Factors associated with failed treatment : an analysis of 121,744 women embarking on their first IVF cycles

Peer reviewedPublisher PD

Design and Integration of Electrical Bio-Impedance Sensing in a Bipolar Forceps for Soft Tissue Identification: A Feasibility Study

This paper presents the integration of electrical bio-impedance sensing technology into a bipolar surgical forceps for soft tissue identification during a robotic assisted procedure. The EBI sensing is done by pressing the forceps on the target tissue with a controlled pressing depth and a controlled jaw opening distance. The impact of these 2 parameters are characterized by finite element simulation. Subsequently, an experiment is conducted with 4 types of ex-vivo tissues including liver, kidney, lung and muscle. The experimental results demonstrate that the proposed EBI sensing method can identify these 4 tissue types with an accuracy higher than 92.82%

Relation Modeling with Graph Convolutional Networks for Facial Action Unit Detection

Most existing AU detection works considering AU relationships are relying on probabilistic graphical models with manually extracted features. This paper proposes an end-to-end deep learning framework for facial AU detection with graph convolutional network (GCN) for AU relation modeling, which has not been explored before. In particular, AU related regions are extracted firstly, latent representations full of AU information are learned through an auto-encoder. Moreover, each latent representation vector is feed into GCN as a node, the connection mode of GCN is determined based on the relationships of AUs. Finally, the assembled features updated through GCN are concatenated for AU detection. Extensive experiments on BP4D and DISFA benchmarks demonstrate that our framework significantly outperforms the state-of-the-art methods for facial AU detection. The proposed framework is also validated through a series of ablation studies.Comment: Accepted by MMM202

Nanoscale LiZnN - luminescent half-Heusler quantum dots

Colloidal semiconductor quantum dots are a well-established technology, with numerous materials available either commercially or through the vast body of literature. The prevalent materials are cadmium-based and are unlikely to find general acceptance in most applications. While the III-V family of materials is a likely substitute, issues remain about its long-term suitability, and other earth-abundant materials are being explored. In this report, we highlight a nanoscale half-Heusler semiconductor, LiZnN, composed of readily available elements as a potential alternative system to luminescent II-VI and III-V nanoparticle quantum dots

Transport and Thermodynamic Evidence for a Marginal Fermi Liquid State in ZrZn2_2

Measurements of low temperature transport and thermodynamic properties have been used to characterize the non-Fermi liquid state of the itinerant ferromagnet ZrZn$_2$. We observe a $T^{5/3}$ temperature dependence of the electrical resistivity at zero field, which becomes $T^2$ like in an applied field of 9 T. In zero field we also measured the thermal conductivity, and we see a novel linear in $T$ dependence of the difference between the thermal and electrical resistivities. Heat capacity measurements, also at zero field, reveal an upturn in the electronic contribution at low temperatures when the phonon term is subtracted. Taken together, we argue that these properties are consistent with a marginal Fermi liquid state which is predicted by a mean-field model of enhanced spin fluctuations on the border of ferromagnetism in three dimensions. We compare our data to quantitative predictions and establish this model as a compelling theoretical framework for understanding ZrZn$_2$.Comment: 10 pages, 10 figure

Differential expression analysis with global network adjustment

<p>Background: Large-scale chromosomal deletions or other non-specific perturbations of the transcriptome can alter the expression of hundreds or thousands of genes, and it is of biological interest to understand which genes are most profoundly affected. We present a method for predicting a gene’s expression as a function of other genes thereby accounting for the effect of transcriptional regulation that confounds the identification of genes differentially expressed relative to a regulatory network. The challenge in constructing such models is that the number of possible regulator transcripts within a global network is on the order of thousands, and the number of biological samples is typically on the order of 10. Nevertheless, there are large gene expression databases that can be used to construct networks that could be helpful in modeling transcriptional regulation in smaller experiments.</p> <p>Results: We demonstrate a type of penalized regression model that can be estimated from large gene expression databases, and then applied to smaller experiments. The ridge parameter is selected by minimizing the cross-validation error of the predictions in the independent out-sample. This tends to increase the model stability and leads to a much greater degree of parameter shrinkage, but the resulting biased estimation is mitigated by a second round of regression. Nevertheless, the proposed computationally efficient “over-shrinkage” method outperforms previously used LASSO-based techniques. In two independent datasets, we find that the median proportion of explained variability in expression is approximately 25%, and this results in a substantial increase in the signal-to-noise ratio allowing more powerful inferences on differential gene expression leading to biologically intuitive findings. We also show that a large proportion of gene dependencies are conditional on the biological state, which would be impossible with standard differential expression methods.</p> <p>Conclusions: By adjusting for the effects of the global network on individual genes, both the sensitivity and reliability of differential expression measures are greatly improved.</p&gt

A Combined Deep Learning-Gradient Boosting Machine Framework for Fluid Intelligence Prediction

The ABCD Neurocognitive Prediction Challenge is a community driven competition asking competitors to develop algorithms to predict fluid intelligence score from T1-w MRIs. In this work, we propose a deep learning combined with gradient boosting machine framework to solve this task. We train a convolutional neural network to compress the high dimensional MRI data and learn meaningful image features by predicting the 123 continuous-valued derived data provided with each MRI. These extracted features are then used to train a gradient boosting machine that predicts the residualized fluid intelligence score. Our approach achieved mean square error (MSE) scores of 18.4374, 68.7868, and 96.1806 for the training, validation, and test set respectively.Comment: Challenge in Adolescent Brain Cognitive Development Neurocognitive Predictio

Beyond element-wise interactions: identifying complex interactions in biological processes

Background: Biological processes typically involve the interactions of a number of elements (genes, cells) acting on each others. Such processes are often modelled as networks whose nodes are the elements in question and edges pairwise relations between them (transcription, inhibition). But more often than not, elements actually work cooperatively or competitively to achieve a task. Or an element can act on the interaction between two others, as in the case of an enzyme controlling a reaction rate. We call “complex” these types of interaction and propose ways to identify them from time-series observations. Methodology: We use Granger Causality, a measure of the interaction between two signals, to characterize the influence of an enzyme on a reaction rate. We extend its traditional formulation to the case of multi-dimensional signals in order to capture group interactions, and not only element interactions. Our method is extensively tested on simulated data and applied to three biological datasets: microarray data of the Saccharomyces cerevisiae yeast, local field potential recordings of two brain areas and a metabolic reaction. Conclusions: Our results demonstrate that complex Granger causality can reveal new types of relation between signals and is particularly suited to biological data. Our approach raises some fundamental issues of the systems biology approach since finding all complex causalities (interactions) is an NP hard problem

Head-mounted Sensory Augmentation Device: Comparing Haptic and Audio Modality

This paper investigates and compares the effectiveness of haptic and audio modality for navigation in low visibility environment using a sensory augmentation device. A second generation head-mounted vibrotactile interface as a sensory augmentation prototype was developed to help users to navigate in such environments. In our experiment, a subject navigates along a wall relying on the haptic or audio feedbacks as navigation commands. Haptic/audio feedback is presented to the subjects according to the information measured from the walls to a set of 12 ultrasound sensors placed around a helmet and a classification algorithm by using multilayer perceptron neural network. Results showed the haptic modality leads to significantly lower route deviation in navigation compared to auditory feedback. Furthermore, the NASA TLX questionnaire showed that subjects reported lower cognitive workload with haptic modality although both modalities were able to navigate the users along the wall

Cactus pear: a natural product in cancer chemoprevention

BACKGROUND: Cancer chemoprevention is a new approach in cancer prevention, in which chemical agents are used to prevent cancer in normal and/or high-risk populations. Although chemoprevention has shown promise in some epithelial cancers, currently available preventive agents are limited and the agents are costly, generally with side effects. Natural products, such as grape seed, green tea, and certain herbs have demonstrated anti-cancer effects. To find a natural product that can be used in chemoprevention of cancer, we tested Arizona cactus fruit solution, the aqueous extracts of cactus pear, for its anti-cancer effects in cultured cells and in an animal model. METHOD: Aqueous extracts of cactus pear were used to treat immortalized ovarian and cervical epithelial cells, as well as ovarian, cervical, and bladder cancer cells. Aqueous extracts of cactus pear were used at six concentrations (0, 0.5, 1, 5, 10 or 25%) to treat cells for 1, 3, or 5 days. Growth inhibition, apoptosis induction, and cell cycle changes were analyzed in the cultured cells; the suppression of tumor growth in nude mice was evaluated and compared with the effect of a synthetic retinoid N-(4-hydroxyphernyl) retinamide (4-HPR), which is currently used as a chemoprevention agent. Immunohistochemistry staining of tissue samples from animal tumors was performed to examine the gene expression. RESULTS: Cells exposed to cactus pear extracts had a significant increase in apoptosis and growth inhibition in both immortalized epithelial cells and cancer cells in a dose- and time-dependent manner. It also affected cell cycle of cancer cells by increasing G1 and decreasing G2 and S phases. Both 4-HPR and cactus pear extracts significantly suppressed tumor growth in nude mice, increased annexin IV expression, and decreased VEGF expression. CONCLUSION: Arizona cactus pear extracts effectively inhibited cell growth in several different immortalized and cancer cell cultures, suppressed tumor growth in nude mice, and modulated expression of tumor-related genes. These effects were comparable with those caused by a synthetic retinoid currently used in chemoprevention trials. The mechanism of the anti-cancer effects of cactus pear extracts needs to be further studied