31,182 research outputs found
A computational study on the hydration-shell properties of antifreeze and non-antifreeze proteins
No full textHere we present a computational approach based on molecular dynamics (MD)
simulation to study the hydration-shell density of several proteins which include a
special group of proteins, namely antifreeze proteins, AFPs. AFPs have the ability
to inhibit ice growth by binding to ice nuclei. Their ice-binding mechanism is still
unclear, yet the hydration layer is thought to play a fundamental role. In particular,
the hydration-shell density of eighteen dierent proteins comprising eight AFPs is
calculated. The results obtained show that an increase in the hydration-shell density,
relative to that of the bulk, is observed (in the range of 4{14%) for all studied proteins
and that this increment strongly correlates with the protein size, while it does not
depend on whether the protein is an AFP or not. In particular, a decrease in the
density increment is observed for decreasing protein size. A simple model is proposed
according to which almost all of the hydration-density increase is located in pockets
within, or at the surface of, the protein molecule. We then further investigated the
local properties of the hydration shell around the ice-binding surface (IBS) of the
AFPs. We found that the hydration shell density of the ice-binding surfaces is always
higher than the bulk density and, thus, no ice-like (i.e. with a density lower than the
bulk) layer is detected at the IBS. However, the local water-density around the IBS
is found to be lower than that around the non-ice-binding surfaces and this dierence
correlates to the higher hydrophobic character of the IBS with respect to the non-IBS.
We hypothesize that the lower solvent density at the ice-binding site can pave the way
to the protein binding to ice nuclei, while the higher solvent density at the non-icebinding
surfaces might provide protection against ice growth. Finally, we tested our
hypothesis by studying the dependence of the antifreeze activity of seven AFPs on
various structural and chemical properties of the IBS and non-IBS and found that the
activity strongly correlates with the dierence in the local hydration-shell properties
of the non-ice-binding surfaces, rather than of the IBSs
Surface assembly of nano-metalorganic framework on amine functionalized indium tin oxide substrate for impedimetric sensing of parathion
No full textThe present paper reports the assembly and pesticide sensing application of a nanometal organic framework [Cd(atc)(H2O)2]n (‘atc’=2-aminoterephthalic acid). The assembly of the NMOF film has been achieved by sequential dipping of a 2-aminobenzylamine (2-ABA) modified indium tin oxide (ITO) slide in organic linker ‘atc’ and metal ion ‘Cd2+’ solutions. The different structural and morphological characteristics of the NMOF thin film have been characterized. The availability of pendent –COOH functional groups on the assembled NMOF film is exploited to synthesize a pesticide immunosensor by conjugating the NMOF film with anti-parathion antibody. This immunosensor has been explored for the electrochemical impedance spectroscopy (EIS) based analysis of parathion in the concentration range of 0.1–20 ng/mL. The proposed detection is specific with respect to other organophosphate compounds, e.g. malathion, paraoxon, fenitrothion, monochrotophos and dichlorovos. The proposed sensor shows the detection limit of 0.1 ng/mL and it is applicable for analysis of parathion in a rice sample. The sensor's performance is validated by comparting the obtained results with gas chromatographic data.Authors gratefully acknowledge the financial grant from CSIR India through project OMEGA/PSC0202/2.2.5. We are thankful to the Director, CSIR-CSIO, Chandigarh, India. The fourth author acknowledges partial financial support from the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST) (No. 2009-0093848)
Polycyclic aromatic hydrocarbons and volatile organic compounds in biochar and biochar-amended soil:a review
No full textResidual pollutants including polycyclic aromatic hydrocarbons (PAHs), volatile organic compounds (VOCs), and carbon (aceous) nanoparticles are inevitably generated during the pyrolysis of waste biomass and remain on the solid coproduct called biochar. Such pollutants could have adverse effects on the plant growth as well as microbial community in soil. Although biochar has been proposed as a ‘carbon negative strategy’ to mitigate the greenhouse gas emissions, the impacts of its application with respect to long-term persistence and bioavailability of hazardous components are not clear. Moreover, the co-occurrence of low molecular weight VOCs with PAHs in biochar may exert further phytotoxic effects. This review describes the basic need to unravel key mechanisms driving the storage vs. emission of these organics and the dynamics between the sorbent (biochar) and soil microbes. Moreover, there is an urgent need for standardized methods for quantitative analysis of PAHs and VOCs in biochar under environmentally relevant conditions. This review is also extended to cover current research gaps including the influence of biochar application on the short- and long-term fate of PAHs and VOCs and the proper control tactics for biochar quality and associated risk.This study was supported by a grant from the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST) (No. 2009-0093848). This work was also carried out with the support of the 'Cooperative Research Program for Agriculture Science & Technology Development' (Project title: Study on model development to control odor from hogpens, Project No. PJ01052101) Rural Development Administration, Republic of Korea. The second author also acknowledges the support made by a National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIP) (No. 2014RA1A004893)
One step in-situ synthesis of amine functionalized graphene for immunosensing of cardiac marker cTnI
No full text2-Aminobenzyl amine (2-ABA) functionalized graphene is proposed for the ultrasensitive immunosensing of Cardiac Troponin I (cTnI). 2-ABA was electrochemically polymerized on the graphene decorated interdigitated electrode to obtain the amine functionalized graphene (f-GN). The f-GN electrode was then modified with monoclonal anti-cTnI antibodies via Schiff reaction based chemistry. Detailed characteristics of the processes involved and the finally developed antibody conjugated f-GN interdigitated electrode have been studied. The above micro-device was used in a drain source configuration for the sensing of cTnI. A wide dynamic linear range of antigen detection (0.01–1 ng/mL) is achieved with the limit of detection of 0.01 ng/mL. The utility of the proposed sensing technique is demonstrated by successfully testing the antigen concentration in spiked serum samples.The authors are thankful to the Director, CSIR-CSIO, Chandigarh. The financial assistance provided by CSIR Grant no. OMEGA/PSC0202/2.2.5 is gratefully acknowledged. Mr. Satish Kumar also thanks CSIR for SRF fellowship
Co-channel interference between WiFi and through-wall micro-Doppler radar
No full textNarrowband through-wall radars have been researched for detecting and classifying indoor movers on the basis of their micro-Doppler signatures. These radars usually operate in the unlicensed 2.4GHz ISM band and are therefore susceptible to interference from WiFi networks operating with the IEEE 802.11g protocol. In this work, we show, through experiments, how the radar degrades the WiFi throughput by lowering the signal to noise and interference ratio at the WiFi receiver. Similarly, WiFi interference causes deterioration in the radar performance by increasing the probability of false alarms.</p
Application and Use of Multivariate Control Charts In a BTA Deep Hole Drilling Process
No full textDeep hole drilling methods are used for producing holes with a high length-to-diameter ratio, good surface finish and straightness. The process is subject to dynamic disturbances usually classified as either chatter vibration or spiralling. In this paper, we will focus on the application and use of multivariate control charts to monitor the process in order to detect chatter vibrations. The results showed that chatter is detected and some alarm signals occurs at time points which can be connected to physical changes of the process. --
Deep Scene Understanding using RF and its Fusion with other Modalities
No full textRich scene understanding is a critical first step in creating autonomous systems with situational awareness -- i.e. systems that can not only perceive and comprehend their environments but also project what the future states are going to be. Current vision-based methods of tackling this problem are inadequate as cameras are restricted to the visible spectrum. While they can detect objects, track movements, and make inferences about human expressions, they suffer from several challenges such as lack of depth information and weakness to bad weather conditions. Moreover, there are many other modalities in which information is present around us, and relying solely on one makes it susceptible to a higher chance of failure.Through my thesis, I aim to include RF (radio-frequency) modality in scene understanding since RF has both complementary and supplementary properties to vision. My hypothesis is that by fusing RF with vision, one can create a richer understanding of their scene which I call ‘deep scene understanding’. There are four key enablers to deep scene understanding -- (1) Detection of objects’ states and activities, (2) Localization of objects in a scene and tracking them, (3) Developing methods to train machine learning models over RF data, and (4) Understanding privacy and societal impacts of instrumenting spaces with sensors.RF comes with its own set of challenges that make this sort of integration hard. Additionally, instrumenting spaces with sensors such as RF sensors itself can lead to privacy concerns. In solving these challenges, we present -- (1) a framework to detect human activities using a mmWave radar that can ingest sparse and noisy radar point clouds and output what activity is being performed in the scene. (2) a framework to detect, identify and localize hidden objects such as cameras in a scene that may be monitoring a user but are not visible to the naked eye. (3) a radar-camera fusion framework that can estimate dense depth in a scene from a sparse radar point cloud and an image. (4) A self-supervised learning approach that can leverage mutual information between a camera and a radar to train the radar. (5) A user study to understand the privacy perceptions of users when spaces are equipped with sensors
DeepFGSS: Anomalous Pattern Detection using Deep Learning
No full textUniversity of Minnesota M.S. thesis. May 2019. Major: Computer Science. Advisor: Edward McFowland III. 1 computer file (PDF); vii, 67 pages.Anomaly detection refers to finding observations which do not conform to expected behavior. It is widely applied in many domains such as image processing, fraud detection, intrusion detection, medical health, etc. However, most of the anomaly detection techniques focus on detecting a single anomalous instance. Such techniques fail when there is only a slight difference between the anomalous instance and a non-anomalous instance. Various collective anomaly detection techniques (based on clustering, deep learning, etc) have been developed that determine whether a group of records form an anomaly even though they are only slightly anomalous instances. However, they do not provide any information about the attributes that make the group anomalous. In other words, they are focussed only on detecting records that are collectively anomalous and are not able to detect anomalous patterns in general. FGSS is a scalable anomalous pattern detection technique that searches over both records and attributes. However, FGSS has several limitations preventing it from functioning on continuous, unstructured and high dimensional data such as images, etc. We propose a general framework called DeepFGSS, which uses Autoencoder, enabling it to operate on any kind of data. We evaluate its performance using four experiments on both structured and unstructured data to determine its accuracy of detecting anomalies and efficiency of distinguishing between datasets containing anomalies and ones that do not.Kulkarni, Akash. (2019). DeepFGSS: Anomalous Pattern Detection using Deep Learning. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/206159
Numerical analysis of film boiling around horizontal cylindrical surfaces
No full textPapers presented to the 11th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, South Africa, 20-23 July 2015.Stability of vapor film over a horizontal heater drags attention
due to its wide spread applications in nuclear reactors, metal
processing, manufacturing and chemical refineries. Existence
of vapor film around the heater can cause advantages as well as
disadvantages for various applications in industry and daily life.
Hence understanding the film formation and its subsequent
release in the form of bubble are dealt carefully by researchers
in heat transfer community. Critical vapor thickness and
average heat flux are the essential parameters which govern the
release of vapor mass in the form of isolated bubbles from the
surface. Experimental evidences have been reported in order to
find out film dynamics for some specific fluids. However
understanding it from the fundamental physics is still due and
becomes a major challenge for heat transfer community. In this
paper, numerical analysis of the film boiling heat transfer on a
horizontal cylinder is presented to determine the effect of
superheating on the heat transfer coefficient and film thickness
in a pool. Findings from the present study will develop
knowhow about the film formation and its role in enhancing the
boiling heat transfer coefficient.am201
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