PNA-induced assembly of fluorescent proteins using DNA as a framework

Controlled alignment of proteins on molecular frameworks requires the development of facile and orthogonal chemical approaches and molecular scaffolds. In this work, protein−PNA conjugates are brought forward as new chemical components allowing efficient assembly and alignment on DNA scaffolds. Site-selective monomeric teal fluorescent protein (mTFP)−peptide nucleic acid (PNA) (mTFP-PNA) conjugation was achieved by covalent linkage of the PNA to the protein through expressed protein ligation (EPL). A DNA beacon, with 6-Fam and Dabcyl at its ends, acts as a framework to create an assembled hetero-FRET system with the mTFP-PNA conjugate. Using fluorescence intensity, frequency domain lifetime measurements, and anisotropy measurements, the system was shown to produce FRET as indicated by decreased donor intensity, decreased donor lifetime, and increased donor anisotropy. Extension of the DNA scaffold allowed for the assembly of multiple mTFP-PNA constructs. Efficient formation of protein dimers and oligomers on the DNA-PNA frameworks could be shown, as visualized via size exclusion chromatography (SEC) and electrophoresis (SDS-PAGE). Assembly of multiple proteins in a row induced homo-FRET for the mTFP-PNA’s assembled on the DNA scaffolds. The oligonucleotide framework allows an induced and controllable assembly of proteins by fusing them to PNAs directed to align on DNA scaffolds

Alteration in incidence and pattern of congenital anomalies among newborns during one decade in Azarshahr, Northwest of Iran

Background and aims: Congenital anomalies are as the major causes of stillbirths, neonatal death, disability and childhood health problems all over the world. The aim of this study was to determine the incidence and pattern of congenital anomalies in newborn during the first 24 hours of life in Shahid-Madani hospital, Azarshahr, Tabriz, during two periods 2002-2003 and 2012-2013 years. Methods: This is a records-based descriptive study with 4515 newborns who were delivered at Shahid-Madani hospital. Results: The incidence of congenital anomalies in newborns during 2002-2003 and 2012-2013 years was 1.31 and 1.06 respectively. We found that the incidence rate of congenital anomalies has declined during one decade, and also the pattern of these has varied. In 2002-2003, the most common anomaly was musculoskeletal system anomaly whereas in 2012-2013, the genitourinary system was the most frequent anomaly. Conclusion: Our findings showed incidence and pattern of congenital anomalies have changed during one decade. Research into the etiology, prevention and prenatal care planning must focus on in prevalent congenital anomalies in this city

Spatial mapping of the provenance of storm dust: Application of data mining and ensemble modelling

Spatial modelling of storm dust provenance is essential to mitigate its on-site and off-site effects in the arid and semi-arid environments of the world. Therefore, the main aim of this study was to apply eight data mining algorithms including random forest (RF), support vector machine (SVM), bayesian additive regression trees (BART), radial basis function (RBF), extreme gradient boosting (XGBoost), regression tree analysis (RTA), Cubist model and boosted regression trees (BRT) and an ensemble modelling (EM) approach for generating spatial maps of dust provenance in the Khuzestan province, a main region with active sources for producing dust in southwestern Iran. This study is the first attempt at predicting storm dust provenance by applying individual data mining models and ensemble modelling. We identified and mapped in a geographic information system (GIS), 12 potential effective factors for dust emissions comprising two for climate (wind speed, precipitation), five soil characteristics (texture, bulk density, Ec, organic matter (OM), available water capacity (AWC)), a normalized difference vegetation index (NDVI), land use, geology, a digital elevation model (DEM) and land type, and used a mean decrease accuracy measure (MDAM) to determine the corresponding importance scores (IS). A multicollinearity test (including the variance inflation factor (VIF) and tolerance coefficient (TC)) was applied to assess relationships between the effective factors, and an existing map of dust provenance was randomly categorized into two groups consisting of training (70%) and validation (30%) data. The individual data mining models were validated using the area under the curve (AUC). Based on the TC and VIF results, no collinearity was detected among the 12 effective factors for dust emissions. The prediction accuracies of the eight data mining models and an EM assessed by the AUC were as follows: EM (with AUC=99.8%) > XGBoost>RBF > Cubist>RF > BART>SVM > BRT > RTA (with AUC=79.1%). Among all models, the EM was found to provide the highest accuracy for predicting storm dust provenance. Using the EM, areas classified as being low, moderate, high and very high susceptibility for storm dust provenance comprised 36, 13, 23 and 28% of the total mapped area, respectively. Based on MDAM results, the highest and lowest IS were obtained for the wind speed (IS=23) and geology (IS=6.5) factors, respectively. Overall, the modelling techniques used in this research are helpful for predicting storm dust provenance and thereby targeting mitigation. Therefore, we recommend applying data mining EM approaches to the spatial mapping of storm dust provenance worldwide

TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks

Glioma is one of the most common types of brain tumors; it arises in the glial cells in the human brain and in the spinal cord. In addition to having a high mortality rate, glioma treatment is also very expensive. Hence, automatic and accurate segmentation and measurement from the early stages are critical in order to prolong the survival rates of the patients and to reduce the costs of the treatment. In the present work, we propose a novel end-to-end cascaded network for semantic segmentation that utilizes the hierarchical structure of the tumor sub-regions with ResNet-like blocks and Squeeze-and-Excitation modules after each convolution and concatenation block. By utilizing cross-validation, an average ensemble technique, and a simple post-processing technique, we obtained dice scores of 88.06, 80.84, and 80.29, and Hausdorff Distances (95th percentile) of 6.10, 5.17, and 2.21 for the whole tumor, tumor core, and enhancing tumor, respectively, on the online test set.Comment: Accepted at MICCAI BrainLes 201

Data on experimental investigation of Methyl Ester Sulphonate and nanopolystyrene for rheology improvement and filtration loss control of water-based drilling fluid

Data presented in this article focused on the application of Methyl Ester Sulphonate (MES) surfactant and nanopolystyrene in water based drilling fluid. Data from rheology study using Bingham and Power law models showed that the synergy of MES and nanopolystyrene improved the formulated drilling fluid. Filtration study under LPLT and HPHT conditions showed that MES and nanopolystyrene drilling fluid reduced filtration loss by 50.7% at LPLT and 61.1% at HPHT conditions. These filtration data were validated by filter cake permeability and scanning electron microscope images

Entropic forces generated by grafted semiflexible polymers

The entropic force exerted by the Brownian fluctuations of a grafted semiflexible polymer upon a rigid smooth wall are calculated both analytically and by Monte Carlo simulations. Such forces are thought to play an important role for several cellular phenomena, in particular, the physics of actin-polymerization-driven cell motility and movement of bacteria like Listeria. In the stiff limit, where the persistence length of the polymer is larger than its contour length, we find that the entropic force shows scaling behavior. We identify the characteristic length scales and the explicit form of the scaling functions. In certain asymptotic regimes we give simple analytical expressions which describe the full results to a very high numerical accuracy. Depending on the constraints imposed on the transverse fluctuations of the filament there are characteristic differences in the functional form of the entropic forces; in a two-dimensional geometry the entropic force exhibits a marked peak.Comment: 21 pages, 18 figures, minor misprints correcte

Measurement of Returns-to-Scale using Interval Data Envelopment Analysis Models

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI linkThe economic concept of Returns-to-Scale (RTS) has been intensively studied in the context of Data Envelopment Analysis (DEA). The conventional DEA models that are used for RTS classification require well-defined and accurate data whereas in reality observations gathered from production systems may be characterized by intervals. For instance, the heat losses of the combined production of heat and power (CHP) systems may be within a certain range, hinging on a wide variety of factors such as external temperature and real-time energy demand. Enriching the current literature independently tackling the two problems; interval data and RTS estimation; we develop an overarching evaluation process for estimating RTS of Decision Making Units (DMUs) in Imprecise DEA (IDEA) where the input and output data lie within bounded intervals. In the presence of interval data, we introduce six types of RTS involving increasing, decreasing, constant, non-increasing, non-decreasing and variable RTS. The situation for non-increasing (non-decreasing) RTS is then divided into two partitions; constant or decreasing (constant or increasing) RTS using sensitivity analysis. Additionally, the situation for variable RTS is split into three partitions consisting of constant, decreasing and increasing RTS using sensitivity analysis. Besides, we present the stability region of an observation while preserving its current RTS classification using the optimal values of a set of proposed DEA-based models. The applicability and efficacy of the developed approach is finally studied through two numerical examples and a case study

Developable Rotationally Symmetric Kirigami‐Based Structures as Sensor Platforms

Developable surfaces based on closed‐shape, planar, rotationally symmetric kirigami (RSK) sheets approximate 3D, globally curved surfaces upon (reversible) out‐of‐plane deflection. The distribution of stress and strain across the structure is characterized experimentally and by finite‐element analysis as a function of the material and cut parameters, enabling the integration with strain gauges to produce a wearable, conformal patch that can capture complex, multiaxis motion. Using the patch, real‐time tracking of shoulder joint and muscle behavior is demonstrated. The facile fabrication and unique properties of the RSK structures potentially enable wearable, textile‐integrated joint monitoring for athletic training, wellness, rehabilitation, feedback control for augmented mobility, motion of soft and traditional robotics, and other applications.This work introduces a new paradigm for realizing 2D to curved, 3D, functional surface transformation using rotationally symmetric kirigami as a platform for deploying wearable sensors; here it is demonstrated for real‐time tracking of complex motion of joints within the body and circumventing longstanding tradeoffs in the design of materials, structures, and devices for conformable, wearable electronics.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/153082/1/admt201900563-sup-0001-SuppMat.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153082/2/admt201900563.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/153082/3/admt201900563_am.pd

Neuropathies and neurological dysfunction induced by coronaviruses

During the recent years, viral epidemic due to coronaviruses, such as SARS (Severe Acute Respiratory Syndrome), Middle East Respiratory Coronavirus Syndrome (MERS), and COVID-19 (coronavirus disese-19), has become a global problem. In addition to causing cardiovascular and respiratory lethal dysfunction, these viruses can cause neurodegeneration leading to neurological disorders. Review of the current scientific literature reveals the multiple neuropathies and neuronal dysfunction associated with these viruses. Here, we review the major findings of these studies and discuss the main neurological sequels and outcomes of coronavirus infections with SARS, MERS, and COVID-19. This article analyzes and discusses the main mechanisms of coronavirus-induced neurodegeneration according to the current experimental and clinical studies. Coronaviruses can damage the nerves directly through endovascular dysfunctions thereby affecting nerve structures and synaptic connections. Coronaviruses can also induce neural cell degeneration indirectly via mitochondrial dysfunction inducing oxidative stress, inflammation, and apoptosis. Thus, coronaviruses can cause neurological disorders by inducing neurovascular dysfunction affecting nerve structures and synaptic connections, and by inducing inflammation, oxidative stress, and apoptosis. While some of these mechanisms are similar to other RNA viruses, the neurotoxic mechanisms of COVID-19, MERS, and SARS-CoV viruses are unknown and need detailed clinical and experimental studies. © 2021, Journal of NeuroVirology, Inc