In this report, we discussed rapid, facile one-pot green synthesis of gold and silver nanoparticles (AuNPs and AgNPs) by using tuber extract of Amorphophallus paeoniifolius, and evaluated their antibacterial activity. AuNPs and AgNPs were synthesized by mixing their respective precursors (AgNO3 and HAuCl4) with tuber extract of Amorphophallus paeoniifolius as the bio-reducing agent. Characterization of AuNPs and AgNPs were confirmed by applying UV-vis spectroscopy, field-emission scanning electron microscopy (FESEM), X-ray diffraction (XRD) analysis, Fourier transform infrared spectroscopy (FTIR), and energy dispersive X-ray spectroscopy (EDS). From UV-vis characterization, surface plasmon resonance spectra were found at 530 nm for AuNPs and 446 nm for AgNPs. XRD data confirmed that both synthesized nanoparticles were face-centered cubic in crystalline nature, and the average crystallite sizes for the assign peaks were 13.3 nm for AuNPs and 22.48 nm for AgNPs. FTIR data evaluated the characteristic peaks of different phytochemical components of tuber extract, which acted as the reducing agent, and possibly as stabilizing agents. The antibacterial activity of synthesized AuNPs and AgNPs were examined in Muller Hinton agar, against two Gram-positive and four Gram-negative bacteria through the disc diffusion method. AuNPs did not show any inhibitory effect, while AgNPs showed good inhibitory effect against both Gram-positive and Gram-negative bacteria.

The research project was conducted to assess the bacteriological quality of buffalo meat samples collected from three upazilas namely Haluaghat, Sreepur and Madhupur of Bangladesh under the districts of Mymensingh, Gazipur and Tangail respectively with particular emphasis on the molecular detection and antimicrobial resistance of the isolate Salmonella species. Total viable count (TVC), total staphylococcal count (TStaC) and total salmonella count (TSC) of meat samples were determined and the mean values of TVC, TStaC and TSC for the Haluaghat, Sreepur and Madhupur were log 8.30, log 7.94, log 8.15; log6.21, log 6.40, log 5.43 and log 4.76, log 4.82, log 4.56 CFU/gm respectively which exceeded the ICMSF recommendations values. The variation of TVC and TSC in meats of different buffalo markets was significant at 5% level where the variation of TStaC was significant at 1% level. Nevertheless no significant variation was demonstrated between the interactions of the three upazilas. Among the samples, 46.67% (n=14) were found to be associated with Salmonella spp. The Salmonella spp. were identified by observing black centered colonies on XLD agar, positive to MR test and negative to VP and Indole test. All isolates of Salmonella spp. were positive to 16s rRNA gene based PCR (574bp). All isolates of Salmonella species were susceptible to ciprofloxacin, streptomycin and gentamicin. All isolates of Salmonella spp. (n=14; 100%) were resistant to amoxicillin and few isolates also resistant to erythromycin, tetracycline, azithromycin and cephradine. The findings of this study revealed the presence of multidrug resistant Salmonella spp. in buffalo meat of Mymensingh, Gazipur and Tangail districts of Bangladesh that posseses a serious threat to public health. Asian Australas. J. Food Saf. Secur. 2018, 2(1), 12-20

The binding of Beta Adrenoceptor antagonist, propranolol and anti-depressant drug, amitriptyline, to bovine serum albumin (BSA) was studied by equilibrium dialysis (ED) method. During concurrent administration, it was found that amitriptyline has the capacity to release propranolol from its binding site on BSA, causing reduced binding of propranolol to BSA. This increment in free concentration of propranolol was from 5.15% to 9.15%, upon the addition of increased concentration of only amitriptyline 0x10 -5 M to 20x10 -5 M, and in the presence of site-II specific probe (diazepam), it was from 6.95% to 10.45%. On the other hand, the release of amitriptyline from the binding sites on BSA was increased from 3.08% to 4.28% upon the addition of increased concentration of only propranolol 0x10 -5 M to 20x10 -5 M, and in the presence of site-II specific probe (diazepam), it was 0.51% to 4.75%. This form of drug-drug interaction at binding sites on BSA has been termed as site-to-site displacement. Drug-drug interactions, more specifically, displacement interaction will affect the free concentrations drugs in blood. Since the pharmacologic activity of a drug is a function of free drug concentration, the displacement of even a small amount of drug bound to plasma protein could produce considerable increase in activity.

The choice of this study has a significant impact on daily life. In various fields such as journalism, academia, business, and more, large amounts of text need to be processed quickly and efficiently. Text summarization is a technique used to generate a precise and shortened summary of spacious texts. The generated summary sustains overall meaning without losing any information and focuses on those parts that contain useful information. The goal is to develop a model that converts lengthy articles into concise versions. The task to be solved is to select an effective procedure to develop the model. Although the present text summarization models give us good results in many recognized datasets such as cnn/daily- mail, newsroom, etc. All the problems can not be resolved by these models. In this paper, a new text summarization method has been proposed: combining the Extractive and Abstractive Text Summarization technique. In the extractive-based method, the model generates a summary using Sentence Ranking Algorithm and passes this generated summary through an abstractive method. When using the sentence ranking algorithm, after rearranging the sentences, the relationship between one sentence and another sentence is destroyed. To overcome this situation, Pronoun to Noun conversion has been proposed with the new system. After generating the extractive summary, the generated summary is passed through the abstractive method. The proposed abstractive model consists of three pre-trained models: google/pegusus-xsum, face-book/bart-large-cnn model, and Yale-LILY/brio-cnndm-uncased, which generates a final summary depending on the maximum final score. The following results were obtained: experimental results on CNN/daily-mail dataset show that the proposed model obtained scores of ROUGE-1, ROUGE-2 and ROUGE-L are respectively 42.67 %, 19.35 %, and 39.57 %. Then, the result has been compared with three state-of-the-art methods: JEANS, DEATS and PGAN-ATSMT. The results outperform state-of-the-art models. Experimental results also show that the proposed model is qualitatively readable and can generate abstract summaries. Conclusion: In terms of ROUGE score, the model outperforms some art-of-the-state models for ROUGE-1 and ROUGE-L, but doesn’t achieve good result in ROUGE-2.

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