Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT

Health sensing for chronic disease management creates immense benefits for social welfare. Existing health sensing studies primarily focus on the prediction of physical chronic diseases. Depression, a widespread complication of chronic diseases, is however understudied. We draw on the medical literature to support depression prediction using motion sensor data. To connect human expertise in the decision-making, safeguard trust for this high-stake prediction, and ensure algorithm transparency, we develop an interpretable deep learning model: Temporal Prototype Network (TempPNet). TempPNet is built upon the emergent prototype learning models. To accommodate the temporal characteristic of sensor data and the progressive property of depression, TempPNet differs from existing prototype learning models in its capability of capturing the temporal progression of depression. Extensive empirical analyses using real-world motion sensor data show that TempPNet outperforms state-of-the-art benchmarks in depression prediction. Moreover, TempPNet interprets its predictions by visualizing the temporal progression of depression and its corresponding symptoms detected from sensor data. We further conduct a user study to demonstrate its superiority over the benchmarks in interpretability. This study offers an algorithmic solution for impactful social good - collaborative care of chronic diseases and depression in health sensing. Methodologically, it contributes to extant literature with a novel interpretable deep learning model for depression prediction from sensor data. Patients, doctors, and caregivers can deploy our model on mobile devices to monitor patients' depression risks in real-time. Our model's interpretability also allows human experts to participate in the decision-making by reviewing the interpretation of prediction outcomes and making informed interventions.Comment: 39 pages, 12 figure

Space Efficient Sequence Alignment for SRAM-Based Computing: X-Drop on the Graphcore IPU

Dedicated accelerator hardware has become essential for processing AI-based workloads, leading to the rise of novel accelerator architectures. Furthermore, fundamental differences in memory architecture and parallelism have made these accelerators targets for scientific computing. The sequence alignment problem is fundamental in bioinformatics; we have implemented the $X$-Drop algorithm, a heuristic method for pairwise alignment that reduces search space, on the Graphcore Intelligence Processor Unit (IPU) accelerator. The $X$-Drop algorithm has an irregular computational pattern, which makes it difficult to accelerate due to load balancing. Here, we introduce a graph-based partitioning and queue-based batch system to improve load balancing. Our implementation achieves $10\times$ speedup over a state-of-the-art GPU implementation and up to $4.65\times$ compared to CPU. In addition, we introduce a memory-restricted $X$-Drop algorithm that reduces memory footprint by $55\times$ and efficiently uses the IPU's limited low-latency SRAM. This optimization further improves the strong scaling performance by $3.6\times$.Comment: 12 pages, 7 figures, 2 table

Dynamic estimation of epidemiological parameters of COVID-19 outbreak and effects of interventions on its spread

Background: A key challenge in estimating epidemiological parameters for a pandemic such as the initial COVID-19 outbreak in Wuhan is the discrepancy between the officially reported number of infections and the true number of infections. A common approach to tackling the challenge is to use the number of infections exported from the originating city to infer the true number. This approach can only provide a static estimate of the epidemiological parameters before city lockdown because there are almost no exported cases thereafter.Methods: We propose a Bayesian estimation method that dynamically estimates the epidemiological parameters by recovering true numbers of infections from day-to-day official numbers. To illustrate the use of this method, we provide a comprehensive retrospection on how the COVID-19 had progressed in Wuhan from January 19 to March 5, 2020. Particularly, we estimate that the outbreak sizes by January 23 and March 5 were 11,239 [95% CI 4,794–22,372] and 124,506 [95% CI 69,526–265,113], respectively.Results: The effective reproduction number attained its maximum on January 24 (3.42 [95% CI 3.34–3.50]) and became less than 1 from February 7 (0.76 [95% CI 0.65–0.92]). We also estimate the effects of two major government interventions on the spread of COVID-19 in Wuhan.Conclusions: This case study by our proposed method affirms the believed importance and effectiveness of imposing tight non-essential travel restrictions and affirm the importance and effectiveness of government interventions (e.g., transportation suspension and large scale hospitalization) for effective mitigation of COVID-19 community spread

Crystal Structure of the C-Terminal Cytoplasmic Domain of Non-Structural Protein 4 from Mouse Hepatitis Virus A59

BACKGROUND:The replication of coronaviruses takes place on cytoplasmic double membrane vesicles (DMVs) originating in the endoplasmic reticulum (ER). Three trans-membrane non-structural proteins, nsp3, nsp4 and nsp6, are understood to be membrane anchors of the coronavirus replication complex. Nsp4 is localized to the ER membrane when expressed alone but is recruited into the replication complex in infected cells. It is revealed to contain four trans-membrane regions and its N- and C-termini are exposed to the cytosol. METHODOLOGY/PRINCIPAL FINDINGS:We have determined the crystal structures of the C-terminal hydrophilic domain of nsp4 (nsp4C) from MHV strain A59 and a C425S site-directed mutant. The highly conserved 89 amino acid region from T408 to Q496 is shown to possess a new fold. The wild-type (WT) structure features two monomers linked by a Cys425-Cys425 disulfide bond in one asymmetric unit. The monomers are arranged with their N- and C-termini in opposite orientations to form an "open" conformation. Mutation of Cys425 to Ser did not affect the monomer structure, although the mutant dimer adopts strikingly different conformations by crystal packing, with the cross-linked C-termini and parallel N-termini of two monomers forming a "closed" conformation. The WT nsp4C exists as a dimer in solution and can dissociate easily into monomers in a reducing environment. CONCLUSIONS/SIGNIFICANCE:As nsp4C is exposed in the reducing cytosol, the monomer of nsp4C should be physiological. This structure may serve as a basis for further functional studies of nsp4

Protective effects of resveratrol on the inhibition of hippocampal neurogenesis induced by ethanol during early postnatal life

AbstractEthanol (EtOH) exposure during early postnatal life triggers obvious neurotoxic effects on the developing hippocampus and results in long-term effects on hippocampal neurogenesis. Resveratrol (RSV) has been demonstrated to exert potential neuroprotective effects by promoting hippocampal neurogenesis. However, the effects of RSV on the EtOH-mediated impairment of hippocampal neurogenesis remain undetermined. Thus, mice were pretreated with RSV and were later exposed to EtOH to evaluate its protective effects on EtOH-mediated toxicity during hippocampal development. The results indicated that a brief exposure of EtOH on postnatal day 7 resulted in a significant impairment in hippocampal neurogenesis and a depletion of hippocampal neural precursor cells (NPCs). This effect was attenuated by pretreatment with RSV. Furthermore, EtOH exposure resulted in a reduction in spine density on the granular neurons of the dentate gyrus (DG), and the spines exhibited a less mature morphological phenotype characterized by a higher proportion of stubby spines and a lower proportion of mushroom spines. However, RSV treatment effectively reversed these responses. We further confirmed that RSV treatment reversed the EtOH-induced down-regulation of hippocampal pERK and Hes1 protein levels, which may be related to the proliferation and maintenance of NPCs. Furthermore, EtOH exposure in the C17.2 NPCs also diminished cell proliferation and activated apoptosis, which could be reversed by pretreatment of RSV. Overall, our results suggest that RSV pretreatment protects against EtOH-induced defects in neurogenesis in postnatal mice and may thus play a critical role in preventing EtOH-mediated toxicity in the developing hippocampus

On a New Hardware Trojan Attack on Power Budgeting of Many Core Systems

In this paper, we study stealthy false-data attacks that exploit the vulnerabilities of power budgeting scheme in NoC, which can cause catastrophic denial of service (DoS) effects. Essentially, when a power budget request packet is routed through a Trojan-infected network-on-chip node's router, the power budget request can be unknowingly modified. The global manager then tends to make really bad power budget allocation decisions with all the tampered power requests it received. That is, legitimate applications will be victimized with lower power budgets than what they initially asked for, and thus, could suffer serious performance degradation; malicious applications, on the other hand, may be entitled to high power budgets and thus see performance boost that they do not deserve. Our study has shown that this new type of DoS attack can be initiated and sustained by a simple hardware Trojan (HT) circuit that is extremely hard to be detected. The effects of this new DoS attack are simulated using a network model, and all the major parameters and factors that impact the attack effects are identified and quantified