Passive optical network (PON) monitoring using optical coding technology

Les réseaux optiques passifs (PON) semblent être la technologie gagnante et ultime du futur pour les "fibres jusqu'au domicile" ayant une haute capacité. L'écoute de contrôle de ce genre de système est nécessaire pour s'assurer un niveau de qualité de service prédéterminé pour chaque client. En outre, l'écoute de contrôle réduit considérablement les dépenses en capital et de fonctionnement (CAPEX et OPEX), tant pour le fournisseur du réseau que les clients. Alors que la capacité des PON est croissante, les gestionnaires de réseau ne disposent pas encore d'une technologie efficace et appropriée pour l'écoute de contrôle des réseaux de capacité aussi élevée. Une variété de solutions a été proposée. Toutes ces dernières solutions ne sont pas pratiques à cause de leur faible capacité (nombre de clients), d'une faible évolutivité, d'une grande complexité et des défis technologiques. Plus important encore, la technologie souhaitable pour l'écoute de contrôle devrait être rentable car le marché des PON est très sensible aux coûts. Dans cette thèse, nous considérons l'application de la technologie du codage optique passif (OC) comme une solution prometteuse pour l'écoute de contrôle centralisée d'un réseau optique ramifié tels que les réseaux PON. Dans la première étape, nous développons une expression pour le signal détecté par l'écoute de contrôle et étudions ses statistiques. Nous trouvons une nouvelle expression explicite pour le rapport signal utile/signal brouillé (SIR) comme outil de mesure métrique de performance. Nous considérons cinq distributions PON géographiques différentes et étudions leurs effets sur l'SIR pour l'écoute de contrôle d'OC. Dans la prochaine étape, nous généralisons notre modèle mathématique et ses expressions pour le contrôle des signaux détectés par un détecteur quadratique et des paramètres réalistes. Nous évaluons ensuite les performances théoriques de la technologie basée sur l'écoute de contrôle selon le rapport signal/bruit (SNR), le rapport signal/bruit plus coefficient d'interférence (SNIR), et la probabilité de fausse alarme. Nous élaborons l'effet de la puissance d'impulsion transmise, la taille du réseau et la cohérence de la source lumineuse sur le rendement des codes unidimensionnels (ID) et bidimensionnels (2D) de l'écoute de contrôle d'OC. Une conception optimale est également abordée. Enfin, nous appliquons les tests de Neyman-Pearson pour le récepteur de notre système d'écoute de contrôle et enquêtons sur la façon dont le codage et la taille du réseau affectent les dépenses de fonctionnement (OPEX) de notre système d'écoute de contrôle. Malgré le fait que les codes ID et 2D fournissent des performances acceptables, elles exigent des encodeurs avec un nombre élevé de composants optiques : ils sont encombrants, causent des pertes, et ils sont coûteux. Par conséquent, nous proposons un nouveau schéma de codage simple et plus approprié pour notre application de l'écoute de contrôle que nous appelons le codage périodique. Par simulation, nous évaluons l'efficacité de l'écoute de contrôle en terme de SNR pour un PON employant cette technologie. Ce système de codage est utilisé dans notre vérification expérimentale de l'écoute de contrôle d'OC. Nous étudions expérimentalement et par simulation, l'écoute de contrôle d'un PON utilisant la technologie de codage périodique. Nous discutons des problèmes de conception pour le codage périodique et les critères de détection optimale. Nous développons également un algorithme séquentiel pour le maximum de vraisemblance avec une complexité réduite. Nous menons des expériences pour valider notre algorithme de détection à l'aide de quatre encodeurs périodiques que nous avons conçus et fabriqués. Nous menons également des simulations de Monte-Carlo pour des distributions géographiques de PON réalistes, avec des clients situés au hasard. Nous étudions l'effet de la zone de couverture et la taille du réseau (nombre d'abonnés) sur l'efficacité de calcul de notre algorithme. Nous offrons une borne sur la probabilité pour un réseau donné d'entraîner l'algorithme vers un temps exorbitant de surveillance du réseau, c'est à dire le délai d'attente de probabilité. Enfin, nous soulignons l'importance du moyennage pour remédier aux restrictions budgétaires en puissance/perte dans notre système de surveillance afin de supporter de plus grandes tailles de réseaux et plus grandes portées de fibres. Ensuite, nous mettrons à niveau notre dispositif expérimental pour démontrer un m PON avec 16 clients. Nous utilisons un laser à modulation d'exploitation directement à 1 GHz pour générer les impulsions sonde. Les données mesurées par le dispositif expérimental est exploité par l'algorithme de MLSE à détecter et à localiser les clients. Trois déploiements PON différents sont réalisés. Nous démontrons une surveillance plus rigoureuse pour les réseaux ayant une répartition géographique à plusieurs niveaux. Nous étudions aussi le budget de la perte de notre dispositif de soutien plus élevés de capacités du réseau. Enfin, nous étudions le budget total admissible de la perte d'exploitation du système de surveillance dans la bande de fréquences à 1650 nm en fonction des spécifications de l'émetteur/récepteur. En particulier, la limite totale de la perte de budget est représentée en fonction du gain de l'amplicateure de transimpédance (TIA) et le résolution de la conversion analogique-numérique (ADC). Par ailleurs, nous enquêtons sur le compromis entre la distance portée et la capacité (taille de fractionnement au niveau du noeud distant) dans notre système de suivi

Federated Learning in Medical Imaging:Part I: Toward Multicentral Health Care Ecosystems

With recent developments in medical imaging facilities, extensive medical imaging data are produced every day. This increasing amount of data provides an opportunity for researchers to develop data-driven methods and deliver better health care. However, data-driven models require a large amount of data to be adequately trained. Furthermore, there is always a limited amount of data available in each data center. Hence, deep learning models trained on local data centers might not reach their total performance capacity. One solution could be to accumulate all data from different centers into one center. However, data privacy regulations do not allow medical institutions to easily combine their data, and this becomes increasingly difficult when institutions from multiple countries are involved. Another solution is to use privacy-preserving algorithms, which can make use of all the data available in multiple centers while keeping the sensitive data private. Federated learning (FL) is such a mechanism that enables deploying large-scale machine learning models trained on different data centers without sharing sensitive data. In FL, instead of transferring data, a general model is trained on local data sets and transferred between data centers. FL has been identified as a promising field of research, with extensive possible uses in medical research and practice. This article introduces FL, with a comprehensive look into its concepts and recent research trends in medical imaging

Federated Learning in Medical Imaging:Part II: Methods, Challenges, and Considerations

Federated learning is a machine learning method that allows decentralized training of deep neural networks among multiple clients while preserving the privacy of each client's data. Federated learning is instrumental in medical imaging due to the privacy considerations of medical data. Setting up federated networks in hospitals comes with unique challenges, primarily because medical imaging data and federated learning algorithms each have their own set of distinct characteristics. This article introduces federated learning algorithms in medical imaging and discusses technical challenges and considerations of real-world implementation of them

Interplaying factors of students personal characteristics in online learning modality: evidence in asian context

Mapping the multidimensional impact of learner attributes on behavior demonstrates the importance of models in learning. To this purpose, we examined the correlations between strategies and student characteristics and utilized regression analysis to determine how learner attributes affect strategy selection. A cross-sectional study of 258 students demonstrated widespread strategy use, as well as statistically significant connections within and between the Strategy Inventory for Language Learning and Student Characteristics of Learning measures. Regression analysis found distinctions in the types of learner characteristics associated with strategy adoption, most notably between direct and indirect strategies. Instrumental motivation predicted both direct and indirect Strategy Inventory for Language Learning scores, but self-efficacy affected memory, cognitive, and compensatory strategies, and perseverance predicted reported metacognitive and emotional strategy choice levels. Additionally, a negative route coefficient occurred between persistence and compensation techniques and between competition and memory strategies, implying mediation and a high degree of complexity in the way learner traits impact behavior. The present study's findings have implications for prospective instructor techniques for motivating students to become fully involved in language learning via the online procedure.Campus At

High-grade Features of Papillary Cystadenocarcinoma of the Parotid Gland

Papillary cystadenocarcinomas (PCAs) are rare low-grade salivary gland tumors first introduced in the World Health Organization classification in 1991. While classically regarded as a low-grade malignancy, PCAs with more clinically and histologically high-grade features have been reported, reflecting the often-underrecognized morphological diversity of this entity. Although no universally advocated grading system exists, high-grade PCAs tend to demonstrate locally aggressive features, cytologic atypia, high mitotic rate, necrosis, and an absence of papillary features. We present a case of a 51-year-old male with slow-onset, progressive right facial fullness over four years. Contrast-enhanced computed tomography of the neck demonstrated a 3.3 cm peripherally enhancing cystic and solid mass in the right superficial lobe of the parotid gland. Following a superficial parotidectomy and a selective right neck dissection, histopathology demonstrated a large cyst with papillary projections lined with cuboidal cells of mild to moderate atypia and surrounding solid tumor nests. The tumor displayed stromal, lymphovascular, and subcutaneous fibroadipose tissue invasion. One of 12 lymph nodes was positive for metastatic carcinoma without extranodal extension. A diagnosis of intermediate-grade PCA was rendered. This case report summarizes the features typical of high-grade PCAs, the few reported cases of intermediate- and high-grade PCAs within the existing literature and provides a brief overview of the radiological and pathological differential diagnosis when considering a parotid gland PCA

Drug inhibition of redox factor-1 restores hypoxia-driven changes in tuberous sclerosis complex 2 deficient cells

Simple Summary: Tuberous sclerosis complex (TSC) is a genetic disease where patients are predisposed to tumors and neurological complications. Current therapies for this disease are not fully curative. We aimed to explore novel drug targets and therapies that could further benefit TSC patients. This work uncovered a novel pathway that drives disease in TSC cell models involving redox factor-1 (Ref-1). Ref-1 is a protein that turns on several key transcription factors that collectively promote tumor growth and survival through direct redox signaling. Processes regulated by Ref-1 include angiogenesis, inflammation, and metabolic transformation. Therefore, this work reveals a new drug target, where inhibitors of Ref-1 could have an additional benefit compared to current drug therapies. Abstract: Therapies with the mechanistic target of rapamycin complex 1 (mTORC1) inhibitors are not fully curative for tuberous sclerosis complex (TSC) patients. Here, we propose that some mTORC1-independent disease facets of TSC involve signaling through redox factor-1 (Ref-1). Ref-1 possesses a redox signaling activity that stimulates the transcriptional activity of STAT3, NF-kB, and HIF-1α, which are involved in inflammation, proliferation, angiogenesis, and hypoxia, respectively. Here, we demonstrate that redox signaling through Ref-1 contributes to metabolic transformation and tumor growth in TSC cell model systems. In TSC2-deficient cells, the clinically viable Ref-1 inhibitor APX3330 was effective at blocking the hyperactivity of STAT3, NF-kB, and HIF-1α. While Ref-1 inhibitors do not inhibit mTORC1, they potently block cell invasion and vasculature mimicry. Of interest, we show that cell invasion and vasculature mimicry linked to Ref-1 redox signaling are not blocked by mTORC1 inhibitors. Metabolic profiling revealed that Ref-1 inhibitors alter metabolites associated with the glutathione antioxidant pathway as well as metabolites that are heavily dysregulated in TSC2-deficient cells involved in redox homeostasis. Therefore, this work presents Ref-1 and associated redox-regulated transcription factors such as STAT3, NF-kB, and HIF-1α as potential therapeutic targets to treat TSC, where targeting these components would likely have additional benefits compared to using mTORC1 inhibitors alone

Evaluating the association of biallelic OGDHL variants with significant phenotypic heterogeneity

BACKGROUND: Biallelic variants in OGDHL, encoding part of the α-ketoglutarate dehydrogenase complex, have been associated with highly heterogeneous neurological and neurodevelopmental disorders. However, the validity of this association remains to be confirmed. A second OGDHL patient cohort was recruited to carefully assess the gene-disease relationship. METHODS: Using an unbiased genotype-first approach, we screened large, multiethnic aggregated sequencing datasets worldwide for biallelic OGDHL variants. We used CRISPR/Cas9 to generate zebrafish knockouts of ogdhl, ogdh paralogs, and dhtkd1 to investigate functional relationships and impact during development. Functional complementation with patient variant transcripts was conducted to systematically assess protein functionality as a readout for pathogenicity. RESULTS: A cohort of 14 individuals from 12 unrelated families exhibited highly variable clinical phenotypes, with the majority of them presenting at least one additional variant, potentially accounting for a blended phenotype and complicating phenotypic understanding. We also uncovered extreme clinical heterogeneity and high allele frequencies, occasionally incompatible with a fully penetrant recessive disorder. Human cDNA of previously described and new variants were tested in an ogdhl zebrafish knockout model, adding functional evidence for variant reclassification. We disclosed evidence of hypomorphic alleles as well as a loss-of-function variant without deleterious effects in zebrafish variant testing also showing discordant familial segregation, challenging the relationship of OGDHL as a conventional Mendelian gene. Going further, we uncovered evidence for a complex compensatory relationship among OGDH, OGDHL, and DHTKD1 isoenzymes that are associated with neurodevelopmental disorders and exhibit complex transcriptional compensation patterns with partial functional redundancy. CONCLUSIONS: Based on the results of genetic, clinical, and functional studies, we formed three hypotheses in which to frame observations: biallelic OGDHL variants lead to a highly variable monogenic disorder, variants in OGDHL are following a complex pattern of inheritance, or they may not be causative at all. Our study further highlights the continuing challenges of assessing the validity of reported disease-gene associations and effects of variants identified in these genes. This is particularly more complicated in making genetic diagnoses based on identification of variants in genes presenting a highly heterogenous phenotype such as "OGDHL-related disorders"

An insight to HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) pathogenesis; evidence from high-throughput data integration and meta-analysis

Background Human T-lymphotropic virus 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a progressive disease of the central nervous system that significantly affected spinal cord, nevertheless, the pathogenesis pathway and reliable biomarkers have not been well determined. This study aimed to employ high throughput meta-analysis to find major genes that are possibly involved in the pathogenesis of HAM/TSP. Results High-throughput statistical analyses identified 832, 49, and 22 differentially expressed genes for normal vs. ACs, normal vs. HAM/TSP, and ACs vs. HAM/TSP groups, respectively. The protein-protein interactions between DEGs were identified in STRING and further network analyses highlighted 24 and 6 hub genes for normal vs. HAM/TSP and ACs vs. HAM/TSP groups, respectively. Moreover, four biologically meaningful modules including 251 genes were identified for normal vs. ACs. Biological network analyses indicated the involvement of hub genes in many vital pathways like JAK-STAT signaling pathway, interferon, Interleukins, and immune pathways in the normal vs. HAM/TSP group and Metabolism of RNA, Viral mRNA Translation, Human T cell leukemia virus 1 infection, and Cell cycle in the normal vs. ACs group. Moreover, three major genes including STAT1, TAP1, and PSMB8 were identified by network analysis. Real-time PCR revealed the meaningful down-regulation of STAT1 in HAM/TSP samples than AC and normal samples (P = 0.01 and P = 0.02, respectively), up-regulation of PSMB8 in HAM/TSP samples than AC and normal samples (P = 0.04 and P = 0.01, respectively), and down-regulation of TAP1 in HAM/TSP samples than those in AC and normal samples (P = 0.008 and P = 0.02, respectively). No significant difference was found among three groups in terms of the percentage of T helper and cytotoxic T lymphocytes (P = 0.55 and P = 0.12). Conclusions High-throughput data integration disclosed novel hub genes involved in important pathways in virus infection and immune systems. The comprehensive studies are needed to improve our knowledge about the pathogenesis pathways and also biomarkers of complex diseases.Peer reviewe

Drug inhibition of redox factor-1 restores hypoxic-driven changes in Tuberous Sclerosis Complex 2-deficient cells

Therapies with mechanistic target of rapamycin complex 1 (mTORC1) inhibitors are not fully curative for Tuberous Sclerosis Complex (TSC) patients. Here we propose that some mTORC1-independent disease facets of TSC involve signaling through redox factor-1 (Ref-1). Ref-1 possesses redox signaling activity that stimulates the transcriptional activity of STAT3, NF-B, and HIF-1 involved in inflammation, proliferation, angiogenesis and hypoxia, respectively. Here we demonstrate that redox signaling through Ref-1 contributes to metabolic transformation and tumor growth in TSC cell model systems. In TSC2-deficient cells, the clinically viable Ref-1 inhibitor, APX3330, was effective at blocking the hyperactivity of STAT3, NF-B, and HIF-1. While Ref-1 inhibitors do not inhibit mTORC1, they potently block cell invasion and vasculature mimicry. Of interest, we show that cell invasion and vasculature mimicry linked to Ref-1 redox signaling are not blocked by mTORC1 inhibitors. Metabolic profiling revealed that Ref-1 inhibitors alter metabolites associated with the glutathione antioxidant pathway as well as metabolites that are heavily dysregulated in TSC2-deficient cells involved in redox homeostasis. Therefore, this work presents Ref-1 and associated redox-regulated transcription factors, such as STAT3, NF-B and HIF-1, as potential therapeutic targets to treat TSC, where targeting these components would likely have additional benefits to just using mTORC1 inhibitors alone