From monoclonal antibody to gene for a neuron-specific glycoprotein in Drosophila

A monoclonal antibody (MAb24B10), derived from mice immunized with Drosophila retina, exclusively stains photoreceptor cells in the retina and their axonal projections to the optic ganglia. The antigen (Ag24B10) is a 160-kDa glycoprotein comprising about 0.8% of the retina protein. By microsequencing, 19 of the first 21 amino acids at the NH2-terminal end of the protein have been determined. Using synthetic oligonucleotide probes corresponding to a portion of this amino acid sequence, we isolated a homologous genomic clone. A partial DNA sequence of this clone, along with blot experiments on genomic DNA and RNA, indicate that this clone is part of the structural gene for Ag24B10. By in situ hybridization, the gene was localized to the tip of chromosome 3R

Timing feedback-inhibition of the male reproductive hormone axis

Hormonal methods of male contraception have addressed feedback-inhbition of the cascade of hormones that has evolved to regulate sperm production but high concentrations of testosterone (T) in the blood have not worked satisfactorily. We hypothesized that an episodic event, such as degranulation of gonadotropin-releasing hormone (GnRH) neurons in the hypothalamus, could be as well inhibited by episodic interference as by continuously-applied suppression. We used a computational model of hypothalamus-pituitary-gonad axis described by Veldhuis et al. to test the hypothesis that episodic administration of T would inhibit GnRH and/or luteinizing hormone (LH) secretion. A set of stochastic differential equations model feedback as well as feed-forward actions of GnRH, LH and T. The model predicted feedback-inhibition of GnRH pulses in a dose and frequency dependent manner when transdermal patches or injections delivering pulsatile T were applied. Since the published model did not address the effect of chronic GnRH deprivation on the viability of pituitary gonadotrophes, we introduced a function to address this issue. Incorporation of this function in the model allowed the prediction of a “holiday period” in the contraceptive regimen, during which exogenous T would not be needed to protect from conception. Pulsatile T delivered by real-life transdermal delivery system applied as per a parsimonious regimen reduced secretion of LH and T and fertility. The vitality function, proposed to account for survival and proliferation of pituitary gonadotrophes correlated with in vivo observations as extensive apoptosis in the anterior pituitary was observed after application of transdermal T

Socio-economic Impact Assessment of Livelihood Security in Agriculture, Animal Husbandry and Aquaculture on the Tsunami-hit Lands of Andaman

Indian subcontinent is highly vulnerable to major natural disasters such as earthquakes, cyclones, floods, droughts, landslides and bushfires. Tsunami, which is a recent addition to this list, had occurred in the early morning of 26th December 2004, after a massive earthquake of 9.2 magnitude on the Richter scale in Andaman & Nicobar islands, resulting in the submergence of large area of farmland, and subsequent drying up of water bodies. It caused moisture stress for the standing crops, livestock and fisheries and affected the livelihood of the people to a large extent. In this context, the present study has been carried out to make the socio-economic impact assessment of livelihood security in agriculture, animal husbandry and aquaculture on the tsunami-hit lands of Andaman. Data have been collected from 150 sample respondents and the survey has been conducted for two periods, pre-tsunami and post-tsunami. The results have indicated that tsunami has ravaged the households, standing crops, farm inputs such as seed, feed and implements, livestock and poultry population, their sheds, fish ponds, etc., thereby affecting the basic livelihood security of the people in Andaman. The rehabilitation measures taken by the government and NGOs have improved their livelihoods by reviving agriculture considerably in the subsequent years and by creating employment opportunities in various farm and non-farm activities. The paper has suggested creating profitable livelihood security to vulnerable sections of the society in the existing socio-economic penury with holistic intervention of the community, government and NGOs.Agricultural and Food Policy,

Optimization of NaCl based spray corrosion test process parameters of heat treated hybrid metal matrix composites

ABSTRACT. Aluminium hybrid metal matrix composites (AHMMCs) have widely employed in aerospace, transportation, and automotive applications since for their excellent mechanical qualities and high corrosion resistance. In this research, Al8079 is selected as a matrix material. The titanium diboride (TiB2) is selected as hard reinforcement and molybdenum disulfide (MoS2) is used as soft reinforcement. The Al8079/15 wt.% TiB2/x wt.% MoS2 (x = 0, 2.5, 5 and 7.5) HMMCs are fabricated by using stir casting. The composites are heat treated under T6 condition. The density and micro hardness tests are conducted. The optimization on NaCl based spray corrosion test process parameters is done using grey relational analysis (GRA). The selected input process parameters are Al8079/15 wt.% TiB2/wt.% MoS2 (x = 0, 2.5 and 5), pH value of NaCl solution (x = 6, 9 and 12), hang time (x = 24, 48, and 72 h) and pressure (x = 0.7, 0.9 and 1.1 kg/cm2). The selected response parameters are micro hardness, mass loss and wear loss. The L9 Taguchi design is used for optimization. The wear test is conducted at the constant speed of 0.5 m/s, loading rate of 20 N and the sliding distance of 1000 m. The percentage of improvement of GRG from initial setting to experimental is 10.4%.   KEY WORDS: Reinforcement, Stir casting, Optimization, GRA, NaCl Bull. Chem. Soc. Ethiop. 2022, 36(4), 903-914.                                                         DOI: https://dx.doi.org/10.4314/bcse.v36i4.1

A Taxonomy of Deep Convolutional Neural Nets for Computer Vision

Traditional architectures for solving computer vision problems and the degree of success they enjoyed have been heavily reliant on hand-crafted features. However, of late, deep learning techniques have offered a compelling alternative -- that of automatically learning problem-specific features. With this new paradigm, every problem in computer vision is now being re-examined from a deep learning perspective. Therefore, it has become important to understand what kind of deep networks are suitable for a given problem. Although general surveys of this fast-moving paradigm (i.e. deep-networks) exist, a survey specific to computer vision is missing. We specifically consider one form of deep networks widely used in computer vision - convolutional neural networks (CNNs). We start with "AlexNet" as our base CNN and then examine the broad variations proposed over time to suit different applications. We hope that our recipe-style survey will serve as a guide, particularly for novice practitioners intending to use deep-learning techniques for computer vision.Comment: Published in Frontiers in Robotics and AI (http://goo.gl/6691Bm