Mineralization and Structural Controls of the AB-Bid Carbonate-Hosted Pb-Zn (±Cu) Deposit, Tabas-Posht e Badam Metallogenic Belt, Iran

The Ab-Bid deposit, located in the Tabas-Posht e Badam metallogenic belt (TPMB) in Central Iran, is the largest Pb-Zn (±Cu) deposit in the Behadad-Kuhbanan mining district. Sulfide mineralization in the Ab-Bid deposit formed in Middle Triassic carbonate rocks and contains galena and sphalerite with minor pyrite, chalcopyrite, chalcocite, and barite. Silicification and dolomitization are the main wall-rock alteration styles. Structural and textural observations indicate that the mineralization occurs as fault fills with coarse-textured, brecciated, and replacement sulfides deposited in a bookshelf structure. The Ab-Bid ore minerals precipitated from high temperature (≈180–200 °C) basinal brines within the dolomitized and silicified carbonates. The sulfur isotope values of ore sulfides suggest a predominant thermochemical sulfate reduction (TSR) process, and the sulfur source was probably Triassic-Jurassic seawater sulfate. Given the current evidence, mineralization at Ab-Bid resulted from focusing of heated, over-pressurized brines of modified basinal origin into an active fault system. The association of the sulfide mineralization with intensely altered wall rock represents a typical example of such features in the Mississippi Valley-type (MVT) metallogenic domain of the TPMB. According to the structural data, the critical ore control is a bookshelf structure having mineralized dextral strike-slip faults in the northern part of the Ab-Bid reverse fault, which seems to be part of a sinistral brittle shear zone. Structural relationships also indicate that the strata-bound, fault-controlled Ab-Bid deposit was formed after the Middle Jurassic, and its formation may be related to compressive and deformation stages of the Mid-Cimmerian in the Middle Jurassic to Laramide orogenic cycle in the Late Cretaceous-Tertiary.

Experimental Analysis and Multiscale Modeling of the Dynamics of a Fiber-Optic Coil

Fiber-optic gyroscopes (FOGs) are common rotation measurement devices in aerospace applications. They have a wide range of diversity in length and in the winding radius of the coil to meet system requirements. Every dimensional parameter in the coil influences the dynamic response of the system, eventually leading to measurement errors. In order to eliminate the errors and to qualify the system, after the design and production stages, a deep and comprehensive testing procedure follows. In this study, the dynamic behavior of a quadrupole wound fiber-optic coil is investigated. First, pre-wound fiber-optic coils are tested with an impact modal test, where the mode shapes and natural frequencies are determined with structural data acquisition. For the modal analysis, a finite element (FE) model is developed where a representative volume element (RVE) analysis is also included to properly consider the influence of the microstructure. The experimental and numerical results are compared and validated. Moreover, an estimation model is proposed for a type of coil with different fiber lengths. Finally, the estimated coil set is produced and tested employing the same methodology in order to illustrate the capacity of the developed framework.

Mapping inhibitory sites on the RNA polymerase of the 1918 pandemic influenza virus using nanobodies

Influenza A viruses cause seasonal epidemics and global pandemics, representing a considerable burden to healthcare systems. Central to the replication cycle of influenza viruses is the viral RNA-dependent RNA polymerase which transcribes and replicates the viral RNA genome. The polymerase undergoes conformational rearrangements and interacts with viral and host proteins to perform these functions. Here we determine the structure of the 1918 influenza virus polymerase in transcriptase and replicase conformations using cryo-electron microscopy (cryo-EM). We then structurally and functionally characterise the binding of single-domain nanobodies to the polymerase of the 1918 pandemic influenza virus. Combining these functional and structural data we identify five sites on the polymerase which are sensitive to inhibition by nanobodies. We propose that the binding of nanobodies at these sites either prevents the polymerase from assuming particular functional conformations or interactions with viral or host factors. The polymerase is highly conserved across the influenza A subtypes, suggesting these sites as effective targets for potential influenza antiviral development.

Elucidating Macular Structure-Function Correlations in Glaucoma

Correlation between structural data from optical coherence tomography (OCT) and functional data from the visual field (VF) may be suboptimal because of poor mapping of OCT measurement locations to VF test stimuli. We tested the hypothesis that stronger structure-function correlations in the macula can be achieved with fundus-tracking perimetery, by precisely mapping OCT measurements to VF sensitivity at the same location. The conventional 64 superpixel (3°x3°) OCT grid was mapped to VF sensitivities averaged in 40 corresponding VF units with standard automated perimetry (conventional mapped approach, CMA) in 38 glaucoma patients and 10 healthy subjects. Similarly, a 144 superpixel (2°x2°) OCT grid was mapped to each of the 68 VF locations with fundus-tracking perimetry (localized mapped approach, LMA). For each approach, the correlation between sensitivity at each VF unit and OCT superpixel was computed and the maximum value used to generate vector maps. CMA yielded significantly higher structure-function correlations compared to LMA. Only 20% of the vectors with CMA and <5% with LMA were within corresponding mapped OCT superpixels, while most were directed towards loci with structural damage. Measurement variability and patterns of glaucomatous damage are more likely to affect the correlations rather than precise mapping of VF stimuli.

The use of Big Data Analytics in healthcare

The introduction of Big Data Analytics (BDA) in healthcare will allow to use new technologies both in treatment of patients and health management. The paper aims at analyzing the possibilities of using Big Data Analytics in healthcare. The research is based on a critical analysis of the literature, as well as the presentation of selected results of direct research on the use of Big Data Analytics in medical facilities. The direct research was carried out based on research questionnaire and conducted on a sample of 217 medical facilities in Poland. Literature studies have shown that the use of Big Data Analytics can bring many benefits to medical facilities, while direct research has shown that medical facilities in Poland are moving towards data-based healthcare because they use structured and unstructured data, reach for analytics in the administrative, business and clinical area. The research positively confirmed that medical facilities are working on both structural data and unstructured data. The following kinds and sources of data can be distinguished: from databases, transaction data, unstructured content of emails and documents, data from devices and sensors. However, the use of data from social media is lower as in their activity they reach for analytics, not only in the administrative and business but also in the clinical area. It clearly shows that the decisions made in medical facilities are highly data-driven. The results of the study confirm what has been analyzed in the literature that medical facilities are moving towards data-based healthcare, together with its benefits.

Differentiating Between Migraine and Post-traumatic Headache Using a Machine Learning Classifier

ObjectiveThe objective was to develop classification models differentiating persistent PTH (PPTH) and migraine using clinical data and MRI-based measures of brain structure and functional connectivity.BackgroundPTH and migraine commonly have similar phenotypes. Furthermore, migraine is a risk factor for developing PTH, sometimes making it difficult to differentiate PTH from exacerbation of migraine symptoms.Design/MethodsThirty-four individuals with migraine without history of TBI and 48 individuals with mild TBI attributed to PPTH but without history of migraine or prior frequent tension type headache were included. Subjects completed questionnaires assessing headache characteristics, mood, sensory hypersensitivities and cognitive function and underwent MRI imaging during the same day. Clinical features and structural brain measures from T1-weighted imaging, diffusion tensor imaging and functional resting-state measures were included as potential variables. A classifier using ridge logistic regression of principal components (PC) was fit. Since PCs can hinder identification of significant variables in a model, a second regression model was fit directly to the data. In the non-PC based model, input variables were selected based on lowest t-test or chi-square p-value by modality. Average accuracy was calculated using leave-one-out cross validation. The importance of variables to the classifier were examined.ResultsThe PC-based classifier achieved an average classification accuracy of 85%. The non-PC based classifier achieved an average classification accuracy of 74.4%. Both classifiers were more accurate at classifying migraine subjects than PPTH. The PC-based model incorrectly classified 9/48 (18.8%) PPTH subjects compared to 3/34 (8.8%) migraine patients, whereas the non-PC classifier incorrectly classed 16/48 (33.3%) vs 5/34 (14.7%) of migraine subjects. Important variables in the non-PC model included static and dynamic functional connectivity values, several questions from the Beck Depression Inventory, and worsening symptoms and headaches with mental activity.ConclusionsMultivariate models including clinical characteristics, functional connectivity, and brain structural data accurately classify and differentiate PPTH vs migraine.

A Structural Perspective of Reps from CRESS-DNA Viruses and Their Bacterial Plasmid Homologues

Rolling circle replication (RCR) is ubiquitously used by cellular and viral systems for genome and plasmid replication. While the molecular mechanism of RCR has been described, the structural mechanism is desperately lacking. Circular-rep encoded single stranded DNA (CRESS-DNA) viruses employ a viral encoded replicase (Rep) to initiate RCR. The recently identified prokaryotic homologues of Reps may also be responsible for initiating RCR. Reps are composed of an endonuclease, oligomerization, and ATPase domain. Recent structural studies have provided structures for all these domains such that an overall mechanism of RCR initiation can begin to be synthesized. However, structures of Rep in complex with its various DNA substrates and/or ligands are lacking. Here we provide a 3D bioinformatic review of the current structural information available for Reps. We combine an excess of 1590 sequences with experimental and predicted structural data from 22 CRESS-DNA groups to identify similarities and differences between Reps that lead to potentially important functional sites. Experimental studies of these sites may shed light on how Reps execute their functions. Furthermore, we identify Rep-substrate or Rep-ligand structures that are urgently needed to better understand the structural mechanism of RCR.

Optimal Compression of Remote Sensing Images Using Deep Learning during Transmission of Data

Industrial internet of things has grown quite popular in recent years and involves a large number of intelligent devices linked together to build a system that can investigate, communicate, gather and observe information. Due to this requirement, there is more demand for compression techniques which compresses data, leading to less usage of resources and low complexity. This is where Convolutional Neural Networks (CNN) play a large role in the field of computer vision, especially in places where high applications such as interpretation coupled with detection is required. Similarly, low-level applications such as image compression cannot be resolved using this methodology. In this paper, a compression technique for remote sensing images using CNN is proposed. This methodology incorporates CNN in a compact learning environment wherein the actual image that consists of structural data is coded using Lempel Ziv Markov chain algorithm. This process is followed by image reconstruction in order to obtain the actual image in high quality. Other methodologies such as optimized trunctiona, JPEG2000, JPEC and binary tree were compared using a large number of experiments in terms of space saving, reconstructed image quality and efficiency. The output obtained indicates that the proposed methodology shows effective improvement, attaining a 50 dB signal to noise ratio and space saving of 90%.

Discovery of Cofactor Competitive Inhibitors against the Human Methyltransferase Fibrillarin

Fibrillarin (FBL) is an essential and evolutionarily highly conserved S-adenosyl methionine (SAM) dependent methyltransferase. It is the catalytic component of a multiprotein complex that facilitates 2′-O-methylation of ribosomal RNAs (rRNAs), a modification essential for accurate and efficient protein synthesis in eukaryotic cells. It was recently established that human FBL (hFBL) is critical for Nipah, Hendra, and respiratory syncytial virus infections. In addition, overexpression of hFBL contributes towards tumorgenesis and is associated with poor survival in patients with breast cancer, suggesting that hFBL is a potential target for the development of both antiviral and anticancer drugs. An attractive strategy to target cofactor-dependent enzymes is the selective inhibition of cofactor binding, which has been successful for the development of inhibitors against several protein methyltransferases including PRMT5, DOT1L, and EZH2. In this work, we solved crystal structures of the methyltransferase domain of hFBL in apo form and in complex with the cofactor SAM. Screening of a fluorinated fragment library, via X-ray crystallography and 19F NMR spectroscopy, yielded seven hit compounds that competed with cofactor binding, two of which resulted in co-crystal structures. One of these structures revealed unexpected conformational variability in the cofactor binding site, which allows it to accommodate a compound significantly different from SAM. Our structural data provide critical information for the design of selective cofactor competitive inhibitors targeting hFBL, and preliminary elaboration of hit compounds has led to additional cofactor site binders.

An Efficient Interpretable Visualization Method of Multidimensional Structural Data Matching Based on Job Seekers and Positions

With the rapid development of Internet technology, millions of small, medium, and microenterprises are using Internet recruitment platforms to host their recruitment information. They have different job requirements and benefits positions. It is important to understand them for job seekers when choosing a position. Existing Internet recruitment platforms do not provide a detailed analysis of positions and visual methods for multidimensional matching of positions and job applicants. Candidates need to spend a lot of energy to screen out suitable positions. In this paper, we propose an efficient interpretable visualization method of multidimensional structural data matching based on job seekers and positions. First, we extract the keywords of the job seeker’s ability and benefits based on personal information, and we generate a job seeker ability table and a job seeker demand table. After that, we calculate the degree of the support, confidence, and promotion of each rule through the association rules generated by each frequent itemset of recruitment data to obtain the association rule table. We further explore the relationship between the skills required for the three types of positions based on the association rule. Finally, we use the regression method to build a salary forecasting model. On this basis, we predict the salary of job seekers based on the work experience, education, and work city provided by the job seeker. Simulation results show that our method has better performance on the job analysis and recommendation.