A Förster Resonance Energy Transfer (FRET)-Based Immune Assay for the Detection of Microcystin-LR in Drinking Water.

Cyanobacteria bloom is the term used to describe an abnormal and rapid growth of cyanobacteria in aquatic ecosystems such as lakes, rivers, and oceans as a consequence of anthropic factors, ecosystem degradation, or climate change. Cyanobacteria belonging to the genera Microcystis, Anabaena, Planktothrix, and Nostoc produce and release toxins called microcystins (MCs) into the water. MCs can have severe effects on human and animal health following their ingestion and inhalation. The MC structure is composed of a constant region (composed of five amino acid residues) and a variable region (composed of two amino acid residues). When the MC variable region is composed of arginine and leucine, it is named MC-LR. The most-common methods used to detect the presence of MC-LR in water are chromatographic-based methods (HPLC, LC/MS, GC/MS) and immunological-based methods (ELISA). In this work, we developed a new competitive Förster resonance energy transfer (FRET) assay to detect the presence of traces of MC-LR in water. Monoclonal antibody anti-MC-LR and MC-LR conjugated with bovine serum albumin (BSA) were labeled with the near-infrared fluorophores CF568 and CF647, respectively. Steady-state fluorescence measurements were performed to investigate the energy transfer process between anti-MC-LR 568 and MC-LR BSA 647 upon their interaction. Since the presence of unlabeled MC-LR competes with the labeled one, a lower efficiency of FRET process can be observed in the presence of an increasing amount of unlabeled MC-LR. The limit of detection (LoD) of the FRET assay is found to be 0.245 nM (0.245 µg/L). This value is lower than the provisional limit established by the World Health Organization (WHO) for quantifying the presence of MC-LR in drinking water.

Label-Free Detection of African Swine Fever and Classical Swine Fever in the Point-of-Care Setting Using Photonic Integrated Circuits Integrated in a Microfluidic Device.

Swine viral diseases have the capacity to cause significant losses and affect the sector's sustainability, a situation further exacerbated by the lack of antiviral drugs and the limited availability of effective vaccines. In this context, a novel point-of-care (POC) diagnostic device incorporating photonic integrated circuits (PICs), microfluidics and information, and communication technology into a single platform was developed for the field diagnosis of African swine fever (ASF) and classical swine fever (CSF). The device targets viral particles and has been validated using oral fluid and serum samples. Sensitivity, specificity, accuracy, precision, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were calculated to assess the performance of the device, and PCR was the reference method employed. Its sensitivities were 80.97% and 79%, specificities were 88.46% and 79.07%, and DOR values were 32.25 and 14.21 for ASF and CSF, respectively. The proposed POC device and PIC sensors can be employed for the pen-side detection of ASF and CSF, thus introducing novel technological advancements in the field of animal diagnostics. The need for proper validation studies of POC devices is highlighted to optimize animal biosecurity.

Transformer-based spatial-temporal detection of apoptotic cell death in live-cell imaging.

Intravital microscopy has revolutionized live-cell imaging by allowing the study of spatial-temporal cell dynamics in living animals. However, the complexity of the data generated by this technology has limited the development of effective computational tools to identify and quantify cell processes. Amongst them, apoptosis is a crucial form of regulated cell death involved in tissue homeostasis and host defense. Live-cell imaging enabled the study of apoptosis at the cellular level, enhancing our understanding of its spatial-temporal regulation. However, at present, no computational method can deliver robust detection of apoptosis in microscopy timelapses. To overcome this limitation, we developed ADeS, a deep learning-based apoptosis detection system that employs the principle of activity recognition. We trained ADeS on extensive datasets containing more than 10,000 apoptotic instances collected both in vitro and in vivo, achieving a classification accuracy above 98% and outperforming state-of-the-art solutions. ADeS is the first method capable of detecting the location and duration of multiple apoptotic events in full microscopy timelapses, surpassing human performance in the same task. We demonstrated the effectiveness and robustness of ADeS across various imaging modalities, cell types, and staining techniques. Finally, we employed ADeS to quantify cell survival in vitro and tissue damage in mice, demonstrating its potential application in toxicity assays, treatment evaluation, and inflammatory dynamics. Our findings suggest that ADeS is a valuable tool for the accurate detection and quantification of apoptosis in live-cell imaging and, in particular, intravital microscopy data, providing insights into the complex spatial-temporal regulation of this process.

A mirror therapy system using virtual reality and an actuated exoskeleton for the recovery of hand motor impairments: a study of acceptability, usability, and embodiment.

Hand motor impairments are one of the main causes of disabilities worldwide. Rehabilitation procedures like mirror therapy are given crucial importance. In the traditional setup, the patient moves the healthy hand in front of a mirror; the view of the mirrored motion tricks the brain into thinking that the impaired hand is moving as well, stimulating the recovery of the lost hand functionalities. We propose an innovative mirror therapy system that leverages and couples cutting-edge technologies. Virtual reality recreates an immersive and effective mirroring effect; a soft hand exoskeleton accompanies the virtual visual perception by physically inducing the mirrored motion to the real hand. Three working modes of our system have been tested with 21 healthy users. The system is ranked as acceptable by the system usability scale; it does not provoke adverse events or sickness in the users, according to the simulator sickness questionnaire; the three execution modes are also compared w.r.t. the sense of embodiment, evaluated through another customized questionnaire. The achieved results show the potential of our system as a clinical tool and reveal its social and economic impact.

Model-Optimizing Radiofrequency Parameters of 3D Finite Element Analysis for Ablation of Benign Thyroid Nodules.

Radiofrequency (RF) ablation represents an efficient strategy to reduce the volume of thyroid nodules. In this study, a finite element model was developed with the aim of optimizing RF parameters, e.g., input power and treatment duration, in order to achieve the target volume reduction rate (VRR) for a thyroid nodule. RF ablation is modelled as a coupled electro-thermal problem wherein the electric field is applied to induce tissue heating. The electric problem is solved with the Laplace equation, the temperature distribution is estimated with the Pennes bioheat equation, and the thermal damage is evaluated using the Arrhenius equation. The optimization model is applied to RF electrode with different active tip lengths in the interval from 5 mm to 40 mm at the 5 mm step. For each case, we also explored the influence of tumour blood perfusion rate on RF ablation outcomes. The model highlights that longer active tips are more efficient as they require lesser power and shorter treatment time to reach the target VRR. Moreover, this condition is characterized by a reduced transversal ablation zone. In addition, a higher blood perfusion increases the heat dispersion, requiring a different combination of RF power and time treatment to achieve the target VRR. The model may contribute to an improvement in patient-specific RF ablation treatment.

Have You Ever Seen a Robot? An Analysis of Children's Drawings Between Technology and Science Fiction.

Technologies have become an essential part of the daily life of our children. Consequently, artifacts that imply the early adoption of abstract thinking affect the imagination of children and young people in relation to the world of technology, now much more than they did in the past. With the emerging importance of robots in many aspects of our everyday lives, the goal of this study is to investigate which mental representations children have about robots. To this end, drawings from 104 children aged between 7 and 12 years old were used as a map of representations, considering the drawings as a proxy capable of evoking learned or emerging mental frameworks. The drawings were analyzed in several steps: they were first labeled using binary descriptors and then classified using clustering methods based on Hamming distances between drawings. Finally, questionnaire items covering children's perceptions about robots were analyzed for each of the resulting cluster separately to identify differences between them. The results show that there are relationships between the way children draw robots and their perception about robots' capabilities as well as their aspirations to pursue a career in science. These findings can provide meaningful insights into how to design educational robots and learning activities for children to learn with and about robots.

Hazards&Robots: A dataset for visual anomaly detection in robotics.

We propose Hazards&Robots, a dataset for Visual Anomaly Detection in Robotics. The dataset is composed of 324,408 RGB frames, and corresponding feature vectors; it contains 145,470 normal frames and 178,938 anomalous ones categorized in 20 different anomaly classes. The dataset can be used to train and test current and novel visual anomaly detection methods such as those based on deep learning vision models. The data is recorded with a DJI Robomaster S1 front facing camera. The ground robot, controlled by a human operator, traverses university corridors. Considered anomalies include presence of humans, unexpected objects on the floor, defects to the robot. Preliminary versions of the dataset are used in [1,3]. This version is available at [12].

Applications of Probiotic-Based Multi-Components to Human, Animal and Ecosystem Health: Concepts, Methodologies, and Action Mechanisms.

Probiotics and related preparations, including synbiotics and postbiotics, are living and non-living microbial-based multi-components, which are now among the most popular bioactive agents. Such interests mainly arise from the wide range and numerous beneficial effects of their use for various hosts. The current minireview article attempts to provide an overview and discuss in a holistic way the concepts, methodologies, action mechanisms, and applications of probiotic-based multi-components in human, animal, plant, soil, and environment health. Probiotic-based multi-component preparations refer to a mixture of bioactive agents, containing probiotics or postbiotics as main functional ingredients, and prebiotics, protectants, stabilizers, encapsulating agents, and other compounds as additional constituents. Analyzing, characterizing, and monitoring over time the traceability, performance, and stability of such multi-component ingredients require relevant and sensitive analytical tools and methodologies. Two innovative profiling and monitoring methods, the thermophysical fingerprinting thermogravimetry-differential scanning calorimetry technique (TGA-DSC) of the whole multi-component powder preparations, and the Advanced Testing for Genetic Composition (ATGC) strain analysis up to the subspecies level, are presented, illustrated, and discussed in this review to respond to those requirements. Finally, the paper deals with some selected applications of probiotic-based multi-components to human, animal, plant, soil and environment health, while mentioning their possible action mechanisms.

Point-of-Care and Label-Free Detection of Porcine Reproductive and Respiratory Syndrome and Swine Influenza Viruses Using a Microfluidic Device with Photonic Integrated Circuits.

Swine viral diseases challenge the sector's sustainability by affecting productivity and the health and welfare of the animals. The lack of antiviral drugs and/or effective vaccines renders early and reliable diagnosis the basis of viral disease management, underlining the importance of point-of-care (POC) diagnostics. A novel POC diagnostic device utilizing photonic integrated circuits (PICs), microfluidics, and information and communication technologies for the detection of porcine reproductive and respiratory syndrome virus (PRRSV) and swine influenza A (SIV) was validated using spiked and clinical oral fluid samples. Metrics including sensitivity, specificity, accuracy, precision, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were calculated to assess the performance of the device. For PRRSV, the device achieved a sensitivity of 83.5%, specificity of 77.8%, and DOR values of 17.66, whereas the values for SIV were 81.8%, 82.2%, and 20.81, respectively. The POC device and PICs can be used for the detection of PRRSV and SIV in the field, paving the way for the introduction of novel technologies in the field of animal POC diagnostics to further optimize livestock biosecurity.

Nature-Based One Health Approaches to Urban Agriculture Can Deliver Food and Nutrition Security.

The increasing global human population is projected to reach 9.7 billion people by 2050. This population growth is currently linked to the trends of world-wide urbanization, growth of megacities and shifting dietary patterns. While humankind faces the daunting challenge of feeding and providing healthy lives for its teeming populations, urban agriculture holds promise for improving the quality of life in cities. Fortunately, policymakers and planners are accepting the need to support peri-urban farmers to increase the resilience of food systems while efficiently managing already strained natural resources. We argue that for urban agriculture to significantly increase food yields, it is crucial to adopt a One Health approach to agriculture and environmental stewardship. Here, we propose six nature-based and climate-smart approaches to accelerate the transition toward more sustainable food systems. These approaches include reducing the reliance on synthetic agricultural inputs, increasing biodiversity through producing locally adapted crops and livestock breeds, using probiotics and postbiotics, and adopting portable digital decision-support systems. Such radical approaches to transforming food production will require cross-sectoral stakeholder engagement at international, national, and community levels to protect biodiversity and the environment whilst ensuring sustainable and nutritious diets that are culturally acceptable, accessible, and affordable for all.

Photonic Label-Free Biosensors for Fast and Multiplex Detection of Swine Viral Diseases.

In this paper we present the development of photonic integrated circuit (PIC) biosensors for the label-free detection of six emerging and endemic swine viruses, namely: African Swine Fever Virus (ASFV), Classical Swine Fever Virus (CSFV), Porcine Reproductive and Respiratory Syndrome Virus (PPRSV), Porcine Parvovirus (PPV), Porcine Circovirus 2 (PCV2), and Swine Influenza Virus A (SIV). The optical biosensors are based on evanescent wave technology and, in particular, on Resonant Rings (RRs) fabricated in silicon nitride. The novel biosensors were packaged in an integrated sensing cartridge that included a microfluidic channel for buffer/sample delivery and an optical fiber array for the optical operation of the PICs. Antibodies were used as molecular recognition elements (MREs) and were selected based on western blotting and ELISA experiments to ensure the high sensitivity and specificity of the novel sensors. MREs were immobilized on RR surfaces to capture viral antigens. Antibody-antigen interactions were transduced via the RRs to a measurable resonant shift. Cell culture supernatants for all of the targeted viruses were used to validate the biosensors. Resonant shift responses were dose-dependent. The results were obtained within the framework of the SWINOSTICS project, contributing to cover the need of the novel diagnostic tools to tackle swine viral diseases.

Integration of Microfluidics, Photonic Integrated Circuits and Data Acquisition and Analysis Methods in a Single Platform for the Detection of Swine Viral Diseases.

Viral diseases challenge the health and welfare of pigs and undermine the sustainability of swine farms. Their efficient control requires early and reliable diagnosis, highlighting the importance of Point of Care (POC) diagnostics in veterinary practice. The objective of this study was to validate a novel POC system that utilizes Photonic Integrated Circuits (PICs) and microfluidics to detect swine viral pathogens using oral fluids and Porcine Parvovirus (PPV) and Porcine Circovirus 2 (PCV-2) as proofs of concept. The sensitivity and specificity of the device were calculated for both viruses, and Receiver Operating Characteristic (ROC) curves were drawn. PPV had an Area Under Curve (AUC) value of 0.820 (95% CI: 0.760 to 0.880, p < 0.0001), and its optimal efficiency threshold of detection shifts was equal to 4.5 pm (68.6% sensitivity, 77.1% specificity and Limit of Detection (LOD) value 106 viral copies/mL). PCV-2 had an AUC value of 0.742 (95% CI: 0.670 to 0.815, p < 0.0001) and an optimal efficiency threshold of shifts equal to 6.5 pm (69.5% sensitivity, 70.3% specificity and LOD 3.3 × 105 copies/mL). In this work, it was proven that PICs can be exploited for the detection of swine viral diseases. The novel device can be directly deployed on farms as a POC diagnostics tool.

Artificial Intelligence in Thyroid Field-A Comprehensive Review.

Artificial intelligence (AI) uses mathematical algorithms to perform tasks that require human cognitive abilities. AI-based methodologies, e.g., machine learning and deep learning, as well as the recently developed research field of radiomics have noticeable potential to transform medical diagnostics. AI-based techniques applied to medical imaging allow to detect biological abnormalities, to diagnostic neoplasms or to predict the response to treatment. Nonetheless, the diagnostic accuracy of these methods is still a matter of debate. In this article, we first illustrate the key concepts and workflow characteristics of machine learning, deep learning and radiomics. We outline considerations regarding data input requirements, differences among these methodologies and their limitations. Subsequently, a concise overview is presented regarding the application of AI methods to the evaluation of thyroid images. We developed a critical discussion concerning limits and open challenges that should be addressed before the translation of AI techniques to the broad clinical use. Clarification of the pitfalls of AI-based techniques results crucial in order to ensure the optimal application for each patient.

Design and Development of Photonic Biosensors for Swine Viral Diseases Detection.

In this paper we introduce a field diagnostic device based on the combination of advanced bio-sensing and photonics technologies, to tackle emerging and endemic viruses causing swine epidemics, and consequently significant economic damage in farms. The device is based on the use of microring resonators fabricated in silicon nitride with CMOS compatible techniques. In the paper, the designed and fabricated photonic integrated circuit (PIC) sensors are presented and characterized, showing an optimized performance in terms of optical losses (30 dB per ring) and extinction ration for ring resonances (15 dB). Furthermore, the results of an experiment for porcine circovirus 2 (PCV2) detection by using the developed biosensors are presented. Positive detection for different virus concentrations has been obtained. The device is currently under development in the framework of the EU Commission co-funded project SWINOSTICS.

A Diagnostic Device for In-Situ Detection of Swine Viral Diseases: The SWINOSTICS Project.

In this paper, we present the concept of a novel diagnostic device for on-site analyses, based on the use of advanced bio-sensing and photonics technologies to tackle emerging and endemic viruses causing swine epidemics and significant economic damage in farms. The device is currently under development in the framework of the EU Commission co-funded project. The overall concept behind the project is to develop a method for an early and fast on field detection of selected swine viruses by non-specialized personnel. The technology is able to detect pathogens in different types of biological samples, such as oral fluids, faeces, blood or nasal swabs. The device will allow for an immediate on-site threat assessment. In this work, we present the overall concept of the device, its architecture with the technical requirements, and all the used innovative technologies that contribute to the advancements of the current state of the art.

WaterSpy: A High Sensitivity, Portable Photonic Device for Pervasive Water Quality Analysis.

In this paper, we present WaterSpy, a project developing an innovative, compact, cost-effective photonic device for pervasive water quality sensing, operating in the mid-IR spectral range. The approach combines the use of advanced Quantum Cascade Lasers (QCLs) employing the Vernier effect, used as light source, with novel, fibre-coupled, fast and sensitive Higher Operation Temperature (HOT) photodetectors, used as sensors. These will be complemented by optimised laser driving and detector electronics, laser modulation and signal conditioning technologies. The paper presents the WaterSpy concept, the requirements elicited, the preliminary architecture design of the device, the use cases in which it will be validated, while highlighting the innovative technologies that contribute to the advancement of the current state of the art.

The identity of Cicinnus orthane Blanchard, 1852 (Lepidoptera, Mimallonidae, Cicinninae), type species of Cicinnus Blanchard, 1852.

In order to clarify the identity of the type species of one of the most diverse Mimallonidae genera, Cicinnus Blanchard, 1852, we designate a lectotype for Cicinnus orthane Blanchard, 1852 with photographs of the lectotype (dorsal and ventral) and its genitalia figured for the first time. Cicinnus orthane, described from Chile, is a close morphological match for a southeastern Brazilian species, which we also illustrate for comparative purposes. Because the lectotype of C. orthane is female, we include both sexes of this related Brazilian species in order to facilitate the figuring of male genitalia as well as comparisons of the female genitalia. We are therefore able to establish sound morphological characteristics for Cicinnus sensu stricto, namely in the males the bifid configuration of the gnathos, a complex juxtal arrangement fused to the phallus, and largely membranous valvae; and in the females a wide and narrow lamella antevaginalis, lack of setae covered bulbous masses on either side of the lamella antevaginalis, well-sclerotized and posteriorly protruding tergite VIII, and reduced, stout apophyses anteriores which are about one quarter the length of the apophyses posteriores.

Affordance Equivalences in Robotics: A Formalism.

Automatic knowledge grounding is still an open problem in cognitive robotics. Recent research in developmental robotics suggests that a robot's interaction with its environment is a valuable source for collecting such knowledge about the effects of robot's actions. A useful concept for this process is that of an affordance, defined as a relationship between an actor, an action performed by this actor, an object on which the action is performed, and the resulting effect. This paper proposes a formalism for defining and identifying affordance equivalence. By comparing the elements of two affordances, we can identify equivalences between affordances, and thus acquire grounded knowledge for the robot. This is useful when changes occur in the set of actions or objects available to the robot, allowing to find alternative paths to reach goals. In the experimental validation phase we verify if the recorded interaction data is coherent with the identified affordance equivalences. This is done by querying a Bayesian Network that serves as container for the collected interaction data, and verifying that both affordances considered equivalent yield the same effect with a high probability.

A High Sensitivity Biosensor to detect the presence of perfluorinated compounds in environment.

A novel surface plasmon resonance (SPR) optical fiber biosensor, able to bind perfluorooctanoate and perfluorooctanesulfonate compounds, is presented. In the first step, an ad hoc antibody compound has been designed, produced and tested by ELISA, then, in the second step, the gold surface of a plastic optical fiber sensor has been derivatizated and functionalized with this new bio-receptor, able to bind target analytes with high affinity and selectivity. The experimental data have shown that the developed SPR optical fiber biosensor makes it possible to detect these compounds. One advantage of this approach stems from the possibility to monitor the perfluorinated compounds in the environment exploiting the remote sensing capability offered by the optical fibers. The measurements were performed in laboratory, also exploiting matrices mimicking the real environment. The limit of detection of the assay was 0.21ppb, a value that is lower than the maximum residue limit fixed by the European Union regulations.

Crowdsourcing the creation of image segmentation algorithms for connectomics.

To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This "deep learning" approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge.