Miracle-dz/UniFAR-FTUs huggingface dataset:
FOXM1 functions collaboratively with PLAU to promote gastric cancer progression

Background: Gastric cancer (GC) is one of the main mortality cause worldwide. Previously, we found Forkhead box protein (FOXM1) or Urokinase-type plasminogen activator (PLAU) are independent prognostic markers of GC. This study aims to explore the combining prognostic efficacy and the potential insights underlying additive effect of FOXM1 to PLAU in GC progression through in-silico analyses. Method: The expression of FOXM1 and PLAU were profiled in 33 cancer types using public data. A merged GC expression dataset containing 598 samples was used for evaluating prognostic significance of FOXM1/PLAU. Gene Set Enrichment Analysis (GSEA) was performed to elucidate the mechanisms underlying FOXM1/PLAU promoted GC progression. The Cancer Genome Atlas (TCGA) was used for analyzing the association between FOXM1/PLAU and tumor immune infiltration. Genomic and proteomic differences between FOXM1+PLAU+ and FOXM1-PLAU- groups were also computed using TCGA GC data. Drugs targeting FOXM1/PLAU associated gene expression pattern was analyzed using LINCs database. Results: FOXM1 and PLAU are overexpressed in 17/33 cancer types including GC. Kaplan-Meier analyses indicate that the FOXM1+PLAU+ subgroup have the worst prognosis, while FOXM1-PLAU- subgroup have the best survival. Bioinformatics analysis indicated that FOXM1+PLAU+ associated genes are enriched in TGF-beta, DNA repair and drug resistance signaling pathways; FOXM1 and PLAU expression are negatively correlated with tumor immune infiltration. Genomic and proteomic differences between FOXM1+PLAU+ and FOXM1-PLAU- groups were presented. Data mining from LINCs suggested several chemicals or drugs that could target the gene expression pattern of FOXM1+PLAU+ patients. Conclusion: FOXM1+PLAU+ can serve as effective prognostic biomarkers and potential therapeutic targets for GC. Due to the additive effect of these two genes, screening for drugs or chemicals that targeting the expression patterns PLAU+FOXM1+ subgroup may exert important clinical impact on GC management.
Prediction of Competition Indices in a Norway Spruce and Silver Fir-Dominated Forest Using Lidar Data

Competitive interactions are important predictors of tree growth. Spatial and temporal changes in resource availability, and variation in species and spatial patterning of trees alter competitive interactions, thus affecting tree growth and, hence, biomass. Competition indices are used to quantify the level of competition among trees. As these indices are normally computed only over small areas, where field measurements are done, it would be useful to have a tool to predict them over large areas. On this regard, remote sensing, and in particular light detection and ranging (lidar) data, could be the perfect tool. The objective of this study was to use lidar metrics to predict competition (on the basis of distance-dependent competition indices) of individual trees and to relate them with tree aboveground biomass (AGB). The selected study area was a mountain forest area located in the Italian Alps. The analyses focused on the two dominant species of the area: Silver fir (Abies alba Mill.) and Norway spruce (Picea abies (L.) H. Karst). The results showed that lidar metrics could be used to predict competition indices of individual trees (R2 above 0.66). Moreover, AGB decreased as competition increased, suggesting that variations in the availability of resources in the soil, and the ability of plants to withstand competition for light may influence the partitioning of biomass.
Relationship Between Spatial Variations in Static Skin Deformation and Perceived Roughness of Macroscopic Surfaces

The perceived roughness of macroscopic surface features is related to finger skin deformation. For example, skin indentation into surface grooves and the spatial variation in slowly adapting type 1 (SAI) mechanoreceptor activities are linked to the magnitude of subjective roughness. However, the perception of macroscopic roughness has not been directly linked to the spatial variations in skin deformation; therefore, this article investigated the relationship between the subjective roughness magnitude and the spatial spectrum of skin deformation through contact with roughened macroscopic surfaces. Experiments were performed to measure deformation of a finger pad when it statically touched different shaped grating scales. Then, the spatial spectra of skin deformation were computed by applying the Gabor filter with varying spatial selectivity. Some spectral components, particularly those for the spatial period ranging from 2.45–4.00 mm, exhibited a good correlation with the perceived roughness magnitude. When the optimal spatial periods were determined for individual subjects, the spectral components and perceived roughness exhibited a strong linear relationship with an average correlation coefficient of 0.94. Therefore, the amount of spatial variation in finger skin deformation can be linked to the subjective roughness intensities of macroscopic surface features.
Weakly Supervised Video Summarization by Hierarchical Reinforcement Learning

Conventional video summarization approaches based on reinforcement learning have the problem that the reward can only be received after the whole summary is generated. Such kind of reward is sparse and it makes reinforcement learning hard to converge. Another problem is that labelling each shot is tedious and costly, which usually prohibits the construction of large-scale datasets. To solve these problems, we propose a weakly supervised hierarchical reinforcement learning framework, which decomposes the whole task into several subtasks to enhance the summarization quality. This framework consists of a manager network and a worker network. For each subtask, the manager is trained to set a subgoal only by a task-level binary label, which requires much fewer labels than conventional approaches. With the guide of the subgoal, the worker predicts the importance scores for video shots in the subtask by policy gradient according to both global reward and innovative defined sub-rewards to overcome the sparse problem. Experiments on two benchmark datasets show that our proposal has achieved the best performance, even better than supervised approaches.
Detecting Mixing Services via Mining Bitcoin Transaction Network With Hybrid Motifs

As the first decentralized peer-to-peer (P2P) cryptocurrency system allowing people to trade with pseudonymous addresses, Bitcoin has become increasingly popular in recent years. However, the P2P and pseudonymous nature of Bitcoin make transactions on this platform very difficult to track, thus triggering the emergence of various illegal activities in the Bitcoin ecosystem. Particularly, mixing services in Bitcoin, originally designed to enhance transaction anonymity, have been widely employed for money laundering to complicate the process of trailing illicit fund. In this article, we focus on the detection of the addresses belonging to mixing services, which is an important task for anti-money laundering in Bitcoin. Specifically, we provide a feature-based network analysis framework to identify statistical properties of mixing services from three levels, namely, network level, account level, and transaction level. To better characterize the transaction patterns of different types of addresses, we propose the concept of attributed temporal heterogeneous motifs (ATH motifs). Moreover, to deal with the issue of imperfect labeling, we tackle the mixing detection task as a positive and unlabeled learning (PU learning) problem and build a detection model by leveraging the considered features. Experiments on real Bitcoin datasets demonstrate the effectiveness of our detection model and the importance of hybrid motifs including ATH motifs in mixing detection.
Awareness of the Signs, Symptoms, and Risk Factors of Cancer and the Barriers to Seeking Help in the UK: Comparison of Survey Data Collected Online and Face-to-Face

Background Cancer is the second leading cause of death globally, causing an estimated 9.6 million deaths in 2018. Low cancer symptom awareness has been associated with poor cancer survival for all cancers combined. The Cancer Awareness Measure (CAM) is a validated, face-to-face survey used since 2008 to measure the UK public’s awareness of the symptoms and risk factors of cancer as well as the barriers to seeking help. Objective The aim of this study is to explore whether online data collection can produce a representative sample of the UK population, compare awareness of cancer signs and risk factors and the barriers to seeking help between data collected online and face-to-face, and examine the relationships between awareness and demographic variables. Methods Differences in awareness of cancer signs, symptoms, and risk factors among samples were explored while adjusting for demographic differences (age, gender, ethnicity, educational level, marital status, and country of residence) to distinguish the effect of data collection method. Multivariate logistic regression models were used to calculate adjusted odds ratios for recall and recognition of signs and symptoms, risk factors, and barriers to seeking help. Results A total of 4075 participants completed the CAM, 20% (n=819) via face-to-face interviews and 80% online (n=3256; agency A: n=1190; agency B: n=2066). Comparisons of data collected using face-to-face interviews and online surveys revealed minor differences between samples. Both methods provided representative samples of the UK population with slight differences in awareness of signs, symptoms, and risk factors and frequency of help-seeking barriers reported. Conclusions These findings support a move to online data collection for the CAM. The flexibility afforded will enable the CAM to explore a wider range of issues related to the prevention, early diagnosis, and treatment of cancer.
Genome Sequence of Lignin-Degrading Arthrobacter sp. Strain RT-1, Isolated from Termite Gut and Rumen Fluid

Here, we report the genome sequence of Arthrobacter sp. strain RT-1, isolated from a cocktail of termite gut and rumen fluid. Strain RT-1 degrades a variety of lignin monomers and dimers as the growth substrates. The genome annotation predicted the genes necessary for the catabolism of lignin-derived aromatic compounds. ABSTRACT Here, we report the genome sequence of Arthrobacter sp. strain RT-1, isolated from a cocktail of termite gut and rumen fluid. Strain RT-1 degrades a variety of lignin monomers and dimers as the growth substrates. The genome annotation predicted the genes necessary for the catabolism of lignin-derived aromatic compounds.
The Fundamental Role of Bicarbonate Transporters and Associated Carbonic Anhydrase Enzymes in Maintaining Ion and pH Homeostasis in Non-Secretory Organs

The bicarbonate ion has a fundamental role in vital systems. Impaired bicarbonate transport leads to various diseases, including immune disorders, cystic fibrosis, tumorigenesis, kidney diseases, brain dysfunction, tooth fracture, ischemic reperfusion injury, hypertension, impaired reproductive system, and systemic acidosis. Carbonic anhydrases are involved in the mechanism of bicarbonate movement and consist of complex of bicarbonate transport systems including bicarbonate transporters. This review focused on the convergent regulation of ion homeostasis through various ion transporters including bicarbonate transporters, their regulatory enzymes, such as carbonic anhydrases, pH regulatory role, and the expression pattern of ion transporters in non-secretory systems throughout the body. Understanding the correlation between these systems will be helpful in order to obtain new insights and design potential therapeutic strategies for the treatment of pH-related disorders. In this review, we have discussed the broad prospects and challenges that remain in elucidation of bicarbonate-transport-related biological and developmental systems.
The effect of interleukin-22 on osteogenic differentiation and the osteoclastogenic response of Human periodontal ligament fibroblasts in vitro.

BACKGROUND Interleukin-22 (IL-22) exerts extensive biological effects, playing both protective and pathological roles in autoimmune and infectious diseases. However, the specific role and mechanism of IL-22 in the pathogenesis of periodontitis have not been clarified. The aim of this study was to analyze the possible roles of IL-22 in the osteoclastogenesis and osteogenesis of periodontitis. METHODS Human periodontal ligament fibroblasts (hPDLFs) were treated with IL-22 and/or lipopolysaccharide from Porphyromonas gingivalis (Pg-LPS), and the mRNA and protein expression of RANKL and OPG were measured by qRT-PCR and Western blotting, respectively. Western blotting was also used to examine the phosphorylated and total protein expression of MAPK signaling molecules. The role of the MAPK pathway in osteoclastogenesis marker expression was further confirmed by inhibition assays. For osteogenic assays, the mRNA expression of osteoblastic markers was quantified by qRT-PCR, the ALP activity of hPDLFs was measured by an ALP assay, and the mineralized nodules formed by hPDLFs were determined by Alizarin Red S staining. RESULTS IL-22 promoted the expression of RANKL in hPDLFs via the MAPK signaling pathway and further upregulated RANKL expression together with Pg-LPS via the p38 MAPK pathway. IL-22 could enhance the ALP activity and mineralized nodule formation of hPDLFs in the early period of osteogenic induction, while exhibiting no profound effect on the expression of osteoblastic markers. CONCLUSION IL-22 plays regulatory roles in bone homeostasis, and it is likely to contribute to osteoclastogenesis as a proinflammatory cytokine in the pathogenesis of periodontitis. This article is protected by copyright. All rights reserved.
Duration of Hybrid Closed-Loop Insulin Therapy to Achieve Representative Glycemic Outcomes in Adults With Type 1 Diabetes

Closed-loop insulin therapy increases the time spent in target glucose range (70–180 mg/dL) while reducing the time spent in hypoglycemia ( 180 mg/dL) (1), and it is being progressively applied in clinical practice. Randomized clinical trials of closed-loop insulin delivery have been performed over a single day to several months, but the time required to achieve representative glucose outcomes, including control algorithm individualization and behavioral adaptation, is currently unknown. To address this issue we analyzed retrospectively data collected during closed-loop clinical trials in adults with type 1 diabetes and A1C ≥58 mmol/mol (≥7.5%) as such information may be useful in determining the optimal duration of future closed-loop studies as well as follow-up assessments under routine care conditions. We combined data collected in adults aged 21 years and older during two multinational (U.K., U.S., Austria, and Germany), randomized, day-and-night, hybrid closed-loop studies (2,3) (AP@home04 and APCam11) of 12 weeks’ duration ( N = 56, mean ± SD A1C 68 ± 7 mmol/mol \[8.4 ± 0.6%\] \[range 58–83 mmol/mol (7.5–9.7%)\], age 39 ± 10 years [range 21 to 65 years], diabetes duration 21 ± 9 years [range 6 to 49 years]). The AP@home04 study (2) utilized Dana Diabecare R insulin pump (Sooil, Seoul, South Korea) and FreeStyle Navigator II glucose sensor (Abbott Diabetes Care, Alameda, CA), while the APCam11 study (3) used a modified Medtronic 640G insulin pump and Guardian 3 glucose sensor (Medtronic, Northridge, CA). Both studies used an identical individually adapting, treat-to-target, model-predictive control algorithm residing on a smartphone. Participants were required to deliver a premeal bolus based on carbohydrate content, were not remotely monitored or supervised, …
Novel Approach for Insertion of Heterologous Sequences into Full-Length ZIKV Genome Results in Superior Level of Gene Expression and Insert Stability

Zika virus (ZIKV) emerged in the Americas in 2015, presenting unique challenges to public health. Unlike other arboviruses of the Flaviviridae family, it is transmissible by sexual contact, which facilitates the spread of the virus into new geographic areas. Additionally, ZIKV can be transmitted from mother to fetus, causing microcephaly and other severe developmental abnormalities. Reliable and easy-to-work-with clones of ZIKV expressing heterologous genes will significantly facilitate studies aimed at understanding the virus pathogenesis and tissue tropism. Here, we developed and characterized two novel approaches for expression of heterologous genes of interest in the context of full-length ZIKV genome and compared them to two previously published strategies for ZIKV-mediated gene expression. We demonstrated that among the four tested viruses expressing nLuc gene, the virus constructed using a newly developed approach of partial capsid gene duplication (PCGD) attained the highest titer in Vero cells and resulted in the highest level of nLuc expression. Suitability of the PCGD approach for expression of different genes of interest was validated by replacing nLuc sequence with that of eGFP gene. The generated constructs were further characterized in cell culture. Potential applications of ZIKV clones stably expressing heterologous genes include development of detection assays, antivirals, therapeutics, live imaging systems, and vaccines.
Effect of tetracycline family of antibiotics on actin aggregation, resulting in the formation of Hirano bodies responsible for neuropathological disorders

Abstract Actin, an ATPase superfamily protein, regulates some vital biological functions like cell locomotion, cytokinesis, synaptic plasticity and cell signaling in higher eukaryotes, and is dependent on the dynamics of actin polymerization process. Impaired regulation of actin polymerization has been implicated in the formation and deposition of rod-like paracrystalline structures called as Hirano bodies in neuronal cells of patients suffering from Alzheimer’s disease, Pick’s disease, Guam amyotrophic lateral sclerosis and parkinsonism–dementia complex. Aggregation of actin forming amorphous deposition in the brain cells is also associated with chronic alcoholism and aging of the neurons. In the current article, we propose the breaking of the highly amorphous and dysregulated actin aggregates using generic compounds like tetracycline, oxytetracycline, doxycycline and minocycline which are used as antibiotics against tuberculosis and infection caused due to various Gram-negative bacteria. We have investigated the effect and affinity of binding of these four compounds to that of actin aggregates using 90° light scattering, size exclusion chromatography, dynamic light scattering, circular dichroism, scanning electron microscopy, transmission electron microscopy imaging and kinetic analysis. The isothermal calorimetric measurements showed that the binding constant for the cycline family molecules used in this study range from 9.8 E4 M−1 to 1.3 E4 M−1. To understand the in vivo effect, we also studied the effect of these drugs on Saccharomyces cerevisiae Δend3 mutant cells. Our data suggest that these generic compounds can plausibly be used for the treatment of various neurodegenerative diseases occurring due to Hirano body formation in brain cells. Communicated by Ramaswamy H. Sarma
Abstract 4506: Computational-driven metronomics: application to gemcitabine in neuroblastoma-bearing mice

Metronomics (i.e., repeated administration of small doses of drugs over a long period of time) is an attractive strategy to reduce treatment-related toxicities while possibly offering unexpected novel mechanisms of action such as anti-angiogenesis or immuno-stimulatory properties. Defining an optimal metronomic schedule remains uneasy because of the variety of possible combinations between dosing, frequency and duration. Our group has developed innovative modeling tools to optimize metronomic schedules. Here, we have used this computational approach to test whether metronomics gemcitabine would make better than standard gemcitabine in mice bearing human neuroblastoma. In silico simulation suggested that shifting from standard 100 mg/kg/w over 4 consecutive weeks to the metronomic 1 mg/kg/d schedule for 28 consecutive days would achieve higher antitumoral efficacy while being well tolerated. To test this hypothesis, 100 000 human GI-ME-N neuroblastoma cells stably transfected with luciferase were subcutaneously grafted in 40 nude mice. Mice were next split in 3 groups: control, 100 mg/kg/w gemcitabine (STD-GEM) and 1 mg/kg/d gemcitabine (MetroGem-1). An additional satellite group was treated with 0.5 mg/kg/d (MetroGem-0.5). Metro-Gem was administrated using an osmotic pump implanted subcutaneously. Efficacy (i.e., comparison in tumor growth) was the main endpoint. Pharmacokinetics, tumor inflammation, and vascular density were the secondary endpoints. Gemcitabine assay showed that whereas Cmax up to 80 μg/ml were reached in mice treated with STD-GEM, steady-state concentrations of 0.1 μg/ml only were observed in animals undergoing MetroGem-1. Measuring cathepsin expression by fluorescence imaging suggested reduced inflammation in mice treated with the metronomic schedule. Similarly, fluorescence imaging showed smaller vascular density in mice treated with MetroGem-1 or -0.5 as compared with control or STD-GEM animals. To further check a possible anti-angiogenic effect, tumor perfusion rate was measuring next using luciferine, and fully confirmed that mice treated with Metro-Gem exhibited reduced blood flow as compared with other groups. Finally, bioluminescence monitoring of tumor growth showed that whereas STD-GEM was totally ineffective, both MetroGem-1 and MetroGem-0.5 achieved 60% reduction in tumor mass at the end of the treatment (p Citation Format: Joseph Ciccolini, Eddy Pasquier, Aurelie Lombard, Sarah Giacometti, Christian Faivre, Raphaelle Fanciullino, Cindy Serdjebi, Dominique Barbolosi, Nicolas Andre. Computational-driven metronomics: application to gemcitabine in neuroblastoma-bearing mice. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4506. doi:10.1158/1538-7445.AM2015-4506
Descriptive Epidemiology of Safety Events at an Academic Medical Center

Background: Adverse safety events in healthcare are of great concern, and despite an increasing focus on the prevention of error and harm mitigation, the epidemiology of safety events remains incomplete. Methods: We performed an analysis of all reported safety events in an academic medical center using a voluntary incident reporting surveillance system for patient safety. Safety events were classified as: serious (reached the patient and resulted in moderate to severe harm or death); precursor (reached the patient and resulted in minimal or no detectable harm); and near miss (did not reach the patient). Results: During a three-year period, there were 31,817 events reported. Most of the safety events were precursor safety events (reached the patient and resulted in minimal harm or no detectable harm), corresponding to 77.3%. Near misses accounted for 10.8%, and unsafe conditions for 11.8%. The number of reported serious safety events was low, accounting for only 0.1% of all safety events. Conclusions: The reports analysis of these events should lead to a better understanding of risks in patient care and ways to mitigate it.