Gas Sensing Ionic Liquids on Quartz Crystal Microbalance

Recent advances in “designer solvents” have facilitated the development of ultrasensitive gas sensing ionic liquids (SILs) based on quartz crystal microbalance (QCM) that can real‐time detect and discriminate volatile molecules. The amalgamation of tailored‐made SILs and label‐free QCM resulted in a new class of qualitative and semi‐quantitative gas sensing device, which represents a model system of electronic nose. Because a myriad of human‐made or naturally occurring volatile organic compounds (VOCs) are of great interest in many areas, several functional SILs have been designed to detect gaseous aldehyde, ketone, amine and azide molecules chemoselectively in our laboratory. The versatility of this platform lies in the selective capture of volatile compounds by thin‐coated reactive SILs on QCM at room temperature. Notably, the detection limit of the prototype system can be as low as single‐digit parts‐per‐billion. This chapter briefly introduces some conventional gas sensing approaches and collates recent research results in the integration of SILs and QCM and finally gives an account of the state‐of‐the‐art gas sensing technology

Modular network construction using eQTL data: an analysis of computational costs and benefits

Background: In this paper, we consider analytic methods for the integrated analysis of genomic DNA variation and mRNA expression (also named as eQTL data), to discover genetic networks that are associated with a complex trait of interest. Our focus is the systematic evaluation of the trade-off between network size and network search efficiency in the construction of these networks. Results: We developed a modular approach to network construction, building from smaller networks to larger ones, thereby reducing the search space while including more variables in the analysis. The goal is achieving a lower computational cost while maintaining high confidence in the resulting networks. As demonstrated in our simulation results, networks built in this way have low node/edge false discovery rate (FDR) and high edge sensitivity comparing to greedy search. We further demonstrate our method in a data set of cellular responses to two chemotherapeutic agents: docetaxel and 5-fluorouracil (5-FU), and identify biologically plausible networks that might describe resistances to these drugs. Conclusion: In this study, we suggest that guided comprehensive searches for parsimonious networks should be considered as an alternative to greedy network searches

How to Backdoor Diffusion Models?

Diffusion models are state-of-the-art deep learning empowered generative models that are trained based on the principle of learning forward and reverse diffusion processes via progressive noise-addition and denoising. To gain a better understanding of the limitations and potential risks, this paper presents the first study on the robustness of diffusion models against backdoor attacks. Specifically, we propose BadDiffusion, a novel attack framework that engineers compromised diffusion processes during model training for backdoor implantation. At the inference stage, the backdoored diffusion model will behave just like an untampered generator for regular data inputs, while falsely generating some targeted outcome designed by the bad actor upon receiving the implanted trigger signal. Such a critical risk can be dreadful for downstream tasks and applications built upon the problematic model. Our extensive experiments on various backdoor attack settings show that BadDiffusion can consistently lead to compromised diffusion models with high utility and target specificity. Even worse, BadDiffusion can be made cost-effective by simply finetuning a clean pre-trained diffusion model to implant backdoors. We also explore some possible countermeasures for risk mitigation. Our results call attention to potential risks and possible misuse of diffusion models

GENERALIZED LIQUID ASSOCIATION

The analysis of interactions among a group of genes is fundamental to fur- ther our understanding of their biological interactions in a cell. Several studies suggested that the co-expression relationship of two genes can be modulated by a third controller gene. These controller genes and the corresponding modulated co-expressed gene pairs are the subjects of interests in this study. This described \controller-modulated genes three-way interactions is referred as liquid association in the literature. Analysis of gene expression data has suggested that these interactions are present in many biological systems. To quantify the magnitude of liquid association for a given gene triplet, we proposed a statistical measure named generalized liquid association (GLA). To estimate the value of GLA given the data, we propose two approaches: the direct and the model-based estimation approach. For the model-based approach, we introduce the conditional normal model (CNM). This is a generalization of the tri-variate normal distribution that allows us to characterize means, variances, as well as liquid association structures. We provide an approach based on generalized estimation equations to estimate the parameters in the CNM. We validate the proposed approaches through simulation studies and illustrate them in experimental data analysis. We also compare them with the three-product-moment measure suggested by Li in various settings and discuss related computational issues

Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective

Dataset distillation offers a potential means to enhance data efficiency in deep learning. Recent studies have shown its ability to counteract backdoor risks present in original training samples. In this study, we delve into the theoretical aspects of backdoor attacks and dataset distillation based on kernel methods. We introduce two new theory-driven trigger pattern generation methods specialized for dataset distillation. Following a comprehensive set of analyses and experiments, we show that our optimization-based trigger design framework informs effective backdoor attacks on dataset distillation. Notably, datasets poisoned by our designed trigger prove resilient against conventional backdoor attack detection and mitigation methods. Our empirical results validate that the triggers developed using our approaches are proficient at executing resilient backdoor attacks.Comment: 19 pages, 4 figure

963. Whole blood transcriptome analysis reveals differences in erythropoiesis and neurologically relevant pathways between cerebral malaria and severe malarial anemia

Background: Plasmodium falciparum malaria can rapidly progress to severe disease that can lead to death if left untreated. Severe malaria cases commonly present as severe malarial anemia (SMA), defined in children as hemoglobin (Hb) \u3c5 g/dL with parasitemia, or as cerebral malaria (CM), which manifests as parasitemia with acute neurological deficits and has an inpatient mortality rate of ~20%. The molecular and cellular processes that lead to CM and SMA are unclear. Methods: In a cross-sectional study, we compared genome-wide transcription profiles of whole blood obtained from Ugandan children with acute CM (n = 17) or SMA (n = 17) and community children without P. falciparum infection (n = 12) who were enrolled in a parent cohort study of severe malaria. We determined the relationships between gene expression, hematological indices, and plasma biomarkers, including inflammatory cytokines. Results: Both CM and SMA demonstrated enrichment of dendritic cell activation, inflammatory/TLR/chemokines, monocyte, and neutrophil modules but depletion of lymphocyte modules. Neurodegenerative disease and neuroinflammation pathways were enriched in CM. Increased Nrf2 pathway gene expression corresponded with increased plasma heme oxygenase-1 and the heme catabolite bilirubin in a manner specific to children with both SMA and sickle cell disease. Reticulocyte-specific gene expression was markedly decreased in CM relative to SMA despite higher Hb levels and appropriate increases in plasma erythropoietin. Viral sensing/interferon regulatory factor (IRF) 2 module (M111) expression and plasma IP-10 levels both negatively correlated with reticulocyte-specific signatures, but only M111 expression independently predicted decreased reticulocyte-specific gene expression after controlling for leukocyte count, Hb level, parasitemia, and clinical syndrome by multiple regression. Conclusion: Differences in the blood transcriptome of CM and SMA relate to neurologically relevant pathways and erythropoiesis. Erythropoietic suppression during severe malaria is more pronounced during CM versus SMA and is positively associated with IRF2 blood signatures. Future studies are needed to validate these findings

Neuroprotective Effect of Paeonol Mediates Anti-Inflammation via Suppressing Toll-Like Receptor 2 and Toll-Like Receptor 4 Signaling Pathways in Cerebral Ischemia-Reperfusion Injured Rats

Paeonol is a phenolic compound derived from Paeonia suffruticosa Andrews (MC) and P. lactiflora Pall (PL). Paeonol can reduce cerebral infarction volume and improve neurological deficits through antioxidative and anti-inflammatory effects. However, the anti-inflammatory pathway of paeonol remains unclear. This study investigated the relationship between anti-inflammatory responses of paeonol and signaling pathways of TLR2 and TLR4 in cerebral infarct. We established the cerebral ischemia-reperfusion model in Sprague Dawley rats by occluding right middle cerebral artery for 60 min, followed by reperfusion for 24 h. The neurological deficit score was examined, and the brains of the rats were removed for cerebral infarction volume and immunohistochemistry (IHC) analysis. The infarction volume and neurological deficits were lower in the paeonol group (pretreatment with paeonol; 20 mg/kg i.p.) than in the control group (without paeonol treatment). The IHC analysis revealed that the number of TLR2-, TLR4-, Iba1-, NF-κB- (P50-), and IL-1β-immunoreactive cells and TUNEL-positive cells was significantly lower in the paeonol group; however, the number of TNF-α-immunoreactive cells did not differ between the paeonol and control groups. The paeonol reveals some neuroprotective effects in the model of ischemia, which could be due to the reduction of many proinflammatory receptors/mediators, although the mechanisms are not clear