Deep Learning-Based Compressed Domain Multimedia for Man and Machine: A Taxonomy and Application to Point Cloud Classification

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Deep Learning-based Compressed Domain Multimedia for Man and Machine: A Taxonomy and Application to Point Cloud Classification

In the current golden age of multimedia, human visualization is no longer the single main target, with the final consumer often being a machine which performs some processing or computer vision tasks. In both cases, deep learning plays a undamental role in extracting features from the multimedia representation data, usually producing a compressed representation referred to as latent representation. The increasing development and adoption of deep learning-based solutions in a wide area of multimedia applications have opened an exciting new vision where a common compressed multimedia representation is used for both man and machine. The main benefits of this vision are two-fold: i) improved performance for the computer vision tasks, since the effects of coding artifacts are mitigated; and ii) reduced computational complexity, since prior decoding is not required. This paper proposes the first taxonomy for designing compressed domain computer vision solutions driven by the architecture and weights compatibility with an available spatio-temporal computer vision processor. The potential of the proposed taxonomy is demonstrated for the specific case of point cloud classification by designing novel compressed domain processors using the JPEG Pleno Point Cloud Coding standard under development and adaptations of the PointGrid classifier. Experimental results show that the designed compressed domain point cloud classification solutions can significantly outperform the spatial-temporal domain classification benchmarks when applied to the decompressed data, containing coding artifacts, and even surpass their performance when applied to the original uncompressed data

Identification of a PhenylthiazoleSmall Molecule with DualAntifungal and Antibiofilm ActivityAgainst Candida albicans andCandida auris

Candida species are a leading source of healthcare infections globally. The limited number of antifungal drugs combined with the isolation of Candida species, namely C. albicans and C. auris, exhibiting resistance to current antifungals necessitates the development of new therapeutics. The present study tested 85 synthetic phenylthiazole small molecules for antifungal activity against drug-resistant C. albicans. Compound 1 emerged as the most potent molecule, inhibiting growth of C. albicans and C. auris strains at concentrations ranging from 0.25–2μg/mL. Additionally, compound 1 inhibited growth of other clinically-relevant yeast (Cryptococcus) and molds (Aspergillus) at a concentration as low as 0.50μg/mL. Compound 1 exhibited rapid fungicidal activity, reducing the burden of C. albicans and C. auris below the limit of detection within 30 minutes. Compound 1 exhibited potent antibiofilm activity, similar to amphotericin B, reducing the metabolic activity of adherent C. albicans and C. auris biofilms by more than 66% and 50%, respectively. Furthermore, compound 1 prolonged survival of Caenorhabditis elegans infected with strains of C. albicans and C. auris, relative to the untreated control. The present study highlights phenylthiazole small molecules, such as compound 1, warrant further investigation as novel antifungal agents for drug-resistant Candida infections

Ospemifene Displays Broad-Spectrum Synergistic Interactions with Itraconazole Through Potent Interference with Fungal Efflux Activities

Azole antifungals are vital therapeutic options for treating invasive mycotic infections. However, the emergence of azole-resistant isolates combined with limited therapeutic options presents a growing challenge in medical mycology. To address this issue, we utilized microdilution checkerboard assays to evaluate nine stilbene compounds for their ability to interact synergistically with azole drugs, particularly against azole-resistant fungal isolates. Ospemifene displayed the most potent azole chemosensitizing activity, and its combination with itraconazole displayed broad-spectrum synergistic interactions against Candida albicans, Candida auris, Cryptococcus neoformans, and Aspergillus fumigatus (ΣFICI = 0.05–0.50). Additionally, in a Caenorhabditis elegans infection model, the ospemifene-itraconazole combination significantly reduced fungal CFU burdens in infected nematodes by ~75–96%. Nile Red efflux assays and RT-qPCR analysis suggest ospemifene interferes directly with fungal efflux systems, thus permitting entry of azole drugs into fungal cells. This study identifies ospemifene as a novel antifungal adjuvant that augments the antifungal activity of itraconazole against a broad range of fungal pathogens

Repurposing Approach IdentifiesPitavastatin as a Potent AzoleChemosensitizing Agent EffectiveAgainst Azole-Resistant CandidaSpecies

The limited number of antifungals and the rising frequency of azole-resistant Candida species are growing challenges to human medicine. Drug repurposing signifies an appealing approach to enhance the activity of current antifungal drugs. Here, we evaluated the ability of Pharmakon 1600 drug library to sensitize an azole-resistant Candida albicans to the effect of fluconazole. The primary screen revealed 44 non-antifungal hits were able to act synergistically with fluconazole against the test strain. Of note, 21 compounds, showed aptness for systemic administration and limited toxic effects, were considered as potential fluconazole adjuvants and thus were termed as “repositionable hits”. A follow-up analysis revealed pitavastatin displaying the most potent fluconazole chemosensitizing activity against the test strain (ΣFICI 0.05) and thus was further evaluated against 18 isolates of C. albicans (n = 9), C. glabrata (n = 4), and C. auris (n = 5). Pitavastatin displayed broad-spectrum synergistic interactions with both fluconazole and voriconazole against ~89% of the tested strains (ΣFICI 0.05–0.5). Additionally, the pitavastatin-fluconazole combination significantly reduced the biofilm-forming abilities of the tested Candida species by up to 73%, and successfully reduced the fungal burdens in a Caenorhabditis elegans infection model by up to 96%. This study presents pitavastatin as a potent azole chemosensitizing agent that warrant further investigation

Antibacterial Characterization of Novel Synthetic Thiazole Compounds against Methicillin-Resistant Staphylococcus pseudintermedius

Staphylococcus pseudintermedius is a commensal organism of companion animals that is a significant source of opportunistic infections in dogs. With the emergence of clinical isolates of S. pseudintermedius (chiefly methicillin-resistant S. pseudintermedius (MRSP)) exhibiting increased resistance to nearly all antibiotic classes, new antimicrobials and therapeutic strategies are urgently needed. Thiazole compounds have been previously shown to possess potent antibacterial activity against multidrug-resistant strains of Staphylococcus aureus of human and animal concern. Given the genetic similarity between S. aureus and S. pseudintermedius, this study explores the potential use of thiazole compounds as novel antibacterial agents against methicillin-sensitive S. pseudintermedius (MSSP) and MRSP. A broth microdilution assay confirmed these compounds exhibit potent bactericidal activity (at sub-microgram/mL concentrations) against both MSSA and MRSP clinical isolates while the MTS assay confirmed three compounds (at 10 μg/mL) were not toxic to mammalian cells. A time-kill assay revealed two derivatives rapidly kill MRSP within two hours. However, this rapid bactericidal activity was not due to disruption of the bacterial cell membrane indicating an alternative mechanism of action for these compounds against MRSP. A multistep resistance selection analysis revealed compounds 4 and 5 exhibited a modest (twofold) shift in activity over ten passages. Furthermore, all six compounds (at a subinihibitory concentration) demonstrated the ability to re-sensitize MRSP to oxacillin, indicating these compounds have potential use for extending the therapeutic utility of β-lactam antibiotics against MRSP. Metabolic stability analysis with dog liver microsomes revealed compound 3 exhibited an improved physicochemical profile compared to the lead compound. In addition to this, all six thiazole compounds possessed a long post-antibiotic effect (at least 8 hours) against MRSP. Collectively the present study demonstrates these synthetic thiazole compounds possess potent antibacterial activity against both MSSP and MRSP and warrant further investigation into their use as novel antimicrobial agents

Doc. WG1m100090

The JPEG Pleno PCC scope is a learning-based PC coding standard offering a singlestream, compact, compressed domain representation, targeting both human visualization, with significant compression efficiency improvement over PC coding standards in common use at equivalent subjective quality, as well as effective performance for PC processing and computer vision tasks.N/

Studying the Benefits of a New JPEG AI Profile for the JPEG PCC Verification Model: ISO/IEC JTC 1/SC29/WG1 M100115

The Joint Photographic Experts Group (JPEG) is a Working Group of ISO/IEC, the International Organisation for Standardization / International Electrotechnical Commission, (ISO/IEC JTC 1/SC 29/WG 1) and of the International Telecommunication Union (ITU-T SG16), responsible for the popular JPEG, JPEG 2000, JPEG XR, JPSearch, JPEG XT and more recently, the JPEG XS, JPEG Systems, JPEG Pleno, JPEG XL and JPEG AI families of imaging standards.JPEG AI: https://jpeg.org/jpegai/documentation.htmlContext and Objective: The current JPEG PCC VM color coding approach first projects the PC color onto 2D images, then uses JPEG AI to code the 2D images.N/

Handling Missing Values Based on Similarity Classifiers and Fuzzy Entropy Measures

Handling missing values (MVs) and feature selection (FS) are vital preprocessing tasks for many pattern recognition, data mining, and machine learning (ML) applications, involving classification and regression problems. The existence of MVs in data badly affects making decisions. Hence, MVs have to be taken into consideration during preprocessing tasks as a critical problem. To this end, the authors proposed a new algorithm for manipulating MVs using FS. Bayesian ridge regression (BRR) is the most beneficial type of Bayesian regression. BRR estimates a probabilistic model of the regression problem. The proposed algorithm is dubbed as cumulative Bayesian ridge with similarity and Luca’s fuzzy entropy measure (CBRSL). CBRSL reveals how the fuzzy entropy FS used for selecting the candidate feature holding MVs aids in the prediction of the MVs within the selected feature using the Bayesian Ridge technique. CBRSL can be utilized to manipulate MVs within other features in a cumulative order; the filled features are incorporated within the BRR equation in order to predict the MVs for the next selected incomplete feature. An experimental analysis was conducted on four datasets holding MVs generated from three missingness mechanisms to compare CBRSL with state-of-the-art practical imputation methods. The performance was measured in terms of R2 score (determination coefficient), RMSE (root mean square error), and MAE (mean absolute error). Experimental results indicate that the accuracy and execution times differ depending on the amount of MVs, the dataset’s size, and the mechanism type of missingness. In addition, the results show that CBRSL can manipulate MVs generated from any missingness mechanism with a competitive accuracy against the compared methods

Deep Learning-Based Compressed Domain Multimedia for Man and Machine: A Taxonomy and Application to Point Cloud Classification

In the current golden age of multimedia, human visualization is no longer the single main target, with the final consumer often being a machine which performs some processing or computer vision tasks. In both cases, deep learning plays a fundamental role in extracting features from the multimedia representation data, usually producing a compressed representation referred to as latent representation. The increasing development and adoption of deep learning-based solutions in a wide area of multimedia applications have opened an exciting new vision where a common compressed multimedia representation is used for both man and machine. The main benefits of this vision are two-fold: i) improved performance for the computer vision tasks, since the effects of coding artifacts are mitigated; and ii) reduced computational complexity, since prior decoding is not required. This paper proposes the first taxonomy for designing compressed domain computer vision solutions driven by the architecture and weights compatibility with an available spatio-temporal computer vision processor. The potential of the proposed taxonomy is demonstrated for the specific case of point cloud classification by designing novel compressed domain processors using the JPEG Pleno Point Cloud Coding standard under development and adaptations of the PointGrid classifier. Experimental results show that the designed compressed domain point cloud classification solutions can significantly outperform the spatial-temporal domain classification benchmarks when applied to the decompressed data, containing coding artifacts, and even surpass their performance when applied to the original uncompressed data