The Cerebral Haemorrhage Anatomical RaTing inStrument (CHARTS): Development and assessment of reliability.

PURPOSE: The causes, risk factors and prognosis of spontaneous intracerebral haemorrhage (ICH) are partly determined by anatomical location (specifically, lobar vs. non-lobar (deep and infratentorial) regions). We systematically developed a rating instrument to reliably classify ICH location. METHODS: We used a two-stage iterative Delphi-style method for instrument development. The resultant Cerebral Haemorrhage Anatomical RaTing inStrument (CHARTS) was validated on CT and MRI scans from a cohort of consecutive patients with acute spontaneous symptomatic ICH by three independent raters. We tested interrater and intrarater reliability using kappa statistics. RESULTS: Our validation cohort included 227 patients (58% male; median age: 72.4 (IQR: 67.1-74.6)). The interrater reliability for the main analyses (i.e. including any lobar ICH; all deep and infratentorial anatomical categories (lentiform, caudate thalamus; brainstem; cerebellum); and uncertain location) was excellent (all kappa values>0.80) both in pair-wise between-rater comparisons and across all raters. The intrarater reliability was substantial to almost perfect (k=0.83; 95%CI: 0.77-0.88 and k=0.95; 95%CI: 0.92-0.96 respectively). All kappa statistics remained consistent for individual cerebral lobar regions. CONCLUSIONS: The CHARTS instrument can be used to reliably and comprehensively map the anatomical location of spontaneous ICH, and may be helpful for studying important questions regarding causes, risk factors, prognosis, and for stratification in clinical trials

Crossover from Kondo assisted suppression to co-tunneling enhancement of tunneling magnetoresistance via ferromagnetic nanodots in MgO tunnel barriers

Recently, it has been shown that magnetic tunnel junctions with thin MgO tunnel barriers exhibit extraordinarily high tunneling magnetoresistance (TMR) values at room temperature1, 2. However, the physics of spin dependent tunneling through MgO barriers is only beginning to be unravelled. Using planar magnetic tunnel junctions in which ultra-thin layers of magnetic metals are deposited in the middle of a MgO tunnel barrier here we demonstrate that the TMR is strongly modified when these layers are discontinuous and composed of small pancake shaped nanodots. At low temperatures, in the Coulomb blockade regime, for layers less than ~1 nm thick, the conductance of the junction is increased at low bias consistent with Kondo assisted tunneling. In the same regime we observe a suppression of the TMR. For slightly thicker layers, and correspondingly larger nanodots, the TMR is enhanced at low bias, consistent with co-tunneling.Comment: Nano Letters (in press

Experimental study on the effect of shape of bolt and nut on fatigue strength for bolted joint

In this study, the effect of curvature radius of the thread bottom and the pitch difference between of M16 bolt and nut on fatigue strength for bolted joint is considered experimentally. The M16 bolt-nut specimens having the two kinds of thread bottom radii and the pitch differences are prepared. The S-N curves for bolted specimens with different thread shapes are obtained by the stress-controlled fatigue test (stress ratio R>0). The experimental results are compared and discussed in terms of stress analysis. The finite element method is used to make a simulation of the fatigue experiment and the mean stress and stress amplitude at each thread bottom of bolt are analysed. It is found that the initiation and propagation of crack are changed by introducing the pitch difference of α=15 μm from the crack observation in cross section of the bolt specimens after the experiment. Furthermore, the fatigue life can be extended by increasing curvature radius of thread bottom and introducing the pitch difference.2018 International Conference on Material Strength and Applied Mechanics (MSAM 2018), 10–13 April 2018, Kitakyushu City, Japa

Spin Diode Based on Fe/MgO Double Tunnel Junction

We demonstrate a spin diode consisting of a semiconductor free nano-scale Fe/MgO-based double tunnel junction. The device exhibits a near perfect spin-valve effect combined with a strong diode effect. The mechanism consistent with our data is resonant tunneling through discrete states in the middle ferromagnetic layer sandwiched by tunnel barriers of different spin-dependent transparency. The observed magneto-resistance is record high, ~4000%, essentially making the structure an on/off spin-switch. This, combined with the strong diode effect, ~100, offers a new device that should be promising for such technologies as magnetic random access memory and re-programmable logic.Comment: 14 page

Exploiting likely-positive and unlabeled data to improve the identification of protein-protein interaction articles

<p>Abstract</p> <p>Background</p> <p>Experimentally verified protein-protein interactions (PPI) cannot be easily retrieved by researchers unless they are stored in PPI databases. The curation of such databases can be made faster by ranking newly-published articles' relevance to PPI, a task which we approach here by designing a machine-learning-based PPI classifier. All classifiers require labeled data, and the more labeled data available, the more reliable they become. Although many PPI databases with large numbers of labeled articles are available, incorporating these databases into the base training data may actually reduce classification performance since the supplementary databases may not annotate exactly the same PPI types as the base training data. Our first goal in this paper is to find a method of selecting likely positive data from such supplementary databases. Only extracting likely positive data, however, will bias the classification model unless sufficient negative data is also added. Unfortunately, negative data is very hard to obtain because there are no resources that compile such information. Therefore, our second aim is to select such negative data from unlabeled PubMed data. Thirdly, we explore how to exploit these likely positive and negative data. And lastly, we look at the somewhat unrelated question of which term-weighting scheme is most effective for identifying PPI-related articles.</p> <p>Results</p> <p>To evaluate the performance of our PPI text classifier, we conducted experiments based on the BioCreAtIvE-II IAS dataset. Our results show that adding likely-labeled data generally increases AUC by 3~6%, indicating better ranking ability. Our experiments also show that our newly-proposed term-weighting scheme has the highest AUC among all common weighting schemes. Our final model achieves an F-measure and AUC 2.9% and 5.0% higher than those of the top-ranking system in the IAS challenge.</p> <p>Conclusion</p> <p>Our experiments demonstrate the effectiveness of integrating unlabeled and likely labeled data to augment a PPI text classification system. Our mixed model is suitable for ranking purposes whereas our hierarchical model is better for filtering. In addition, our results indicate that supervised weighting schemes outperform unsupervised ones. Our newly-proposed weighting scheme, TFBRF, which considers documents that do not contain the target word, avoids some of the biases found in traditional weighting schemes. Our experiment results show TFBRF to be the most effective among several other top weighting schemes.</p

Retrospective time-trend study of polybrominated diphenyl ether and polybrominated and polychlorinated biphenyl levels in human serum from the United States.

Six polybrominated diphenyl ethers (PBDEs), one hexabromobiphenyl [polybrominated biphenyl (PBB)], and one hexachlorobiphenyl [polychlorinated biphenyl (PCB)] were measured in 40 human serum pools collected in the southeastern United States during 1985 through 2002 and in Seattle, Washington, for 1999 through 2002. The concentrations of most of the PBDEs, which are commercially used as flame retardants in common household and commercial applications, had significant positive correlations with time of sample collection, showing that the concentrations of these compounds are increasing in serum collected in the United States. In contrast, PCB and PBB levels were negatively correlated with sample collection year, indicating that the levels of these compounds have been decreasing since their phaseout in the 1970s

Linguistic feature analysis for protein interaction extraction

<p>Abstract</p> <p>Background</p> <p>The rapid growth of the amount of publicly available reports on biomedical experimental results has recently caused a boost of text mining approaches for protein interaction extraction. Most approaches rely implicitly or explicitly on linguistic, i.e., lexical and syntactic, data extracted from text. However, only few attempts have been made to evaluate the contribution of the different feature types. In this work, we contribute to this evaluation by studying the relative importance of deep syntactic features, i.e., grammatical relations, shallow syntactic features (part-of-speech information) and lexical features. For this purpose, we use a recently proposed approach that uses support vector machines with structured kernels.</p> <p>Results</p> <p>Our results reveal that the contribution of the different feature types varies for the different data sets on which the experiments were conducted. The smaller the training corpus compared to the test data, the more important the role of grammatical relations becomes. Moreover, deep syntactic information based classifiers prove to be more robust on heterogeneous texts where no or only limited common vocabulary is shared.</p> <p>Conclusion</p> <p>Our findings suggest that grammatical relations play an important role in the interaction extraction task. Moreover, the net advantage of adding lexical and shallow syntactic features is small related to the number of added features. This implies that efficient classifiers can be built by using only a small fraction of the features that are typically being used in recent approaches.</p

Combination therapy with irinotecan and cisplatin as neoadjuvant chemotherapy in locally advanced cervical cancer

To evaluate the response rate and toxicity of the combination of irinotecan (CPT-11) and cisplatin in a neoadjuvant setting, a phase II study was conducted regarding the regimen of this combination in patients with locally advanced cervical cancer. Eligibility included patients with previously untreated stage Ib2, IIb, or IIIb squamous cell carcinoma with good performance status. CPT-11 (60 mg m−2) was administered intravenously on days 1, 8 and 15, followed by cisplatin (60 mg m−2) given intravenously on day 1. Treatment was repeated every 4 weeks for a total of two or three cycles. Among 23 eligible patients (median age: 59 years), three showed complete response (13%), 15 showed partial response (65%), for an overall response rate of 78% (95% confidence interval 58–90%). Stable disease was observed in four cases (17%) and progressive disease in one (4%). The median time to failure and median survival time have not yet been reached. Of the 52 treatment cycles administered, diarrhoea and grade 3 or 4 neutropenia were observed in 10% and 75% respectively. There were no therapy-related deaths. The combination of CPT-11 with cisplatin is a promising regimen for neoadjuvant chemotherapy in locally advanced cervical cancer. The toxicities of this regimen are well tolerated. © 1999 Cancer Research Campaig