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

Holographic recording of laser-induced plasma

We report on a holographic probing technique that allows for measurement of free-electron distribution with fine spatial detail. Plasma is generated by focusing a femtosecond pulse in air. We also demonstrate the capability of the holographic technique of capturing the time evolution of the plasma-generation process

Transgenic and optogenetic manipulation of inhibition in mouse visual thalamus

This thesis examines GABAA receptor (GABAAR) and GABAB receptor (GABABR) mediated inhibition within the mouse dorsal lateral geniculate nucleus (dLGN) with a particular emphasis on the significance of GABA release from local interneurons. The removal of the gamma2 subunit from thalamic relay neurons of the dLGN in the HDC-gamma2 mouse strain was shown to reduce the overall sIPSC frequency across all relay neurons with an absence of IPSCs in a subset of Y-type thalamic relay neurons. The IPSCs associated with the remaining relay neurons exhibited slower rise-times and decays and were insensitive to diazepam, indicating the absence of the gamma2 subunit. Potentiation of these slower IPSCs by DMCM further suggested that the remaining IPSCs were mediated by gamma1 subunit-containing GABAA receptors. In contrast, removal of the GABAB1 subunit resulted in a complete loss of postsynaptic GABABR responses within the mouse dLGN in all cells so far examined. The baclofen-induced membrane hyperpolarization was lost from HDC-GABAB1 cells and elevated ambient GABA concentrations resulted in a significantly smaller membrane hyperpolarization. Although HDC-GABAB1 mice did not exhibit a major visual deficit in a novel object recognition task, local field potential recordings during the animals sleep period revealed a shift in the power spectrum towards 1-4 Hz delta band of oscillatory activity locally within the visual cortex. I also identified the Sox14 gene as a marker for dLGN interneurons. Channelrhodopsin-2 (ChR2) activation of Sox14 interneurons not only gave rise to time-locked phasic inhibition in the dLGN relay neurons, this stimulation also induced tonic inhibition in an activity-dependent manner, mediated by the activation of extrasynaptic delta-containing GABAARs. However, action potential induced GABA release from interneuron is not a conspicuous feature of simultaneous paired interneuron to relay neuron recordings.Open Acces

Positive-unlabeled learning for binary and multi-class cell detection in histopathology images with incomplete annotations

Cell detection in histopathology images is of great interest to clinical practice and research, and convolutional neural networks (CNNs) have achieved remarkable cell detection results. Typically, to train CNN-based cell detection models, every positive instance in the training images needs to be annotated, and instances that are not labeled as positive are considered negative samples. However, manual cell annotation is complicated due to the large number and diversity of cells, and it can be difficult to ensure the annotation of every positive instance. In many cases, only incomplete annotations are available, where some of the positive instances are annotated and the others are not, and the classification loss term for negative samples in typical network training becomes incorrect. In this work, to address this problem of incomplete annotations, we propose to reformulate the training of the detection network as a positive-unlabeled learning problem. Since the instances in unannotated regions can be either positive or negative, they have unknown labels. Using the samples with unknown labels and the positively labeled samples, we first derive an approximation of the classification loss term corresponding to negative samples for binary cell detection, and based on this approximation we further extend the proposed framework to multi-class cell detection. For evaluation, experiments were performed on four publicly available datasets. The experimental results show that our method improves the performance of cell detection in histopathology images given incomplete annotations for network training.Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2022:027. arXiv admin note: text overlap with arXiv:2106.1591

Distributional Domain-Invariant Preference Matching for Cross-Domain Recommendation

Learning accurate cross-domain preference mappings in the absence of overlapped users/items has presented a persistent challenge in Non-overlapping Cross-domain Recommendation (NOCDR). Despite the efforts made in previous studies to address NOCDR, several limitations still exist. Specifically, 1) while some approaches substitute overlapping users/items with overlapping behaviors, they cannot handle NOCDR scenarios where such auxiliary information is unavailable; 2) often, cross-domain preference mapping is modeled by learning deterministic explicit representation matchings between sampled users in two domains. However, this can be biased due to individual preferences and thus fails to incorporate preference continuity and universality of the general population. In light of this, we assume that despite the scattered nature of user behaviors, there exists a consistent latent preference distribution shared among common people. Modeling such distributions further allows us to capture the continuity in user behaviors within each domain and discover preference invariance across domains. To this end, we propose a Distributional domain-invariant Preference Matching method for non-overlapping Cross-Domain Recommendation (DPMCDR). For each domain, we hierarchically approximate a posterior of domain-level preference distribution with empirical evidence derived from user-item interactions. Next, we aim to build distributional implicit matchings between the domain-level preferences of two domains. This process involves mapping them to a shared latent space and seeking a consensus on domain-invariant preference by minimizing the distance between their distributional representations therein. In this way, we can identify the alignment of two non-overlapping domains if they exhibit similar patterns of domain-invariant preference.Comment: 9 pages, 5 figures, full research paper accepted by ICDM 202

The contribution of δ subunit-containing GABAA receptors to phasic and tonic conductance changes in cerebellum, thalamus and neocortex

We have made use of the delta subunit-selective allosteric modulator DS2 (4-chloro-N-[2-(2-thienyl)imidazo[1,2-a]pyridine-3-yl benzamide) to assay the contribution of delta-GABAARs to tonic and phasic conductance changes in the cerebellum, thalamus and neocortex. In cerebellar granule cells, an enhancement of the tonic conductance was observed for DS2 and the orthosteric agonist THIP (4,5,6,7-tetrahydroisoxazolo[5,4-c]pyridin-3-ol). As expected, DS2 did not alter the properties of GABAA receptor-mediated inhibitory postsynaptic synaptic currents (IPSCs) supporting a purely extrasynaptic role for delta-GABAARs in cerebellar granule cells. DS2 also enhanced the tonic conductance recorded from thalamic relay neurons of the visual thalamus with no alteration in IPSC properties. However, in addition to enhancing the tonic conductance DS2 also slowed the decay of IPSCs recorded from layer II/III neocortical neurons. A slowing of the IPSC decay also occurred in the presence of the voltage-gated sodium channel blocker TTX. Moreover, under conditions of reduced GABA release the ability of DS2 to enhance the tonic conductance was attenuated. These results indicate that delta-GABAARs can be activated following vesicular GABA release onto neocortical neurons and that the actions of DS2 on the tonic conductance may be influenced by the ambient GABA levels present in particular brain regions