Health and economic benefits of building ventilation interventions for reducing indoor PM2.5 exposure from both indoor and outdoor origins in urban Beijing, China

China is confronted with serious PM2.5 pollution, especially in the capital city of Beijing. Exposure to PM2.5 could lead to various negative health impacts including premature mortality. As people spend most of their time indoors, the indoor exposure to PM2.5 from both indoor and outdoor origins constitutes the majority of personal exposure to PM2.5 pollution. Different building interventions have been introduced to mitigate indoor PM2.5 exposure, but always at the cost of energy expenditure. In this study, the health and economic benefits of different ventilation intervention strategies for reducing indoor PM2.5 exposure are modelled using a representative urban residence in Beijing, with consideration of different indoor PM2.5 emission strengths and outdoor pollution. Our modelling results show that the increase of envelope air-tightness can achieve significant economic benefits when indoor PM2.5 emissions are absent; however, if an indoor PM2.5 source is present, the benefits only increase slightly in mechanically ventilated buildings, but may show negative benefit without mechanical ventilation. Installing mechanical ventilation in Beijing can achieve annual economic benefits ranging from 200yuan/capita to 800yuan/capita if indoor PM2.5 sources exist. If there is no indoor emission, the annual benefits above 200yuan/capita can be achieved only when the PM2.5 filtration efficiency is no less than 90% and the envelope air-tightness is above Chinese National Standard Level 7. Introducing mechanical ventilation with low PM2.5 filtration efficiency to current residences in urban Beijing will increase the indoor PM2.5 exposure and result in excess costs to the resident

The Impact of Employer Attitude to Green Commuting Plans on Reducing Car Driving: A Mixed Method Analysis

Reducing car trips and promoting green commuting modes are generally considered important solutions to reduce the increase of energy consumption and transportation CO2 emissions. One potential solution for alleviating transportation CO2 emissions has been to identify a role for the employer through green commuter programs. This paper offers an approach to assess the effects of employer attitudes towards green commuting plans on commuter mode choice and the intermediary role car ownership plays in the mode choice decision process. A mixed method which extends the traditional discrete choice model by incorporating latent variables and mediating variables with a structure equation model was used to better understand the commuter mode choice behaviour. The empirical data were selected from Washington-Baltimore Regional Household Travel Survey in 2007-2008, including all the trips from home to workplace during the morning hours. The model parameters were estimated using the simultaneous estimation approach and the integrated model turns out to be superior to the traditional multinomial logit (MNL) model accounting for the impact of employer attitudes towards green commuting. The direct and indirect effects of socio-demographic attributes and employer attitudes towards green commuting were estimated. Through the structural equation modelling with mediating variable, this approach confirmed the intermediary nature of car ownership in the choice process. The results found in this paper provide helpful information for transportation and planning policymakers to test the transportation and planning policies effects and encourage green commuting reducing transportation CO2 emissions.</p

Persistent Occurrence of Cryptosporidium hominis and Giardia duodenalis Subtypes in a Welfare Institute

Few data are available on the transmission dynamics of intestinal protozoa in children in welfare institutes. In this study, fecal specimens were collected from 396 children in a welfare institute in Shanghai, China during December 2011 (207 specimens), June 2012 (78 specimens), and September 2013 (111 specimens), and examined for Cryptosporidium spp., Giardia duodenalis, and Enterocytozoon bieneusi by PCR analysis of the small subunit rRNA, triosephosphate isomerase, and internal transcribed spacer genes, respectively. The Cryptosporidium hominis and G. duodenalis assemblage A identified were further subtyped by multilocus sequence typing. Altogether, Cryptosporidium was detected in 39 (9.8%) children, with infection rates of 11.6% (24/207), 9.0% (7/78), and 7.2% (8/111) in December 2011, June 2012, and September 2013, respectively. Infection rates were higher in children of 0–12 months (20.4% compared to 0–7.3% in other age groups, P = 0.0001) and those with diarrhea (17.9% compared to 7.7% in those with no diarrhea, P = 0.006). In contrast, G. duodenalis was detected in 161/396 (40.7%), with infection rates of 48.3% (100/207), 35.9% (28/78), and 29.7% (33/111) in December 2011, June 2012, and September 2013, respectively. There were no significant gender- or diarrhea-associated differences, but the G. duodenalis infection rate in children of 13–24 months (50%) was significantly higher than in the age groups of 0–12 months and &gt; 48 months (29.8–36.5%, P = 0.021). Co-infection of Cryptosporidium and G. duodenalis was seen in 19 (4.8%) children, but no E. bieneusi infection was detected in this study. All Cryptosporidium-positive specimens belonged to the subtype IaA14R4 of C. hominis, while all G. duodenalis-positive specimens belonged to sub-assemblage AII. Both were the same subtypes in a previous outbreak of cryptosporidiosis and giardiasis in a hospital ward hosting children from the welfare institute. Results of the study indicate that there was a persistent occurrence of limited C. hominis and G. duodenalis subtypes in the small enclosed community, with differences in age distribution and association with diarrhea occurrence between cryptosporidiosis and giardiasis

Primordial magnetic field as a common solution of nanohertz gravitational waves and Hubble tension

The origin of interstellar and intergalactic magnetic fields is largely unknown, and the primordial magnetic fields (PMFs) produced by, e.g., phase transitions of the early Universe are expected to provide seeds for those magnetic fields. The PMFs affect the evolution of the Universe at an early time, resulting in a series of phenomena. In this work, we show that the PMF-induced turbulence can give rise to nanohertz (nHz) gravitational waves reported by several pulsar timing arrays, including NANOGrav, PPTA, EPTA, and CPTA. Using the nHz gravitational wave data, we obtain the constraints on the characteristic magnetic field strength ($B_{\rm ch}^* \sim \mathcal{O}(1)~\rm{\mu G}$) and coherent length scale ($\ell_{\rm ch}^* \sim \mathcal{O}(1)~\rm{pc}$) of PMFs, assuming a generation temperature of approximately the QCD temperature ($\sim 100$ MeV). In addition, the PMFs which evolve to the recombination era can induce baryon density inhomogeneities, and then alter the ionization process. This naturally results in an alleviation of the tension of the Hubble parameter $H_0$ and the matter clumpiness parameter $S_8$ between early and late-time measurements. Assuming an evolution form of $B_{\rm ch}\sim \ell_{\rm ch}^{-\alpha}$ from the epoch of the production of PMFs to the epoch of recombination, we find $0.91<\alpha<1.08$ (95\% credible region).Comment: 7 pages, 4 figure

Integrating TSPO PET imaging and transcriptomics to unveil the role of neuroinflammation and amyloid-β deposition in Alzheimer's disease.

PURPOSE Despite the revealed role of immunological dysfunctions in the development and progression of Alzheimer's disease (AD) through animal and postmortem investigations, direct evidence regarding the impact of genetic factors on microglia response and amyloid-β (Aβ) deposition in AD individuals is lacking. This study aims to elucidate this mechanism by integrating transcriptomics and TSPO, Aβ PET imaging in clinical AD cohort. METHODS We analyzed 85 patients with PET/MR imaging for microglial activation (TSPO, [18F]DPA-714) and Aβ ([18F]AV-45) within the prospective Alzheimer's Disease Immunization and Microbiota Initiative Study Cohort (ADIMIC). Immune-related differentially expressed genes (IREDGs), identified based on AlzData, were screened and verified using blood samples from ADIMIC. Correlation and mediation analyses were applied to investigate the relationships between immune-related genes expression, TSPO and Aβ PET imaging. RESULTS TSPO uptake increased significantly both in aMCI (P < 0.05) and AD participants (P < 0.01) and showed a positive correlation with Aβ deposition (r = 0.42, P < 0.001). Decreased expression of TGFBR3, FABP3, CXCR4 and CD200 was observed in AD group. CD200 expression was significantly negatively associated with TSPO PET uptake (r =-0.33, P = 0.013). Mediation analysis indicated that CD200 acted as a significant mediator between TSPO uptake and Aβ deposition (total effect B = 1.92, P = 0.004) and MMSE score (total effect B =-54.01, P = 0.003). CONCLUSION By integrating transcriptomics and TSPO PET imaging in the same clinical AD cohort, this study revealed CD200 played an important role in regulating neuroinflammation, Aβ deposition and cognitive dysfunction

Comparative genomics reveals Cyclospora cayetanensis possesses coccidia-like metabolism and invasion components but unique surface antigens

Assessment of the completeness of sequenced Toxoplasma gondii, Eimeria tenella and Cyclospora cayetanensis genomes based on core eukaryotic protein-encoding genes search using BUSCO. (DOCX 14 kb

PyPose: A Library for Robot Learning with Physics-based Optimization

Deep learning has had remarkable success in robotic perception, but its data-centric nature suffers when it comes to generalizing to ever-changing environments. By contrast, physics-based optimization generalizes better, but it does not perform as well in complicated tasks due to the lack of high-level semantic information and the reliance on manual parametric tuning. To take advantage of these two complementary worlds, we present PyPose: a robotics-oriented, PyTorch-based library that combines deep perceptual models with physics-based optimization techniques. Our design goal for PyPose is to make it user-friendly, efficient, and interpretable with a tidy and well-organized architecture. Using an imperative style interface, it can be easily integrated into real-world robotic applications. Besides, it supports parallel computing of any order gradients of Lie groups and Lie algebras and $2^{\text{nd}}$-order optimizers, such as trust region methods. Experiments show that PyPose achieves 3-20$\times$ speedup in computation compared to state-of-the-art libraries. To boost future research, we provide concrete examples across several fields of robotics, including SLAM, inertial navigation, planning, and control

PyPose v0.6: The Imperative Programming Interface for Robotics

PyPose is an open-source library for robot learning. It combines a learning-based approach with physics-based optimization, which enables seamless end-to-end robot learning. It has been used in many tasks due to its meticulously designed application programming interface (API) and efficient implementation. From its initial launch in early 2022, PyPose has experienced significant enhancements, incorporating a wide variety of new features into its platform. To satisfy the growing demand for understanding and utilizing the library and reduce the learning curve of new users, we present the fundamental design principle of the imperative programming interface, and showcase the flexible usage of diverse functionalities and modules using an extremely simple Dubins car example. We also demonstrate that the PyPose can be easily used to navigate a real quadruped robot with a few lines of code