Face2Multi-modal: in-vehicle multi-modal predictors via facial expressions

Towards intelligent Human-Vehicle Interaction systems and innovative Human-Vehicle Interaction designs, in-vehicle drivers' physiological data has been explored as an essential data source. However, equipping multiple biosensors is considered the limited extent of user-friendliness and impractical during the driving procedure. The lack of a proper approach to access physiological data has hindered wider applications of advanced biosignal-driven designs in practice (e.g. monitoring systems and etc.). Hence, the demand for a user-friendly approach to measuring drivers' body statuses has become more intense. In this Work-In-Progress, we present Face2Multi-modal, an In-vehicle multi-modal Data Streams Predictors through facial expressions only. More specifically, we have explored the estimations of Heart Rate, Skin Conductance, and Vehicle Speed of the drivers. We believe Face2Multi-modal provides a user-friendly alternative to acquiring drivers' physiological status and vehicle status, which could serve as the building block for many current or future personalized Human-Vehicle Interaction designs. More details and updates about the project Face2Multi-modal is online at https://github.com/unnc-ucc/Face2Multimodal/

Unraveling the mechanisms of intervertebral disc degeneration: an exploration of the p38 MAPK signaling pathway

Intervertebral disc (IVD) degeneration (IDD) is a worldwide spinal degenerative disease. Low back pain (LBP) is frequently caused by a variety of conditions brought on by IDD, including IVD herniation and spinal stenosis, etc. These conditions bring substantial physical and psychological pressure and economic burden to patients. IDD is closely tied with the structural or functional changes of the IVD tissue and can be caused by various complex factors like senescence, genetics, and trauma. The IVD dysfunction and structural changes can result from extracellular matrix (ECM) degradation, differentiation, inflammation, oxidative stress, mechanical stress, and senescence of IVD cells. At present, the treatment of IDD is basically to alleviate the symptoms, but not from the pathophysiological changes of IVD. Interestingly, the p38 mitogen-activated protein kinase (p38 MAPK) signaling pathway is involved in many processes of IDD, including inflammation, ECM degradation, apoptosis, senescence, proliferation, oxidative stress, and autophagy. These activities in degenerated IVD tissue are closely relevant to the development trend of IDD. Hence, the p38 MAPK signaling pathway may be a fitting curative target for IDD. In order to better understand the pathophysiological alterations of the intervertebral disc tissue during IDD and offer potential paths for targeted treatments for intervertebral disc degeneration, this article reviews the purpose of the p38 MAPK signaling pathway in IDD

Unraveling Molecular Mechanisms of Antibiotic Resistance Through Multiscale Simulations and Explainable Machine Learning

Pathogen resistance to β-lactam antibiotics compromises effective treatments of superbug infections. One major source of β-lactam resistance is the bacterial production of β-lactamases, which could effectively hydrolyze β-lactam drugs. In this thesis, the hydrolysis of various β-lactam antibiotics by class A serine-based β-lactamases (ASβLs) were investigated using hybrid Quantum Mechanical / Molecular Mechanical (QM/MM) minimum energy pathway (MEP) calculations and explainable machine learning (ML) approaches. The TEM-1/benzylpenicillin acylation reaction with QM/MM chain-of-states reaction pathways was firstly revisited. I proposed two decomposition methods for energy contribution analysis based on perturbing ML regression models. Both methods were shown to be model implementation invariant and successfully bridged the discrepancies between two pioneering mechanistic studies. The Toho-1 ASβL acylations of ampicillin and cefalexin were then investigated. I reported that the acylation pathway selection can be ligand dependent: ampicillin could undergo acylation via Lys73 or Glu166 acting as the general base while cefalexin acylation is limited to Lys73 as the general base. An explainable artificial intelligence (XAI) method, the Boltzmann-weighted Cumulative Integrated Gradients (BCIG), was developed to explain the different acylation pathway viability found for ampicillin and cefalexin. Lastly, conformational factors determining the GES-5/imipenem deacylation activity was investigated using edge-conditioned convolutional graph-learning (GL) methods. Critical vi mechanistic insights were derived from perturbative response of the GL latent representations, which explained the different deacylation reactivity between the two imipenem pyrroline tautomer states and identified the orientation of the carbapenem 6α-hydroxyethyl as the key factor that impacts the deacylation barrier heights. In summary, my thesis focuses on bridging QM/MM chain-of-states reaction pathway calculations and explainable ML to derive essential mechanistic insights into β-lactam resistance driven by ASβLs

Unraveling Molecular Mechanisms of Antibiotic Resistance Through Multiscale Simulations and Explainable Machine Learning

Pathogen resistance to β-lactam antibiotics compromises effective treatments of superbug infections. One major source of β-lactam resistance is the bacterial production of β-lactamases, which could effectively hydrolyze β-lactam drugs. In this thesis, the hydrolysis of various β-lactam antibiotics by class A serine-based β-lactamases (ASβLs) were investigated using hybrid Quantum Mechanical / Molecular Mechanical (QM/MM) minimum energy pathway (MEP) calculations and explainable machine learning (ML) approaches. The TEM-1/benzylpenicillin acylation reaction with QM/MM chain-of-states reaction pathways was firstly revisited. I proposed two decomposition methods for energy contribution analysis based on perturbing ML regression models. Both methods were shown to be model implementation invariant and successfully bridged the discrepancies between two pioneering mechanistic studies. The Toho-1 ASβL acylations of ampicillin and cefalexin were then investigated. I reported that the acylation pathway selection can be ligand dependent: ampicillin could undergo acylation via Lys73 or Glu166 acting as the general base while cefalexin acylation is limited to Lys73 as the general base. An explainable artificial intelligence (XAI) method, the Boltzmann-weighted Cumulative Integrated Gradients (BCIG), was developed to explain the different acylation pathway viability found for ampicillin and cefalexin. Lastly, conformational factors determining the GES-5/imipenem deacylation activity was investigated using edge-conditioned convolutional graph-learning (GL) methods. Critical vi mechanistic insights were derived from perturbative response of the GL latent representations, which explained the different deacylation reactivity between the two imipenem pyrroline tautomer states and identified the orientation of the carbapenem 6α-hydroxyethyl as the key factor that impacts the deacylation barrier heights. In summary, my thesis focuses on bridging QM/MM chain-of-states reaction pathway calculations and explainable ML to derive essential mechanistic insights into β-lactam resistance driven by ASβLs

Modeling the loyalty of individuals towards banks

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On Complete Targets Coverage in Rechargeable IoT Networks: A Message-Passing Approach

This article studies targets coverage in an energy harvesting (EH) Internet of Things (IoT) network. Specifically, it addresses the problem of activating subsets of EH sensor nodes to monitor targets, such as valuable assets or the ingresses and egresses of a building. A key requirement, so called complete targets coverage, is that all targets must be monitored by at least one sensor node at all times. To meet this requirement, we need to derive set covers, where each set cover is comprised of one or more sensor nodes that are activated simultaneously in each time slot. To this end, we show for the first time how the belief propagation message-passing framework can be used to derive these set covers. Advantageously, our approach does not require future energy arrivals information at devices. The results show that our message-passing approach is within 90% of the optimal coverage lifetime

COMPARISON OF TWO CHEMICAL PRETREATMENTS OF RICE STRAW FOR BIOGAS PRODUCTION BY ANAEROBIC DIGESTION

Lignocellulosic biomass is considered the most abundant renewable resource that has the potential to contribute remarkably in the supply of biofuel. Previous studies have shown that chemical pretreatment prior to anaerobic digestion (AD) can increase the digestibility of lignocellulosic biomass and methane yield. In the present study, the effect of rice straw pretreatment using ammonium hydroxide (NH3•H2O) and hydrogen peroxide (H2O2) on the biogasification performance through AD was investigated. A self-designed, laboratory-scale, and continuous anaerobic biogas digester was used for the evaluation. Results showed that the contents of the rice straw, i.e. the lignin, cellulose, and hemicellulose were degraded significantly after the NH3•H2O and H2O2 treatments, and that biogas production from all pretreated rice straw increased. In addition, the optimal treatments for biogas production were the 4% and 3% H2O2 treatments (w/w), which yielded 327.5 and 319.7 mL/gVS, biogas, respectively, higher than the untreated sample. Biogas production from H2O2 pretreated rice straw was more favorable than rice straw pretreated with same concentration of ammonia, ranking in the order of 4% ≈ 3% > 2% > 1%. The optimal amount of H2O2 treatment for rice straw biogas digestion is 3% when economics and biogas yields are considered