Structure of the deactive state of mammalian respiratory complex I

Complex I (NADH:ubiquinone oxidoreductase) is central to energy metabolism in mammalian mitochondria. It couples NADH oxidation by ubiquinone to proton transport across the energy-conserving inner membrane, catalyzing respiration and driving ATP synthesis. In the absence of substrates, ‘active’ complex I gradually enters a pronounced resting or ‘deactive’ state. The active-deactive transition occurs during ischemia and is crucial for controlling how respiration recovers upon reperfusion. Here, we set a highly-active preparation of Bos taurus complex I into the biochemically-defined deactive state, and used single-particle electron cryomicroscopy to determine its structure to 4.1 Å resolution. We show that the deactive state arises when critical structural elements that form the ubiquinone-binding site become disordered, and we propose reactivation is induced when substrate binding to the NADH-reduced enzyme templates their reordering. Our structure both rationalizes biochemical data on the deactive state, and offers new insights into its physiological and cellular roles.Data were recorded at the UK National Electron Bio-Imaging Centre (eBIC) at Diamond (proposal EM13581, funded by the Wellcome Trust, MRC and BBSRC) with help from Dan Clare and Alistair Siebert. This work was supported by The Medical Research Council, grant numbers U105663141 (to J.H.) and U105184322 (K.R.V. in R. Henderson's group)

Effect of air flow on tubular solar still efficiency

BACKGROUND: An experimental work was reported to estimate the increase in distillate yield for a compound parabolic concentrator-concentric tubular solar still (CPC-CTSS). The CPC dramatically increases the heating of the saline water. A novel idea was proposed to study the characteristic features of CPC for desalination to produce a large quantity of distillate yield. A rectangular basin of dimension 2 m × 0.025 m × 0.02 m was fabricated of copper and was placed at the focus of the CPC. This basin is covered by two cylindrical glass tubes of length 2 m with two different diameters of 0.02 m and 0.03 m. The experimental study was operated with two modes: without and with air flow between inner and outer tubes. The rate of air flow was fixed throughout the experiment at 4.5 m/s. On the basis of performance results, the water collection rate was 1445 ml/day without air flow and 2020 ml/day with air flow and the efficiencies were 16.2% and 18.9%, respectively. FINDINGS: The experimental study was operated with two modes: without and with air flow between inner and outer tubes. The rate of air flow was fixed throughout the experiment at 4.5 m/s. CONCLUSIONS: On the basis of performance results, the water collection rate was 1445 ml/day without air flow and 2020 ml/day with air flow and the efficiencies were 16.2% and 18.9%, respectively

Neoplasia in oil sardine from Palk Bay

During a routine fishery survey programme at Irumeni fishing village, Palk Bay on 03rd December 2016, a single specimen of oil sardine Sardinella longiceps with neoplasia, measuring 163 mm in total length (TL) and weighing 119 gram was collected from the gillnet landings. The specimen was a female with empty stomach

Antimagicness of Tensor product for some wheel related graphs with star

A graph $G$ with $p$ vertices and $q$ edges has an antimagic labelling if there is a bijection from the graph's edge set to the label set $\left\{1,2, \cdots, q \right\}$ such that $p$ vertices must have distinct vertex sums, where the vertex sums are determined by adding up all the edge labels incident to each vertex $v$ in $V(G)$. Hartsfield and Ringel \cite{Ringel1} in the book "Pearls in Graph Theory" conjectured that every connected graph is antimagic, with the exception of $P_2$. In this study, we identified a class of connected graphs that lend credence to the conjecture. In this article, we proved that the tensor product of a wheel and a star, a helm and a star, and a flower and a star is antimagic.Comment: 28 pages, 3 figure

EFFECTS OF THREATS ON ETHICAL BEHAVIOR OF PROFESSIONAL VALUERS IN SRI LANKA

Ethical behavior means compliance with the ethical standards. Though there are fundamental standards, unethical behavior may emerge due to many obstructions (threats) arise in property valuation profession. To date, there is no evidence of adequate research on this issue. This paper focus on analyzing the factors affecting on the ethical behavior of valuers over mortgagevaluation in Sri Lanka. Based on the quantitative approach the data was collected from 100 professionals. In particular, following factors such as self-interest threat, self-review threat, client conflict threat, advocacy threat, familiarity threat, and intimidation threat were analyzed. Results revealed that, all other tested factors except intimidation threat, influenced the ethicalbehavior of valuers in mortgage valuation. It is recommended to organize awareness programmes on ethical behavior frequently

Accumulation efficiency of sunflower for lead and cadmium along with sustainable crop productivity under soil stress

By nature coastal saline soils having several constraints in crop production in addition to that of heavy metals contamination deteriorate the soil productivity. To restore these contaminated soils, various remediation techniques in practices must be revamped. The present study was conducted to enhance the accumulation of heavy metals lead and cadmium in sunflower and improve the crop productivity using organic and inorganic soil amendments along with NPK fertilizers in completely randomized design. Soil samples were admitted to estimating soil physico chemical properties and DTPA extractable lead (Pb) and cadmium (Cd) and plant samples analyzed for DTPA extractable Pb and Cd concentrations under ICP-OES. The physico-chemical properties and DTPA extractable Pb and Cd concentrations were significantly influenced by amendments. Sunflower exhibited significant differences concerning accumulation of Pb and Cd against amendments tested along with higher biomass production. Higher shoot and root concentration of Pb(0.72,0.81 and 0.94,0.97 mg kg-1) and Cd (1.78, 2.32 and 0.35,0.32 mg kg-1)were recorded in the treatment RDF + EDTA, which was followed by RDF + Potassium humate and RDF + Zeolite application at 45 DAS and at harvest. Remediation efficiency of sunflower increased by application of RDF + EDTA through enhanced solubility of Pb and Cd in soil and thus increased Pb and Cd accumulation in root and shoot of sunflower. Whereas, the application of RDF+ FYM or press mud reduced the bioavailability of Pb and Cd in soil and thus restricted the accumulation of Pb and Cd by sunflower. Further, application of NPK fertilizers maintained the availability of nutrients and enhanced the yield of sunflower. The application of EDTA along with NPK fertilizer enhanced the bioaccumulation of lead and cadmium by sunflower without yield loss. Since, there is a possibility to cause leaching of HMs to ground water by EDTA. Hence, RDF plus Potassium humate or Zeolite can be recommended for lead and cadmium removal by sunflower in coastal saline soils with no loss in crop productivity

A COMPREHENSIVE STUDY OF CRYPTOGRAPHY AND KEY MANAGEMENT BASED SECURITY IN CLOUD COMPUTING

Cloud computing is a cost effective flexible and proven delivery platform for providing consumer IT services or business services over internet. It has an ability to provide many services over internet. It not only provides computing services but additional computing resources. To interact with various services in the cloud and to store retrieve data from cloud several security mechanism is required. Cryptography and key management mechanism are one of the import services in the cloud to secure data. In this context, this paper investigates the basic problem of cloud computing with cryptography and key management system for enabling support of interoperability between cloud cryptography client and key management services

Multimodal recognition with deep learning: audio, image, and text

Emotion detection is essential in many domains including affective computing, psychological assessment, and human computer interaction (HCI). It contrasts the study of emotion detection across text, image, and speech modalities to evaluate state-of-the-art approaches in each area and identify their benefits and shortcomings. We looked at present methods, datasets, and evaluation criteria by conducting a comprehensive literature review. In order to conduct our study, we collect data, clean it up, identify its characteristics and then use deep learning (DL) models. In our experiments we performed text-based emotion identification using long short-term memory (LSTM), term frequency-inverse document frequency (TF-IDF) vectorizer, and image-based emotion recognition using a convolutional neural network (CNN) algorithm. Contributing to the body of knowledge in emotion recognition, our study's results provide light on the inner workings of different modalities. Experimental findings validate the efficacy of the proposed method while also highlighting areas for improvement

Deep-SFER: deep convolutional neural network and MFCC an effective speech and face emotion recognition

There has been a lot of progress in recent years in the fields of expert systems, artificial intelligence (AI) and human machine interface (HMI). The use of voice commands to engage with machinery or instruct it to do a certain task is becoming more common. Numerous consumer electronics have SIRI, Alexa, Cortana, and Google Assistant built in. In the field of human-device interaction, emotion recognition from speech is a complex research subject. We can't imagine modern life without machines, so naturally there's a need to create a more robust framework for human-machine communication. A number of academics are now working on speech emotion recognition (SER) in an effort to improve the interaction between humans and machines. We aimed to identify four fundamental emotions: angry, unhappy, neutral and joyful from speech in our experiment. As you can hear below, we trained and tested our model using audio data of brief Manipuri speeches taken from films. This task makes use of convolutional neural networks (CNNs) to extract functions from speech in order to recognize different moods using the Mel-frequency cepstral coefficient (MFCC)