Histological characteristic of interrenal and chromaffin cells in relation to ovarian activities in Mystus vittatus (Bloch) during growth, maturation, spawning and post-spawning phases

The histological status of adrenocortical tissues and the correlated seasonal changes in ovarian activities in Mystus vittatus was performed. The tubules and nests of interrenal and chromaffin cells were located in cephalic kidney around the main branches of posterior cardinal vein. Various female germ line cells were identified in the ovary based on size, distinctive features and histoarchitechture of the cells. However, on the basis of relative abundance and size of the different oocytes, the event of oogenesis has been found to occur in four distinct phases, including growth, maturation, spawning and post-spawning. The cytoplasmic features and the architecture of the interrenal and chromaffin cells varied during different phases of the reproductive cycle. During growth and maturation phases, the amount of cytoplasmic granules of interrenal cells increased than chromaffin cells that was in coincidence with the increase of early and late perinucleolar oocytes followed by highest frequency percentage of oocyte at stages IV and V. The cytoplasmic mass of interrenal cells was gradually elevated along with hypertrophied nuclei from the end of maturation and spawning phases also correlated with the increased frequency of mature oocytes. Therefore, gradual accumulation of cytoplasmic granules in the interrenal cells was noticed during post-spawning phase. The cytological variations in the interrenal and chromaffin cells harmonized with constitution of different ovarian cells during different reproductive phase in M. vittatus

GOPro: Generate and Optimize Prompts in CLIP using Self-Supervised Learning

Large-scale foundation models, such as CLIP, have demonstrated remarkable success in visual recognition tasks by embedding images in a semantically rich space. Self-supervised learning (SSL) has also shown promise in improving visual recognition by learning invariant features. However, the combination of CLIP with SSL is found to face challenges due to the multi-task framework that blends CLIP's contrastive loss and SSL's loss, including difficulties with loss weighting and inconsistency among different views of images in CLIP's output space. To overcome these challenges, we propose a prompt learning-based model called GOPro, which is a unified framework that ensures similarity between various augmented views of input images in a shared image-text embedding space, using a pair of learnable image and text projectors atop CLIP, to promote invariance and generalizability. To automatically learn such prompts, we leverage the visual content and style primitives extracted from pre-trained CLIP and adapt them to the target task. In addition to CLIP's cross-domain contrastive loss, we introduce a visual contrastive loss and a novel prompt consistency loss, considering the different views of the images. GOPro is trained end-to-end on all three loss objectives, combining the strengths of CLIP and SSL in a principled manner. Empirical evaluations demonstrate that GOPro outperforms the state-of-the-art prompting techniques on three challenging domain generalization tasks across multiple benchmarks by a significant margin. Our code is available at https://github.com/mainaksingha01/GOPro.Comment: Accepted at BMVC 202

C-SAW: Self-Supervised Prompt Learning for Image Generalization in Remote Sensing

We focus on domain and class generalization problems in analyzing optical remote sensing images, using the large-scale pre-trained vision-language model (VLM), CLIP. While contrastively trained VLMs show impressive zero-shot generalization performance, their effectiveness is limited when dealing with diverse domains during training and testing. Existing prompt learning techniques overlook the importance of incorporating domain and content information into the prompts, which results in a drop in performance while dealing with such multi-domain data. To address these challenges, we propose a solution that ensures domain-invariant prompt learning while enhancing the expressiveness of visual features. We observe that CLIP's vision encoder struggles to identify contextual image information, particularly when image patches are jumbled up. This issue is especially severe in optical remote sensing images, where land-cover classes exhibit well-defined contextual appearances. To this end, we introduce C-SAW, a method that complements CLIP with a self-supervised loss in the visual space and a novel prompt learning technique that emphasizes both visual domain and content-specific features. We keep the CLIP backbone frozen and introduce a small set of projectors for both the CLIP encoders to train C-SAW contrastively. Experimental results demonstrate the superiority of C-SAW across multiple remote sensing benchmarks and different generalization tasks.Comment: Accepted in ACM ICVGIP 202

Natural pesticides for pest control in agricultural crops: an alternative and eco-friendly method

Biological pesticides are pesticides derived from natural materials such as bacteria, plants, and minerals that are applied to crops to kill pests. Biopesticides are targeted, inexpensive, eco-friendly, sustainable, leave no trace, and are not associated with the production of greenhouse gases. It contributes significantly to the agricultural bio-economy's sustainability. The advantages to the ecosystem provided by many significant biological resources justify the incorporation of biopesticides in Integrated Pest Management (IPM) programs. Through advancements in research and development, the use of biopesticides has significantly reduced environmental contamination. The development of biopesticides promotes agricultural modernization and will surely result in a gradual phase-out of chemical pesticides. Although synthetic pesticides have positive effects on crop yield and productivity, they also have some negative impacts on soil biodiversity, animals, aquatic life, and humans. In general, synthetic pesticides make the soil brittle, decrease soil respiration, and reduce the activity of some soil microorganisms, such as earthworms. Pesticide buildup in bodies of water can spread from aquatic life to animals including people, as their biomagnification can cause fatal diseases like cancer, kidney disease, rashes on the skin, diabetes, etc. Biopesticides, on the other hand, have surfaced and have proven to be quite beneficial in the management of pests and are safe for the environment and hence have emerged as very useful in the control of pests with a lot of merits.  The present review provides a broad perspective on the different kinds of pesticides. We analyzed suitable and environmentally friendly ways to improve the acceptance and industrial application of microbial herbicides, phytopesticides, and nano biopesticides for plant nutrition, crop protection/yield, animal/human health promotion, as well as their potential integration into the integrated pest management system

Other regarding principal and moral hazard: the single agent case

Using the classic moral hazard problem with limited liability we characterize the optimal incentive contracts when first an other-regarding principal interacts with a self-regarding agent. The optimal contract differs considerably when the principal is ‘inequity averse’ vis-a-vis the self-regarding case. Also the agent is generally (weakly) better-off under an ‘inequity averse’ principal compared to a ‘status seeking’ principal. Then we extend our analysis and characterize the optimal contracts when both other-regarding principal and other-regarding agent interact

Other regarding principal and moral hazard: the single agent case

Using the classic moral hazard problem with limited liability we characterize the optimal incentive contracts when first an other-regarding principal interacts with a self-regarding agent. The optimal contract differs considerably when the principal is ‘inequity averse’ vis-a-vis the self-regarding case. Also the agent is generally (weakly) better-off under an ‘inequity averse’ principal compared to a ‘status seeking’ principal. Then we extend our analysis and characterize the optimal contracts when both other-regarding principal and other-regarding agent interact

Plant growth promotion and antifungal activities of the mango phyllosphere bacterial consortium for the management of Fusarium wilt disease in pea (Pisum sativum L.)

Root rot caused by the pathogen Fusarium oxysporum is the number one cause of pea plant (P. sativum L.) death. There are many potential advantages to using rhizobacteria, endophytic bacteria and phyllospheric bacteria for managing plant diseases and promoting plant growth. This study investigated the potentiality of consortium species of bacteria to suppress root rot disease and their ability to promote the growth of pea plants compared with their individual and control plants. A total of 55 phyllospheric bacteria were isolated from mango flower and Bacillus sp. LBF- 02, Bacillus sp. LBF- 03 and Bacillus sp. LBF- 05 showed the most potent antimicrobial activity against root rot pathogens in a dual culture assay. Identification of phyllobacterial strain LBF- 01, LBF- 03 and LBF-05 were done by 16S rDNA sequence analysis using 704f forward primer (50-AGATTTTCCGACGGCAGGTT-30) and 907r reverse primer (50-CCGTCAATTCMTTTRAGTTT-30) with the PCR conditions. Their ability to solubilize phosphate, produce ammonia, siderophore and indole acetic acid, as well as produce extracellular enzymes in vitro was excellent. The results of a greenhouse study found that pea seed treated with consortium isolate significantly increased high germination rates and vigour indexes, as well as shoot and root length, fresh and dry weights, as compared with seed treated with single isolate and control. The defense enzyme activities in consortium treated pots were higher than those in individual and control pots. The plants treated with consortium exhibited higher levels of chlorophyll and carotenoids content in their leaves compared to the untreated control and single treated plants. A significant variation in the chemical profile of pea plants was found (F7,16 ? 2.598; P ? 0.048) resulting from different treatments (T1-T8). After evaluating a variety of growth and microbiological parameters, it was concluded that inoculation with the microbial consortium contributed to raising healthy and vigorously growing pea seedlings in greenhouse conditions, which is applicable in the field in future for sustainable farming

Accessibility of Enzymatically Delignified Bambusa bambos for Efficient Hydrolysis at Minimum Cellulase Loading: An Optimization Study

In the present investigation, Bambusa bambos was used for optimization of enzymatic pretreatment and saccharification. Maximum enzymatic delignification achieved was 84%, after 8 h of incubation time. Highest reducing sugar yield from enzyme-pretreated Bambusa bambos was 818.01 mg/g dry substrate after 8 h of incubation time at a low cellulase loading (endoglucanase, β-glucosidase, exoglucanase, and xylanase were 1.63 IU/mL, 1.28 IU/mL, 0.08 IU/mL, and 47.93 IU/mL, respectively). Enzyme-treated substrate of Bambusa bambos was characterized by analytical techniques such as Fourier transformed infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM). The FTIR spectrum showed that the absorption peaks of several functional groups were decreased after enzymatic pretreatment. XRD analysis indicated that cellulose crystallinity of enzyme-treated samples was increased due to the removal of amorphous lignin and hemicelluloses. SEM image showed that surface structure of Bambusa bambos was distorted after enzymatic pretreatment

Usporedno ispitivanje metode odzivnih površina, umjetne neuralne mreže i genetskog algoritma radi optimiranja hidratacije zrna soje

The present investigation deals with the modelling and optimization of soybean hydration for facilitating soybean processing and it focuses on maximization of mass gain, water uptake and protein retention in the bean. Process variables considered for optimization were: soybean to water ratio (1:2.48 obtained with response surface methodology, RSM, and 1.19 obtained with artificial neural network and genetic algorithm, ANN/GA), time (2.0 h using RSM and 8.0 h using ANN/GA) and temperature (40.0 °C using RSM and 45.1 °C using ANN/GA). The findings in this first report on optimization of soaking conditions for soybean hydration employing response surface methodology, hybrid artificial neural network and genetic algorithms reveal a substantially better alternative to the time-consuming soaking process, extensively practiced in industries, in terms of process time economy. Reasonably accurate neural network model (regression coefficient of 0.9443) was obtained based on the experimental data. The optimized set of process conditions was predicted through genetic algorithm, and the effectiveness of the ANN/GA model, validated through experiments, was indicated by significant correlations (R2 and mean squared error (MSE) being 0.9380 and 5.9299, respectively). RSM also resulted in accurate models for predicting percentage mass gain, percentage water uptake and percentage protein retention (R2 and MSE in the range of 0.889–0.9297 and 0.80–4.94, respectively).U radu je modelirana i optimirana hidratacija zrna radi ubrzavanja prerade soje, pri čemu se pokušao ostvariti maksimalni prinos mase, usvajanje vode i retencija proteina. Metodom odzivnih površina te umjetnom neuralnom mrežom i genetskim algoritmom optimirane su sljedeće varijable procesa: omjer zrna soje i vode (optimalni omjer od 1:2,48 i 1:1,19), vrijeme (2 odnosno 8 sati) i temperatura (40 i 45, 1 °C). Tako je pronađena bolja alternativa klasičnom postupku namakanja zrna soje koji se učestalo koristi u industriji, a zahtijeva veliki utrošak vremena. Na osnovi rezultata razvijen je vrlo precizan model neuralne mreže (koeficijent regresije od 0,9443). Genetskim su algoritmom predviđeni optimalni uvjeti prerade, a učinkovitost je modela umjetne neuralne mreže i genetskog algoritma potvrđena ispitivanjem (koeficijent determinacije R2=0,938 i srednja kvadratna pogreška MSE=5,9299). Metodom odzivnih površina također je razvijen točan model procjene prinosa mase, usvajanja vode i retencije proteina (R2=0,8890–0,9297 i MSE=0,80–4,94)