Increasing temperature and consequent changes in climate adversely affect plant growth and development, resulting in catastrophic loss of wheat productivity. For each degree rise in temperature, wheat production is estimated to reduce by 6%. A detailed overview of morpho-physiological responses of wheat to heat stress may help formulating appropriate strategies for heat-stressed wheat yield improvement. Additionally, searching for possible management strategies may increase productivity and sustainability of growing wheat. The major findings from this review are as follows: (1) heat stress significantly reduces seed germination and seedling growth, cell turgidity, and plant water-use efficiency; (2) at a cellular level, heat stress disturbs cellular functions through generating excessive reactive oxygen species, leading to oxidative stress; (3) the major responses of wheat to heat stress include the enhancement of leaf senescence, reduction of photosynthesis, deactivation of photosynthetic enzymes, and generation of oxidative damages to the chloroplasts; (4) heat stress also reduces grain number and size by affecting grain setting, assimilate translocation and duration and growth rate of grains; (5) effective approaches for managing heat stress in wheat include screening available germplasm under field trials and/or employing marker-assisted selection, application of exogenous protectants to seeds or plants, mapping quantitative trait locus conferring heat resistance and breeding; (6) a well-integrated genetic and agronomic management option may enhance wheat tolerance to heat. However, the success of applying various techniques of heat stress management requires greater understanding of heat tolerance features, molecular cloning, and characterization of genes. The overall success of the complex plant heat stress management depends on the concerted efforts of crop modelers, molecular biologists, and plant physiologists.

Certain Trichoderma strains protect plants from diverse pathogens using multiple mechanisms. We report a novel mechanism that may potentially play an important role in Trichoderma-based biocontrol. Trichoderma virens and T. viride significantly increased the amount/activity of secreted antifungal metabolites in response to volatile compounds (VCs) produced by 13 strains of Fusarium oxysporum, a soilborne fungus that infects diverse plants. This response suggests that both Trichoderma spp. recognize the presence of F. oxysporum by sensing pathogen VCs and prepare for attacking pathogens. However, T. asperellum did not respond to any, while T. harzianum responded to VCs from only a few strains. Gene expression analysis via qPCR showed up-regulation of several biocontrol-associated genes in T. virens in response to F. oxysporum VCs. Analysis of VCs from seven F. oxysporum strains tentatively identified a total of 28 compounds, including six that were produced by all of them. All four Trichoderma species produced VCs that inhibited F. oxysporum growth. Analysis of VCs produced by T. virens and T. harzianum revealed the production of compounds that had been reported to display antifungal activity. F. oxysporum also recognizes Trichoderma spp. by sensing their VCs and releases VCs that inhibit Trichoderma, suggesting that both types of VC-mediated interaction are common among fungi.

The research was conducted to study the effect of heat stress on behavior, some physiological and blood parameters with nine goats of almost similar in age, sex and weight into three groups. Three groups were divided as zero hour (T 0 ), four hours (T 4 ) and eight hours (T 8 ) heat exposure. Temperature-humidity index (THI) value was calculated as 28.17 which indicate the experimental animals were in extreme severe heat stress. Skin and rectal temperature had no significant differences among the treatment groups but respiration/panting and pulse rate were increased with the increased of heat stress from T 0 to T 8 group (P<0.01). Significant difference was found in standing time and lying time (P<0.01) in experimental groups. There were significant changes (P<0.01) in number of urination and defecation per hour but no significant changes was found in duration per urination in heat treated groups. The amount of RBC, PCV%, Hb%, WBC were increased with the increased of heat stress (P<0.01). Neutrophil, eosinophil, lymphocyte and monocyte numbers increased significantly (P<0.01) in heat treated groups. It can be concluded that heat stress had significant changes on some behavioral, physiological and blood parameters of goat.

Topology and histology were performed in the lymphoid tissues (thymus, bursa of Fabricius, spleen and cecal tonsils) of the fifteen 28-days-old "Kasilla" broilers by observation of H & E stained sections in the Department of Anatomy and Histology, Faculty of Veterinary Science, Bangladesh Agricultural University, Mymensingh during the period from October to December 2005. In the present study, it was observed that the thymus was enclosed by a thin connective tissue capsule. Numerous fine septa of connective tissue originated from the capsule and divide the organ into incompletely separated lobules. Each lobule organized into a peripheral cortex and a central medulla. The population of the immunocompetent cells (lymphocytes and immunoglobulin containing plasma cells) in the cortex were denser rather than that of medulla of the thymic lobule. The bursa of Fabricius was consisting of long thick mucosal folds (plicae). Numerous follicles filled the lamina propria of each fold and each bursal follicle was composed a peripheral cortex and a central medulla. The population of the immunocompetent cells in the cortex of the bursal follicle were denser rather than that of medulla of the bursal follicle. The spleen was surrounded by a thick splenic capsule and there was a small number of trabeculi. The network of the splenic tissue was consisting of a network of reticular cells and fibers and was arranged into red pulps, which were scatteredly distributed within the white pulps. The white pulp was composed of network of reticular cells and reticular fibers within which the immunocompetent cells were diffusely distributed. It contained sheathed arteries and lymphatic nodules. The red pulp of the spleen was formed from venous sinuses and anastomosing cord of reticular cells, macrophages, lymphocytes and blood cells. Cecal tonsil was composed of four histological layers i.e. tunica mucosa, submucosa, muscularis and serosa. Their lining epithelium was simple columnar epithelium. More diffuse lymphoid tissue and unorganized lymphatic nodules were present both in the mucosa and submucosa of the cecal tonsil of broiler. The length and breadth of the thymic lobules were 629.30 ± 118.95 µm and 376.03 ± 98.92 µm, bursal follicles 468.83 ± 52.26 µm and 240.70 ± 34.19 µm, white pulp of the spleen 112.62 ± 13.25 µm and 89.42 ± 12.20 µm, lymphatic nodules of the cecal tonsil 255.20 ± 20.46 µm and 186.08 ± 24.90 µm respectively. The result of the present study revealed that the immunocompetent cells were arranged scatteredly or densely and as unorganized or organized lymphatic nodules in the lymphoid tissues and the length and breadth of the thymic lobule, bursal follicle, splenic white pulp and lymphatic nodule of cecal tonsils were varied within the lymphoid tissues and even one another.

The genotype and genotype by environment biplot model is an excellent tool for visual multienvironment trials data analysis. In this study we investigated grain yield of six rice genotypes (three tested, one released hybrids and two inbred check varieties) in five environments. The combined analysis of variance for grain yield data indicated that the differences among all sources of variation were highly significant (P<0.001). Environment (E), Genotype (G) and G × E interaction effects accounted for 12.49, 76.51 and 10.21% of the total sum of squares respectively. The first two principal components (PC1 and PC2) were used to display a two-dimensional GGE biplot. Thus, genotypic PC1 scores>0 classified the high yielding genotypes while PC1 scores<0 identified low yielding genotypes. Unlike genotypic PC1, genotypic PC2 scores discriminated the unstable ones. The GGE biplot analysis was useful in identifying stable genotypes with high yield performance. In this study, the polygon view of GGE biplot showed that the vertex genotypes were BRRI1A/BR168R (G1), BRRI10A/BRRI10R (G2) and BRRI dhan28 (G5) having the largest distance from the origin, which was most discriminated genotypes with the unstable ones. These vertex genotypes BRRI1A/BR168R (G1) and BRRI10A/BRRI10R (G2) gave higher yield (PC1 scores>0) while another vertex genotype BRRI dhan28 (G5) produced low yield (PC1 score<0). Hence, the vertex genotype BRRI10A/BRRI10R (G2) was high yielding for all environments and it fell into section 1 following IR58025A/BRRI10R (G3) and BRRI hybrid dhan1 (G4). Mean yield and stability performance over environments of each genotype is explored by using the average environment (tester) coordinate (AEC) methods. These methods show that the genotypes BRRI10A/BRRI10R (G2), IR58025A/BRRI10R (G3) and BRRI hybrid dhan1 (G4) had higher stability as well as higher mean yield while the genotype IR58025A/BRRI10R (G3) had the highest stability out of these three genotypes. The ideal genotype biplot suggests that the closer to 'ideal' genotype was IR58025A/BRRI10R (G3) followed by G2 and G4 being more desirable than the other genotypes. Similarly, the environment Barisal (E3) was 'ideal' environment followed by E1 (Gazipur), E2 (Comilla) and E5 (Satkhira). Hence, the environment Barisal (E3) is more stable and suitable for all genotypes following Satkhira (E5) because it has large PC1 and small PC2 score but Rangpur (E4) is a discriminating environment because it has large PC2 score. The interrelationship among the environments according to the small angles of test environments was highly positively correlated. Gazipur (E1), Comilla (E2), Barisal (E3) and Satkhira (E5) were closely correlated with small angles but Rangpur (E4) had medium long angles. Comparison between two genotypes showed that BRRI10A/BRRI10R (G2) and IR58025A/BRRI10R (G3) were high yielder in test environments. Thus, the difference between G2 and G3 was relatively small in test environments.