This study set out to assess the performance of an artificial intelligence (AI) algorithm based on clinical data and dermatoscopic imaging for the early diagnosis of melanoma, and its capacity to define the metastatic progression of melanoma through serological and histopathological biomarkers, enabling dermatologists to make more informed decisions about patient management. Integrated analysis of demographic data, images of the skin lesions, and serum and histopathological markers were analyzed in a group of 196 patients with melanoma. The interleukins (ILs) IL-4, IL-6, IL-10, and IL-17A as well as IFNγ (interferon), GM-CSF (granulocyte and macrophage colony-stimulating factor), TGFβ (transforming growth factor), and the protein DCD (dermcidin) were quantified in the serum of melanoma patients at the time of diagnosis, and the expression of the RKIP, PIRIN, BCL2, BCL3, MITF, and ANXA5 proteins was detected by immunohistochemistry (IHC) in melanoma biopsies. An AI algorithm was used to improve the early diagnosis of melanoma and to predict the risk of metastasis and of disease-free survival. Two models were obtained to predict metastasis (including "all patients" or only patients "at early stages of melanoma"), and a series of attributes were seen to predict the progression of metastasis: Breslow thickness, infiltrating BCL-2 expressing lymphocytes, and IL-4 and IL-6 serum levels. Importantly, a decrease in serum GM-CSF seems to be a marker of poor prognosis in patients with early-stage melanomas.

In this paper, we present an implementation of a Job Selection Problem (JSP) — a generalization of the well-known Travelling Salesperson Problem (TSP)— of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N=9$</tex> jobs on its Quadratic Unconstrained Binary Optimization (QUBO) form, using <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{O}(N)$</tex> qubits on DWave's Advantage_system4.1 quantum annealing device. The best known quantum algorithm for TSP to date uses <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(N^{2})$</tex> qubits. A solution is found using the quantum method. However, since hardware is not yet able to compensate the increase in search-space size, no present overall advantage is achieved when comparing the quantum results with either exhaustive or equiprobably sampled classical solutions of the problem.

Using implicit responses to determine consumers’ response to different stimuli is becoming a popular approach, but research is still needed to understand the outputs of the different technologies used to collect data. During the present research, electroencephalography (EEG) responses and self-reported liking and emotions were collected on different stimuli (odor, taste, flavor samples) to better understand sweetness perception. Artificial intelligence analytics were used to classify the implicit responses, identifying decision trees to discriminate the stimuli by activated sensory system (odor/taste/flavor) and by nature of the stimuli (‘sweet’ vs. ‘non-sweet’ odors; ‘sweet-taste’, ‘sweet-flavor’, and ‘non-sweet flavor’; and ‘sweet stimuli’ vs. ‘non-sweet stimuli’). Significant differences were found among self-reported-liking of the stimuli and the emotions elicited by the stimuli, but no clear relationship was identified between explicit and implicit data. The present research sums interesting data for the EEG-linked research as well as for EEG data analysis, although much is still unknown about how to properly exploit implicit measurement technologies and their data.

In the last years, MLOps (Machine Learning Operations) paradigm is attracting the attention from the community, extrapolating the DevOps (Development and Operations) paradigm to the artificial intelligence (AI) development life-cycle. In this area, some challenges must be addressed to successfully deliver solutions since there are specific nuances when dealing with AI operationalization such as the model packaging or monitoring. Fortunately, interesting and helpful approaches, both from the research community and industry have emerged. However, further research is still necessary to fulfil key gaps. This paper presents a tool, MLPacker, for addressing some of them. Concretely, this tool provides mechanisms to package and deploy analytic pipelines both in REST APIs and in streaming mode. In addition, the analytic pipelines can be deployed atomically (i.e., the whole pipeline in the same machine) or in a distributed fashion (i.e., deploying each stage of the pipeline in distinct machines). In this way, users can take advantage from the cloud continuum paradigm considering edge-fog-cloud computing layers. Finally, the tool is decoupled from the training stage to avoid data scientists the integration of blocks of code in their experiments for the operationalization. Besides the package mode (REST API or streaming), the tool can be configured to perform the deployments in local or in remote machines and by using or not containers. For this aim, this paper describes the gaps this tool addresses, the detailed components and flows supported, as well as an scenario with three different case studies to better explain the research conducted.

Background: Alzheimer’s is a degenerative dementing disorder that starts with a mild memory impairment and progresses to a total loss of mental and physical faculties. The sooner the diagnosis is made, the better for the patient, as preventive actions and treatment can be started. Although tests such as the Mini-Mental State Tests Examination are usually used for early identification, diagnosis relies on magnetic resonance imaging (MRI) brain analysis. Methods: Public initiatives such as the OASIS (Open Access Series of Imaging Studies) collection provide neuroimaging datasets openly available for research purposes. In this work, a new method based on deep learning and image processing techniques for MRI-based Alzheimer’s diagnosis is proposed and compared with previous literature works. Results: Our method achieves a balance accuracy (BAC) up to 0.93 for image-based automated diagnosis of the disease, and a BAC of 0.88 for the establishment of the disease stage (healthy tissue, very mild and severe stage). Conclusions: Results obtained surpassed the state-of-the-art proposals using the OASIS collection. This demonstrates that deep learning-based strategies are an effective tool for building a robust solution for Alzheimer’s-assisted diagnosis based on MRI data.

<b>Introduction:</b> Diagnosis method of sleep apnea&nbsp;is polysomnography (PSG) and respiratory polygraphy (RP). Both systems are costly, time consuming and uncomfortable. The objective of our study was to validate an infrared thermographic camera with an artificial intelligence system, in order to identify apneic respiratory events in adults with clinical suspicion of OSA. <b>Methods:</b> Total number of respiratory events (apneas and hypopneas) detected by facial temperature micro-changes were analyzed and compared with events detected by nasal flow quantified by the PSG nasal cannula and thermistor. The result of both methods was classified by binary form as normal study if it presented an AHI≤10 or pathological if the AHI was&gt;10. <b>Results:</b> 30 records were full valuable (age 48±11 years, 83% men, body mass index 27± 3.7 kg/m2, and Epworth of 9±4 points). 16 studies were normal on PSG and 14 were pathological. Thermographic camera was able to detect all normal studies (100% specificity) and 12 of the 14 pathological studies, classifying only 2 pathological studies as normal. Table I presents the validity results. <b>Conclusions:</b> Use of this system is highly valid, especially for diagnosing the presence of OSA (AHI &gt;10). If these results are confirmed with a greater number of patients, the potential implantation of thermography treated with artificial intelligence could result in a new “no contact” diagnostic system for patients with suspected OSA.

<b>Introduction:</b> Thermography has been implemented in different fields of medicine, but its use in sleep medicine has been limited. The objective of this study was to validate the use of a thermographic camera, using an artificial intelligence algorithm, to differentiate wakefulness and sleep in comparison with the conventional polysomnographic (PSG) results. <b>Methods:</b> Staging of wakefulness and sleep was compared between the EEG of PSG recordings and the facial thermographic images obtained by means of a thermographic camera, treated with an artificial intelligence system. The periods of sleep and wakefulness were estimated second by second. Videos of a duration greater than 2.7 hours were considered as valid (61 videos). <b>Results:</b> Characteristics of subjet analized were: age 48±10 years, 73 % men; body mass index 27.0±3.8 kg/m2 and Epworth sleepness scale of 9.0±4.0 points). Of the 943 seconds detected as sleep by thermography, 940 seconds corresponded to sleep phases in PSG (99.7% correct), and of the 867 seconds classified as wakefulness, 766 corresponded to wakefulness in the PSG (88.4% of these). Table I presents the validity results. <b>Conclusions:</b> Treatment of thermographic images, through an artificial intelligence algorithm, is a valid and non-invasive system for determining sleep and wakefulness. Development of these systems could facilitate the implementation of “no contact” diagnosis methods for sleep analysis.

Quantum Computing is considered as the next frontier in computing, and it is attracting a lot of attention from the current scientific community. This kind of computation provides to researchers with a revolutionary paradigm for addressing complex optimization problems, offering a significant speed advantage and an efficient search ability. Anyway, Quantum Computing is still in an incipient stage of development. For this reason, present architectures show certain limitations, which have motivated the carrying out of this paper. In this paper, we introduce a novel solving scheme coined as hybrid Quantum Computing - Tabu Search Algorithm. Main pillars of operation of the proposed method are a greater control over the access to quantum resources, and a considerable reduction of non-profitable accesses. To assess the quality of our method, we have used 7 different Traveling Salesman Problem instances as benchmarking set. The obtained outcomes support the preliminary conclusion that our algorithm is an approach which offers promising results for solving partitioning problems while it drastically reduces the access to quantum computing resources. We also contribute to the field of Transfer Optimization by developing an evolutionary multiform multitasking algorithm as initialization method.