The Evaluation of a Long-Term Experiment on the Relationships between Weather, Nitrogen Fertilization, Preceding Crop, and Winter Wheat Grain Yield on Cambisol.

In this paper, a sequence (1979-2022) of a long-term trial established in Lukavec in 1956 (Czech Republic) focusing on the effect of weather, various nitrogen (N) fertilization methods (control, PK, N1PK, N2PK, and N3PK) and preceding crops (cereals, legumes, and oil plants) on winter wheat grain yield is presented. The weather significantly changed at the site of the long-term trial. While the trend in the mean temperature significantly increased, precipitation did not change significantly over the long term. Four relationships between weather and grain yield were evaluated to be significant: (a) the mean temperature in February (r = -0.4) and the precipitation in (b) February (r = -0.4), (c) March (r = -0.4), and (d) May (r = 0.5). The yield trends for all the fertilizer treatments increased, including the unfertilized control. The N3PK treatment provided the highest mean grain yields, while the unfertilized control had the lowest yields. Comparing the preceding crops, the highest yields were harvested when the wheat followed the legumes. On the other hand, the cereals were evaluated as the least suitable preceding crop in terms of grain yield. According to the linear-plateau model, the optimal nitrogen (N) dose for modern wheat varieties, following legumes and under the trial's soil climate conditions, was 131 kg ha-1 N, corresponding to a mean grain yield of 8.2 t ha-1.

Miniaturized fluorescence pH sensor with assembly free ball lens on a tapered multimode optical fiber.

In biochemistry, the absence of a compact, assembly-free pH sensor with high sensitivity and signal-to-noise ratio has been a persistent hurdle in achieving accurate pH measurements in real time, particularly in complex liquid environments. This manuscript introduces what we believe to be a novel solution in the form of a miniaturized pH sensor utilizing an assembly-free ball lens on a tapered multimode optical fiber (TMMF), offering the potential to revolutionize pH sensing in biochemical applications. A multimode optical fiber (MMF) was subjected to tapering processes, leading to the creation of an ultra-thin needle-like structure with a cross-sectional diameter of about 12.5 µm and a taper length of 3 mm. Subsequently, a ball lens possessing a diameter of 20 µm was fabricated at the apex of the taper. The resultant structure was coated utilizing the dip-coating technique, involving a composite mixture of epoxy and pH-sensitive dye, 2',7'-bis(2-carboxyethyl)-5-(and-6)-carboxyfluorescein (BCECF), thereby ensconcing the tapered ball lens with dye molecules for pH sensing. This study encompassed the fabrication and evaluation of six distinct fiber structures, incorporating the cleaved endface, the convex lens, and the ball lens structures to compare light focal lengths and propagation intensities. Computational simulations and numerical analyses were conducted to elucidate the encompassing light focal distances across the full array of lens configurations. The efficacy of the proposed pH sensor was subsequently assessed through its deployment within a complex liquid medium spanning a pH spectrum ranging from 6 to 8. Real-time data acquisition was performed with a fast response time of 0.5 seconds. A comparative analysis with a pH sensor predicated upon a single TMMF embedded with the fluorescent dye underscored the substantial signal enhancement achieved by the proposed system twice the fluorescence signal magnitude. The proposed assembly-free miniaturized pH sensor not only substantiates enhanced signal collection efficiency but also decisively addresses the persistent challenges of poor signal-to-noise ratio encountered within contemporary miniaturized pH probes.

Entropy generation in magneto couple stress bionanofluid flow containing gyrotactic microorganisms towards a stagnation point on a stretching/shrinking sheet.

The study focuses on the behavior of an electrically conducting non-Newtonian fluid with couple stress properties, using water-based bionanofluid. The fluid is analyzed as it flows across a porous stretching/shrinking sheet within its own plane. This Study also explores the Bejan Number and Entropy Generation. To facilitate this investigation, the governing nonlinear partial differential equations undergo a transformation, wherein they are converted into nonlinear ordinary differential equations through a suitable similarity transformation. An ideal strategy has been employed to achieve the desired results from the modeled challenge. The Homotopy Analysis Method is applied to determine the solution of the system of differential equations. The convergence of the applied method and their comparison with the numerical method are described through graphs and tables. The main features of the different profiles are briefly described. Graphs are used to analyze the impact of the Bejan number, concentration, temperature, velocity profile, and entropy production rate. Tables present the characteristics of skin friction, Nusselt, and Sherwood numbers for various limitations. The stretching and ambient fluid velocities should fluctuate linearly as the distance from the stagnation point increases. A rise in the magnetic and porosity parameters is accompanied by an increase in the velocity profile. While the velocity profile falls off as a Couple of fluid parameters are increased. The phenomenon of temperature boost is observed to be positively correlated with the increase in Brownian motion parameter while exhibiting no significant dependence on other parameters such as Brinkman number, Prandtl number Lewis number and Thermophoresis parameter. Entropy generation increases with the Brinkman number while decreasing with the radiation parameter and diffusion parameter as is plainly demonstrated.

Geraniol Suppresses Oxidative Stress, Inflammation, and Interstitial Collagenase to Protect against Inflammatory Arthritis.

Geraniol (GER) is a plant-derived acyclic isoprenoid monoterpene that has displayed anti-inflammatory effects in numerous in vivo and in vitro models. This study was therefore designed to evaluate the antiarthritic potential of GER in complete Freund's adjuvant (CFA)-induced inflammatory arthritis (IA) model in rats. IA was induced by intraplantar injection of CFA (0.1 mL), and a week after CFA administration, rats were treated with various doses of methotrexate (MTX; 1 mg/kg) or GER (25, 50, and 100 mg/kg). Treatments were given on every alternate day, and animals were sacrificed on the 35th day. Paw volume, histopathological, hematological, radiographic, and qPCR analyses were performed to analyze the severity of the disease. GER significantly reduced paw edema after 35 days of treatment, and these results were comparable to the MTX-treated group. GER-treated animals displayed a perfect joint structure with minimal inflammation and no signs of cartilage or bone damage. Moreover, GER restored red blood cell and hemoglobin levels, normalized erythrocyte sedimentation rate, platelet, and c-reactive protein values, and also attenuated the levels of rheumatoid factor. RT-qPCR analysis demonstrated that GER decreased mRNA expression of pro-inflammatory cytokines like tumor necrosis factor-alpha (TNF-α) and interleukin-1 beta. GER also down-regulated the transcript levels of cyclooxygenase-2 (COX-2), microsomal prostaglandin E synthase-1, prostaglandin D2 synthase, and interstitial collagenase (MMP-1). Molecular docking of GER with COX-2, TNF-α, and MMP-1 also revealed that the antiarthritic effects of GER could be due to its direct interactions with these mediators. Based on our findings, it is conceivable that the antiarthritic effects of GER could be attributed to downregulation of pro-inflammatory mediators and protease like MMP-1.

Molecular Characterization of Extensively Drug Resistant Salmonella Enterica Serovar Typhi Clinical Isolates from Lahore, Pakistan.

Background: The emergence of extensively drug-resistant (XDR) typhoid in Pakistan has endangered the treatment options available to manage this infection. Third generation cephalosporin were the empiric choice to treat typhoid fever in Pakistan, but acquisition of ESBLs have knocked them out of the arsenal. The current empiric choice is azithromycin which is vulnerable to resistance too. This study aimed to assess the burden of XDR typhoid and the frequency of resistance determinants in blood culture samples collected from different hospitals in Lahore, Pakistan. Methods: A total of 835 blood cultures were collected from different tertiary care hospitals in Lahore during January 2019 to December 2021. Among 835 blood cultures, 389 Salmonella Typhi were identified, and 150 were XDR S. Typhi (resistant to all recommended antibiotics). Antibiotics resistance genes of the first-line drugs (blaTEM-1, catA1, sul1, and dhfR7) and second line drugs (gyrB, gyrA, qnrS, ParC and ParE) were investigated among XDR S. Typhi. There were different CTX-M genes isolated using the specific primers, blaCTX-M-U, blaCTX-M-1, blaCTX-M-15, blaCTX-M-2, blaCTX-M-8 and blaCTX-M-9. Results: Antibiotic resistant genes of the first-line drugs were isolated with different frequency, blaTEM-1 (72.6%), catA1 (86.6%), sul1 (70%), and dhfR7 (56%). Antibiotics resistance genes of second-line drugs were isolated as: gyrB (60%), gyrA (49.3%), qnrS (32.6%), parC (44%) and parE (28%). Among CTX-M genes, blaCTX-M-U (63.3%) was the most frequent followed by blaCTX-M-15 (39.3%) and blaCTX-M-1 (26%). Conclusion: Our study concluded that XDR isolates circulating in Pakistan have acquired first-line and second-line antibiotic resistant genes quite successfully along with CTX-M genes (ESBLs) rendering them resistant to the third generation cephalosporins as well. Emergence of azithromycin resistance in XDR S. Typhi which is currently used as an empiric treatment option is worrisome and needs to be monitored carefully in endemic countries like Pakistan.

Identification of Volatile Organic Liquids by Combining an Array of Fiber-Optic Sensors and Machine Learning.

In this paper, we report an array of fiber-optic sensors based on the Fabry-Perot interference principle and machine learning-based analyses for identifying volatile organic liquids (VOLs). Three optical fiber tip sensors with different surfaces were included in the array of sensors to improve the accuracy for identifying liquids: an intrinsic (unmodified) flat cleaved endface, a hydrophobic-coated endface, and a hydrophilic-coated endface. The time-transient responses of evaporating droplets from the optical fiber tip sensors were monitored and collected following the controlled immersion tests of 11 different organic liquids. A continuous wavelet transform was used to convert the time-transient response signal into images. These images were then utilized to train convolution neural networks for classification (identification of VOLs). We show that diversity in the information collected using the array of three sensors helps machine learning-based methods perform significantly better. We explore different pipelines for combining the information from the array of sensors within a machine learning framework and their effect on the robustness of models. The results showed that the machine learning-based methods achieved high accuracy in their classification of different liquids based on their droplet evaporation time-transient events.

Thermally robust and highly stable method for splicing silica glass fiber to crystalline sapphire fiber.

This research reports an advancement in splicing silica glass fiber to sapphire single-crystal optical fiber (SCF) using a specialized glass processing device, including data that demonstrate the thermal stability of the splice to 1000°C. A filament heating process was used to produce a robust splice between the dissimilar fibers. A femtosecond laser is used to inscribe a fiber Bragg gratings sensor into the SCF to measure the high-temperature capabilities and signal attenuation characteristics of the splice joint. The experimental results demonstrate that the proposed splicing method produces a splice joint that is robust, stable, repeatable, and withstands temperatures up to 1000°C with a low attenuation of 0.5 dB. The proposed method allows placement of SCF-based sensors in the extreme environments encountered in various engineering fields, such as nuclear, chemical, aviation, and metals manufacturing, to enable improvements in process monitoring, product quality, and production efficiency.

Highly Sensitive Strain Sensor by Utilizing a Tunable Air Reflector and the Vernier Effect.

A highly sensitive strain sensor based on tunable cascaded Fabry-Perot interferometers (FPIs) is proposed and experimentally demonstrated. Cascaded FPIs consist of a sensing FPI and a reference FPI, which effectively generate the Vernier effect (VE). The sensing FPI comprises a hollow core fiber (HCF) segment sandwiched between single-mode fibers (SMFs), and the reference FPI consists of a tunable air reflector, which is constituted by a computer-programable fiber holding block to adjust the desired cavity length. The simulation results predict the dispersion characteristics of modes carried by HCF. The sensor's parameters are designed to correspond to a narrow bandwidth range, i.e., 1530 nm to 1610 nm. The experimental results demonstrate that the proposed sensor exhibits optimum strain sensitivity of 23.9 pm/µÎµ, 17.54 pm/µÎµ, and 14.11 pm/µÎµ cascaded with the reference FPI of 375 µm, 365 µm, and 355 µm in cavity length, which is 13.73, 10.08, and 8.10 times higher than the single sensing FPI with a strain sensitivity of 1.74 pm/µÎµ, respectively. The strain sensitivity of the sensor can be further enhanced by extending the source bandwidth. The proposed sensor exhibits ultra-low temperature sensitivity of 0.49 pm/°C for a temperature range of 25 °C to 135 °C, providing good isolation for eliminating temperature-strain cross-talk. The sensor is robust, cost-effective, easy to manufacture, repeatable, and shows a highly linear and stable response for strain sensing. Based on the sensor's performance, it may be a good candidate for high-resolution strain sensing.

Staphylococcal Cassette Chromosome mec Typing and Multilocus Variable Number Tandem Repeat Analysis of Methicillin Resistant Staphylococcus aureus Clinical Isolates with Vancomycin Creep Phenomenon.

Background: The association of treatment failure and mortality with vancomycin minimum inhibitory concentration creep (MIC) is a matter of serious concern in patients with severe methicillin resistant Staphylococcus aureus (MRSA) infections. The purpose of the study was to identify and characterize staphylococcal cassette chromosome mec (SCCmec) and clonal types of MRSA strains, exhibiting the vancomycin MIC creep phenomenon. Methods: A total of 3305 S. aureus strains were isolated from various clinical samples of Lahore General Hospital, Lahore, Pakistan. MRSA strains were identified by cefoxitin resistant (≤21mm) followed by mecA and mecC gene genotyping. Vancomycin MIC creep was determined by E-test. Isolates having MIC values >1.5 µg/mL were further subjected for SCCmec typing (I-V and XI) and multiple-locus variable number tandem repeat analysis (MLVA) by amplification of spa, sspA, clfA, clfB, and sdrCDE genes. A dendrogram was created based on the similarity index using bioneumerics software. Results: About 13.3% (440/3305) isolates were MRSA with 99.3% (437/440) and 0.7% (3/440) carried mecA and mecC genes, respectively. In 120 MRSA isolates, the MIC of vancomycin was >1.5µg/mL. In MRSA isolates with high vancomycin MIC (>1.5µg/mL), the most common SCCmec type was SCCmec III (38.3%), followed by SCCmec IVa (15.8%), SCCmec IIIa (13.3%,), SCCmec IVc (7.5%), SCCmec IVe (5.8%), SCCmec IVd (5.8%), SCCmec IVb (4.2%), SCCmec II (2.5%), SCCmec V (1.7%), SCCmec I (1.7%) and SCCmec XI (1.7%). MLVA revealed 60 genotypic groups of MRSA isolates having a 92% similarity index. Conclusion: SCCmec III was the most common type in genetically related MRSA isolates showing vancomycin MIC creep. The presence of SCCmec XI may further add burden to infection control measures.

Distributed fiber optic sensing with enhanced sensitivity based on microwave-photonic Vernier effect.

The Vernier effect has been widely used in the field of measurement and instrumentation for sensitivity enhancement. Single-point optical fiber sensors based on the Vernier effect have been extensively reported in recent years. In this Letter, for the first time, a distributed optical fiber sensor based on microwave photonics with improved sensitivity enabled by the Vernier effect is demonstrated. Distributed sensing is realized by interrogating a Fabry-Perot interferometer (FPI) array formed by cascaded reflectors along an optical fiber using an optical carrier-based microwave interferometry (OCMI) system. A reference FPI is also included in the system. The interferogram of each of the sensing FPIs can be unambiguously reconstructed and superimposed with the reconstructed interferogram of the reference FPI to generate the Vernier effect. By tracking the spectral shift of the envelope signals in the superimposed spectra, the measurement sensitivities of the sensing FPIs can be significantly improved. A simple direct modulation-based OCMI system is used in the proof-of-concept demonstration, showing sensitivity-enhanced distributed sensing capability. Moreover, the sensitivity amplification factor can be adjusted by varying the optical length difference of the sensing and reference FPIs, similar to that of Vernier effect-based single-point optical fiber sensors.

Impact analysis of keyword extraction using contextual word embedding.

A document's keywords provide high-level descriptions of the content that summarize the document's central themes, concepts, ideas, or arguments. These descriptive phrases make it easier for algorithms to find relevant information quickly and efficiently. It plays a vital role in document processing, such as indexing, classification, clustering, and summarization. Traditional keyword extraction approaches rely on statistical distributions of key terms in a document for the most part. According to contemporary technological breakthroughs, contextual information is critical in deciding the semantics of the work at hand. Similarly, context-based features may be beneficial in the job of keyword extraction. For example, simply indicating the previous or next word of the phrase of interest might be used to describe the context of a phrase. This research presents several experiments to validate that context-based key extraction is significant compared to traditional methods. Additionally, the KeyBERT proposed methodology also results in improved results. The proposed work relies on identifying a group of important words or phrases from the document's content that can reflect the authors' main ideas, concepts, or arguments. It also uses contextual word embedding to extract keywords. Finally, the findings are compared to those obtained using older approaches such as Text Rank, Rake, Gensim, Yake, and TF-IDF. The Journals of Universal Computer (JUCS) dataset was employed in our research. Only data from abstracts were used to produce keywords for the research article, and the KeyBERT model outperformed traditional approaches in producing similar keywords to the authors' provided keywords. The average similarity of our approach with author-assigned keywords is 51%.

Epidemiology, Clinico-Pathological Characteristics, and Comorbidities of SARS-CoV-2-Infected Pakistani Patients.

SARS-CoV-2 is a causative agent for COVID-19 disease, initially reported from Wuhan, China. The infected patients experienced mild to severe symptoms, resulting in several fatalities due to a weak understanding of its pathogenesis, which is the same even to date. This cross-sectional study has been designed on 452 symptomatic mild-to-moderate and severe/critical patients to understand the epidemiology and clinical characteristics of COVID-19 patients with their comorbidities and response to treatment. The mean age of the studied patients was 58 ± 14.42 years, and the overall male to female ratio was 61.7 to 38.2%, respectively. In total, 27.3% of the patients had a history of exposure, and 11.9% had a travel history, while for 60% of patients, the source of infection was unknown. The most prevalent signs and symptoms in ICU patients were dry cough, myalgia, shortness of breath, gastrointestinal discomfort, and abnormal chest X-ray (p < 0.001), along with a high percentage of hypertension (p = 0.007) and chronic obstructive pulmonary disease (p = 0.029) as leading comorbidities. The complete blood count indicators were significantly disturbed in severe patients, while the coagulation profile and D-dimer values were significantly higher in mild-to-moderate (non-ICU) patients (p < 0.001). The serum creatinine (1.22 µmol L-1; p = 0.016) and lactate dehydrogenase (619 µmol L-1; p < 0.001) indicators were significantly high in non-ICU patients, while raised values of total bilirubin (0.91 µmol L-1; p = 0.054), C-reactive protein (84.68 mg L-1; p = 0.001), and ferritin (996.81 mg L-1; p < 0.001) were found in ICU patients. The drug dexamethasone was the leading prescribed and administrated medicine to COVID-19 patients, followed by remdesivir, meropenem, heparin, and tocilizumab, respectively. A characteristic pattern of ground glass opacities, consolidation, and interlobular septal thickening was prominent in severely infected patients. These findings could be used for future research, control, and prevention of SARS-CoV-2-infected patients.

Novel acetamide derivatives of 2-aminobenzimidazole prevent inflammatory arthritis in rats via suppression of pro-inflammatory mediators.

Benzimidazole ring system is an important pharmacophore with diverse pharmacological activities. In this study, we explored the anti-arthritic effects of newly synthesized acetamide derivatives of 2-aminobenzimidazole (N1 and N2) in rats. FTIR and NMR spectroscopies were used to characterize these compounds. Carrageenan (CRG) induced paw edema model was used to test the acute anti-inflammatory activity of various doses (10, 20 and 30 mg/kg) of N1 and N2 compounds. Based on acute anti-inflammatory effects, the most potent dose of each compound was selected and investigated in complete freund's adjuvant (CFA) induced inflammatory arthritis (RA) model (n = 4 in each group). Histopathological, hematological, radiographic, and RT-qPCR analyses were performed to assess the progression or resolution of inflammatory arthritis. The tested compounds produced a dose-dependent anti-inflammatory activity against CRG induced paw inflammation and similarly reduced edema in CFA induced inflammatory arthritis model. Histopathological and X-ray analyses of ankle joints revealed minimal inflammation and normal joint structures in N1 and N2 treated groups. The tested compounds also reduced the levels of autoantibodies and restored hematological parameters. Interestingly, the tested compounds did not elevate aspartate aminotransferase and alanine transaminase levels and displayed a better safety profile than methotrexate. N1 and N2 compounds also attenuated the transcript levels of IRAK1, NF-kB1, TNF-α, IL-1ß, IL17 and MMP1. In addition, N1 displayed a greater inhibition of mRNA levels of COX1, COX2, mPGES1 and PTGDS as compared to N2. Our findings demonstrate that N1 and N2 compounds possess strong anti-arthritic activity which can be attributed to the suppression of pro-inflammatory mediators.

Pharmacological evaluation of anti-arthritic potential of terpinen-4-ol using in vitro and in vivo assays.

Terpinen 4-ol, a phytochemical is a monoterpene which has been reported for its anti-inflammatory effect. Present research was planned to check its effect against arthritis through in vitro and in vivo models. Terpinen 4-ol was evaluated through in-vitro procedures including blocking of protein (BSA and egg albumin) denaturation and human RBC membrane stabilization. In in vivo study, terpinen 4-ol (15, 30 and 60 mg/kg) was evaluated using formaldehyde and CFA arthritic models. Terpinen 4-ol significantly inhibited increase in paw and joint swelling as compared to diseased group. Terpinen 4-ol showed remarkable antioxidant effect (SOD, reducing power) and also improved body weight, haematological, histopathological and radiological parameters in CFA model. Also, moreover, the excess production of IL-1ß, TNF-α, IRAK, and NF-kB were noticeably attenuated in all terpinen 4-ol treated rats, however, IL-17 and IL-10 were distinctly increased compared to arthritic control rats in RT-PCR. Also, terpinen 4-ol showed promising antioxidant effect in DPPH assay. Henceforth, it might be concluded that terpinen 4-ol has anti-arthritic effect which can be attributed to the downregulation of pro-inflammatory cytokines.

Assessing English language sentences readability using machine learning models.

Readability is an active field of research in the late nineteenth century and vigorously persuaded to date. The recent boom in data-driven machine learning has created a viable path forward for readability classification and ranking. The evaluation of text readability is a time-honoured issue with even more relevance in today's information-rich world. This paper addresses the task of readability assessment for the English language. Given the input sentences, the objective is to predict its level of readability, which corresponds to the level of literacy anticipated from the target readers. This readability aspect plays a crucial role in drafting and comprehending processes of English language learning. Selecting and presenting a suitable collection of sentences for English Language Learners may play a vital role in enhancing their learning curve. In this research, we have used 30,000 English sentences for experimentation. Additionally, they have been annotated into seven different readability levels using Flesch Kincaid. Later, various experiments were conducted using five Machine Learning algorithms, i.e., KNN, SVM, LR, NB, and ANN. The classification models render excellent and stable results. The ANN model obtained an F-score of 0.95% on the test set. The developed model may be used in education setup for tasks such as language learning, assessing the reading and writing abilities of a learner.

Noscapine hydrochloride (benzyl-isoquinoline alkaloid) effectively prevents protein denaturation through reduction of IL-6, NF-kB, COX-2, Prostaglandin-E2 in rheumatic rats.

Noscapine hydrochloride (benzyl-isoquinoline antitussive alkaloid) is an opium derivative and generally used as a cough suppressant. Numerous studies on noscapine hydrochloride have reported that it has potent anti-inflammatory activity. However, the mechanisms by which it exerts an anti-inflammatory function is not well understood. Protein denaturation is the primary step that leads to the organ destruction and permanent arthritic disability. The above-mentioned facts provided the ground to plan this study using different in-vitro and in-vivo approaches. RT-qPCR and ELISA assays were used to assess the inflammatory markers related to protein denaturation in complete adjuvant persuaded rheumatism in Sprague - Dawley rats. The results were collected as paw volume and body weight changes, arthritic scoring and serum antioxidant enzymes assays. These findings demonstrated that all doses of noscapine hydrochloride (10, 20 and 40 mg/kg) studied in this study, significantly (p < 0.001) decreased the protein denaturation by preventing the increase in levels of interleukin-1ß (IL-1ß), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), nuclear factor-kB (NF-kB), cyclooxygenase-2 (COX-2) and prostaglandin E2. Noscapine hydrochloride significantly reduced the paw volume (p < 0.001), arthritic scoring and reversed the body mass as compared to arthritic control diseased rats.

SARS-CoV-2 variants of concern dominate in Lahore, Pakistan in April 2021.

The SARS-CoV-2 pandemic continues to expand globally, with case numbers rising in many areas of the world, including the Indian sub-continent. Pakistan has one of the world's largest populations, of over 200 million people and is experiencing a severe third wave of infections caused by SARS-CoV-2 that began in March 2021. In Pakistan, during the third wave until now only 12 SARS-CoV-2 genomes have been collected and among these nine are from Islamabad. This highlights the need for more genome sequencing to allow surveillance of variants in circulation. In fact, more genomes are available among travellers with a travel history from Pakistan, than from within the country itself. We thus aimed to provide a snapshot assessment of circulating lineages in Lahore and surrounding areas with a combined population of 11.1 million. Within a week of April 2021, 102 samples were sequenced. The samples were randomly collected from two hospitals with a diagnostic PCR cutoff value of less than 25 cycles. Analysis of the lineages shows that the Alpha variant of concern (first identified in the UK) dominates, accounting for 97.9 % (97/99) of cases, with the Beta variant of concern (first identified in South Africa) accounting for 2.0 % (2/99) of cases. No other lineages were observed. In depth analysis of the Alpha lineages indicated multiple separate introductions and subsequent establishment within the region. Eight samples were identical to genomes observed in Europe (seven UK, one Switzerland), indicating recent transmission. Genomes of other samples show evidence that these have evolved, indicating sustained transmission over a period of time either within Pakistan or other countries with low-density genome sequencing. Vaccines remain effective against Alpha, however, the low level of Beta against which some vaccines are less effective demonstrates the requirement for continued prospective genomic surveillance.

Analyses of ABO blood groups with susceptibility and symptomatic variations of COVID-19 infection, a questionnaire-based survey.

Coronavirus disease 2019 (COVID-19) is a novel respiratory disease that has led to a global pandemic and created a havoc. The COVID-19 disease severity varies among individuals, depending on fluctuating symptoms. Many infectious diseases such as hepatitis B and dengue hemorrhagic fever have been associated with ABO blood groups. The aim of this study was to explore whether ABO blood groups might serve as a risk or a protective factor for COVID-19 infection. Moreover, the symptomatic variations of COVID-19 infection among the individuals with different blood groups were also analyzed. An online questionnaire-based survey was conducted in which 305 partakers were included, who had successfully recovered from coronavirus infection. The ABO blood groups of 1294 healthy individuals were also taken as a control. The results of the current study demonstrated that antibody A containing blood groups (blood group B, p-value: 0.049 and blood group O, p-value: 0.289) had a protective role against COVID-19 infection. The comparison of symptomatic variations among COVID-19-infected subjects showed that blood group O subjects had lower chances of experiencing severe symptoms relating to respiratory distress, while subjects with AB blood group were more prone to develop symptoms, but the differences in both groups were found to be statistically non-significant. In conclusion, subjects who do not have anti-A antibodies in their serum (i.e., subjects with group A and AB) are more likely to be infected with COVID-19. The current data showed that there was no significant association of signs and symptoms variations of COVID-19 infection among individuals with different blood groups.

TKFIM: Top-K frequent itemset mining technique based on equivalence classes.

Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially. For frequent Itemset (FIs) mining applications, it imposes new challenges. Misconceived information may be found in recent algorithms, including both threshold and size based algorithms. Threshold value plays a central role in generating frequent itemsets from the given dataset. Selecting a support threshold value is very complicated for those unaware of the dataset's characteristics. The performance of algorithms for finding FIs without the support threshold is, however, deficient due to heavy computation. Therefore, we have proposed a method to discover FIs without the support threshold, called Top-k frequent itemsets mining (TKFIM). It uses class equivalence and set-theory concepts for mining FIs. The proposed procedure does not miss any FIs; thus, accurate frequent patterns are mined. Furthermore, the results are compared with state-of-the-art techniques such as Top-k miner and Build Once and Mine Once (BOMO). It is found that the proposed TKFIM has outperformed the results of these approaches in terms of execution and performance, achieving 92.70, 35.87, 28.53, and 81.27 percent gain on Top-k miner using Chess, Mushroom, and Connect and T1014D100K datasets, respectively. Similarly, it has achieved a performance gain of 97.14, 100, 78.10, 99.70 percent on BOMO using Chess, Mushroom, Connect, and T1014D100K datasets, respectively. Therefore, it is argued that the proposed procedure may be adopted on a large dataset for better performance.

A Spatially Distributed Fiber-Optic Temperature Sensor for Applications in the Steel Industry.

This paper presents a spatially distributed fiber-optic sensor system designed for demanding applications, like temperature measurements in the steel industry. The sensor system employed optical frequency domain reflectometry (OFDR) to interrogate Rayleigh backscattering signals in single-mode optical fibers. Temperature measurements employing the OFDR system were compared with conventional thermocouple measurements, accentuating the spatially distributed sensing capability of the fiber-optic system. Experiments were designed and conducted to test the spatial thermal mapping capability of the fiber-optic temperature measurement system. Experimental simulations provided evidence that the optical fiber system could resolve closely spaced temperature features, due to the high spatial resolution and fast measurement rates of the OFDR system. The ability of the fiber-optic system to perform temperature measurements in a metal casting was tested by monitoring aluminum solidification in a sand mold. The optical fiber, encased in a stainless steel tube, survived both mechanically and optically at temperatures exceeding 700 °C. The ability to distinguish between closely spaced temperature features that generate information-rich thermal maps opens up many applications in the steel industry.