Identification of the shortest species-specific oligonucleotide sequences.

Despite the exponential increase in sequencing information driven by massively parallel DNA sequencing technologies, universal and succinct genomic fingerprints for each organism are still missing. Identifying the shortest species-specific nucleotide sequences offers insights into species evolution and holds potential practical applications in agriculture, wildlife conservation, and healthcare. We propose a new method for sequence analysis termed nucleic quasi-primes, the shortest occurring sequences in each of 45,076 organismal reference genomes, present in one genome and absent from every other examined genome. In the human genome, we find that the genomic loci of nucleic quasi-primes are most enriched for genes associated with brain development and cognitive function. In a single-cell case study focusing on the human primary motor cortex, nucleic quasi-prime genes account for a significantly larger proportion of the variation based on average gene expression. Nonneuronal cell types, including astrocytes, endothelial cells, microglia perivascular-macrophages, oligodendrocytes, and vascular and leptomeningeal cells, exhibit significant activation of quasi-prime-containing gene associations related to cancer, whereas simultaneously suppressing quasi-prime-containing genes are associated with cognitive, mental, and developmental disorders. We also show that human disease-causing variants, eQTLs, mQTLs, and sQTLs are 4.43-fold, 4.34-fold, 4.29-fold, and 4.21-fold enriched at human quasi-prime loci, respectively. These findings indicate that nucleic quasi-primes are genomic loci linked to the evolution of species-specific traits, and in humans, they provide insights in the development of cognitive traits and human diseases, including neurodevelopmental disorders

Το σύστημα ιστολογικής βαθμοποίησης GELA και η εφαρμογή του στην καθημερινή πρακτική ως αξιόπιστος και αποτελεσματικός τρόπος παρακολούθησης ασθενών με λεμφώματα MALT στομάχου μετά από θεραπεία

Τα λεμφώματα της οριακής ζώνης αποτελούν ξεχωριστό τύπο μη Hodgkin Β-λεμφωμάτων και διαχωρίζονται σε τρεις κατηγορίες: Τα σπληνικά, τα λεμφαδενικά και τα εξωλεμφαδενικά, τα τελευταία σχετιζόμενα τις περισσότερες φορές με τον λεμφικό ιστό των βλεννογόνων. Πρόκειται για λεμφώματα αποτελούμενα από μικρά Β νεοπλασματικά λεμφοκύτταρα με μορφολογικούς χαρακτήρες λεμφοκυττάρων της οριακής ζώνης, μονοκυτταροειδών λεμφοκυττάρων, μικρών και διάσπαρτων ανοσοβλαστών καθώς και λεμφοκυττάρων δίκην κεντροβλαστών, ενίοτε με στοιχεία πλασματοκυτταροειδούς διαφοροποίησης. Τα εξωλεμφαδενικά λεμφώματα της οριακής ζώνης αναπτύσσονται σε θέσεις όπου φυσιολογικά δεν υπάρχει οργανωμένος λεμφικός ιστός αλλά δημιουργείται επίκτητα συνέπεια αντιγονικού ερεθισμού – λοίμωξης, ενεργοποίησης αυτοανόσων μηχανισμών είτε ως αποτέλεσμα μοριακών και γενετικών φαινομένων. Τα λεμφώματα της οριακής ζώνης του βλεννογόνου (MALT) του στομάχου αποτελούν τον συνηθέστερο τύπο γαστρικού πρωτοπαθούς λεμφώματος και σχετίζονται σε ένα μεγάλο ποσοστό με λοίμωξη από Helicobacter pylori. Καθοριστικοί παράγοντες στην διάγνωση και την θεραπεία τους είναι γαστροσκόπηση και η ιστολογική εξέταση. Η θεραπεία τις περισσότερες φορές περιλαμβάνει την εκρίζωση του Helicobacter pylori, ενώ μετά την ολοκλήρωση αυτής διενεργούνται σε τακτική βάση γαστροσκοπήσεις με λήψη πολλαπλών βιοψιών. Η αξιολόγηση των ιστολογικών ευρημάτων σε αυτή τη φάση έχει πολύ σημαντικό ρόλο, γεγονός το οποίο καθιστά επιτακτική την ανάγκη ενός εύχρηστου και πρακτικού συστήματος βαθμοποίησης και κατηγοριοποίησης αυτών των ευρημάτων, πάντα με σκοπό την υιοθέτηση της βέλτιστης στρατηγικής παρακολούθησης και περαιτέρω αντιμετώπισης των ασθενών μετά από την λήψη θεραπείας. Ένα τέτοιο σύστημα είναι το σύστημα βαθμοποίησης της απόκρισης στην θεραπεία GELA για λεμφώματα MALT στομάχου, το οποίο κατηγοριοποιεί τα ιστολογικά ευρήματα σε τέσσερεις κατηγορίες (CR, pMRD, rRD, NC), συμβάλλοντας στην άμεση λήψη αποφάσεων κατά το στάδιο παρακολούθησης των ασθενών, αποτελώντας παράλληλα ένα εύχρηστο και πρακτικό εργαλείο για τους κλινικούς και ιδιαίτερα για τους θεράποντες ιατρούς.Marginal zone lymphomas are a distinct type of non-Hodgkin B-lymphomas and they are classified into three categories: Splenic, Nodal, and Extranodal, the latter most commonly associated with mucosal lymphoid tissue. These are lymphomas consisting of small B neoplastic lymphocytes with morphological features of marginal zone lymphocytes, monocytes, small and scattered immunoblasts as well as lymphocytes simulating centroblasts, sometimes with plasmacytic differentiation. The extranodal marginal zone lymphomas develop in sites where normally there is no organized lymphatic tissue, but it is formed as a consequence of antigenic irritation - infection, activation of autoimmune mechanisms or as a result of molecular and genetic phenomena. Gastric mucosa-associated lymphoid tissue (MALT) lymphomas are the most common type of gastric primary lymphomas and they are strongly associated with Helicobacter pylori infection. The determinants for their diagnosis and treatment are gastroscopy and histological examination. Treatment most often involves the eradication of Helicobacter pylori and after its completion gastroscopies are performed on a regular basis with multiple biopsies. The evaluation of histological findings at this stage has a very important role, a fact that makes imperative the need for an easy and practical system for grading and classification of these findings, always with the aim of adopting the optimal monitoring and further treatment strategy for patients that have received treatment. One such system is the GELA treatment response grading system for gastric MALT lymphomas, which classifies histological findings into four categories (CR, pMRD, rRD, NC), facilitating immediate decision making during patient follow-up, being an easy to use and practical tool for clinicians and especially for attending physicians

A Survey on Parameter Server Architecture: Approaches for Optimizing Distributed Centralized Learning

Deep learning has emerged as a cornerstone technology across various domains, from image classification to natural language processing. However, the computational and data demands of training large-scale neural networks pose significant challenges. Distributed learning approaches, particularly those leveraging data parallelism, have become critical to addressing these challenges. Among these, the parameter server architecture stands out as a widely adopted and scalable solution, enabling efficient training of large models across distributed systems. This survey provides a comprehensive exploration of the parameter server architecture, detailing its design principles and operation. It categorizes and critically analyzes research advancements across five key aspects: consistency control, network optimization, parameter management, straggler handling, and fault tolerance. By synthesizing insights from a wide range of studies, this work highlights the trade-offs and practical effectiveness of various approaches while identifying open challenges and future research directions. The survey aims to serve as a foundational resource for researchers and practitioners striving to enhance the performance and scalability of distributed deep learning systems

Strategy-Switch: From All-Reduce to Parameter Server for Faster Efficient Training

Deep learning plays a pivotal role in numerous big data applications by enhancing the accuracy of models. However, the abundance of available data presents a challenge when training neural networks on a single node. Consequently, various distributed training methods have emerged. Among these, two prevalent approaches are All-Reduce and Parameter Server. All-Reduce, operating synchronously, faces synchronization-related bottlenecks, while the Parameter Server, often used asynchronously, can potentially compromise the model’s performance. To harness the strengths of both setups, we introduce Strategy-Switch, a hybrid approach that offers the best of both worlds, combining speed with efficiency and high-quality results. This method initiates training under the All-Reduce system and, guided by an empirical rule, transitions to asynchronous Parameter Server training once the model stabilizes. Our experimental analysis demonstrates that we can achieve comparable accuracy to All-Reduce training but with significantly accelerated training