iCpG-Pos: an accurate computational approach for identification of CpG sites using positional features on single-cell whole genome sequence data.

MOTIVATION: The investigation of DNA methylation can shed light on the processes underlying human well-being and help determine overall human health. However, insufficient coverage makes it challenging to implement single-stranded DNA methylation sequencing technologies, highlighting the need for an efficient prediction model. Models are required to create an understanding of the underlying biological systems and to project single-cell (methylated) data accurately. RESULTS: In this study, we developed positional features for predicting CpG sites. Positional characteristics of the sequence are derived using data from CpG regions and the separation between nearby CpG sites. Multiple optimized classifiers and different ensemble learning approaches are evaluated. The OPTUNA framework is used to optimize the algorithms. The CatBoost algorithm followed by the stacking algorithm outperformed existing DNA methylation identifiers. AVAILABILITY AND IMPLEMENTATION: The data and methodologies used in this study are openly accessible to the research community. Researchers can access the positional features and algorithms used for predicting CpG site methylation patterns. To achieve superior performance, we employed the CatBoost algorithm followed by the stacking algorithm, which outperformed existing DNA methylation identifiers. The proposed iCpG-Pos approach utilizes only positional features, resulting in a substantial reduction in computational complexity compared to other known approaches for detecting CpG site methylation patterns. In conclusion, our study introduces a novel approach, iCpG-Pos, for predicting CpG site methylation patterns. By focusing on positional features, our model offers both accuracy and efficiency, making it a promising tool for advancing DNA methylation research and its applications in human health and well-being.

Software defined radio frequency sensing framework for intelligent monitoring of sleep apnea syndrome.

Healthy sleep is vital to all functions in the body. It improves physical and mental health, strengthens resistance against diseases, and develops strong immunity against metabolism and chronic diseases. However, a sleep disorder can cause the inability to sleep well. Sleep apnea syndrome is a critical breathing disorder that occurs during sleeping when breathing stops suddenly and starts when awake, causing sleep disturbance. If it is not treated timely, it can produce loud snoring and drowsiness or causes more acute health problems such as high blood pressure or heart attack. The accepted standard for diagnosing sleep apnea syndrome is full-night polysomnography. However, its limitations include a high cost and inconvenience. This article aims to develop an intelligent monitoring framework for detecting breathing events based on Software Defined Radio Frequency (SDRF) sensing and verify its feasibility for diagnosing sleep apnea syndrome. We extract the wireless channel state information (WCSI) for breathing motion using channel frequency response (CFR) recorded in time at every instant at the receiver. The proposed approach simplifies the receiver structure with the added functionality of communication and sensing together. Initially, simulations are conducted to test the feasibility of the SDRF sensing design for the simulated wireless channel. Then, a real-time experimental setup is developed in a lab environment to address the challenges of the wireless channel. We conducted 100 experiments to collect the dataset of 25 subjects for four breathing patterns. SDRF sensing system accurately detected breathing events during sleep without subject contact. The developed intelligent framework uses machine learning classifiers to classify sleep apnea syndrome and other breathing patterns with an acceptable accuracy of 95.9%. The developed framework aims to build a non-invasive sensing system to diagnose patients conveniently suffering from sleep apnea syndrome. Furthermore, this framework can easily be further extended for E-health applications.

Dynamical properties of solid and hydrated collagen: Insight from nuclear magnetic resonance relaxometry.

1H spin-lattice Nuclear Magnetic Resonance relaxometry experiments have been performed for collagen and collagen-based artificial tissues in the frequency range of 10 kHz-20 MHz. The studies were performed for non-hydrated and hydrated materials. The relaxation data have been interpreted as including relaxation contributions originating from 1H-1H and 1H-14N dipole-dipole interactions, the latter leading to Quadrupole Relaxation Enhancement effects. The 1H-1H relaxation contributions have been decomposed into terms associated with dynamical processes on different time scales. A comparison of the parameters for the non-hydrated and hydrated systems has shown that hydration leads to a decrease in the dipolar relaxation constants without significantly affecting the dynamical processes. In the next step, the relaxation data for the hydrated systems were interpreted in terms of a model assuming two-dimensional translational diffusion of water molecules in the vicinity of the macromolecular surfaces and a sub-diffusive motion leading to a power law of the frequency dependencies of the relaxation rates. It was found that the water diffusion process is slowed down by at least two orders of magnitude compared to bulk water diffusion. The frequency dependencies of the relaxation rates in hydrated tissues and hydrated collagen are characterized by different power laws (ωH-ß, where ωH denotes the 1H resonance frequency): the first of about 0.4 and the second close to unity.

Regulatory impacts of PPARGC1A gene expression on milk production and cellular metabolism in buffalo mammary epithelial cells.

The PPARGC1A gene plays a fundamental role in regulating cellular energy metabolism, including adaptive thermogenesis, mitochondrial biogenesis, adipogenesis, gluconeogenesis, and glucose/fatty acid metabolism. In a previous study, our group investigated seven SNPs in Mediterranean buffalo associated with milk production traits, and the current study builds on this research by exploring the regulatory influences of the PPARGC1A gene in buffalo mammary epithelial cells (BuMECs). Our findings revealed that knockdown of PPARGC1A gene expression significantly affected the growth of BuMECs, including proliferation, cell cycle, and apoptosis. Additionally, we observed downregulated triglyceride secretion after PPARGC1A knockdown. Furthermore, the critical genes related to milk production, including the STATS, BAD, P53, SREBF1, and XDH genes were upregulated after RNAi, while the FABP3 gene, was downregulated. Moreover, Silencing the PPARGC1A gene led to a significant downregulation of ß-casein synthesis in BuMECs. Our study provides evidence of the importance of the PPARGC1A gene in regulating cell growth, lipid, and protein metabolism in the buffalo mammary gland. In light of our previous research, the current study underscores the potential of this gene for improving milk production efficiency and overall dairy productivity in buffalo populations.

Hormetic effects of thiamethoxam on Schizaphis graminum: demographics and feeding behavior.

In agroecosystems, insects contend with chemical insecticides often encountered at sublethal concentrations. Insects' exposure to these mild stresses may induce hormetic effects, which has consequences for managing insect pests. In this study, we used an electrical penetration graph (EPG) technique to investigate the feeding behavior and an age-stage, two-sex life table approach to estimate the sublethal effects of thiamethoxam on greenbug, Schizaphis graminum. The LC5 and LC10 of thiamethoxam significantly decreased longevity and fecundity of directly exposed adult aphids (F0). However, the adult longevity, fecundity, and reproductive days (RPd)-indicating the number of days in which the females produce offspring - in the progeny generation (F1) exhibited significant increase when parental aphids (F0) were treated with LC5 of the active ingredient. Subsequently, key demographic parameters such as intrinsic rate of increase (r) and net reproductive rate (R0) significantly increased at LC5 treatment. EPG recordings showed that total durations of non-probing (Np), intercellular stylet pathway (C), and salivary secretion into the sieve element (E1) were significantly increased, while mean duration of probing (Pr) and total duration of phloem sap ingestion and concurrent salivation (E2) were decreased in F0 adults exposed to LC5 and LC10. Interestingly, in the F1 generation, total duration of Np was significantly decreased while total duration of E2 was increased in LC5 treatment. Taken together, our results showed that an LC5 of thiamethoxam induces intergenerational hormetic effects on the demographic parameters and feeding behavior of F1 individuals of S. graminum. These findings have important implications on chemical control against S. graminum and highlight the need for a deeper understanding of the ecological consequences of such exposures within pest management strategies across the agricultural landscapes.

A Vehicle-Edge-Cloud Framework for Computational Analysis of a Fine-Tuned Deep Learning Model.

The cooperative, connected, and automated mobility (CCAM) infrastructure plays a key role in understanding and enhancing the environmental perception of autonomous vehicles (AVs) driving in complex urban settings. However, the deployment of CCAM infrastructure necessitates the efficient selection of the computational processing layer and deployment of machine learning (ML) and deep learning (DL) models to achieve greater performance of AVs in complex urban environments. In this paper, we propose a computational framework and analyze the effectiveness of a custom-trained DL model (YOLOv8) when deployed in diverse devices and settings at the vehicle-edge-cloud-layered architecture. Our main focus is to understand the interplay and relationship between the DL model's accuracy and execution time during deployment at the layered framework. Therefore, we investigate the trade-offs between accuracy and time by the deployment process of the YOLOv8 model over each layer of the computational framework. We consider the CCAM infrastructures, i.e., sensory devices, computation, and communication at each layer. The findings reveal that the performance metrics results (e.g., 0.842 mAP@0.5) of deployed DL models remain consistent regardless of the device type across any layer of the framework. However, we observe that inference times for object detection tasks tend to decrease when the DL model is subjected to different environmental conditions. For instance, the Jetson AGX (non-GPU) outperforms the Raspberry Pi (non-GPU) by reducing inference time by 72%, whereas the Jetson AGX Xavier (GPU) outperforms the Jetson AGX ARMv8 (non-GPU) by reducing inference time by 90%. A complete average time comparison analysis for the transfer time, preprocess time, and total time of devices Apple M2 Max, Intel Xeon, Tesla T4, NVIDIA A100, Tesla V100, etc., is provided in the paper. Our findings direct the researchers and practitioners to select the most appropriate device type and environment for the deployment of DL models required for production.

Association of Anemia with Parathyroid Hormone Levels and Other Factors in Patients with End-Stage Renal Disease Undergoing Hemodialysis: A Cross-Sectional, Real-World Data Study in Pakistan.

End-stage renal disease (ESRD) patients are mostly managed with maintenance hemodialysis (MHD). ESRD patients on MHD also present with many complications, such as anemia, hyperparathyroidism, and hepatitis prevalence. This study depicts the real-world scenario of anemia among MHD and end-stage renal disease patients in the Pakistani population. A retrospective, multicentric, and real-world data analytical study was conducted at 4 dialysis centers in Pakistan. The study had a sample size of n = 342 patients on maintenance hemodialysis. The data were gathered from the medical records of patients. Data analysis was performed using STATA Version 16. Statistical significance was gauged at a 0.05 level of significance. According to our results, the mean age of the patients was 45 (±15) years. Most of the patients were male (n = 234, 68.4%), whereas 58.1% of the patients were maintained on twice-weekly hemodialysis. The most commonly reported comorbidities were hypertension and diabetes mellitus. The frequency of dialysis (P < 0.01) and comorbidities (P = 0.009) had a significant association with anemia in MHD patients. The majority of the patients had hyperparathyroidism (52%) with anemia. Upon performing binary logistic regression, multivariate analysis displayed a similar odds value for having anemia in patients with every additional month in the duration of hemodialysis (OR 1.01, P = 0.001), the odds of anemic patients having a positive antihepatitis-C antibody (OR 2.22, P = 0.013), and the odds of having anemia in patients in the age category below 45 years (OR 1.93, P = 0.013). In conclusion, the study results depict that every additional month in the duration of hemodialysis, age (<45 years), and positive anti-HCV antibody status, these variables were more likely to have anemia in our study MHD patients. While in our final multivariate model, no statistically significant association was observed between hyperparathyroidism and anemia.

Variation in ovine KRTAP8-1 affects mean staple length and opacity of wool fiber.

In this study, keratin-associated proteins gene (KRTAP8-1) from five different sheep breeds and breed-crosses (n = 310) was genotyped using a Polymerase Chain Reaction-Single Strand confirmation Polymorphism (PCR-SSCP). Six unique genotypes were observed: AA, AC, AD, AE, DD and EE, with AA being the most common in the different breeds and crosses. Twelve wool characteristics: yield, mean staple length (MSL), bulk, mean fiber diameter (MFD), fiber diameter standard deviation (FDSD), coefficient of variation of fiber diameter (CVFD), medullation, standard deviation of medullation (MeSD), coefficient of variation of medullation (CVMed), opacity, standard deviation of opacity (OpSD), and coefficient of variation of opacity (CVOp) were measured on wool derived from the sheep. Variation in KRTAP8-1 was found to have strong association with MSL, OpSD and CVOp (p ≤ 0.027). The MSL of sheep of genotype AE was greater (p = 0.027) than for sheep of genotype AA. The OpSD of sheep of genotype AA was less (p = 0.017) than sheep with the AE genotype, and the CVOp of sheep with genotype AA was less (p = 0.018) than sheep with genotype AE. Further studies are required to confirm the role of variation in KRTAP8-1 in improving quality wool production.

Determination of the relationship between class IV sirtuin genes and growth traits in Chinese black Tibetan sheep.

Class IV sirtuin (SIRT6 and SIRT7) played essential roles in biometabolism processes via deacetylating specific transcription factors. The present study was conducted to search for mutations in SIRT6/7 and determine their associations with growth traits in black Tibetan sheep. Via DNA sequencing methods, three single-nucleotide polymorphisms (SNPs) were identified in 427 ewes, including a mutation (g.3724C > T) in the intron 1 of SIRT6 and two mutations (g.3668G > T and g.4223C > G) in SIRT7 intron 6 and 8, respectively. Based on the χ2 test, both g.3724C > T and g.4223C > G loci fitted with Hardy-Weinberg equilibrium (p > 0.05). Compared with animals with genotype TT, the CC genotype at g.3724C > T locus (SIRT6) exhibited the highest mean for body weight (p < 0.05) and heart girth (p < 0.05). At g.3668G > T locus (SIRT7), individuals carrying the GG genotype tended to have heavier body weight than those of TT genotype (p < 0.05). With the exception of body weight, body measurement traits not affected by combinative genotype (p > 0.05). Our results could be used as genetic markers for marker-assisted selection and maybe guide sheep breeding in economic traits.

Impact of sublethal concentrations of flonicamid on key demographic parameters and feeding behavior of Schizaphis graminum.

Flonicamid is a novel systemic insecticide that efficiently controls sap-sucking insect pests. However, the impact of sublethal concentrations of flonicamid on key demographic parameters and the feeding behavior of greenbug, Schizaphis graminum has not yet been studied. In this study, we used the age stage, two-sex life table approach, and electrical penetration graphs (EPGs) to investigate the sublethal effects of flonicamid on the biological traits and feeding behavior of S. graminum. Bioassays showed that flonicamid possesses high toxicity to adult S. graminum with LC50 of 5.111 mg L-1 following 48 h exposure. Sublethal concentrations of flonicamid (LC5 and LC10) significantly decreased the longevity and fecundity of directly exposed parental aphids (F0), while the reproductive days were reduced only at LC10. The pre-adult stage and total pre-reproductive period (TPRP) increased in F1 individuals after exposure of F0 aphids to the sublethal concentrations of flonicamid. Furthermore, the adult longevity, fecundity and key demographic parameters (R0, r, and λ) were significantly reduced in progeny generation (F1). EPG recordings showed that the total duration of phloem sap ingestion and concurrent salivation (E2) decreased substantially in F0 and F1 aphids after exposure to LC5 and LC10 of flonicamid. Taken together, our results showed that the sublethal concentrations of flonicamid affect the demographic parameters and feeding behavior that ultimately suppress the population growth of S. graminum. This study provides in-depth information about the overall effects of flonicamid on S. graminum that might help to manage this key pest.

Sublethal and transgenerational effects of lufenuron on biological characteristics and expression of reproductive related genes in the fall armyworm, Spodoptera frugiperda.

The fall armyworm, Spodoptera frugiperda, is a notorious polyphagous pest that causes serious economic losses in crucial crops and has invaded Africa and Asia. Lufenuron is widely used for controlling S. frugiperda in China, owing to its high toxicity against this key pest, and less pollution and little impact on natural enemies. In the present study, the sublethal and transgenerational effects of lufenuron on S. frugiperda were investigated to provide in-depth information for the rational use of lufenuron. Results showed that the development time and pupae weight were not significantly affected following exposure of females to LC10 and LC25 and male S. frugiperda to the LC10 of lufenuron. However, LC25 exposure significantly reduced pupal and total development time and pupae weight of male S. frugiperda. The longevity of S. frugiperda adults was prolonged by lufenuron and the fecundity of S. frugiperda treated with LC10 of lufenuron was significantly increased by 40% compared to the control. In addition, our study demonstrated that the LC25 of lufenuron had transgenerational effects on the progeny generation. The development time of female S. frugiperda whose parents were exposed to LC25 of lufenuron was significantly decreased compared to the control. And then, the expression profiles of Vg, VgR, JHEH, JHE, JHAMT, JHBP, CYP307A1, CYP306A1, CYP302A1 and CYP314A1 genes involved in insect reproduction and development were analyzed using Quantitative Real-Time PCR (RT-qPCR). Results showed that Vg, VgR, JHE, JHAMT, and CYP306A1 were significantly upregulated at the LC10 of lufenuron, which revealed that these upregulated genes might be linked with increased fecundity of S. frugiperda. Taken together, these findings highlighted the importance of sublethal and transgenerational effects under laboratory conditions and these effects may change the population dynamics in the field. Therefore, our study provided valuable information for promoting the rational use of lufenuron for controlling S. frugiperda.

COVID-19 spread control policies based early dynamics forecasting using deep learning algorithm.

Many severe epidemics and pandemics have hit human civilizations throughout history. The recent Sever Actuate Respiratory disease SARS-CoV-2 known as COVID-19 became a global disease and is still growing around the globe. It has severely affected the world's economy and ways of life. It necessitates predicting the spread in advance and considering various control policies to avoid the country's complete closure. In this paper, we propose deep learning-based stacked Bi-directional long short-term memory (Stacked Bi-LSTM) network that forecasts COVID-19 more accurately for the country of South Korea. The paper's main objectives are to present a lightweight, accurate, and optimized model to predict the spread considering restriction policies such as school closure, workspace closing, and the canceling of public events. Based on the fourteen parameters (including control policies), we predict and forecast the future value of the number of positive, dead, recovered, and quarantined cases. In this paper, we use the dataset of South Korea comprised of several control policies implemented for minimizing the spread of COVID-19. We compare the performance of the stacked Bi-LSTM with the traditional time-series models and LSTM model using the performance metrics mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). Moreover, we study the impact of control policies on forecasting accuracy. We further study the impact of changing the Bi-LSTM default activation functions Tanh with ReLU on forecasting accuracy. The research provides insight to policymakers to optimize the pooling of resources more optimally on the correct date and time prior to the event and to control the spread by employing various strategies in the meantime.

Fusion-Based Body-Worn IoT Sensor Platform for Gesture Recognition of Autism Spectrum Disorder Children.

The last decade's developments in sensor technologies and artificial intelligence applications have received extensive attention for daily life activity recognition. Autism spectrum disorder (ASD) in children is a neurological development disorder that causes significant impairments in social interaction, communication, and sensory action deficiency. Children with ASD have deficits in memory, emotion, cognition, and social skills. ASD affects children's communication skills and speaking abilities. ASD children have restricted interests and repetitive behavior. They can communicate in sign language but have difficulties communicating with others as not everyone knows sign language. This paper proposes a body-worn multi-sensor-based Internet of Things (IoT) platform using machine learning to recognize the complex sign language of speech-impaired children. Optimal sensor location is essential in extracting the features, as variations in placement result in an interpretation of recognition accuracy. We acquire the time-series data of sensors, extract various time-domain and frequency-domain features, and evaluate different classifiers for recognizing ASD children's gestures. We compare in terms of accuracy the decision tree (DT), random forest, artificial neural network (ANN), and k-nearest neighbour (KNN) classifiers to recognize ASD children's gestures, and the results showed more than 96% recognition accuracy.

STEM Image Analysis Based on Deep Learning: Identification of Vacancy Defects and Polymorphs of MoS2.

Scanning transmission electron microscopy (STEM) is an indispensable tool for atomic-resolution structural analysis for a wide range of materials. The conventional analysis of STEM images is an extensive hands-on process, which limits efficient handling of high-throughput data. Here, we apply a fully convolutional network (FCN) for identification of important structural features of two-dimensional crystals. ResUNet, a type of FCN, is utilized in identifying sulfur vacancies and polymorph types of MoS2 from atomic resolution STEM images. Efficient models are achieved based on training with simulated images in the presence of different levels of noise, aberrations, and carbon contamination. The accuracy of the FCN models toward extensive experimental STEM images is comparable to that of careful hands-on analysis. Our work provides a guideline on best practices to train a deep learning model for STEM image analysis and demonstrates FCN's application for efficient processing of a large volume of STEM data.

In silico analysis of a novel pathogenic variant c.7G > A in C14orf39 gene identified by WES in a Pakistani family with azoospermia.

Infertility is a multifactorial disorder that affects approximately 12% of couples of childbearing ages worldwide. Few studies have been conducted to understand the genetic causes of infertility in depth. The synaptonemal complex (SC), which is essential for the progression of meiosis, is a conserved tripartite structure that binds homologous chromosomes together and is thus required for fertility. This study investigated genetic causes of infertility in a Pakistani consanguineous family containing two patients suffering from non-obstructive azoospermia (NOA). We performed whole-exome sequencing, followed by Sanger sequencing, and identified a novel pathogenic variant (c.7G > A [p.D3N]) in the SC coding gene C14orf39, which was recessively co-segregated with NOA. In silico analysis revealed that charges on wild-type residues were lost, which may result in loss of interactions with other molecules and residues, and a reduction in protein stability occurred, which was caused by the p.D3N mutation. The novel variant generated the mutant protein C14ORF39D3N, and homozygous mutations in C14orf39 resulted in NOA. The transcriptome profile of C14ORF39 shows that it is specifically expressed in early brain development, which suggests that research in this area is required to study other functions of C14ORF39 in addition to its role in the germline. This research highlights the conserved role of C14orf39/SIX6OS1 in assembly of the SC and its indispensable role in facilitating genetic diagnosis in patients with infertility, which may enable the development of future treatments.

Identification of thienopyrimidine glycinates as selective inhibitors for h-NTPDases.

The h-NTPDases is an essential family of ectonucleotidases that consists of eight isozymes with various physiological functions. The undesired activity of the h-NTPDases leads to pathological conditions such as cancer, diabetes, inflammation, and thrombosis. In the present study, a series of thienopyrimidines was synthesized employing a sequential SNAr and Suzuki coupling to synthesize diverse aryl substituted thienopyrimidine glycinate derivatives. The synthesized compounds constituted electron donating, electron-deficient, heteroaryl, and fluorinated substituents. The thienopyrimidines were screened against h-NTPDases to determine the effect on the activity of the h-NTPDases-1, -2, -3, and -8. The compound 3j selectively blocked the isozyme h-NTPDases1, while the compounds 3e, 3m, and 4a were selective inhibitors of h-NTPDases2. The activity of the isozyme h-NTPDases3 was selectively reduced by inhibitor 3k whereas, the compound 3d was found as the most active inhibitor against isozyme h-NTPDase8. The molecular docking study interpreted the interactions of the potent inhibitors of the respective isozymes with important amino acid residues i.e., Asp54, Ser57, His59, Ser58, His59, Asp213, and Phe360 of h-NTPDases1 protein; residues Arg 392, Ala393, Ala347, Tye350 and Arg245 of h-NTPDases2; amino acids Arg67, Ser65, Ala323, Gly222, and Tyr375 of h-NTPDases3 whereas in case of h-NTPDases8, the residues Val436, Gln74, Gly179, and Val71 were involved in interaction with the inhibitors docked into the active sites of these isozymes.

Attention based parameter estimation and states forecasting of COVID-19 pandemic using modified SIQRD Model.

In this work, we propose a new mathematical modeling of the spread of COVID-19 infection in an arbitrary population, by modifying the SIQRD model as m-SIQRD model, while taking into consideration the eight governmental interventions such as cancellation of events, closure of public places etc., as well as the influence of the asymptomatic cases on the states of the model. We introduce robustness and improved accuracy in predictions of these models by utilizing a novel deep learning scheme. This scheme comprises of attention based architecture, alongside with Generative Adversarial Network (GAN) based data augmentation, for robust estimation of time varying parameters of m-SIQRD model. In this regard, we also utilized a novel feature extraction methodology by employing noise removal operation by Spline interpolation and Savitzky-Golay filter, followed by Principal Component Analysis (PCA). These parameters are later directed towards two main tasks: forecasting of states to the next 15 days, and estimation of best policy encodings to control the infected and deceased number within the framework of data driven synergetic control theory. We validated the superiority of the forecasting performance of the proposed scheme over countries of South Korea and Germany and compared this performance with 7 benchmark forecasting models. We also showed the potential of this scheme to determine best policy encodings in South Korea for 15 day forecast horizon.

Biodiesel Production Using Wild Apricot (Prunus aitchisonii) Seed Oil via Heterogeneous Catalysts.

We confined the formation and characterization of heterogenous nano-catalysts and then used them to produce biodiesel from the novel non-edible seed oil of Prunus aitchisonii. P. aitchisonii seeds' oil content was extracted at about 52.4 ± 3% with 0.77% FFA. Three different heterogenous nano-catalysts-calcined (CPC), KPC, and KOH-activated P. aitchisonii cake Titanium Dioxide (TiO2)-were synthesized using calcination and precipitation methods. The mentioned catalysts were characterized through XRD, SEM, and EDX to inspect their crystallin dimension, shape, and arrangement. Titanium dioxide has morphological dimensions so that the average particle size ranges from 49-60 nm. The result shows that the crystal structure of TiO2 is tetragonal (Anatase). The surface morphology of CPC illustrated that the roughness of the surface was increased after calcination, many macropores and hollow cavities appeared, and the external structure became very porous. These changes in morphology may increase the catalytic efficiency of CPC than non-calcined Prunus aitchisonii oil cake. The fuel belonging to PAOB stood according to the series suggested by ASTM criteria. All the characterization reports that P. aitchisonii is a novel and efficient potential source of biodiesel as a green energy source.

Prevalence of antibiotic resistance pattern in shigella isolates procured from pediatric patients at Faisalabad - Pakistan.

Shigella infection (shigellosis) is an intestinal disease caused by a shigella isolates belongs to a family Enterobacteriacea. Watery diarrhea, abdominal pain and tenesmus are the prominent symptoms of shigella infection. The present study was designed to determine period prevalence and antimicrobial susceptibility of Shigella species recovered from stool specimens obtained from diarrheal paediatric patients under 5 years of age. This cross-sectional study was carried out for a period of six months (Jan to June, 2016). All Shigella isolates were identified based on colony morphology, microscopic characteristics, and biochemical characteristics. After applying Kirby Baur disc diffusion method only 22 (18.96%) stool specimens were found positive for Shigella isolates among the 116 stool specimens. The isolates were also found susceptible to Levofloxacin (72.72%), Azithromycin (59.09%), and Cefotaxime (40.90%). However, the said isolates were resistant to Lincomycin (100%) and Penicillin-G (100%), followed by Amoxicillin (95.45%) and Oxacillin (95.45%). The chi-square test was used to check the close association among antimicrobial agents used and as highly significant (p-value < 2.2e-16). Based on antimicrobial susceptibility findings, Levofloxacin, Azithromycin and Cefotoxime were found effective for the control of shigellosis.

Sublethal concentrations of clothianidin affect fecundity and key demographic parameters of the chive maggot, Bradysia odoriphaga.

Bradysia odoriphaga is a major insect pest that infests Chinese chive in northern China. Clothianidin is a second-generation neonicotinoid insecticide that is commonly used against B. odoriphaga. In this study, the effect of sublethal clothianidin concentrations (LC5 and LC10) on key biological characteristics of B. odoriphaga was investigated using an age-stage, two-sex life table method. Bioassays results showed that clothianidin exhibited high toxicity against B. odoriphaga with LC50 of 1.898 mg L-1 following 24 h exposure. The developmental duration of larvae was significantly increased when exposed to the LC5 (0.209 mg L-1) and LC10 (0.340 mg L-1) of clothianidin. No significant effects were observed on the pupal stage, adult pre-oviposition period (APOP), total pre-oviposition period (TPOP), and mean longevities of male and female. The oviposition period and fecundity of B. odoriphaga were reduced in clothianidin-treated groups. Moreover, key demographic parameters, including the intrinsic rate of increase (r), finite rate of increase (λ), and net reproductive rate (R0), were significantly decreased by the LC5 and LC10 of clothianidin, while no effects were noted on mean generation time (T). Overall, this study showed that sublethal concentrations of clothianidin have a detrimental effect on B. odoriphaga developmental period, fecundity, and life table parameters. Therefore, clothianidin has the potential to suppress the population of B. odoriphaga even at sublethal concentrations.