Mutual Information Decay Curves and Hyper-Parameter Grid Search Design for Recurrent Neural Architectures

We present an approach to design the grid searches for hyper-parameter optimization for recurrent neural architectures. The basis for this approach is the use of mutual information to analyze long distance dependencies (LDDs) within a dataset. We also report a set of experiments that demonstrate how using this approach, we obtain state-of-the-art results for DilatedRNNs across a range of benchmark datasets.Comment: Published at the 27th International Conference on Neural Information Processing, ICONIP 2020, Bangkok, Thailand, November 18-22, 2020. arXiv admin note: text overlap with arXiv:1810.0296

Creation and suppression of point defects through a kick-out substitution process of Fe in InP

Indium antisite defect In P-related photoluminescence has been observed in Fe-diffused semi-insulating (SI) InP. Compared to annealed undoped or Fe-predoped SI InP, there are fewer defects in SI InP obtained by long-duration, high-temperature Fe diffusion. The suppression of the formation of point defects in Fe-diffused SI InP can be explained in terms of the complete occupation by Fe at indium vacancy. The In P defect is enhanced by the indium interstitial that is caused by the kick out of In and the substitution at the indium site of Fe in the diffusion process. Through these Fe-diffusion results, the nature of the defects in annealed undoped SI InP is better understood. © 2002 American Institute of Physics.published_or_final_versio

Characterizing the malignancy and drug resistance of cancer cells from their membrane resealing response

In this report, we showed that two tumor cell characteristics, namely the malignancy and drug-resistance status can be evaluated by their membrane resealing response. Specifically, membrane pores in a number of pairs of cancer and normal cell lines originated from nasopharynx, lung and intestine were introduced by nano-mechanical puncturing. Interestingly, such nanometer-sized holes in tumor cells can reseal ∼ 2-3 times faster than those in the corresponding normal cells. Furthermore, the membrane resealing time in cancer cell lines exhibiting resistance to several leading chemotherapeutic drugs was also found to be substantially shorter than that in their drug-sensitive counterparts, demonstrating the potential of using this quantity as a novel marker for future cancer diagnosis and drug resistance detection. Finally, a simple model was proposed to explain the observed resealing dynamics of cells which suggested that the distinct response exhibited by normal, tumor and drug resistant cells is likely due to the different tension levels in their lipid membranes, a conclusion that is also supported by direct cortical tension measurement.published_or_final_versio

Can oral infection be a risk factor for Alzheimer’s disease?

Alzheimer’s disease (AD) is a scourge of longevity that will drain enormous resources from public health budgets in the future. Currently, there is no diagnostic biomarker and/or treatment for this most common form of dementia in humans. AD can be of early familial-onset or sporadic with a late-onset. Apart from the two main hallmarks, amyloid-beta and neurofibrillary tangles, inflammation is a characteristic feature of AD neuropathology. Inflammation may be caused by a local central nervous system insult and/or by peripheral infections. Numerous microorganisms are suspected in AD brains ranging from bacteria (mainly oral and non-oral Treponema species), viruses (Herpes simplex type I) and yeasts (Candida species). A causal relationship between periodontal pathogens/non-oral Treponema species of bacteria has been proposed via the amyloid-beta and inflammatory links. Periodontitis constitutes a peripheral oral infection that can provide the brain with intact bacteria and virulence factors and inflammatory mediators due to daily, transient bacteraemias. If and when genetic risk factors meet environmental risk factors in the brain, disease is expressed, in which neurocognition may be impacted, leading to the development of dementia. To achieve the goal of finding a diagnostic biomarker and possible prophylactic treatment for AD, there is an initial need to solve the etiological puzzle contributing to its pathogenesis. This review therefore addresses oral infection as the plausible aetiology of late onset AD (LOAD)

On the Inability of Markov Models to Capture Criticality in Human Mobility

We examine the non-Markovian nature of human mobility by exposing the inability of Markov models to capture criticality in human mobility. In particular, the assumed Markovian nature of mobility was used to establish a theoretical upper bound on the predictability of human mobility (expressed as a minimum error probability limit), based on temporally correlated entropy. Since its inception, this bound has been widely used and empirically validated using Markov chains. We show that recurrent-neural architectures can achieve significantly higher predictability, surpassing this widely used upper bound. In order to explain this anomaly, we shed light on several underlying assumptions in previous research works that has resulted in this bias. By evaluating the mobility predictability on real-world datasets, we show that human mobility exhibits scale-invariant long-range correlations, bearing similarity to a power-law decay. This is in contrast to the initial assumption that human mobility follows an exponential decay. This assumption of exponential decay coupled with Lempel-Ziv compression in computing Fano's inequality has led to an inaccurate estimation of the predictability upper bound. We show that this approach inflates the entropy, consequently lowering the upper bound on human mobility predictability. We finally highlight that this approach tends to overlook long-range correlations in human mobility. This explains why recurrent-neural architectures that are designed to handle long-range structural correlations surpass the previously computed upper bound on mobility predictability

Dehydroepiandrosterone (DHEA) supplementation improves in vitro fertilization outcomes of poor ovarian responders, especially in women with low serum concentration of DHEA-S: a retrospective cohort study

Background: Dehydroepiandrosterone (DHEA) is now widely used as an adjuvant for in vitro fertilization (IVF) cycles in poor ovarian responders (PORs). Several studies showed that DHEA supplementation could improve IVF outcomes of PORs. However, most of the PORs do not respond to DHEA clinically. Therefore, the aim of this study is to confirm the beneficial effects of DHEA on IVF outcomes of PORs and to investigate which subgroups of PORs can best benefit from DHEA supplementation. Methods: This retrospective cohort study was performed between January 2015 and December 2017. A total of 151 PORs who fulfilled the Bologna criteria and underwent IVF cycles with the gonadotropin-releasing hormone antagonist protocol were identified. The study group (n = 67) received 90 mg of DHEA daily for an average of 3 months before the IVF cycles. The control group (n = 84) underwent the IVF cycles without DHEA pretreatment. The basic and cycle characteristics and IVF outcomes between the two groups were compared using independent t-tests, Chi-Square tests and binary logistic regression. Results: The study and control groups did not show significant differences in terms of basic characteristics. The study group demonstrated a significantly greater number of retrieved oocytes, metaphase II oocytes, fertilized oocytes, day 3 embryos and top-quality embryos at day 3 and a higher clinical pregnancy rate, ongoing pregnancy rate and live birth rate than those measures in the control group. The multivariate analysis revealed that DHEA supplementation was positively associated with clinical pregnancy rate (OR = 4.93, 95% CI 1.68–14.43, p = 0.004). Additionally, in the study group, the multivariate analysis showed that serum dehydroepiandrosterone-sulfate (DHEA-S) levels < 180 μg/dl were significantly associated with a rate of retrieved oocytes > 3 (OR = 5.92, 95% CI 1.48–23.26, p = 0.012). Conclusions: DHEA supplementation improves IVF outcomes of PORs. In PORs with DHEA pretreatment, women with lower DHEA-S level may have greater possibility of attaining more than 3 oocytes

Discovering monotonic stemness marker genes from time-series stem cell microarray data

© 2015 Wang et al.; licensee BioMed Central Ltd. Background: Identification of genes with ascending or descending monotonic expression patterns over time or stages of stem cells is an important issue in time-series microarray data analysis. We propose a method named Monotonic Feature Selector (MFSelector) based on a concept of total discriminating error (DEtotal) to identify monotonic genes. MFSelector considers various time stages in stage order (i.e., Stage One vs. other stages, Stages One and Two vs. remaining stages and so on) and computes DEtotal of each gene. MFSelector can successfully identify genes with monotonic characteristics.Results: We have demonstrated the effectiveness of MFSelector on two synthetic data sets and two stem cell differentiation data sets: embryonic stem cell neurogenesis (ESCN) and embryonic stem cell vasculogenesis (ESCV) data sets. We have also performed extensive quantitative comparisons of the three monotonic gene selection approaches. Some of the monotonic marker genes such as OCT4, NANOG, BLBP, discovered from the ESCN dataset exhibit consistent behavior with that reported in other studies. The role of monotonic genes found by MFSelector in either stemness or differentiation is validated using information obtained from Gene Ontology analysis and other literature. We justify and demonstrate that descending genes are involved in the proliferation or self-renewal activity of stem cells, while ascending genes are involved in differentiation of stem cells into variant cell lineages.Conclusions: We have developed a novel system, easy to use even with no pre-existing knowledge, to identify gene sets with monotonic expression patterns in multi-stage as well as in time-series genomics matrices. The case studies on ESCN and ESCV have helped to get a better understanding of stemness and differentiation. The novel monotonic marker genes discovered from a data set are found to exhibit consistent behavior in another independent data set, demonstrating the utility of the proposed method. The MFSelector R function and data sets can be downloaded from: http://microarray.ym.edu.tw/tools/MFSelector/

Adaptive Evolutionary Clustering

In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a static clustering method. In this paper, we introduce a different approach to evolutionary clustering by accurately tracking the time-varying proximities between objects followed by static clustering. We present an evolutionary clustering framework that adaptively estimates the optimal smoothing parameter using shrinkage estimation, a statistical approach that improves a naive estimate using additional information. The proposed framework can be used to extend a variety of static clustering algorithms, including hierarchical, k-means, and spectral clustering, into evolutionary clustering algorithms. Experiments on synthetic and real data sets indicate that the proposed framework outperforms static clustering and existing evolutionary clustering algorithms in many scenarios.Comment: To appear in Data Mining and Knowledge Discovery, MATLAB toolbox available at http://tbayes.eecs.umich.edu/xukevin/affec