Computational fluid dynamics (CFD) has recently become a pivotal tool in the design and scale-up of bioprocesses. While CFD has been extensively utilized for stirred tank reactors (STRs), there exists a relatively limited body of literature focusing on CFD applications for shake flasks, almost exclusively concentrated on fluids at waterlike viscosity. The importance of CFD model validation cannot be overstated. While techniques to elucidate the internal flow field are necessary for model validation in STRs, the liquid distribution, caused by the orbital shaking motion of shake flasks, can be exploited for model validation. An OpenFOAM CFD model for shake flasks has been established. Calculated liquid distributions were compared to suitable, previously published experimental data. Across a broad range of shaking conditions, at waterlike and moderate viscosity (16.7 mPa∙s), the CFD model's liquid distributions align excellently with the experimental data, in terms of overall shape and position of the liquid relative to the direction of the centrifugal force. Additionally, the CFD model was used to calculate the volumetric power input, based on the energy dissipation. Depending on the shaking conditions, the computed volumetric power inputs range from 0.1 to 7 kW/m3 and differed on average by 0.01 kW/m3 from measured literature data.
Despite the importance of methicillin-resistant Staphylococcus aureus (MRSA) as a priority nosocomial pathogen, the genome sequences of Malaysian MRSA isolates are currently limited to a small pool of samples. Here, we present the genome sequence analyses of 88 clinical MRSA isolates obtained from the main tertiary hospital in Terengganu, Malaysia in 2016-2020, to obtain in-depth insights into their characteristics. The EMRSA-15 (ST22-SCCmec IV) clone of the clonal complex 22 (CC22) lineage was predominant with a total of 61 (69.3%) isolates. Earlier reports from other Malaysian hospitals indicated the predominance of the ST239 clone, but only two (2.3%) isolates were identified in this study. Two Indian-origin clones, the Bengal Bay clone ST772-SCCmec V (n = 2) and ST672 (n = 10) were also detected, with most of the ST672 isolates obtained in 2020 (n = 7). Two new STs were found, with one isolate each, and were designated ST7879 and ST7883. From the core genome phylogenetic tree, the HSNZ MRSA isolates could be grouped into seven clades. Antimicrobial phenotype-genotype concordance was high (> 95%), indicating the accuracy of WGS in predicting most resistances. Majority of the MRSA isolates were found to harbor more than 10 virulence genes, demonstrating their pathogenic nature.
Greenhouse gases can cause acid rain, which in turn degrades soil chemical properties. This research was conducted to determine the effects of simulated acid rain (SAR) on the chemical properties of Nyalau series (Typic paleudults). A 45-day laboratory leaching and incubation study (control conditions) was conducted following standard procedures include preparing simulated acid rain with specific pH levels, followed by experimental design/plan and systematically analyzing both soil and leachate for chemical changes over the 45-day period. Six treatments five of which were SAR (pH 3.5, 4.0, 4.5, 5.0, and 5.5) and one control referred to as natural rainwater (pH 6.0) were evaluated. From the study, the SAR had significant effects on the chemical properties of the soil and its leachate. The pH of 3.5 of SAR treatments decreased soil pH, K+, and fertility index. In contrast, the contents of Mg2+, Na+, SO42-, NO3-, and acidity were higher at the lower SAR pH. Furthermore, K+ and Mg2+ in the leachate significantly increased with increasing acidity of the SAR. The changes in Ca2+ and NH4+ between the soil and its leachate were positively correlated (r = 0.84 and 0.86), whereas the changes in NO3- negatively correlated (r = - 0.82). The novelty of these results lies in the discovery of significant alterations in soil chemistry due to simulated acid rain (SAR), particularly impacting soil fertility and nutrient availability, with notable positive and negative correlations among specific ions where prolonged exposure to acid rain could negatively affect the moderately tolerant to acidic and nutrient-poor soils. Acid rain can negatively affect soil fertility and the general soils ecosystem functions. Long-term field studies are required to consolidate the findings of this present study in order to reveal the sustained impact of SAR on tropical forest ecosystems, particularly concerning soil health, plant tolerance, and potential shifts in biodiversity and ecological balance.
The digital healthcare (DH) system has recently emerged as an advanced rehabilitation approach that promotes rehabilitation training based on virtual reality (VR) and augmented reality (AR). The purpose of this meta-analysis study is to review and assess the impact of DH systems on pain and physical function among patients diagnosed with knee joint pain. Between January 2003 and September 2023, studies that met the listed inclusion criteria were gathered from Scopus, PubMed, Web of Science, and EBSCO databases. The analysis of standardized mean difference (SMD) was carried out with 95% confidence interval (95% CI) (PROSPERO registration number: CRD42023462538). Eight research papers were selected, which collectively involved 194 males and 279 females. The meta-analysis outcomes revealed that DH intervention significantly improved balance (SMD, 0.41 [0.12, 0.69], p
The use of a reflector can increase the solar radiation on the photovoltaic module (PV) surface, whereby the energy output can be improved. However, the economic feasibility may need to be considered too. This study is conducted, for the first time, due to the lack of studies regarding the economic feasibility assessment of implementing reflectors under the Malaysian meteorological conditions. The outcome will give information about the suitability for implementing a PV reflector in Malaysia through an experimental setup at a sewage treatment site, for two months in 2022. The Malaysian meteorological data, which include daily solar radiation, ambient temperature and wind velocity, were collected to study the output energy, efficiency and the economic perspective of a PV. In February 2022, the PV was operating without a reflector and the averaged values for the monthly solar radiation, ambient temperature and wind velocity were 539.9 MJ/m2, 28.4 °C and 2.2 m/s, respectively, which resulted in an output energy of 106.43 kWh. On the other hand, for April 2022, the PV was operating with a reflector. With the respective averaged input parameters 544.98 MJ/m2, 28.9 °C and 1.51 m/s, the output energy was 121.94 kWh. It is thus shown that the PV with a reflector increases the PV's output energy by 14.57%. Also, it is shown that the cost-effective factor value is 0.955 which means that the PV reflector is economically feasible to be implemented under the Malaysian meteorological conditions. Hence, extensive research should be conducted to improve the performance of PV reflectors. The findings of this paper maybe useful for researchers and/or manufacturers of PV reflectors.
The infection of human papilloma virus (HPV) poses a global public health challenge, particularly in regions with limited access to health care and preventive measures, contributing to health disparities and increased disease burden. In this research work, we present a new model to explore the transmission dynamics of HPV infection, incorporating the impact of vaccination through the Atangana-Baleanu derivative. We establish the positivity and uniqueness of the solution for the proposed model HPV infection. The threshold parameter is determined through the next-generation matrix method, symbolized by [Formula: see text]. Moreover, we investigate the local asymptotic stability of the infection-free steady-state of the system. The existence of the solutions of the recommended model is determined through fixed-point theory. A numerical scheme is presented to visualize the dynamical behavior of the system with variation of input factors. We have shown the impact of input parameters on the dynamics of the system through numerical simulations. The findings of our investigation delineated the principal parameters exerting significant influence for the control and prevention of HPV infection.
DNA-binding proteins (DBPs) play a significant role in all phases of genetic processes, including DNA recombination, repair, and modification. They are often utilized in drug discovery as fundamental elements of steroids, antibiotics, and anticancer drugs. Predicting them poses the most challenging task in proteomics research. Conventional experimental methods for DBP identification are costly and sometimes biased toward prediction. Therefore, developing powerful computational methods that can accurately and rapidly identify DBPs from sequence information is an urgent need. In this study, we propose a novel deep learning-based method called Deep-WET to accurately identify DBPs from primary sequence information. In Deep-WET, we employed three powerful feature encoding schemes containing Global Vectors, Word2Vec, and fastText to encode the protein sequence. Subsequently, these three features were sequentially combined and weighted using the weights obtained from the elements learned through the differential evolution (DE) algorithm. To enhance the predictive performance of Deep-WET, we applied the SHapley Additive exPlanations approach to remove irrelevant features. Finally, the optimal feature subset was input into convolutional neural networks to construct the Deep-WET predictor. Both cross-validation and independent tests indicated that Deep-WET achieved superior predictive performance compared to conventional machine learning classifiers. In addition, in extensive independent test, Deep-WET was effective and outperformed than several state-of-the-art methods for DBP prediction, with accuracy of 78.08%, MCC of 0.559, and AUC of 0.805. This superior performance shows that Deep-WET has a tremendous predictive capacity to predict DBPs. The web server of Deep-WET and curated datasets in this study are available at https://deepwet-dna.monarcatechnical.com/ . The proposed Deep-WET is anticipated to serve the community-wide effort for large-scale identification of potential DBPs.
Although knowledge of the composition and genetic diversity of disease vectors is important for their management, this is limiting in many instances. In this study, the population structure and phylogenetic relationship of the two Aedes aegypti subspecies namely Aedes aegypti aegypti (Aaa) and Aedes aegypti formosus (Aaf) in eight geographical areas in Sudan were analyzed using seven microsatellite markers. Hardy-Weinberg Equilibrium (HWE) for the two subspecies revealed that Aaa deviated from HWE among the seven microsatellite loci, while Aaf exhibited departure in five loci and no departure in two loci (A10 and M201). The Factorial Correspondence Analysis (FCA) plots revealed that the Aaa populations from Port Sudan, Tokar, and Kassala clustered together (which is consistent with the unrooted phylogenetic tree), Aaf from Fasher and Nyala populations clustered together, and Gezira, Kadugli, and Junaynah populations also clustered together. The Bayesian cluster analysis structured the populations into two groups suggesting two genetically distinct groups (subspecies). Isolation by distance test revealed a moderate to strong significant correlation between geographical distance and genetic variations (p = 0.003, r = 0.391). The migration network created using divMigrate demonstrated that migration and gene exchange between subspecies populations appear to occur based on their geographical proximity. The genetic structure of the Ae. aegypti subspecies population and the gene flow among them, which may be interpreted as the mosquito vector's capacity for dispersal, were revealed in this study. These findings will help in the improvement of dengue epidemiology research including information on the identity of the target vector/subspecies and the arboviruses vector surveillance program.
This study was conducted to investigate the energy mobilisation preference and ionoregulation pattern of female tilapia, Oreochromis sp. living in different environments. Three different treatments of tilapia as physiology compromising model were compared; tilapia cultured in recirculating aquaculture system (RAS as Treatment I-RAS), tilapia cultured in open water cage (Treatment II-Cage) and tilapia transferred from cage and cultured in RAS (Treatment III-Compensation). Results revealed that tilapia from Treatment I and III mobilised lipid to support gonadogenesis, whilst Treatment II tilapia mobilised glycogen as primary energy for daily exercise activity and reserved protein for growth. The gills and kidney Na+/K+ ATPase (NKA) activities remained relatively stable to maintain homeostasis with a stable Na+ and K+ levels. As a remark, this study revealed that tilapia strategized their energy mobilisation preference in accessing glycogen as an easy energy to support exercise metabolism and protein somatogenesis in cage culture condition, while tilapia cultured in RAS mobilised lipid for gonadagenesis purposes.
It has been well established that pandemics affect mental health, yet few studies have been conducted in China regarding this issue following COVID-19's gradual decline and the recent H1N1 influenza outbreak. In response to this research gap, this investigation explores the risk factors linked to depression and anxiety symptoms among young adults in this specific setting. Data were collected via an online cross-sectional survey of 385 young adults living in Anyang city, Henan Province, China, between June 15 and July 21, 2023. Respondents were assessed for anxiety and depression symptoms using the GAD-7 and PHQ-9 scales. Additionally, to examine the factors that influenced the study, we utilized an ordered logit regression model. Results revealed depression and anxiety prevalence rates of 33.3% and 21.6%, respectively. Several factors were found to increase the likelihood of depression and anxiety among young adults, including gender, age, education status, marital status, and attitudes towards epidemics. Participants' concerns about pandemics and viruses had a significant negative impact relationship on depression levels. Women report moderate to severe anxiety more frequently than men. An evident correlation can be observed between the educational attainment level and the influence of depression and anxiety.
The present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270 clayey soil samples stabilized with geopolymer, with ground-granulated blast-furnace slag and fly ash as source materials and sodium hydroxide solution as alkali activator. The database was randomly divided into training (80%) and testing (20%) sets for model development and validation. Several performance metrics, including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE), were utilized to assess the accuracy and reliability of the developed models. The statistical results of this research showed that the GB and AdaBoost are reliable models based on the obtained values of R2 (= 0.980, 0.975), MAE (= 0.585, 0.655), RMSE (= 0.969, 1.088), and MSE (= 0.940, 1.185) for the testing dataset, respectively compared to the widely used artificial neural network, random forest, extreme gradient boosting, multivariable regression, and multi-gen genetic programming based models. Furthermore, the sensitivity analysis result shows that ground-granulated blast-furnace slag content was the key parameter affecting the UCS.
We experimentally demonstrated the generation of domain-wall dark pulse in an Erbium-doped fiber laser using the combination of a 10 cm graded index multimode fiber sandwiched by single mode fibers as artificial saturable absorber. The interaction of phase difference in grade index multimode fiber allowed the stable dual-wavelength oscillation in the cavity. The dual-wavelength centered at 1567.2 nm and 1569.4 nm produces the topological defect in temporal domain and achieved a dark pulse formation with repetition rate of 21.5 MHz. The highest average pulse energy is calculated as 769.6 pJ with pulse width of 5 ns. Throughout the operating pump power range, the average pulse energy and output power increase linearly, with R2 of 0.9999 and achieved the laser efficiency of 9.33%. From the measurement in frequency domain, the signal-to-noise ratio is measured as 49 dB. As compared to reported DW dark pulse works, the proposed structure only required a short length of multimode fiber, which allowed the domain-wall dark pulse to achieve higher pulse repetition rate. The venture of domain wall dark pulse is potentially to pave the foundation toward sustainable industrial growth.
In this article, an attribute control chart is proposed when the lifetime of a product follows a Weibull distribution in two-stage sampling, which is based on the number of failures from a truncated life test. The coefficients of the proposed double sampling attribute control chart and the test duration are determined so that the average run length when the process is in control is close to the target value. An overview is reported on how double sampling np control charts work. Tables reporting the out-of-control average run lengths are given for various shift parameters. A case study is given to illustrate the proposed control chart for industrial use. A comparison of two-stage and single-stage sampling of failure of products is discussed.
To analyze the public opinion related to the employment situation, a combined approach is proposed to study the valuable ideas from social media. Firstly, the popularity of public opinion was analyzed according to the time series from a statistical point of view. Secondly, the feature extraction was carried out on the public opinion information, and the thematic analysis of the employment environment was carried out based on the Latent Dirichlet Allocation model. Thirdly, the Bert model was used to analyze the sentiment classification and trend of the employment-related public opinion data. Finally, the employment public opinion texts in different regions were studied based on the spatial sequence popularity analysis, keyword difference analysis. A case study in China is conducted to verify the effectiveness of proposed combined approach. Results shown that the popularity of employment public opinion reached the highest level in March 2022. Public opinions towards employment situation are negative. There is a specific relationship between the popularity of employment public opinion in different provinces.
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal of polyps are highly operator-dependent procedures and occur in a highly complex organ topology. There exists a high missed detection rate and incomplete removal of colonic polyps. To assist in clinical procedures and reduce missed rates, automated methods for detecting and segmenting polyps using machine learning have been achieved in past years. However, the major drawback in most of these methods is their ability to generalise to out-of-sample unseen datasets from different centres, populations, modalities, and acquisition systems. To test this hypothesis rigorously, we, together with expert gastroenterologists, curated a multi-centre and multi-population dataset acquired from six different colonoscopy systems and challenged the computational expert teams to develop robust automated detection and segmentation methods in a crowd-sourcing Endoscopic computer vision challenge. This work put forward rigorous generalisability tests and assesses the usability of devised deep learning methods in dynamic and actual clinical colonoscopy procedures. We analyse the results of four top performing teams for the detection task and five top performing teams for the segmentation task. Our analyses demonstrate that the top-ranking teams concentrated mainly on accuracy over the real-time performance required for clinical applicability. We further dissect the devised methods and provide an experiment-based hypothesis that reveals the need for improved generalisability to tackle diversity present in multi-centre datasets and routine clinical procedures.
Gastrointestinal (GI) cancers account for a significant incidence and mortality rates of cancers globally. Utilization of a phenomic data approach allows researchers to reveal the mechanisms and molecular pathogenesis of these conditions. We aimed to investigate the association between the phenomic features and GI cancers in a large cohort study. We included 502,369 subjects aged 37-73 years in the UK Biobank recruited since 2006, followed until the date of the first cancer diagnosis, date of death, or the end of follow-up on December 31st, 2016, whichever occurred first. Socio-demographic factors, blood chemistry, anthropometric measurements and lifestyle factors of participants collected at baseline assessment were analysed. Unvariable and multivariable logistic regression were conducted to determine the significant risk factors for the outcomes of interest, based on the odds ratio (OR) and 95% confidence intervals (CI). The analysis included a total of 441,141 participants, of which 7952 (1.8%) were incident GI cancer cases and 433,189 were healthy controls. A marker, cystatin C was associated with total and each gastrointestinal cancer (adjusted OR 2.43; 95% CI 2.23-2.64). In this cohort, compared to Asians, the Whites appeared to have a higher risk of developing gastrointestinal cancers. Several other factors were associated with distinct GI cancers. Cystatin C and race appear to be important features in GI cancers, suggesting some overlap in the molecular pathogenesis of GI cancers. Given the small proportion of Asians within the UK Biobank, the association between race and GI cancers requires further confirmation.
Artificial intelligence models, like ChatGPT, have the potential to revolutionize higher education when implemented properly. This study aimed to investigate the factors influencing university students' attitudes and usage of ChatGPT in Arab countries. The survey instrument "TAME-ChatGPT" was administered to 2240 participants from Iraq, Kuwait, Egypt, Lebanon, and Jordan. Of those, 46.8% heard of ChatGPT, and 52.6% used it before the study. The results indicated that a positive attitude and usage of ChatGPT were determined by factors like ease of use, positive attitude towards technology, social influence, perceived usefulness, behavioral/cognitive influences, low perceived risks, and low anxiety. Confirmatory factor analysis indicated the adequacy of the "TAME-ChatGPT" constructs. Multivariate analysis demonstrated that the attitude towards ChatGPT usage was significantly influenced by country of residence, age, university type, and recent academic performance. This study validated "TAME-ChatGPT" as a useful tool for assessing ChatGPT adoption among university students. The successful integration of ChatGPT in higher education relies on the perceived ease of use, perceived usefulness, positive attitude towards technology, social influence, behavioral/cognitive elements, low anxiety, and minimal perceived risks. Policies for ChatGPT adoption in higher education should be tailored to individual contexts, considering the variations in student attitudes observed in this study.
Here we describe a molecular approach to assess conspecific identity that relies on the comparison of an evolved mutated transposable element sequence and its genomic insertion site in individuals from closely related species. This was explored with the IFP2 piggyBac transposon, originally discovered in Trichoplusia ni as a 2472 bp functional element, that was subsequently found as mutated elements in seven species within the Bactrocera dorsalis species complex. In a B. dorsalis [Hendel] strain collected in Kahuku, Hawaii, a degenerate 2420 bp piggyBac sequence (pBacBd-Kah) having ~ 94.5% sequence identity to IFP2 was isolated, and it was reasoned that common species, or strains within species, should share the same evolved element and its precise genomic insertion site. To test this assumption, PCR using primers to pBacBd-Kah and adjacent genomic sequences was used to isolate and compare homologous sequences in strains of four sibling species within the complex. Three of these taxa, B. papayae, B. philippinensis, and B. invadens, were previously synonymized with B. dorsalis, and found to share nearly identical pBacBd-Kah homologous elements (> 99% nucleotide identity) within the identical insertion site consistent with conspecific species. The fourth species tested, B. carambolae, considered to be a closely related yet independent species sympatric with B. dorsalis, also shared the pBacBd-Kah sequence and insertion site in one strain from Suriname, while another divergent pBacBd-Kah derivative, closer in identity to IFP2, was found in individuals from French Guiana, Bangladesh and Malaysia. This data, along with the absence of pBacBd-Kah in distantly related Bactrocera, indicates that mutated descendants of piggyBac, as well as other invasive mobile elements, could be reliable genomic markers for common species identity.