Miracle-dz/UniFAR-FTUs huggingface dataset:
POLYMERIC SUPERHYDROPHOBIC COATINGS BASED ON FUNCTIONALIZED SiO 2 NANOCOMPOSITES

Functionalized SiO2 nanoparticles (NPs) are highly versatile systems, which can be used for the development of tailor-made smart coatings. For example, proper functionalization and structuring of the NPs coatings may result in efficient self-cleaning films, resembling the lotus effect, although this kind of films are easily removed, because the particles are not firmly attached to the substrate. However, for exterior applications in which the system is exposed to the environment, such as in solar panels, excellent mechanical properties are also a requirement. This could be achieved by integrating such functionalized NPs into polymeric nanocomposites [1]. In this investigation, SiO2 NPs were synthesized by sol-gel method, followed by in-situ functionalization with various organosilane and fluorosilane molecules. Dispersions of the functionalized SiO2 NPs were used for the preparation of poly(acrylic acid) and polyurethane composite films by spin coating technique. The functionalized SiO2 NPs were characterized by FTIR, XPS, AFM, and SEM. Scanning electron microscopy micrographs of the composites showed spherical SiO2 NPs (~100 nm) that are embedded in a polymeric matrix. With the propose of confirm the hydrophobic effect of the SiO2 NPs in the composites, the contact angle technique was used, and water contact angles up to 140o were obtained (Figure 1); water-droplet rolling up was achieved with composites based on SiO2 NPs functionalized with PFOS (perfluorooctyl(trimethoxy)silane), in resemblance to the lotus effect. The optical and mechanical properties of composite were also evaluated, showing a promising behavior for its outdoor application.
Metabolomics analysis of follicular fluid in women with ovarian endometriosis undergoing in vitro fertilization

ABSTRACT The purpose of this study was to investigate whether a change in the follicular fluid metabolomics profile due to endometrioma is identifiable. Twelve women with ovarian endometriosis (aged<40 years, with a body mass index [BMI] of <30 kg/m2) and 12 age- and BMI-matched controls (women with infertility purely due to a male factor) underwent ovarian stimulation for intracytoplasmic sperm injection (ICSI). Follicular fluid samples were collected from both of groups at the time of oocyte retrieval for ICSI. Next, nuclear magnetic resonance (NMR) spectroscopy was performed for the collected follicular fluids. The metabolic compositions of the follicular fluids were then compared using univariate and multivariate statistical analyses of NMR data. Univariate and multivariate statistical analyses of NMR data showed that the metabolomic profiles of the follicular fluids obtained from the women with ovarian endometriosis were distinctly different from those obtained from the control group. In comparison with the controls, the follicular fluids of the women with ovarian endometriosis had statistically significant elevated levels of lactate, β-glucose, pyruvate, and valine. We conclude that the levels of lactate, β-glucose, pyruvate, and valine in the follicular fluid of the women with endometrioma were higher than those of the controls. Abbreviations: ASRM: American Society for Reproductive Medicine; BMI: body mass index; CPMG: Carr-Purcell-Meiboom-Gill; E2: estradiol; ESHRE: European Society of Human Reproduction and Embryology; ERETIC: electronic to access in vivo concentration; FF: follicular fluid; FSH: follicle-stimulating hormone; hCG: human chorionic gonadotropin; HEPES: 2-hydroxyethyl-1-piperazineethanesulfonic acid; ICSI: intracytoplasmic sperm injection; IVF: in vitro fertilization; NMR: nuclear magnetic resonance spectroscopy; PCA: principal component analysis; PCOS: polycystic ovary syndrome; PLS-DA: partial least squares discriminant analysis; ppm: parts per million; PULCON: pulse length-based concentration determination; TSP: 3-(trimethylsilyl)-1-propanesulfonic acid sodium salt; VIP: variable importance in projection
Title Osteological variation among extreme morphological forms in the Mexican salamander genus Chiropterotriton ( Amphibia : Plethodontidae ) : Morphological evolution and homoplasy Permalink

Osteological variation is recorded among and within four of the most distinctive species of the Mexican salamander genus Chiropterotriton. Analysis of the data is consistent with the monophyletic status of the genus and documents previously unrecorded intraspecific and interspecific variation. Most of the recorded variation involves qualitative and quantitative proportional differences, but four fixed differences constitute autapomorphic states that affirm and diagnose some species (C. dimidiatus, C.magnipes). Osteological variation in 15 characters is analyzed with respect to predictions generated from four hypotheses: 1) phylogeny, 2) adaptation to specific habitats (the four species include cave-dwelling, terrestrial, and arboreal forms), 3) size-free shape, and 4) size. High levels of intraspecific variation suggest that the characters studied are not subject to rigid functional constraints in salamanders, regardless of size. The pattern predicted by the hypothesis based on size differences seen among these four Chiropterotriton species matches most closely the observed pattern of relative skull robustness. Since size change and heterochrony are often associated in plethodontid evolution, it is likely that changes in developmental timing play a role in the morphological transitions among these morphologically diverse taxa. Webbed feet, miniaturization, body shape, and an unusual tarsal arrangement are morphologies exhibited in species of Chiropterotrition that are shown to be homoplastic with other clades of tropical plethodontids. Although extensive homoplasy in salamanders might be seen as a roadblock to unraveling phylogenetic hypotheses, the homologous developmental systems that appear to underlie such homoplasy may reveal common and consistent evolutionary process-
A Storage-Computation-Communication Tradeoff for Distributed Computing

This paper investigates distributed computing systems where computations are split into “Map” and “Reduce” functions. A new coded scheme, called distributed computing and coded communication (D3C), is proposed, and its communication load is analyzed as a function of the available storage space and the number of intermediate values (IVA) to be computed. D3C achieves the smallest possible communication load for a given storage space, while a smaller number of IVAs need to be computed compared to Li et al.'s coded distributed computing (CDC) scheme. More generally, our scheme can flexibly trade between storage space and the number of IVAs to be computed. Communication load is then analyzed for any given tradeoff.
Using Inter-Sentence Diverse Beam Search to Reduce Redundancy in Visual Storytelling

Visual storytelling includes two important parts: coherence between the story and images as well as the story structure. For image to text neural network models, similar images in the sequence would provide close information for story generator to obtain almost identical sentence. However, repeatedly narrating same objects or events will undermine a good story structure. In this paper, we proposed an inter-sentence diverse beam search to generate a more expressive story. Comparing to some recent models of visual storytelling task, which generate story without considering the generated sentence of the previous picture, our proposed method can avoid generating identical sentence even given a sequence of similar pictures.
A Novel Parallel Image Encryption Scheme Using Chaos

In recent years, chaos-based image cipher has been widely studied and a growing number of schemes based on permutation-substitution architecture have been proposed. To better meet the challenge of real-time secure image communication applications, this paper suggests a new image encryption scheme using a parallel substitution. In the permutation stage, the Arnold cat map is employed to shuffle the pixel positions so as to erase the strong relationship between adjacent pixels. In the substitution stage, the scrambled image is firstly decomposed into eight bit-planes which are then parallelly mixed with keystreams generated by the chaotic logistic map. Theoretically, the parallel substitution strategy runs eight times faster than the serial strategy on an 8-thread processor as the volume of data processed by each substitution unit is 1/8 of that of the input image. Experimental results show that the proposed parallel scheme runs more than five times faster than the serial scheme. Extensive security analysis is carried out with detailed analysis, demonstrating the satisfactory security of the proposed scheme.
Distributed Optimization of Collaborative Regions in Large-Scale Inhomogeneous Fog Computing

Fog computing enables resource-limited network devices to help each other with computationally demanding tasks, but has yet to be implemented in large scales due to sophisticated control and network inhomogeneity. This paper presents a new fully distributed online optimization to asymptotically minimize the time-average cost of fog computing, where tasks are selected to be offloaded and processed independently between different links and devices by measuring their cost effectiveness at each time slot. A key contribution is that we optimize the cost-effectiveness measures which achieve the asymptotic optimality over infinite time. Another contribution is that we optimize placeholders at the devices; which create collaborative computing regions of tasks in the vicinity of the point of capture, prevent tasks being offloaded beyond, preserve the asymptotic optimality and reduce delay. This is achieved in a distributed fashion by discovering the optimal substructure of the placeholders. Simulations show that the average size of collaborative regions is only 3.2 out of total 500 servers, and the system income increases by 43% as compared with existing techniques.
Novel Light Source Integration Approaches for Silicon Photonics

Silicon does not emit light efficiently, therefore the integration of other light‐emitting materials is highly demanded for silicon photonic integrated circuits. A number of integration approaches have been extensively explored in the past decade. Here, the most recent progress in this field is reviewed, covering the integration approaches of III‐V‐to‐silicon bonding, transfer printing, epitaxial growth and the use of colloidal quantum dots. The basic approaches to create waveguide‐coupled on‐chip light sources for different application scenarios are discussed, both for silicon and silicon nitride based waveguides. A selection of recent representative device demonstrations is presented, including high speed DFB lasers, ultra‐dense comb lasers, short (850nm) and long (2.3μm) wavelength lasers, wide‐band LEDs, monolithic O‐band lasers and micro‐disk lasers operating in the visible. The challenges and opportunities of these approaches are discussed.
Travel and us: the impact of mode share on sentiment using geo-social media and GIS

Abstract Commute stress is a serious health problem that impacts nearly everyone. Considering that microblogged geo-locational information offers new insight into human attitudes, the present research examined the utility of geo-social media data for understanding how different active and inactive travel modes affect feelings of pleasure, or displeasure, in two major US cities: Chicago, Illinois and Washington DC. A popular approach was used to derive a sentiment index (pleasure or valence) for each travel Tweet. Methodologically, exploratory spatial data analysis (ESDA) and global and spatial regression models were used to examine the geography of all travel modes and factors affecting their valence. After adjusting for spatial error associated with socioeconomic, environmental, weather and temporal factors, spatial autoregression models proved superior to the base global model. The results showed that water and pedestrian travel were universally associated with positive valences. Bicycling also favourably influenced valence, albeit only in DC. A noteworthy finding was the negative influence temperature and humidity had on valence. The outcomes from this research should be considered when additional evidence is needed to elevate commuter sentiment values in practice and policy, especially in regards to active transportation.
To Construct an Engineered (S)-Equol Resistant E. coli for in Vitro (S)-Equol Production

(S)-equol is one of the major metabolites of daidzein that is produced by human and animal gut bacteria. Most of the physiological functions of soybean isoflavones, such as anti-oxidative activity, anti-cancer activity, and cardiovascular protection have been ascribed to (S)-equol. However, only 30–50% people contain this kind of equol-producing bacteria, and therefore are able to convert daidzein to (S)-equol. Administration of (S)-equol may be more beneficial than soybean isoflavones. The aim of this study was to construct an engineered (S)-equol resistant Escherichia coli to enhance (S)-equol production in vitro. First, transposon mutagenesis libraries were constructed and screened to isolate the (S)-equol resistant mutant E. coli strain BL21 (ydiS) in order to overcome the inhibitory effects of (S)-equol on bacterial growth. Bacterial full genome scan sequencing and in vitro overexpression results revealed that the ydiS gene was responsible for this resistance. Second, the (S)-equol-producing genes L-dznr, L-ddrc, L-dhdr, and L-thdr of Lactococcus strain 20–92 were synthesized and cloned into compatible vectors, pETDuet-1 and pCDFDuet-1. These plasmids were subsequently transformed into BL21 (DE3) and its mutant BL21 (ydiS). Both engineered BL21 (DE3) and BL21 (ydiS) could use daidzein as substrate to produce (S)-equol under both anaerobic and aerobic conditions. As expected, engineered BL21 (ydiS) had faster growth rates than BL21 (DE3) when supplemented with high concentrations of (S)-equol. The yield and the daidzein utilization ratio were higher for engineered BL21 (ydiS). Interestingly, engineered BL21 (ydiS) was able to convert daidzein to (S)-equol efficiently under aerobic conditions, providing a convenient method for (S)-equol production in vitro. In addition, a two-step method was developed to produce (S)-equol using daidzin as substrate.
Understanding and Designing the Strategies for the Microbe-Mediated Remediation of Environmental Contaminants Using Omics Approaches

Rapid industrialization and population explosion has resulted in the generation and dumping of various contaminants into the environment. These harmful compounds deteriorate the human health as well as the surrounding environments. Current research aims to harness and enhance the natural ability of different microbes to metabolize these toxic compounds. Microbial-mediated bioremediation offers great potential to reinstate the contaminated environments in an ecologically acceptable approach. However, the lack of the knowledge regarding the factors controlling and regulating the growth, metabolism, and dynamics of diverse microbial communities in the contaminated environments often limits its execution. In recent years the importance of advanced tools such as genomics, proteomics, transcriptomics, metabolomics, and fluxomics has increased to design the strategies to treat these contaminants in ecofriendly manner. Previously researchers has largely focused on the environmental remediation using single omics-approach, however the present review specifically addresses the integrative role of the multi-omics approaches in microbial-mediated bioremediation. Additionally, we discussed how the multi-omics approaches help to comprehend and explore the structural and functional aspects of the microbial consortia in response to the different environmental pollutants and presented some success stories by using these approaches.
Complex Network of Damage Assessment Using GMM Based FAIR

With the recent increase in IoT devices, network connectivity devices such as routers or switches are increasing. This increase in equipment could increase the degree of a particular node (hub node or hub router) in terms of network characteristics. In other words, the structure of the network can be changed into a more complicated network form. The convergence of links to these specific nodes provides an opportunity for attackers to enjoy maximum benefit at minimal cost. An attacker can attack only a few hub routers with a high degree of network structure, which can paralyze networks in some areas. This possibility is because the actual network follows the power degree distribution. These research measures threats based on network characteristics and data on exploits of network equipment. The threat measurement is applied to the FAIR(Factor Analysis of Information Risk) model, and the damage caused by the macro network structure is assessed.
Target classification using convolutional deep learning and auto-encoder models

Targets recognition in radar images presents an essential task for monitoring and surveillance of sensitive areas such as military zones. The fundamental problem in radar imaging is related to the recognition of objects in radar images, that its needs a whole chain of treatment. To classify radar images a feature extraction method is used to detect an appropriate subspace in the original feature space, which is based on transformation of the original feature. This subspace should be big enough to maintain minimal loss of information and small enough to minimize the complexity of classifier. Since the feature extractor is difficult to build in manual mode and needs to be redesigned for each application, a Deep Learning in automatic mode is used with a training process subdivided into several modules. In this paper, we lay out an approach to classify Synthetic aperture radar (SAR) and Inverse Synthetic aperture radar (ISAR) images using Deep learning techniques. At first, in order to evaluate the effect of convolution layers and the number of hidden layers of the perceptron we thought of implementing 4 configurations of CNN (Convolutional neural network). In the second time, we use the CAE (Convolutional auto-encoder) to learn the optimal filters that minimize the reconstruction error, after we use these filters to feed the CNN retained and evaluate the effect on performance's system.
Plasmas meet nanoparticles—where synergies can advance the frontier of medicine

Nanoparticles and low-temperature plasmas have been developed, independently and often along different routes, to tackle the same set of challenges in biomedicine. There are intriguing similarities and contrasts in their interactions with cells and living tissues, and these are reflected directly in the characteristics and scope of their intended therapeutic solutions, in particular their chemical reactivity, selectivity against pathogens and cancer cells, safety to healthy cells and tissues and targeted delivery to diseased tissues. Time has come to ask the inevitable question of possible plasma–nanoparticle synergy and the related benefits to the development of effective, selective and safe therapies for modern medicine. This perspective paper offers a detailed review of the strengths and weakenesses of nanomedicine and plasma medicine as a stand-alone technology, and then provides a critical analysis of some of the major opportunities enabled by synergizing nanotechnology and plasma technology. It is shown that the plasma–nanoparticle synergy is best captured through plasma nanotechnology and its benefits for medicine are highly promising.
Effects of a Brief Empowerment Program for Families of Persons with Mental Illness in South Korea: A Pilot Study

ABSTRACT Families of persons with mental illness (PMIs) are considered important resources for PMIs rather than as contributors to their mental illness. However, these families experience not only the burden of caregiving but also social stigma and discrimination in various aspects of their lives, and their psychosocial needs tend to be overlooked. This was a pilot study to explore the effects of a brief empowerment program on the empowerment and quality of life of families of PMIs in South Korea. A repeated-measures design with a control group and pre/post-follow-up testing was used. We enrolled 18 participants (experimental group = 9, control group = 9). The experimental group participated in an empowerment program consisting of four sessions over 4 weeks. Data were collected before and after the program, and again 4 weeks later. The χ2-test, Fisher's exact probability test, t-test, and repeated-measures analysis of covariance were used, as appropriate, to analyze data. The program significantly increased empowerment (F = 4.66, p = .020) and quality of life (F = 5.83, p = .009) among participants in the experimental group over time. Its therapeutic features, such as sharing their experiences, discussion, and presentations, can be applied to create effective psychosocial interventions for families of PMIs.
Understanding the usage of online forums in engineering: A case study in industrial organization

Conceptual understanding of the management of real situations and heterogeneous problems in manufacturing and engineering in general is one important competence in industrial organization. This paper describes a series of practical experiments conducted to determine how online discussion forums make an influence in the behavior of the students, enhance the quality of the discourse and improve the learning performance of industrial organization.
Past, Present, and Future of Rituximab—The World’s First Oncology Monoclonal Antibody Therapy

Rituximab is a chimeric mouse/human monoclonal antibody (mAb) therapy with binding specificity to CD20. It was the first therapeutic antibody approved for oncology patients and was the top-selling oncology drug for nearly a decade with sales reaching $8.58 billion in 2016. Since its initial approval in 1997, it has improved outcomes in all B-cell malignancies, including diffuse large B-cell lymphoma, follicular lymphoma, and chronic lymphocytic leukemia. Despite widespread use, most mechanistic data have been gathered from in vitro studies while the roles of the various response mechanisms in humans are still largely undetermined. Polymorphisms in Fc gamma receptor and complement protein genes have been implicated as potential predictors of differential response to rituximab, but have not yet shown sufficient influence to impact clinical decisions. Unlike most targeted therapies developed today, no known biomarkers to indicate target engagement/tumor response have been identified, aside from reduced tumor burden. The lack of companion biomarkers beyond CD20 itself has made it difficult to predict which patients will respond to any given anti-CD20 antibody. In the past decade, two new anti-CD20 antibodies have been approved: ofatumumab, which binds a distinct epitope of CD20, and obinutuzumab, a mAb derived from rituximab with modifications to the Fc portion and to its glycosylation. Both are fully humanized and have biological activity that is distinct from that of rituximab. In addition to these new anti-CD20 antibodies, another imminent change in targeted lymphoma treatment is the multitude of biosimilars that are becoming available as rituximab’s patent expires. While the widespread use of rituximab itself will likely continue, its biosimilars will increase global access to the therapy. This review discusses current research into mechanisms and potential biomarkers of rituximab response, as well as its biosimilars and the newer CD20 binding mAb therapies. Increased ability to assess the effectiveness of rituximab in an individual patient, along with the availability of alternative anti-CD20 antibodies will likely lead to dramatic changes in how we use CD20 antibodies going forward.