Blue Phosphorene Oxide: Strain-tunable Quantum Phase Transitions and Novel 2D Emergent Fermions

Tunable quantum phase transitions and novel emergent fermions in solid state materials are fascinating subjects of research. Here, we propose a new stable two-dimensional (2D) material, the blue phosphorene oxide (BPO), which exhibits both. Based on first-principles calculations, we show that its equilibrium state is a narrow-bandgap semiconductor with three bands at low energy. Remarkably, a moderate strain can drive a semiconductor-to-semimetal quantum phase transition in BPO. At the critical transition point, the three bands cross at a single point at Fermi level, around which the quasiparticles are a novel type of 2D pseudospin-1 fermions. Going beyond the transition, the system becomes a symmetry-protected semimetal, for which the conduction and valence bands touch quadratically at a single Fermi point that is protected by symmetry, and the low-energy quasiparticles become another novel type of 2D double Weyl fermions. We construct effective models characterizing the phase transition and these novel emergent fermions, and we point out several exotic effects, including super Klein tunneling, supercollimation, and universal optical absorbance. Our result reveals BPO as an intriguing platform for the exploration of fundamental properties of quantum phase transitions and novel emergent fermions, and also suggests its great potential in nanoscale device applications.Comment: 23 pages, 5 figure

Association between genetic variants and development of antibodies to infliximab: A cross-sectional study in Chinese patients with Crohn’s disease

Aims: Genetic variants increase the susceptibility to anti-drug antibodies (ADA) in response to anti-TNF therapy in chronic inflammatory diseases. However, little is known about genetic variants in Chinese populations. This study aimed to identify genetic variants contributing to the risk of the development of antibodies to infliximab (ATI) in Chinese patients with Crohn’s disease (CD).Methods: CD patients (n = 104) treated with infliximab (IFX) during the maintenance therapy were enrolled in this cross-sectional study. ATI was assessed by an in-house developed drug-tolerant ELISA method. ATI titers of 1:20 and ≥1:60 were considered a low titer and a high titer, respectively. Thirteen types of single nucleotide polymorphisms (SNPs) within 13 genes involved in the immune process, the susceptibility to chronic inflammatory diseases, cytokines and apoptosis pathways were investigated.Results: The median trough levels of infliximab (TLI) in patients with clinical remission (CR) were higher than those in patients without CR (3.80 vs. 1.50 μg/mL, p < .001). The median TLI in patients with high-titer ATI was significantly lower than that in ATI-negative patients (1.15 vs. 4.48 μg/mL, p < .001) or those with low-titer ATI (1.15 vs. 2.95 μg/mL, p = .03). The HLA-DQA1*05 rs2097432 GG and GA genotypes were more frequent in patients with ATI (GG and AG vs. AA, 27/38 = 71.05% vs. 29/66 = 43.94%, OR 2.94, 95% CI 1.19–7.30, p = .02). Patients carrying the CC and AC genotypes of rs396991 in FCGR3A were associated with a higher frequency of ATI formation (CC and AC vs. AA, 37/57 = 64.91% vs. 19/47 = 40.43%, OR 2.94, 95% CI 1.24–6.96, p = .01). According to the number of variants in rs2097432 and rs393991, patients with two variants had a higher proportion of producing ATI (two variants vs. no variant, 17/21 = 80.95% vs. 9/30 = 30.00%, OR 9.92, 95% CI 2.59–37.87, p = .001; single variant vs. no variant, 30/53 = 56.60% vs. 9/30 = 30.00%, OR 3.04, 95% CI 1.18–7.88, p = .02). No association was found between other SNPs and ATI production.Conclusion: Rs2097432 in HLA-DQA1*05 and rs396991 in FCGR3A are associated with ATI production in Chinese patients with CD. A pharmacogenomic strategy could help with the clinical management of CD

Liver Damage in Patients with HCV/HIV Coinfection Is Linked to HIV-Related Oxidative Stress

HIV infection aggravates the progression of liver damage in HCV-coinfected patients, with the underlying pathogenesis being multifactorial. Although high level of oxidative stress has been observed frequently in patients infected with HIV or HCV, the status of oxidative stress in HIV/HCV coinfection and its contribution to HCV liver damage have not been determined. This study involved 363 HBsAg-negative, anti-HCV-positive former blood donors recruited from a village in central China in July 2005; of these, 140 were positive for HIV. Of these 363 subjects, 282 were successfully followed up through July 2009. HIV/HCV-coinfected subjects had higher rates of end-stage liver disease-related death than those monoinfected with HCV. Liver ultrasound manifestations were poor in HIV-positive than in HIV-negative individuals, in both chronic HCV carriers and those with resolved HCV. Serum concentrations of total glutathione (tGSH), malondialdehyde (MDA), glutathione peroxidase (GSH-Px), GSSG, and reduced GSH were higher in HIV-positive than HIV-negative subjects. GSSG concentrations were higher in HIV-infected subjects with abnormal ALT/AST levels than in those with normal ALT/AST levels and were associated with poorer liver ultrasound manifestations. These finding indicated that HIV infection accelerated HCV-associated liver damage in HIV/HCV-coinfected individuals. Increased oxidative stress, induced primarily by HIV coinfection, may contribute to aggravated liver damage

Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data

Recently, the Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus disease 2019 (COVID-19) incidence (hotspots) and offers support to local health departments to limit the spread of the disease. Understanding the spatio-temporal dynamics of hotspot events is of great importance to support policy decisions and prevent large-scale outbreaks. This paper presents a spatio-temporal Bayesian framework for early detection of COVID-19 hotspots (at the county level) in the United States. We assume both the observed number of cases and hotspots depend on a class of latent random variables, which encode the underlying spatio-temporal dynamics of the transmission of COVID-19. Such latent variables follow a zero-mean Gaussian process, whose covariance is specified by a non-stationary kernel function. The most salient feature of our kernel function is that deep neural networks are introduced to enhance the model's representative power while still enjoying the interpretability of the kernel. We derive a sparse model and fit the model using a variational learning strategy to circumvent the computational intractability for large data sets. Our model demonstrates better interpretability and superior hotspot-detection performance compared to other baseline methods