Detecting head and neck lymph node metastases with white light reflectance spectroscopy; a pilot study

Introduction: A challenge in the treatment of patients with head and neck cancer is the management of occult cervical lymph node (LN) metastases. Single-fiber reflectance (SFR) spectroscopy has the potential to detect physiological tissue changes that occur in a positive LN. This pilot study aimed to investigate whether SFR spectroscopy could serve as an alternative or additional technique to detect cervical lymph node metastases. Materials and Methods: We performed intraoperative SFR spectroscopy measurements of LNs with and without malignancies. We analyzed if physiological and scattering parameters were significantly altered in positive LNs. Results: Nine patients with a total of nineteen LNs were included. Three parameters, blood volume fraction (BVF), microvascular saturation (StO2), and Rayleigh amplitude, were significantly lower in positive LNs. They were combined into one optical parameter ‘delta’, using discriminant analysis. Delta was significantly decreased in positive LNs, p = 0,0006. It had a high diagnostic accuracy where the sensitivity, specificity, PPV, and NPV were 90,0%, 88.9%, 90,0%, and 88.9%, respectively. The area under the ROC curve was 96.7% (95% confidence interval 89.7–100.0%). Conclusion: This proof of principle study is a first step in the development of an SFR spectroscopy technique to detect LN metastases in real time. A next step towards this goal is replicating these results in LNs with smaller metastases and in a larger cohort of patients. This future study will combine SFR spectroscopy with fine-needle aspiration, using the same needle, to perform preoperative in vivo measurements.</p

Optical detection of field cancerization in the buccal mucosa of patients with esophageal cancer

Introduction: Esophageal cancer is an increasingly common type of neoplasm with a very poor prognosis. This prognosis could improve with more early tumor detection. We have previously shown that we can use an optical spectroscopy to detect field cancerization in the buccal mucosa of patients with laryngeal cancer. The aim of this prospective study was to investigate whether we could detect field cancerization of buccal mucosa of patients with esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). Methods: Optical measurements were performed in vivo using a novel optical technique: multidiameter single-fiber reflectance (MDSFR) spectroscopy. MDSFR spectra were acquired by a handheld probe incorporating three fiber diameters. Multiple absorption and scattering parameters that are related to the physiological and ultrastructural properties of the buccal mucosa were derived from these spectra. A linear discriminant analysis of the parameters was performed to create a combined biomarker σ to discriminate oncologic from non-oncologic patients. Results: Twelve ESCC, 12 EAC, and 24 control patients were included in the study. The median value of our biomarker σ was significantly higher in patients with ESCC (2.07 [1.93-2.10]) than control patients (1.86 [1.73-1.95], p = 0.022). After cross-validation σ was able to identify ESCC patients with a sensitivity of 66.7% and a specificity of 70.8%. There were no significant differences between the EAC group and the control group. Conclusion: Field cancerization in the buccal mucosa can be detected using optical spectroscopy in ESCC patients. This may be the first step towards non-invasive ESCC cancer screening

The ESCAPE project : Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche a l'Operationnel a Meso-Echelle) and ALADIN (Aire Limitee Adaptation Dynamique Developpement International); and COSMO-EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU-GPU arrangements

The ESCAPE project: Energy-efficient Scalable Algorithms for Weather Prediction at Exascale

Abstract. In the simulation of complex multi-scale flows arising in weather and climate modelling, one of the biggest challenges is to satisfy strict service requirements in terms of time to solution and to satisfy budgetary constraints in terms of energy to solution, without compromising the accuracy and stability of the application. These simulations require algorithms that minimise the energy footprint along with the time required to produce a solution, maintain the physically required level of accuracy, are numerically stable, and are resilient in case of hardware failure. The European Centre for Medium-Range Weather Forecasts (ECMWF) led the ESCAPE (Energy-efficient Scalable Algorithms for Weather Prediction at Exascale) project, funded by Horizon 2020 (H2020) under the FET-HPC (Future and Emerging Technologies in High Performance Computing) initiative. The goal of ESCAPE was to develop a sustainable strategy to evolve weather and climate prediction models to next-generation computing technologies. The project partners incorporate the expertise of leading European regional forecasting consortia, university research, experienced high-performance computing centres, and hardware vendors. This paper presents an overview of the ESCAPE strategy: (i) identify domain-specific key algorithmic motifs in weather prediction and climate models (which we term Weather & Climate Dwarfs), (ii) categorise them in terms of computational and communication patterns while (iii) adapting them to different hardware architectures with alternative programming models, (iv) analyse the challenges in optimising, and (v) find alternative algorithms for the same scheme. The participating weather prediction models are the following: IFS (Integrated Forecasting System); ALARO, a combination of AROME (Application de la Recherche à l'Opérationnel à Meso-Echelle) and ALADIN (Aire Limitée Adaptation Dynamique Développement International); and COSMO–EULAG, a combination of COSMO (Consortium for Small-scale Modeling) and EULAG (Eulerian and semi-Lagrangian fluid solver). For many of the weather and climate dwarfs ESCAPE provides prototype implementations on different hardware architectures (mainly Intel Skylake CPUs, NVIDIA GPUs, Intel Xeon Phi, Optalysys optical processor) with different programming models. The spectral transform dwarf represents a detailed example of the co-design cycle of an ESCAPE dwarf. The dwarf concept has proven to be extremely useful for the rapid prototyping of alternative algorithms and their interaction with hardware; e.g. the use of a domain-specific language (DSL). Manual adaptations have led to substantial accelerations of key algorithms in numerical weather prediction (NWP) but are not a general recipe for the performance portability of complex NWP models. Existing DSLs are found to require further evolution but are promising tools for achieving the latter. Measurements of energy and time to solution suggest that a future focus needs to be on exploiting the simultaneous use of all available resources in hybrid CPU–GPU arrangements

Lattice Sieving in Three Dimensions for Discrete Log in Medium Characteristic

Lattice sieving in two dimensions has proven to be an indispensable practical aid in integer factorization and discrete log computations involving the number field sieve. The main contribution of this article is to show that a different method of lattice sieving in three dimensions will provide a significant speedup in medium characteristic. Our method is to use the successive minima and shortest vectors of the lattice instead of transition vectors to iterate through lattice points. We showcase the new method by a record computation in a 133-bit subgroup of Fp6${{\mathbb{F}}_{{{p}^{6}}}}$, with p6 having 423 bits. Our overall timing is nearly 3 times faster than the previous record of a 132-bit subgroup in a 422-bit field. The approach generalizes to dimensions 4 or more, overcoming one key obstruction to the implementation of the tower number field sieve

Investigating Structural Property Relationships to Enable Repurposing of Pharmaceuticals as Zinc Ionophores

The importance of zinc in biology has gained greater recognition in recent years due to its essential contributions to the function of many endogenous enzymes. Disruption of zinc homeostasis may be useful in treating pathological conditions, such as Alzheimer&rsquo;s, and for antiviral purposes. Despite the growth of knowledge and increased interest in zinc, little is known about the structure and function of zinc ionophores. In this study we analyse the Cambridge Structural Database and solution complexation studies found in the literature to identify key functional groups which may confer zinc ionophorism. Pharmaceuticals, nutraceuticals and amino acids with these functionalities were selected to enable us to explore the translatability of ionophoric activity from in vitro assays to cellular systems. We find that although certain species may complex to zinc in the solid and solution states, and may carry ions across simple membrane systems, this does not necessarily translate into ionophoric activity. We propose that the CSD can help refine key functionalities but that ionophoric activity must be confirmed in cellular systems

Investigating structural property relationships to enable repurposing of pharmaceuticals as zinc ionophores

The importance of zinc in biology has gained greater recognition in recent years due to its essential contributions to the function of many endogenous enzymes. Disruption of zinc homeostasis may be useful in treating pathological conditions, such as Alzheimer’s, and for antiviral purposes. Despite the growth of knowledge and increased interest in zinc, little is known about the structure and function of zinc ionophores. In this study we analyse the Cambridge Structural Database and solution complexation studies found in the literature to identify key functional groups which may confer zinc ionophorism. Pharmaceuticals, nutraceuticals and amino acids with these functionalities were selected to enable us to explore the translatability of ionophoric activity from in vitro assays to cellular systems. We find that although certain species may complex to zinc in the solid and solution states, and may carry ions across simple membrane systems, this does not necessarily translate into ionophoric activity. We propose that the CSD can help refine key functionalities but that ionophoric activity must be confirmed in cellular systems

Hydroxychloroquine Does Not Function as a Direct Zinc Ionophore

Drug-mediated correction of abnormal biological zinc homeostasis could provide new routes to treating neurodegeneration, cancer, and viral infections. Designing therapeutics to facilitate zinc transport intracellularly is hampered by inadequate concentrations of endogenous zinc, which is often protein-bound in vivo. We found strong evidence that hydroxychloroquine, a drug used to treat malaria and employed as a potential treatment for COVID-19, does not bind and transport zinc across biological membranes through ionophoric mechanisms, contrary to recent claims. In vitro complexation studies and liposomal transport assays are correlated with cellular zinc assays in A549 lung epithelial cells to confirm the indirect mechanism of hydroxychloroquine-mediated elevation in intracellular zinc without ionophorism. Molecular simulations show hydroxychloroquine-triggered helix perturbation in zinc-finger protein without zinc chelation, a potential alternative non-ionophoric mechanism. </p

Early Upper Aerodigestive Tract Cancer Detection Using Electron Microscopy to Reveal Chromatin Packing Alterations in Buccal Mucosa Cells

A profound characteristic of field cancerization is alterations in chromatin packing. This study aimed to quantify these alterations using electron microscopy image analysis of buccal mucosa cells of laryngeal, esophageal, and lung cancer patients. Analysis was done on normal-appearing mucosa, believed to be within the cancerization field, and not tumor itself. Large-scale electron microscopy (nanotomy) images were acquired of cancer patients and controls. Within the nuclei, the chromatin packing of euchromatin and heterochromatin was characterized. Furthermore, the chromatin organization was quantified through chromatin packing density scaling. A significant difference was found between the cancer and control groups in the chromatin packing density scaling parameter for length scales below the optical diffraction limit (200 nm) in both the euchromatin (p = 0.002) and the heterochromatin (p = 0.006). The chromatin packing scaling analysis also indicated that the chromatin organization of cancer patients deviated significantly from the control group. They might allow for novel strategies for cancer risk stratification and diagnosis with high sensitivity. This could aid clinicians in personalizing screening strategies for high-risk patients and follow-up strategies for treated cancer patients