Tutorial : applying machine learning in behavioral research

Machine-learning algorithms hold promise for revolutionizing how educators and clinicians make decisions. However, researchers in behavior analysis have been slow to adopt this methodology to further develop their understanding of human behavior and improve the application of the science to problems of applied significance. One potential explanation for the scarcity of research is that machine learning is not typically taught as part of training programs in behavior analysis. This tutorial aims to address this barrier by promoting increased research using machine learning in behavior analysis. We present how to apply the random forest, support vector machine, stochastic gradient descent, and k-nearest neighbors algorithms on a small dataset to better identify parents of children with autism who would benefit from a behavior analytic interactive web training. These step-by-step applications should allow researchers to implement machine-learning algorithms with novel research questions and datasets

Effects of robotic-assisted laparoscopic prostatectomy on surgical pathology specimens

Background Robotic-assisted laparoscopic prostatectomy (RALP) has greatly changed clinical management of prostate cancer. It is important for pathologists and urologists to compare RALP with conventional open radical retropubic prostatectomy (RRP), and evaluate their effects on surgical pathology specimens. Methods We retrospectively reviewed and statistically analyzed 262 consecutive RALP (n = 182) and RRP (n = 80) procedures performed in our institution from 2007 to 2010. From these, 49 RALP and 33 RRP cases were randomly selected for additional microscopic examination to analyze the degree of capsular incision and the amount of residual prostate surface adipose tissue. Results Positive surgical margins were present in 28.6% RALP and 57.5% RRP cases, a statistically significant difference. In patients with stage T2c tumors, which represent 61.2% RALP and 63.8% RRP patients, the positive surgical margin rate was 24.1% in the RALP group and 58.8% in the RRP group (statistically significant difference). For other pathologic stages, the differences in positive margins between RALP and RRP groups were not statistically significant. The incidence of positive surgical margins after RALP was related to higher tumor stage, higher Gleason score, higher tumor volume and lower prostate weight, but was not related to the surgeons performing the procedure. When compared with RRP, RALP also caused less severe prostatic capsular incision and maintained larger amounts of residual surface adipose tissue in prostatectomy specimens. Conclusions In this study RALP showed a statistically significant lower positive surgical margin rate than RRP. Analysis of capsular incision and amount of prostatic surface residual adipose tissue suggested that RALP caused less prostatic capsular damage than RRP

The effects of a muscle resistance program on the functional capacity, knee extensor muscle strength and plasma levels of IL-6 and TNF-α in pre-frail elderly women: a randomized crossover clinical trial - a study protocol

<p>Abstract</p> <p>Background</p> <p>With the increase in the elderly population, a growing number of chronic degenerative diseases and a greater dependency on caregivers have been observed. Elderly persons in states of frailty remain more susceptible to significant health complications. There is evidence of an inverse relationship between plasma levels of inflammatory mediators and levels of functionality and muscle strength, suggesting that muscle-strengthening measures can aid in inflammatory conditions. The purpose of this study will be verified the effect of a muscle-strengthening program with load during a ten-week period in pre-frail elderly women with attention to the following outcomes: (1) plasma levels of interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α), (2) functional capacity and (3) knee extensor muscle strength.</p> <p>Methods/Design</p> <p>The study design is a randomized crossover clinical trial evaluating 26 elderly women (regardless of their race and/or social condition) who are community residents, older than 65, and classified as pre-frail according to the criteria previously described by Fried et al. (2004). All subjects will be assessed using the Timed up and go and 10-Meter Walk Test functional tests. The plasma levels of IL-6 and TNF-α will be assessed by ELISA (<it>enzyme-linked immunosorbent assay</it>) with high sensitivity kits (Quantikine^®HS, R&D Systems Minneapolis, MN, U.S.). Knee extensor muscle strength will be assessed using the <it>Byodex System 3 Pro</it>^{<it>® </it>}isokinetic dynamometer at angular speeds of 60 and 180°/s. The intervention will consist of strengthening exercises of the lower extremities at 50 to 70% of 1RM (maximal resistance) three times per week for ten weeks. The volunteers will be randomized into two groups: group E, the intervention group, and group C, the control group that did not initiate any new activities during the initial study period (ten weeks). After the initial period, group C will begin the intervention and group E will maintain everyday activities without exercising. At the end of the total study period, all volunteers will be reassessed.</p> <p>Discussion</p> <p>To demonstrate and discuss possible influences of load-bearing exercises on the modification of plasma levels of IL-6 and TNF-α and in the functional performance of pre-frail elderly women.</p> <p>Trial Registration</p> <p>ISRCTN62824599</p

Streaming histogram sketching for rapid microbiome analytics

Background: The growth in publically available microbiome data in recent years has yielded an invaluable resource for genomic research, allowing for the design of new studies, augmentation of novel datasets and reanalysis of published works. This vast amount of microbiome data, as well as the widespread proliferation of microbiome research and the looming era of clinical metagenomics, means there is an urgent need to develop analytics that can process huge amounts of data in a short amount of time. To address this need, we propose a new method for the compact representation of microbiome sequencing data using similarity-preserving sketches of streaming k-mer spectra. These sketches allow for dissimilarity estimation, rapid microbiome catalogue searching and classification of microbiome samples in near real time. Results: We apply streaming histogram sketching to microbiome samples as a form of dimensionality reduction, creating a compressed ‘histosketch’ that can efficiently represent microbiome k-mer spectra. Using public microbiome datasets, we show that histosketches can be clustered by sample type using the pairwise Jaccard similarity estimation, consequently allowing for rapid microbiome similarity searches via a locality sensitive hashing indexing scheme. Furthermore, we use a ‘real life’ example to show that histosketches can train machine learning classifiers to accurately label microbiome samples. Specifically, using a collection of 108 novel microbiome samples from a cohort of premature neonates, we trained and tested a random forest classifier that could accurately predict whether the neonate had received antibiotic treatment (97% accuracy, 96% precision) and could subsequently be used to classify microbiome data streams in less than 3 s. Conclusions: Our method offers a new approach to rapidly process microbiome data streams, allowing samples to be rapidly clustered, indexed and classified. We also provide our implementation, Histosketching Using Little K-mers (HULK), which can histosketch a typical 2 GB microbiome in 50 s on a standard laptop using four cores, with the sketch occupying 3000 bytes of disk space