Inducible antiviral activity and rapid production of the Ribosome-Inactivating Protein I from Phytolacca heterotepala in tobacco

We studied the in vitro and in planta antiviral activity of the PhRIP I, a type 1 Ribosome-Inactivating Protein originally purified from leaves of the Phytolacca heterotepala. This protein inhibited protein translation in a cell-free assay and limited the local lesion formation from PVX infection on tobacco leaves. We used a transient expression system based on leaf infiltration with recombinant Agrobacteria to show that tobacco can produce a correctly processed PhRIP I enzyme that retains its antiviral activity. Hence, it is possible to rapidly yield in plants a type 1 RIP by means of this transient expression system. To analyse the possible increase of virus resistance in plants, Nicotiana tabacum lines that were transformed with the PhRIP I coding sequence under the control of the wound-inducible PGIP promoter were challenged by PVX. A significantly lower number of viral lesions compared to untransformed plants was observed only after the induction of the transgene, indicating that the controlled gene expression of an antiviral protein can increase virus resistance

CFM: a convolutional neural network for first-motion polarity classification of seismic records in volcanic and tectonic areas

First-motion polarity determination is essential for deriving volcanic and tectonic earthquakes’ focal mechanisms, which provide crucial information about fault structures and stress fields. Manual procedures for polarity determination are time-consuming and prone to human error, leading to inaccurate results. Automated algorithms can overcome these limitations, but accurately identifying first-motion polarity is challenging. In this study, we present the Convolutional First Motion (CFM) neural network, a label-noise robust strategy based on a Convolutional Neural Network, to automatically identify first-motion polarities of seismic records. CFM is trained on a large dataset of more than 140,000 waveforms and achieves a high accuracy of 97.4% and 96.3% on two independent test sets. We also demonstrate CFM’s ability to correct mislabeled waveforms in 92% of cases, even when they belong to the training set. Our findings highlight the effectiveness of deep learning approaches for first-motion polarity determination and suggest the potential for combining CFM with other deep learning techniques in volcano seismology

Signal classification using neural networks

The aim of this paper is to classify two kind of signals recorded by seismic station: artificial explosions and seismic activity. The problem is approached from both the preprocessing and the classification point of view. For the preprocessing stage, instead of the conventional Fourier Transform, we use a Linear Prediction Coding (LPC) algorithm, which allows to compress the data and extract robust features for the signal representation, For the classification stage, we have compared the performance of several neural models. An unsupervised method, based on the Principal Component Analysis (PCA) and the Mixture of Gaussian (MoG) clustering algorithm, gives a 70% percentage of correct classification. The Elman Recurrent Neural Nets (RNN) is able to reach 91% of correct classification on the test set. However this performance is strongly and critically dependent on the order of presentation of the events. Instead a MLP with a single hidden layer gives the 86% of correct classification on the test set, independently of the order of presentation of the patterns

Antimicrobial susceptibilitiy of Salmonella spp. strains isolated from layer hens in Campania regione from 2000 to 2003

The aim of this study was to determine the antimicrobial resistance in 60 Salmonella strains (S. enteritidis, S. thyphimurium, S. gallinarum) isolated from layer hens in Campania region from 2000 to 2003. S. gallinarum showed resistance against ciprofloxacin and enrofloxacin, in contrast, S. enteritidis and S. typhimurium were fully susceptibile. In all of isolates high levels of resistance were observed for neomycin, gentamicin and oxytetracycline. Also, one significant observation was that all of the isolates showed full susceptibility to Sulphamethoxazole/Trimethoprime. These results suggest importance to restrict the use of antibiotics in layers hens flocks in order to reduce the selection and spread of multiresistant strains

Antimicrobial susceptibility of Salmonella spp. strains isolated from Layer Hens in Campania Region from 2000 to 2003

Scopo del presente lavoro è stato quello di testare la sensibilità antibiotica di 60 ceppi di Salmonella (S. enteritidis, S. typhimurium, S. gallinarum) isolati da galline ovaiole nel periodo compreso tra il 2000 e il 2003. S. gallinarum mostrava resistenza nei confronti di ciprofloxacina ed enrofloxacina (rispettivamente 15% e 23%), al contrario di S. enteritidis e S. typhimurium che manifestavano una completa sensibilità. Tutti i sierotipi valutati presentavano alte percentuali di resistenza nei confronti di neomicina, gentamicina e ossitetraciclina. Nei confronti dei sulfamidici i ceppi testati presentavano resistenza nulla. Tali risultati suggeriscono un uso più moderato e mirato degli antibiotici negli allevamenti in modo da ridurre la selezione e diffusione di ceppi multiresistenti

DataSheet1_CFM: a convolutional neural network for first-motion polarity classification of seismic records in volcanic and tectonic areas.PDF

First-motion polarity determination is essential for deriving volcanic and tectonic earthquakes’ focal mechanisms, which provide crucial information about fault structures and stress fields. Manual procedures for polarity determination are time-consuming and prone to human error, leading to inaccurate results. Automated algorithms can overcome these limitations, but accurately identifying first-motion polarity is challenging. In this study, we present the Convolutional First Motion (CFM) neural network, a label-noise robust strategy based on a Convolutional Neural Network, to automatically identify first-motion polarities of seismic records. CFM is trained on a large dataset of more than 140,000 waveforms and achieves a high accuracy of 97.4% and 96.3% on two independent test sets. We also demonstrate CFM’s ability to correct mislabeled waveforms in 92% of cases, even when they belong to the training set. Our findings highlight the effectiveness of deep learning approaches for first-motion polarity determination and suggest the potential for combining CFM with other deep learning techniques in volcano seismology.</p