Palladium-Catalyzed Direct Selanylation of Chalcogenophenes and Arenes Assisted by 2-(Methylthio)amide.

The direct and selective conversion of a C-H bond into a C-Se bond remains a significant challenge, which is even more intricate with substrates having an innate regioselectivity under several reaction conditions, such as chalcogenophenes. We overrode their selectivity toward selanylation using palladium, copper, and the 2-(methylthio)amide directing group. This chelation-assisted direct selanylation was also suitable for mono and double ortho functionalization of arenes. The mechanistic studies indicate high-valent Pd(IV) species in the catalytic cycle, a reversible C-H activation step, and Cu(II) as a sequestering agent for organoselenide byproducts.

Using Mobile Edge AI to Detect and Map Diseases in Citrus Orchards.

Deep Learning models have presented promising results when applied to Agriculture 4.0. Among other applications, these models can be used in disease detection and fruit counting. Deep Learning models usually have many layers in the architecture and millions of parameters. This aspect hinders the use of Deep Learning on mobile devices as they require a large amount of processing power for inference. In addition, the lack of high-quality Internet connectivity in the field impedes the usage of cloud computing, pushing the processing towards edge devices. This work describes the proposal of an edge AI application to detect and map diseases in citrus orchards. The proposed system has low computational demand, enabling the use of low-footprint models for both detection and classification tasks. We initially compared AI algorithms to detect fruits on trees. Specifically, we analyzed and compared YOLO and Faster R-CNN. Then, we studied lean AI models to perform the classification task. In this context, we tested and compared the performance of MobileNetV2, EfficientNetV2-B0, and NASNet-Mobile. In the detection task, YOLO and Faster R-CNN had similar AI performance metrics, but YOLO was significantly faster. In the image classification task, MobileNetMobileV2 and EfficientNetV2-B0 obtained an accuracy of 100%, while NASNet-Mobile had a 98% performance. As for the timing performance, MobileNetV2 and EfficientNetV2-B0 were the best candidates, while NASNet-Mobile was significantly worse. Furthermore, MobileNetV2 had a 10% better performance than EfficientNetV2-B0. Finally, we provide a method to evaluate the results from these algorithms towards describing the disease spread using statistical parametric models and a genetic algorithm to perform the parameters' regression. With these results, we validated the proposed pipeline, enabling the usage of adequate AI models to develop a mobile edge AI solution.

Silver-catalyzed direct selanylation of indoles: synthesis and mechanistic insights.

Herein we describe the Ag(i)-catalyzed direct selanylation of indoles with diorganoyl diselenides. The reaction gave 3-selanylindoles with high regioselectivity and also allowed direct access to 2-selanylindoles when the C3 position of the indole ring was blocked via a process similar to Plancher rearrangement. Experimental analyses and density functional theory calculations were carried out in order to picture the reaction mechanism. Among the pathways considered (via concerted metalation-deprotonation, Ag(iii), radical, and electrophilic aromatic substitution), our findings support a classic electrophilic aromatic substitution via Lewis adducts between Ag(i) and diorganoyl diselenides. The results also afforded new insights into the interactions between Ag(i) and diorganoyl diselenides.

Transition metal catalysed direct sulfanylation of unreactive C-H bonds: an overview of the last two decades.

Transition metal catalysed direct sulfanylations of unreactive C-H bonds have become a unique and straightforward synthetic strategy in late-stage C-S bond formation of relevant complex molecules. Such transformations represent a breakthrough in modern synthetic organic chemistry, as they offer unusual reactivity patterns and avoid pre-functionalization of the starting materials. Despite inherent challenges in activating/functionalizing unreactive C-H bonds, a considerable number of different transition metals have shown the ability to selectively catalyze these processes toward C-S bond formation. In this sense, this review article covers the development and mechanistic analysis of the direct sulfanylation of Csp3-H and Csp2-H bonds through transition metal catalysed reactions in the last two decades, providing an essential guide for organic chemists working on this research area.

CapsProm: a capsule network for promoter prediction.

Locating the promoter region in DNA sequences is of paramount importance in bioinformatics. This problem has been widely studied in the literature, but it has not yet been fully resolved. Some researchers have shown remarkable results using convolutional networks that allowed the automatic extraction of features from a DNA chain. However, a single architecture schema that could learn the promoter prediction task competitively for several organisms has not yet been achieved. Thus, researchers must seek new architectures by hand-designing or by Neural Architecture Search for each new evaluated organism dataset. This work proposes a versatile architecture based on a capsule network that can accurately identify promoter sequences in raw DNA data from five different organisms, eukaryotic and prokaryotic. Our architecture, the CapsProm, could help create models with minimum effort to learn the promoter identification task between different datasets. Furthermore, the CapsProm showed competitive results, overcoming the baseline method in five out of seven tested datasets (F1-score). The models and source code are made available at https://github.com/lauromoraes/CapsNet-promoter.

Crystal structures of 3-halo-2-organochalcogenylbenzo[b]chalcogenophenes.

The structure of the title compounds 3-bromo-2-(phenyl-sulfan-yl)benzo[b]thiophene (C14H9BrS2; 1), 3-iodo-2-(phenyl-sulfan-yl)benzo[b]thio-phene (C14H9IS2; 2), 3-bromo-2-(phenyl-selan-yl)benzo[b]seleno-phene (C14H9BrSe2; 3), and 3-iodo-2-(phenyl-selan-yl)benzo[b]seleno-phene (C14H9ISe2; 4) were determined by single-crystal X-ray diffraction; all structures presented monoclinic (P21/c) symmetry. The phenyl group is distant from the halogen atom to minimize the steric hindrance repulsion for all structures. Moreover, the structures of 3 and 4 show an almost linear alignment of halogen-selenium-carbon atoms arising from the intra-molecular orbital inter-action between a lone pair of electrons on the halogen atom and the anti-bonding σ*Se-C orbital (n halogen→σ*Se-C). This inter-action leads to significant differences in the three-dimensional packing of the mol-ecules, which are assembled through π-π and C-H⋯π inter-actions. These data provide a better comprehension of the inter-molecular packing in benzo[b]chalcogenophenes, which is relevant for optoelectronic applications.

Dataset for Hop varieties classification.

Humulus lupulus L., also known as hops, is a vine whose flowers are a major component in brewing. It delivers flavor, bitterness, and aroma to beer and also aids in foam stabilization. Furthermore, it plays an important role in beer conservation due to its antimicrobial and antioxidant properties, which have recently been studied for food preservation. Hops can also be found in the production of cosmetics and is considered healthy food. There are more than 250 cataloged varieties of hops, and among the main attributes that differ from each other are alpha-acids, beta-acids, and essential oils. Those components give the beer a unique combination of characteristics, and may even influence its category. There are many ways to identify the hop variety from its acids and essential oils using methods such as chromatography, mass spectrometry, capillary electrophoresis, and nuclear magnetic resonance. However, these methods demand expensive and complex equipment, inaccessible or unavailable to most beer producers. In this work, we present a database that includes 1592 images of hop leaves, from 12 popular hop varieties in southeastern Brazil. From these images, it is possible to explore methods of pattern recognition and machine learning to classify hop varieties.

A deep descriptor for cross-tasking EEG-based recognition.

Due to the application of vital signs in expert systems, new approaches have emerged, and vital signals have been gaining space in biometrics. One of these signals is the electroencephalogram (EEG). The motor task in which a subject is doing, or even thinking, influences the pattern of brain waves and disturb the signal acquired. In this work, biometrics with the EEG signal from a cross-task perspective are explored. Based on deep convolutional networks (CNN) and Squeeze-and-Excitation Blocks, a novel method is developed to produce a deep EEG signal descriptor to assess the impact of the motor task in EEG signal on biometric verification. The Physionet EEG Motor Movement/Imagery Dataset is used here for method evaluation, which has 64 EEG channels from 109 subjects performing different tasks. Since the volume of data provided by the dataset is not large enough to effectively train a Deep CNN model, it is also proposed a data augmentation technique to achieve better performance. An evaluation protocol is proposed to assess the robustness regarding the number of EEG channels and also to enforce train and test sets without individual overlapping. A new state-of-the-art result is achieved for the cross-task scenario (EER of 0.1%) and the Squeeze-and-Excitation based networks overcome the simple CNN architecture in three out of four cross-individual scenarios.

Arrhythmia classification from single-lead ECG signals using the inter-patient paradigm.

BACKGROUND AND OBJECTIVES: Arrhythmia is a heart disease characterized by the change in the regularity of the heartbeat. Since this disorder can occur sporadically, Holter devices are used for continuous long-term monitoring of the subject's electrocardiogram (ECG). In this process, a large volume of data is generated. Consequently, the use of an automated system for detecting arrhythmias is highly desirable. In this work, an automated system for classifying arrhythmias using single-lead ECG signals is proposed. METHODS: The proposed system uses a combination of three groups of features: RR intervals, signal morphology, and higher-order statistics. To validate the method, the MIT-BIH database was employed using the inter-patient paradigm. Besides, the robustness of the system against segmentation errors was tested by adding jitter to the R-wave positions given by the MIT-BIH database. Additionally, each group of features had its robustness against segmentation error tested as well. RESULTS: The experimental results of the proposed classification system with jitter show that the sensitivities for the classes N, S, and V are 93.7, 89.7, and 87.9, respectively. Also, the corresponding positive predictive values are 99.2, 36.8, and 93.9, respectively. CONCLUSIONS: The proposed method was able to outperform several state-of-the-art methods, even though the R-wave position was synthetically corrupted by added jitter. The obtained results show that our approach can be employed in real scenarios where segmentation errors and the inter-patient paradigm are present.

Towards better heartbeat segmentation with deep learning classification.

The confidence of medical equipment is intimately related to false alarms. The higher the number of false events occurs, the less truthful is the equipment. In this sense, reducing (or suppressing) false positive alarms is hugely desirable. In this work, we propose a feasible and real-time approach that works as a validation method for a heartbeat segmentation third-party algorithm. The approach is based on convolutional neural networks (CNNs), which may be embedded in dedicated hardware. Our proposal aims to detect the pattern of a single heartbeat and classifies them into two classes: a heartbeat and not a heartbeat. For this, a seven-layer convolution network is employed for both data representation and classification. We evaluate our approach in two well-settled databases in the literature on the raw heartbeat signal. The first database is a conventional on-the-person database called MIT-BIH, and the second is one less uncontrolled off-the-person type database known as CYBHi. To evaluate the feasibility and the performance of the proposed approach, we use as a baseline the Pam-Tompkins algorithm, which is a well-known method in the literature and still used in the industry. We compare the baseline against the proposed approach: a CNN model validating the heartbeats detected by a third-party algorithm. In this work, the third-party algorithm is the same as the baseline for comparison purposes. The results support the feasibility of our approach showing that our method can enhance the positive prediction of the Pan-Tompkins algorithm from [Formula: see text]/[Formula: see text] to [Formula: see text]/[Formula: see text] by slightly decreasing the sensitivity from [Formula: see text]/[Formula: see text] to [Formula: see text] [Formula: see text] on the MIT-BIH/CYBHi databases.

COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis.

Early detection and diagnosis are critical factors to control the COVID-19 spreading. A number of deep learning-based methodologies have been recently proposed for COVID-19 screening in CT scans as a tool to automate and help with the diagnosis. These approaches, however, suffer from at least one of the following problems: (i) they treat each CT scan slice independently and (ii) the methods are trained and tested with sets of images from the same dataset. Treating the slices independently means that the same patient may appear in the training and test sets at the same time which may produce misleading results. It also raises the question of whether the scans from the same patient should be evaluated as a group or not. Moreover, using a single dataset raises concerns about the generalization of the methods. Different datasets tend to present images of varying quality which may come from different types of CT machines reflecting the conditions of the countries and cities from where they come from. In order to address these two problems, in this work, we propose an Efficient Deep Learning Technique for the screening of COVID-19 with a voting-based approach. In this approach, the images from a given patient are classified as group in a voting system. The approach is tested in the two biggest datasets of COVID-19 CT analysis with a patient-based split. A cross dataset study is also presented to assess the robustness of the models in a more realistic scenario in which data comes from different distributions. The cross-dataset analysis has shown that the generalization power of deep learning models is far from acceptable for the task since accuracy drops from 87.68% to 56.16% on the best evaluation scenario. These results highlighted that the methods that aim at COVID-19 detection in CT-images have to improve significantly to be considered as a clinical option and larger and more diverse datasets are needed to evaluate the methods in a realistic scenario.

ChimericalDataset Creation Protocol Based on Doddington Zoo: A Biometric Application with Face, Eye, and ECG.

Multimodal systems are a workaround to enhance the robustness and effectiveness of biometric systems. A proper multimodal dataset is of the utmost importance to build such systems. The literature presents some multimodal datasets, although, to the best of our knowledge, there are no previous studies combining face, iris/eye, and vital signals such as the Electrocardiogram (ECG). Moreover, there is no methodology to guide the construction and evaluation of a chimeric dataset. Taking that fact into account, we propose to create a chimeric dataset from three modalities in this work: ECG, eye, and face. Based on the Doddington Zoo criteria, we also propose a generic and systematic protocol imposing constraints for the creation of homogeneous chimeric individuals, which allow us to perform a fair and reproducible benchmark. Moreover, we have proposed a multimodal approach for these modalities based on state-of-the-art deep representations built by convolutional neural networks. We conduct the experiments in the open-world verification mode and on two different scenarios (intra-session and inter-session), using three modalities from two datasets: CYBHi (ECG) and FRGC (eye and face). Our multimodal approach achieves impressive decidability of 7.20 ± 0.18, yielding an almost perfect verification system (i.e., Equal Error Rate (EER) of 0.20% ± 0.06) on the intra-session scenario with unknown data. On the inter-session scenario, we achieve a decidability of 7.78 ± 0.78 and an EER of 0.06% ± 0.06. In summary, these figures represent a gain of over 28% in decidability and a reduction over 11% of the EER on the intra-session scenario for unknown data compared to the best-known unimodal approach. Besides, we achieve an improvement greater than 22% in decidability and an EER reduction over 6% in the inter-session scenario.

Transition metal catalysed direct selanylation of arenes and heteroarenes.

Transition metal catalysed C-H functionalization has reached an exciting level of sophistication, and, today, it represents a paradigm shift from the standard logic of synthetic chemistry. The direct conversion of C-H bonds into C-heteroatoms remains, however, a critical challenge. Nowadays, there is a great demand in general synthetic chemistry in, for example, the materials science for the development of straightforward C-Se bond formation, in order to fulfil the practical requirements. In this sense, this review summarizes recent outstanding advances in the C-Se bond formation through transition metal-catalysed direct selanylation, providing new insights into their mechanistic aspects and disclosing effective synthetic routes with high atom economy. In addition, this review intends to show the growing opportunities to construct complex chemical scaffolds containing selenium atoms.

Inter-Patient ECG Heartbeat Classification with Temporal VCG Optimized by PSO.

Classifying arrhythmias can be a tough task for a human being and automating this task is highly desirable. Nevertheless fully automatic arrhythmia classification through Electrocardiogram (ECG) signals is a challenging task when the inter-patient paradigm is considered. For the inter-patient paradigm, classifiers are evaluated on signals of unknown subjects, resembling the real world scenario. In this work, we explore a novel ECG representation based on vectorcardiogram (VCG), called temporal vectorcardiogram (TVCG), along with a complex network for feature extraction. We also fine-tune the SVM classifier and perform feature selection with a particle swarm optimization (PSO) algorithm. Results for the inter-patient paradigm show that the proposed method achieves the results comparable to state-of-the-art in MIT-BIH database (53% of Positive predictive (+P) for the Supraventricular ectopic beat (S) class and 87.3% of Sensitivity (Se) for the Ventricular ectopic beat (V) class) that TVCG is a richer representation of the heartbeat and that it could be useful for problems involving the cardiac signal and pattern recognition.

Green Hydroselenation of Aryl Alkynes: Divinyl Selenides as a Precursor of Resveratrol.

A simple and efficient protocol to prepare divinyl selenides has been developed by the regio- and stereoselective addition of sodium selenide species to aryl alkynes. The nucleophilic species was generates in situ, from the reaction of elemental selenium with NaBH4, utilizing PEG-400 as the solvent. Several divinyl selenides were obtained in moderate to excellent yields with selectivity for the (Z,Z)-isomer by a one-step procedure that was carried out at 60 °C in short reaction times. The methodology was extended to tellurium, giving the desired divinyl tellurides in good yields. Furthermore, the Fe-catalyzed cross-coupling reaction of bis(3,5-dimethoxystyryl) selenide 3f with (4-methoxyphenyl)magnesium bromide 5 afforded resveratrol trimethyl ether 6 in 57% yield.

ECG-based heartbeat classification for arrhythmia detection: A survey.

An electrocardiogram (ECG) measures the electric activity of the heart and has been widely used for detecting heart diseases due to its simplicity and non-invasive nature. By analyzing the electrical signal of each heartbeat, i.e., the combination of action impulse waveforms produced by different specialized cardiac tissues found in the heart, it is possible to detect some of its abnormalities. In the last decades, several works were developed to produce automatic ECG-based heartbeat classification methods. In this work, we survey the current state-of-the-art methods of ECG-based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing, the heartbeat segmentation techniques, the feature description methods and the learning algorithms used. In addition, we describe some of the databases used for evaluation of methods indicated by a well-known standard developed by the Association for the Advancement of Medical Instrumentation (AAMI) and described in ANSI/AAMI EC57:1998/(R)2008 (ANSI/AAMI, 2008). Finally, we discuss limitations and drawbacks of the methods in the literature presenting concluding remarks and future challenges, and also we propose an evaluation process workflow to guide authors in future works.

Pattern of cytokine and chemokine production by THP-1 derived macrophages in response to live or heat-killed Mycobacterium bovis bacillus Calmette-Guérin Moreau strain.

Tuberculosis has great public health impact with high rates of mortality and the only prophylactic measure for it is the Mycobacterium bovis bacillus Calmette-Guérin (BCG) vaccine. The present study evaluated the release of cytokines [interleukin (IL)-1, tumour necrosis factor and IL-6] and chemokines [macrophage inflammatory protein (MIP)-1α and MIP-1ß] by THP-1 derived macrophages infected with BCG vaccine obtained by growing mycobacteria in Viscondessa de Moraes Institute medium medium (oral) or Sauton medium (intradermic) to compare the effects of live and heat-killed (HK) mycobacteria. Because BCG has been reported to lose viability during the lyophilisation process and during storage, we examined whether exposing BCG to different temperatures also triggers differences in the expression of some important cytokines and chemokines of the immune response. Interestingly, we observed that HK mycobacteria stimulated cytokine and chemokine production in a different pattern from that observed with live mycobacteria.

Pattern of cytokine and chemokine production by THP-1 derived macrophages in response to live or heat-killed Mycobacterium bovis bacillus Calmette-Guérin Moreau strain

Tuberculosis has great public health impact with high rates of mortality and the only prophylactic measure for it is the Mycobacterium bovisbacillus Calmette-Guérin (BCG) vaccine. The present study evaluated the release of cytokines [interleukin (IL)-1, tumour necrosis factor and IL-6] and chemokines [macrophage inflammatory protein (MIP)-1α and MIP-1β] by THP-1 derived macrophages infected with BCG vaccine obtained by growing mycobacteria in Viscondessa de Moraes Institute medium medium (oral) or Sauton medium (intradermic) to compare the effects of live and heat-killed (HK) mycobacteria. Because BCG has been reported to lose viability during the lyophilisation process and during storage, we examined whether exposing BCG to different temperatures also triggers differences in the expression of some important cytokines and chemokines of the immune response. Interestingly, we observed that HK mycobacteria stimulated cytokine and chemokine production in a different pattern from that observed with live mycobacteria.

Automatic cardiac arrhythmia detection and classification using vectorcardiograms and complex networks.

This paper intends to bring new insights in the methods for extracting features for cardiac arrhythmia detection and classification systems. We explore the possibility for utilizing vectorcardiograms (VCG) along with electrocardiograms (ECG) to get relevant informations from the heartbeats on the MIT-BIH database. For this purpose, we apply complex networks to extract features from the VCG. We follow the ANSI/AAMI EC57:1998 standard, for classifying the beats into 5 classes (N, V, S, F and Q), and de Chazal's scheme for dataset division into training and test set, with 22 folds validation setup for each set. We used the Support Vector Machinhe (SVM) classifier and the best result we chose had a global accuracy of 84.1%, while still obtaining relatively high Sensitivities and Positive Predictive Value and low False Positive Rates, when compared to other papers that follows the same evaluation methodology that we do.

How the choice of samples for building arrhythmia classifiers impact their performances.

Arrhythmia (i.e., irregular cardiac beat) classification in electrocardiogram (ECG) signals is an important issue for heart disease diagnosis due to the non-invasive nature of the ECG exam. In this paper, we analyze and criticize the results of some arrhythmia classification methods presented in the literature in terms of how the samples are chosen for training/testing the classifier and the impact this choice has on their performance (i.e., accuracy/sensitivity/specificity). From our implementation, we also report new accuracies for these methods, establishing a new state-of-the-art method, in terms of results.