Living in the past, present, and future: measuring temporal orientation with language

OBJECTIVE: Temporal orientation refers to individual differences in the relative emphasis one places on the past, present, or future, and is related to academic, financial, and health outcomes. We propose and evaluate a method for automatically measuring temporal orientation through language expressed on social media. METHOD: Judges rated the temporal orientation of 4,302 social media messages. We trained a classifier based on these ratings, which could accurately predict the temporal orientation of new messages in a separate validation set (accuracy/mean sensitivity = .72; mean specificity = .77). We used the classifier to automatically classify 1.3 million messages written by 5,372 participants (50% female, aged 13-48). Finally, we tested whether individual differences in past, present, and future orientation differentially related to gender, age, Big Five personality, satisfaction with life, and depressive symptoms. RESULTS: Temporal orientations exhibit several expected correlations with age, gender, and Big Five personality. More future-oriented people were older, more likely to be female, more conscientious, less impulsive, less depressed, and more satisfied with life; present orientation showed the opposite pattern. CONCLUSION: Language-based assessments can complement and extend existing measures of temporal orientation, providing an alternative approach and additional insights into language and personality relationships. This article is protected by copyright. All rights reserved.Support for this article was provided by grant #63597 from the Robert Wood Johnson Foundation (M. E. P. Seligman, PI) and by a grant from the Templeton Religion Trust (M.E.P. Seligman, H. A. Schwartz, L. H. Ungar, co-PIs)

Feasibility of Automated Deep Learning Design for Medical Image Classification by Healthcare Professionals with Limited Coding Experience

Deep learning has huge potential to transform healthcare. However, significant expertise is required to train such models and this is a significant blocker for their translation into clinical practice. In this study, we therefore sought to evaluate the use of automated deep learning software to develop medical image diagnostic classifiers by healthcare professionals with limited coding – and no deep learning – expertise. We used five publicly available open-source datasets: (i) retinal fundus images (MESSIDOR); (ii) optical coherence tomography (OCT) images (Guangzhou Medical University/Shiley Eye Institute, Version 3); (iii) images of skin lesions (Human against Machine (HAM)10000) and (iv) both paediatric and adult chest X-ray (CXR) images (Guangzhou Medical University/Shiley Eye Institute, Version 3 and the National Institute of Health (NIH)14 dataset respectively) to separately feed into a neural architecture search framework that automatically developed a deep learning architecture to classify common diseases. Sensitivity (recall), specificity and positive predictive value (precision) were used to evaluate the diagnostic properties of the models. The discriminative performance was assessed using the area under the precision recall curve (AUPRC). In the case of the deep learning model developed on a subset of the HAM10000 dataset, we performed external validation using the Edinburgh Dermofit Library dataset. Diagnostic properties and discriminative performance from internal validations were high in the binary classification tasks (range: sensitivity of 73.3-97.0%, specificity of 67-100% and AUPRC of 0.87-1). In the multiple classification tasks, the diagnostic properties ranged from 38-100% for sensitivity and 67-100% for specificity. The discriminative performance in terms of AUPRC ranged from 0.57 to 1 in the five automated deep learning models. In an external validation using the Edinburgh Dermofit Library dataset, the automated deep learning model showed an AUPRC of 0.47, with a sensitivity of 49% and a positive predictive value of 52%. The quality of the open-access datasets used in this study (including the lack of information about patient flow and demographics) and the absence of measurement for precision, such as confidence intervals, constituted the major limitation of this study. All models, except for the automated deep learning model trained on the multi-label classification task of the NIH CXR14 dataset, showed comparable discriminative performance and diagnostic properties to state-of-the-art performing deep learning algorithms. The performance in the external validation study was low. The availability of automated deep learning may become a cornerstone for the democratization of sophisticated algorithmic modelling in healthcare as it allows the derivation of classification models without requiring a deep understanding of the mathematical, statistical and programming principles. Future studies should compare several application programming interfaces on thoroughly curated datasets

Spatial and Temporal Variations in SO₂ and PM₂.₅ Levels Around Kīlauea Volcano, Hawai'i During 2007–2018

Among the hazards posed by volcanoes are the emissions of gases and particles that can affect air quality and damage agriculture and infrastructure. A recent intense episode of volcanic degassing associated with severe impacts on air quality accompanied the 2018 lower East Rift Zone (LERZ) eruption of Kīlauea volcano, Hawai'i. This resulted in a major increase in gas emission rates with respect to usual emission values for this volcano, along with a shift in the source of the dominant plume to a populated area on the lower flank of the volcano. This led to reduced air quality in downwind communities. We analyse open-access data from the permanent air quality monitoring networks operated by the Hawai'i Department of Health (HDOH) and National Park Service (NPS), and report on measurements of atmospheric sulfur dioxide (SO2) between 2007 and 2018 and PM2.5 (aerosol particulate matter with diameter <2.5 μm) between 2010 and 2018. Additional air quality data were collected through a community-operated network of low-cost PM2.5 sensors during the 2018 LERZ eruption. From 2007 to 2018 the two most significant escalations in Kīlauea's volcanic emissions were: the summit eruption that began in 2008 (Kīlauea emissions averaged 5–6 kt/day SO2 from 2008 until summit activity decreased in May 2018) and the LERZ eruption in 2018 when SO2 emission rates reached a monthly average of 200 kt/day during June. In this paper we focus on characterizing the airborne pollutants arising from the 2018 LERZ eruption and the spatial distribution and severity of volcanic air pollution events across the Island of Hawai'i. The LERZ eruption caused the most frequent and severe exceedances of the Environmental Protection Agency (EPA) PM2.5 air quality threshold (35 μg/m3 as a daily average) in Hawai'i in the period 2010–2018. In Kona, for example, the maximum 24-h-mean mass concentration of PM2.5 was recorded as 59 μg/m3 on the twenty-ninth of May 2018, which was one of eight recorded exceedances of the EPA air quality threshold during the 2018 LERZ eruption, where there had been no exceedances in the previous 8 years as measured by the HDOH and NPS networks. SO2 air pollution during the LERZ eruption was most severe in communities in the south and west of the island, as measured by selected HDOH and NPS stations in this study, with a maximum 24-h-mean mass concentration of 728 μg/m3 recorded in Ocean View (100 km west of the LERZ emission source) in May 2018. Data from the low-cost sensor network correlated well with data from the HDOH PM2.5 instruments, confirming that these low-cost sensors provide a robust means to augment reference-grade instrument networks

A Study of Brain Networks Associated with Swallowing Using Graph-Theoretical Approaches

Functional connectivity between brain regions during swallowing tasks is still not well understood. Understanding these complex interactions is of great interest from both a scientific and a clinical perspective. In this study, functional magnetic resonance imaging (fMRI) was utilized to study brain functional networks during voluntary saliva swallowing in twenty-two adult healthy subjects (all females, 23.1±1.52 years of age). To construct these functional connections, we computed mean partial correlation matrices over ninety brain regions for each participant. Two regions were determined to be functionally connected if their correlation was above a certain threshold. These correlation matrices were then analyzed using graph-theoretical approaches. In particular, we considered several network measures for the whole brain and for swallowing-related brain regions. The results have shown that significant pairwise functional connections were, mostly, either local and intra-hemispheric or symmetrically inter-hemispheric. Furthermore, we showed that all human brain functional network, although varying in some degree, had typical small-world properties as compared to regular networks and random networks. These properties allow information transfer within the network at a relatively high efficiency. Swallowing-related brain regions also had higher values for some of the network measures in comparison to when these measures were calculated for the whole brain. The current results warrant further investigation of graph-theoretical approaches as a potential tool for understanding the neural basis of dysphagia. © 2013 Luan et al

An Action-Based Approach to Presence: Foundations and Methods

This chapter presents an action-based approach to presence. It starts by briefly describing the theoretical and empirical foundations of this approach, formalized into three key notions of place/space, action and mediation. In the light of these notions, some common assumptions about presence are then questioned: assuming a neat distinction between virtual and real environments, taking for granted the contours of the mediated environment and considering presence as a purely personal state. Some possible research topics opened up by adopting action as a unit of analysis are illustrated. Finally, a case study on driving as a form of mediated presence is discussed, to provocatively illustrate the flexibility of this approach as a unified framework for presence in digital and physical environment

The language of religious affiliation: social, emotional, and cognitive differences

Religious affiliation is an important identifying characteristic for many individuals and relates to numerous life outcomes including health, well-being, policy positions, and cognitive style. Using methods from computational linguistics, we examined language from 12,815 Facebook users in the United States and United Kingdom who indicated their religious affiliation. Religious individuals used more positive emotion words (β = .278, p < .0001) and social themes such as family (β = .242, p < .0001), while nonreligious people expressed more negative emotions like anger (β = −.427, p < .0001) and categories related to cognitive processes, like tentativeness (β = −.153, p < .0001). Nonreligious individuals also used more themes related to the body (β = −.265, p < .0001) and death (β = −.247, p < .0001). The findings offer directions for future research on religious affiliation, specifically in terms of social, emotional, and cognitive differences

Implementation of a cloud-based referral platform in ophthalmology: making telemedicine services a reality in eye care

BACKGROUND: Hospital Eye Services (HES) in the UK face an increasing number of optometric referrals driven by progress in retinal imaging. The National Health Service (NHS) published a 10-year strategy (NHS Long-Term Plan) to transform services to meet this challenge. In this study, we implemented a cloud-based referral platform to improve communication between optometrists and ophthalmologists. METHODS: Retrospective cohort study conducted at Moorfields Eye Hospital, Croydon (NHS Foundation Trust, London, UK). Patients classified into the HES referral pathway by contributing optometrists have been included into this study. Main outcome measures was the reduction of unnecessary referrals. RESULTS: After reviewing the patient's data in a web-based interface 54 (52%) out of 103 attending patients initially classified into the referral pathway did not need a specialist referral. Fourteen (14%) patients needing urgent treatment were identified. Usability was measured in duration for data input and reviewing which was an average of 9.2 min (median: 5.4; IQR: 3.4-8.7) for optometrists and 3.0 min (median: 3.0; IQR: 1.7-3.9) min for ophthalmologists. A variety of diagnosis was covered by this tool with dry age-related macular degeneration (n=34) being most common. CONCLUSION: After implementation more than half of the HES referrals have been avoided. This platform offers a digital-first solution that enables rapid-access eye care for patients in community optometrists, facilitates communication between healthcare providers and may serve as a foundation for implementation of artificial intelligence

Evaluation of HER-2/neu gene amplification and protein expression in non-small cell lung carcinomas

HER-2/neu gene amplification and cell surface overexpression are important factors in breast cancer for prognosis and prediction of sensitivity to anti-HER-2/neu monoclonal antibody therapy. In lung cancer, the clinical significance of HER-2/neu expression is currently under evaluation. We investigated 238 non-small lung carcinomas for HER-2/neu protein overexpression by immunohistochemistry using the HercepTest. We found 2+ or 3+ overexpression in 39 patients (16%), including 35% in adenocarcinomas and 20% in large cell carcinomas, but only 1% of squamous cell carcinomas. Marked (3+) overexpression was uncommon (4%). The association between protein expression and gene copy number per cell, as determined by fluorescence in situ hybridisation assay, was investigated in 51 of these NSCLC tumours. Twenty-seven tumours (53%) were negative by both tests. Marked (3+) protein expression and gene amplification were present in only 4% of samples. In 11 tumours (21%), gene gain was accompanied by chromosomal aneusomy and did not result in high protein levels while in 7 (14%) the score 2+ was associated with maximum number of signals per cell <9. The prognostic implication of HER-2/neu protein expression was studied in 187 surgically resected tumours. No statistical difference in survival was observed comparing patients with positive (2+/3+) and negative tumours (0/1+), although 3+ patients showed a tendency to shorter survival. The therapeutic implications of protein expression and gene amplification in lung cancer need to be examined in prospective clinical trials

One-step isolation and biochemical characterization of a highlyactive plant PSII monomeric core

We describe a one-step detergent solubilization protocol for isolating a highly active form of Photosystem II (PSII) from Pisum sativum L. Detailed characterization of the preparation showed that the complex was a monomer having no light harvesting proteins attached. This core reaction centre complex had, however, a range of low molecular mass intrinsic proteins as well as the chlorophyll binding proteins CP43 and CP47 and the reaction centre proteins D1 and D2. Of particular note was the presence of a stoichiometric level of PsbW, a low molecular weight protein not present in PSII of cyanobacteria. Despite the high oxygen evolution rate, the core complex did not retain the PsbQ extrinsic protein although there was close to a full complement of PsbO and PsbR and partial level of PsbP. However, reconstitution of PsbP and PsbPQ was possible. The presence of PsbP in absence of LHCII and other chlorophyll a/b binding proteins confirms that LHCII proteins are not a strict requirement for the assembly of this extrinsic polypeptide to the PSII core in contrast with the conclusion of Caffarri et al. (2009)