Relative Efficacy of Different Insecticides Againsts Chilli Pod Borer, Spodoptera Litura Fabr. (Lepidoptera - Noctuidae) in Manipur

ABSTRACT: The bio-efficacy of seven insecticides namely Endosulfan (0.07%), Monocrotophos (0.05%), Malathion (0.05%), Dimethoate (0.04%), Phosalone (0.04%), Cypermethrine (0.01%) and Neem oil (3.5%) were applied on Chili. Capsicum annum was grown in the field for the control of Chilli pod borer, Spodoptera litura Fabr. The insecticide like Phosalone (0.04%) and Endosulfan (0.07%) were most effective and the neem oil were found to be the least effective in reducing borer population.Â

Detection of mutations in gyrB using denaturing high performance liquid chromatography (DHPLC) among Salmonella enterica serovar Typhi and Paratyphi A

Background:- Fluoroquinolone resistance is mediated by mutations in the quinolone-resistance determining region (QRDR) of the topoisomerase genes. Denaturing high performance liquid chromatography (DHPLC) was evaluated for detection of clinically important mutations in gyrB among Salmonella. Method:- S. Typhi and S. ParatyphiA characterised for mutation in QRDR of gyrA, parC and parE were studied for mutation in gyrB by DHPLC and validated by sequencing. Result:- The DHPLC analysis was able to resolve the test mutant from isolates with wild type gyrB and distinguished mutants from other mutant by peak profile and shift in retention time. Three sequence variants were detected at codon 464, and a novel mutation Ser→Thr was also detected. gyrB mutation was associated with non classical quinolone resistance (NALS-CIPDS) in 34 isolates of S. Typhi only and was distinct from classical quinolone resistance associated with gyrA mutations (NALR-CIPDS). Conclusion: DHPLC is effective for the detection of mutation and can reduce the need forsequencing to detect clinically significant gyrB mutations.

Antimicrobial Susceptibility Patterns of Salmonella enterica Serotype Typhi in Eastern Nepal

The aim of the present study was to evaluate antimicrobial susceptibility patterns with special reference to multidrug resistance, susceptibility to ciprofloxacin, and bacteriophage typing of Salmonella enterica serotype Typhi isolated from blood sent for culture in a tertiary-care teaching hospital in eastern Nepal during January 2000\u2013December 2004. In total, 132 strains of S. enterica Typhi, isolated from 2,568 blood culture samples collected from cases of suspected enteric fever, were tested for susceptibility to commonly-used antimicrobials by the disc-diffusion method. There were 35 multidrug-resistant strains. None of the isolates were resistant to ciprofloxacin.Of 52 isolates tested for minimum inhibitory concentration (MIC) of ciprofloxacin, 36 (69.23%) showed reduced susceptibility (MIC 650.25 mg/L). Of 112 strains tested for nalidixicacid susceptibility,86(76%) were resistant. Strains with reduced susceptibility to ciprofloxacin and resistance to nalidixic acid could be correlated. The commonest phage type was E1. Nalidixic acid susceptibility could be a useful screening test for the detection of decreased susceptibility of S. Typhi to ciprofloxacin, a drug which is commonly used even for minor ailments in this area

Ciprofloxacin-Resistant Neisseria meningitidis, Delhi, India

Decreased susceptibility of Neisseria meningitidis isolates to ciprofloxacin emerged from an outbreak in Delhi, India. Results of antimicrobial susceptibility testing of the meningococcal isolates to ciprofloxacin and further sequencing of DNA gyrase A quinolone-resistance–determining region confirmed the emergence of ciprofloxacin resistance in the outbreak

Clinically and microbiologically derived azithromycin susceptibility breakpoints for Salmonella enterica serovars Typhi and Paratyphi A.

Azithromycin is an effective treatment for uncomplicated infections with Salmonella enterica serovar Typhi and serovar Paratyphi A (enteric fever), but there are no clinically validated MIC and disk zone size interpretative guidelines. We studied individual patient data from three randomized controlled trials (RCTs) of antimicrobial treatment in enteric fever in Vietnam, with azithromycin used in one treatment arm, to determine the relationship between azithromycin treatment response and the azithromycin MIC of the infecting isolate. We additionally compared the azithromycin MIC and the disk susceptibility zone sizes of 1,640 S. Typhi and S. Paratyphi A clinical isolates collected from seven Asian countries. In the RCTs, 214 patients who were treated with azithromycin at a dose of 10 to 20 mg/ml for 5 to 7 days were analyzed. Treatment was successful in 195 of 214 (91%) patients, with no significant difference in response (cure rate, fever clearance time) with MICs ranging from 4 to 16 μg/ml. The proportion of Asian enteric fever isolates with an MIC of ≤ 16 μg/ml was 1,452/1,460 (99.5%; 95% confidence interval [CI], 98.9 to 99.7) for S. Typhi and 207/240 (86.3%; 95% CI, 81.2 to 90.3) (P 16 μg/ml and to determine MIC and disk breakpoints for S. Paratyphi A

Advanced Fusion-Based Speech Emotion Recognition System Using a Dual-Attention Mechanism with Conv-Caps and Bi-GRU Features

Recognizing the speaker’s emotional state from speech signals plays a very crucial role in human–computer interaction (HCI). Nowadays, numerous linguistic resources are available, but most of them contain samples of a discrete length. In this article, we address the leading challenge in Speech Emotion Recognition (SER), which is how to extract the essential emotional features from utterances of a variable length. To obtain better emotional information from the speech signals and increase the diversity of the information, we present an advanced fusion-based dual-channel self-attention mechanism using convolutional capsule (Conv-Cap) and bi-directional gated recurrent unit (Bi-GRU) networks. We extracted six spectral features (Mel-spectrograms, Mel-frequency cepstral coefficients, chromagrams, the contrast, the zero-crossing rate, and the root mean square). The Conv-Cap module was used to obtain Mel-spectrograms, while the Bi-GRU was used to obtain the rest of the spectral features from the input tensor. The self-attention layer was employed in each module to selectively focus on optimal cues and determine the attention weight to yield high-level features. Finally, we utilized a confidence-based fusion method to fuse all high-level features and pass them through the fully connected layers to classify the emotional states. The proposed model was evaluated on the Berlin (EMO-DB), Interactive Emotional Dyadic Motion Capture (IEMOCAP), and Odia (SITB-OSED) datasets to improve the recognition rate. During experiments, we found that our proposed model achieved high weighted accuracy (WA) and unweighted accuracy (UA) values, i.e., 90.31% and 87.61%, 76.84% and 70.34%, and 87.52% and 86.19%, respectively, demonstrating that the proposed model outperformed the state-of-the-art models using the same datasets

Advanced Fusion-Based Speech Emotion Recognition System Using a Dual-Attention Mechanism with Conv-Caps and Bi-GRU Features

Recognizing the speaker’s emotional state from speech signals plays a very crucial role in human–computer interaction (HCI). Nowadays, numerous linguistic resources are available, but most of them contain samples of a discrete length. In this article, we address the leading challenge in Speech Emotion Recognition (SER), which is how to extract the essential emotional features from utterances of a variable length. To obtain better emotional information from the speech signals and increase the diversity of the information, we present an advanced fusion-based dual-channel self-attention mechanism using convolutional capsule (Conv-Cap) and bi-directional gated recurrent unit (Bi-GRU) networks. We extracted six spectral features (Mel-spectrograms, Mel-frequency cepstral coefficients, chromagrams, the contrast, the zero-crossing rate, and the root mean square). The Conv-Cap module was used to obtain Mel-spectrograms, while the Bi-GRU was used to obtain the rest of the spectral features from the input tensor. The self-attention layer was employed in each module to selectively focus on optimal cues and determine the attention weight to yield high-level features. Finally, we utilized a confidence-based fusion method to fuse all high-level features and pass them through the fully connected layers to classify the emotional states. The proposed model was evaluated on the Berlin (EMO-DB), Interactive Emotional Dyadic Motion Capture (IEMOCAP), and Odia (SITB-OSED) datasets to improve the recognition rate. During experiments, we found that our proposed model achieved high weighted accuracy (WA) and unweighted accuracy (UA) values, i.e., 90.31% and 87.61%, 76.84% and 70.34%, and 87.52% and 86.19%, respectively, demonstrating that the proposed model outperformed the state-of-the-art models using the same datasets

Rapid Characterization of Mycobacterium tuberculosis Complex isolated from Clinical Samples by SD TB Ag MPT 64 kits

Purpose: The present study aimed at evaluation of SD TB Ag MPT 64 Rapid kit for confirmation of isolates of Mycobacterium tuberculosis complex (MTBC) grown from clinical samples.Material & Methodology: The present study included a total of 105 mycobacterial growths recovered from various pulmonary and extra-pulmonary clinical specimens in a tertiary care center. Culture was performed using Lowenstein Jensen’s media and BacT ALERT 3D automation system. The growths were subjected to both Accuprobe culture identification (Genprobe, San Deigo, CA) and SD TB Ag MPT 64 rapid kit (Standard Diagnostics, INC, Korea). SD TB Ag MPT 64 rapid kit was evaluated in the present study using Accuprobe as gold standard.Results: The sensitivity and specificity of SD TB Ag MPT 64 rapid kit was 92.3% and 100% respectively.Conclusion: SD TB Ag MPT 64 rapid kit is easy to perform, rapid and cheap test which can be used as a screening tool for rapid confirmation of the MTBC isolates in a routine tertiary care setting