Egg parameters of the Red Wattled Lapwing (Vanellus indicus) in agricultural ecosystem of Punjab

The Red Wattled Lapwing is an important bird of the agro-ecosystem of the Punjab state, feeding on insects, seeds and nectar. The present study was carried out to gather information on the egg parameters (morphometry and its components), which is lacking for this species. The eggs were observed in the nest construct-ed in the agricultural fields of Punjab Agricultural University (PAU), Ludhiana, Punjab. Egg length, width and weight were measured using vernier caliper and portable weighing balance, respectively, in laboratory. The egg parameters like egg volume, specific gravity and shape index were also calculated. Weight of egg components (yolk, albumen and shell) were also measured using weighing balance. Results revealed that average values of egg breadth, length and shape index were: 30.05±0.331 (mm), 41.29±0.573 (mm) and 72.83±0.930, respectively. Whereas whole egg weight, albumen weight, yolk weight, shell weight, albumen percentage, yolk percentage, shell percentage, egg volume and specific gravity were 17.49±0.634(gm),7.17±0.374 (gm), 9.05±0.233 (gm), 1.26±0.070 (gm), 40.84±0.941 (%), 51.92±0.830 (%), 7.22±0.280 (%), 17.07±0.531(cm3) and 1.02±0.009 (gm/cm3), respectively. This study pro-vides important information that can help the avian taxonomists in species classification, as bird’s egg diverges widely in shape, volume, weight and percentage of albumen, yolk and shell. Therefore, we can use the egg parameters as additional information in bird systematic

Solid-state diffusion reaction and formation of intermetallic compounds in the nickel-zirconium system

Chemical diffusion studies in the nickel-zirconium system are investigated in the temperature range of 1046 to 1213 K employing diffusion couples of pure nickel and pure zirconium. Electron microprobe and X-ray diffraction studies have been employed to investigate the formation of different compounds and to study their layer growth kinetics in the diffusion zone. It is observed that growth of each phase is controlled by the process of volume diffusion as the layer growth obeys the parabolic law. The activation energies for interdiffusion in NiZr and NiZr2, which are the dominant phases in the diffusion zone, are 119.0 ±13.4 and 103.0 ±25.0 kJ/ mole, respectively. The formation and stability of compounds over the temperature range have been discussed on the basis of existing thermodynamic and kinetic data

Transition Metal Chemistry of Oxime Containing Ligands: Part III-Complexes of Fe(II), Co(II) & Ni(II) with Pyridine-2-aldoxime & 6-Methylpyridine-2-aldoxime

696-69

Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosis

Advances in self-supervised learning (SSL) have shown that self-supervised pretraining on medical imaging data can provide a strong initialization for downstream supervised classification and segmentation. Given the difficulty of obtaining expert labels for medical image recognition tasks, such an "in-domain" SSL initialization is often desirable due to its improved label efficiency over standard transfer learning. However, most efforts toward SSL of medical imaging data are not adapted to video-based medical imaging modalities. With this progress in mind, we developed a self-supervised contrastive learning approach, EchoCLR, catered to echocardiogram videos with the goal of learning strong representations for efficient fine-tuning on downstream cardiac disease diagnosis. EchoCLR leverages (i) distinct videos of the same patient as positive pairs for contrastive learning and (ii) a frame re-ordering pretext task to enforce temporal coherence. When fine-tuned on small portions of labeled data (as few as 51 exams), EchoCLR pretraining significantly improved classification performance for left ventricular hypertrophy (LVH) and aortic stenosis (AS) over other transfer learning and SSL approaches across internal and external test sets. For example, when fine-tuning on 10% of available training data (519 studies), an EchoCLR-pretrained model achieved 0.72 AUROC (95% CI: [0.69, 0.75]) on LVH classification, compared to 0.61 AUROC (95% CI: [0.57, 0.64]) with a standard transfer learning approach. Similarly, using 1% of available training data (53 studies), EchoCLR pretraining achieved 0.82 AUROC (95% CI: [0.79, 0.84]) on severe AS classification, compared to 0.61 AUROC (95% CI: [0.58, 0.65]) with transfer learning. EchoCLR is unique in its ability to learn representations of medical videos and demonstrates that SSL can enable label-efficient disease classification from small, labeled datasets