From electronic structure to catalytic activity: A single descriptor for adsorption and reactivity on transition-metal carbides

Adsorption and catalytic properties of the polar (111) surface of transition-metal carbides (TMC's) are investigated by density-functional theory. Atomic and molecular adsorption are rationalized with the concerted-coupling model, in which two types of TMC surface resonances (SR's) play key roles. The transition-metal derived SR is found to be a single measurable descriptor for the adsorption processes, implying that the Br{\o}nsted-Evans-Polanyi relation and scaling relations apply. This gives a picture with implications for ligand and vacancy effects and which has a potential for a broad screening procedure for heterogeneous catalysts.Comment: 5 pages, 3 figure

On the efficacy of handcrafted and deep features for seed image classification

Computer vision techniques have become important in agriculture and plant sciences due to their wide variety of applications. In particular, the analysis of seeds can provide meaningful information on their evolution, the history of agriculture, the domestication of plants, and knowledge of diets in ancient times. This work aims to propose an exhaustive comparison of several different types of features in the context of multiclass seed classification, leveraging two public plant seeds data sets to classify their families or species. In detail, we studied possible optimisations of five traditional machine learning classifiers trained with seven different categories of handcrafted features. We also fine-tuned several well-known convolutional neural networks (CNNs) and the recently proposed SeedNet to determine whether and to what extent using their deep features may be advantageous over handcrafted features. The experimental results demonstrated that CNN features are appropriate to the task and representative of the multiclass scenario. In particular, SeedNet achieved a mean F-measure of 96%, at least. Nevertheless, several cases showed satisfactory performance from the handcrafted features to be considered a valid alternative. In detail, we found that the Ensemble strategy combined with all the handcrafted features can achieve 90.93% of mean F-measure, at least, with a considerably lower amount of times. We consider the obtained results an excellent preliminary step towards realising an automatic seeds recognition and classification framework

On The Potential of Image Moments for Medical Diagnosis

Medical imaging is widely used for diagnosis and postoperative or post-therapy monitoring. The ever-increasing number of images produced has encouraged the introduction of automated methods to assist doctors or pathologists. In recent years, especially after the advent of convolutional neural networks, many researchers have focused on this approach, considering it to be the only method for diagnosis since it can perform a direct classification of images. However, many diagnostic systems still rely on handcrafted features to improve interpretability and limit resource consumption. In this work, we focused our efforts on orthogonal moments, first by providing an overview and taxonomy of their macrocategories and then by analysing their classification performance on very different medical tasks represented by four public benchmark data sets. The results confirmed that convolutional neural networks achieved excellent performance on all tasks. Despite being composed of much fewer features than those extracted by the networks, orthogonal moments proved to be competitive with them, showing comparable and, in some cases, better performance. In addition, Cartesian and harmonic categories provided a very low standard deviation, proving their robustness in medical diagnostic tasks. We strongly believe that the integration of the studied orthogonal moments can lead to more robust and reliable diagnostic systems, considering the performance obtained and the low variation of the results. Finally, since they have been shown to be effective on both magnetic resonance and computed tomography images, they can be easily extended to other imaging techniques

A Shallow Learning Investigation for COVID-19 Classification

COVID-19, an infectious coronavirus disease, triggered a pandemic that resulted in countless deaths. Since its inception, clinical institutions have used computed tomography as a supplemental screening method to reverse transcription-polymerase chain reaction. Deep learning approaches have shown promising results in addressing the problem; however, less computationally expensive techniques, such as those based on handcrafted descriptors and shallow classifiers, may be equally capable of detecting COVID-19 based on medical images of patients. This work proposes an initial investigation of several handcrafted descriptors well known in the computer vision literature already been exploited for similar tasks. The goal is to discriminate tomographic images belonging to three classes, COVID-19, pneumonia, and normal conditions, and present in a large public dataset. The results show that kNN and ensembles trained with texture descriptors achieve outstanding accuracy in this task, reaching accuracy and F-measure of 93.05% and 89.63%, respectively. Although it did not exceed state of the art, it achieved satisfactory performance with only 36 features, enabling the potential to achieve remarkable improvements from a computational complexity perspective

Automatic Monitoring Cheese Ripeness Using Computer Vision and Artificial Intelligence

Ripening is a very important process that contributes to cheese quality, as its characteristics are determined by the biochemical changes that occur during this period. Therefore, monitoring ripening time is a fundamental task to market a quality product in a timely manner. However, it is difficult to accurately determine the degree of cheese ripeness. Although some scientific methods have also been proposed in the literature, the conventional methods adopted in dairy industries are typically based on visual and weight control. This study proposes a novel approach aimed at automatically monitoring the cheese ripening based on the analysis of cheese images acquired by a photo camera. Both computer vision and machine learning techniques have been used to deal with this task. The study is based on a dataset of 195 images (specifically collected from an Italian dairy industry), which represent Pecorino cheese forms at four degrees of ripeness. All stages but the one labeled as 'day 18', which has 45 images, consist of 50 images. These images have been handled with image processing techniques and then classified according to the degree of ripening, i.e., 18, 22, 24, and 30 days. A 5-fold cross-validation strategy was used to empirically evaluate the performance of the models. During this phase, each training fold was augmented online. This strategy allowed to use 624 images for training, leaving 39 original images per fold for testing. Experimental results have demonstrated the validity of the approach, showing good performance for most of the trained models

Origanum vulgare subsp. hirtum essential oil prevented biofilm formation and showed antibacterial activity against planktonic and sessile bacterial cells

Essential oils from six different populations of Origanum vulgare subsp. hirtum were compared for their antibiofilm properties. The six essential oils (A to F) were characterized by a combination of gas chromatography with flame ionization detector and gas chromatography with mass spectrometer detector analyses. All oils showed weak activity against the planktonic form of a group of Staphylococcus aureus strains and against a Pseudomonas aeruginosa ATCC 15442 reference strain. The ability to inhibit biofilm formation was investigated at sub-MIC levels of 200, 100, and 50 m g/ml by staining sessile cells with safranin. Sample E showed the highest average effectiveness against all tested strains at 50 m g/ml and had inhibition percentages ranging from 30 to 52%. In the screening that used preformed biofilm from the reference strain P. aeruginosa, essential oils A through E were inactive at 200 m g/ml; F was active with a percentage of inhibition equal to 53.2%. Oregano essential oil can inhibit the formation of biofilms of various food pathogens and food spoilage organisms.Essential oils from six different populations of Origanum vulgare subsp. hirtum were compared for their antibiofilm properties. The six essential oils (A to F) were characterized by a combination of gas chromatography with flame ionization detector and gas chromatography with mass spectrometer detector analyses. All oils showed weak activity against the planktonic form of a group of Staphylococcus aureus strains and against a Pseudomonas aeruginosa ATCC 15442 reference strain. The ability to inhibit biofilm formation was investigated at sub-MIC levels of 200, 100, and 50 m g/ml by staining sessile cells with safranin. Sample E showed the highest average effectiveness against all tested strains at 50 m g/ml and had inhibition percentages ranging from 30 to 52%. In the screening that used preformed biofilm from the reference strain P. aeruginosa, essential oils A through E were inactive at 200 m g/ml; F was active with a percentage of inhibition equal to 53.2%. Oregano essential oil can inhibit the formation of biofilms of various food pathogens and food spoilage organisms

Steam reforming on transition-metal carbides from density-functional theory

A screening study of the steam reforming reaction (CH_4 + H_2O -> CO + 3H_2) on early transition-metal carbides (TMC's) is performed by means of density-functional theory calculations. The set of considered surfaces includes the alpha-Mo_2C(100) surfaces, the low-index (111) and (100) surfaces of TiC, VC, and delta-MoC, and the oxygenated alpha-Mo_2C(100) and TMC(111) surfaces. It is found that carbides provide a wide spectrum of reactivities towards the steam reforming reaction, from too reactive via suitable to too inert. The reactivity is discussed in terms of the electronic structure of the clean surfaces. Two surfaces, the delta-MoC(100) and the oxygen passivated alpha-Mo_2C(100) surfaces, are identified as promising steam reforming catalysts. These findings suggest that carbides provide a playground for reactivity tuning, comparable to the one for pure metals.Comment: 6 pages, 4 figure

Essential Oils of Dennettia Tripetala Bak. f. Stem Bark and Leaf – Constituents and Biological Activities:

The essential oil from the stem bark and leaves of Dennettia tripetala Bak. f. (Annonaceae) growing wild in Ondo State, Nigeria, has been characterized by combined gas chromatography (GC) and gas chromatography-mass spectrometry (GC- MS) analyses. Overall, thirty-six components have been fully identified, thirty-two in the stem-bark oil, and only seven in the leaf oil. In both oils, 2-phenylnitroethane was the main component, ranging between 70 – 76% of the total oils. The profile of the stem bark oil was characterized by a large number of sesquiterpenes, whereas among the few components in the leaf oil, linalool reaches over 17%. When both oils were assayed for antimicrobial activity, only Staphylococcus aureus was susceptible to the stem-bark oil which was more active than leaf oil. For protective effects against UV radiation–induced peroxidation in phosphatidylcholine (PC) liposomes, stem-bark oil also showed greater effectiveness. Activity of the leaf oil against Trichomonas gallinae, was also remarkable

VISUOMOTOR INTEGRATION SKILLS IN CHILDREN AFFECTED BY OBSTRUCTIVE SLEEP APNEA SYNDROME: A CASE-CONTROL STUDY

Introduction: Sleep related breathing disorders (SRBD) consist of frequent and repetitive episodes of pharyngeal obstruction during sleep, with consequent intermittent hypoxia, sleep architecture fragmentation, daytime sleepiness and/or behavioural problems and executive impairment in children. When untreated, SRBD and obstructive sleep apnea syndrome (OSA) mainly, may impact school performance, cognition, metabolism, and cardiovascular function. Aim of the present study is assessing the visuomotor integration skills in children affected by OSA. Materials and methods: 57 subjects affected by mild-to severe OSA, PSG diagnosed according to international diagnostic criteria, (31 males and 26 females) (mean age 10.8; SD \ub1 2.49) and 83 healthy children (45 males and 38 females) (mean age 9.95; SD \ub1 1.87; p = 0.725). All subjects underwent assessment of motor coordination skills with Movement-ABC tests and visual-motor integration ability with Visual Motor Integration (VMI) test. Results: The subjects with OSA show a worse average performances in all items of Movement ABC (p <0.001) respect of controls. Specifically, children with OSAS show significantly higher values of total points (p <0.001), manual dexterity (p <0.001), ball skills (p <0.001) and balance (p <0.001). Accordingly, the average centile in OSA children at the MABC-test is significantly reduced compared with controls (p <0.001). (Table 1) On the other hand, the VMI test evaluation among children with OSAS shows worst result in total Visuo-Motor Integration (p <0.001), and in Motor Coordination sub-item (p <0.001) than controls. (Table 1). Conclusion: Our results also support for children and adolescents the hypothesis that executive functioning deficits might be linked primarily to the degree of severity nocturnal hypoxemia rather than daytime sleepiness, although several other studies are needed