Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.MCIU - Nvidia(UMA18-FEDERJA-084

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Computational approaches to Explainable Artificial Intelligence:Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.</p

Functional Genetic Diversity among Mycobacterium tuberculosis Complex Clinical Isolates: Delineation of Conserved Core and Lineage-Specific Transcriptomes during Intracellular Survival

Tuberculosis exerts a tremendous burden on global health, with ∼9 million new infections and ∼2 million deaths annually. The Mycobacterium tuberculosis complex (MTC) was initially regarded as a highly homogeneous population; however, recent data suggest the causative agents of tuberculosis are more genetically and functionally diverse than appreciated previously. The impact of this natural variation on the virulence and clinical manifestations of the pathogen remains largely unknown. This report examines the effect of genetic diversity among MTC clinical isolates on global gene expression and survival within macrophages. We discovered lineage-specific transcription patterns in vitro and distinct intracellular growth profiles associated with specific responses to host-derived environmental cues. Strain comparisons also facilitated delineation of a core intracellular transcriptome, including genes with highly conserved regulation across the global panel of clinical isolates. This study affords new insights into the genetic information that M. tuberculosis has conserved under selective pressure during its long-term interactions with its human host

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Long-term dominance of Mycobacterium tuberculosis Uganda family in peri-urban Kampala-Uganda is not associated with cavitary disease

Previous studies have shown that Mycobacterium tuberculosis (MTB) Uganda family, a sub-lineage of the MTB Lineage 4, is the main cause of tuberculosis (TB) in Uganda. Using a well characterized patient population, this study sought to determine whether there are clinical and patient characteristics associated with the success of the MTB Uganda family in Kampala.; A total of 1,746 MTB clinical isolates collected from1992-2009 in a household contact study were genotyped. Genotyping was performed using Single Nucleotide Polymorphic (SNP) markers specific for the MTB Uganda family, other Lineage 4 strains, and Lineage 3, respectively. Out of 1,746 isolates, 1,213 were from patients with detailed clinical data. These data were used to seek associations between MTB lineage/sub-lineage and patient phenotypes.; Three MTB lineages were found to dominate the MTB population in Kampala during the last two decades. Overall, MTB Uganda accounted for 63% (1,092/1,746) of all cases, followed by other Lineage 4 strains accounting for 22% (394/1,746), and Lineage 3 for 11% (187/1,746) of cases, respectively. Seventy-three (4 %) strains remained unclassified. Our longitudinal data showed that MTB Uganda family occurred at the highest frequency during the whole study period, followed by other Lineage 4 strains and Lineage 3. To explore whether the long-term success of MTB Uganda family was due to increased virulence, we used cavitary disease as a proxy, as this form of TB is the most transmissible. Multivariate analysis revealed that even though cavitary disease was associated with known risk factors such as smoking (adjusted odds ratio (aOR) 4.8, 95% confidence interval (CI) 3.33-6.84) and low income (aOR 2.1, 95% CI 1.47-3.01), no association was found between MTB lineage and cavitary TB.; The MTB Uganda family has been dominating in Kampala for the last 18 years, but this long-term success is not due to increased virulence as defined by cavitary disease

Construction and characterization of the mycobacterium tuberculosis sigE fadD26 unmarked double mutant as a vaccine candidate

Despite the great increase in the understanding of the biology and pathogenesis of Mycobacterium tuberculosis achieved by the scientific community in recent decades, tuberculosis (TB) still represents one of the major threats to global human health. The only available vaccine (Mycobacterium bovis BCG) protects children from disseminated forms of TB but does not effectively protect adults from the respiratory form of the disease, making the development of new and more-efficacious vaccines against the pulmonary forms of TB a major goal for the improvement of global health. Among the different strategies being developed to reach this goal is the construction of attenuated strains more efficacious and safer than BCG. We recently showed that a sigE mutant of M. tuberculosis was more attenuated and more efficacious than BCG in a mouse model of infection. In this paper, we describe the construction and characterization of an M. tuberculosis sigE fadD26 unmarked double mutant fulfilling the criteria of the Geneva Consensus for entering human clinical trials. The data presented suggest that this mutant is even more attenuated and slightly more efficacious than the previous sigE mutant in different mouse models of infection and is equivalent to BCG in a Guinea pig model of infection