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Clostridium perfringens (C. perfringens) is widely distributed in broiler chickens causing clinical and subclinical enteritis and is especially known for causing necrotic enteritis (NE). There are numerous reports of NE outbreaks in Pakistan as well as China but there is a lack of information related to PFGE profile from both the countries. To close this gap, we designed this study and obtained samples from broiler chicken farms located in 3 different regions of Pakistan and 4 different regions of China. A total of 79 fecal swabs (Pakistan=29; China=50) were collected and grown on FTA media. Further, isolates were grown on TSE agar and black colonies were selected for DNA extraction. All 79 isolates were tested for toxin profiles by PCR (a-gene; beta-2; netB gene) and PFGE profiling (pulsotypes analysis). Toxinotyping results revealed that all the isolates (n=50) from China were type A (a-toxin positive) while 23 and 6 isolates (n=29) from Pakistan were type A (a-toxin positive) and type G (a-toxin, NetB positive), respectively. Toxinotyping revealed a-toxin is highly prevalent in both the countries while from Pakistani isolates, NetB toxin was also detected. PFGE discriminated 79 isolates into 45 different PFGE patterns (pulsotypes). The analysis further showed different pulsotypes originating from China and Pakistan and isolates were subtyped by SmaI. The results showed high genetic polymorphism in C. perfringens even within the same strain. These preliminary findings of genetic variations will further help to design control strategies. [ABSTRACT FROM AUTHOR]

Background and Objective: Multiple Choice Questions (MCQs) can sample broad domains of knowledge efficiently and reliably. The MCQs of lower order C1(Cognitive Level1=Recall of knowledge) do not fulfill this purpose and those of higher cognitive order C2 (Cognitive Level2=Interpret) &C3(Cognitive Level3=Analyze) are better at assessing the problem solving capabilities of the student. Every good educational activity must be supported by quality examination to complete the objectives of a curriculum. The objective of the study was to evaluate MCQs presently being used in internal examinations of medical colleges in Lahore. Methods: Papers consisting of MCQs from Orthopedics other specialties were collected in June 2019 from different medical colleges of Lahore and reviewed by a senior medical teacher without blinding and without his knowing the scores the students had been awarded before. Question statement, clinical scenarios, options and other mistakes were assessed in each item on predetermined criteria. Cognitive level of the item was determined if it was asking for a recall/identify/analyze response. The results were tabulated and compared in two groups i.e. Miscellaneous and Orthopedics. Results: Most of the items(total=589) in both groups were of C1 cognitive level though Orthopedics (229) were slightly better (X2 = 49.882 P-Value = 0.000 (Statistically Significant). Miscellaneous group (360) was better in quality in making clinical scenarios (X2 = 29.952 P-Value = 0.000 (Statistically Significant) and writing a question statement without confusion. Options were better written in both groups. A good percentage of items needed to be corrected for mistakes in spellings, grammar and segregation into under graduate level. Conclusions: The cognitive level of assessment tool s MCQs is quiet low in both groups especially clinical scenario construction can be improved. Mistakes in spellings, grammar and conceptual mediocrity is common in both groups. [ABSTRACT FROM AUTHOR]

Single-domain antibody (sdAb) or nanobody possesses specific features non-accessible for conventional antibodies that make them suitable for research and biotechnological applications. Porcine epidemic diarrhea virus (PEDV) causes lethal diarrhea in piglets, resulting in great economic losses all over the world. To detect and isolate PEDV rapidly and accurately is important for the control and further research of the clinical PEDV strains. In this study, four sdAb fragments (sdAb-Mc19/29/30/37) targeting the membrane (M) protein of PEDV were selected from sdAb library that was constructed through M protein-immunized Camelus bactrianus. The selected sdAb-Mcs were solubly expressed in Escherichia coli. The functional characteristics analysis revealed that the recombinant sdAb-Mcs have excellent binding activity and specificity to M protein but have no neutralizing activity to PEDV. For further application, sdAb-Mc37 was conjugated with quantum dots to synthesize a nanoprobe for imaging PEDV in vero cells. The observed fluorescence in vero cells clearly reflects that PEDV virions can be reliably recognized and labeled by the nanoprobe. Furthermore, the sdAb-Mc29 was conjugated with superparamagnetic nanobeads to construct immunomagnetic nanobeads (IMNBs) used to isolate PEDV. One PEDV strain was successfully isolated from clinical fecal sample, suggesting IMNBs as a novel and efficient tool suitable for PEDV isolation from clinical samples. This study provided a novel application and substantiated the suitability of sdAb as a specific binder for the isolation of viruses. [ABSTRACT FROM AUTHOR]

Power Quality (PQ) disturbances of a few milliseconds duration may lead to malfunctioning the sensitive devices connected to the network. This necessitates detecting these PQ disturbances in a minimum time before affecting the sensitive load and adopting countermeasures. An expert PQ recognition system (XPQRS) is proposed to identify seventeen types of PQ events using only one power cycle acquired at 5 kHz. In XPQRS, the input PQ signal is first concatenated with its four derivatives to highlight the deviations. Log energy, Shannon energy, and mobility parameters, extracted from the resultant vectors, are then fed to Quadratic Support Vector Machine. XPQRS offers 96.5% accuracy and provides fast decisions in 34.99 ms which makes it suitable for its implementation on an embedded system. XPQRS shows noise immunity and outperforms the previous studies in terms of decision time, accuracy, and computational complexity. • XPQRS is proposed for sensitive load applications to classify 17 types of PQDs. • The first study to use only one cycle of power signal acquired at 5 kHz. • Derivative-based operators are used to magnify the deviations in the input PQDs. • XPQRS achieved a mean accuracy of 96.5% while performing fast decisions in 34.99 ms. • XPQRS shows noise immunity and good performance as compared to previous studies. [ABSTRACT FROM AUTHOR]

Vibration signal analysis is a significant approach in fault diagnosis. This article presents a new vibration signal preprocessing scheme called Automated Relative Energy based Empirical Mode Decomposition (AREEMD) and a novel Cepstral Autoregressive (cAR) feature extraction. AREEMD generates the modes based on high relative energies and automates the signal reconstruction. The cAR features take advantage of the cepstrum information and provide discriminant markers for various machine faults. The weighted K-nearest neighbor delivered the best results when validated on four different datasets. The proposed scheme yielded promising performances of average accuracies of 99.3% using SUBF, 97.4% for CWRU, 100% on the Ottawa, and 99.1% using the combined dataset. The significant generalizability of the technique is demonstrated by its high performance in identifying faults that will aid in the detection of early warnings, regardless of motor speeds and size of faults, assuring a reduction in system shutdowns brought on by bearing failure. [Display omitted] • A new in-house machine fault vibration dataset (SUBF v1.0) is created. • A novel AREEMD technique is devised for intelligent vibration signal preprocessing. • Novel powerful discriminative Cepstral Autoregressive Features are proposed. • Proposed framework is validated on SUBF, CWRU, Ottawa, and a combined dataset. [ABSTRACT FROM AUTHOR]

Matching the output impedance of SONAR system's transmitter with the input impedance of high power electro-acoustic transducer is important to improve its transmitting efficiency by maximizing the transfer of power. In this work, an attempt has been made to match the output impedance of power amplifier (4 Ω)with the input impedance of high power (2000 watts) and high impedance (1387 Ω with phase angle of 85° at its resonant frequency i.e 2.5 KHz) Piezoelectric material based Electroacoustic Transducer, by realizing a Broadband Matching Transformer. Designing of the matching transformer is undertaken using Lump Circuit Modeling and Network Theorem. Matching Transformer has been designed to meet the required specification by sequentially following measurement of transducer's impedance as a function of frequency using Impedance Analyzer and computation of its equivalent circuit element's values based on measured parameters of the transducer. Matching transformer was developed using composite Iron alloy based C-type and Toroid-type Cores. Impedance and phase angle of the tuned transducer were found to be equal to 3.6 Ω and phase angle of 14.5°, respectively. It is pertinent to mention that no significant influence of the core type variation was observed on results. Impedance results of the tuned transducer were also taken as a function of frequency to evaluate the broadband performance of the Matching Transformer. Source Pressure Level of the resonating transducer with matching transformer was observed to increase from 137 to 164 dB/ˇPa @ 1m when gain of the amplifier was set on 6dB. Results of the work are promising and expected to contribute a lot in the development of Underwater Surveillance Systems in future. [ABSTRACT FROM AUTHOR]

Timely and accurate detection and diagnosis of heart disorders is a significant problem in the medical community since the mortality rate is increasing. Pulsing of cardiac structures and blood turbulence creates heart sounds recorded and detected through Phonococardiogram (PCG). As a non‐invasive technique, PCG signals have a strong ability to be used for designing automatic classification of possible heart disorders. This paper presents an expert system design for the detection and classification of PCG signals for five classes, namely, healthy, aortic stenosis, mitral stenosis, mitral regurgitation, and mitral valve prolapse. In this work, a single‐channel PCG signal is first decomposed using Empirical Mode Decomposition (EMD) into different modes known as intrinsic mode functions (IMFs). Manual signal analysis is applied to identify the relevant IMFs to construct a preprocessed signal. We proposed an automated energy‐based signal reconstruction through IMFs. The proposed algorithms automatically identify the relevant IMFs and added them together to form a preprocessed signal. After preprocessing, the first nine features of Mel Frequency Cepstral Coefficients (MFCC) were computed and passed to several classification methods such as Fine Tree, Quadratic Discriminant, Kernel Naive Bayes, Support Vector Machines (SVM), Fine K‐Nearest Neighbours (Fine‐KNN), Ensemble Bagged Trees and Neural Network. The best performance of 99.3% accuracy was obtained via Fine‐KNN using 10‐fold cross‐validation. The proposed method was evaluated on a publicly available dataset of heart sounds. The proposed method demonstrated improved performance as compared to the existing state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

Background: Pseudostellaria heterophylla (P. heterophylla), a herbaceous perennial, belongs to Caryophyllaceae family and is one of the Chinese herbal medicine with high pharmacodynamic value. It can be used to treat the spleen deficiency, anorexia, weakness after illness and spontaneous perspiration symptoms. Our previous study found that consecutive monoculture of Pseudostellaria heterophylla could lead to the deterioration of the rhizosphere microenvironment. The specialized forms of pathogenic fungus Fusarium oxysporum f.Sp. heterophylla (F. oxysporum) in rhizosphere soils of P. heterophylla plays an important role in the consecutive monoculture of P. heterophylla. Results: In this study, F. oxysporum was used to infect the tissue culture plantlets of P. heterophylla to study the responding process at three different infection stages by using RNA-sequencing. We obtained 127,725 transcripts and 47,655 distinct unigenes by de novo assembly and obtained annotated information in details for 25,882 unigenes. The Kyoto Encyclopedia of Genes and Genomes pathway analysis and the real-time quantitative PCR results suggest that the calcium signal system and WRKY transcription factor in the plant-pathogen interaction pathway may play an important role in the response process, and all of the WRKY transcription factor genes were divided into three different types. Moreover, we also found that the stimulation of F. oxysporum may result in the accumulation of some phenolics in the plantlets and the programmed cell death of the plantlets. Conclusions: This study has partly revealed the possible molecular mechanism of the population explosion of F. oxysporum in rhizosphere soils and signal response process, which can be helpful in unraveling the role of F. oxysporum in consecutive monoculture problems of P. heterophylla. [ABSTRACT FROM AUTHOR]

Resonant propagating waves created on the surface of a dielectric multilayer stack, called Bloch surface waves (BSW), can be designed for high sensitivity monitoring of the adjacent refractive index as an alternative platform to the metal-based surface plasmon resonance (SPR) sensing. The resonant wavelength and polarization can be designed by engineering of the dielectric layers unlike the fixed resonance of SPR, while the wide bandwidth low loss of dielectrics permits sharper resonances, longer propagation lengths and thus their use in waveguiding devices. The transparency of the dielectrics allows the excitation and monitoring of surfacebound fluorescent molecules. We review the recent developments in this technology. We show the advantages that can be obtained by using high index contrast layered structures. Operating at 1550 nm wavelengths will allow the BSW sensors to be implemented in the silicon photonics platform where active waveguiding can be used in the realization of compact planar integrated circuits for multi-parameter sensing. [ABSTRACT FROM AUTHOR]

Background subtraction (BS) is a crucial machine vision scheme for detecting moving objects in a scene. With the advent of smart cameras, the embedded implementation of BS finds ever-increasing applications. This paper presents a new BS scheme called efficient BS for smart cameras (EBSCam). EBSCam thresholds the change in the estimated background model, which suppresses variance of the estimates, resulting in competitive performance compared with standard BS schemes. The percentage of wrong classification of EBSCam is lower than those of the Gaussian mixture model (GMM) (10.97%) and the pixel-based adaptive segmenter (PBAS) (4.66%) algorithms in FPGA implementations. Moreover, the memory bandwidth requirement of EBSCam is 6.66%, 41.36%, and 90.48% lower than the state-of-the-art FPGA implementation of GMM, ViBe, and PBAS algorithms, respectively. EBSCam achieves a significant speed up compared with the FPGA implementations of GMM (by 43.3%), ViBe (by 118.6%), and PBAS (by 144.8%) schemes. Similarly, the energy consumption of EBSCam is 80.56% and 99.9% less compared with GMM and PBAS, respectively. In summary, the advantages of EBSCam in accuracy, speed, and energy consumption combined together make it especially suitable for embedded applications. [ABSTRACT FROM AUTHOR]

Cardiac disorders cause a large number of human mortalities every year. This raises a sheer need for an early and accurate diagnosis of cardiac disorders to provide early meaningful intervention. Ischemic heart disease (IHD) and rheumatic heart disease (RHD) are the leading causes of heart failure. In this article, we proposed a novel framework for the classification of IHD and RHD using Pulse Plethysmograph (PuPG) signals obtained from a subject's fingertip. The presented framework comprises a combination of variational mode decomposition (VMD), cosine-based soft segmentation, and novel cepstral quinary patterns (CQPs). The PuPG signals were first preprocessed through VMD by decomposing them in various modes. After an extensive time–frequency analysis, only relevant modes were selected and combined to reconstruct a preprocessed PuPG signal. The preprocessed signals were segmented through developed cosine-based soft segmentation to eliminate similar content in various classes. Features were extracted from the preprocessed signal using novel CQPs. CQPs were able to extract the hidden discriminative information about the disease through cepstrum transformed representation. The extracted CQP features were further reduced through the ReliefF ranking algorithm. The extracted reduced features were exposed to a range of well-known classification methods such as Support Vector Machines (SVM) with linear and non-linear kernels, Ensemble classifiers, and K-nearest neighbors. SVM-Gaussian (SVMG) provides the best performance of 99% accuracy using 10-fold cross-validation. The proposed CQPs were also compared with time, frequency, and cepstral features. Comparative analysis confirms that the proposed method outperforms the existing renowned techniques for the diagnosis of cardiac disorders. [Display omitted] • Classification of multiple cardiac disorders using PuPG signals is proposed. • A new dataset of PuPG signals was recorded from healthy, IHD, and RHD subjects. • Cepstral Quinary Patterns (CQPs) are proposed as a novel feature extraction method. • CQPs outperforms conventional time, frequency, and cepstral features. • SVM classifier yielded 99% accuracy with only five CQP features. [ABSTRACT FROM AUTHOR]