mbonsai: Application Package for Sequence Classification by Tree Methodology

In many applications such as transaction data analysis, the classification of long chains of sequences is required. For example, brand purchase history in customer transaction data is in a form like AABCABAA, where A, B, and C are brands of a consumer product. The decision tree-based package mbonsai is designed to handle sequence data of varying lengths using one or multiple variables of interest as predictor variables. This software package uses tree growing and pruning strategies adopted from C4.5 and CART algorithms, and includes new features for handling sequence data and indexing for classification purpose. The software uses a simple command line program for learning and predicting processes, and has the ability to generate user-friendly graphics depicting decision trees. The underlying C++ codes are designed to efficiently process large data sets in ASCII files. Two examples from transaction data sets are used to illustrate the application of mbonsai

Biomarkers Predictive of Distant Disease-free Survival Derived from Diffusion-weighted Imaging of Breast Cancer

Purpose: To investigate whether intravoxel incoherent motion (IVIM) and/or non-Gaussian diffusion parameters are associated with distant disease-free survival (DDFS) in patients with invasive breast cancer. Methods: From May 2013 to March 2015, 101 patients (mean age 60.0, range 28-88) with invasive breast cancer were evaluated prospectively. IVIM parameters (flowing blood volume fraction [ɪᴠɪᴍ] and pseudodiffusion coefficient [D*]) and non-Gaussian diffusion parameters (theoretical apparent diffusion coefficient [ADC] at a b value of 0 s/mm² [ADC₀] and kurtosis [K]) were estimated using a diffusion-weighted imaging series of 16 b values up to 2500 s/mm². Shifted ADC values (sADC₂₀₀-₁₅₀₀) and standard ADC values (ADC₀-₈₀₀) were also calculated. The Kaplan-Meier method was used to generate survival analyses for DDFS, which were compared using the log-rank test. Univariable Cox proportional hazards models were used to assess any associations between each parameter and distant metastasis-free survival. Results: The median observation period was 80 months (range, 35-92 months). Among the 101 patients, 12 (11.9%) developed distant metastasis, with a median time to metastasis of 79 months (range, 10-92 months). Kaplan-Meier analysis showed that DDFS was significantly shorter in patients with K > 0.98 than in those with K ≤ 0.98 ( = 0.04). Cox regression analysis showed a marginal statistical association between K and distant metastasis-free survival ( = 0.05). Conclusion: Non-Gaussian diffusion may be associated with prognosis in invasive breast cancer. A higher K may be a marker to help identify patients at an elevated risk of distant metastasis, which could guide subsequent treatment

Comparison of TGSE-BLADE DWI, RESOLVE DWI, and SS-EPI DWI in healthy volunteers and patients after cerebral aneurysm clipping

Diffusion-weighted magnetic resonance imaging is prone to have susceptibility artifacts in an inhomogeneous magnetic field. We compared distortion and artifacts among three diffusion acquisition techniques (single-shot echo-planar imaging [SS-EPI DWI], readout-segmented EPI [RESOLVE DWI], and 2D turbo gradient- and spin-echo diffusion-weighted imaging with non-Cartesian BLADE trajectory [TGSE-BLADE DWI]) in healthy volunteers and in patients with a cerebral aneurysm clip. Seventeen healthy volunteers and 20 patients who had undergone surgical cerebral aneurysm clipping were prospectively enrolled. SS-EPI DWI, RESOLVE DWI, and TGSE-BLADE DWI of the brain were performed using 3 T scanners. Distortion was the least in TGSE-BLADE DWI, and lower in RESOLVE DWI than SS-EPI DWI near air–bone interfaces in healthy volunteers (P < 0.001). Length of clip-induced artifact and distortion near the metal clip were the least in TGSE-BLADE DWI, and lower in RESOLVE DWI than SS-EPI DWI (P < 0.01). Image quality scores for geometric distortion, susceptibility artifacts, and overall image quality in both healthy volunteers and patients were the best in TGSE-BLADE DWI, and better in RESOLVE DWI than SS-EPI DWI (P < 0.001). Among the three DWI sequences, image quality was the best in TGSE-BLADE DWI in terms of distortion and artifacts, in both healthy volunteers and patients with an aneurysm clip

Feasibility of Dedicated Breast Positron Emission Tomography Image Denoising Using a Residual Neural Network

Objective(s): This study aimed to create a deep learning (DL)-based denoising model using a residual neural network (Res-Net) trained to reduce noise in ring-type dedicated breast positron emission tomography (dbPET) images acquired in about half the emission time, and to evaluate the feasibility and the effectiveness of the model in terms of its noise reduction performance and preservation of quantitative values compared to conventional post-image filtering techniques.Methods: Low-count (LC) and full-count (FC) PET images with acquisition durations of 3 and 7 minutes, respectively, were reconstructed. A Res-Net was trained to create a noise reduction model using fifteen patients’ data. The inputs to the network were LC images and its outputs were denoised PET (LC + DL) images, which should resemble FC images. To evaluate the LC + DL images, Gaussian and non-local mean (NLM) filters were applied to the LC images (LC + Gaussian and LC + NLM, respectively). To create reference images, a Gaussian filter was applied to the FC images (FC + Gaussian). The usefulness of our denoising model was objectively and visually evaluated using test data set of thirteen patients. The coefficient of variation (CV) of background fibroglandular tissue or fat tissue were measured to evaluate the performance of the noise reduction. The SUVmax and SUVpeak of lesions were also measured. The agreement of the SUV measurements was evaluated by Bland–Altman plots.Results: The CV of background fibroglandular tissue in the LC + DL images was significantly lower (9.10 2.76) than the CVs in the LC (13.60  3.66) and LC + Gaussian images (11.51  3.56). No significant difference was observed in both SUVmax and SUVpeak of lesions between LC + DL and reference images. For the visual assessment, the smoothness rating for the LC + DL images was significantly better than that for the other images except for the reference images.Conclusion: Our model reduced the noise in dbPET images acquired in about half the emission time while preserving quantitative values of lesions. This study demonstrates that machine learning is feasible and potentially performs better than conventional post-image filtering in dbPET denoising

High-density Integrated Linkage Map Based on SSR Markers in Soybean

A well-saturated molecular linkage map is a prerequisite for modern plant breeding. Several genetic maps have been developed for soybean with various types of molecular markers. Simple sequence repeats (SSRs) are single-locus markers with high allelic variation and are widely applicable to different genotypes. We have now mapped 1810 SSR or sequence-tagged site markers in one or more of three recombinant inbred populations of soybean (the US cultivar ‘Jack’ × the Japanese cultivar ‘Fukuyutaka’, the Chinese cultivar ‘Peking’ × the Japanese cultivar ‘Akita’, and the Japanese cultivar ‘Misuzudaizu’ × the Chinese breeding line ‘Moshidou Gong 503’) and have aligned these markers with the 20 consensus linkage groups (LGs). The total length of the integrated linkage map was 2442.9 cM, and the average number of molecular markers was 90.5 (range of 70–114) for the 20 LGs. We examined allelic diversity for 1238 of the SSR markers among 23 soybean cultivars or lines and a wild accession. The number of alleles per locus ranged from 2 to 7, with an average of 2.8. Our high-density linkage map should facilitate ongoing and future genomic research such as analysis of quantitative trait loci and positional cloning in addition to marker-assisted selection in soybean breeding

Analysis Using Popularity Awareness Index, Recency Index and Purchase Diversity in Group Buying

We propose new metrics for customers’ purchasing behaviors in a group buying coupon website, based on HITS algorithms and information entropy: that is, popularity awareness index, recency index, and purchase diversity. These indices are used to classify customers and predict future behaviors. This paper includes definitions of these new indices to be used in real group buying websites. In these websites, adequate characteristics for customers are strongly required and are critical for marketing purpose. We will also provide some experimental results on real data set, including customer segmentation used in future marketing planning