Ki67 nuclei detection and ki67-index estimation: A novel automatic approach based on human vision modeling

Background: The protein ki67 (pki67) is a marker of tumor aggressiveness, and its expression has been proven to be useful in the prognostic and predictive evaluation of several types of tumors. To numerically quantify the pki67 presence in cancerous tissue areas, pathologists generally analyze histochemical images to count the number of tumor nuclei marked for pki67. This allows estimating the ki67-index, that is the percentage of tumor nuclei positive for pki67 over all the tumor nuclei. Given the high image resolution and dimensions, its estimation by expert clinicians is particularly laborious and time consuming. Though automatic cell counting techniques have been presented so far, the problem is still open. Results: In this paper we present a novel automatic approach for the estimations of the ki67-index. The method starts by exploiting the STRESS algorithm to produce a color enhanced image where all pixels belonging to nuclei are easily identified by thresholding, and then separated into positive (i.e. pixels belonging to nuclei marked for pki67) and negative by a binary classification tree. Next, positive and negative nuclei pixels are processed separately by two multiscale procedures identifying isolated nuclei and separating adjoining nuclei. The multiscale procedures exploit two Bayesian classification trees to recognize positive and negative nuclei-shaped regions. Conclusions: The evaluation of the computed results, both through experts' visual assessments and through the comparison of the computed indexes with those of experts, proved that the prototype is promising, so that experts believe in its potential as a tool to be exploited in the clinical practice as a valid aid for clinicians estimating the ki67-index. The MATLAB source code is open source for research purposes

Whole-genome analysis uncovers recurrent IKZF1 inactivation and aberrant cell adhesion in blastic plasmacytoid dendritic cell neoplasm

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare and highly aggressive hematological malignancy with a poorly understood pathobiology and no effective therapeutic options. Despite a few recurrent genetic defects (eg, single nucleotide changes, indels, large chromosomal aberrations) have been identified in BPDCN, none are disease-specific, and more importantly, none explain its genesis or clinical behavior. In this study, we performed the first high resolution whole-genome analysis of BPDCN with a special focus on structural genomic alterations by using whole-genome sequencing and RNA sequencing. Our study, the first to characterize the landscape of genomic rearrangements and copy number alterations of BPDCN at nucleotide-level resolution, revealed that IKZF1, a gene encoding a transcription factor required for the differentiation of plasmacytoid dendritic cell precursors, is focally inactivated through recurrent structural alterations in this neoplasm. In concordance with the genomic data, transcriptome analysis revealed that conserved IKZF1 target genes display a loss-of-IKZF1 expression pattern. Furthermore, up-regulation of cellular processes responsible for cell-cell and cell-ECM interactions, which is a hallmark of IKZF1 deficiency, was prominent in BPDCN. Our findings suggest that IKZF1 inactivation plays a central role in the pathobiology of the disease, and consequently, therapeutic approaches directed at reestablishing the function of this gene might be beneficial for patients

Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction

Methods for phenotype and outcome prediction are largely based on inductive supervised models that use selected biomarkers to make predictions, without explicitly considering the functional relationships between individuals. We introduce a novel network-based approach named Patient-Net (P-Net) in which biomolecular profiles of patients are modeled in a graph-structured space that represents gene expression relationships between patients. Then a kernel-based semi-supervised transductive algorithm is applied to the graph to explore the overall topology of the graph and to predict the phenotype/clinical outcome of patients. Experimental tests involving several publicly available datasets of patients afflicted with pancreatic, breast, colon and colorectal cancer show that our proposed method is competitive with state-of-the-art supervised and semi-supervised predictive systems. Importantly, P-Net also provides interpretable models that can be easily visualized to gain clues about the relationships between patients, and to formulate hypotheses about their stratification

A GPU-based algorithm for fast node label learning in large and unbalanced biomolecular networks

Background: Several problems in network biology and medicine can be cast into a framework where entities are represented through partially labeled networks, and the aim is inferring the labels (usually binary) of the unlabeled part. Connections represent functional or genetic similarity between entities, while the labellings often are highly unbalanced, that is one class is largely under-represented: for instance in the automated protein function prediction (AFP) for most Gene Ontology terms only few proteins are annotated, or in the disease-gene prioritization problem only few genes are actually known to be involved in the etiology of a given disease. Imbalance-aware approaches to accurately predict node labels in biological networks are thereby required. Furthermore, such methods must be scalable, since input data can be large-sized as, for instance, in the context of multi-species protein networks. Results: We propose a novel semi-supervised parallel enhancement of COSNet, an imbalance-aware algorithm build on Hopfield neural model recently suggested to solve the AFP problem. By adopting an efficient representation of the graph and assuming a sparse network topology, we empirically show that it can be efficiently applied to networks with millions of nodes. The key strategy to speed up the computations is to partition nodes into independent sets so as to process each set in parallel by exploiting the power of GPU accelerators. This parallel technique ensures the convergence to asymptotically stable attractors, while preserving the asynchronous dynamics of the original model. Detailed experiments on real data and artificial big instances of the problem highlight scalability and efficiency of the proposed method. Conclusions: By parallelizing COSNet we achieved on average a speed-up of 180x in solving the AFP problem in the S. cerevisiae, Mus musculus and Homo sapiens organisms, while lowering memory requirements. In addition, to show the potential applicability of the method to huge biomolecular networks, we predicted node labels in artificially generated sparse networks involving hundreds of thousands to millions of nodes

Glass groups, glass supply and recycling in late Roman Carthage

Carthage played an important role in maritime exchange networks during the Roman and late antique periods. One hundred ten glass fragments dating to the third to sixth centuries CE from a secondary deposit at the Yasmina Necropolis in Carthage have been analysed by electron microprobe analysis (EPMA) to characterise the supply of glass to the city. Detailed bivariate and multivariate data analysis identified different primary glass groups and revealed evidence of extensive recycling. Roman mixed antimony and manganese glasses with MnO contents in excess of 250 ppm were clearly the product of recycling, while iron, potassium and phosphorus oxides were frequent contaminants. Primary glass sources were discriminated using TiO2 as a proxy for heavy minerals (ilmenite/spinel), Al2O3 for feldspar and SiO2 for quartz in the glassmaking sands. It was thus possible to draw conclusions about the chronological and geographical attributions of the primary glass types. Throughout much of the period covered in this study, glassworkers in Carthage utilised glass from both Egyptian and Levantine sources. Based on their geochemical characteristics, we conclude that Roman antimony and Roman manganese glasses originated from Egypt and the Levant, respectively, and were more or less simultaneously worked at Carthage in the fourth century as attested by their mixed recycling (Roman Sb-Mn). In the later fourth and early fifth centuries, glasses from Egypt (HIMT) and the Levant (two Levantine I groups) continued to be imported to Carthage, although the Egyptian HIMT is less well represented at Yasmina than in many other late antique glass assemblages. In contrast, in the later fifth and sixth centuries, glass seems to have been almost exclusively sourced from Egypt in the form of a manganese-decolourised glass originally described and characterised by Foy and colleagues (2003). Hence, the Yasmina assemblage testifies to significant fluctuations in the supply of glass to Carthage that require further attention

Cutaneous-group histiocytoses associated with myeloid malignancies: A systematic review of 102 cases

Background: Histiocytoses are haematological disorders of bone marrow origin that share many biological and clinical features with haematological neoplasms. The association between histiocytoses of the cutaneous-group and myeloid malignancies is a poorly investigated topic of high biological and clinical impact. Methods: We performed a systematic review of the scientific literature, compliant with PRISMA guidelines, to unravel the clinical and pathological features of this intriguing association. Findings: We gathered and analysed 102 patients. Most were children with generalised cutaneous eruptions and displayed risk organ involvement (i.e. bone marrow, spleen, liver). Interestingly, all these features are uncommonly encountered in C-group histiocytosis not associated with haematological neoplasms. Conclusions: Our review shows that generalised eruptions and risk organ involvement in cutaneous-group histiocytosis should raise a suspicion for a concomitant myeloid neoplasm both in children and in adults and warrant further investigations. A rapid recognition of this association is required to start a prompt and effective therapeutic management given the aggressive behaviour of the associated myeloid neoplasm in most instances

In the footsteps of Pliny: tracing the sources of Garamantian carnelian from Fazzan, south-west Libya

References in the ancient sources indicate that the Libyan desert was a source of \u2018carbunculi\u2019: semiprecious red stones and gemstones variously interpreted as ruby, garnet and spinel, amongst others. While gemstones are not attested in the geological strata of Fazzan (south-west Libya), a range of silicabased stones including chert, chalcedony, agate and carnelian are known to originate in this area, linked to an early civilisation known as the Garamantes. It has been long proposed that the geochemical signature and the variations in the relative proportions of quartz:moganite phases can be used to distinguish between groups of stones of different origin. The proposed methodology was tested on a number of archaeological samples from the Garamantian sites of Jarma (ancient Garama) and Saniat Jibril, in Fazzan. Fragments of chert, carnelian and amazonite found at the two sites have been identified as raw materials associated with beadmaking. Trace elemental data obtained by LA-ICP-MS were combined with mineralogical data obtained by X-ray powder diffraction and Raman spectroscopy on the same samples and a group of reference samples. The dataset has been compared with the available literature and data from other localities around the world. To this purpose a preliminary database of silica-based materials was established for provenance work. Based on the scarce data available in the literature, the importation of these stones from Eastern localities such as India may be ruled out. The measured data on archaeological samples and debitage allow us to define a reliable reference group of parameters for materials from Fazzan, which are likely to be derived from a unique geological source. The methodology should be extended and compared with cherts and carnelians from a range of Mediterranean and Sub-Saharan sites. This characterisation work is a tool of high potential utility for a new investigation of ancient contact and trade across the Trans-Saharan zone

Human Digital Twin for Fitness Management

Our research work describes a team of human Digital Twins (DTs), each tracking fitness-related measurements describing an athlete's behavior in consecutive days (e.g. food income, activity, sleep). After collecting enough measurements, the DT firstly predicts the physical twin performance during training and, in case of non-optimal result, it suggests modifications in the athlete's behavior. The athlete's team is integrated into SmartFit, a software framework for supporting trainers and coaches in monitoring and manage athletes' fitness activity and results. Through IoT sensors embedded in wearable devices and applications for manual logging (e.g. mood, food income), SmartFit continuously captures measurements, initially treated as the dynamic data describing the current physical twins' status. Dynamic data allows adapting each DT's status and triggering the DT's predictions and suggestions. The analyzed measurements are stored as the historical data, further processed by the DT to update (increase) its knowledge and ability to provide reliable predictions. Results show that, thanks to the team of DTs, SmartFit computes trustable predictions of the physical twins' conditions and produces understandable suggestions which can be used by trainers to trigger optimization actions in the athletes' behavior. Though applied in the sport context, SmartFit can be easily adapted to other monitoring tasks