Dexamethasone induces apoptosis in pulmonary arterial smooth muscle cells

BACKGROUND: Dexamethasone suppressed inflammation and haemodynamic changes in an animal model of pulmonary arterial hypertension (PAH). A major target for dexamethasone actions is NF-κB, which is activated in pulmonary vascular cells and perivascular inflammatory cells in PAH. Reverse remodelling is an important concept in PAH disease therapy, and further to its anti-proliferative effects, we sought to explore whether dexamethasone augments pulmonary arterial smooth muscle cell (PASMC) apoptosis. METHODS: Analysis of apoptosis markers (caspase 3, in-situ DNA fragmentation) and NF-κB (p65 and phospho-IKK-α/β) activation was performed on lung tissue from rats with monocrotaline (MCT)-induced pulmonary hypertension (PH), before and after day 14–28 treatment with dexamethasone (5 mg/kg/day). PASMC were cultured from this rat PH model and from normal human lung following lung cancer surgery. Following stimulation with TNF-α (10 ng/ml), the effects of dexamethasone (10(−8)–10(−6) M) and IKK2 (NF-κB) inhibition (AS602868, 0–3 μM (0-3×10(−6) M) on IL-6 and CXCL8 release and apoptosis was determined by ELISA and by Hoechst staining. NF-κB activation was measured by TransAm assay. RESULTS: Dexamethasone treatment of rats with MCT-induced PH in vivo led to PASMC apoptosis as displayed by increased caspase 3 expression and DNA fragmentation. A similar effect was seen in vitro using TNF-α-simulated human and rat PASMC following both dexamethasone and IKK2 inhibition. Increased apoptosis was associated with a reduction in NF-κB activation and in IL-6 and CXCL8 release from PASMC. CONCLUSIONS: Dexamethasone exerted reverse-remodelling effects by augmenting apoptosis and reversing inflammation in PASMC possibly via inhibition of NF-κB. Future PAH therapies may involve targeting these important inflammatory pathways

Graph Distillation for Action Detection with Privileged Modalities

We propose a technique that tackles action detection in multimodal videos under a realistic and challenging condition in which only limited training data and partially observed modalities are available. Common methods in transfer learning do not take advantage of the extra modalities potentially available in the source domain. On the other hand, previous work on multimodal learning only focuses on a single domain or task and does not handle the modality discrepancy between training and testing. In this work, we propose a method termed graph distillation that incorporates rich privileged information from a large-scale multimodal dataset in the source domain, and improves the learning in the target domain where training data and modalities are scarce. We evaluate our approach on action classification and detection tasks in multimodal videos, and show that our model outperforms the state-of-the-art by a large margin on the NTU RGB+D and PKU-MMD benchmarks. The code is released at http://alan.vision/eccv18_graph/.Comment: ECCV 201

Low-Rank Subspace Override for Unsupervised Domain Adaptation

Current supervised learning models cannot generalize well across domain boundaries, which is a known problem in many applications, such as robotics or visual classification. Domain adaptation methods are used to improve these generalization properties. However, these techniques suffer either from being restricted to a particular task, such as visual adaptation, require a lot of computational time and data, which is not always guaranteed, have complex parameterization, or expensive optimization procedures. In this work, we present an approach that requires only a well-chosen snapshot of data to find a single domain invariant subspace. The subspace is calculated in closed form and overrides domain structures, which makes it fast and stable in parameterization. By employing low-rank techniques, we emphasize on descriptive characteristics of data. The presented idea is evaluated on various domain adaptation tasks such as text and image classification against state of the art domain adaptation approaches and achieves remarkable performance across all tasks

Economic footprint of California wildfires in 2018

Recent increases in the frequency and scale of wildfires worldwide have raised concerns about the influence of climate change and associated socioeconomic costs. In the western United States, the hazard of wildfire has been increasing for decades. Here, we use a combination of physical, epidemiological and economic models to estimate the economic impacts of California wildfires in 2018, including the value of destroyed and damaged capital, the health costs related to air pollution exposure and indirect losses due to broader economic disruption cascading along with regional and national supply chains. Our estimation shows that wildfire damages in 2018 totalled $148.5 (126.1–192.9) billion (roughly 1.5$27.7 billion (19%) in capital losses, $32.2 billion (22$88.6 billion (59%) in indirect losses (all values in US$). Our results reveal that the majority of economic impacts related to California wildfires may be indirect and often affect industry sectors and locations distant from the fires (for example, 52% of the indirect losses—31% of total losses—in 2018 were outside of California). Our findings and methods provide new information for decision makers tasked with protecting lives and key production sectors and reducing the economic damages of future wildfires

Channel selection for multispectral color imaging using binary differential evolution

2013-2014 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

On the relative expressiveness of higher-order session processes

By integrating constructs from the λ-calculus and the π-calculus, in higher-order process calculi exchanged values may contain processes. This paper studies the relative expressiveness of HOπ, the higher-order π-calculus in which communications are governed by session types. Our main discovery is that HO, a subcalculus of HOπ which lacks name-passing and recursion, can serve as a new core calculus for session-typed higher-order concurrency. By exploring a new bisimulation for HO, we show that HO can encode HOπ fully abstractly (up to typed contextual equivalence) more precisely and efficiently than the first-order session π-calculus (π). Overall, under session types, HOπ, HO, and π are equally expressive; however, HOπ and HO are more tightly related than HOπ and π

Users’ experiences of lighting controls: a case-study

The aim of this paper is to elucidate how occupants perceive their lit environments in a university setting and how they interact with lighting controls using qualitative methods. Semi-structured interviews were carried out with academic teaching and research staff. Thematic analysis identified four main themes: control and choice, connection with the outdoors, concentration, and comfort. Participants were largely able to control and adapt their lighting using small power lighting in office spaces and they perceived this as beneficial to comfort and concentration. Participants expressed frustration with the light switches in classrooms, a lack of consistency in lighting controls across the university buildings was particularly notable. Installers should consider how piecemeal upgrades on large estates affect the perception of buildings where occupiers face multiple control systems. The management of the lighting in classroom spaces including the type and location of blinds, lack of regular window cleaning in some buildings and difficulty in minimising light on projection screens in upgraded classrooms were cited as areas for improvement. Wider implications for lighting control and management highlighted by this study include most notably that a lack of end users consultation has serious consequences on their perception of lighting upgrades and their willingness to employ “workarounds”

Adverse prognostic and predictive significance of low DNA-dependent protein kinase catalytic subunit (DNA-PKcs) expression in early-stage breast cancers

Background: DNA-dependent protein kinase catalytic subunit (DNA-PKcs), a serine threonine kinase belonging to the PIKK family (phosphoinositide 3-kinase-like-family of protein kinase), is a critical component of the non-homologous end joining (NHEJ) pathway required for the repair of DNA double strand breaks. DNA-PKcs may be involved in breast cancer pathogenesis. Methods: We evaluated clinicopathological significance of DNA-PKcs protein expression in 1161 tumours and DNA-PKcs mRNA expression in 1950 tumours. We correlated DNA-PKcs to other markers of aggressive phenotypes, DNA repair, apoptosis and cell cycle regulation. Results: Low DNA-PKcs protein expression was associated with higher tumour grade, higher mitotic index, tumour de-differentiation and tumour type (ps<0.05). Absence of BRCA1, low XRCC1/SMUG1/APE1/Polβ were also more likely in low DNA-PKcs expressing tumours (ps<0.05). Low DNA-PKcs protein expression was significantly associated with worse breast cancer specific survival (BCCS) in univariate and multivariate analysis (ps<0.01). At the mRNA level, low DNA-PKcs was associated with PAM50.Her2 and PAM50.LumA molecular phenotypes (ps<0.01) and poor BCSS. In patients with ER positive tumours who received endocrine therapy, low DNA-PKcs (protein and mRNA) was associated with poor survival. In ER negative patients, low DNA-PKcs mRNA remains significantly associated with adverse outcome. Conclusions: Our study suggests that low DNA-PKcs expression may have prognostic and predictive significance in breast cancers