DDT and metabolites residues in the southern bent-wing bat (Miniopterus schreibersii bassanii) of south-eastern Australia

The southern bent-wing bat (Miniopterus schreibersii bassanii) is an insectivorous, obligate cave dwelling species found in south-eastern South Australia and western Victoria, Australia. In recent times, the finger of blame for an apparent population decline at Bat Cave, Naracoorte (one of only two known maternity roosts for this species, the other being Starlight Cave, Warrnambool) has been pointed at pesticide use in the region, following the finding of organochlorine and organophosphate insecticide residues in bat guano. This study sampled juvenile southern bent-wing bats from Bat Cave and Starlight Cave, and determined DDT, DDD and DDE concentrations in liver, pectoral muscle, brain and back-depot fat tissues.

Organochlorine and trace metal residues in adult southern bent-wing bat (Miniopterus schreibersii bassanii) in Southeastern Australia

In recent times, the apparent population decline of the southern bent-wing bat (Miniopterus schreibersii bassanii) at Bat Cave, Naracoorte has been ascribed to pesticide use in the region, following the finding of organochlorine and orgaonophosphate insecticide residues in bat guano. Adult southern bent-wing bats were collected from Bat Cave and Starlight Cave in 2003. Organochlorine contaminants were detected in all carcass samples: p,p′-DDE was by far the most dominant contaminant with concentrations ranging from 11 000 to 59 000 ng g⁻¹, followed by p,p′-DDT (110–1600 ng g⁻¹), p,p′-DDD (35–620 ng g⁻¹), ∑PCBs (33–490 ng g⁻¹), ∑chlordane and related compounds (7.9–270 ng g⁻¹), HCB (1.6–120 ng g⁻¹), HP epox. (3.1–230 ng g⁻¹), TCPMOH (3.8–38 ng g⁻¹), ∑HCHs (1.4–9.6 ng g⁻¹), and TCPMe (0.1–4.2 ng g⁻¹) (all values on lipid-weight basis). No significant difference in DDE, DDD, DDT, ∑DDT, ∑PCB, trans-chlordane, heptachlor epoxide, trans-nonachlor, α-HCH, β-HCH, γ-HCH, TCPMOH or TCPMe concentrations were observed either between sexes within sites, or between sites (p > 0.05). However, there were significant differences in HCB and oxychlordane concentrations between sexes and between sites (p < 0.05), between site differences in cis-nonachlor concentrations in male bats (p < 0.05), and cis-chlordane concentrations between sexes at Starlight Cave, and between males of each site (p < 0.05). There were also significant differences in the liver concentrations of some metals between sexes within sites (Ag, Cd, Co, Cu, Pb, Se, Zn), and between sites (Ag, Cd, Co, Cu, Hg, Pb, Se, V, Zn). Clustering or grouping of sites was observed when the OC data was expressed on a lipid-weight basis. These inter-site differences in OC concentrations reflect local exposure over a period of time, and do not unambiguously support any suggestion that we are witnessing incipient speciation. However, for conservation purposes, it may be prudent to assume that there are two sub-populations of M. s. bassani feeding in different locations in this region of southern Australia, rather than the single homogeneous population suggested by genetic studies.

A neural networks learning algorithm for minor component analysis and its convergence analysis

The eigenvector associated with the smallest eigenvalue of the autocorrelation matrix of input signals is called minor component. Minor component analysis (MCA) is a statistical approach for extracting minor component from input signals and has been applied in many fields of signal processing and data analysis. In this letter, we propose a neural networks learning algorithm for estimating adaptively minor component from input signals. Dynamics of the proposed algorithm are analyzed via a deterministic discrete time (DDT) method. Some sufficient conditions are obtained to guarantee convergence of the proposed algorithm.

On the discrete time dynamics of a self-stabilizing MCA learning algorithm

The stability of minor component analysis (MCA) learning algorithms is an important problem in many signal processing applications. In this paper, we propose an effective MCA learning algorithm that can offer better stability. The dynamics of the proposed algorithm are analyzed via a corresponding deterministic discrete time (DDT) system. It is proven that if the learning rate satisfies some mild conditions, almost all trajectories of the DDT system starting from points in an invariant set are bounded, and will converge to the minor component of the autocorrelation matrix of the input data. Simulation results will be furnished to illustrate the theoretical results achieved.

A stable MCA learning algorithm

Minor component analysis (MCA) is an important statistical tool for signal processing and data analysis. Neural networks can be used to extract online minor component from input data. Compared with traditional algebraic  approaches, a neural network method has a lower computational complexity. Stability of neural networks learning algorithms is crucial to practical applications. In this paper, we propose a stable MCA neural networks learning algorithm, which has a more satisfactory numerical stability than some existing MCA algorithms. Dynamical behaviors of the proposed algorithm are analyzed via deterministic discrete time (DDT) method and the conditions are obtained to guarantee convergence. Simulations are carried out to illustrate the theoretical results achieved.

Potential antiarrhythmic and cardioprotective agents based on adenosine

N-Ethylcarboxamidoadenosine (12) was synthesised from adenosine (1) and the 6-chloro-2’,3’-O-isopropylidene-AT-ethylcarboxamidoadenosine (25) was synthesised from inosine (19). Employing molecular modelling techniques and the results from previous structure activity relationships it was possible to design and synthesise a N6-substituted N-ethylcarboxamidoadenosines which possessed an oxygen in the N6-substituent either in the form of an epoxide (which was obtained by cpoxidising an alkene with m-CPBA or dimethyldioxirane) or in the form of a cyclic ether as was the case for N6-((tetrahydro-2H--pyran--2-yl)methyl-N-ethylcarboxamidoadenosine (78). These compounds were tested for their biological activity at the A1 adenosine receptor by their ability to inhibit cAMP accumulation in DDT, MF2 cells. The EC50 values obtained indicated that the N6-(norborn-5-en-2-yl)-N-ethylcarboxamidoadenosines were the most potent. Of theseN6-(S-endo-norbrn-5-en-2-yI)-N-ethylcarboxaniidoadenosine (56) was the most potent (0.2 nM). N6-(exo-norborn-5-en-2-yl)-2-iodo-N-ethylcarboxamidoadenosine (79) was synthesised from guanosine (22) and was also evaluated for its potency at the A, receptor (24.8 ± 1.5 nM). At present 79 is being evaluated for its selectivity for the A1 receptor compared to the other three receptor subtypes (A2a, A2b, A3). A series of N6-(benzyl)-N-ethylcarboxamidoadenosines were synthesised with substitutions at the 4-position of the phenyl ring. Another series of compounds were synthesised which replaced the methylene spacer between the N6H and the N6-aromatic or lipophilic substituent The replacement groups -were carbonyl and trans-2- cyclopropyl moieties. The N6-acyl compounds were obtained by reacting 2’,3’-O- di(tert-butyldimethylsilyl)-AT-ethylcarboxamidoadenosinc (59) with the appropriate acid chloride and then deprotecting with lelrabutylammonium fluoride in tetrahydrofuran. The compound N6-(4-(1,2-dihydroxy)ethyl)benzyl-N- ethylcarboxamidoadenosine (125) was synthesised by the reaction of 4-(1,2-0- isopropylidene-ethyl)benzyl aminc (123) with 6-chloro-2,3-0-isopropylidene-N- ethylcarboxamidoadenosine (25). Compound 123 was synthesised from an epoxidation of vinylbenzyl phthalimide (118) followed by an acidic ring opening to yield the diol which was isopropylidenated to yield 4-(l,2-O-isopropylidene- elhyl)benzyl phlhalimide (122), It was hoped that the presence of the diol functionality in 125 would increase water solubility whilst maintaining potency at the A3 receptor.

A unified learning algorithm to extract principal and minor components

Recently, many unified learning algorithms have been developed to solve the task of principal component analysis (PCA) and minor component analysis (MCA). These unified algorithms can be used to extract principal component and if altered simply by the sign, it can also serve as a minor component extractor. This is of practical significance in the implementations of algorithms. Convergence of the existing unified algorithms is guaranteed only under the condition that the learning rates of algorithms approach zero, which is impractical in many practical applications. In this paper, we propose a unified PCA & MCA algorithm with a constant learning rate, and derive the sufficient conditions to guarantee convergence via analyzing the discrete-time dynamics of the proposed algorithm. The achieved theoretical results lay a solid foundation for the applications of our proposed algorithm.

Structure–activity relationships of adenosines with heterocyclic N6-substituents

Two series of N⁶-substituted adenosines with monocyclic and bicyclic N⁶ substituents containing a heteroatom were synthesized in good yields. These derivatives were assessed for their affinity ([³H]CPX), potency, and intrinsic activity (cAMP accumulation) at the A₁ adenosine receptor in DDT₁ MF-2 cells. In the monocyclic series, the N⁶-tetrahydrofuran-3-yl and thiolan-3-yl adenosines (1 and 26, respectively) were found to possess similar activities, whereas the corresponding selenium analogue 27 was found to be more potent. A series of nitrogen containing analogues showed varying properties, N⁶-((3R)-1-benzyloxycarbonylpyrrolidin-3-yl)adenosine (30) was the most potent at the A₁AR; IC₅₀ = 3.2 nM. In the bicyclic series, the effect of a 7-azabicyclo[2.2.1]heptan-2-yl substituent in the N⁶-position was explored. N⁶-(7-Azabicyclo[2.2.1]heptan-2-yl)adenosine (38) proved to be a reasonably potent A₁ agonist (K_i = 51 nM, IC₅₀ = 35 nM) while further substitution on the 7″-nitrogen with tert-butoxycarbonyl (31, IC₅₀ = 2.5 nM) and 2-bromobenzyloxycarbonyl (34, IC₅₀ = 9.0 nM) gave highly potent A₁AR agonists.

Copy number variation and transposable elements feature in recent, ongoing adaptation at the Cyp6g1 locus

The increased transcription of the Cyp6g1 gene of Drosophila melanogaster, and consequent resistance to insecticides such as DDT, is a widely cited example of adaptation mediated by cis-regulatory change. A fragment of an Accord transposable element inserted upstream of the Cyp6g1 gene is causally associated with resistance and has spread to high frequencies in populations around the world since the 1940s. Here we report the existence of a natural allelic series at this locus of D. melanogaster, involving copy number variation of Cyp6g1, and two additional transposable element insertions (a P and an HMS-Beagle). We provide evidence that this genetic variation underpins phenotypic variation, as the more derived the allele, the greater the level of DDT resistance. Tracking the spatial and temporal patterns of allele frequency changes indicates that the multiple steps of the allelic series are adaptive. Further, a DDT association study shows that the most resistant allele, Cyp6g1-[BP], is greatly enriched in the top 5% of the phenotypic distribution and accounts for ~16% of the underlying phenotypic variation in resistance to DDT. In contrast, copy number variation for another candidate resistance gene, Cyp12d1, is not associated with resistance. Thus the Cyp6g1 locus is a major contributor to DDT resistance in field populations, and evolution at this locus features multiple adaptive steps occurring in rapid succession.