Multivariate classification of cotton cultivars tolerant to salt stress

ABSTRACT Two multivariate methods were adopted to classify salt-tolerant cotton genotypes based on their growth and physiological traits. The genotypes were cultivated in a greenhouse and subjected to 45 days of irrigation with saline water from the V4 phase onwards. Irrigation was performed with saline water with electrical conductivity (ECw) of 6.0 dS m-1. A factorial-randomized block design was adopted with nine cultivars, two treatments of ECw (0.6 as the control, and 6.0 dS m-1), and four replicates. Plants were evaluated for growth, gas exchange, and photosynthesis. The data were statistically analyzed using univariate and multivariate methods. For the latter, non-hierarchical (principal component, PC) and hierarchical (UPGMA) models were used for the classification of cultivars. Significant differences were found between cultivars based on univariate analyses, and the traits that differed statistically were used for multivariate analyses. Four groups were identified with the same composition in both the PC and UPGMA methods. Among them, one contained the cultivars BRS Seridó, BRS 286, FMT 705, and BRS Rubi, which were tolerant to salt stress imposed on the plants. Photosynthesis, transpiration, and stomatal conductance data were the main contributors to the classification of cultivars using the principal component method.

Impedance Characterization of AlGaN/GaN/Si High Electron Mobility Transistors

AlGaN/GaN HEMTs grown on high resistive silicon (111) substrate grown by molecular beam epitaxy have been investigated using impedance measurements. Passivation of the HEMT devices is made in order to improve the electron transport. As has been found from conductance data, the electron traps are eliminated after passivation. The impedance spectroscopy has been, on the other hand, studied from the electrical transport. As a result, a complex impedance plot was revealed an equivalent circuit models indicating single semicircles and the solid interface.

A single-molecule electrical approach for amino acid detection and chirality recognition

One of the ultimate goals of analytic chemistry is to efficiently discriminate between amino acids. Here we demonstrate this ability using a single-molecule electrical methodology based on molecular nanocircuits formed from stable graphene-molecule-graphene single-molecule junctions. These molecular junctions are fabricated by covalently bonding a molecular machine featuring a permethylated-β-cyclodextrin between a pair of graphene point contacts. Using pH to vary the type and charge of the amino acids, we find distinct multimodal current fluctuations originating from the different host-guest interactions, consistent with theoretical calculations. These conductance data produce characteristic dwell times and shuttling rates for each amino acid, and allow accurate, statistical real-time, in situ measurements. Testing four amino acids and their enantiomers shows the ability to distinguish between them within a few microseconds, thus paving a facile and precise way to amino acid identification and even single-molecule protein sequencing.

Axial Addition in Diastereoisomeric [Ni(Me8[14]ane)](ClO4)2 Complexes and Their Antifungal Activities

A series of diastereoisomeric square planar nickel(II) diperchlorate complexes, [Ni(L)](ClO4)2{L= isomeric Me8[14]ane (LAα, LBα or LCα)}undergo axial ligand addition reactions with NO3-, Cl-, Br-, SCN- or ClO4- to yield six coordinate octahedral derivatives [NiL(X)x(Y)y].z(H2O)(X=NO3,Cl, Br or SCN; Y= ClO4; x = 1 or 2; y = 1 or 0 and z = 0, 1 or 2). The products have been characterized on the basis of analytical, spectroscopic, magnetic and conductance data. All the derivatives are unstable in open air except one derivative and revert back to original square planar complexes. Antifungal activities of the ligands and their nickel(II) complexes have been investigated against some phytopathogenic fungi. The Chittagong Univ. J. Sci. 40(1) : 122-147, 2019

Interplay of filling fraction and coherence in symmetry broken graphene p-n junction

Graphene p–n junction (PNJ) with co-propagating spin-valley polarized quantum Hall (QH) edges is a promising platform for studying electron interferometry. Though several conductance measurements have been attempted for such PNJs, the edge dynamics of the spin-valley symmetry broken edge states remain unexplored. In this work, we present the measurements of conductance together with shot noise, an ideal tool to unravel the dynamics, at low temperature, in a dual graphite gated hexagonal boron nitride encapsulated high mobility graphene device. The conductance data show that the symmetry broken QH edges at the PNJ follow spin selective equilibration. The shot noise results as a function of both p and n side filling factors reveal the unique dependence of the scattering mechanism. Remarkably, the scattering is found to be fully tunable from incoherent to coherent regime with the increasing number of QH edges at the PNJ, shedding crucial insights of edge dynamics.

Computational Modeling of Threat Learning Reveals Links with Anxiety and Neuroanatomy

Influential theories implicate variations in the mechanisms supporting threat learning in variations in the severity of anxiety symptoms. We use computational models of associative learning in conjunction with structural imaging to explicate links among the computational mechanisms underlying threat learning, their neuroanatomical substrates, and anxiety severity. We recorded skin-conductance data during a threat-learning task from individuals with and without anxiety disorders (N=251; 8-50 years; 116 females). Reinforcement-learning model variants quantified processes hypothesized to relate to anxiety: threat conditioning, threat generalization, safety learning, and threat extinction. We then tested associations among latent learning parameters, whole-brain morphometry, and anxiety severity, as well as age moderation. Results indicate that greater anxiety severity related specifically to slower safety learning and threat extinction. Gray-matter volume in several subcortical structures moderated extinction-anxiety associations. Using a modeling approach, we identify computational mechanisms linking threat learning and anxiety severity and their neuroanatomical substrates.

CONDUCTOMETRIC STUDY OF LIGAND STRUCTURE INFLUENCE ON THE Pb(II) COMPLEXATION WITH CROWN ETHERS

The conductometric study of ligand structure influence on the Pb(II) complexation with crown ethers in different solvents has been investigated. In this paper, the complexation reaction of macrocyclic ligand, 18-crown-6 (18C6), dibenzo-18-crown-6 (DB18C6), and Pb(II) cation was studied in different solvents: dichloromethane (DCM) and 1,2- dichloroethane (1,2-DCE). The effects of surfactant structure (Triton X-100 and Triton X-45) on the conductivity of the Pb(II) complex with 18-crown-6 and dibenzo-18-crown-6 ether have been investigated. The conductance data showed that the stoichiometry of the complexes in most cases is 1:1(ML). It is also demonstrated that the influence of crown ethers is deeply affected by the organic solvent used. In the solvents studied, the stability of the resulting complexes showed higher stability in dichloromethane comparing with 1,2- dichloroethane. Macrocyclic ligand 18-crown-6 showed more suitable for complexation of Pb(II) ions compared to dibenzo-18-crown-6. Adding a surfactant affected the higher absolute values of the conductivity of systems, but not the change in the stoichiometric ratio between a metal ion and macrocyclic ligand.

Non Invasive Skin Hydration Level Detection Using Machine Learning

Dehydration and overhydration can help to improve medical implications on health. Therefore, it is vital to track the hydration level (HL) specifically in children, the elderly and patients with underlying medical conditions such as diabetes. Most of the current approaches to estimate the hydration level are not sufficient and require more in-depth research. Therefore, in this paper, we used the non-invasive wearable sensor for collecting the skin conductance data and employed different machine learning algorithms based on feature engineering to predict the hydration level of the human body in different body postures. The comparative experimental results demonstrated that the random forest with an accuracy of 91.3% achieved better performance as compared to other machine learning algorithms to predict the hydration state of human body. This study paves a way for further investigation in non-invasive proactive skin hydration detection which can help in the diagnosis of serious health conditions.

A Chemically Soldered Polyoxometalate Single-Molecule Transistor

Polyoxometalates have been proposed in the literature as promising components for nanoelectronic applications, where they could offer key advantages with their structural versatility and rich electrochemistry. Apart from a few studies on their ensemble behaviour (for instance, as monolayers or thin films) this potential remains largely unexplored. We synthesised a pyridyl-capped Anderson-Evans polyoxometalate and used it to fabricate single-molecule junctions, by using the organic termini to chemically “solder” a single metal oxide cluster to two nanoelectrodes through coordination bonds. Operating the device in an electrochemical environment allowed us to probe charge transport through different oxidation states of the polyoxometalate, and we report here an efficient three-state transistor behaviour. Conductance data fits a quantum tunnelling transport mechanism, with charge having different tunnelling probabilities through different oxidation states of the polyoxometalate. Our results show the promise of such compounds in nanoelectronics, and are, to our best knowledge, the first report on the single-entity electrochemical behaviour of polyoxometalates.<p></p>