Mating disruption of citrus leafminer mediated by a noncompetitive mechanism at a remarkably low pheromone release rate.

The citrus leafminer, Phyllocnistis citrella Stainton (Lepidoptera: Gracillariidae), is a worldwide pest of citrus. A season-long investigation was conducted that evaluated mating disruption for this pest. Effective disruption of the male P. citrella orientation to pheromone traps (98%) and reduced flush infestation by larvae was achieved for 221 d with two deployments of a 3:1 blend of (Z,Z,E)-7,11,13-hexadecatrienal/(Z,Z)-7,11-hexadecadienal at a remarkably low rate of 1.5 g active ingredient (AI)/ha per deployment. To gain insight into the mechanism that mediates the disruption of P. citrella, male moth catch was quantified in replicated plots of citrus treated with varying densities of pheromone dispensers. The densities of septum dispensers compared were: 0 (0/ha, 0.0 g AI/ha), 0.2 (one every fifth tree or 35/ha, 0.05 g AI/ha), 1 (215/ha, 0.29 g AI/ha), and 5 per tree (1,100/ha, 1.5 g AI/ha). Profile analysis by previously published mathematical methods matched predictions of noncompetitive mating disruption. Behavioral observations of male P. citrella in the field revealed that males did not approach mating disruption dispensers in any of the dispenser density treatments. The current report presents the first set of profile analyses combined with direct behavioral observations consistent with previously published theoretical predictions for a noncompetitive mechanism of mating disruption. The results suggest that disruption of P. citrella should be effective even at high population densities given the density-independent nature of disruption for this species and the remarkably low rate of pheromone per hectare required for efficacy

Modeling the adoption and use of social media by nonprofit organizations

This study examines what drives organizational adoption and use of social media through a model built around four key factors - strategy, capacity, governance, and environment. Using Twitter, Facebook, and other data on 100 large US nonprofit organizations, the model is employed to examine the determinants of three key facets of social media utilization: 1) adoption, 2) frequency of use, and 3) dialogue. We find that organizational strategies, capacities, governance features, and external pressures all play a part in these social media adoption and utilization outcomes. Through its integrated, multi-disciplinary theoretical perspective, this study thus helps foster understanding of which types of organizations are able and willing to adopt and juggle multiple social media accounts, to use those accounts to communicate more frequently with their external publics, and to build relationships with those publics through the sending of dialogic messages.Comment: Seungahn Nah and Gregory D. Saxton. (in press). Modeling the adoption and use of social media by nonprofit organizations. New Media & Society, forthcomin

Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet

Skin cancer, a major form of cancer, is a critical public health problem with 123,000 newly diagnosed melanoma cases and between 2 and 3 million non-melanoma cases worldwide each year. The leading cause of skin cancer is high exposure of skin cells to UV radiation, which can damage the DNA inside skin cells leading to uncontrolled growth of skin cells. Skin cancer is primarily diagnosed visually employing clinical screening, a biopsy, dermoscopic analysis, and histopathological examination. It has been demonstrated that the dermoscopic analysis in the hands of inexperienced dermatologists may cause a reduction in diagnostic accuracy. Early detection and screening of skin cancer have the potential to reduce mortality and morbidity. Previous studies have shown Deep Learning ability to perform better than human experts in several visual recognition tasks. In this paper, we propose an efficient seven-way automated multi-class skin cancer classification system having performance comparable with expert dermatologists. We used a pretrained MobileNet model to train over HAM10000 dataset using transfer learning. The model classifies skin lesion image with a categorical accuracy of 83.1 percent, top2 accuracy of 91.36 percent and top3 accuracy of 95.34 percent. The weighted average of precision, recall, and f1-score were found to be 0.89, 0.83, and 0.83 respectively. The model has been deployed as a web application for public use at (https://saketchaturvedi.github.io). This fast, expansible method holds the potential for substantial clinical impact, including broadening the scope of primary care practice and augmenting clinical decision-making for dermatology specialists.Comment: This is a pre-copyedited version of a contribution published in Advances in Intelligent Systems and Computing, Hassanien A., Bhatnagar R., Darwish A. (eds) published by Chaturvedi S.S., Gupta K., Prasad P.S. The definitive authentication version is available online via https://doi.org/10.1007/978-981-15-3383-9_1

Exercise Program Design Considerations for Head and Neck Cancer Survivors.

The present study aimed to establish exercise preferences, barriers, and perceived benefits among head and neck cancer survivors, as well as their level of interest in participating in an exercise program. Patients treated for primary squamous cell carcinoma of the head and neck between 2010 and 2014 were identified from the hospital database and sent a postal questionnaire pack to establish exercise preferences, barriers, perceived benefits, current physical activity levels, and quality of life. A postal reminder was sent to non-responders 4 weeks later. The survey comprised 1021 eligible patients of which 437 (43%) responded [74% male, median (interquartile range) age, 66 (60-73) years]. Of the repondents, 30% said ‘Yes’ they would be interested in participating in an exercise program and 34% said ‘Maybe’. The most common exercise preferences were a frequency of three times per week, moderate-intensity, and 15-29 minutes per bout. The most popular exercise types were walking (68%), flexibility exercises (35%), water activites/swimming (33%), cycling (31%), and weight machines (19%). Home (55%), outdoors (46%) and health club/gym (33%) were the most common preferred choices for where to regularly exercise. Percieved exercise benefits relating to improved physical attributes were commonly cited, whereas potential social and work-related benefits were less well acknowledged. The most commonly cited exercise barriers were dry mouth or throat (40%), fatigue (37%), shortness of breath (30%), muscle weakness (28%) difficulty swallowing (25%), and shoulder weakness and pain (24%). The present findings inform the design of exercise programs for head and neck cancer survivors

Disease prevention versus data privacy : using landcover maps to inform spatial epidemic models

The availability of epidemiological data in the early stages of an outbreak of an infectious disease is vital for modelers to make accurate predictions regarding the likely spread of disease and preferred intervention strategies. However, in some countries, the necessary demographic data are only available at an aggregate scale. We investigated the ability of models of livestock infectious diseases to predict epidemic spread and obtain optimal control policies in the event of imperfect, aggregated data. Taking a geographic information approach, we used land cover data to predict UK farm locations and investigated the influence of using these synthetic location data sets upon epidemiological predictions in the event of an outbreak of foot-and-mouth disease. When broadly classified land cover data were used to create synthetic farm locations, model predictions deviated significantly from those simulated on true data. However, when more resolved subclass land use data were used, moderate to highly accurate predictions of epidemic size, duration and optimal vaccination and ring culling strategies were obtained. This suggests that a geographic information approach may be useful where individual farm-level data are not available, to allow predictive analyses to be carried out regarding the likely spread of disease. This method can also be used for contingency planning in collaboration with policy makers to determine preferred control strategies in the event of a future outbreak of infectious disease in livestock

Thalamic inputs to dorsomedial striatum are involved in inhibitory control: evidence from the five-choice serial reaction time task in rats

Rationale Corticostriatal circuits are widely implicated in the top-down control of attention including inhibitory control and behavioural flexibility. However, recent neurophysiological evidence also suggests a role for thalamic inputs to striatum in behaviours related to salient, reward-paired cues. Objectives Here, we used designer receptors exclusively activated by designer drugs (DREADDs) to investigate the role of parafascicular (Pf) thalamic inputs to the dorsomedial striatum (DMS) using the five-choice serial reaction time task (5CSRTT) in rats. Methods The 5CSRTT requires sustained attention in order to detect spatially and temporally distributed visual cues and provides measures of inhibitory control related to impulsivity (premature responses) and compulsivity (perseverative responses). Rats underwent bilateral Pf injections of the DREADD vector, AAV2-CaMKIIa-HA-hM4D(Gi)-IRES-mCitrine. The DREADD agonist, clozapine N-oxide (CNO; 1 μl bilateral; 3 μM) or vehicle, was injected into DMS 1 h before behavioural testing. Task parameters were manipulated to increase attention load or reduce stimulus predictability respectively. Results We found that inhibition of the Pf-DMS projection significantly increased perseverative responses when stimulus predictability was reduced but had no effect on premature responses or response accuracy, even under increased attentional load. Control experiments showed no effects on locomotor activity in an open field. Conclusions These results complement previous lesion work in which the DMS and orbitofrontal cortex were similarly implicated in perseverative responses and suggest a specific role for thalamostriatal inputs in inhibitory control

Which older people decline participation in a primary care trial of physical activity and why: insights from a mixed methods approach

This article is available through the Brunel Open Access Publishing Fund. Copyright 2014 Rogers et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Background: Physical activity is of vital importance to older peoples’ health. Physical activity intervention studies with older people often have low recruitment, yet little is known about non-participants. Methods: Patients aged 60–74 years from three UK general practices were invited to participate in a nurse-supported pedometer-based walking intervention. Demographic characteristics of 298 participants and 690 non-participants were compared. Health status and physical activity of 298 participants and 183 non-participants who completed a survey were compared using age, sex adjusted odds ratios (OR) (95% confidence intervals). 15 non-participants were interviewed to explore perceived barriers to participation. Results: Recruitment was 30% (298/988). Participants were more likely than non-participants to be female (54% v 47%; p = 0.04) and to live in affluent postcodes (73% v 62% in top quintile; p < 0.001). Participants were more likely than non-participants who completed the survey to have an occupational pension OR 2.06 (1.35-3.13), a limiting longstanding illness OR 1.72 (1.05-2.79) and less likely to report being active OR 0.55 (0.33-0.93) or walking fast OR 0.56 (0.37-0.84). Interviewees supported general practice-based physical activity studies, particularly walking, but barriers to participation included: already sufficiently active, reluctance to walk alone or at night, physical symptoms, depression, time constraints, trial equipment and duration. Conclusion: Gender and deprivation differences suggest some selection bias. However, trial participants reported more health problems and lower activity than non-participants who completed the survey, suggesting appropriate trial selection in a general practice population. Non-participant interviewees indicated that shorter interventions, addressing physical symptoms and promoting confidence in pursuing physical activity, might increase trial recruitment and uptake of practice-based physical activity endeavours.The National Institute for Health Research (NIHR) under its Research for Patient Benefit Programme (Grant Reference Number PB-PG-0909-20055)

Theories for influencer identification in complex networks

In social and biological systems, the structural heterogeneity of interaction networks gives rise to the emergence of a small set of influential nodes, or influencers, in a series of dynamical processes. Although much smaller than the entire network, these influencers were observed to be able to shape the collective dynamics of large populations in different contexts. As such, the successful identification of influencers should have profound implications in various real-world spreading dynamics such as viral marketing, epidemic outbreaks and cascading failure. In this chapter, we first summarize the centrality-based approach in finding single influencers in complex networks, and then discuss the more complicated problem of locating multiple influencers from a collective point of view. Progress rooted in collective influence theory, belief-propagation and computer science will be presented. Finally, we present some applications of influencer identification in diverse real-world systems, including online social platforms, scientific publication, brain networks and socioeconomic systems.Comment: 24 pages, 6 figure