Bio-inspired 0.35μm CMOS Time-to-Digital Converter with 29.3ps LSB

Time-to-digital converter (TDC) integrated circuit is introduced in this paper. It is based on chain of delay elements composing a regular scalable structure. The scheme is analogous to the sound direction sensitivity nerve system found in barn owl. The circuit occupies small silicon area, and its direct mapping from time to position-code makes conversion rates up to 500Msps possible. Specialty of the circuit is the structural and functional symmetry. Therefore the role of start and stop signals are interchangeable. In other words negative delay is acceptable: the circuit has no dead time problems. These are benefits of the biology model of the auditory scene representation in the bird's brain. The prototype chip is implemented in 0.35μm CMOS having less than 30ps single-shot resolution in the measurements.Hungarian National Research Foundation TS4085

An Intelligent Model for Pairs Trading Using Genetic Algorithms

Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice

A powerful and efficient multivariate approach for voxel-level connectome-wide association studies

We describe an approach to multivariate analysis, termed structured kernel principal component regression (sKPCR), to identify associations in voxel-level connectomes using resting-state functional magnetic resonance imaging (rsfMRI) data. This powerful and computationally efficient multivariate method can identify voxel-phenotype associations based on the whole-brain connectivity pattern of voxels, and it can detect linear and non-linear signals in both volume-based and surface-based rsfMRI data. For each voxel, sKPCR first extracts low-dimensional signals from the spatially smoothed connectivities by structured kernel principal component analysis, and then tests the voxel-phenotype associations by an adaptive regression model. The method's power is derived from appropriately modelling the spatial structure of the data when performing dimension reduction, and then adaptively choosing an optimal dimension for association testing using the adaptive regression strategy. Simulations based on real connectome data have shown that sKPCR can accurately control the false-positive rate and that it is more powerful than many state-of-the-art approaches, such as the connectivity-wise generalized linear model (GLM) approach, multivariate distance matrix regression (MDMR), adaptive sum of powered score (aSPU) test, and least-square kernel machine (LSKM). Moreover, since sKPCR can reduce the computational cost of non-parametric permutation tests, its computation speed is much faster. To demonstrate the utility of sKPCR for real data analysis, we have also compared sKPCR with the above methods based on the identification of voxel-wise differences between schizophrenic patients and healthy controls in four independent rsfMRI datasets. The results showed that sKPCR had better between-sites reproducibility and a larger proportion of overlap with existing schizophrenia meta-analysis findings. Code for our approach can be downloaded from https://github.com/weikanggong/sKPCR. [Abstract copyright: Copyright © 2018 Elsevier Inc. All rights reserved.

Increased functional connectivity of the posterior cingulate cortex with the lateral orbitofrontal cortex in depression

To analyze the functioning of the posterior cingulate cortex (PCC) in depression, we performed the first fully voxel-level resting state functional-connectivity neuroimaging analysis of depression of the PCC, with 336 patients with major depressive disorder and 350 controls. Voxels in the PCC had significantly increased functional connectivity with the lateral orbitofrontal cortex, a region implicated in non-reward and which is thereby implicated in depression. In patients receiving medication, the functional connectivity between the lateral orbitofrontal cortex and PCC was decreased back towards that in the controls. In the 350 controls, it was shown that the PCC has high functional connectivity with the parahippocampal regions which are involved in memory. The findings support the theory that the non-reward system in the lateral orbitofrontal cortex has increased effects on memory systems, which contribute to the rumination about sad memories and events in depression. These new findings provide evidence that a key target to ameliorate depression is the lateral orbitofrontal cortex

Functional connectivity of the human amygdala in health and in depression

To analyze the functioning of the amygdala in depression, we performed the first voxel-level resting state functional-connectivity neuroimaging analysis of depression of voxels in the amygdala with all other voxels in the brain, with 336 patients with major depressive disorder and 350 controls. Amygdala voxels had decreased functional connectivity with the orbitofrontal cortex, temporal lobe areas, including the temporal pole, inferior temporal gyrus, and the parahippocampal gyrus. The reductions in the strengths of the functional connectivity of the amygdala voxels with the medial orbitofrontal cortex and temporal lobe voxels were correlated with increases in the Beck Depression Inventory score and in the duration of illness measures of depression. Parcellation analysis in 350 healthy controls based on voxel-level functional connectivity showed that the basal division of the amygdala has high functional connectivity with medial orbitofrontal cortex areas, and the dorsolateral amygdala has strong functional connectivity with the lateral orbitofrontal cortex and related ventral parts of the inferior frontal gyrus. In depression, the basal amygdala division had especially reduced functional connectivity with the medial orbitofrontal cortex which is involved in reward; and the dorsolateral amygdala subdivision had relatively reduced functional connectivity with the lateral orbitofrontal cortex which is involved in non-reward

FLJ10540 is associated with tumor progression in nasopharyngeal carcinomas and contributes to nasopharyngeal cell proliferation, and metastasis via osteopontin/CD44 pathway

BACKGROUND: Nasopharyngeal carcinoma (NPC) is well-known for its highly metastatic characteristics, but little is known of its molecular mechanisms. New biomarkers that predict clinical outcome, in particular the ability of the primary tumor to develop metastatic tumors are urgently needed. The aim of this study is to investigate the role of FLJ10540 in human NPC development. METHODS: A bioinformatics approach was used to explore the potentially important regulatory genes involved in the growth/metastasis control of NPC. FLJ10540 was chosen for this study. Two co-expression strategies from NPC microarray were employed to identify the relationship between FLJ10540 and osteopontin. Quantitative-RT-PCR, immunoblotting, and immunohistochemistry analysis were used to investigate the mRNA and protein expression profiles of FLJ10540 and osteopontin in the normal and NPC tissues to confirm microarray results. TW01 and Hone1 NPC cells with overexpression FLJ10540 or siRNA to repress endogenous FLJ10540 were generated by stable transfection to further elucidate the molecular mechanisms of FLJ10540-elicited cell growth and metastasis under osteopontin stimulation. RESULTS: We found that osteopontin expression exhibited a positive correlation with FLJ10540 in NPC microarray. We also demonstrated comprehensively that FLJ10540 and osteopontin were not only overexpressed in NPC specimens, but also significantly correlated with advanced tumor and lymph node-metastasis stages, and had a poor 5-year survival rate, respectively. Stimulation of NPC parental cells with osteopontin results in an increase in FLJ10540 mRNA and protein expressions. Functionally, FLJ10540 transfectant alone, or stimulated with osteopontin, exhibited fast growth and increased metastasis as compared to vehicle control with or without osteopontin stimulation. Conversely, knockdown of FLJ10540 by siRNA results in the suppression of NPC cell growth and motility. Treatment with anti-CD44 antibodies in NPC parental cells not only resulted in a decrease of FLJ10540 protein, but also affected the abilities of FLJ10540-elicited cell growth and motility in osteopontin stimulated-NPC cells. CONCLUSIONS: These findings suggest that FLJ10540 may be critical regulator of disease progression in NPC, and the underlying mechanism may involve in the osteopontin/CD44 pathway

Prevalence of PIK3CA mutations in Taiwanese patients with breast cancer: a retrospective next-generation sequencing database analysis

BackgroundBreast cancer is the most common cancer type that affects women. In hormone receptor–positive (HR+), human epidermal growth factor receptor 2−negative (HER2–) advanced breast cancer (ABC), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA) is the most frequently mutated gene associated with poor prognosis. This study evaluated the frequency of PIK3CA mutations in the Taiwanese breast cancer population.MethodologyThis is a retrospective study; patient data were collected for 2 years from a next-generation sequencing database linked to electronic health records (EHRs). The primary endpoint was the regional prevalence of PIK3CA mutation. The secondary endpoints were to decipher the mutation types across breast cancer subtype, menopausal status, and time to treatment failure after everolimus (an mTOR inhibitor) or cyclin-dependent kinase 4/6 (CDK4/6) inhibitor treatment.ResultsPIK3CA mutations were identified in 278 of 728 patients (38%). PIK3CA mutations were reported in 43% of patients with HR−/HER2+ subtype and 42% of patients with HR+/HER2– postmenopausal status. A lower prevalence of PIK3CA mutations was observed in triple-negative (27%) and HR+/HER2– premenopausal patients (29%). The most common mutation was at exon 20 (H1047R mutation, 41.6%), followed by exon 9 (E545K mutation, 18.9% and E542K mutation, 10.3%). Among patients treated with CDK4/6 inhibitors, the median time to treatment failure was 12 months (95% CI: 7-21 months) in the PIK3CA mutation cohort and 16 months (95% CI: 11-23 months) in the PIK3CA wild-type cohort, whereas patients receiving an mTOR inhibitor reported a median time to treatment failure of 20.5 months (95% CI: 8-33 months) in the PIK3CA mutation cohort and 6 months (95% CI: 2-9 months) in the PIK3CA wild-type cohort.ConclusionA high frequency of PIK3CA mutations was detected in Taiwanese patients with breast cancer, which was consistent with previous studies. Early detection of PIK3CA mutations might influence therapeutic decisions, leading to better treatment outcomes

An Overview of Regional Experiments on Biomass Burning Aerosols and Related Pollutants in Southeast Asia: From BASE-ASIA and the Dongsha Experiment to 7-SEAS

By modulating the Earth-atmosphere energy, hydrological and biogeochemical cycles, and affecting regional-to-global weather and climate, biomass burning is recognized as one of the major factors affecting the global carbon cycle. However, few comprehensive and wide-ranging experiments have been conducted to characterize biomass-burning pollutants in Southeast Asia (SEA) or assess their regional impact on meteorology, the hydrological cycle, the radiative budget, or climate change. Recently, BASEASIA (Biomass-burning Aerosols in South-East Asia: Smoke Impact Assessment) and the 7-SEAS (7- South-East Asian Studies) Dongsha Experiment were conducted during the spring seasons of 2006 and 2010 in northern SEA, respectively, to characterize the chemical, physical, and radiative properties of biomass-burning emissions near the source regions, and assess their effects. This paper provides an overview of results from these two campaigns and related studies collected in this special issue, entitled Observation, modeling and impact studies of biomass burning and pollution in the SE Asian Environment. This volume includes 28 papers, which provide a synopsis of the experiments, regional weatherclimate, chemical characterization of biomass-burning aerosols and related pollutants in source and sink regions, the spatial distribution of air toxics (atmospheric mercury and dioxins) in source and remote areas, a characterization of aerosol physical, optical, and radiative properties, as well as modeling and impact studies. These studies, taken together, provide the first relatively complete dataset of aerosol chemistry and physical observations conducted in the sourcesink region in the northern SEA, with particular emphasis on the marine boundary layer and lower free troposphere (LFT). The data, analysis and modeling included in these papers advance our present knowledge of source characterization of biomass-burning pollutants near the source regions as well as the physical and chemical processes along transport pathways. In addition, we raise key questions to be addressed by a coming deployment during springtime 2013 in northern SEA, named 7-SEASBASELInE (Biomass-burning Aerosols Stratocumulus Environment: Lifecycles and Interactions Experiment). This campaign will include a synergistic approach for further exploring many key atmospheric processes (e.g., complex aerosol-cloud interactions) and impacts of biomass burning on the surface-atmosphere energy budgets during the lifecycles of biomass burning emissions