Renal Manifestations in Scleroderma: Evidence for Subclinical Renal Disease as a Marker of Vasculopathy

Scleroderma is a disease characterized by immune activation, vasculopathy, fibroblast stimulation, and connective tissue fibrosis. End-organ damage occurs due to progressive tissue fibrosis and vasculopathy. Markers of incipient vasculopathy have not been well studied in scleroderma. However, reduced renal functional reserve and proteinuria are common indicators of progressive vasculopathy in diabetic and hypertensive vasculopathy. Recent studies suggest a strong association between renal involvement and outcomes in scleroderma, with a threefold increased risk of mortality from pulmonary hypertension if renal insufficiency is present. We review the types of renal involvement seen in scleroderma and the data to support the use of renal parameters including proteinuria, glomerular filtration rate, and renal vascular dynamics measured with Doppler ultrasound to identify subclinical renal insufficiency. Further studies are warranted to investigate the use of renal parameters as prognostic indicators in scleroderma

Adaptive filtering of radar images for autofocus applications

Autofocus techniques are being designed at the Jet Propulsion Laboratory to automatically choose the filter parameters (i.e., the focus) for the digital synthetic aperture radar correlator; currently, processing relies upon interaction with a human operator who uses his subjective assessment of the quality of the processed SAR data. Algorithms were devised applying image cross-correlation to aid in the choice of filter parameters, but this method also has its drawbacks in that the cross-correlation result may not be readily interpretable. Enhanced performance of the cross-correlation techniques of JPL was hypothesized given that the images to be cross-correlated were first filtered to improve the signal-to-noise ratio for the pair of scenes. The results of experiments are described and images are shown

Insights from within activity based learning (ABL) classrooms in Tamil Nadu, India: Teachers perspectives and practices

Quality has been an Education for All (EFA) goal since the 2000 Dakar framework positioned it ‘at the heart of education’ as a fundamental determinant of student enrolment, retention and achievement. Over the years, classroom pedagogy has been consistently regarded as ‘the crucial variable for improving learning outcomes’ (e.g., Hattie, 2009) and is thus seen as critical to reforms aimed at improving educational quality (UNESCO, 2005 p.152). The quality of teacher–pupil classroom interaction remains of central importance, rather research evidence (e.g., Borich, 1996) suggests that it is the single most important factor accounting for wide variation in the learning attainments of students who have used the same curriculum materials and purportedly experienced similar teaching methods. Other more recent studies (e.g., Aslam and Kingdon, 2011) have also reported that teacher ‘process’ variables have a more significant impact on student achievement than standard background characteristics. In the current era of the ‘global learning crisis’ (UNESCO, 2014) many developing economies have embarked on major pedagogical reforms. In India, the notion of energising schools and transforming classrooms has received unprecedented attention in the last 15 years. A number of programmes have been introduced in various states to provide meaningful access (Jandhyala and Ramachandran, 2007). The Activity Based Learning (ABL) Programme is one such effort to change the nature of teaching and learning in mainstream classrooms. In a national context, where there are innumerable on-going efforts aimed at pedagogical reform, ABL is hailed as a success story in terms of replication of a small model to a grand scale. From modest beginnings in 2003 in 13 Chennai (the capital city of Tamil Nadu) schools, ABL was rolled out in a phased manner across the entire state of Tamil Nadu for all children in classes 1–4, in all government and aided schools. The last few years have witnessed its adaptation under various guises in several other Indian states, such as Ekalavya in Madhya Pradesh, Digantar in Rajasthan and Nali Kali in Karnataka. Efforts to promote it internationally in other parts of the developing world, such as Ghana, Bangladesh, Ethiopia and Mozambique (Fennell and Shanmugam, 2016)have also been made. Though as Nudzor et al., 2015 note it has been met with mixed success in the case of Ghana. Nonetheless, ABL is an interesting programme to examine given its rapid growth and international outreach.The project was funded by Department for International Development (DFID, India)

Light‐Activated Electron Transfer and Catalytic Mechanism of Carnitine Oxidation by Rieske‐Type Oxygenase from Human Microbiota

Oxidation of quaternary ammonium substrate, carnitine by non-heme iron containing Acinetobacter baumannii (Ab) oxygenase CntA/reductase CntB is implicated in the onset of human cardiovascular disease. Herein, we develop a blue-light (365 nm) activation of NADH coupled to electron paramagnetic resonance (EPR) measurements to study electron transfer from the excited state of NADH to the oxidized, Rieske-type, [2Fe-2S]2+ cluster in the AbCntA oxygenase domain with and without the substrate, carnitine. Further electron transfer from one-electron reduced, Rieske-type [2Fe-2S]1+ center in AbCntA-WT to the mono-nuclear, non-heme iron center through the bridging glutamate E205 and subsequent catalysis occurs only in the presence of carnitine. The electron transfer process in the AbCntA-E205A mutant is severely affected, which likely accounts for the significant loss of catalytic activity in the AbCntA-E205A mutant. The NADH photo-activation coupled with EPR is broadly applicable to trap reactive intermediates at low temperature and creates a new method to characterize elusive intermediates in multiple redox-centre containing proteins

Light‐Activated Electron Transfer and Catalytic Mechanism of Carnitine Oxidation by Rieske‐Type Oxygenase from Human Microbiota

Oxidation of quaternary ammonium substrate, carnitine by non-heme iron containing Acinetobacter baumannii (Ab) oxygenase CntA/reductase CntB is implicated in the onset of human cardiovascular disease. Herein, we develop a blue-light (365 nm) activation of NADH coupled to electron paramagnetic resonance (EPR) measurements to study electron transfer from the excited state of NADH to the oxidized, Rieske-type, [2Fe-2S]2+ cluster in the AbCntA oxygenase domain with and without the substrate, carnitine. Further electron transfer from one-electron reduced, Rieske-type [2Fe-2S]1+ center in AbCntA-WT to the mono-nuclear, non-heme iron center through the bridging glutamate E205 and subsequent catalysis occurs only in the presence of carnitine. The electron transfer process in the AbCntA-E205A mutant is severely affected, which likely accounts for the significant loss of catalytic activity in the AbCntA-E205A mutant. The NADH photo-activation coupled with EPR is broadly applicable to trap reactive intermediates at low temperature and creates a new method to characterize elusive intermediates in multiple redox-centre containing proteins

Automated Weed Detection Systems: A Review

A weed plant can be described as a plant that is unwanted at a specific location at a given time. Farmers have fought against the weed populations for as long as land has been used for food production. In conventional agriculture this weed control contributes a considerable amount to the overall cost of the produce. Automatic weed detection is one of the viable solutions for efficient reduction or exclusion of chemicals in crop production. Research studies have been focusing and combining modern approaches and proposed techniques which automatically analyze and evaluate segmented weed images. This study discusses and compares the weed control methods and gives special attention in describing the current research in automating the weed detection and control. Keywords: Detection, Weed, Agriculture 4.0, Computational vision, Robotic

Lower Extremity Ulcers in Systemic Sclerosis: Features and Response to Therapy

Nondigital lower extremity ulcers are a difficult to treat complication of scleroderma, and a significant cause of morbidity. The purpose of this study was to evaluate the prevalence of nondigital lower extremity ulcers in scleroderma and describe the associations with autoantibodies and genetic prothrombotic states. A cohort of 249 consecutive scleroderma patients seen in the Georgetown University Hosptial Division of Rheumatology was evaluated, 10 of whom had active ulcers, giving a prevalence of 4.0%. Patients with diffuse scleroderma had shorter disease duration at the time of ulcer development (mean 4.05 years ± 0.05) compared to those with limited disease (mean 22.83 years ± 5.612, P value .0078). Ulcers were bilateral in 70%. In the 10 patients with ulcers, antiphospholipid antibodies were positive in 50%, and genetic prothrombotic screen was positive in 70% which is higher than expected based on prevalence reports from the general scleroderma population. Of patients with biopsy specimens available (n = 5), fibrin occlusive vasculopathy was seen in 100%, and all of these patients had either positive antiphospholipid antibody screen, or positive genetic prothrombotic profile. We recommend screening scleroderma patients with lower extremity ulcers for the presence of anti-phospholipid antibodies and genetic prothrombotic states

A Internet of Things Improvng Deep Neural Network Based Particle Swarm Optimization Computation Prediction Approach for Healthcare System

Internet of Things (IoT) systems tend to generate with energy and good data to process and responding. In internet of things devices, the most important challenge when sending data to the cloud the level of energy consumption. This paper introduces an energy-efficient abstraction method data collection in medical with IoT-based for the exchange. Initially, the data required for IoT devices is collected from the person. First, Adaptive Optimized Sensor-Lamella Zive Welch (AOSLZW) is a pressure sensing prior to the data transmission technique used in the process. A cloud server is used data reducing  the amount of data sent from IoT devices to the AOSLZW strategy. Finally, a deep neural network (DNN) based on Particle Swarm Optimization (PSO) known as DNN-PSO algorithm is used for data sensed result model make decisions based as a predictive to make it. The results are studied under distinct scenarios of the presented of the performance for AOSLZW-DNN-PSO method, for that simation are studied under different sections. This current pattern of simalation results indicates that the AOSLZW-DNN-PSO method is effective under several aspects