Thunderstorm Predictions Using Artificial Neural Networks

Artificial neural network (ANN) model classifiers were developed to generate ≤15h predictions of thunderstorms within three 400-km2 domains. The feed-forward, multi-layer perceptron and single hidden layer network topology, scaled conjugate gradient learning algorithm, and the sigmoid (linear) transfer function in the hidden (output) layer were used. The optimal number of neurons in the hidden layer was determined iteratively based on training set performance. Three sets of nine ANN models were developed: two sets based on predictors chosen from feature selection (FS) techniques and one set with all 36 predictors. The predictors were based on output from a numerical weather prediction (NWP) model. This study amends an earlier study and involves the increase in available training data by two orders of magnitude. ANN model performance was compared to corresponding performances of operational forecasters and multi-linear regression (MLR) models. Results revealed improvement relative to ANN models from the previous study. Comparative results between the three sets of classifiers, NDFD, and MLR models for this study were mixed—the best performers were a function of prediction hour, domain, and FS technique. Boosting the fraction of total positive target data (lightning strikes) in the training set did not improve generalization

Validation of the French Version of the Experiences in Close Relationships– Revised (ECR-R) Adult Romantic Attachment Questionnaire

This study aimed to validate the French version of the Experiences in Close Relationships-Revised (ECR-R) adult attachment questionnaire by investigating its internal structure and construct validity. The sample (N = 600) consisted of an equal number of male and female participants aged 25-45 years. Variables linked to adult romantic attachment (marital satisfaction, sexual satisfaction and fears associated with sexual activities, and self-esteem) were assessed using a set of questionnaires. The reliability of the two attachment dimensions (viz., avoidance and anxiety) was satisfactory. Confirmatory factor analyses revealed that the original two-factor model explained the data collected with the French ECR-R most satisfactorily. The assessment of measurement invariance showed that the structure is the same across the original U. S. sample and our sample, across men and women, and across single individuals and those in a couple relationship. Our evaluation of construct validity showed that the higher avoidance and anxiety, the lower self-esteem and sexual satisfaction and the higher the fears associated with sexuality. These results are theoretically coherent and consistent with those of previous studies of the English version of the scale. We conclude that the French version is valid

Artificial Neural Network Predictions of Water Levels in a Gulf of Mexico Shallow Embayment

Tide tables are the method of choice for water level predictions in most coastal regions. However, for many locations along the coast of the Gulf of Mexico, tide tables do not meet United States National Ocean Service (NOS) standards. Wind forcing has been recognized as the main variable not included. The performance of the tide tables is particularly poor in shallow embayments. Recent research has shown that Artificial Neural Network (ANN) models including input variables such as previous water levels, tidal forecasts, wind speed, wind direction, wind forecasts and barometricpressure can greatly improve over the tide charts for locations including open coast and deep embayments. In this paper, the ANN modeling technique is applied to a shallow embayment, the station of Rockport, located near Corpus Christi, Texas. The ANN model performance is compared against the NOS tide charts and the persistence model for the years 1997 to 2001. The performance is assessed using NOS criteria including Central Frequency (CF of 15 cm), Maximum Duration of Positive Outliers (MDPO), and Maximum Duration of Negative Outliers (MDNO). Over the study period, the performances of the three models (tide table, persistence, ANN) are respectively CF’s of 85%, 95.8% and 96.9%, MDPOs of 16, 14 and 5.9 hours, and MDNOs of 72.8 hours, 0.6 and 9.5 hours.Tablas de mareas son el método escogido generalmente para la predicción del nivel del agua en regiones costeras. Sin embargo, para muchas localidades en la costa del Golfo de México, las tablas de mareas no satisfacen las normas del Servicio Nacional Oceánico de los Estados Unidos (NOS, por sus siglas en inglés). La fuerza del viento ha sido reconocida como la principal variable no incluida. El rendimiento de las tablas de mareas es particularmente pobre en aguas poco profundas. Investigaciones recientes han mosrado que los modelos de redes de neuronas artificiales (ANN, por sus siglas en inglés) que incluyen variables de entrada como niveles previos de agua, previsiones de mareas, velocidad del viento, dirección del viento, predicción del viento, y presión atmosférica, pueden mejorar en gran medida los gráficos de mareas para localizaciones que incluyen mar abierto y aguas profundas. En este artículo, la técnica de modelación de ANN es aplicada a una estación de aguas poco profundas, la estación de Rockport, localizada cerca de Corpus Christi, Texas. El rendimiento del modelo ANN es comparado contra los gráficos de mareas NOS y el modelo de persistencia para los años 1007 a 2001. El rendimiento es medido usando los criterios NOS, que incluyen Frecuencia Central (FC de 15 cm), Máxima Duración de Puntos Atípicos Positivos (MNPO), y Máxima Duración de Puntos Atípicos Necagativos (MDNO). Sobre el período de estudio, el rendimiento de los tres modelos (tabla de mareas, persistencia, ANN) son, respectivamente, CF de 85%, 95.8% y 96.9%, para MDPO es 16, 14 y 5.9 horas, y para MDNO es de 72.8, 0.6 y 0.5 horas

Q Fever Outbreak in Homeless Shelter

Urban outbreaks of Q fever have occurred after exposure to slaughterhouses or parturient cats. We detected an outbreak of Q fever in a homeless shelter in Marseilles. Investigations showed that the main factors exposing persons to Coxiella burnetii were an abandoned slaughterhouse, used for an annual Muslim sheep feast, and wind

Halofuginone regulates keloid fibroblast fibrotic response to TGF-β induction

Keloids are characterized by increased deposition of fibrous tissue in the skin and subcutaneous tissue following an abnormal wound healing process. Although keloid etiology is yet to be fully understood, fibroblasts are known to be key players in its development. Here we analyze the antifibrotic mechanisms of Halofuginone (HF), a drug reportedly able to inhibit the TGF-β1-Smad3 pathway and to attenuate collagen synthesis, in an in-vitro keloid model using patient-derived Keloid Fibroblasts (KFs) isolated from fibrotic tissue collected during the "Scar Wars" clinical study (NCT NCT03312166). TGF-β1 was used as a pro-fibrotic agent to stimulate fibroblasts response under HF treatment. The fibrotic related properties of KFs, including survival, migration, proliferation, myofibroblasts conversion, ECM synthesis and remodeling, were investigated in 2D and 3D cultures. HF at 50 nM concentration impaired KFs proliferation, and decreased TGF-β1-induced expression of α-SMA and type I procollagen production. HF treatment also reduced KFs migration, prevented matrix contraction and increased the metallo-proteases/inhibitors (MMP/TIMP) ratio. Overall, HF elicits an anti-fibrotic contrasting the TGF-β1 stimulation of KFs, thus supporting its therapeutic use for keloid prevention and management

Rickettsia slovaca and R. raoultii in Tick-borne Rickettsioses

Tick-borne lymphadenopathy (TIBOLA), also called Dermacentor-borne necrosis erythema and lymphadenopathy (DEBONEL), is defined as the association of a tick bite, an inoculation eschar on the scalp, and cervical adenopathies. We identified the etiologic agent for 65% of 86 patients with TIBOLA/DEBONEL as either Rickettsia slovaca (49/86, 57%) or R. raoultii (7/86, 8%)

Versatile Virus-Like Particle Carrier for Epitope Based Vaccines

BACKGROUND: Recombinant proteins and in particular single domains or peptides are often poorly immunogenic unless conjugated to a carrier protein. Virus-like-particles are a very efficient means to confer high immunogenicity to antigens. We report here the development of virus-like-particles (VLPs) derived from the RNA bacteriophage AP205 for epitope-based vaccines. METHODOLOGY/PRINCIPAL FINDINGS: Peptides of angiotensin II, S.typhi outer membrane protein (D2), CXCR4 receptor, HIV1 Nef, gonadotropin releasing hormone (GnRH), Influenza A M2-protein were fused to either N- or C-terminus of AP205 coat protein. The A205-peptide fusions assembled into VLPs, and peptides displayed on the VLP were highly immunogenic in mice. GnRH fused to the C-terminus of AP205 induced a strong antibody response that inhibited GnRH function in vivo. Exposure of the M2-protein peptide at the N-terminus of AP205 resulted in a strong M2-specific antibody response upon immunization, protecting 100% of mice from a lethal influenza infection. CONCLUSIONS/SIGNIFICANCE: AP205 VLPs are therefore a very efficient and new vaccine system, suitable for complex and long epitopes, of up to at least 55 amino acid residues in length. AP205 VLPs confer a high immunogenicity to displayed epitopes, as shown by inhibition of endogenous GnRH and protective immunity against influenza infection

Perspectives for integrating human and environmental risk assessment and synergies with socio-economic analysis

International audienceFor more than a decade, the integration of human and environmental risk assessment (RA) has become an attractive vision. At the same time, existing European regulations of chemical substances such as REACH (EC Regulation No. 1907/2006), the Plant Protection Products Regulation (EC regulation 1107/2009) and Biocide Regulation (EC Regulation 528/2012) continue to ask for sector-specific RAs, each of which have their individual information requirements regarding exposure and hazard data, and also use different methodologies for the ultimate risk quantification. In response to this difference between the vision for integration and the current scientific and regulatory practice, the present paper outlines five medium-term opportunities for integrating human and environmental RA, followed by detailed discussions of the associated major components and their state of the art. Current hazard assessment approaches are analyzed in terms of data availability and quality, and covering non-test tools, the integrated testing strategy (ITS) approach, the adverse outcome pathway (AOP) concept, methods for assessing uncertainty, and the issue of explicitly treating mixture toxicity. With respect to exposure, opportunities for integrating exposure assessment are discussed, taking into account the uncertainty, standardization and validation of exposure modeling as well as the availability of exposure data. A further focus is on ways to complement RA by a socio-economic assessment (SEA) in order to better inform about risk management options. In this way, the present analysis, developed as part of the EU FP7 project HEROIC, may contribute to paving the way for integrating, where useful and possible, human and environmental RA in a manner suitable for its coupling with SEA

The Presence of Acinetobacter baumannii DNA on the Skin of Homeless People and Its Relationship With Body Lice Infestation. Preliminary Results

The presence of Acinetobacter baumannii was demonstrated in body lice, however, little is known about the mechanism of natural lice infection. In 2013 and 2014, cross-sectional one-day studies were therefore performed within two Marseille homeless shelters to assess the presence of A. baumannii DNA on human skin, blood and in body lice collected from the same homeless individuals. All 332 participants completed questionnaires, were examined for dermatologic signs, and provided four skin samples (hair, neck, armpits, and pelvic belt), blood samples and body lice (if any). We developed a new real-time PCR tool targeting the ompA/motB gene for the detection of A. baumannii for all collected samples. Blood culture was also performed. Body lice were found in 24/325 (7.4%) of subjects. We showed a prevalence of A. baumannii DNA skin-carriage in 33/305 (10.8%) of subjects. No difference was found in A. baumannii DNA prevalence according to body sites. A strong association between body lice infestation (OR = 3.07, p = 0.029) and A. baumannii DNA skin-carriage was noted. In lice, A. baumannii DNA was detected in 59/219 arthropods (26.9%). All blood cultures and real-time PCR on blood samples were negative for A. baumannii. Lice probably get infected with A. baumannii while biting through the colonized skin and likely transmit the bacteria in their feces. We found no evidence that lice facilitate the invasion of A. baumannii into the blood stream. Further investigations are needed to compare phenotypic and genotypic features of A. baumannii isolates from human skin and lice from the same individuals

Framework for sustained climate assessment in the United States

Author Posting. © American Meteorological Society, 2019. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society, 100(5), (2019): 897-908, doi:10.1175/BAMS-D-19-0130.1.As states, cities, tribes, and private interests cope with climate damages and seek to increase preparedness and resilience, they will need to navigate myriad choices and options available to them. Making these choices in ways that identify pathways for climate action that support their development objectives will require constructive public dialogue, community participation, and flexible and ongoing access to science- and experience-based knowledge. In 2016, a Federal Advisory Committee (FAC) was convened to recommend how to conduct a sustained National Climate Assessment (NCA) to increase the relevance and usability of assessments for informing action. The FAC was disbanded in 2017, but members and additional experts reconvened to complete the report that is presented here. A key recommendation is establishing a new nonfederal “climate assessment consortium” to increase the role of state/local/tribal government and civil society in assessments. The expanded process would 1) focus on applied problems faced by practitioners, 2) organize sustained partnerships for collaborative learning across similar projects and case studies to identify effective tested practices, and 3) assess and improve knowledge-based methods for project implementation. Specific recommendations include evaluating climate models and data using user-defined metrics; improving benefit–cost assessment and supporting decision-making under uncertainty; and accelerating application of tools and methods such as citizen science, artificial intelligence, indicators, and geospatial analysis. The recommendations are the result of broad consultation and present an ambitious agenda for federal agencies, state/local/tribal jurisdictions, universities and the research sector, professional associations, nongovernmental and community-based organizations, and private-sector firms.This report would not have been possible without the support and participation of numerous organizations and individuals. We thank New York State Governor Andrew M. Cuomo for announcing in his 2018 State of the State agenda that the IAC would be reconvened. The New York State Energy Research and Development Authority (Contract ID 123416), Columbia University’s Earth Institute, and the American Meteorological Society provided essential financial support and much more, including sage advice and moral support from John O’Leary, Shara Mohtadi, Steve Cohen, Alex Halliday, Peter deMenocal, Keith Seitter, Paul Higgins, and Bill Hooke. We thank the attendees of a workshop, generously funded by the Kresge Foundation in November of 2017, that laid a foundation for the idea to establish a civil-society-based assessment consortium. During the course of preparing the report, IAC members consulted with individuals too numerous to list here—state, local, and tribal officials; researchers; experts in nongovernmental and community-based organizations; and professionals in engineering, architecture, public health, adaptation, and other areas. We are so grateful for their time and expertise. We thank the members and staff of the National Academy of Sciences, Engineering, and Medicine’s Committee to Advise the U.S. Global Change Research Program for providing individual comments on preliminary recommendations during several discussions in open sessions of their meetings. The following individuals provided detailed comments on an earlier version of this report, which greatly sharpened our thinking and recommendations: John Balbus, Tom Dietz, Phil Duffy, Baruch Fischhoff, Brenda Hoppe, Melissa Kenney, Linda Mearns, Claudia Nierenberg, Kathleen Segerson, Soroosh Sorooshian, Chris Weaver, and Brian Zuckerman. Mary Black provided insightful copy editing of several versions of the report. We also thank four anonymous reviewers for their effort and care in critiquing and improving the report. It is the dedication, thoughtful feedback, expertise, care, and commitment of all these people and more that not only made this report possible, but allow us all to continue to support smart and insightful actions in a changing climate. We are grateful as authors and as global citizens. Author contributions: RM, SA, KB, MB, AC, JD, PF, KJ, AJ, KK, JK, ML, JM, RP, TR, LS, JS, JW, and DZ were members of the IAC and shared in researching, discussing, drafting, and approving the report. BA, JF, AG, LJ, SJ, PK, RK, AM, RM, JN, WS, JS, PT, GY, and RZ contributed to specific sections of the report