Objective:The present study was carried out to appraise the antibiotic resistance and to detect some of the target resistant genes in Escherichia coli (E. coli) isolated from apparently healthy broilers.Materials and Methods:Cloacal swab samples (n = 60) were collected from apparently healthy broilers (n = 60) sold at two different live bird markets (LBMs) of Chattogram, Bangladesh. Isolation and identification of the Escherichia coli was done by the following standard bacteriological techniques followed by biochemical tests. The antibiotic susceptibility of E. coli isolates was determined by the disk diffusion method. The antibiotic resistant genes were detected by polymerase chain reaction (PCR) using specific primers.Results:The overall prevalence of E. coli in broilers was 61.67% (n = 37/60) (95% CI = 49–72.93). The antibiogram study showed that the isolates were 100% resistant to ampicillin and tetracycline followed by sulfomethoxazole-trimethoprim (94.59%, n = 35/37) and nalidixic acid (91.89%, n = 34/37). To the contrary, 56.76% (n = 21/37) isolates were sensitive to both ceftriaxone and gentamicin followed by colistin (48.65%, n = 18/37). All of E. coli isolates were multidrug resistant (MDR) and carried blaTEM, tetA, and Sul2 genes.Conclusion:The presence of MDR genes in E. coli isolates in broilers could pose a serious public health threat.
Nitrogen (N) is the prime nutrient for crop production and carbon-based functions associated with soil quality. The objective of our study (2012 to 2019) was to evaluate the impact of variable rates of N fertilization on soil organic carbon (C) pools and their stocks, stratification, and lability in subtropical wheat (Triticum aestivum)—mungbean (Vigna radiata)—rice (Oryza sativa L) agroecosystems. The field experiment was conducted in a randomized complete block design (RCB) with N fertilization at 60, 80, 100, 120, and 140% of the recommended rates of wheat (100 kg/ha), mungbean (20 kg/ha), and rice (80 kg/ha), respectively. Composite soils were collected at 0–15 and 15–30 cm depths from each replicated plot and analyzed for microbial biomass (MBC), basal respiration (BR), total organic C (TOC), particulate organic C (POC), permanganate oxidizable C (POXC), carbon lability indices, and stratification. N fertilization (120 and 140%) significantly increased the POC at both depths; however, the effect was more pronounced in the surface layer. Moreover, N fertilization (at 120% and 140%) significantly increased the TOC and labile C pools when compared to the control (100%) and the lower rates (60 and 80%). N fertilization significantly increased MBC, C pool (CPI), lability (CLI), and management indices (CMI), indicating improved and efficient soil biological activities in such systems. The MBC and POC stocks were significantly higher with higher rates of N fertilization (120% and 140%) than the control. Likewise, higher rates of N fertilization significantly increased the stocks of labile C pools. Equally, the stratification values for POC, MBC, and POXC show evidence of improved soil quality because of optimum N fertilization (120–140%) to maintain and/or improve soil quality under rice-based systems in subtropical climates.
The present study was conducted to analyze the fresh and post-thaw semen quality and fertility from native bulls of Red Chittagong Cattle (RCC), BLRI Cattle Breed 1 (BCB1), and Munshiganj Cattle of Bangladesh. One hundred and seventy-two ejaculates were collected by artificial vagina set and semen analysis was performed using Computer Assisted Sperm Analyzer (CASA) at Bangladesh Livestock Research Institute. Commercial extender (AndroMed) was used to dilute the fresh semen. After equilibration (4˚C for 4 hr), freezing was done using a programmable bio-freezer. Post-thawed semen was evaluated for sperm motility and kinematics. Cryopreserved semen straws were used for artificial insemination (AI) and determined the bull fertility based on 60 days non-return rate. Motility of the sperm differs significantly (p < 0.01) among the genotypes. Total motility was higher in Munshiganj bulls and static motility was higher in BCB1 bulls. However, the semen volume and sperm concentration did not vary significantly (p > 0.05) among the bulls but the highest concentration was found in Munshiganj bull (1669.60 ± 192.07 million/ml) followed by RCC (1648.70 ± 91.07 million/ml) and BCB1 bull (1481.60 ± 167.35 million/ml). Moreover, the highest bent tail (5.89 ± 0.75%), coiled tail (1.01% ± 0.22%) and distal mid-piece reflex (2.26% ± 0.28%) were observed in BCB1 followed by Munshiganj and RCC. Amplitude of lateral head displacement (ALH) was recorded higher in post-thaw than in fresh semen. Kinematics parameters of post-thaw semen decreased than fresh semen irrespective of genotypes. More number of doses/ejaculates can be produced from Munshiganj bull (394.34 ± 127.95) followed by RCC (349.01 ± 120.91
Various technological devices have been developed to meet the ever-increasing demand of today’s cities. Cities, as we can see today, have become highly technology-oriented. However, technological advancement comes at a cost which in this case is the environment of our planet. Therefore, it is necessary to design green cities, which impose as less harm as it possibly can to the environment. One of the most important characteristics of these new cities is the way of managing their waste. Traditional waste management, which employs various sizes and shapes of trash cans at multiple places that are collected by hand after each use is space-consuming, manual, inefficient and often leads to environmental pollution. Therefore, we require a novel approach that is free from these constraints. This paper proposes a garbage management system integrating the fundamental ideas of smart waste management and ‘Internet of Things’ (IoT). The proposed system employs smart cyclic containers that rotate one by one after being filled with rubbish like ‘Merry Go Round’. This approach of waste management solves the problem of space-constraint in an innovative way. A central server monitors the whole system, which further dispatches an autonomous car to collect the waste when necessary. This IoT integrated waste management system is named ‘Internet of Garbage Bins’ (IoGB).
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