Dolichopoda cave crickets from Peloponnese (Orthoptera, Rhaphidophoridae): molecular and morphological investigations reveal four new species for Greece

Three species belonging to the genus Dolichopoda (Orthoptera; Rhaphidopohoridae) are known so far from the Peloponnese, all endemic to the area. In particular, D. matsakisi is known from two mountains in the North, while D. dalensi is present in the east region. The third species, D. unicolor, is distributed in the southern part of the Peloponnese, inhabiting caves on Mt Taygetos and Mani Peninsula. Recently, extensive sampling work in most of the Peloponnese has led to the discovery of new taxa, morphologically differentiated by the above three known species. To investigate the delimitation of the Peloponnesian species of Dolichopoda, we performed both morphological and molecular analyses. Morphological analysis was carried out by considering diagnostic characters generally used to distinguish different taxa, as the shape of epiphallus in males and the subgenital plate in females. Molecular analysis was performed by sequencing three mitochondrial genes, 12S rRNA, 16S rRNA, and COI, and one nuclear gene, 28S rRNA. Results from both morphological and molecular analyses were used to revise the taxonomic arrangement of the Peloponnesian species. On the whole, we were able to distinguish seven lineages of Peloponnesian Dolichopoda species, of which D. kofinasi n.sp., D.epidavrii n.sp., D. poseidonica n.sp., and D. propanti n.sp. are described as new species

Instant recovery of shape from spectrum via latent space connections

We introduce the first learning-based method for recovering shapes from Laplacian spectra. Our model consists of a cycle-consistent module that maps between learned latent vectors of an auto-encoder and sequences of eigenvalues. This module provides an efficient and effective linkage between Laplacian spectrum and geometry. Our data-driven approach replaces the need for ad-hoc regularizers required by prior methods, while providing more accurate results at a fraction of the computational cost. Our learning model applies without modifications across different dimensions (2D and 3D shapes alike), representations (meshes, contours and point clouds), as well as across different shape classes, and admits arbitrary resolution of the input spectrum without affecting complexity. The increased flexibility allows us to address notoriously difficult tasks in 3D vision and geometry processing within a unified framework, including shape generation from spectrum, mesh superresolution, shape exploration, style transfer, spectrum estimation from point clouds, segmentation transfer and pointtopoint matching

Integrated waveguides and deterministically positioned nitrogen vacancy centers in diamond created by femtosecond laser writing

Diamond's nitrogen vacancy (NV) center is an optically active defect with long spin coherence times, showing great potential for both efficient nanoscale magnetometry and quantum information processing schemes. Recently, both the formation of buried 3D optical waveguides and high quality single NVs in diamond were demonstrated using the versatile femtosecond laser-writing technique. However, until now, combining these technologies has been an outstanding challenge. In this work, we fabricate laser written photonic waveguides in quantum grade diamond which are aligned to within micron resolution to single laser-written NVs, enabling an integrated platform providing deterministically positioned waveguide-coupled NVs. This fabrication technology opens the way towards on-chip optical routing of single photons between NVs and optically integrated spin-based sensing

Superconducting Heater Cryotron-Based Reconfigurable Logic Towards Cryogenic IC Camouflaging

Superconducting electronics are among the most promising alternatives to conventional CMOS technology thanks to the ultra-fast speed and ultra-high energy efficiency of the superconducting devices. Having a cryogenic control processor is also a crucial requirement for scaling the existing quantum computers up to thousands of qubits. Despite showing outstanding speed and energy efficiency, Josephson junction-based circuits suffer from several challenges such as flux trapping leading to limited scalability, difficulty in driving high impedances, and so on. Three-terminal cryotron devices have been proposed to solve these issues which can drive high impedances (>100 k{\Omega}) and are free from any flux trapping issue. In this work, we develop a reconfigurable logic circuit using a heater cryotron (hTron). In conventional approaches, the number of devices to perform a logic operation typically increases with the number of inputs. However, here, we demonstrate a single hTron device-based logic circuit that can be reconfigured to perform 1-input copy and NOT, 2-input AND and OR, and 3-input majority logic operations by choosing suitable biasing conditions. Consequently, we can perform any processing task with a much smaller number of devices. Also, since we can perform different logic operations with the same circuit (same layout), we can develop a camouflaged system where all the logic gates will have the same layout. Therefore, this proposed circuit will ensure enhanced hardware security against reverse engineering attacks.Comment: 12 pages, 5 figure

Machine Learning-powered Compact Modeling of Stochastic Electronic Devices using Mixture Density Networks

The relentless pursuit of miniaturization and performance enhancement in electronic devices has led to a fundamental challenge in the field of circuit design and simulation: how to accurately account for the inherent stochastic nature of certain devices. While conventional deterministic models have served as indispensable tools for circuit designers, they fall short when it comes to capture the subtle yet critical variability exhibited by many electronic components. In this paper, we present an innovative approach that transcends the limitations of traditional modeling techniques by harnessing the power of machine learning, specifically Mixture Density Networks (MDNs), to faithfully represent and simulate the stochastic behavior of electronic devices. We demonstrate our approach to model heater cryotrons, where the model is able to capture the stochastic switching dynamics observed in the experiment. Our model shows 0.82% mean absolute error for switching probability. This paper marks a significant step forward in the quest for accurate and versatile compact models, poised to drive innovation in the realm of electronic circuits

The oncofetal protein IMP3: a novel grading tool and predictor of poor clinical outcome in human gliomas

Morphologic criteria illustrated in WHO guidelines are the most significant prognostic factor in human gliomas, but novel biomarkers are needed to identify patients with a poorer outcome. The present study examined the expression of the oncofetal protein IMP3 in a series of 135 patients affected by high-grade (grade III and IV) gliomas, correlating the results with proliferative activity, molecular parameters, and clinical and follow-up data. Overall, IMP3 expression was higher in glioblastomas (68%) than in grade III tumors (20%, P < 0.0001), and IMP3-positive high-grade gliomas showed a shorter overall and disease-free survival than negative ones (P = 0.0002 and P = 0.006, resp.). IMP3 expression was significantly associated with the absence of mutations of IDH1 gene (P = 0.0001) and with the unmethylated phenotype of MGMT in high-grade gliomas (P = 0.004). High Ki67 levels were correlated with better prognosis in glioblastomas but IMP3 expression was not correlated with the proliferation index. These findings confirm the role of IMP3 as a marker of poor outcome, also in consideration of its association with IDH1 wild-type phenotype and MGMT unmethylated status. The data suggest that IMP3 staining could identify a subgroup of patients with poor prognosis and at risk of recurrence in high-grade gliomas

Prognostic value of preoperative von Willebrand factor plasma levels in patients with Glioblastoma

Circulating biomarker for malignant gliomas could improve both differential diagnosis and clinical management of brain tumor patients. Among all gliomas, glioblastoma (GBM) is considered the most hypervascularized tumor with activation of multiple proangiogenic signaling pathways that enhance tumor growth. To investigate whether preoperative antigen plasma level of von Willebrand Factor (VWF:Ag) might be possible marker for GBM onset, progression, and prognosis, we retrospectively examined 57 patients with histological diagnosis for GBM and 23 meningiomas (MNGs), benign intracranial expansive lesions, enrolled as controls. Blood samples were collected from all the patients before tumor resection. Plasma von Willebrand Factor (VWF):Ag levels were determined by using a latex particle-enhanced immunoturbidimetric assay. The median levels of vWF:Ag were significantly higher in GBMs than in meningiomas (MNGs) (183 vs. 133IU/dL, P=0.01). The cumulative 1-year survival was significantly shorter in patients with VWF:Ag levels 200IU/dL than in those with levels <200IU/dL and increased VWF levels were associated with a threefold higher risk of death in GBM patients. Our data suggest that VWF:Ag could be a circulating biomarker of disease malignancy, that could be considered, in association with other genetic and epigenetic factors, currently available in the GBM management. Future studies should investigate whether plasma VWF:Ag levels could also be used to monitor therapeutic effects and whether it may have a prognostic value