bluuebunny/crossref_metadata_2025_split huggingface dataset:
WITHDRAWN: Robust Biofilm-Forming Ionizing Radiation Resistant Bacillus subtilis isolated from Ramsar-Iran, Emphasis on Molecular Genetic profiling Analysis
Authors: ali dhuha saeed , Author) reza vazifehmand(Former Corresponding , Rukayadi Yaya , Radu Son , Mirpour Mirsasan , sekawi zamberi , Author) Nor-Khaizura Mahmud Ab Rashid(New Corresponding 
Published: January 2020

Abstract

Background The production of structurally complex multicellular communities or biofilms is a mode of growth that is believed to require and involves a differential gene expression as compared to their planktonic counterpart. There is a poor knowledge of biofilm formation and its molecular signaling function in a radiation resistance bacteria. Therefore, the aim of the present study was to investigate the genetic variations and gene expression levels of some critical genes involved in the biofilm formation and matrix production in an ionizing-radiation-resistant B. subtilis (RAM-04) isolated from Abe-Siah hot spring, Ramsar- Iran. Bacillus subtilis DSM-10 a non-ionizing-radiation-resistant and standard strain model considered as a negative control. Results Quantification of biofilms performed using crystal violet assay and Scanning Electron Microscopy that the results presented a significant difference of a high level of the biofilm formation in terms of quantity and structure by Bacillus subtilis (RAM-04) as compared to Bacillus subtilis DSM-10. At the Molecular level, a total of 13 genes including ( tapA ) yqxM-sipW-tasA (operon), sinR , sinI , ccpA , epsA-O , spoOB , spoOA , slrA , slrR , ymcA and abrB were selected to study the genetic variations and gene expression levels using direct sequencing method and quantitative PCR technique. Primary analysis revealed genetic variations in ( tapA ) yqxM-sipW-tasA (operon), sinR , sinI , epsA-O , and slrR in B. subtilis (RAM-04) as compared to the negative control. The result of gene expression revealed a significant difference between test and control bacteria. ( tapA ) yqxM-sipW-tasA operon was significantly over expressed ( p = 0.000 with expression folding of 13, 11, 8 respectively) and the expression level of ccpA and slrR were significantly down ( p = 0.000 with expression folding of 0.2 and 0.3 respectively). Conclusions These results support the hypothesis that the induction of biofilm formation is performed by ( tapA ) yqxM-sipW-tasA operon signaling pathway, which in turn indicate that this operon has a positive role in biofilm formation in ionizing-radiation-resistant B. subtilis (RAM-04) under natural radiation environment.
Study on the suitability of ZnO thin film for dynamic pressure sensing application
Authors: M. N. Suma , Gaddam Venkateswarlu , Prasad M. V. N. , Nayak M. M. , Rajanna K. 
Published: January 2020

Abstract
ZnO thin films were prepared by RF reactive magnetron sputtering on phynox substrate at room temperature for four different thicknesses by varying deposition duration. The structural and morphological properties and composition of these films were characterized using XRD, SEM, and EDS, respectively. Suitability of these films for dynamic pressure sensing applications and the effect of film thickness on dynamic pressure sensing were evaluated experimentally using a shock tube equipment. Shock tube test results show the pure dynamic behavior of ZnO films with fast rise and discharge. Sensors with higher film thickness showed improved sensitivity which is on par with commercially available dynamic pressure sensors. This work demonstrates that the cost-effective sensors based on ZnO thin film are capable of sensing dynamic pressures for different pressure ranges.
Partition coefficients of trace elements between carbonates and melt and suprasolidus phase relation of Ca-Mg-carbonates at 6 GPa
Authors: Sieber Melanie J. , Wilke Franziska , Koch-Müller Monika 
Published: January 2020

Abstract
The presence of Ca-Mg-carbonates affects the melting and phase relations of peridotites and eclogites in the mantle, and (partial) melting of carbonates liberates carbon from the mantle to shallower depths. The onset and composition of incipient melting of carbonated peridotites and carbonated eclogites are influenced by the pure CaCO3-MgCO3-system making the understanding of the phase relations of Ca-Mg-carbonates fundamental in assessing carbon fluxes in the mantle. By performing high-pressure and high-temperature experiments, this study clarifies the suprasolidus phase relations of the nominally anhydrous CaCO3-MgCO3-system at 6 GPa showing that Ca-Mg-carbonates will (partially) melt for temperatures above ~1300 °C. A comparison with data from thermodynamic modeling confirms the experimental results. Furthermore, partition coefficients for Li, Na, K, Sr, Ba, Nb, Y, and rare earth elements between calcite and dolomitic melt, Ca-magnesite and dolomitic melt, and magnesite and dolomitic melt are established.
Experiments were performed at 6 GPa and between 1350 to 1600 °C utilizing a rotating multi-anvil press. Rotation of the multi-anvil press is indispensable to establish equilibrium between solids and carbonate liquid. Major and trace elements were quantified with EPMA and LA-ICP-MS, respectively.
The melting temperature and phase relations of Ca-Mg-carbonates depend on the Mg/Ca-ratio. For instance, Ca-rich carbonates with a molar Mg/(Mg+Ca)-ratio (XMg) of 0.2 will transform into a dolomitic melt (XMg = 0.33–0.31) and calcite crystals (XMg = 0.19–0.14) at 1350–1440 °C. Partial melting of Mg-rich carbonates (XMg = 0.85) will produce a dolomitic melt (XMg = 0.5–0.8) and Ca-bearing magnesite (XMg = 0.89–0.96) at 1400–1600 °C. Trace element distribution into calcite and magnesite seems to follow lattice constraints for divalent cations. For instance, the compatibility of calcite (XMg = 0.14–0.19) for Sr and Ba decreases as the cation radii increases. Ca-Mg-carbonates are incompatible for rare earth elements (REEs), whereby the distribution between carbonates and dolomitic melt depends on the Mg/Ca ratio and temperature. For instance, at 1600 °C, partition coefficients between magnesite (XMg = 0.96) and dolomitic melt (XMg = 0.8) vary by two orders of magnitudes from 0.001 to 0.1 for light-REEs to heavy-REEs. In contrast, partition coefficients of REEs (and Sr, Ba, Nb, and Y) between magnesite (XMg = 0.89) and dolomitic melt (XMg = 0.5) are more uniform scattering marginal between ~0.1–0.2 at 1400 °C.
Towards a comprehensive uncertainty assessment in environmental research and decision support
Authors: Reichert Peter 
Published: January 2020

Abstract
Uncertainty quantification is very important in environmental management to allow decision makers to consider the reliability of predictions of the consequences of decision alternatives and relate them to their risk attitudes and the uncertainty about their preferences. Nevertheless, uncertainty quantification in environmental decision support is often incomplete and the robustness of the results regarding assumptions made for uncertainty quantification is often not investigated. In this article, an attempt is made to demonstrate how uncertainty can be considered more comprehensively in environmental research and decision support by combining well-established with rarely applied statistical techniques. In particular, the following elements of uncertainty quantification are discussed: (i) using stochastic, mechanistic models that consider and propagate uncertainties from their origin to the output; (ii) profiting from the support of modern techniques of data science to increase the diversity of the exploration process, to benchmark mechanistic models, and to find new relationships; (iii) analysing structural alternatives by multi-model and non-parametric approaches; (iv) quantitatively formulating and using societal preferences in decision support; (v) explicitly considering the uncertainty of elicited preferences in addition to the uncertainty of predictions in decision support; and (vi) explicitly considering the ambiguity about prior distributions for predictions and preferences by using imprecise probabilities. In particular, (v) and (vi) have mostly been ignored in the past and a guideline is provided on how these uncertainties can be considered without significantly increasing the computational burden. The methodological approach to (v) and (vi) is based on expected expected utility theory, which extends expected utility theory to the consideration of uncertain preferences, and on imprecise, intersubjective Bayesian probabilities.
Micropollutant emissions from combined sewer overflows
Authors: Nickel Jan Philip , Fuchs Stephan 
Published: January 2020

Abstract
In an extensive monitoring programme, event mean concentrations of 12 heavy metals, 16 polycyclic aromatic hydrocarbons (PAH), nine pesticides/biocides, three pharmaceuticals, three benzotriazoles, acesulfame, and DEHP (di-(2-ethylhexyl)phthalate) were measured at 10 combined sewer overflow (CSO) facilities throughout Bavaria, Germany, for more than 110 overflow events. A harmonised approach with large volume samplers was used to produce volume-proportional event composite samples. A wide range of event durations and volumes was covered successfully. All substances analysed were detected in CSO samples and the majority were quantified in more than 80% of the samples. Our results confirm that CSOs need to be considered in the debate on micropollutant emissions, and knowledge regarding their concentrations at a regional level needs to be solidified. Distinct substance-specific patterns can be observed in the variability between events and sites as well as in a correlation analysis of substance concentrations. These trends underline the need for differentiation of the substances by their predominant sources, pathways, and transport behaviours. Compared to wastewater treatment plants, CSOs are an important pollution source especially for ubiquitous, primarily stormwater-transported pollutants, including substances causing failure to achieve good chemical status of surface waters, such as the uPBT (ubiquitous, persistent, bioaccumulative and toxic) substances Hg and PAH.
Elucidating Molecular Interactions of Ten Natural Compounds Targeting E6 HPV High Risk Oncoproteins Using Microsecond Molecular Dynamics Simulations
Authors: Meza-Menchaca Thuluz , Lizano-Soberón Marcela , Trigos Angel , Zepeda Rossana C. , Medina Manuel E. , Galindo-Murillo Rodrigo 
Published: January 2020

Background:
Cervical cancer is a major public health issue worldwide, occurring in the vast majority of cases (85%) in low-income countries. Human papillomavirus (HPV) mainly infects the mucosal epithelium, and a small portion causes over 600,000 cases every year worldwide at various anatomical spots, mainly leading to anogenital and head and neck.

Introduction:
The E6 oncoprotein encoded by cancer-associated alpha HPV can transform epithelial cells into tumorigenic tissue. Therapy for this infection and blocking of the HPV E6 oncoprotein could be provided with cost-effective and abundant natural products which are an exponentially growing topic in the literature. Finding an active natural compound that readily blocks HPV E6 oncoprotein which could be available for developing countries without expensive extraction processes or costly synthetic pathways is of major interest.

Method:
Molecular dynamics simulations was performed using the most up-to-date AMBER protein force field ff14SB and a GPU enabled high performance computing cluster.

Results:
In this research we present a study of the binding properties between 10 selected natural compounds that are readily available with two variants of the E6 oncoprotein types (HPV-16 and HPV-18) using 10+ microsecond molecular dynamics simulations.

Conclusions:
Our results suggest that crocetin, ergosterol peroxide and κ-carrageenan natural products binds strongly to both HPV-16 and HPV-18 and could potentially serve as a scaffolding for further drug development.
In silico Studies on the Interaction Between Bioactive Ligands and DPPIV: Insights on Potential Candidates for the Treatment of type 2 Diabetes Mellitus
Authors: Martins Michelle C.M.R. , Pantaleao Simone Q. , de Oliveira Almeida Michell , Weber Karen C. , Honorio Kathia M. 
Published: January 2020

Introduction:
The enzyme called dipeptidyl peptidase IV (DPP-IV) is related to the
glycemic control associated with the stimulation of the pancreas to produce insulin. So, its inhibition
is a good strategy for the treatment of type 2 diabetes mellitus.

Methods:
In this study, we have employed molecular modeling strategies such as CoMFA, molecular
docking, molecular dynamics, and binding free energy calculations of a set of DPP-IV inhibitors
in order to understand the main characteristics related to the biological activity of these ligands
against the enzyme.

Results:
The models obtained from CoMFA presented significant values of internal (0.768) and
external (0.988) validations. Important interactions with some residues, such as Glu205, Tyr666,
Arg125, Ser630, Phe357 and Tyr662, were also identified. In addition, calculations of the electronic
properties allowed relating the LUMO and HOMO energies with the biological activity of the
compounds studied. The results obtained from the molecular dynamics simulations and the SIE
calculations (ΔG) indicated that the inhibitor 40 increases the stability of the DPP-IV target.

Conclusions:
Therefore, from this study, it is possible to propose molecular modifications of these
DPP-IV inhibitors in order to improve their potential to treat type 2 diabetes.
A Study on the Investigation of Diffusion Coefficient for Oil Extraction from Terebinth (Pistacia terebinthus L.) Seeds
Authors: Kaya Fatih , Özer Ahmet 
Published: January 2020

Objective:
In this study, the extraction of oil from Pistacia terebinthus L’s seeds
grown in Elazig-TURKEY and called menengic in domestic region was investigated.
Crude oil content of the seeds obtained from this region was determined as approximately
47% (w/w).

Methods:
Effects of the parameters such as extraction time, temperature, seeds/solvent ratio
(dosage), the particle size of seeds and type of solvent were examined on the oil extraction
yield. In this context, it has concluded that up to a certain point, the extraction time has increased
the yield of oil extracted. But the extraction temperature showed activity as depending
on the solvent type.

Results:
As expected, it has been observed that the yield of oil has decreased depending on
the increase in particle size and dosage as well. The mathematical model obtained by solving
Fick's second law under the appropriate boundary and initial conditions were used to
calculate diffusion coefficients for the extraction process. Diffusion coefficients for the
seeds with a particle size of 0.55 mm were found to be between 1.15x10-11 and 1.86x10-11
m2s-1. To compare the extraction yield of Pistacia terebinthus L’s seeds with that of sunflower
at the same conditions, the diffusion coefficients of sunflower seeds were calculated
in the range of 9.11x10-12 and 1.13x10-11 m2s-1.

Conclusion:
These figures show that the diffusion coefficients calculated for both oily
seeds are nearly equivalent to each other. The fatty acid composition of extracted oil from
Pistacia terebinthus L’s seeds was determined by GC-FID. The GC-FID results showed
that oleic, linoleic (ω-6) and palmitic acid were main fatty acids in the oil obtained from
menengic seeds.
FbD Supported Development and In Vitro Evaluation of Carbomer based Resveratrol Loaded Topical Antipsoriatic Nanoemulgel for its Targeted Skin Delivery
Authors: Khurana Bharat , Arora Daisy , Narang Raj K. 
Published: January 2020

Background:
Resveratrol is a wonder therapy for the treatment of several skin
disorders, including psoriasis, but its skin permeation limits its applications.

Objective:
The present work dealt with optimizing and formulating resveratrol loaded vitamin
E based nanoemulsion and carbomer based nanoemulgel intended for topical application
in the treatment of plaque psoriasis.

:
The major objective of this study was to achieve the quality target product profile with respect
to enhanced skin permeation and superior skin deposition of the formulated
nanoemulgel to achieve the superlative therapeutic advantages.

Methods:
Formulation by design (FbD) approach was employed to optimize varied critical
material attributes such as the concentration of oil and Smix to achieve the desired quality
characteristics. Carbomer based nanoemulgel was formulated and evaluated.

Results:
Optimized formulation was having globule size (168.3 ± 4.98 nm), percentage
cumulative permeation (4.81 ± 0.65%), permeation flux (7.62 ± 0.39 μg hr-1cm-2), and skin
deposition (668.65 ± 11.98 μg cm-2). Nanoemulgel was found to have optimum physical
properties in terms of viscosity, spreadability, pH and physical stability. The extent of skin
deposition was approximately 6.682 times higher while the permeation enhancement ratio
was around 2.872 as compared to conventional formulation indicating its higher skin targeting
abilities, which was further ratified by Confocal Laser Scanning Microscopy results.

Conclusion:
Nanoemulgel formulated by the current FbD approach has enhanced skin
permeation and skin deposition properties as compared to conventional carbomer gel.
Thus, it could augment the therapeutic benefits of encapsulated bioactive in the treatment
of several skin disorders like psoriasis.
CBIR-CNN: Content-Based Image Retrieval on Celebrity Data Using Deep Convolution Neural Network
Authors: Singh Pushpendra , Hrisheekesha P.N. , Singh Vinai Kumar. 
Published: January 2020

Background:
Finding a region of interest in an image and content-based image analysis
has been a challenging task for the last two decades. With the advancement in image processing,
computer vision and a huge amount of image data generation, the Content-Based Image Retrieval
System (CBIR) has attracted several researchers as a common technique to manage this huge
amount of data. It is an approach of searching user interest based on visual information present in
an image. The requirement of high computation power and huge memory limits the deployment of
the CBIR technique in real-time scenarios.

Objective:
In this paper, an advanced deep learning model is applied to the CBIR on facial image
data. We designed a deep convolution neural network architecture where activation of the convolution
layer is used for feature representation and included max-pooling as a feature reduction technique.
Furthermore, our model uses partial feature mapping as image descriptor to incorporate the
property that facial image contains repeated information.

Methods:
Existing CBIR approaches primarily consider colour, texture and low-level features for
mapping and localizing image segments. While deep learning has shown high performance in numerous
fields of research, its application in CBIR is still very limited. Human face contains significant
information to be used in a content driven task and applicable to various applications of computer
vision and multimedia systems. In this research work, a deep learning-based model has been
discussed for Content-Based Image Retrieval (CBIR). In CBIR, there are two important things 1)
classification and 2) retrieval of image based on similarity. For the classification purpose, a fourconvolution
layer model has been proposed. For the calculation of the similarity, Euclidian distance
measure has been used between the images.

Results:
The proposed model is completely unsupervised, and it is fast and accurate in comparison
to other deep learning models applied for CBIR over the facial dataset. The proposed method provided
satisfactory results from the experiment, and it outperforms other CNN-based models such as VGG16,
Inception V3, ResNet50, and MobileNet. Moreover, the performance of the proposed model has been
compared with pre-trained models in terms of accuracy, storage space and inference time.

Conclusion:
: The experimental analysis over the dataset has shown promising results with more
than 90% classification accuracy.
Facial Feature Extraction for Emotion Classification Using Fuzzy C-Mean Clustering
Authors: Sharma Garima , Singh Latika , Gautam Sumanlata 
Published: January 2020

Background:
Automatic human emotion recognition system is an active area of research
due to its wide applications in the field of Human Computer Interaction(HCI) systems, driver fatigue
monitoring systems, surveillance systems, human assistance systems, smile detectors etc.

Objective:
The study presents a fuzzy based approach to extract facial features from input image and
builds different classification models to classify the image into two emotion classes i.e. happy and
neutral. The system has potential implications in the field of smile detection systems, customer experience
analysis and patient monitoring systems.

Methods:
The proposed system determines the dimensional attributes (l-attribute and w-attribute) of
mouth region extracted from the facial image using viola-jones algortithm. The feature set is
generated by using a total of 136 images from JAFFE, NimStim and MUG dataset. The
differentiating power of the attribures is then evaluated using five different classification models.

Results:
The accuracy, precision and recall is determined for each classification model. The results
show good accuracy of 70% for the grayscale JAFFE and NimStim databases and 95% for the
coloured MUG database.

Conclusion:
The mouth features calculated in the study are based on the geometric coordinates
which eliminates the possibility of false distance measurements due to presence of noise or shadows.
Evaluasi Pengaruh Kecepatan Rotasi Rotor terhadap Efektifitas Rotary Air Preheater Menggunakan Metode ε-NTU (Halaman 5 s.d. 8)
Authors: Sakarum Dewo , Hady Ariwibowo Teguh 
Published: January 2020

Air Preheater (APH) merupakan komponen pendukung dalam PLTU yang berfungsi sebagai pemanasan awal (preheater) pada saat pembakaran di dalam ruang bakar. Salah satu parameter yang mempengaruhi besarnya efektivitas pada rotary air preheater adalah kecepatan rotasi rotor. Untuk mengetahui pengaruh besarnya kecepatan rotasi rotor terhadap performansi rotary air preheater digunakan metode ε-NTU. Variasi dilakukan pada kecepatan rotasi 6 RPM, 9 RPM, 12 RPM, 15 RPM, 18 RPM dan pada C* 0,64 ; 0,78 ; 0,84. Dari lima variasi yang dilakukan menunjukkan bahwa kecepatan rotasi rotor mempengaruhi besarnya efektifitas. Kecepatan tersebut juga mempengaruhi besarnya NTU sehingga efektifitasnya ikut berubah. Efektifitas tertinggi yaitu pada kecepatan 18 RPM dan C* 0,64 sebesar 0,8750. Pada kecepatan rotasi yang sama efektifitas pada C* 0,64 mempunyai nilai yang lebih besar dari pada C* 0,78 dan 0,84.
Fabrikasi Lapisan Antirefleksi dengan Bahan Methyl Methacrylate (MMA) Menggunakan Metode Spin Coating (Halaman 30 s.d. 33)
Authors: - Sudarsono , Yudoyono Gatut , Indarto Bachtera , Hadi Pramono Yono , - Faridawati 
Published: January 2020

Telah dilakukan fabrikasi lapisan antirefleksi dengan bahan methyl methacrylate dengan metode spin coating. Lapisan methyl methacrylate dilapiskan pada substrat kaca kemudian diputar dengan kecepatan 1000 rpm dengan metode spin coating. Untuk membuat lapisan anti refleksi dibutuhkan indeks bias substrat lebih besar dari pada lapisan film, sehingga salah satu bahan yang dipilih pada penelitian ini adalah methyl methacrylate dengan indeks bias 1,49. Dari hasil fabrikasi didapatkan nilai ketebalan lapisan adalah 10,816 µm. karakterisasi hasil fabrikasi dilakukan menggunakan spektrometer UV Vis dengan panjang gelombang (390- 450) nm dan menghasilkan transmisi lebih dari 87,842% dan absorbsi kurang dari 0,056 %. Dari hasil transmisi dan absorbsi bahan tersebut maka hasil refleksi relatif kecil sehingga dapat digunakan sebagai lapisan antirefleksi.