Application of Artificial Intelligence to the Prediction of the Antimicrobial Activity of Essential Oils

Essential oils (EOs) are vastly used as natural antibiotics in Complementary and Alternative Medicine (CAM). Their intrinsic chemical variability and synergisms/antagonisms between its components make difficult to ensure consistent effects through different batches. Our aim is to evaluate the use of artificial neural networks (ANNs) for the prediction of their antimicrobial activity. Methods. The chemical composition and antimicrobial activity of 49 EOs, extracts, and/or fractions was extracted from NCCLS compliant works. The fast artificial neural networks (FANN) software was used and the output data reflected the antimicrobial activity of these EOs against four common pathogens: Staphylococcus aureus, Escherichia coli, Candida albicans, and Clostridium perfringens as measured by standardised disk diffusion assays. Results. ANNs were able to predict >70% of the antimicrobial activities within a 10 mm maximum error range. Similarly, ANNs were able to predict 2 or 3 different bioactivities at the same time. The accuracy of the prediction was only limited by the inherent errors of the popular antimicrobial disk susceptibility test and the nature of the pathogens. Conclusions. ANNs can be reliable, fast, and cheap tools for the prediction of the antimicrobial activity of EOs thus improving their use in CAM

From on-road to off : transfer learning within a deep convolutional neural network for segmentation and classification of off-road scenes.

Real-time road-scene understanding is a challenging computer vision task with recent advances in convolutional neural networks (CNN) achieving results that notably surpass prior traditional feature driven approaches. Here, we take an existing CNN architecture, pre-trained for urban road-scene understanding, and retrain it towards the task of classifying off-road scenes, assessing the network performance within the training cycle. Within the paradigm of transfer learning we analyse the effects on CNN classification, by training and assessing varying levels of prior training on varying sub-sets of our off-road training data. For each of these configurations, we evaluate the network at multiple points during its training cycle, allowing us to analyse in depth exactly how the training process is affected by these variations. Finally, we compare this CNN to a more traditional approach using a feature-driven Support Vector Machine (SVM) classifier and demonstrate state-of-the-art results in this particularly challenging problem of off-road scene understanding

Categorizing facial expressions : a comparison of computational models

The original publication is available at www.springerlink.com Copyright SpringerRecognizing expressions is a key part of human social interaction, and processing of facial expression information is largely automatic for humans, but it is a non-trivial task for a computational system. The purpose of this work is to develop computational models capable of differentiating between a range of human facial expressions. Raw face images are examples of high-dimensional data, so here we use two dimensionality reduction techniques: principal component analysis and curvilinear component analysis. We also preprocess the images with a bank of Gabor filters, so that important features in the face images may be identified. Subsequently, the faces are classified using a support vector machine. We show that it is possible to differentiate faces with a prototypical expression from the neutral expression. Moreover, we can achieve this with data that has been massively reduced in size: in the best case the original images are reduced to just 5 components. We also investigate the effect size on face images, a concept which has not been reported previously on faces. This enables us to identify those areas of the face that are involved in the production of a facial expression.Peer reviewe

The Role of Phosphatidic Acid and Cardiolipin in Stability of the Tetrameric Assembly of Potassium Channel KcsA

In this study, the roles of two anionic phospholipids—phosphatidic acid (PA), which is an important signaling molecule, and cardiolipin (CL), which plays a crucial role in the bioenergetics of the cell—in stabilizing the oligomeric structure of potassium channel KcsA were determined. The stability of KcsA was drastically increased as a function of PA or CL content (mol%) in phosphatidylcholine (PC) bilayers. Deletion of the membrane-associated N terminus significantly reduced channel stability at high levels of PA content; however, the intrinsic stability of this protein was marginally affected in the presence of CL. These studies indicate that the electrostatic-hydrogen bond switch between PA and N terminus, involving basic residues, is much stronger than the stabilizing effect of CL. Furthermore, the unique properties of the PA headgroup alter protein assembly and folding properties differently from the CL headgroup, and both lipids stabilize the tetrameric assembly via their specific interaction on the extra- or the intracellular side of KcsA

Transcription of toll-like receptors 2, 3, 4 and 9, FoxP3 and Th17 cytokines in a susceptible experimental model of canine Leishmania infantum infection

Canine leishmaniosis (CanL) due to Leishmania infantum is a chronic zoonotic systemic disease resulting from complex interactions between protozoa and the canine immune system. Toll-like receptors (TLRs) are essential components of the innate immune system and facilitate the early detection of many infections. However, the role of TLRs in CanL remains unknown and information describing TLR transcription during infection is extremely scarce. The aim of this research project was to investigate the impact of L. infantum infection on canine TLR transcription using a susceptible model. The objectives of this study were to evaluate transcription of TLRs 2, 3, 4 and 9 by means of quantitative reverse transcription polymerase chain reaction (qRT-PCR) in skin, spleen, lymph node and liver in the presence or absence of experimental L. infantum infection in Beagle dogs. These findings were compared with clinical and serological data, parasite densities in infected tissues and transcription of IL-17, IL-22 and FoxP3 in different tissues in non-infected dogs (n = 10), and at six months (n = 24) and 15 months (n = 7) post infection. Results revealed significant down regulation of transcription with disease progression in lymph node samples for TLR3, TLR4, TLR9, IL-17, IL-22 and FoxP3. In spleen samples, significant down regulation of transcription was seen in TLR4 and IL-22 when both infected groups were compared with controls. In liver samples, down regulation of transcription was evident with disease progression for IL-22. In the skin, upregulation was seen only for TLR9 and FoxP3 in the early stages of infection. Subtle changes or down regulation in TLR transcription, Th17 cytokines and FoxP3 are indicative of the silent establishment of infection that Leishmania is renowned for. These observations provide new insights about TLR transcription, Th17 cytokines and Foxp3 in the liver, spleen, lymph node and skin in CanL and highlight possible markers of disease susceptibility in this model

Trastuzumab and pertuzumab without chemotherapy in early-stage HER2+ breast cancer: a plain language summary of the PHERGain study

This is a summary of a publication about the PHERGain study, which was published in The Lancet Oncology in May 2021. The study includes 376 women with a type of breast cancer called HER2-positive breast cancer that can be removed by surgery. In the study, researchers wanted to learn if participants could be treated with two medicines called trastuzumab and pertuzumab without the need for chemotherapy. To identify HER2-positive tumors with more sensitivity to anti-HER2 therapies, the researchers used a type of imaging called a FDG-PET scan to check how well the treatments were working.Participants took a treatment before surgery, consisting of either chemotherapy (docetaxel and carboplatin) plus trastuzumab and pertuzumab (group A) or trastuzumab and pertuzumab alone (plus hormone therapy if the tumor was hormone receptor-positive; group B). After two cycles of treatment, participants underwent a FDG-PET scan. Participants assigned to group A completed 6 cycles of treatment regardless of 18F-FDG-PET results. Participants in group B continued the same treatment until surgery if their FDG-PET scan showed the treatment was working. While participants who did not show a response started treatment with chemotherapy in addition to trastuzumab and pertuzumab. All participants then had surgery.The results revealed that, of the participants in group B who showed a response using FDG-PET scan, 37.9% achieved a disappearance of all invasive cancer in the breast and axillary lymph nodes. This rate appears to be higher than those reported in previous studies evaluating the same treatment. These participants also had less side effects and improved overall quality of life compared with participants taking chemotherapy plus trastuzumab and pertuzumab.Early monitoring of how well participants respond to treatment by FDG-PET scan seems to identify participants with operable HER2-positive breast cancer who were more likely to benefit from trastuzumab and pertuzumab without the need to have chemotherapy. The PHERGain study is still ongoing and results on long-term survival are expected to be released in 2023. Clinical Trial Registration: NCT03161353 (ClinicalTrials.gov)

BPS dyons and Hesse flow

We revisit BPS solutions to classical N=2 low energy effective gauge theories. It is shown that the BPS equations can be solved in full generality by the introduction of a Hesse potential, a symplectic analog of the holomorphic prepotential. We explain how for non-spherically symmetric, non-mutually local solutions, the notion of attractor flow generalizes to gradient flow with respect to the Hesse potential. Furthermore we show that in general there is a non-trivial magnetic complement to this flow equation that is sourced by the momentum current in the solution.Comment: 25 pages, references adde

Genetic diversity and population structure of Ascochyta rabiei from the western Iranian Ilam and Kermanshah provinces using MAT and SSR markers

Knowledge of genetic diversity in A. rabiei provides different levels of information that are important in the management of crop germplasm resources. Gene flow on a regional level indicates a significant potential risk for the regional spread of novel alleles that might contribute to fungicide resistance or the breakdown of resistance genes. Simple sequence repeat (SSR) and mating type (MAT) markers were used to determine the genetic structure, and estimate genetic diversity and the prevalence of mating types in 103 Ascochyta rabiei isolates from seven counties in the Ilam and Kermanshah provinces of western Iran (Ilam, Aseman abad, Holaylan, Chardavol, Dareh shahr, Gilangharb, and Sarpul). A set of 3 microsatellite primer pairs revealed a total of 75 alleles; the number of alleles varied from 15 to 34 for each marker. A high level of genetic variability was observed among A. rabiei isolates in the region. Genetic diversity was high (He = 0.788) within populations with corresponding high average gene flow and low genetic distances between populations. The smallest genetic distance was observed between isolates from Ilam and Chardavol. Both mating types were present in all populations, with the majority of the isolates belonging to Mat1-1 (64%), but within populations the proportions of each mating type were not significantly different from 50%. Results from this study will be useful in breeding for Ascochyta blight-resistant cultivars and developing necessary control measures

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur