bluuebunny/crossref_metadata_2025 huggingface dataset:
Novel Cardiac Arrhythmia Processing using Machine Learning Techniques
Authors: Prashar Navdeep , Sood Meenakshi , Jain Shruti
Published: August 2020

Electrocardiography (ECG) signals provides assistance to the cardiologists for identification of various cardiovascular diseases (CVD). ECG machine records the electrical activity of the heart with the assistance of electrodes placed on the patient’s body. Qualitative characterization of ECG signal reflects its sensitiveness towards distinct artifacts that resulted in low diagnostic accuracy and may lead to incorrect decision of the clinician. The artifacts are removed utilizing a robust noise estimator employing DTCWT using various threshold values and functions. The segments and intervals of ECG signals are calculated using the peak detection algorithm followed by particle swarm optimization (PSO) and the proposed optimization technique to select the best features from a considerable pool of features. Out of the 12 features, the best four features are selected using PSO and the proposed optimization technique. Comparative analysis with other feature selection methods and state-of-the-art techniques demonstrated that the proposed algorithm precisely selects principle features for handling the ECG signal and attains better classification utilizing distinctive machine learning algorithms. The obtained accuracy using our proposed optimization technique is 95.71% employing [Formula: see text]-NN and neural networks. Also, 4% and 10% improvements have been observed while using [Formula: see text]-NN over ANN and SVM, respectively, when the PSO technique is executed. Similarly, a 14.16% improvement is achieved while using [Formula: see text]-NN and ANN over the SVM machine learning technique for the proposed optimization technique. Heart rate is calculated using the proposed estimator and optimization technique, which is in consensus with the gold standard.
Effects of Color Quantization on JPEG Compression
Authors: Araujo Leonardo C. , Sansao Joao P. H. , Junior Mario C. S.
Published: August 2020

This paper analyzes the effects of color quantization on standard JPEG compression. Optimized color palettes were used to quantize natural images, using dithering and chroma subsampling as optional. The resulting variations on file size and quantitative quality measures were analyzed. Preliminary results, using a small image database, show that file size suffered an average 20% increase and a concomitant loss in quality was perceived ([Formula: see text]6dB PSNR, [Formula: see text]0.16 SSIM and [Formula: see text]9.6 Butteraugli). Color quantization present itself as an ineffective tool on JPEG compression but if necessarily imposed, on high quality compressed images, it might lead to a negligible increase in data size and quality loss. In addition dithering seems to always decrease JPEG compression ratio.
Prediction of Visibility Under Radiation Fog by DNA Computing
Authors: Mondal Mandrita , Ray Kumar S.
Published: August 2020

In this paper, we propose a wet lab algorithm for prediction of visibility under radiation fog by DNA computing. The model is based on a concept of similarity based fuzzy reasoning suitable for wet lab implementation. The concept of similarity based fuzzy reasoning using DNA sequences is different from conventional approach to fuzzy reasoning. It replaces the logical aspect of classical fuzzy reasoning by DNA chemistry. By the proposed algorithm the tedious job to choose suitable implication operator, which is absolutely necessary for classical fuzzy reasoning, can be avoided. If the fuzzified forms of five observed parameters, i.e. dew point, dew point spread, the rate of change of dew point spread per day, wind speed and sky condition are given, the newly proposed algorithm efficiently predicts the possibility of visibility under radiation fog. The final result of the wet lab algorithm, which is in form of fuzzy DNA, produces multi valued status which can be linguistically interpreted to match the perception of an expert.
Sustainability: Analysis Using Mathematics of Uncertainty
Authors: Mordeson John N. , Mordeson Matthew A. , Mathew Sunil
Published: August 2020

All member states of the United Nations adopted Agenda 2030 and the Sustainable Development Goals (SDGs) in 2015. The 17 SDGs describe a universal agenda that applies to and must be implemented by all countries. We take the metrics and data provided in the SDG Index and Dashboards Reports and the Report of a Study by Stakeholder Forum and transform them into a fuzzy logic setting. This allows for the analysis of the results in these reports by using techniques of mathematics of uncertainty. We focus on countries making up the Organization for Economic Cooperation and Development (OECD). We provide a ranking of the countries in the OECD as to their achievement of the SDGs.
Conception and Implementation of a New Data-Driven Fuzzy Method for Reliability and Safety Analysis
Authors: Abbasi F. , Allahviranloo T.
Published: August 2020

In this paper, the probability of failure of the components is introduced as a new type of fuzzy number called Gaussian patchy fuzzy number to insert parameters’ uncertainty. The reason for the use of such fuzzy numbers is also due to their realistic estimation. Hence, in order to obtain a more accurate estimate of each failure occurrence and overall system reliability analysis, we assume that the fundamental events of fault tree are in the form of Gaussian patchy fuzzy number on [0, 1], and then, by applying the fuzzy transmission average (TA) [F. Abbasi, T. Allahviranloo and S. Abbasbandy, A new attitude coupled with fuzzy thinking to fuzzy rings and fields, Journal of Intelligent and Fuzzy Systems 29 (2015) 851–861.], we will model the reliability of the fuzzy system (especially the series and the parallel). Applying such fuzzy operations and numbers will lead to a more realistic analysis of the reliability of the fuzzy system. Finally, the proposed model has been used for the lack of satisfaction of the University’s administrative and financial assistant.
Inverse Fuzzy Graphs with Applications
Authors: Borzooei R. A. , Almallah R. , Jun Y. B. , Ghaznavi H.
Published: August 2020

Rosenfeld [A. Rosenfeld, Fuzzy Graphs, Fuzzy Sets and Their Applications, eds. L. A. Zadeh, K. S. Fu and M. Shimura (Academic Press, New York, 1975), pp. 77–95.] defined the fuzzy relations on the fuzzy sets and developed the structure of fuzzy graph, as a graph with a membership degree (between zero and one) for the vertices and edges such that the membership degree of every edge is less than or equal to the minimum of the membership degree of its endpoints. Although this model of graph has many applications in the real life, it fails to solve a lot of problems, which we can use graph for its representation. This paper aimed to demonstrate a new type of graph with a membership degree (between zero and one) for the vertices and edges so that the membership degree of every edge becomes more than or equals the minimum of the membership degrees of its endpoints. This new type of graph is called inverse fuzzy graph “or” I-fuzzy graph, which can play a role in solving many problems which are not solved by fuzzy graph.
THE EFFECTIVE BENCHMARK SELECTION MODEL AND SIMULATION IN THE POWER SECTOR OF CHINA’S ETS
Authors: QI SHAOZHOU , HE ANQI , ZHANG JIHONG
Published: August 2020

In 2019, the National Carbon Emission Trading Scheme (ETS) of China’s power generation industry was officially launched, and the free allocation and benchmark are the primary ways for allowance allocation in the first phase of the national ETS. The carbon allowance allocation method is the basis for the effective operation of the ETS. In order to evaluate China’s carbon allowance allocation method published by policy, we constructed a selection model of the effective benchmark and combined with three different scenarios such as no generation-right trading market, incomplete generation-right trading market and complete generation-right trading market to simulate the operation of the national ETS in China’s power generation industry. The study shows that: (1) according to the current benchmark value published by policy, the carbon allowance supply will exceed demand in all scenarios. (2) Regardless of the scenario, in order to balance the supply and demand of allowance in the ETS, the effective benchmark value should be adjusted to 35th–50th quantile of the carbon emissions per unit power generation.
THE SPEECHES OF THE EUROPEAN CENTRAL BANK’s PRESIDENTS: AN NLP STUDY
Authors: WARIN THIERRY , SANGER WILLIAM
Published: August 2020

This paper introduces natural language processing into the study of central banking. It studies the evolution of the ECB’s communication through time, considering its three subsequent presidents (W. Duisenberg, J. C. Trichet and M. Draghi) and the pre- and post-2008 financial crisis era. It helps understand the history of the ECB since its inception. From a methodological standpoint, we study the evolution of the ECB’s speeches. The speech analysis is based on text classification and sentiment/polarity analyses. For that purpose, we have built a unique dataset of the ECB’s speeches. We have coded algorithms to run the text analysis through time. They help us capture the evolution in the ECB’s understanding of the actual economic situation and also measure — for instance — the stress level at the ECB through a polarity analysis through time.
On the Perception Analysis of User Feedback for Interactive Face Retrieval
Authors: Fang Yuchun , Zhang Wei , Liu Ningjie
Published: August 2020

In this article, we explore the coherence of face perception between human and machine in the scenario of interactive face retrieval. In the part of human perception, we collect user feedback to the stimuli of a target face and groups of displayed candidate face images in a face database with a large number of subjects. In the part of machine vision, we compare the benchmark features and general metrics to measure face similarity. We propose a series of coherence measurements to evaluate the statistic characteristic of human and machine face perception. We discover that despite the unfamiliarity of users to most faces in the database, the coherence between human and machine remains in a stable level across multiple variations in metrics, features, size of databases, and demographics. The simulation experiments with the coherence distributions demonstrate that the embedded information is valuable to speed up interactive retrieval. The comparisons over multiple parameter settings provide feasible instructions in designing the interactive face retrieval system with more consideration of human factors.
A quick look at impredicativity
Authors: Serrano Alejandro , Hage Jurriaan , Peyton Jones Simon , Vytiniotis Dimitrios
Published: August 2020

Type inference for parametric polymorphism is wildly successful, but has always suffered from an embarrassing flaw: polymorphic types are themselves not first class. We present Quick Look, a practical, implemented, and deployable design for impredicative type inference. To demonstrate our claims, we have modified GHC, a production-quality Haskell compiler, to support impredicativity. The changes required are modest, localised, and are fully compatible with GHC's myriad other type system extensions.
Effects for efficiency: asymptotic speedup with first-class control
Authors: Hillerström Daniel , Lindley Sam , Longley John
Published: August 2020

We study the fundamental efficiency of delimited control. Specifically, we show that effect handlers enable an asymptotic improvement in runtime complexity for a certain class of functions. We consider thegeneric countproblem using a pure PCF-like base language λband its extension with effect handlers λh.

We show that λhadmits an asymptotically more efficient implementation of generic count than any λbimplementation.

We also show that this efficiency gap remains when λbis extended with mutable state.

To our knowledge this result is the first of its kind for control operators.
Elaboration with first-class implicit function types
Authors: Kovács András
Published: August 2020

Implicit functions are dependently typed functions, such that arguments are provided (by default) by inference machinery instead of programmers of the surface language. Implicit functions in Agda are an archetypal example. In the Haskell language as implemented by the Glasgow Haskell Compiler (GHC), polymorphic types are another example. Implicit function types are
first-class
if they are treated as any other type in the surface language. This holds in Agda and partially holds in GHC. Inference and elaboration in the presence of first-class implicit functions poses a challenge; in the context of Haskell and ML-like languages, this has been dubbed “impredicative instantiation” or “impredicative inference”. We propose a new solution for elaborating first-class implicit functions, which is applicable to full dependent type theories and compares favorably to prior solutions in terms of power, generality and simplicity. We build atop Norell’s bidirectional elaboration algorithm for Agda, and we note that the key issue is incomplete information about insertions of implicit abstractions and applications. We make it possible to track and refine information related to such insertions, by adding a function type to a core Martin-L'of type theory, which supports strict (definitional) currying. This allows us to represent undetermined domain arities of implicit function types, and we can decide at any point during elaboration whether implicit abstractions should be inserted.
Regular language type inference with term rewriting
Authors: Haudebourg Timothée , Genet Thomas , Jensen Thomas
Published: August 2020

This paper defines a new type system applied to the fully automatic verification of safety properties of tree-processing higher-order functional programs. We use term rewriting systems to model the program and its semantics and tree automata to model algebraic data types. We define the regular abstract interpretation of the input term rewriting system where the abstract domain is a set of regular languages. From the regular abstract interpretation we derive a type system where each type is a regular language. We define an inference procedure for this type system which allows us check the validity of safety properties. The inference mechanism is built on an invariant learning procedure based on the tree automata completion algorithm. This invariant learning procedure is regularly-complete and complete in refutation, meaning that if it is possible to give a regular type to a term then we will eventually find it, and if there is no possible type (regular or not) then we will eventually find a counter-example.
TLC: temporal logic of distributed components
Authors: Griffin Jeremiah , Lesani Mohsen , Shadab Narges , Yin Xizhe
Published: August 2020

Distributed systems are critical to reliable and scalable computing; however, they are complicated in nature and prone to bugs. To manage this complexity, network middleware has been traditionally built in layered stacks of components.We present a novel approach to compositional verification of distributed stacks to verify each component based on only the specification of lower components. We present TLC (Temporal Logic of Components), a novel temporal program logic that offers intuitive inference rules for verification of both safety and liveness properties of functional implementations of distributed components. To support compositional reasoning, we define a novel transformation on the assertion language that lowers the specification of a component to be used as a subcomponent. We prove the soundness of TLC and the lowering transformation with respect to a novel operational semantics for stacks of composed components in partially synchronous networks. We successfully apply TLC to compose and verify a stack of fundamental distributed components.
Operation of SOFC Short-Stacks with Simulated Bio-Syngas: Influence of Model Tars Naphthalene and Phenol
Authors: Hauser Michael , Herrmann Stephan , Hauck Maximilian , Fendt Sebastian , Lenser Christian , Menzler Norbert H. , Spliethoff Hartmut
Published: August 2020

Operation of solid oxide fuel cells (SOFCs) with bio-syngas from the gasification of biomass is a promising approach to highly efficient and sustainable power generation. At the same time, the coupling is challenging as several biogenic impurities in the bio-syngas have a negative effect on the SOFC. For this paper the impacts of the impurities naphthalene and phenol on SOFC short-stacks were investigated experimentally for the first time. The cell in the stacks were anode-supported SOFCs with Ni/YSZ anode. The experiments were performed at 700 °C under load with simulated bio-syngas consisting of hydrogen, carbon monoxide, carbon dioxide, methane and water vapor. 2 g Nm−3 of naphthalene (350 ppm) caused a pronounced voltage drop and an increase in cell temperature. By analysing the anode off-gas and recording of I–V-curves, it could be shown that naphthalene blocked the electrochemical hydrogen oxidation as well as the reforming of methane and the shift reaction of carbon monoxide. Up to 8 g Nm−3 of phenol (1900 ppm), on the other hand, led to carbon deposition and irreversibly damaged the structure of the anode substrate by metal dusting. This form of degradation was not visible in the electrochemical data during operation.
Despite initial recovery of GFR, long-term renal functions deteriorate following short periods of unilateral ureteral obstruction
Authors: Hammad Fayez T. , Al-Salam Suhail , Hammad Waheed F. , Yasin Javed , Lubbad Loay
Published: August 2020

Following the release of short periods of unilateral ureteral obstruction (UUO), glomerular filtration rate (GFR) recovers by time. However, research in experimental animal models has demonstrated the presence of an ongoing element of renal interstitial fibrosis a few weeks following UUO reversal. Interstitial fibrosis can cause deterioration in GFR, and it is not known whether it leads to an ongoing slow deterioration in other renal functions despite the apparent initial recovery postreversal. To investigate this, rats underwent a 72-h reversible UUO. Renal functions of nonobstructed and previously obstructed kidneys were measured 1, 4, and 18 mo postreversal. GFR in nonobstructed and previously obstructed kidneys was similar up to 18 mo postreversal. However, there was ongoing tubulointerstitial fibrosis, and the degree of tubular atrophy and dilatation deteriorated by time. This was associated with an increase in urinary albumin leakage and alterations in renal injury markers, proinflammatory and profibrotic cytokines, and p53 from 4 mo onward despite the recovery in GFR. In conclusion, several aspects of renal functions continue to deteriorate following reversal of relatively short periods of UUO despite the initial recovery in GFR. This might stimulate further research in this area and might have clinical implications in terms of determining the best time for intervention following acute ureteral obstruction and long-term monitoring of these individuals.
PF-06869206 is a selective inhibitor of renal Pitransport: evidence from in vitro and in vivo studies
Authors: Thomas Linto , Xue Jianxiang , Tomilin Viktor N. , Pochynyuk Oleh M. , Dominguez Rieg Jessica A. , Rieg Timo
Published: August 2020

Plasma phosphate (Pi) levels are tightly controlled, and elevated plasma Pilevels are associated with an increased risk of cardiovascular complications and death. Two renal transport proteins mediate the majority of Pireabsorption: Na+-phosphate cotransporters Npt2a and Npt2c, with Npt2a accounting for 70–80% of Pireabsorption. The aim of the present study was to determine the in vitro effects of a novel Npt2a inhibitor (PF-06869206) in opossum kidney (OK) cells as well as determine its selectivity in vivo in Npt2a knockout (Npt2a−/−) mice. In OK cells, Npt2a inhibitor caused dose-dependent reductions of Na+-dependent Piuptake (IC50: ~1.4 μmol/L), whereas the unselective Npt2 inhibitor phosphonoformic acid (PFA) resulted in an ~20% stronger inhibition of Piuptake. The dose-dependent inhibitory effects were present after 24 h of incubation with both low- and high-Pimedia. Michaelis-Menten kinetics in OK cells identified an ~2.4-fold higher Kmfor Piin response to Npt2a inhibition with no significant change in apparent Vmax. Higher parathyroid hormone concentrations decreased Piuptake equivalent to the maximal inhibitory effect of Npt2a inhibitor. In vivo, the Npt2a inhibitor induced a dose-dependent increase in urinary Piexcretion in wild-type mice (ED50: ~23 mg/kg), which was completely absent in Npt2a−/−mice, alongside a lack of decrease in plasma Pi. Of note, the Npt2a inhibitor-induced dose-dependent increase in urinary Na+excretion was still present in Npt2a−/−mice, a response possibly mediated by an off-target acute inhibitory effect of the Npt2a inhibitor on open probability of the epithelial Na+channel in the cortical collecting duct.
Sex differences in solute transport along the nephrons: effects of Na+transport inhibition
Authors: Hu Rui , McDonough Alicia A. , Layton Anita T.
Published: August 2020

Each day, ~1.7 kg of NaCl and 180 liters of water are reabsorbed by nephron segments in humans, with urinary excretion fine tuned to meet homeostatic requirements. These tasks are coordinated by a spectrum of renal Na+transporters and channels. The goal of the present study was to investigate the extent to which inhibitors of transepithelial Na+transport (TNa) along the nephron alter urinary solute excretion and how those effects may vary between male and female subjects. To accomplish that goal, we developed sex-specific multinephron models that represent detailed transcellular and paracellular transport processes along the nephrons of male and female rat kidneys. We simulated inhibition of Na+/H+exchanger 3 (NHE3), bumetanide-sensitive Na+-K+-2Cl−cotransporter (NKCC2), Na+-Cl−cotransporter (NCC), and amiloride-sensitive epithelial Na+channel (ENaC). NHE3 inhibition simulations predicted a substantially reduced proximal tubule TNa, and NKCC2 inhibition substantially reduced thick ascending limb TNa. Both gave rise to diuresis, natriuresis, and kaliuresis, with those effects stronger in female rats. While NCC inhibition was predicted to have only minor impact on renal TNa, it nonetheless had a notable effect of enhancing excretion of Na+, K+, and Cl−, particularly in female rats. Inhibition of ENaC was predicted to have opposite effects on the excretion of Na+(increased) and K+(decreased) and to have only a minor impact on whole kidney TNa. Unlike inhibition of other transporters, ENaC inhibition induced stronger natriuresis and diuresis in male rats than female rats. Overall, model predictions agreed well with measured changes in Na+and K+excretion in response to diuretics and Na+transporter mutations.
Inflammatory mediators in the adipo-renal axis: leptin, adiponectin, and soluble ICAM-1
Authors: Hua Yifan , Herder Christian , Kalhoff Hermann , Buyken Anette E. , Esche Jonas , Krupp Danika , Wudy Stefan A. , Remer Thomas
Published: August 2020

A lower 24-h urine pH (24h-pH), i.e., a higher renal excretion of free protons, at a given acid load to the body, denotes a reduction in the kidney’s capacity for net acid excretion (NAE). There is increasing evidence, not only for patients with type 2 diabetes but also for healthy individuals, that higher body fatness or waist circumference (WC) has a negative impact on renal function to excrete acids (NAE). We hypothesized that adiposity-related inflammation molecules might mediate this relation between adiposity and renal acid excretion function. Twelve biomarkers of inflammation were measured in fasting blood samples from 162 adult participants (18–25 yr old) of the Dortmund Nutritional and Anthropometric Longitudinally Designed (DONALD) study who had undergone anthropometric measurements and collected 24-h urine samples. Both Baron and Kenny’s (B&K’s) steps to test mediation and causal mediation analysis were conducted to examine the potential mediatory roles of biomarkers of inflammation in the WC-24-h pH relationship after strictly controlling for laboratory-measured NAE. In B&K’s mediation analysis, leptin, soluble intercellular adhesion molecule 1 (sICAM-1), and adiponectin significantly associated with the outcome 24-h pH and attenuated the WC-pH relation. In agreement herewith, causal mediation analysis estimated the “natural indirect effects” of WC on 24-h pH via leptin ( P = 0.01) and adiponectin ( P = 0.03) to be significant, with a trend for sICAM-1 ( P = 0.09). The calculated proportions mediated by leptin, adiponectin, and sICAM-1 were 64%, 23%, and 12%, respectively. Both mediation analyses identified an inflammatory cytokine (leptin) and an anti-inflammatory cytokine (adiponectin) along with sICAM-1 as being potentially involved in mediating adiposity-related influences on renal acid excretion capacity.
Response of Teff (Eragrostis tef (Zucc) Trotter) to Seeding Rate and Methods of Sowing on Yield and Yield Attributes in a Subhumid Environment, Northern Ethiopia
Authors: Arefaine Abraha , Adhanom Daniel , Tekeste Negasi
Published: August 2020

Teff is a dominantly cultivated and stable crop in Ethiopia primarily grown for its grain which is used for preparing injera. In spite of its importance, the productivity is very low due to many factors among them, and poor agronomic practices are the major ones. In view of this, a field experiment, under rain-fed condition, was conducted at Laelay Machew district with the objective of evaluating the response of teff to seeding rate and methods of sowing during 2017/18 main cropping season. The experiment comprised four levels of seeding rate (10, 15, 20, and 25 kg/ha) and two methods of sowing (broad casting and row planting), and the experiment was laid in a 2 × 4 factorial arrangement in randomized complete block design (RCBD), replicated three times. Data related to phenology, growth, yield, and yield attributes were collected and analyzed using SAS software. Results indicated that days to panicle emergence, plant height, total number of tillers, productive tillers, main panicle seed weight, thousand-seed weight, panicle length, and harvest index were significantly (P<0.05) affected by the main effects of seeding rate and methods of sowing but these parameters showed no response to interaction effects of the two treatment factors. However, the interaction of the two treatment factors affected grain yield, biological yield, and straw yield. Plots sown with low seeding rate (10 kg/ha) combined with row planting gave high grain yield (2333 kg/ha), biological yield (7666 kg/ha), and straw yield (5333 kg/ha). Therefore, using treatment combinations of seeding rate of 10 kg·ha−1 together with the row method of sowing can be advised for teff production in the subhumid areas of central zone of Tigray.
Exploring the Pathological Role of Collagen in Paravertebral Muscle in the Progression of Idiopathic Scoliosis
Authors: Peng Haidong , Jin Feng , Meng Depeng , Li Jun , Yu Shuhan , Zhang Shen , Zeng Guigang
Published: August 2020

Background. Paravertebral muscle (PVM) is considered as a contributing factor of idiopathic scoliosis (IS); collagen is crucial for maintaining the mechanical properties of PVM, but only a few researches have described this field. In this study, we observed the muscle stiffness of PVM and the curvature of the spine by adjusting the content of collagen in PVM of rats and explored the role of collagen in the progression of IS. Methods. 32 female Sprague Dawley rats were randomly divided into four groups: neutralizing antibody (NA) group (group 1), normal control group (group 2), IS group (group 3), and IS with NA group (group 4). TGF‐β1 NA was injected into PVM in group 1 and group 4, while Normal saline in group 2 and group 3. The Cobb angle and muscle stiffness were measured before and after injection; the rats were sacrificed at one week after injection, and performed histological, Western Blot, and qRT‐PCR examinations. Results. X‐rays showed that scoliosis occurred in group 1 and relieved in group 4. The stiffness of PVM was decreased significantly on the convex side in group 1, while on the concave side in group 4. The expression of TGF‐β1 and COL1 on the concave side in IS rats (group 3) was significantly increased than that in normal rats (group 2), the concentration of COL1 and COL3 in group 3 was significantly higher than that in group 2, and the addition of TGF‐β1 NA significantly downregulated COL1 and COL3 in group 1 and group 4. The concentration of COL1 in convex PVM was negatively related to Cobb angle in group 1 and group 2, and in concave PVM was positively related to Cobb angle in group 3 and group 4. However, no significant correlation was found between COL3 and Cobb angle in group 3 and group 4. Conclusions. Asymmetric biomechanical characteristics of PVM was an important etiological factor of IS, which was directly correlated with collagen, it could be adjusted by local intramuscular injecting of TGF‐β1 NA, and finally had an effect on the shape of the spine.
Assistant Diagnosis of Basal Cell Carcinoma and Seborrheic Keratosis in Chinese Population Using Convolutional Neural Network
Authors: Huang Kai , He Xiaoyu , Jin Zhentao , Wu Lisha , Zhao Xinyu , Wu Zhe , Wu Xian , Xie Yang , Wan Miaojian , Li Fangfang , Liu Dihui , Yu Nianzhou , Li Mingjia , Su Juan , Zhao Shuang , Chen Xiang
Published: August 2020

Objectives. To evaluate CNN models’ performance of identifying the clinical images of basal cell carcinoma (BCC) and seborrheic keratosis (SK) and to compare their performance with that of dermatologists. Methods. We constructed a Chinese skin diseases dataset which includes 1456 BCC and 1843 SK clinical images and the corresponding medical history. We evaluated the performance using four mainstream CNN structures and transfer learning techniques. We explored the interpretability of the CNN model and compared its performance with that of 21 dermatologists. Results. The fine-tuned InceptionResNetV2 achieved the best performance, with an accuracy and area under the curve of 0.855 and 0.919, respectively. Further experimental results suggested that the CNN model was not only interpretable but also had a performance comparable to that of dermatologists. Conclusions. This study is the first on the assistant diagnosis of BCC and SK based on the proposed dataset. The promising results suggested that CNN model’s performance was comparable to that of expert dermatologists.
The Efficacy of Mesenchymal Stem Cells in Therapy of Acute Kidney Injury Induced by Ischemia-Reperfusion in Animal Models
Authors: Zhou Tianbiao , Liao Chunling , Lin Shujun , Lin Wenshan , Zhong Hongzhen , Huang Shuangyi
Published: August 2020

Mesenchymal stem cells (MSCs), discovered and isolated from the bone marrow in the 1960s and with self-renewal capacity and multilineage differentiation potential, have valuable immunomodulatory abilities. Acute kidney injury (AKI) refers to rapid renal failure, which exhibits as quickly progressive decreasing excretion in few hours or days. This study was performed to assess the efficacy of MSCs in the treatment of AKI induced by ischemia-reperfusion using a meta-analysis method. A literature search using corresponding terms was performed in the following databases: Embase, Cochrane Library, PubMed, and ISI Web of Science databases up to Dec 31, 2019. Data for outcomes were identified, and the efficacy of MSCs for AKI was assessed using Cochrane Review Manager Version 5.3. Nineteen studies were eligible and recruited for this meta-analysis. MSC treatment can reduce the Scr levels at 1 day, 2 days, 3 days, 5 days, and >7 days (1 day: WMD=−0.56, 95% CI: -0.78, -0.34, P<0.00001; 2 days: WMD=−0.58, 95% CI: -0.89, -0.28, P=0.0002; 3 days: WMD=−0.65, 95% CI: -0.84, -0.45, P<0.00001; 5 days: WMD=−0.35, 95% CI: -0.54, -0.16, P=0.0003; and >7 days: WMD=−0.22, 95% CI: -0.36, -0.08, P=0.002) and can reduce the levels of BUN at 1 day, 2 days, 3 days, and 5 days (1 day: WMD=−11.72, 95% CI: -18.80, -4.64, P=0.001; 2 days: WMD=−33.60, 95% CI: -40.15, -27.05, P<0.00001; 3 days: WMD=−21.14, 95% CI: -26.15, -16.14, P<0.00001; and 5 days: WMD=−8.88, 95% CI: -11.06, -6.69, P<0.00001), and it also can reduce the levels of proteinuria at 3 days and >7 days and alleviate the renal damage in animal models of AKI. In conclusion, MSCs might be a promising therapeutic agent for AKI induced by ischemia-reperfusion.
Developing Eco-Driving Strategies considering City Characteristics
Authors: Coloma J. F. , Garcia M. , Boggio-Marzet A. , Monzón A.
Published: August 2020

CO2 emissions reduction is a top element of transport policy agenda. Among other mitigation policy measures, eco-driving techniques have proven to be effective in reducing fuel consumption and CO2 emissions. The aim of this paper is to compare the impacts of adopting eco-driving in different cities, road segments, traffic, and driver features. It intends to gain an insight into how city size and driving characteristics can reduce fuel consumption and CO2 emissions in order to develop specific eco-driving strategies. Field trials were conducted in two Spanish cities (Madrid and Caceres). 24 drivers, with different driving experiences, drove two different vehicles (petrol and diesel) along roads with different characteristics. The experiment was divided into two periods of 2 weeks; after the first one, drivers received an eco-driving training course. The impacts of eco-driving were measured comparing before and after results. They showed that eco-driving is highly effective in reducing fuel consumption and CO2 emissions in both, large-congested and small, cities. Savings between 5% and 12% were achieved. The efficiency increases with road capacity and decreased with city size. Eco-driving appears to be more effective in small, uncongested cities. In addition, limiting speeds on high capacity roads has proven to be a good energy saving measure.
Dental Service Utilization and Barriers to Dental Care for Individuals with Autism Spectrum Disorder in Jordan: A Case-Control Study
Authors: Alshatrat Sabha Mahmoud , Al-Bakri Isra Abdelkarim , Al-Omari Wael Mousa
Published: August 2020

Individuals with disabilities are at higher risk for oral diseases such as caries and periodontal disease. Therefore, regular dental care is essential to maintain oral health. However, individuals with disabilities encounter difficulties in accessing dental care. The challenges and barriers to oral care faced by individuals with autism spectrum disorder (ASD) have not been addressed in Jordan. The aim of this study was to examine the use of dental services in individuals with ASD in Jordan and identify barriers that affect their access to dental care in comparison with individuals without ASD. A case-control study was carried out among 296 parents/caregivers of individuals with ASD and individuals without ASD, which involved completion of a self-designed questionnaire. The majority of the participants in both groups had visited the dentist in the year preceding completion of the questionnaire. The main reason for visiting dental services was toothache (43%), and the least common reason was routine checkup (11.6%), with a significant difference (P<0.05) observed between the two groups. Barriers including embarrassment (43.5%), a lack of specialized dental staff (28.6%), a lack of knowledge of how to treat people with disabilities (26.6%), and inadequate facilities (34%) were significantly (P<0.05) more likely to be reported by individuals with ASD than the controls. In conclusion, knowing and understanding the barriers to accessing dental care could improve overall health for individuals with ASD. Furthermore, recognizing the challenges in accessing dental care for this population could help oral health professionals to minimize these difficulties.
Moving-Window-Improved Monte Carlo Uninformative Variable Elimination Combining Successive Projections Algorithm for Near-Infrared Spectroscopy (NIRS)
Authors: Jiang Weiwei , Lu Changhua , Zhang Yujun , Ju Wei , Wang Jizhou , Hong Feng , Wang Tao , Ou Chunsheng
Published: August 2020

The MC-UVE-SPA method is commonly proposed as a variable selection approach for multivariate calibration. However, the SPA tends to select wavelength variables that are sparsely distributed over the wavelength ranges of the variables selected by the MC-UVE algorithm, and the MC-UVE-SPA cascade cannot improve the problem of wavelength point discontinuity. It is addressed in this paper by proposing a moving-window- (MW-) improved MC-UVE-SPA wavelength selection algorithm. The proposed algorithm improves the continuity of the selected wavelength variables and thereby better exploits the advantages of the MC-UVE algorithm and the SPA to obtain regression models with high prediction accuracy. The MC-UVE, MC-UVE-SPA, and MC-UVE-SPA-MW algorithms are applied for conducting wavelength variable selection for the NIR spectral absorbance data of corn, diesel fuel, and ethylene. Here, partial least squares regression (PLSR) models reflecting the oil content of corn, the boiling point of diesel fuel, and the ethylene concentration are established after conducting wavelength selection using the MC-UVE algorithm, and corresponding multiple linear regression (MLR) models are established after conducting wavelength selection using the MC-UVE-SPA and MC-UVE-SPA-MW algorithms. Experimental results demonstrate that the progressive elimination of uncorrelated and collinear variables generates increasingly simplified partial-spectrum models with greater prediction accuracy than the full-spectrum model. Among the three wavelength selection algorithms, the MC-UVE-SPA selected the least number of wavelength variables, while the proposed MC-UVE-SPA-MW algorithm provided models with the greatest prediction accuracy.
Traceable Attribute-Based Secure Data Sharing with Hidden Policies in Mobile Health Networks
Authors: Liu Xueyan , Luo Yukun , Yang Xiaotao
Published: August 2020

The growing need to store, share, and manage medical and health records has resulted in electronic medical health sharing system (mHealth), which provides intelligent medical treatment for people. Attribute-based encryption (ABE) is regarded as a new cryptology to enhance fine-grained access control over encrypted sharing data in mHealth. However, some existing attribute-based mHealth systems not only violate the one-to-many application characteristics of attribute-based encryption mechanism but also destroy the anonymity of user. In this study, an efficient scheme is proposed to tackle the above defaults and offer two-way anonymity of data owner and data user by introducing a pseudoidentity. The computation of hidden access policy is reduced by removing the bilinear pairing, whereas the interaction between cloud storage and data user is avoided to save bandwidth during trapdoor generation. We also consider the temporal factor of the uploaded information by introducing access validity. Security and performance analyses show that the proposed scheme is efficient without reducing security.
Dynamic Inequalities in Quotients with General Kernels and Measures
Authors: El-Hamid H. A. Abd , Rezk H. M. , Ahmed A. M. , AlNemer Ghada , Zakarya M. , El Saify H. A.
Published: August 2020

By utilizing the precepts related to the punctuation of time scales T, we present some nouveau forms in quotients for Hardy’s and related inequalities on time scales. In particular, some recent results for the Pólya-Knopp, Hardy-Hilbert, and Hardy-Littlewood-Pólya-type inequalities are presented.
On the Cryptanalysis of a Bit-Level Image Chaotic Encryption Algorithm
Authors: Hu Yingchun , Yu Simin , Zhang Zeqing
Published: August 2020

In this paper, the security analysis of a bit-level image chaotic encryption algorithm based on the 1D chaotic map is proposed. The original image chaotic encryption algorithm includes bit-level permutation encryption, diffusion encryption, and linear transform. Deciphering of it can be divided into two stages. First, bit-level permutation encryption, diffusion encryption, and linear transform can be simplified into bit-level equivalent permutation encryption and equivalent diffusion encryption, which is a key breakthrough point of cryptanalysis. Second, the chaotic sequence generated by this algorithm is independent of the plaintext image. Therefore, the equivalent diffusion key and the equivalent permutation key can be obtained by chosen-plaintext attack, respectively. Theoretical analysis and numerical simulation experiment results verify the effectiveness of the analytical method. Finally, some suggestions are proposed to promote the security of the original image chaotic encryption algorithm.
A Dihydroflavonoid Naringin Extends the Lifespan of C. elegans and Delays the Progression of Aging-Related Diseases in PD/AD Models via DAF-16
Authors: Zhu Qing , Qu Yuan , Zhou Xiao-Gang , Chen Jian-Ning , Luo Huai-Rong , Wu Gui-Sheng
Published: August 2020

Naringin is a dihydroflavonoid, which is rich in several plant species used for herbal medicine. It has a wide range of biological activities, including antineoplastic, anti-inflammatory, antiphotoaging, and antioxidative activities. So it would be interesting to know if naringin has an effect on aging and aging-related diseases. We examined the effect of naringin on the aging of Caenorhabditis elegans (C. elegans). Our results showed that naringin could extend the lifespan of C. elegans. Moreover, naringin could also increase the thermal and oxidative stress tolerance, reduce the accumulation of lipofuscin, and delay the progress of aging-related diseases in C. elegans models of AD and PD. Naringin could not significantly extend the lifespan of long-lived mutants from genes in insulin/IGF-1 signaling (IIS) and nutrient-sensing pathways, such as daf-2, akt-2, akt-1, eat-2, sir-2.1, and rsks-1. Naringin treatment prolonged the lifespan of long-lived glp-1 mutants, which have decreased reproductive stem cells. Naringin could not extend the lifespan of a null mutant of the fox-head transcription factor DAF-16. Moreover, naringin could increase the mRNA expression of genes regulated by daf-16 and itself. In conclusion, we show that a natural product naringin could extend the lifespan of C. elegans and delay the progression of aging-related diseases in C. elegans models via DAF-16.
Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning
Authors: Li Duowei , Wu Jianping , Xu Ming , Wang Ziheng , Hu Kezhen
Published: August 2020

Controlling traffic signals to alleviate increasing traffic pressure is a concept that has received public attention for a long time. However, existing systems and methodologies for controlling traffic signals are insufficient for addressing the problem. To this end, we build a truly adaptive traffic signal control model in a traffic microsimulator, i.e., “Simulation of Urban Mobility” (SUMO), using the technology of modern deep reinforcement learning. The model is proposed based on a deep Q-network algorithm that precisely represents the elements associated with the problem: agents, environments, and actions. The real-time state of traffic, including the number of vehicles and the average speed, at one or more intersections is used as an input to the model. To reduce the average waiting time, the agents provide an optimal traffic signal phase and duration that should be implemented in both single-intersection cases and multi-intersection cases. The co-operation between agents enables the model to achieve an improvement in overall performance in a large road network. By testing with data sets pertaining to three different traffic conditions, we prove that the proposed model is better than other methods (e.g., Q-learning method, longest queue first method, and Webster fixed timing control method) for all cases. The proposed model reduces both the average waiting time and travel time, and it becomes more advantageous as the traffic environment becomes more complex.
Functionalization of Graphene Oxide Nanosheets Can Reduce Their Cytotoxicity to Dental Pulp Stem Cells
Authors: Gholami Ahmad , Emadi Fatemeh , Amini Abbas , Shokripour Mansoureh , Chashmpoosh Mostafa , Omidifar Navid
Published: August 2020

Background. The dental pulp is a heterogeneous soft tissue that supplies nutrients and acts as a biosensor to identify pathogenic stimuli. Regeneration of the dental pulp is one of the desirable topics for researchers. Graphene oxide nanosheets (nGOs) help overexpression of the genes related to odontogenic differentiation of stem cells from dental pulps and increases attachment and proliferation of dental pulp stem cells. Despite its benefits, nGO may be considered as a threat to the environment and human health. Therefore, the purpose of this study was to evaluate the biocompatibility potential of graphene oxide (nGO), chitosan functionalized graphene oxide (nGO-CS), and carboxylated graphene (nGO-COOH) when exposed to human dental pulp stem cells (hDPSCs). Material and Methods. Some different aspects of biocompatibility of nGO, nGO-CS, and nGO-COOH were synthesized, and several intracellular effects induced by different concentrations of graphene-based nanosheets, including cell viability, intracellular oxidative damages, and various factors such as LDH, GSH, SOD, MDA, and MMP, were studied on hDPSCs. Results. According to results, IC50 was determined as 232.01, 467.81, and ≥1000 μg/mL for nGO, nGO-CS, and nGO-COOH, respectively. These results demonstrated the lower toxicity and higher cytocompatibility of nGO-CS and nGO-COOH compared to nGO. nGO-COOH not only has any adverse effect on the cell membrane and mitochondrial activity but also shows slight antioxidant activity at some concentrations. Conclusion. The findings help design safe and cytocompatible nGO derivatives for biomedical applications in dental fields.
Construction of Protein‐related Risk Score Model in Bladder Urothelial Carcinoma
Authors: Luo Qizhan , Zhang Xiaobo
Published: August 2020

Background. Though there are several prognostic models, there is no protein‐related prognostic model. The aim of this study is to identify possible prognostic‐related proteins in bladder urothelial carcinoma and to try to predict the prognosis of bladder urothelial carcinoma based on these proteins. Methods. Profile data and corresponding clinical traits were obtained from The Cancer Proteome Atlas (TCPA) and The Cancer Genome Atlas (TCGA) expression. Survival‐associated protein in bladder urothelial carcinoma patients were estimated with Kaplan‐Meier (KM) test and COX regression analysis. The potential molecular mechanisms and properties of these bladder urothelial carcinoma‐specific proteins were also explored with the help of computational skills. The risk score model was validated in different clinical traits. Sankey diagram representation is for protein correlation. A new prognostic‐related risk model based on proteins was developed by using multivariable COX analysis. Next, the alteration of the corresponding genes to the 6 prognostic‐related proteins was analyzed. Finally, the relation between the corresponding genes and the immune infiltration was analyzed using the TIMER. Results. Six proteins were identified to be associated with the prognosis of bladder urothelial carcinoma. A prognostic signature based on proteins (BECLIN, EGFR, PKCALPHA, SRC, ANNEXIN1, and AXL) performed moderately in prognostic predictions. The alteration of corresponding genes was in 31(24%) sequenced cases. ANXA1, AXL, and EGFR were positively related to CD8+ T cell. Conclusion. Our results screened six proteins of clinical significance. The importance of a personalized protein signature model in the recognition, surveillance. The abnormal expression of six prognostic‐related proteins may be caused by corresponding gene alteration. Furthermore, these proteins may affect survival via the immune infiltration.