Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcriptomic analysis with the progress of symbiosis in ‘crack-entry’ legume Arachis hypogaea highlights its contrast with ‘infection thread’ adapted legumes

In root-nodule symbiosis, rhizobial invasion and nodule organogenesis is host controlled. In most legumes, rhizobia enter through infection-threads and nodule primordium in the cortex is induced from a distance. But in dalbergoid legumes like Arachis hypogaea, rhizobia directly invade cortical cells through epidermal cracks to generate the primordia. Herein we report the transcriptional dynamics with the progress of symbiosis in A. hypogaea at 1dpi: invasion; 4dpi: nodule primordia; 8dpi: spread of infection in nodule-like structure; 12dpi: immature nodules containing rod-shaped rhizobia; and 21dpi: mature nodules with spherical symbiosomes. Expression of putative orthologue of symbiotic genes in ‘crack-entry’ legume A. hypogaea was compared with infection thread adapted model legumes. The contrasting features were (i) higher expression of receptors like LYR3, EPR3 as compared to canonical NFRs (ii) late induction of transcription factors like NIN, NSP2 and constitutive high expression of ERF1, EIN2, bHLH476 and (iii) induction of divergent pathogenesis responsive PR-1 genes. Additionally, symbiotic orthologues of SymCRK, FLOT4, ROP6, RR9, NOOT and SEN1 were not detectable and microsynteny analysis indicated the absence of RPG and DNF2 homologues in diploid parental genomes of A. hypogaea. The implications are discussed and a molecular framework that guide ‘crack-entry’ symbiosis in A. hypogaea is proposed.
Impact of coexistent chronic obstructive pulmonary disease on the survival of patients with small cell lung cancer receiving chemotherapy

While there is growing interest in the correlation between chronic obstructive pulmonary disease (COPD) and non‐small cell lung cancer, very few studies have examined the interaction between COPD and small cell lung cancer (SCLC). Therefore, the aim of this study was to examine the impact of COPD on the survival of patients with SCLC.
CosmoFlow: Using Deep Learning to Learn the Universe at Scale

Deep learning is a promising tool to determine the physical model that describes our universe. To handle the considerable computational cost of this problem, we present CosmoFlow: a highly scalable deep learning application built on top of the TensorFlow framework. CosmoFlow uses efficient implementations of 3D convolution and pooling primitives, together with improvements in threading for many element-wise operations, to improve training performance on Intel® Xeon Phi™ processors. We also utilize the Cray PE Machine Learning Plugin for efficient scaling to multiple nodes. We demonstrate fully synchronous data-parallel training on 8192 nodes of Cori with 77% parallel efficiency, achieving 3.5 Pflop/s sustained performance. To our knowledge, this is the first large-scale science application of the TensorFlow framework at supercomputer scale with fully-synchronous training. These enhancements enable us to process large 3D dark matter distribution and predict the cosmological parameters ΩsubM/sub, σsub8/sub and nsubs/sub with unprecedented accuracy.
Lower Order Modulation Aided BER Reduction in OFDM With Index Modulation

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) is a multi-carrier transmission scheme in which a set of sub-carriers is chosen and modulated according to the incoming bit-stream. It is already established that for the same average bits per channel use, OFDM-IM can provide a better error rate performance when compared to conventional OFDM. In this letter, we propose a novel method to further improve the error rate performance of OFDM-IM with the aid of lower order signal constellations, by using fewer bits for symbol selection and using the remaining bits to select the index combinations. As the increased number of indexing bits demand more index combinations for mapping them, certain index combinations are reused, with the reuse made unambiguous by rotating lower order constellation. This approach provides significant signal-to-noise ratio gains without compromising spectral efficiency when compared to existing OFDM-IM schemes.
Tamoxifen Directly Interacts with the Dopamine Transporter

The selective estrogen receptor modulator tamoxifen increases extracellular dopamine in vivo and acts as a neuroprotectant in models of dopamine neurotoxicity. We investigated the effect of tamoxifen on dopamine transporter (DAT)–mediated dopamine uptake, dopamine efflux, and [3H]WIN 35,428 [(–)-2-β-carbomethoxy-3-β-(4-fluorophenyl)tropane] binding in rat striatal tissue. Tamoxifen dose-dependently blocked dopamine uptake (54% reduction at 10 μM) and amphetamine-stimulated efflux (59% reduction at 10 μM) in synaptosomes. It also produced a small but significant reduction in [3H]WIN 35,428 binding in striatal membranes, indicating a weak interaction with the substrate binding site in the DAT. Biotinylation and cysteine accessibility studies indicated that tamoxifen stabilizes the outward-facing conformation of the DAT in a cocaine-like manner and does not affect surface expression of the DAT. Additional studies with mutant DAT constructs D476A and I159A suggested a direct interaction between tamoxifen and a secondary substrate binding site of the transporter. Locomotor studies revealed that tamoxifen attenuates amphetamine-stimulated hyperactivity in rats but has no depressant or stimulant activity in the absence of amphetamine. These results suggest a complex mechanism of action for tamoxifen as a regulator of the DAT. Due to its effectiveness against amphetamine actions and its central nervous system permeant activity, the tamoxifen structure represents an excellent starting point for a structure-based drug-design program to develop a pharmacological therapeutic for psychostimulant abuse.
Optimization of GFP Fluorescence Preservation by a Modified uDISCO Clearing Protocol

Tissue optical clearing techniques provide alternative approaches for imaging large-volume specimens. uDISCO, an organic-solvent-based method, stands out from the enormous array of available optical clearing methods by achieving whole-brain imaging with high transparency, size reduction and fluorescence preservation. In this study, we aimed to modify the uDISCO protocol to achieve better fluorescence preservation and to thereby further improve its optical imaging quality. First, we determined the optimal pH value for optimized uDISCO, termed “a-uDISCO” (alkaline pH-based uDISCO). Then, we compared fluorescence preservation between a-uDISCO and uDISCO. In addition, we validated the clearing performance of the optimized method according to several parameters, including tissue transparency, size changes, and the maintenance of cell morphology. Finally, we demonstrated that a-uDISCO enabled the high-quality brain-wide visualization of neuronal structures. This method potentially provides a better alternative for high-throughput imaging of samples with low-level fluorescence protein expression or for archiving and repetitive revisiting of rare samples.
Structures of Repetitive Controllers Based on GDSC with Feedforward Action

This work presents a vector repetitive control scheme for three-phase systems, based on the generalized delayed signal cancelation transform. Three proposed control structures are presented, allowing to regulate a family of harmonic components (nk+m, k Z), where the negative harmonics indicate negative-sequence components. Differently from most repetitive controllers, distinct negative- and positive-sequence components can be regulated. A stability evaluation is presented. Experimental results are shown in order to validate the proposed control scheme and demonstrate its good performance when applied to a three-phase active power filter.
Ecological Momentary Assessment Is a Feasible and Valid Methodological Tool to Measure Older Adults’ Physical Activity and Sedentary Behavior

Ecological momentary assessment (EMA) has the potential to yield new insights into the prediction and modeling of physical activity (PA) and sedentary behavior (SB). The objective of this study was to determine the feasibility and validity of an EMA protocol to assess older adults’ PA and SB. Feasibility was determined by examining factors associated with EMA survey compliance and if PA or SB were impacted by EMA survey compliance. Validity was determined by comparing EMA-reported PA and SB to objectively measured PA and SB at the EMA prompt. Over 10 days, older adults (n = 104; Agerange = 60–98 years) received 6 randomly prompted EMA questionnaires on a smartphone each day and wore an ActivPAL activity monitor to provide a device-based measure of PA and SB. Participants reported whether they were currently engaged in PA or SB. Older adults were compliant with the EMA and ActivPAL protocol on 92% of occasions. Differences in EMA compliance differed by weight status. Among overweight and obese older adults EMA compliance differed by sex (OR = 3.15, 95% CI: 1.43, 6.92) and day of week (OR = 1.79, 95% CI: 1.33, 2.41). Among normal weight older adults, EMA compliance differed by time of day (OR = 1.52, 95% CI: 1.02, 2.30). EMA compliance did not differ for device-based PA or SB in the 15 min before versus the 15 min after the EMA prompt, suggesting that these behaviors did not influence likelihood of responding and responding did not influence these behaviors (ps > 0.05). When PA was reported through EMA, participants engaged in less device-based PA in the 15 min after compared to the 15 min before the EMA prompt (p = 0.01), suggesting possible reactance or a disruption of PA. EMA-reported PA and SB were positively associated with higher device-based PA and SB in the ±15 min, respectively, supporting criterion validity (ps < 0.05). The assessment of older adults’ PA and SB through EMA is feasible and valid, although there may be PA reactance to EMA prompting. Therefore, EMA represents a significant methodological tool that can aid in our understanding of the environmental, social, and psychological processes regulating older adults’ PA and SB in the context of everyday life.
$H_{\infty}$ Synchronization of Networked Master–Slave Oscillators With Delayed Position Data: The Positive Effects of Network-Induced Delays

This paper is concerned with <inline-formula> <tex-math notation="LaTeX">${H}_{\infty }$ </tex-math></inline-formula> synchronization of coupled oscillators in a master–slave framework, in which the oscillators cannot be stabilized by nondelayed sampled position data, but can be stabilized by sampled position data with delays restricted by nonzero lower bounds and upper bounds. A configuration of networked master–slave oscillators with a remote controller is first constructed. Then the positive effects of delays on master–slave synchronization are investigated. Some delay-dependent <inline-formula> <tex-math notation="LaTeX">${H}_{\infty }$ </tex-math></inline-formula> synchronization criteria are derived by constructing augmented discretized Lyapunov–Krasovskii functionals for determinate sampling and stochastic sampling, respectively. The controller can be designed by solving a set of linear matrix inequalities. Finally, two numerical examples are given to verify the theoretical results. It is shown that the maximum allowable sampling period in the case of stochastic sampling is larger than the one in the case of determinate sampling. Stochastic sampling can also provide a tradeoff between network-induced delays and the sampling periods, enhancing the master–slave synchronization performance.
Tristetraprolin Is a Prognostic Biomarker for Poor Outcomes among Patients with Low-Grade Prostate Cancer

Background: We studied the utility of the tumor suppressor Tristetraprolin (TTP, ZFP36) as a clinically relevant biomarker of aggressive disease in prostate cancer patients after radical prostatectomy (RP). Methods: TTP RNA expression was measured in an RP cohort of patients treated at Moffitt Cancer Center (MCC) and obtained from six publically available RP datasets with biochemical recurrence (BCR; total n = 1,394) and/or metastatic outcome data (total n = 1,222). TTP protein expression was measured by immunohistochemistry in a tissue microarray of 153 MCC RP samples. The time to BCR or metastasis based on TTP RNA or protein levels was calculated using the Kaplan–Meier analysis. Univariable and multivariable Cox proportional hazard models were performed on multiple cohorts to evaluate if TTP is a clinically relevant biomarker and to assess if TTP improves upon the Cancer of the Prostate Risk Assessment postsurgical (CAPRA-S) score for predicting clinical outcomes. Results: In all of the RP patient cohorts, prostate cancer with low TTP RNA or protein levels had decreased time to BCR or metastasis versus TTP-high tumors. Further, the decreased time to BCR in TTP-low prostate cancer was more pronounced in low-grade tumors. Finally, pooled survival analysis suggests that TTP RNA expression provides independent information beyond CAPRA-S to predict BCR. Conclusions: TTP is a promising prostate cancer biomarker for predicting which RP patients will have poor outcomes, especially for low-grade prostate cancer patients. Impact: This study suggests that TTP RNA expression can be used to enhance the accuracy of CAPRA-S to predict outcomes in patients treated with RP. Cancer Epidemiol Biomarkers Prev; 27(11); 1376–83. ©2018 AACR.
Emotion Recognition of Stressed Speech Using Teager Energy and Linear Prediction Features

In this paper, a Speech Emotion Recognition (SER) system is proposed using the feature combination of Teager Energy Operator (TEO) and Linear Prediction Coefficient (LPC) features as T-LPC feature extraction. The stressed speech signals which were not accurately recognized in the previous SER systems were recognized using the proposed methods. Gaussian Mixture Model (GMM) classifier is used to categorize the emotions of EMO-DB database in this analysis. The Stressed Speech Emotion Recognition (SSER) proposed using the T-LPC feature extraction technique acquired better performance compared to the existing Pitch, LPC, and LPC + Pitch feature based recognition systems. This proposed emotion recognition system can be used to motivate the students by finding their emotional state providing better accuracy compared to the existing ones.
Immunohistochemical Coexpression of Epithelial Cell Adhesion Molecule and Alpha-Fetoprotein in Hepatocellular Carcinoma

Background and Aim The epithelial cell adhesion molecule (EpCAM) has been proposed as a marker for cancer stem cells in human hepatocellular carcinoma (HCC) as well as in the development of novel target therapies. This study aimed to investigate the immunohistochemical expression of EpCAM and alpha-fetoprotein (AFP) in HCC patients and their association with clinicopathological characteristics. Methods This study included Child-Pugh A HCC patients undergoing curative surgical resection. Results A significant difference was observed in the ratio between the different phenotypes (p = 0.002), identifying 12 (29.3%) EPCAM positive tumors and 29 (70.7%) negative tumors. EpCAM+ expression was associated with AFP + (OR = 12.5, 95% CI, 1.9-84.1, p<0.001). In univariate analysis, a significant association was observed between AFP+ and EPCAM+ and the serum AFP level. A diameter of ≤ 5 cm was associated with EPCAM+, while angiolymphatic invasion was associated with APF+. In a multivariate analysis, only tumors of ≤ 5 cm were significantly associated with EpCAM+ (OR = 8.7; 95%CI, 1.27-100.0; p = 0.022). The overall survival rate was 74.9%, 69.4%, 69.4%, and 53.5% at 12, 24, 36, and 48 months, respectively. Conclusion A considerable number of patients with EpCAM+ HCC would benefit from a specific target therapy.
Recommendations for Integrating a P300-Based Brain Computer Interface in Virtual Reality Environments for Gaming

The integration of a P300-based brain–computer interface (BCI) into virtual reality (VR) environments is promising for the video games industry. However, it faces several limitations, mainly due to hardware constraints and limitations engendered by the stimulation needed by the BCI. The main restriction is still the low transfer rate that can be achieved by current BCI technology, preventing movement while using VR. The goal of this paper is to review current limitations and to provide application creators with design recommendations to overcome them, thus significantly reducing the development time and making the domain of BCI more accessible to developers. We review the design of video games from the perspective of BCI and VR with the objective of enhancing the user experience. An essential recommendation is to use the BCI only for non-complex and non-critical tasks in the game. Also, the BCI should be used to control actions that are naturally integrated into the virtual world. Finally, adventure and simulation games, especially if cooperative (multi-user), appear to be the best candidates for designing an effective VR game enriched by BCI technology.
Complexity in disease management: A linked data analysis of multimorbidity in Aboriginal and non-Aboriginal patients hospitalised with atherothrombotic disease in Western Australia

Background Hospitalisation for atherothrombotic disease (ATD) is expected to rise in coming decades. However, increasingly, associated comorbidities impose challenges in managing patients and deciding appropriate secondary prevention. We investigated the prevalence and pattern of multimorbidity (presence of two or more chronic conditions) in Aboriginal and non-Aboriginal Western Australian residents with ATDs. Methods and findings We used population-based de-identified linked administrative health data from 1 January 2000 to 30 June 2014 to identify a cohort of patients aged 25–59 years admitted to Western Australian hospitals with a discharge diagnosis of ATD. The prevalence of common chronic diseases in these patients was estimated and the patterns of comorbidities and multimorbidities empirically explored using two different approaches: identification of the most commonly occurring pairs and triplets of comorbid diseases, and through latent class analysis (LCA). Half of the cohort had multimorbidity, although this was much higher in Aboriginal people (Aboriginal: 79.2% vs. non-Aboriginal: 39.3%). Only a quarter were without any documented comorbidities. Hypertension, diabetes, alcohol abuse disorders and acid peptic diseases were the leading comorbidities in the major comorbid combinations across both Aboriginal and non-Aboriginal cohorts. The LCA identified four and six distinct clinically meaningful classes of multimorbidity for Aboriginal and non-Aboriginal patients, respectively. Out of the six groups in non-Aboriginal patients, four were similar to the groups identified in Aboriginal patients. The largest proportion of patients (33% in Aboriginal and 66% in non-Aboriginal) was assigned to the “minimally diseased” (or relatively healthy) group, with most patients having less than two conditions. Other groups showed variability in degree and pattern of multimorbidity. Conclusion Multimorbidity is common in ATD patients and the comorbidities tend to interact and cluster together. Physicians need to consider these in their clinical practice. Different treatment and secondary prevention strategies are likely to be useful for management in these cluster groups.
Exploiting in vitro potential and characterization of surface modified Zinc oxide nanoparticles of Isodon rugosus extract: Their clinical potential towards HepG2 cell line and human pathogenic bacteria

Little is known about biogenically synthesized Zinc oxide nanoparticles (ZnONPs) from Isodon rugosus. Synthesis of metal oxide NPs from essential oil producing medicinal plants results in less harmful side effects to the human population as compared to chemically synthesized NPs. In this article, we report biogenic synthesis of ZnONPs from in vitro derived plantlets and thidiazuron (TDZ) induced callus culture of Isodon rugosus. Synthesized NPs were characterized using UV-spectra, XRD, FTIR, SEM and EDX. Furthermore, the NPs were evaluated for their potential cytotoxic (against HepG2 cell line) and antimicrobial (against drug resistant Staphylococcus epidermidis, Bacillus subtilis, Klebsiella pneumoniae and Pseudomonas aeruginosa) activities. Pure crystalline ZnONPs with hexagonal and triangular shapes were obtained as a result of callus extract (CE) and whole plant extract (WPE), respectively. ZnONPs showed potent cytotoxic and antimicrobial potential. The antimicrobial and cytotoxic activities of ZnONPs were found to be shape and surface bound phytochemicals dependent. CE mediated hexagonal ZnONPs showed superior anti-cancer and antimicrobial activities as compared to WPE mediated triangular shaped ZnONPs. It is concluded that biogenic ZnONPs have incredible potential as theranostic agents and can be adopted as useful drug delivery system in next generation treatment strategies.
Ultraviolet radiation and its effects on pregnancy: A review study

Seasons and Vitamin D are factors that are directly and indirectly related to ultraviolet (UV) radiations and can affect pregnancy. Therefore, the present study aims at investigating the effects of being exposed to direct UV radiation during pregnancy period and its effects on fetal growth, premature birth, and high blood pressure. This study was conducted by searching different websites such as Medline, Embase, ProQuest, Global Health, Google Scholar, and Scopus. Of the 430 papers found, between 1985 and 2017, seventeen related articles were used. The results showed that being exposed to UV radiation during the first 3 months of pregnancy is associated with improved fetal growth and causes high blood pressure during pregnancy. The literature shows that being women exposure to UV radiation had beneficial effects on fetal growth and blood pressure during pregnancy period. However, since this issue has not been extensively studied in the past, the results from previous studies should be generalized with extreme care and caution. Therefore, it is suggested that further studies be carried out in this area.
QRASSH - A Self-Adaptive SSH Honeypot Driven by Q-Learning

Developed for the first time in the 80s, honeypot systems research increased tremendously in the last decade. Moving from simple monitored, emulated, Internet Services, towards intelligent systems that autonomously interact with attackers, was a normal engagement in the context of higher development of artificial intelligence as science. In this paper we present a newly developed SSH self-adaptive honeypot system that uses a Deep Q-Learning algorithm in order to decide how to interact with external attackers. The honeypot system is developed in Python and integrates an existing implementation of a Reinforcement Learning algorithm that makes use of neural network (NN).