocks in developing the WAP-8294A antibiotics is the very low yield in Lysobacter. Here, we carried out a systematic investigation of the nutritional and environmental conditions in an engineered L. enzymogenes strain for the optimal production of WAP-8294A. We developed an activity-based simple method for quick screening of various factors, which enabled us to optimize the culture conditions. With the method, we were able to improve the WAP-8294A yield by 10-fold in small-scale cultures and approximately 15-fold in scale-up fermentation. Additionally, we found the ratio of WAP-8294A2 to WAP-8294A1 in the strains could be manipulated through medium optimization. The development of a practical method for yield improvement in Lysobacter will facilitate the ongoing basic research and clinical studies to develop WAP- 8294A into true therapeutics

On the Performance of RIS-Aided Spatial Scattering Modulation for mmWave Transmission

In this paper, we investigate a state-of-the-art reconfigurable intelligent surface (RIS)-assisted spatial scattering modulation (SSM) scheme for millimeter-wave (mmWave) systems, where a more practical scenario that the RIS is near the transmitter while the receiver is far from RIS is considered. To this end, the line-of-sight (LoS) and non-LoS links are utilized in the transmitter-RIS and RIS-receiver channels, respectively. By employing the maximum likelihood detector at the receiver, the conditional pairwise error probability (CPEP) expression for the RIS-SSM scheme is derived under the two scenarios that the received beam demodulation is correct or not. Furthermore, the union upper bound of average bit error probability (ABEP) is obtained based on the CPEP expression. Finally, the derivation results are exhaustively validated by the Monte Carlo simulations.Comment: arXiv admin note: substantial text overlap with arXiv:2307.1466

Reconfigurable Intelligent Surface Aided Space Shift Keying With Imperfect CSI

In this paper, we investigate the performance of reconfigurable intelligent surface (RIS)-aided spatial shift keying (SSK) wireless communication systems in the presence of imperfect channel state information (CSI). Specifically, we analyze the average bit error probability (ABEP) of two RIS-SSK systems respectively based on intelligent reflection and blind reflection of RIS. For the intelligent RIS-SSK scheme, we first derive the conditional pairwise error probability of the composite channel through maximum likelihood (ML) detection. Subsequently, we derive the probability density function of the combined channel. Due to the intricacies of the composite channel formulation, an exact closed-form ABEP expression is unattainable through direct derivation. To this end, we resort to employing the Gaussian-Chebyshev quadrature method to estimate the results. In addition, we employ the Q-function approximation to derive the non-exact closed-form expression when CSI imperfections are present. For the blind RIS-SSK scheme, we derive both closed-form ABEP expression and asymptotic ABEP expression with imperfect CSI by adopting the ML detector. To offer deeper insights, we explore the impact of discrete reflection phase shifts on the performance of the RIS-SSK system. Lastly, we extensively validate all the analytical derivations using Monte Carlo simulations.Comment: arXiv admin note: text overlap with arXiv:2307.0199

Clinicopathologic features of sporadic inclusion body myositis in China

This study is to investigate the clinical and pathologic features of sporadic inclusion body myositis (sIBM) in China. We retrospectively evaluated the clinical and pathological features of consecutive patients in our department between January 1986 to May 2012. Total 28 cases of sIBM (20 males, 8 females, mean age was 56.93±8.79) were obtained by review of all 4099 muscle biopsy reports. The proportion of sIBM was 0.68% (28/4099) in China. Muscle weakness of quadriceps appeared 100% in 28 cases, while conspicuous atrophy of quadriceps appeared only in five cases (17.86%). Creatase values of 28 patients with sIBM were normal or mildly elevated. Muscle biopsies showed that atrophic fibers resembled more frequent in small angular and irregular shape (82.14%), less common in small round shape (17.86%). Rimmed vacuoles resembled crack (67.86%) and round (32.14%) shape. Mononuclear cell invasion into necrotic muscle fibers (35.71%) was more frequent than non-necrotic muscle fibers (7.14%). sIBM was still a rare disease in China compared to other countries. There were some certain specific pathological characteristics existed in Chinese sIBM patients