Miracle-dz/UniFAR-FTUs huggingface dataset:
Raman Spectroscopy and Machine-Learning for Early Detection of Bacterial Canker of Tomato: The Asymptomatic Disease Condition

Bacterial canker of tomato is caused by Clavibacter michiganensis subsp. michiganensis (Cmm). The disease is highly destructive, because it produces latent asymptomatic infections that favor contagion rates. The present research aims consisted on the implementation of Raman spectroscopy (RS) and machine-learning spectral analysis as a method for the early disease detection. Raman spectra were obtained from infected asymptomatic tomato plants (BCTo) and healthy controls (HTo) with 785 nm excitation laser micro-Raman spectrometer. Spectral data were normalized and processed by principal component analysis (PCA), then the classifiers algorithms multilayer perceptron (PCA + MLP) and linear discriminant analysis (PCA + LDA) were implemented. Bacterial isolation and identification (16S rRNA gene sequencing) were realized of each plant studied. The Raman spectra obtained from tomato leaf samples of HTo and BCTo exhibited peaks associated to cellular components, and the most prominent vibrational bands were assigned to carbohydrates, carotenoids, chlorophyll, and phenolic compounds. Biochemical changes were also detectable in the Raman spectral patterns. Raman bands associated with triterpenoids and flavonoids compounds can be considered as indicators of Cmm infection during the asymptomatic stage. RS is an efficient, fast and reliable technology to differentiate the tomato health condition (BCTo or HTo). The analytical method showed high performance values of sensitivity, specificity and accuracy, among others.
The Promise and Challenges of Cyclic Dinucleotides as Molecular Adjuvants for Vaccine Development

Cyclic dinucleotides (CDNs), originally discovered as bacterial second messengers, play critical roles in bacterial signal transduction, cellular processes, biofilm formation, and virulence. The finding that CDNs can trigger the innate immune response in eukaryotic cells through the stimulator of interferon genes (STING) signalling pathway has prompted the extensive research and development of CDNs as potential immunostimulators and novel molecular adjuvants for induction of systemic and mucosal innate and adaptive immune responses. In this review, we summarize the chemical structure, biosynthesis regulation, and the role of CDNs in enhancing the crosstalk between host innate and adaptive immune responses. We also discuss the strategies to improve the efficient delivery of CDNs and the recent advance and future challenges in the development of CDNs as potential adjuvants in prophylactic vaccines against infectious diseases and in therapeutic vaccines against cancers.
Indirect Estimation of Breathing Rate from Heart Rate Monitoring System during Running

Recent advances in wearable technologies integrating multi-modal sensors have enabled the in-field monitoring of several physiological metrics. In sport applications, wearable devices have been widely used to improve performance while minimizing the risk of injuries and illness. The objective of this project is to estimate breathing rate (BR) from respiratory sinus arrhythmia (RSA) using heart rate (HR) recorded with a chest belt during physical activities, yielding additional physiological insight without the need of an additional sensor. Thirty-one healthy adults performed a run at increasing speed until exhaustion on an instrumented treadmill. RR intervals were measured using the Polar H10 HR monitoring system attached to a chest belt. A metabolic measurement system was used as a reference to evaluate the accuracy of the BR estimation. The evaluation of the algorithms consisted of exploring two pre-processing methods (band-pass filters and relative RR intervals transformation) with different instantaneous frequency tracking algorithms (short-term Fourier transform, single frequency tracking, harmonic frequency tracking and peak detection). The two most accurate BR estimations were achieved by combining band-pass filters with short-term Fourier transform, and relative RR intervals transformation with harmonic frequency tracking, showing 5.5% and 7.6% errors, respectively. These two methods were found to provide reasonably accurate BR estimation over a wide range of breathing frequency. Future challenges consist in applying/validating our approaches during in-field endurance running in the context of fatigue assessment.
A Unified Approach to Spatial Proximity Query Processing in Dynamic Spatial Networks

Nearest neighbor (NN) and range (RN) queries are basic query types in spatial databases. In this study, we refer to collections of NN and RN queries as spatial proximity (SP) queries. At peak times, location-based services (LBS) need to quickly process SP queries that arrive simultaneously. Timely processing can be achieved by increasing the number of LBS servers; however, this also increases service costs. Existing solutions evaluate SP queries sequentially; thus, such solutions involve unnecessary distance calculations. This study proposes a unified batch algorithm (UBA) that can effectively process SP queries in dynamic spatial networks. With the proposed UBA, the distance between two points is indicated by the travel time on the shortest path connecting them. The shortest travel time changes frequently depending on traffic conditions. The goal of the proposed UBA is to avoid unnecessary distance calculations for nearby SP queries. Thus, the UBA clusters nearby SP queries and exploits shared distance calculations for query clusters. Extensive evaluations using real-world roadmaps demonstrated the superiority and scalability of UBA compared with state-of-the-art sequential solutions.
Isolation and Characterization of APETALA3 Orthologs and Promoters from the Distylous Fagopyrum esculentum

Common buckwheat (Fagopyrum esculentum) produces distylous flowers with undifferentiated petaloid tepals, which makes it obviously different from flowers of model species. In model species Arabidopsis, APETALA3 (AP3) is expressed in petal and stamen and specifies petal and stamen identities during flower development. Combining with our previous studies, we found that small-scale gene duplication (GD) event and alternative splicing (AS) of common buckwheat AP3 orthologs resulted in FaesAP3_1, FaesAP3_2 and FaesAP3_2a. FaesAP3_2 and FaesAP3_2a were mainly expressed in the stamen of thrum and pin flower. Promoters functional analysis suggested that intense GUS staining was observed in the whole stamen in pFaesAP3_2::GUS transgenic Arabidopsis, while intense GUS staining was observed only in the filament of stamen in pFaesAP3_1::GUS transgenic Arabidopsis. These suggested that FaesAP3_1 and FaesAP3_2 had overlapping functions in specifying stamen filament identity and work together to determine normal stamen development. Additionally, FaesAP3_2 and FaesAP3_2a owned the similar ability to rescue stamen development of Arabidopsis ap3-3 mutant, although AS resulted in a frameshift mutation and consequent omission of the complete PI-derived motif and euAP3 motif of FaesAP3_2a. These suggested that the MIK region of AP3-like proteins was crucial for determining stamen identity, while the function of AP3-like proteins in specifying petal identity was gradually obtained after AP3 Orthologs acquiring a novel C-terminal euAP3 motif during the evolution of core eudicots. Our results also provide a clue to understanding the early evolution of the functional specificity of euAP3-type proteins involving in floral organ development in core eudicots, and also suggested that FaesAP3_2 holds the potential application for biotechnical engineering to develop a sterile male line of F. esculentum.
Recombinant Antigens Based on Non-Glycosylated Regions from RBD SARS-CoV-2 as Potential Vaccine Candidates against COVID-19

The Receptor-Binding Domain (RBD) of the Spike (S) protein from Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has glycosylation sites which can limit the production of reliable antigens expressed in prokaryotic platforms, due to glycan-mediated evasion of the host immune response. However, protein regions without glycosylated residues capable of inducing neutralizing antibodies could be useful for antigen production in systems that do not carry the glycosylation machinery. To test this hypothesis, the potential antigens NG06 and NG19, located within the non-glycosylated S-RBD region, were selected and expressed in Escherichia coli, purified by FPLC and employed to determine their immunogenic potential through detection of antibodies in serum from immunized rabbits, mice, and COVID-19 patients. IgG antibodies from sera of COVID-19-recovered patients detected the recombinant antigens NG06 and NG19 (A450 nm = 0.80 ± 0.33; 1.13 ± 0.33; and 0.11 ± 0.08 for and negatives controls, respectively). Also, the purified antigens were able to raise polyclonal antibodies in animal models evoking a strong immune response with neutralizing activity in mice model. This research highlights the usefulness of antigens based on the non-N-glycosylated region of RBD from SARS-CoV-2 for candidate vaccine development.
Explainable Artificial Intelligence for Bias Detection in COVID CT-Scan Classifiers

Problem: An application of Explainable Artificial Intelligence Methods for COVID CT-Scan classifiers is presented. Motivation: It is possible that classifiers are using spurious artifacts in dataset images to achieve high performances, and such explainable techniques can help identify this issue. Aim: For this purpose, several approaches were used in tandem, in order to create a complete overview of the classificatios. Methodology: The techniques used included GradCAM, LIME, RISE, Squaregrid, and direct Gradient approaches (Vanilla, Smooth, Integrated). Main results: Among the deep neural networks architectures evaluated for this image classification task, VGG16 was shown to be most affected by biases towards spurious artifacts, while DenseNet was notably more robust against them. Further impacts: Results further show that small differences in validation accuracies can cause drastic changes in explanation heatmaps for DenseNet architectures, indicating that small changes in validation accuracy may have large impacts on the biases learned by the networks. Notably, it is important to notice that the strong performance metrics achieved by all these networks (Accuracy, F1 score, AUC all in the 80 to 90% range) could give users the erroneous impression that there is no bias. However, the analysis of the explanation heatmaps highlights the bias.
Synergetic Effect of Dual Functional Monomers in Molecularly Imprinted Polymer Preparation for Selective Solid Phase Extraction of Ciprofloxacin

Background: Ciprofloxacin (CIP), an important broad-spectrum fluoroquinolone antibiotic, was often used as a template molecule for the preparation of imprinted materials. In this study, methacrylic acid and 2-vinylpyridine were employed for the first time as dual functional monomers for synthesizing ciprofloxacin imprinted polymers. Methods: The chemical and physicochemical properties of synthesized polymers were characterized using Fourier transform-infrared spectroscopy, thermogravimetric analysis-differential scanning calorimetry, scanning electron microscopy, and nitrogen adsorption-desorption isotherm. The adsorption properties of ciprofloxacin onto synthesized polymers were determined by batch experiments. The extraction performances were studied using the solid phase extraction and HPLC-UV method. Results: The molecularly imprinted polymer synthesized with dual functional monomers showed a higher adsorption capacity and selectivity toward the template molecule. The adsorbed amounts of ciprofloxacin onto the imprinted and non-imprinted polymer were 2.40 and 1.45 mg g−1, respectively. Furthermore, the imprinted polymers were employed as a selective adsorbent for the solid phase extraction of ciprofloxacin in aqueous solutions with the recovery of 105% and relative standard deviation of 7.9%. This work provides an alternative approach for designing a new adsorbent with high adsorption capacity and good extraction performance for highly polar template molecules.
Xanthones Production in Gentiana dinarica Beck Hairy Root Cultures Grown in Simple Bioreactors

The hairy root clones of Gentiana dinarica cl-B, cl-D, cl-3, and cl-14 were cultivated in parallel in diverse simple bioreactors, including temporary immersion systems RITA® (TIS RITA®), bubble column bioreactors (BCB), and Erlenmeyer flasks (EF), and evaluated for biomass production and xanthone content. The obtained results showed that TIS RITA® and BCB containing ½ MS medium with 4% sucrose provided equally good growth conditions in which the majority of the clones displayed the higher percentage of dry matter (DM%), and xanthones norswertianin-1-O-primeveroside (nor-1-O-prim) and norswertianin production than those cultivated in EF. Thin and well branched hairy root clone cl-B grown in BCB for 7 weeks was superior regarding all growth parameters tested, including growth index (19.97), dry weight (2.88 g), and DM% (25.70%) compared to all other clones. Cl-B cultured in TIS RITA® contained the highest amount of nor-1-O-prim (56.82 mg per vessel). In BCB with constant aeration, cl-B accumulated the highest norswertianin content reaching 18.08 mg/vessel. The optimized conditions for cultivation of selected G. dinarica hairy root clones in highly aerated TIS RITA® and BCB systems contribute to the development of bioreactor technology designed for the large scale commercial production of xanthones nor-1-O-prim and norswertianin.
An Analysis of Android Malware Classification Services

The increasing number of Android malware forced antivirus (AV) companies to rely on automated classification techniques to determine the family and class of suspicious samples. The research community relies heavily on such labels to carry out prevalence studies of the threat ecosystem and to build datasets that are used to validate and benchmark novel detection and classification methods. In this work, we carry out an extensive study of the Android malware ecosystem by surveying white papers and reports from 6 key players in the industry, as well as 81 papers from 8 top security conferences, to understand how malware datasets are used by both. We, then, explore the limitations associated with the use of available malware classification services, namely VirusTotal (VT) engines, for determining the family of an Android sample. Using a dataset of 2.47 M Android malware samples, we find that the detection coverage of VT’s AVs is generally very low, that the percentage of samples flagged by any 2 AV engines does not go beyond 52%, and that common families between any pair of AV engines is at best 29%. We rely on clustering to determine the extent to which different AV engine pairs agree upon which samples belong to the same family (regardless of the actual family name) and find that there are discrepancies that can introduce noise in automatic label unification schemes. We also observe the usage of generic labels and inconsistencies within the labels of top AV engines, suggesting that their efforts are directed towards accurate detection rather than classification. Our results contribute to a better understanding of the limitations of using Android malware family labels as supplied by common AV engines.
Biofilm Formation of Multidrug-Resistant MRSA Strains Isolated from Different Types of Human Infections

Methicillin-resistant Staphylococcus aureus (MRSA) is one of the main pathogens causing chronic infections, mainly due to its capacity to form biofilms. However, the mechanisms underlying the biofilm formation of MRSA strains from different types of human infections are not fully understood. MRSA strains isolated from distinct human infections were characterized aiming to determine their biofilm-forming capacity, the biofilm resistance to conventional antibiotics and the prevalence of biofilm-related genes, including, icaA, icaB, icaC, icaD, fnbA, fnbB, clfA, clfB, cna, eno, ebpS, fib and bbp. Eighty-three clinical MRSA strains recovered from bacteremia episodes, osteomyelitis and diabetic foot ulcers were used. The biofilm-forming capacity was evaluated by the microtiter biofilm assay and the biofilm structure was analyzed via confocal scanning laser microscopy. The antimicrobial susceptibility of 24-h-old biofilms was assessed against three antibiotics and the biomass reduction was measured. The metabolic activity of biofilms was evaluated by the XTT assay. The presence of biofilm-related genes was investigated by whole-genome sequencing and by PCR. Despite different intensities, all strains showed the capacity to form biofilms. Most strains had also a large number of biofilm-related genes. However, strains isolated from osteomyelitis showed a lower capacity to form biofilms and also a lower prevalence of biofilm-associated genes. There was a significant reduction in the biofilm biomass of some strains tested against antibiotics. Our results provide important information on the biofilm-forming capacity of clinical MRSA strains, which may be essential to understand the influence of different types of infections on biofilm production and chronic infections.
A Simulation-Data-Based Machine Learning Model for Predicting Basic Parameter Settings of the Plasticizing Process in Injection Molding

The optimal machine settings in polymer processing are usually the result of time-consuming and expensive trials. We present a workflow that allows the basic machine settings for the plasticizing process in injection molding to be determined with the help of a simulation-driven machine learning model. Given the material, screw geometry, shot weight, and desired plasticizing time, the model is able to predict the back pressure and screw rotational speed required to achieve good melt quality. We show how data sets can be pre-processed in order to obtain a generalized model that performs well. Various supervised machine learning algorithms were compared, and the best approach was evaluated in experiments on a real machine using the predicted basic machine settings and three different materials. The neural network model that we trained generalized well with an overall absolute mean error of 0.27% and a standard deviation of 0.37% on unseen data (the test set). The experiments showed that the mean absolute errors between the real and desired plasticizing times were sufficiently small, and all predicted operating points achieved good melt quality. Our approach can provide the operators of injection molding machines with predictions of suitable initial operating points and, thus, reduce costs in the planning phase. Further, this approach gives insights into the factors that influence melt quality and can, therefore, increase our understanding of complex plasticizing processes.
Soft Polymer-Based Technique for Cellular Force Sensing

Soft polymers have emerged as a vital type of material adopted in biomedical engineering to perform various biomechanical characterisations such as sensing cellular forces. Distinct advantages of these materials used in cellular force sensing include maintaining normal functions of cells, resembling in vivo mechanical characteristics, and adapting to the customised functionality demanded in individual applications. A wide range of techniques has been developed with various designs and fabrication processes for the desired soft polymeric structures, as well as measurement methodologies in sensing cellular forces. This review highlights the merits and demerits of these soft polymer-based techniques for measuring cellular contraction force with emphasis on their quantitativeness and cell-friendliness. Moreover, how the viscoelastic properties of soft polymers influence the force measurement is addressed. More importantly, the future trends and advancements of soft polymer-based techniques, such as new designs and fabrication processes for cellular force sensing, are also addressed in this review.
Characteristic Structural Knowledge for Morphological Identification and Classification in Meso-Scale Simulations Using Principal Component Analysis

Meso-scale simulations have been widely used to probe aggregation caused by structural formation in macromolecular systems. However, the limitations of the long-length scale, resulting from its simulation box, cause difficulties in terms of morphological identification and insufficient classification. In this study, structural knowledge derived from meso-scale simulations based on parameters from atomistic simulations were analyzed in dissipative particle dynamic (DPD) simulations of PS-b-PI diblock copolymers. The radial distribution function and its Fourier-space counterpart or structure factor were proposed using principal component analysis (PCA) as key characteristics for morphological identification and classification. Disorder, discrete clusters, hexagonally packed cylinders, connected clusters, defected lamellae, lamellae and connected cylinders were effectively grouped.
Contribution to the Knowledge of Rocky Plant Communities of the Southwest Iberian Peninsula

The rocky habitats of southern Portugal are ecosystems with extreme xericity conditions, associated with special abiotic strains. In these unstable ecological conditions, a considerable diversity of plant communities occurs. The objective of this study, carried out in the Algarve and Monchique, and the Mariánica Range biogeographical sectors, is to compare chasmo-chomophytic communities of the southwestern Iberian Peninsula, using a phytosociological approach (Braun–Blanquet methodology) and numerical analysis (hierarchical cluster analysis). From these results, two new communities were identified, Sanguisorbo rupicolae-Dianthetum crassipedis and Antirrhinetum onubensis, as a result of floristic and biogeographical differences from other associations already described within the alliances Rumici indurati-Dianthion lusitani and Calendulo lusitanicae-Antirrhinion linkiani, both included in the Phagnalo saxatilis-Rumicetea indurate class.
A Review on Impedimetric and Voltammetric Analysis Based on Polypyrrole Conducting Polymers for Electrochemical Sensing Applications

Conducting polymers have been widely used in electrochemical sensors as receptors of the sensing signal’s analytes and transducers. Polypyrrole (PPy) conducting polymers are highlighted due to their good electrical conductive properties, ease in preparation, and flexibility of surface characteristics. The objective of this review paper is to discuss the theoretical background of the two main types of electrochemical detection: impedimetric and voltammetric analysis. It also reviews the application and results obtained from these two electrochemical detections when utilizing PPy as a based sensing material in electrochemical sensor. Finally, related aspects in electrochemical sensor construction using PPy will also be discussed. It is anticipated that this review will provide researchers, especially those without an electrochemical analysis background, with an easy-to-understand summary of the concepts and technologies used in electrochemical sensor research, particularly those interested in utilizing PPy as a based sensing material.