Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of real-time GIS analytics to support spatial intelligent decision-making in the era of big data for smart cities

The rapid growth of the urban landscape from natural development, traffic congestion, crime, and waste management has become a challenge to the city dwellers and decision-makers, thus requiring smart and efficient ways to tackle these problems. The combination of big data and Internet of Things (IoT) is guiding local communities in achieving the goal of building creative communities. This study aims to use real-time spatial analytics that integrates state–of–the art ICT approaches with interdisciplinary synthesis for smart solution making process across different sectors of the community. The use of real-time data and coordination of information from multiple city agencies enable policymakers to adjust management strategies in near real-time and can also provide citizens with situational awareness on emergency and non-emergency conditions. Using state-of-the-art technologies, and communication-based applications within the context of smart cities, three smart city examples were developed with the case study of Grand Forks, North Dakota. Using Web AppBuilder, a health resources inventory for the town of Grand Forks was designed with real-time integration of medical information and treatment facilities within the community. For emergency response and preparedness, we developed and deployed a solution that combines information from all the sectors of the city using Survey123 for ArcGIS to collect data and display it on an operational dashboard with multiple visualizations for real-time monitoring of people, services, assets and analysis of events or activities.This operation offers real-time verification and can be an essential resource for team leaders and emergency operations centers. The information collected also provides the flexibility to show layers of information, and task force leaders have a view of their task forces and, consequently, the enrichment of life by making the community safe, healthy, and sustainable.
Phosphorylation of proliferating cell nuclear antigen promotes cancer progression by activating the ATM/Akt/GSK3β/Snail signaling pathway

Proliferating cell nuclear antigen (PCNA) and its posttranslational modifications regulate DNA metabolic reactions, including DNA replication and repair, at replication forks. PCNA phosphorylation at Tyr-211 (PCNA-Y211p) inhibits DNA mismatch repair and induces misincorporation during DNA synthesis. Here, we describe an unexpected role of PCNA-Y211p in cancer promotion and development. Cells expressing phosphorylation-mimicking PCNA, PCNA-Y211D, show elevated hallmarks specific to the epithelial-mesenchymal transition (EMT), including the up-regulation of the EMT-promoting factor Snail and the down-regulation of EMT-inhibitory factors E-cadherin and GSK3β. The PCNA-Y211D–expressing cells also exhibited active cell migration and underwent G2/M arrest. Interestingly, all of these EMT-associated activities required the activation of ATM and Akt kinases, as inactivating these protein kinases by gene knockdown or inhibitors blocked EMT-associated signaling and cell migration. We concluded that PCNA phosphorylation promotes cancer progression via the ATM/Akt/GSK3β/Snail signaling pathway. In conclusion, this study identifies a novel PCNA function and reveals the molecular basis of phosphorylated PCNA-mediated cancer development and progression.
Resting‐state fMRI detects the effects of learning in short term: A visual search training study

Can resting‐state functional connectivity (rs‐FC) detect the impact of learning on the brain in the short term? To test this possibility, we have combined task‐FC and rs‐FC tested before and after a 30‐min visual search training. Forty‐two healthy adults (20 men) divided into no‐contact control and trained groups completed the study. We studied the connectivity between four different regions of the brain involved in visual search: the primary visual area, the right posterior parietal cortex (rPPC), the right dorsolateral prefrontal cortex (rDLPFC), and the dorsal anterior cingulate cortex (dACC). Task‐FC showed increased connectivity between the rPPC and rDLPFC and between the dACC and rDLPFC from pretraining to posttraining for both the control group and the trained group, suggesting that connectivity between these areas increased with task repetition. In rs‐FC, we found enhanced connectivity between these regions in the trained group after training, especially in those with better learning. Whole brain independent component analyses did not reveal any change in main networks after training. These results imply that rs‐FC may not only predict individual differences in task performance, but rs‐FC might also serve to monitor the impact of learning on the brain after short periods of cognitive training, localizing them in brain areas specifically involved in training.
Marginal analysis of ordinal clustered longitudinal data with informative cluster size

The issue of informative cluster size (ICS) often arises in the analysis of dental data. ICS describes a situation where the outcome of interest is related to cluster size. Much of the work on modeling marginal inference in longitudinal studies with potential ICS has focused on continuous outcomes. However, periodontal disease outcomes, including clinical attachment loss, are often assessed using ordinal scoring systems. In addition, participants may lose teeth over the course of the study due to advancing disease status. Here we develop longitudinal cluster‐weighted generalized estimating equations (CWGEE) to model the association of ordinal clustered longitudinal outcomes with participant‐level health‐related covariates, including metabolic syndrome and smoking status, and potentially decreasing cluster size due to tooth‐loss, by fitting a proportional odds logistic regression model. The within‐teeth correlation coefficient over time is estimated using the two‐stage quasi‐least squares method. The motivation for our work stems from the Department of Veterans Affairs Dental Longitudinal Study in which participants regularly received general and oral health examinations. In an extensive simulation study, we compare results obtained from CWGEE with various working correlation structures to those obtained from conventional GEE which does not account for ICS. Our proposed method yields results with very low bias and excellent coverage probability in contrast to a conventional generalized estimating equations approach.
Welcome to Volume 8 of the International Journal of Hematologic Oncology

As the new Editor for the International Journal of Hematologic Oncology, it is with great excitement that I introduce the first issue of Volume 8. The year 2019 has lots of interesting things to be excited about; new projects, new ideas and, of course, more excellent content from all of our authors. The return of our Journal Watch series will help to broaden the range of topics we are able to publish, increasing our coverage of the latest information in the field. However, before we start this new period I would like to look back over the past year and our highlights from 2018. The year 2018 was a year of change for the editorial team at International Journal of Hematologic Oncology with new faces starting and the journal gaining recognition. Our presence on PubMed Central has been a huge help in widening our audience and increasing the reach of the articles we publish.
Optimizing the Spectral Characterisation of a CMYK Printer with Embedded CMY Printer Modelling

In the digital printing process, reliable colour reproduction is commonly achieved by printer characterisation, which defines the correspondence between the input device control values and the output colour information. The cellular Yule–Nielsen spectral Neugebauer model, together with its variants, is widely adopted in this topic because of its superb colorimetric and spectral accuracy. However, it seems that current studies have neglected an inconspicuous defect in such models when characterising printers equipped with black ink. That is, the cellular structure of these models overemphasises the sampling for dark-tone colours, and thus leads to relatively large errors in light tones. In this paper, taking a CMYK printer as an example, a simple and effective solution is proposed with no need of extra sampling. With the aid of a newly built cellular spectral Neugebauer model for the embedded CMY printer, this approach optimises the printer characterisation for light tones, slightly improves the precision for middle tones while it maintains the accuracy for dark tones. The performance of the proposed method was evaluated with regard to three different kinds of substrates and the experimental results validated its improvement in spectral printer characterisation.
Continuity and Change in the Late iron age-roman transition within the environs of Quarry wood opppidum: exCavations at furfieLd Quarry, Boughton monCheLsea

This article reports on the excavations at Furfield Quarry, Boughton Monchelsea, to the south-east of maidstone (ngr 78320 51680). the site was bounded to the north and north-west by Brishing Lane, to the north-east by Brishing Road, a disused quarry (Furfield Quarry) to the south and to the east by an area of woods known as Hogstrough Shaw. The wood known as Long Shaw divides the site in two; West Field and East Field (Fig. 1). Evaluation of the whole site by trial trenching took place in August 1996 (Philp 1996). This identified archaeological remains of Iron Age, Romano-British and post-medieval date in the East Field resulted in two specifications for archaeological investigation (KCC 2005a; KCC 2005b): a strip, map and record of the East Field, carried out from AprilJuly 2005, and a watching brief in advance of, and during development of the West Field for new housing, undertaken from October 2006 to April
Tyrosine kinase inhibitors vandetanib, lenvatinib and cabozantinib modulate oxidation of an anticancer agent ellipticine catalyzed by cytochromes P450 in vitro.

OBJECTIVES Vandetanib¸ lenvatinib, and cabozantinib are tyrosine kinase inhibitors (TKIs) targeting VEGFR subtypes 1 and 2, EGFR and the RET-tyrosine kinase, thus considered as multiple TKIs. These TKIs have already been approved for treating patients suffering from thyroid cancer and renal cell carcinoma. Ellipticine, a DNA-damaging drug, is another anticancer agent that is effective against certain tumors of the thyroid gland, ovarian carcinoma, breast cancer and osteolytic breast cancer metastasis. Its anticancer efficiency is dictated by its oxidation with cytochrome P450 (CYP) and peroxidase enzymes. A number of studies testing the effectiveness of individual anticancer drugs, the pharmacological efficiencies of which are affected by their metabolism, alone or in a combination with other cytostatics demonstrated that such combination can have both positive and negative effects on treatment regimen. The aim of this study was to study the effect of vandetanib, lenvatinib and cabozantinib on oxidation of ellipticine which dictates its pharmacological efficiency. METHODS Ellipticine oxidation catalyzed by hepatic microsomes, recombinant CYP enzymes and peroxidases (horseradish peroxidase, lactoperoxidase and myeloperoxidase) and the effect of TKIs (vandetanib, lenvatinib and cabozantinib) on this oxidation were analyzed by HPLC used for separation of ellipticine metabolites and quantification of their amounts formed during oxidation. RESULTS The CYP enzymatic system oxidizes ellipticine up to five metabolites (9-hydroxy-, 12-hydroxy-, 13-hydroxy-, 7-hydroxyellipticine, and ellipticine N2- oxide), while peroxidases form predominantly ellipticine dimer. Ellipticine oxidation catalyzed by rat and human hepatic microsomes was inhibited by vandetanib and cabozantinib, but essentially no inhibition was caused by lenvatinib. Of individual CYP enzymes catalyzing oxidation of ellipticine, TKIs inhibited oxidation of ellipticine catalyzed by CYP2D6 > 2D1 > 2C9 > 3A1 > 3A4, the CYP enzymes participating in ellipticine oxidation to metabolites increasing the ellipticine anticancer efficiency. On the contrary, they have essentially no inhibition effect on ellipticine oxidation catalyzed by CYP1A1 and 1A2, which are the enzymes that predominantly detoxify this drug. All tested TKIs had essentially no effect on oxidation of ellipticine by used peroxidases. CONCLUSION The results found demonstrate that TKIs vandetanib, lenvatinib and cabozantinib cause a decrease in oxidative activation of DNA-damaging drug ellipticine by several CYP enzymes in vitro which might lead to a decrease in its pharmacological efficiency. In contrast, they practically do not influence its detoxification catalyzed by CYP1A1, 1A2 and peroxidases. The present study indicates that tested TKIs seem not to have a potency to increase ellipticine anticancer efficiency.
Long Term Efficacy of a Single Session RFA of Benign Thyroid Nodules: A Longitudinal 5-Year Observational Study.

CONTEXT Radiofrequency ablation (RFA) of benign thyroid nodules has been gaining consensus. However, solid information on its long-term efficacy is yet to be obtained. OBJECTIVE To analyze the long-term results of single-session RFA. DESIGN Retrospective longitudinal observational study. SETTING Primary care center. PATIENTS OR OTHER PARTICIPANTS Adult outpatients who underwent a single-session RFA and post-treatment follow-up of at least three years. INTERVENTION RFA was performed after local anesthesia, under ultrasound guidance, by means of "moving-shot" technique and by using the trans-isthmic approach. RFA was performed with a median power of 55W; the median treatment time was 14 minutes. RFA system with internally cooled 18-gauge electrode and with an active 10 mm tip was used. MAIN OUTCOME MEASURES Objective (nodule volume trend) and subjective (compressive and cosmetic concerns) response to RFA were evaluated. Continuous variables were analyzed by the Wilcoxon and Anova test and correlations thereof by using the Spearman test. Categorical variables were compared by Pearson χ2 test. RESULTS Two hundred and fifteen patients were included. An early significant reduction of nodule volume was found after one year, lasting up to five years. At the end of the observation, nodules showed a median volume reduction of 66.9% (25-75% percentile 58-81). The best volume response after five years (81.8%) was observed in nodules with a volume below 10 mL at baseline. Patients' symptoms were significantly reduced. CONCLUSIONS This study, by demonstrating a durable shrinkage of benign thyroid nodules treated by RFA, as well as an improvement of compressive and visual symptoms, establishes the reliability of RFA as an alternative to surgery in the management of thyroid nodules, thus representing a remarkable therapeutic tool for clinical practice.
Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control

Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning power. However, the centralized RL is infeasible for large-scale ATSC due to the extremely high dimension of the joint action space. The multi-agent RL (MARL) overcomes the scalability issue by distributing the global control to each local RL agent, but it introduces new challenges: now, the environment becomes partially observable from the viewpoint of each local agent due to limited communication among agents. Most existing studies in MARL focus on designing efficient communication and coordination among traditional Q-learning agents. This paper presents, for the first time, a fully scalable and decentralized MARL algorithm for the state-of-the-art deep RL agent, advantage actor critic (A2C), within the context of ATSC. In particular, two methods are proposed to stabilize the learning procedure, by improving the observability and reducing the learning difficulty of each local agent. The proposed multi-agent A2C is compared against independent A2C and independent Q-learning algorithms, in both a large synthetic traffic grid and a large real-world traffic network of Monaco city, under simulated peak-hour traffic dynamics. The results demonstrate its optimality, robustness, and sample efficiency over the other state-of-the-art decentralized MARL algorithms.
Centrality Measures and Information Flows in Venture Capital Syndication Networks

This paper examines the performance of four centrality measures in describing flowing and accumulation of information in venture capitalists syndication networks. Although centrality measures should differentiate between network positions indicating those units that are more central than others, the basis of the differentiation does not lie on any theoretical foundation. Thus, applicability of these measures to describe flows and accumulation of information on certain actors is not evident. Utilising an actual syndication network of 161 US venture capitalists, we build a simulation emulating the information flows in this network in order to examine whether the measures correlate with the accumulation of information. The results demonstrate that there are unexpected differences between measures with respect to their explanation of information accumulation. The measure the simulation was built on failed worst to meet the information index, whereas simpler measure performed without flaws. The results contribute to the understanding of centrality measures and their performance in the context of information flows.
Siamese Convolutional Neural Network for Sub-millimeter-accurate Camera Pose Estimation and Visual Servoing

Visual Servoing (VS), where images taken from a camera typically attached to the robot end-effector are used to guide the robot motions, is an important technique to tackle robotic tasks that require a high level of accuracy. We propose a new neural network, based on a Siamese architecture, for highly accurate camera pose estimation. This, in turn, can be used as a final refinement step following a coarse VS or, if applied in an iterative manner, as a standalone VS on its own. The key feature of our neural network is that it outputs the relative pose between any pair of images, and does so with sub-millimeter accuracy. We show that our network can reduce pose estimation errors to 0.6 mm in translation and 0.4 degrees in rotation, from initial errors of 10 mm / 5 degrees if applied once, or of several cm / tens of degrees if applied iteratively. The network can generalize to similar objects, is robust against changing lighting conditions, and to partial occlusions (when used iteratively). The high accuracy achieved enables tackling low-tolerance assembly tasks downstream: using our network, an industrial robot can achieve 97.5% success rate on a VGA-connector insertion task without any force sensing mechanism.
Effect of pregnancy weight gain on infant birth weight among mothers attending antenatal care from private clinics in Mekelle City, Northern Ethiopia: A facility based follow-up study

Introduction Weight gain during pregnancy is an important indicator of maternal and fetal nutrition during pregnancy. However, information regarding the effect of pregnancy weight gain on birth weight is lacking from developing countries. Objective To determine the effect of pregnancy weight gain on the newborn’s birth weight in mothers attending antenatal care (ANC) services from private clinics. Methods Health facility-based follow-up study was conducted among 332 pregnant mothers attending their antenatal care in Mekelle city, from October 2016 to June 2017. Before 28 weeks of gestation, pregnancy weight was collected retrospectively, then, mothers were followed-up until the time of infant delivery to record their birth weight. Data were also collected by a structured questionnaire and checklists and analyzed using SPSS version 21. The relationship between dependent and independent variables was assessed and presented using descriptive statistics, as well as t-test, ANOVA, and multivariable linear regression analysis. Variables:—pre-pregnancy BMI, maternal age, parity, decision making power on monetary resources, pregnancy interval, availability of housemaid, women dietary diversity score, maternal occupation, and pregnancy weight gain were included in the multivariable analysis. Results Maternal weight increased monthly at a mean ± SD rate of 2 ± 0.7 kg in the second trimester, and 1.5 ± 0.7 kg in the third trimester. The mean ± SD of pre-pregnancy body mass index (BMI) and total pregnancy weight gain was 23.8 ± 4.6 kg/m2, and 12 ± 2.8 kg respectively. The mean ± SD of birth weight was 3440 ± 542 grams. Weight gain has a significant effect on infant birth weight, a 1 kg increase in the pregnancy weight was associated with 94 g increase in BW (β = 97, 95% CI: 73–120). After dividing the pre-pregnancy weight into four groups (< 18.5, 18.5–24.9, 25–29.9 and ≥30) kg/m2 based on the Institute of Medicine (IOM), we found a statistically significant birth weight difference between each group. Conclusion Pregnancy weight gain has a significant effect on birth weight. Thus, ANC counseling services should focus on maternal weight gain to prevent sub-optimal birth weight.
Poverty Traps and Economic Growth: Evidence from Pakistan

The research on economic growth and poverty has largely considered poverty as an outcome of lower growth performance. In contrary to this commonly held belief that growth is necessary to reduce poverty, in this study we argue that poverty traps create self-perpetuating mechanisms that impede long-run economic growth. Poverty results in forgone growth opportunities because of higher transaction cost. Moreover, the poor people who are financially distress and also are distrustful are likely to be excluded from the active and efficient participation in economic activities. This study investigates the impact of poverty on economic growth in Pakistan using annual data for the period 1975 to 2016. The empirical analysis for the effect of poverty on economic growth is based on ARDL approach to cointegration, generalized method of moments, fully modified OLS, and dynamic ordinary least square estimation techniques. The main finding suggests that poverty inhibits economic growth performance of Pakistan and this finding is robust to diverse estimation methods. Therefore, growth policies should be designed not only to enhance economic growth but also should exert an independent influence on poverty reduction, thereby reducing the drag of poverty on growth.
Novel 8-Substituted Coumarins That Selectively Inhibit Human Carbonic Anhydrase IX and XII

A novel series of 8-substituted coumarin-based compounds, characterized by the presence of alkylpiperazine and arylpiperazine chains, were synthesized and tested for their inhibitory activity against four human carbonic anhydrase (hCA) isoforms. All compounds displayed nanomolar potency against the cancer-related hCA IX and hCA XII; moreover, they were shown to be devoid of any inhibitory activity toward the cytosolic hCA I and hCA II up to 10 µM concentration in the assay system. Therefore, the synthesized coumarin ligands demonstrated to be potent and selective hCA IX/XII inhibitors, and were shown to be as potent as the reference inhibitor acetazolamide against hCA XII, with single-digit nanomolar Ki values. Molecular modeling studies provided a rationale for explaining the selectivity profile of these non-classic hCA inhibitors and their interactions with the enzymes, according to their specific mechanism of action, thus paving the way for future structure-based lead optimization studies.