Miracle-dz/UniFAR-FTUs huggingface dataset:
“PERISH” flowchart for selection of the patients with resectable hepatocellular carcinoma

165 ABSTRACT A selection of patients with hepatocellular carcinoma (HCC) for surgical resection is crucial and algorithms/staging systems help surgeons to decide on a standard treatment for each patient and each HCC stage. However, there are always diffi culties in remembering and/or recalling the contents of the algorithms/staging systems. Moreover, most algorithms/staging systems do not include data about the extent of hepatectomy, intra-hepatic distribution of tumor(s), and technical feasibility of resection, all of which are vital in the surgeons’ decision-making process. Here, we aimed to present a simple and handy mnemonic acronym for selecting resectable HCCs in surgical practice. This was reproduced from the existing well-known staging systems. The designed mnemonic acronym is a phrase “PERISH” and it includes asking for Performance of patient, Extra-hepatic disease, Reserve of the liver, Intra-hepatic distribution, Stratifying risk factors, and Hepatectomy size in order. Performance based on whether the patient is mostly bedridden or not should be the fi rst step of evaluation. Next, asking for suspicious metastasis as bone pain and radiological evaluation of abdomen/thorax is mandatory. The calculation of Child-Pugh score is only the third step. Good candidates for surgical resection should be Child-Pugh “A” with normal bilirubin levels. Technical feasibility of resection, according to the intra-hepatic distribution of tumor(s) should be done later, and the candidates preferably should not have portal hypertension (no splenomegaly, no thrombocytopenia). If the patient fulfi lls all the previous steps, the surgeon may perform indo-cyanine green clearance test. Consequently, following the PERISH fl owchart may prevent “perish” of the surgeons while selecting the appropriate resectable HCCs.
Changes in Achievement Motivation among University Freshmen.

Changes in achievement motivation over the first semester of university studies were examined with N = 229 freshmen, who were surveyed twice in the present study. Students’ academic self-concepts, achievement goals, and subjective values were chosen as theoretically central components of achievement motivation. The results indicated significant deterioration in achievement motivation over the course of the first semester of university studies, a phenomenon which affected a large proportion of the first-year students. Regression analyses provided evidence that motivation, prior to the start of university studies, and the changes in motivation had incremental validity over measures of prior school-achievement in predicting self-regulated learning, performance in examinations, as well as the intention to change university majors or drop out completely (partially in interaction with prior school-achievement).
Incrementally Added GVFs are Learned Faster with the Successor Representation

We propose using the successor representation (SR) to accelerate learning in a constructive knowledge system based on general value functions (GVFs). We consider an agent which incrementally constructs a predictive model of the world as a collection of GVFs. Using the SR provides a way of separating the dynamics of the world from the prediction targets and thus captures regularities that can be reused across multiple GVFs. The learning problem then consists of two parts—learning the SR and learning one-step predictions about a signal of interest. SR-based predictions are then produced by combining the SR with different one-step predictions. As a primary contribution of this work, we show in both a grid world and a robotic task that using SRbased predictions can improve sample efficiency and learning speed in a lifelong learning setting where new predictions are incrementally added and learned over time.
Advancement toward coupling of the VAMPER permafrost model within the earth system model iLOVECLIM (version 1.0): description and validation

Abstract. The VU Amsterdam Permafrost (VAMPER) permafrost model has been enhanced with snow thickness and active layer calculations in preparation for coupling within the iLOVECLIM Earth system model of intermediate complexity (EMIC). In addition, maps of basal heat flux and lithology were developed within ECBilt, the atmosphere component of iLOVECLIM, so that VAMPER may use spatially varying parameters of geothermal heat flux and porosity values. The enhanced VAMPER model is validated by comparing the simulated modern-day extent of permafrost thickness with observations. To perform the simulations, the VAMPER model is forced by iLOVECLIM land surface temperatures. Results show that the simulation which did not include the snow cover option overestimated the present permafrost extent. However, when the snow component is included, the simulated permafrost extent is reduced too much. In analyzing simulated permafrost depths, it was found that most of the modeled thickness values and subsurface temperatures fall within a reasonable range of the corresponding observed values. Discrepancies between simulated and observed permafrost depth distribution are due to lack of captured effects from features such as topography and organic soil layers. In addition, some discrepancy is also due to disequilibrium with the current climate, meaning that some observed permafrost is a result of colder states and therefore cannot be reproduced accurately with constant iLOVECLIM preindustrial forcings.
Differentiable Unbiased Online Learning to Rank

Online Learning to Rank (OLTR) methods optimize rankers based on user interactions. State-of-the-art OLTR methods are built specifically for linear models. Their approaches do not extend well to non-linear models such as neural networks. We introduce an entirely novel approach to OLTR that constructs a weighted differentiable pairwise loss after each interaction: Pairwise Differentiable Gradient Descent (PDGD). PDGD breaks away from the traditional approach that relies on interleaving or multileaving and extensive sampling of models to estimate gradients. Instead, its gradient is based on inferring preferences between document pairs from user clicks and can optimize any differentiable model. We prove that the gradient of PDGD is unbiased w.r. t. user document pair preferences. Our experiments on the largest publicly available Learning to Rank (LTR) datasets show considerable and significant improvements under all levels of interaction noise. PDGD outperforms existing OLTR methods both in terms of learning speed as well as final convergence. Furthermore, unlike previous OLTR methods, PDGD also allows for non-linear models to be optimized effectively. Our results show that using a neural network leads to even better performance at convergence than a linear model. In summary, PDGD is an efficient and unbiased OLTR approach that provides a better user experience than previously possible.
Enhanced Quantum Dot Spontaneous Emission with Multilayer Metamaterial Nanostructures

The Purcell effect of quantum dot (QD) spontaneous emission with Ag-SiO2 multilayer metamaterial nanostructures has been demonstrated in experiment and simulation. A broadband enhanced spontaneous emission rate of QDs is observed due to large local density of states in the epsilon-near-zero and hyperbolic regions of multilayer structures. Multilayer gratings are utilized to further enhance the QD spontaneous emission as the QDs located inside the grating grooves strongly interact with high-k coupled surface plasmon polariton modes. Photoluminescence decay measurements are in good agreement with both analytical treatment with a nonlocal effect and three-dimensional finite-element simulation. Detailed studies of QD position and polarization effects on emission rate enhancement for multilayer and multilayer grating nanostructures provide important insight for understanding the coupling mechanisms of emitter–multilayer interaction and the engineering of local density of states in metamaterial nanostructures.
Potentials of cocoa pod husk ash as fertilizer and liming materials on nutrient uptake and growth performance of cocoa

An investigation was carried out to evaluate the potentials of cocoa pod husk ash (CPHA) as fertilizer and liming materials on nutrient uptake and growth performance of cocoa (Theobroma cacao) seedlings in the nursery. Eight treatments: 5t/ha CPHA (6.25g), 10 t/ha CPHA (12.5g), 15t/ ha CPHA (18.75g), 20t/ha CPHA (25g), 25t/ha CPHA (31.25g), 30t/ha CPHA (37.5g), 400 kg/ha urea (0.6g) per 2.5g of soil filled polythene bags replicated three times, and a control (no fertilizer, no manure) were arranged in completely randomized design (CRD). The results showed that the cocoa pod husk ash increased significantly (P<0.05) the plant height, stem diameter, number of leaves, leaf area, number of branches, root and shoot lengths, root and shoot fresh weights, root and shoot dry weights, N, P, K, Ca, Mg of soil and leaf, soil pH and organic matter contents of cocoa seedlings relative to the control treatment. 25 t/ha CPHA increased the soil pH, soil organic carbon, organic matter, P, K, Mg, Na, and Ca by 40%, 41%, 48%, 56% 58%, 42%, 14% and 39% respectively with exception of soil N compared to urea fertilizer treatment. 25 t/ha CPHA increased significantly (P<0.05) soil, leaf nutrient uptake and morphological growth of cocoa seedlings compared to other levels of CPHA treatments. 25t/ha CPHA also increased soil pH, organic matter, N, P, K, Na, Mg, Ca by 46%, 83%, 29%, 70%, 59%, 3%, 29%, 43% respectively compared to the control. Again 25t/ha CPHA treatment increased the plant height, stem diameter, leaf area, number of leaves, number of branches, root and shoot lengths, fresh and dry root weights, fresh and dry shoot weights of cocoa seedlings by 30%, 54%, 71%, 63%, 98%, 84%, 25%, 56%, 57%, 83% and 80% respectively compared to the control. When compared with urea fertilizer treatment, 25t/ha CPHA increased the plant height, stem diameter, leaf area, number of leaves, number of branches, root and shoot lengths, fresh and dry root weights, fresh and dry shoot weights of cocoa seedlings by 4%, 37%, 60%, 43%, 72%, 47%, 55%, 16%, 14%, 39% and 32% respectively. Generally cocoa pod ash applied at 25 tons/ha was the most effective treatment improving cocoa growth and yield parameters, soil chemical composition.
Employment Stability and the Role of Sectoral Dominance in Rural Economies

Rural development has received renewed attention, particularly in the West, where there are opportunities for enhancing tourism in communities once reliant on extractive resources. The historical focus of rural development has been on economic growth, with little concern about the sustainability or socioeconomic impacts of such growth. This study analyzes and discusses the employment dynamics of rural economies with varying employment bases to illustrate some possible impacts of targeting specific employment sectors as part of a rural economic development strategy. The empirical research focuses on the rural counties of Utah, where the trade and service sectors associated with increased tourism and retirement communities have been rapidly expanding throughout the past two decades. Copyright 1998, Oxford University Press.
PHENOLIC ACIDS PROFILING AND ANTIOXIDANT POTENTIAL OF MULBERRY (MORUS LAEVIGATA W., MORUS NIGRA L., MORUS ALBA L.) LEAVES AND FRUITS GROWN IN PAKISTAN

Mulberry trees are distributed throughout Pakistan. Besides its use in forage and food for animals, it is also used as herbal medicine. This study is carried out to determine phenolic acids profile, sugar content, and the antioxidant activity of the leaves and fruits of three mulberry species (Morus alba L., Morus nigra L. and Morus laevigata W.) grown in Pakistan. Dried fruits and leaves of three mulberry plant species were extracted by three different methods. The extracts were analysed by HPLC with diode array detection for phenolic acids. Antioxidant activity and sugar content were determined using spectrophotometric assays. The structure of chlorogenic acid was confirmed by LC-MS/MS analysis. The major phenolic acids were identified as gallic, protocatechuic, p-hydroxybenzoic, vanillic, chlorogenic, syringic, p-coumaric, ferulic and m-coumaric acids. Chlorogenic acid was the prominent phenolic acid in all leaves samples collected from three mulberry species and optimum extraction of chlorogenic acid was achieved using 80% aqueous methanol with ultrasonic-assisted extraction. Total phenolic compounds as determined by Folin-Ciocalteu assay were in the range of 3.89 to 11.79 mmol/ 100 g gallic acid equivalent, whereas antioxidant activities by DPPH assay ranged from 22.85–76.88 µmol/100 g quercetin equivalent. The concentration of sugars in different samples ranged between 36.56-82.15 mmol/ 100 g sucrose equivalent. This is the first report on identification of phenolic acids and antioxidant activity in the extracts of three cultivars of mulberry plants grown in Pakistan. This information will be of considerable value to the commercial producers of mulberry trees in the country.
Towards a descriptive Framework of Demand Side Flexibility

The objective of this paper is to contribute to the understanding of Demand Side Management (DSM) in future energy markets. DSM is considered one of the most promising concepts to meet the challenge of balancing demand and supply in the future power grid. In contrast to traditional electricity systems, where manageable supply is used to ensure grid stability, DSM strives to influence demand in accordance with current electricity production. From a market perspective, this generates a new, turned-around supply chain: While electricity is traded from generation to consumption, demand side flexibility (DSF) is offered by consumers to grid operators. We address the requirement of a theoretical foundation for DSF by conducting the first step towards a descriptive theory to classify different dimensions of DSF. Further, we suggest an overarching, interdisciplinary unification for further research in the topic and enable various research disciplines to join forces on this challenge. The targeted descriptive theory shall be fundamental research on DSM (e.g. electric vehicle charging, flexible industry processes).
REVIEW: Time lag between photosynthesis and carbon dioxide efflux from soil: a review of mechanisms and controls

CO2 efflux from soil depends on the availability of organic substances respired by roots and microorganisms. Therefore, photosynthetic activity supplying carbohydrates from leaves to roots and rhizosphere is a key driver of soil CO2. This fact has been overlooked in most soil CO2 studies because temperature variations are highly correlated with solar radiation and mask the direct effect of photosynthesis on substrate availability in soil. This review highlights the importance of photosynthesis for rhizosphere processes and evaluates the time lag between carbon (C) assimilation and CO2 release from soil. Mechanisms and processes contributing to the lag were evaluated. We compared the advantages and shortcomings of four main approaches used to estimate this time lag: (1) interruption of assimilate flow from leaves into the roots and rhizosphere, and analysis of the decrease of CO2 efflux from soil, (2) time series analysis (TSA) of CO2 fluxes from soil and photosynthesis proxies, (3) analysis of natural δ13C variation in CO2 with photosynthesis‐related parameters or δ13C in the phloem and leaves, and (4) pulse labeling of plants in artificial 14CO2 or 13CO2 atmosphere with subsequent tracing of 14C or 13C in CO2 efflux from soil. We concluded that pulse labeling is the most advantageous approach. It allows clear evaluation not only of the time lag, but also of the label dynamics in soil CO2, and helps estimate the mean residence time of recently assimilated C in various above‐ and belowground C pools. The impossibility of tracing the phloem pressure–concentration waves by labeling approach may be overcome by its combination with approaches based on TSA of CO2 fluxes and its δ13C with photosynthesis proxies. Numerous studies showed that the time lag for grasses is about 12.5±7.5 (SD) h. The time lag for mature trees was much longer (∼4–5 days). Tree height slightly affected the lag, with increasing delay of 0.1 day m−1. By evaluating bottle‐neck processes responsible for the time lag, we conclude that, for trees, the transport of assimilates in phloem is the rate‐limiting step. However, it was not possible to predict the lag based on the phloem transport rates reported in the literature. We conclude that studies of CO2 fluxes from soil, especially in ecosystems with a high contribution of root‐derived CO2, should consider photosynthesis as one of the main drivers of C fluxes. This calls for incorporating photosynthesis in soil C turnover models.
Genetic Algorithm for Outlier Detection

The primary objective of the outlier detection is to find the data that are noticeably different from other data in a particular dataset. Outlier detection has been a key research area in data mining since it has wide application in various areas like Intrusion Detection System, Fraud Detection, Bio Science and Drugs Research. Many approaches have been proposed for efficient and accurate outlier detection. In this paper authors propose a soft computing technique; Genetic Algorithms for outlier detection. The proposed algorithm was tested on some standard dataset for performance evaluation . The result was found to be very encouraging.
Gr upSM Chronic Pancreatitis-Recent Advances

Recent advances in chronic pancreatitis have demonstrated it as a heterogeneous group of disease with various etiologies, such as alcohol related chronic pancreatitis, autoimmune mediated pancreatitis, hereditary pancreatitis and idiopathic pancreatitis. Correct classification based on thorough history and workup is important, e.g. autoimmune mediated pancreatitis can present with abdominal pain and pancreatic mass therefore mimicking a pancreatic malignancy. This review mainly focuses on the recent advances on the histopathological changes, diagnosis, etiology, treatment as well as serum/tissue biomarker research and animal models. Min Cui M1,2, Zhicheng Jin1,2, Lan Zhou3 and Wei Xin1,2* 1Department of Pathology, University Hospitals Case Medical Center, USA 2Department of Pathology, Case Western Reserve University, USA 3Department of Pharmaceutical Sciences, Northeast Ohio Medical University, USA *Corresponding author: Wei Xin, Department of Pathology, University Hospitals Case Medical Center, USA, Email: wxx10@case.edu Published Date: May 18, 2016
TRIPLE BAND NOTCHED UWB ANTENNA DESIGN USING ELECTROMAGNETIC BAND GAP STRUCTURES

A circular monopole antenna for ultra wideband (UWB) applications with triple band notches is proposed. The proposed antenna rejects worldwide interoperability for microwave access WiMAX band (3.3 GHz–3.8 GHz), wireless local area network WLAN band (5.15 GHz–5.825 GHz) and X-Band downlink satellite communication band (7.1 GHz–7.9 GHz). The antenna utilises mushroomtype and uniplanar Electromagnetic Band Gap (EBG) structures to achieve band-notched designs. The advantages of band-notched designs using EBG structures such as notch-frequency tuning, triple-notch antenna designs and stable radiation pattern are shown. The effect of variation of EBG structure parameters on which notched frequency depends is also investigated. Fabricated and measured results are in good agreement with simulated ones.
The impact of information quality on information research

This contribution analyses the impact of information quality on research. It presents a theoretical discussion of the concept ‘information quality’ and shows that good information quality entails good research. The discussion is relevant for both researchers and research users, because it shows that information quality is a critical determinant of research success, as measured by the creation of new knowledge. Thus, information quality fundamentally impacts research. Reasons for the existence of low quality information as well as proposed solutions to this problem are provided in the paper.
Preparation and therapeutic evaluation of rutin-phospholipid complex for antidiabetic activity -

Article history: Received on: 21/07/2015 Revised on: 03/09/2015 Accepted on: 29/10/2015 Available online: 26/01/2016 The objective of the present work to prepare Rutin-phospholipid complex (RPC), a phytoformulation and characterization, evaluation for its antidiabetic activity in streptozotocin induced diabetic model. Prepared RPC was characterized and screened for antidiabetic activity by OGTT in normoglycemic and diabetic rats for RT and RPC at different time intervals. Effect of RT and RPC (50 and 100 mg/kg b.w. p.o. respectively) in STZ induced diabetic rats for one day and fifteen days was studied. This is followed by estimation of Estimation of SG, and lipid parameters. Histopathology studies of pancreatic tissue and bioavailability studies of RT & RPC were also carried out. SEM data showed that RPC has irregular size vesicles consisting of phosphatidylcholine. RPC showed a single endothermal peak at 147.68°C in DSC studies. OH group has shifted to lower frequency in phytosome compared to rutin phospholipid in FTIR spectra. Treatment with RPC (50 and 100 mg/kg b.w. p.o.) significantly reduced the blood glucose levels and restored the altered lipid parameters as compared to RT. Histopathological studies revealed that RPC also restored back the size of pancreatic islets and maintained the normal β-cells. A higher serum concentration of RT (13.20 μg/mL) in RPC treated group was observed in bioavailability studies as compared to RT. RPC maintained effective concentration of rutin for a longer period in rat serum.
Training Samples MaliciousBenign Dynamic Feature Extraction Training Classi cation

Mobile operating systems are a prime target of today’s malware authors and cyber criminals. In particular, Google’s Android suffers from an ever increasing number of malware attacks in the form of malicious apps. These typically originate from poorly policed third-party app stores that fail to vet the apps prior to publication. In this paper, we present Hugin, a machine learning-based app vetting system that uses features derived from dynamic, as well as static analysis and thus falls into the scarcely studied class of hybrid approaches. Hugin is unique with respect to using IPC/RPC monitoring as source for dynamically extracted features. Furthermore, Hugin uses a short (and yet effective) feature vector that leads to a high efficiency in training as well as classification. Our evaluation shows that Hugin achieves a detection accuracy of up to 99.74% on an up-todate data set consisting of more than 14,000 malware samples and thus, is easily capable of competing with other current systems. Keywords–mobile malware detection; app vetting; machinelearning.
Stimuli-responsive biocompatible nanovalves based on β-cyclodextrin modified poly(glycidyl methacrylate)

β-Cyclodextrins (β-CDs) were grafted onto star-shaped poly(glycidyl methacrylate)s (S5-PGMAs) with a straightforward and efficient ring-opening addition of amine groups to result in PGMA–CDs, which not only possess good water-solubility and biocompatibility, but also can serve as polymeric supramolecular hosts to form inclusion complexes with suitable guests. They can be easily assembled on the surface of azobenzene-functionalized mesoporous silica nanoparticles (MSNs) via host–guest interactions to obtain MSN@PGMA–CD hybrid nanoparticles, which have been fully characterized by Fourier transform infrared (FT-IR) spectroscopy, thermogravimetric analysis (TGA), transmission electron microscopy (TEM) and elemental analysis. The experimental results showed that these types of inorganic–organic hybrid mesoporous nanocomposites possess good cargo encapsulation and release properties, as compared with the simple supramolecular nanovalves with β-CD itself as the gating component, upon activation by light, temperature variation, and competitive binding agents. In addition, the extremely low cytotoxicity of the nanocomposites demonstrated by MTT assay can further broaden their applications in controlled drug release.