bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genetic differentiation and restricted gene flow in rice landraces from Yunnan, China: effects of isolation-by-distance and isolation-by-environment
Authors: Cui Di , Tang Cuifeng , Lu Hongfeng , Li Jinmei , Ma Xiaoding , A Xinxiang , Han Bing , Yang Yayun , Dong Chao , Zhang Feifei , Dai Luyuan , Han Longzhi 
Published: July 2020

Abstract
Background

Understanding and identifying the factors responsible for genetic differentiation is of fundamental importance for efficient utilization and conservation of traditional rice landraces. In this study, we examined the spatial genetic differentiation of 594 individuals sampled from 28 locations in Yunnan Province, China, covering a wide geographic distribution and diverse growing conditions. All 594 accessions were studied using ten unlinked target genes and 48 microsatellite loci, and the representative 108 accessions from the whole collection were sampled for resequencing.
Results

The genetic diversity of rice landraces was quite different geographically and exhibited a geographical decline from south to north in Yunnan, China. Population structure revealed that the rice landraces could be clearly differentiated into japonica and indica groups, respectively. In each group, the rice accessions could be further differentiated corresponded to their geographic locations, including three subgroups from northern, southern and middle locations. We found more obvious internal geographic structure in the japonica group than in the indica group. In the japonica group, we found that genetic and phenotypic differentiation were strongly related to geographical distance, suggesting a pattern of isolation by distance (IBD); this relationship remained highly significant when we controlled for environmental effects, where the likelihood of gene flow is inversely proportional to the distance between locations. Moreover, the gene flow also followed patterns of isolation by environment (IBE) whereby gene flow rates are higher in similar environments. We detected 314 and 216 regions had been differentially selected between Jap-N and Jap-S, Ind-N and Ind-S, respectively, and thus referred to as selection signatures for different geographic subgroups. We also observed a number of significant and interesting associations between loci and environmental factors, which implies adaptation to local environment.
Conclusion

Our findings highlight the influence of geographical isolation and environmental heterogeneity on the pattern of the gene flow, and demonstrate that both geographical isolation and environment drives adaptive divergence play dominant roles in the genetic differentiation of the rice landraces in Yunnan, China as a result of limited dispersal.
Determination of heavy metal content in surface sediments and soil from the River Nile Khartoum City (Sudan)
Authors: Shumo Muna I. , Mohammed Mazin M. , Musa Hawa A. , Khider Tarig Osman 
Published: July 2020

Abstract
The surface sediment and surface soil samples were taken from the River Nile (Abroof, Shambat, AL-Halfaia, and AL-Hetana), Khartoum, to determine the available levels of Lead (Pb), Zinc (Zn), and Cadmium (cd) to establish a baseline concentration of each of these available metals, and to investigate their possible correlations with soil and sediment properties. The content of each of the three heavy metals in soil and sediment samples was determined using atomic absorption spectroscopy, AAS.The values of Zinc in the soil and sediment samples, were found to be between (0.192 to 0.294 ppm) and (0.101 to 0.181) respectively, showing the highest content in soil and sediment samples in Shambat (S) and Abroof (A), and the lowest values recorded for samples in AL-Halfia (H) and AL-Hetana (E). While Lead content was ranged between (0.105 to 0.136 ppm) for soils samples (AL-Hetana(E) and Shambat(S)), and (0.078 to 0.141) for sediment samples (Abroof(A) and shambat (S)). Whereas, content for Cadmium ranged between (0.010 to 0.015 ppm) for soil samples Shambat, AL-Hetana and Abroof and (0.011 to 0.016) for sediment samples Shambat, AL-Halfia and Abroof.Soil and sediment physicochemical properties (pH, temperature & conductivity) were found to be correlated with the available heavy metals content, suggesting that the enhanced mobility of heavy metals are related to anthropogenic activities, the high percentage of organic carbon in soil (11.6%) and sediment (15.0%).
Thromboprophylaxis, Delta SOFA and Main Outcomes in Ventilated Patients: An Analysis of the MIMIC-III Clinical Database
Authors: Bastola Mrigendra M. , Locatis Craig , Fontelo Paul 
Published: July 2020

Abstract
Introduction: Critical care patients are at higher risk for thromboembolic disorders. There are limited studies regarding the effect of Heparin, Warfarin, Enoxaparin and Aspirin on ventilated patients, who are likely to both benefit from prophylaxis and suffer from adverse effects of blood thinners. Methods: This study analyzed the MIMIC-III clinical database on 4192 ventilated patients using Statistical Analysis Software (SAS, Version 9.4). Relevant data was systematically analyzed on the thromboprophylaxis agents and their effects on major treatment outcomes. Parameters studied were the length of ventilation, length of Intensive Care Unit (ICU) stays, ICU mortality, inpatient mortality, improvement in SOFA score, and occurrence of major thromboembolic events such as pulmonary embolism (PE) and deep venous thrombosis (DVT). Results: Except Aspirin, all thromboprophylactic agents showed statistically significant reduction on ICU mortality. None of the blood thinners showed statistically significant reduction in occurrences of pulmonary embolism and deep venous thrombosis. Heparin, Warfarin and Enoxaparin had adjusted Odds ratios of 0.59(p<0.01, 0.47-0.77), 0.23(p<0.05, 0.1-0.57) and 0.36(P<0.05, 0.16-0.83) for ICU mortality. Heparin, Warfarin and Enoxaparin had adjusted Odds ratios of 0.51(p<0.01, 0.38-0.68), 0.19(p<0.01, 0.06-0.59) and 0.42(P=0.06, 0.17-1.05) for overall ventilated patient hospital mortality, including after transfers to the inpatient ward.. Only Heparin (P<0.05, OR 1.52(1.07-2.15)) was associated with thrombocytopenia episodes, which required platelets transfusion. None of the drugs showed statistically significant relationships with development of thromboembolic events after thromboprophylaxis. Only Heparin had mild effect on improvement in sequential organ failure assessment (delta SOFA) scores at 7 and 10 day after ICU admission (P<0.05, OR 1.17 (1.03-1.32)). Conclusion: Although the results supported the use of thromboprophylaxis in ventilated patients to improve treatment outcomes and decrease thromboembolic events, no benefits were indicated for using newer blood thinners (Enoxaparin) than older ones (Heparin & Warfarin). The results of this analysis might help practitioners select, maintain, and switch blood thinners in ventilated patients.
Clot Waveform of APTT Has Abnormal Patterns in Subjects with COVID-19
Authors: Shimura Takuya , Kurano Makoto , Kanno Yoshiaki , Ikeda Mahoko , Okamoto Koh , Jubishi Daisuke , Harada Sohei , Okugawa Shu , Moriya Kyoji , Yatomi Yutaka 
Published: July 2020

Abstract
In Coronavirus disease 2019 (COVID-19) subjects, recent evidence suggests the presence of unique coagulation abnormalities. In this study, we performed clot waveform analyses to investigate whether specific modulations are observed in COVID-19 subjects. We analyzed the second derivative of the absorbance in routine APTT tests performed using an ACL-TOP system. We observed high frequencies of abnormal patterns in APTT second-derivative curves that could be classified into an early shoulder type, a late shoulder type, or a biphasic type, high maximum first-derivative and second-derivative peak levels, and a low minimum second-derivative peak level in COVID-19 subjects. These modulations were not observed in subjects with disseminated intravascular coagulation. These abnormal patterns are also observed in patients with lupus anticoagulant, hemophilia, or factor IX deficiency. The plasma fibrinogen levels might also be involved in the abnormal APTT waveforms, especially the high maximum first-derivative and second-derivative peak levels. The abnormal patterns in the APTT second-derivative curves appear with highest frequency at around 2 weeks after the onset of COVID-19 and were not associated with the severity of COVID-19. These results suggest the possible presence of a specific abnormal coagulopathy in COVID-19.
Identification of Cancer Biomarkers in Human Body Fluids by Using Enhanced Physicochemical-incorporated Evolutionary Conservation Scheme
Authors: Zhang Jian , Zhang Yu , Li Yanlin , Guo Song , Yang Guifu 
Published: July 2020

Objective:
Cancer is one of the most serious diseases affecting human health. Among all current
cancer treatments, early diagnosis and control significantly help increase the chances of cure. Detecting
cancer biomarkers in body fluids now is attracting more attention within oncologists. In-silico
predictions of body fluid-related proteins, which can be served as cancer biomarkers, open a door for
labor-intensive and time-consuming biochemical experiments.

Methods:
In this work, we propose a novel method for high-throughput identification of cancer biomarkers
in human body fluids. We incorporate physicochemical properties into the weighted observed
percentages (WOP) and position-specific scoring matrices (PSSM) profiles to enhance their attributes
that reflect the evolutionary conservation of the body fluid-related proteins. The least absolute selection
and shrinkage operator (LASSO) feature selection strategy is introduced to generate the optimal feature
subset.

Results:
The ten-fold cross-validation results on training datasets demonstrate the accuracy of the proposed
model. We also test our proposed method on independent testing datasets and apply it to the identification
of potential cancer biomarkers in human body fluids.

Conclusion:
The testing results promise a good generalization capability of our approach.
Nanomaterial LaNiTiO3-Fe3O4-based Peroxidase Biomimetic Sensor with High Sensitivity
Authors: Xu Yanhong , Sun Ying , Feng Qiao 
Published: July 2020

Background:
Hydrogen peroxide (H2O2) is widely used in various fields and it plays a
quintessential role in a variety of biomolecular processes. H2O2 concentration level is an essential biological
parameter in monitoring and maintaining the physiological balance of a living cell, and its
variation causes some related diseases. Therefore, it is extremely significant to fabricate biosensor
with low cost, which can quickly, accurately and sensitively detect H2O2 in a wide range. The aims
of this paper are to explore a novel electrochemical sensor with high intrinsic peroxidase-like activity,
high sensitivity and stability to effectively detect H2O2 concentration in real samples.

Methods:
The chemically modified electrode LaNiTiO3-Fe3O4/GCE was fabricated based on the nanomaterial
LaNiTiO3-Fe3O4 by a simple process, and its electrochemical properties were investigated
in the supporting electrolyte of 0.1 M NaOH by the techniques of cyclic voltammetry and currenttime
curves on an electrochemical workstation with a conventional three-electrode system.

Results:
LaNiTiO3-Fe3O4 nanoparticles show good peroxidase-like activity for H2O2 at a low applied
potential of +0.50 V. Under the optimum conditions, the peroxidase biomimetic sensor LaNiTiO3-Fe3O4/GCE exhibited a wide linear response for H2O2 oxidation in the range of 0.05 μM - 3.0 mM (R
= 0.9994) with a high sensitivity of 3946.2 μA·mM-1·cm-2 and fast response time of 2 s; besides, the
detection limit of H2O2 was found to be ca. 5.15 nM (S/N = 3). Moreover, the biosensor exhibited
good repeatability, stability and anti-interference. Satisfactory results were obtained when the sensor
LaNiTiO3-Fe3O4/GCE was applied to determine H2O2 in real samples. All of these results provide
support to practical application.

Conclusion:
A highly sensitive peroxidase biomimetic sensor based on LaNiTiO3-Fe3O4 with nanoscaled
material has been successfully explored, and shows good activity for H2O2. The proposed biosensor
with simple and low cost exhibited excellent advantages of quick response, wide linear range,
low detection limit, high sensitivity, long-term stability and good anti-interference ability, which offer
promising applications.
Melia azedarach Activated Carbon and its novel TiO2 Nanocomposite for Chemisorption and Photodecoloration of Reactive Orange 16: Isotherm and Kinetic Modeling
Authors: Shah Jehanzeb A. , Ashfaq Tayyab , Khan Muhammad S. , Riaz Nadia , Shah Khizar H. , Arshad Muhammad , Shah Sajid H. , Amin Bilal A.Z. , Arfan Muhammad , Zhang Yongqing , Bilal Muhammad 
Published: July 2020

Background:
Bulk generated textile wastewater loaded with dyes is posing a stern threat
to aquatic health, especially when dumped without prior treatment. Lignocellulosic waste based activated
carbon (AC) and Titania (TiO2) suspension can constitute the emerging technological solution.

Objectives:
Best lignocellulosic precursor biomass, Melia azedarach (Darek sawdust - DSD), was
selected for ortho-phosphoric acid impregnated AC production and novel AC-DSD-TiO2 nanocomposite
was developed. AC-DSD and AC-DSD-TiO2 nanocomposites were employed for reactive orange
16 (RO16) dye adsorption in batch and decoloration in photocatalytic reactors, respectively.

Methods:
Materials were characterized by Scanning electron microscope (SEM), energy dispersion
X-ray (EDX) spectroscopy and Fourier transform infrared spectroscopy (FTIR). For AC-DSD production,
the raw powdered biomass of DSD impregnated (value = 2) with H3PO4 at room temperature
and after shaking, was placed in a muffle furnace at 100°C for 12 h in glass tubes and subsequently
carbonized at a high temperature of 400°C for 30 min. Batch reactor parameters for the ACDSD-
RO16 system were optimized as a function of contact time, adsorbent dose, temperature, initial
dye concentration and pH. For AC-DSD-TiO2 nanocomposite synthesis, AC-DSD and TiO2 paste
was dried in the furnace at 90°C and calcined at 300°C and stored in a desiccator.

Results:
AC-DSD exhibited RO16 adsorption capacity of 92.84 mg/g. The experimental data were
best described by Langmuir and Dubinin-Radushkevich isotherms with high R2 of 0.9995 and 0.9895
and closeness of predicted adsorption capacities of 94.15 and 88.58 mg/g respectively. This determines
the chemisorption nature for RO16 adsorption onto AC-DSD. The experimental data was well
explained by the pseudo-second order kinetic model. Thermodynamic parameters also suggest the
endothermic, chemisorption and spontaneous adsorption reaction. Photocatalytic studies of novel
AC-DSD-TiO2 revealed the higher Kc = 0.1833 value over Kad= 0.0572.

Conclusions:
Melia azedarach AC-DSD and its novel AC-DSD-TiO2 nanocomposite prove that these
materials could provide an optimal solution for treating textile dye solutions effectively as the good
adsorbent and photocatalyst.
Android Malware Detection Techniques: A Literature Review
Authors: Dhalaria Meghna , Gandotra Ekta 
Published: July 2020

Objective:
This paper provides the basics of Android malware, its evolution and tools and
techniques for malware analysis. Its main aim is to present a review of the literature on Android
malware detection using machine learning and deep learning and identify the research gaps. It provides
the insights obtained through literature and future research directions which could help researchers
to come up with robust and accurate techniques for the classification of Android malware.

Methods:
This paper provides a review of the basics of Android malware, its evolution timeline and
detection techniques. It includes the tools and techniques for analyzing the Android malware statically
and dynamically for extracting features and finally classifying these using machine learning
and deep learning algorithms.

Results:
The number of Android users is increasing at an exponential rate due to the popularity of
Android devices. As a result, there are more risks to Android users due to the exponential growth of
Android malware. On-going research aims to overcome the constraints of earlier approaches for
malware detection. As the evolving malware is complex and sophisticated, earlier approaches like
signature-based and machine learning-based approaches are not able to identify it timely and accurately.
The findings from the review show various limitations of earlier techniques, i.e. requirement
of more detection time, high false-positive and false-negative rates, low accuracy in detecting sophisticated
malware and less flexibility.

Conclusion:
This paper provides a systematic and comprehensive review on the tools and techniques
being employed for analysis, classification and identification of Android malicious applications. It
includes the timeline of Android malware evolution, tools and techniques for analyzing these statically
and dynamically for the purpose of extracting features and finally using these features for their
detection and classification using machine learning and deep learning algorithms. On the basis of the
detailed literature review, various research gaps are listed. The paper also provides future research
directions and insights that could help researchers to come up with innovative and robust techniques
for detecting and classifying Android malware.
A Virtual Machine Migration Mechanism Based on Firefly Optimization for Cloud Computing
Authors: Singh Shalu , Singh Dinesh 
Published: July 2020

Background:
Cloud computing is one of the prominent technology revolutions around
us. It is changing the ways the consumer expends services, changing the ways the organization develop
and run applications and is completely reshaping the old business models in multiple industries.
Cloud service providers need large-scale data centers for offering cloud resources to users, the
electric power consumed by these data centers has become a concrete and prudential concern. Most
of the energy is dissipated in these data centers due to under-utilized hosts, which also subsidies to
global warming. The broadly adept technology is virtual machine migration in cloud computing,
therefore, the main focus is to save energy.

Objective:
Virtual Machine (VM) migration can reap various objectives like load balancing, ubiquitous
computing, power management, fault tolerance, server maintenance, etc. This paper presents an
energy-oriented mechanism for VM migration based on firefly optimization that reduces energy
consumption and the number of VM migrations to a great extent.

Method:
A Firefly Optimization (FFO) oriented VM migration mechanism has been proposed,
which allocates tasks to the physical machines in cloud data centers. It strives to migrates high loaded
VMs from one physical node to another, which induces minimum energy consumption after VM
migration.

Results:
The empirical result shows that the FFO based mechanism, implemented in the CloudSim
simulator, performs better in terms of the number of hosts saved up to 13.91% in contrast to the
First Fit Decreasing (FFD) algorithm and 8.21% as compared to Ant Colony Optimization (ACO). It
reduced energy consumption up to 12.76% as compared to FFD and 7.78% as compared to ACO
and, ultimately lesser number of migrations up to 52.49% when compared to FFD and 44.51% as
compared to ACO.

Conclusion:
The proposed scheme performs better in terms of saving hosts, reducing energy consumption,
and decreasing the number of migrations in contrast to FFD and ACO techniques. The
research paper also presents challenges and issues in cloud computing, VM migration process, VM
migration techniques, their comparative review as well.
Hybrid Model using Firefly and BBO for Feature Selection in Software Product Line
Authors: Yadav Hitesh , Chhikara Rita , Kumari Charan 
Published: July 2020

Background:
Software Product Line is the group of multiple software systems which share the similar set of
features with multiple variants. Feature model is used to capture and organize features used in different multiple
organization.

Objective:
The objective of this research article is to obtain an optimized subset of features which are capable of providing
high performance.

Methods:
In order to achieve the desired objective, two methods have been proposed. a) An improved objective function
which is used to compute the contribution of each feature with weight based methodology. b) A hybrid model is employed
to optimize the Software Product Line problem.

Results:
Feature sets varying in size from 100 to 1000 have been used to compute the performance of the Software
Product Line.

Conclusion:
The results shows that proposed hybrid model outperforms the state of art metaheuristic algorithms.
Adaptive Energy-Aware Algorithms to Minimize Power Consumption and SLA Violation in Cloud Computing
Authors: Singh Monika , Kumar Pardeep , Tyagi Sanjay 
Published: July 2020

Objective:
With the establishment of virtualized datacenters on a large scale, cuttingedge
technology requires more energy to deliver the services 24*7 hours. With this expansion and
accumulation of information on a massive scale on data centers, the consumption of an excessive
amount of power results in high operational costs. Therefore, there is an urgent need to make the
environment more adaptive and dynamic, where the overutilization and underutilization of hosts are
well known to the system and active measures can be taken accordingly. To serve this purpose, an
energy-efficient method, for the detection of overloaded and under-loaded hosts, has been proposed
in this paper. For implementing VM migration, VM placement decision has also been taken to save
energy and reduce SLA (Service Level Agreement) rate over the cloud.

Methods:
In the paper, a novel adaptive heuristics approach has been presented that concerns with
the utilization of resources for dynamic consolidation of VMs based on the mustered data from the
usage of resources by VMs, while ensuring the high level of relevancy to the SLA. After identification
of under-load and overload hosts, VM placement decision has been taken in the way that takes
minimum energy consumption. A minimum migration policy has been adopted in the proposed
methodology to minimize execution time. The validation of the effectiveness and efficiency of the
suggested approach has been performed by using real-world workload traces in the CloudSim simulator.

Results:
The results shows that the proposed methodology is ideal for SLA, but costs more for VM
migration.

Conclusion:
A deep analysis must be done in existing energy efficient approaches and a new
platform should be suggested to save energy in real life.