The Creation of the Catholic School Leadership Program at Seton Hall University

This article summarizes the development and implementation of a Catholic school leadership program at a diocesan university. Supported by university faculty as well as seminary faculty, this program offers a unique response to the training of future school leaders. The course work blends leadership theory, theology, and educational administration and is delivered via a cohort model

Precision agriculture to improve the monitoring and management of tomato insect pests

Human-based monitoring of arthropod pests of agricultural importance is usually a time-consuming and costly activity. The advent of technologies such as automatic traps opens new opportunities for remote monitoring. In this article, we present a novel Artificial Intelligence (AI)-based approach aimed to developing a smart trap for monitoring two major pests of greenhouse tomatoes, namely whiteflies, i.e., Bemisia tabaci and Trialeurodes vaporariorum (Hemiptera: Aleyrodidae), and leaf miner flies, Liriomyza spp. (Diptera: Agromyzidae)

A deep learning-based pipeline for whitefly pest abundance estimation on chromotropic sticky traps

Integrated Pest Management (IPM) is an essential approach used in smart agriculture to manage pest populations and sustainably optimize crop production. One of the cornerstones underlying IPM solutions is pest monitoring, a practice often performed by farm owners by using chromotropic sticky traps placed on insect hot spots to gauge pest population densities. In this paper, we propose a modular model-agnostic deep learning-based counting pipeline for estimating the number of insects present in pictures of chromotropic sticky traps, thus reducing the need for manual trap inspections and minimizing human effort. Additionally, our solution generates a set of raw positions of the counted insects and confidence scores expressing their reliability, allowing practitioners to filter out unreliable predictions. We train and assess our technique by exploiting PST - Pest Sticky Traps, a new collection of dot-annotated images we created on purpose and we publicly release, suitable for counting whiteflies. Experimental evaluation shows that our proposed counting strategy can be a valuable Artificial Intelligence-based tool to help farm owners to control pest outbreaks and prevent crop damages effectively. Specifically, our solution achieves an average counting error of approximately compared to human capabilities requiring a matter of seconds, a large improvement respecting the time-intensive process of manual human inspections, which often take hours or even days

Learning algorithms estimate pose and detect motor anomalies in flies exposed to minimal doses of a toxicant

Pesticide exposure, even at low doses, can have detrimental effects on ecosystems. This study aimed at validating the use of machine learning for recognizing motor anomalies, produced by minimal insecticide exposure on a model insect species. The Mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae), was exposed to food contaminated with low concentrations of Carlina acaulis essential oil (EO). A deep learning approach enabled fly pose estimation on video recordings in a custom-built arena. Five machine learning algorithms were trained on handcrafted features, extracted from the predicted pose, to distinguish treated individuals. Random Forest and K-Nearest Neighbor algorithms best performed, with an area under the receiver operating characteristic (ROC) curve of 0.75 and 0.73, respectively. Both algorithms achieved an accuracy of 0.71. Results show the machine learning potential for detecting sublethal effects arising from insecticide exposure on fly motor behavior, which could also affect other organisms and environmental health

Caracterização anatômica dos anéis de crescimento de espécies arbóreas de Florestas Ombrófila das Terras Baixas e Ombrófila Mista, no Estado do Paraná.

Organizado por Patricia Póvoa de Mattos, Celso Garcia Auer, Rejane Stumpf Sberze, Katia Regina Pichelli e Paulo César Botosso

Value of systolic pulmonary arterial pressure as a prognostic factor of death in the systemic sclerosis EUSTAR population.

The aim of this study was to assess the prognostic value of systolic pulmonary artery pressure (sPAP) estimated by echocardiography in the multinational European League Against Rheumatism Scleroderma Trial and Research (EUSTAR) cohort.Data for patients with echocardiography documented between 1 January 2005 and 31 December 2011 were extracted from the EUSTAR database. Stepwise forward multivariable statistical Cox pulmonary hypertension analysis was used to examine the independent effect on survival of selected variables.Based on our selection criteria, 1476 patients were included in the analysis; 87\% of patients were female, with a mean age of 56.3 years (s.d. 13.5) and 31\% had diffuse SSc. The mean duration of follow-up was 2.0 years (s.d. 1.2, median 1.9). Taking index sPAP of 50 mmHg. In a multivariable Cox model, sPAP and the diffusing capacity for carbon monoxide (DLCO) were independently associated with the risk of death [HR 1.833 (95\% CI 1.035, 3.247) and HR 0.973 (95\% CI 0.955, 0.991), respectively]. sPAP was an independent risk factor for death with a HR of 3.02 (95\% CI 1.91, 4.78) for sPAP ≥36 mmHg.An estimated sPAP >36 mmHg at baseline echocardiography was significantly and independently associated with reduced survival, regardless of the presence of pulmonary hypertension based on right heart catheterization

Occupational exposure to electromagnetic fields in magnetic resonance environment: an update on regulation, exposure assessment techniques, health risk evaluation, and surveillance

Magnetic resonance imaging (MRI) is one of the most-used diagnostic imaging methods worldwide. There are ∼50,000 MRI scanners worldwide each of which involves a minimum of five workers from different disciplines who spend their working days around MRI scanners. This review analyzes the state of the art of literature about the several aspects of the occupational exposure to electromagnetic fields (EMF) in MRI: regulations, literature studies on biological effects, and health surveillance are addressed here in detail, along with a summary of the main approaches for exposure assessment. The original research papers published from 2013 to 2021 in international peer-reviewed journals, in the English language, are analyzed, together with documents published by legislative bodies. The key points for each topic are identified and described together with useful tips for precise safeguarding of MRI operators, in terms of exposure assessment, studies on biological effects, and health surveillance. Graphical abstract: [Figure not available: see fulltext.

Developing a highly stable Carlina acaulis essential oil nanoemulsion for managing Lobesia botrana

The growing interest in the development of green pest management strategies is leading to the exploitation of essential oils (EOs) as promising botanical pesticides. In this respect, nanotechnology could efficiently support the use of EOs through their encapsulation into stable nanoformulations, such as nanoemulsions (NEs), to improve their stability and efficacy. This technology assures the improvement of the chemical stability, hydrophilicity, and environmental persistence of EOs, giving an added value for the fabrication of natural insecticides effective against a wide spectrum of insect vectors and pests of public and agronomical importance. Carlina acaulis (Asteraceae) root EO has been recently proposed as a promising ingredient of a new generation of botanical insecticides. In the present study, a highly stable C. acaulis-based NE was developed. Interestingly, such a nanosystem was able to encapsulate 6% (w/w) of C. acaulis EO, showing a mean diameter of around 140 nm and a SOR (surfactant-to-oil ratio) of 0.6. Its stability was evaluated in a storage period of six months and corroborated by an accelerated stability study. Therefore, the C. acaulis EO and C. acaulis-based NE were evaluated for their toxicity against 1st instar larvae of the European grapevine moth (EGVM), Lobesia botrana (Denis & Schiffermüller, 1775) (Lepidoptera: Tortricidae), a major vineyard pest. The chemical composition of C. acaulis EO was investigated by gas chromatography–mass spectrometry (GC–MS) revealing carlina oxide, a polyacetylene, as the main constituent. In toxicity assays, both the C. acaulis EO and the C. acaulis-based NE were highly toxic to L. botrana larvae, with LC50 values of 7.299 and 9.044 µL/mL for C. acaulis EO and NE, respectively. The C. acaulis-based NE represents a promising option to develop highly stable botanical insecticides for pest management. To date, this study represents the first evidence about the insecticidal toxicity of EOs and EO-based NEs against this major grapevine pest