Autonomous Polycrystalline Material Decomposition for Hyperspectral Neutron Tomography

Hyperspectral neutron tomography is an effective method for analyzing crystalline material samples with complex compositions in a non-destructive manner. Since the counts in the hyperspectral neutron radiographs directly depend on the neutron cross-sections, materials may exhibit contrasting neutron responses across wavelengths. Therefore, it is possible to extract the unique signatures associated with each material and use them to separate the crystalline phases simultaneously. We introduce an autonomous material decomposition (AMD) algorithm to automatically characterize and localize polycrystalline structures using Bragg edges with contrasting neutron responses from hyperspectral data. The algorithm estimates the linear attenuation coefficient spectra from the measured radiographs and then uses these spectra to perform polycrystalline material decomposition and reconstructs 3D material volumes to localize materials in the spatial domain. Our results demonstrate that the method can accurately estimate both the linear attenuation coefficient spectra and associated reconstructions on both simulated and experimental neutron data

Targeting tissue factor on tumour cells and angiogenic vascular endothelial cells by factor VII-targeted verteporfin photodynamic therapy for breast cancer in vitro and in vivo in mice

<p>Abstract</p> <p>Background</p> <p>The objective of this study was to develop a ligand-targeted photodynamic therapy (tPDT) by conjugating factor VII (fVII) protein with photosensitiser verteporfin in order to overcome the poor selectivity and enhance the effect of non-targeted PDT (ntPDT) for cancer. fVII is a natural ligand for receptor tissue factor (TF) with high affinity and specificity. The reason for targeting receptor TF for the development of tPDT is that TF is a common but specific target on angiogenic tumour vascular endothelial cells (VEC) and many types of tumour cells, including solid tumours and leukaemia.</p> <p>Methods</p> <p>Murine factor VII protein (mfVII) containing a mutation (Lys341Ala) was covalently conjugated via a cross linker EDC with Veterporfin (VP) that was extracted from liposomal Visudyne, and then free VP was separated by Sephadex G50 spin columns. fVII-tPDT using mfVII-VP conjugate, compared to ntPDT, was tested <it>in vitro </it>for the killing of breast cancer cells and VEGF-stimulated VEC and <it>in vivo </it>for inhibiting the tumour growth of breast tumours in a mouse xenograft model.</p> <p>Results</p> <p>We showed that: (i) fVII protein could be conjugated with VP without affecting its binding activity; (ii) fVII-tPDT could selectively kill TF-expressing breast cancer cells and VEGF-stimulated angiogenic HUVECs but had no side effects on non-TF expressing unstimulated HUVEC, CHO-K1 and 293 cells; (iii) fVII targeting enhanced the effect of VP PDT by three to four fold; (iii) fVII-tPDT induced significantly stronger levels of apoptosis and necrosis than ntPDT; and (iv) fVII-tPDT had a significantly stronger effect on inhibiting breast tumour growth in mice than ntPDT.</p> <p>Conclusions</p> <p>We conclude that the fVII-targeted VP PDT that we report here is a novel and effective therapeutic with improved selectivity for the treatment of breast cancer. Since TF is expressed on many types of cancer cells including leukaemic cells and selectively on angiogenic tumour VECs, fVII-tPDT could have broad therapeutic applications for other solid cancers and leukaemia.</p

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

The influence of fiber migration on the mechanical properties of yarns with hierarchical helical structures

An analytical method is presented for studying the influence of fiber migration on the mechanical properties of a yarn with two-level helical structures. The theory of ideal migration is applied to the seven-ply yarn, which results in the exchange of the interior structure of the yarn. A bottom-up method for analyzing the internal forces and stresses of the yarn under axial tension and torsion is developed. The influence of fiber migration is demonstrated by making contrast between the mechanical responses of carbon nanotube yarns with and without fiber migration. The numerical results show that there is a periodical non-monotonic variation in both the internal forces and the stresses with the length of yarn. A stress concentration is revealed around the half-cycle migration point and the one-cycle migration point. It is shown that the chirality, initial helix angle, and the fiber migration pattern can be used to control the mechanical performance of yarns

Predicting learners' effortful behaviour in adaptive assessment using multimodal data

Many factors influence learners' performance on an activity beyond the knowledge required. Learners' on-task effort has been acknowledged for strongly relating to their educational outcomes, reflecting how actively they are engaged in that activity. However, effort is not directly observable. Multimodal data can provide additional insights into the learning processes and may allow for effort estimation. This paper presents an approach for the classification of effort in an adaptive assessment context. Specifically, the behaviour of 32 students was captured during an adaptive self-assessment activity, using logs and physiological data (i.e., eye-tracking, EEG, wristband and facial expressions). We applied k-means to the multimodal data to cluster students' behavioural patterns. Next, we predicted students' effort to complete the upcoming task, based on the discovered behavioural patterns using a combination of Hidden Markov Models (HMMs) and the Viterbi algorithm. We also compared the results with other state-of-the-art classification algorithms (SVM, Random Forest). Our findings provide evidence that HMMs can encode the relationship between effort and behaviour (captured by the multimodal data) in a more efficient way than the other methods. Foremost, a practical implication of the approach is that the derived HMMs also pinpoint the moments to provide preventive/prescriptive feedback to the learners in real-time, by building-upon the relationship between behavioural patterns and the effort the learners are putting in