Miracle-dz/UniFAR-FTUs huggingface dataset:
Tenofovir Hampers the Efficacy of Sorafenib in Prolonging Overall Survival in Hepatocellular Carcinoma

Sorafenib is a first-line treatment for patients with advanced hepatocellular carcinoma (HCC). These patients may simultaneously receive anti-hepatitis B treatment if they are viremic. The N-Acetylgalactosaminyltransferase 14 (GALNT14) gene can serve as a biomarker to guide HCC treatments. However, the enzyme substrates of its gene product, GalNAc-T14 (a glycosyltransferase), remained uncharacterized. Here, we conducted a glycoproteome-wide search for GalNAc-T14 substrates using lectin affinity chromatography followed by tandem mass spectrometry. Seventeen novel GalNAc-T14 substrates were identified. A connective map analysis showed that an antiviral drug, tenofovir, was the leading medicinal compound to down-regulate the expression of these substrates. In vitro assays showed that HCC cells were resistant to sorafenib if pretreated by tenofovir but not entecavir. Clinical analysis showed that the concomitant use of tenofovir and sorafenib was a previously unrecognized predictive factor for unfavorable overall survival (hazard ratio = 2.060, 95% confidence interval = [1.256, 3.381], p = 0.004) in a cohort of 181 hepatitis-B-related, sorafenib-treated HCC patients (concomitant tenofovir versus entecavir treatment; p = 0.003). In conclusion, by conducting a glycoproteome-wide search for GalNAc-T14 substrates, we unexpectedly found that tenofovir was a major negative regulator of GalNAc-T14 substrates and an unfavorable anti-hepatitis B drug in HCC patients receiving sorafenib.
Risk Factors of Influenza-Associated Respiratory Illnesses Reported to a Sentinel Hospital of Lahore, Pakistan: 2015-2016

Epidemiological data about determinants of influenza A virus (IAV) in the Pakistani population is scarce. We aimed to conduct a prospective hospital-based active surveillance study from October 2015 to May 2016 to identify potential risk factors associated with IAV infection among patients with influenza-like illness (ILI) and severe acute respiratory illness (SARI). Surveillance was conducted in Lahore General Hospital, selected as a sentinel site in Lahore District, Pakistan. Nasal/throat samples were collected along with epidemiological and clinical data from enrolled patients. Real-time reverse-transcription polymerase chain reaction (rRT-PCR) was performed to identify IAV and its subtypes (H1N1pdm09, H3N2). Data were analyzed to determine risk factors and risk markers associated with IAV infections. A total of 311 suspected ILI and SARI cases were enrolled in the study, and among these 50 were IAV-positive. Of these 50 confirmed cases of IAV, 14 were subtyped as H1N1pdm09 and 15 were H3N2; the remaining 21 were untyped. A final multivariable model identified four independent risk factors/markers for IAV infection: exposure history to ILI patients within last 7 days and gender being male were identified as risk factors of IAV infection, while use of antibiotics prior to hospital consultation and presence of fever were identified as risk markers. We concluded that adopting nonpharmaceutical interventions like hand hygiene, masks, social distancing, and where possible, avoiding identified risk factors could decrease the risk of IAV infection and may prevent imminent outbreaks of IAV in the community.
Combination of Analytical and Statistical Methods in Order to Optimize Antibacterial Activity of Clary Sage Supercritical Fluid Extracts

The extraction of clary sage (Salvia sclarea L.) using supercritical carbon dioxide (SC-CO2) was systematically studied by using thin layer chromatography-direct bioautography (TLC-DB) and response surface methodology (RSM). The three parameters temperature, pressure, and cosolvent ratio were optimized for the maximum antibacterial activity of clary sage extracts against Pseudomonas aeruginosa (P. aeruginosa) and methicillin-resistant Staphylococcus aureus (MRSA). The highest inhibition zone was 7.51 mm for P. aeruginosa and 7.57 mm for MRSA. According to RSM analysis, the predicted optimum extraction parameters are 18.6 MPa pressure, 40 °C temperature, and 2% ethanol (EtOH) ratio. The combination of this analytical and statistical method allows saving time, money, and instrument runtime in the optimization of essential oil composition, which is tailored to a specific task and could be useful on any kind of herbs in a wide range of use from perfume manufacturing to the food industry.
Ultrasensitive detection of Bisphenol A based on an aptasensor with DNA amplification

ABSTRACT An ultrasensitive and selective method for Bisphenol A (BPA) detection using aptamer-BPA and real-time quantitative polymerase chain reaction (RT-qPCR) technology is reported, in which biotin-modified aptamer DNA (BPA) is fixed on the inner wall of a streptavidin-coated PCR tube via biotin–avidin interactions and the template is fixed through the complementary base pairing with aptamer DNA. Upon the addition of BPA, the template DNA could be off from the inner wall of PCR tube, which results from aptamer DNA interacted specifically with BPA. Thus, the number of template DNA change result from the BPA concentration is readily seen by cycle threshold (Ct) values. The sensor exhibited an excellent linear response between Ct values and BPA concentration in the range of 1–500 nM and the detection limit of this method for aqueous BPA was as low as 0.7 nM. In addition, this method showed good selectivity for BPA over its analogues.
Business resilience in the Sustainable Development Goals (SDGs) era: A conceptual review

Amidst the global COVID-19 pandemic, the term resilience has gained significant momentum in global news and management studies. Although scholars from different domains have investigated resilience, there is a need to provide clarity on its definitions and assessment (Anderson, 2015). This paper provides a conceptual review on resilience and explores business resilience as a framework to guide sustainability strategy by mitigating social and environmental risks. The study contributes to the literature on resilience and tabulates the key definitions of business resilience covered in a sample of 80 peer-reviewed articles and books (Hillmann & Guenther, 2021; McKnight & Linnenluecke, 2017). We challenge the existing literature on adaptive capacity models that are short in anticipating unprecedented operational disruptions. To build business resilience we argue for the adoption of the Sustainable Development Goals (SDGs). Given their strategic outlook until 2030, the SDGs offer a framework for corporate sustainability that helps decision-makers within organizations identify social and environmental risks and establish business strategies that build resilience and meet the expectations of a firm’s diverse stakeholders
Robust multi-objective optimization for solving the RFID network planning problem

Radio-frequency identification (RFID) is a new technology used for identifying and tracking objects or people by radio-frequency waves to facilitate automated traceability and data collection. The RFID system consists of an electronic tag attached to an object, readers, and a middleware. In the latest real applications based on the RFID technology, the deployment of readers has become a central issue for RFID network planning by means of optimizing several objectives such as the coverage of tags, the number of readers, and the readers/tags interferences. In practice, the system is affected by uncertainty and uncontrollable environmental parameters. Therefore, the optimal solutions to the RFID network planning problem can be significantly reduced with uncontrollable variations in some parameters, such as the reader's transmitted power. In this work, we propose a robust multi-objective optimization approach to solve the deployment of RFID readers. In this way, we achieve robust optimal solutions that are insensitive to uncertainties in the optimization parameters.
Quality ensured non‐orthogonal multiple access based downlink scalable video coding multicasting over cognitive radio network

The combination of the cognitive radio network (CRN) and non‐orthogonal multiple access (NOMA) technologies for scalable video coding (SVC) can be considered as a promising approach for quality‐driven video transmission over a fifth‐generation (5G) network. Advanced techniques such as efficient channel allocation, advanced sensing strategy, and optimum power allocation can combinedly improve the overall system performance. In this context, we propose an intergroup content‐aware proportionate channel allocation policy, sensing time optimization strategy and quality of service (QoS) ensured intragroup power allocation scheme for downlink SVC multicasting over CRN using NOMA technology to enhance the quality of experience (QoE) of secondary users (SUs). We propose an optimum power allocation strategy for different NOMA layers to maximize the weighted sum rate of different SVC groups ensuring the individual QoS requirements. Although the power allocation problem seems to be nonconvex, however, it can be transformed into a convex function under some specific conditions which are discussed in this article. Extensive simulation reveals that our proposed power allocation scheme for NOMA with QoS constraint outperforms the other two conventional schemes, that is, NOMA fixed power allocation and NOMA without QoS constraint in terms of average weighted sum rate and average QoE of SUs.
Effect of Pulsed Current on Charging Performance of Lithium-Ion Batteries

The pulsed current has been proposed as a promising battery charging technique to improve the charging performance and maximize the lifetime for lithium-ion (Li-ion) batteries. However, the effect of the pulsed current charging is inconclusive due to the changeable current mode and conditions. This article systematically investigates the effect of various pulsed current charging modes, i.e., positive pulsed current mode, pulsed current-constant current mode, negative pulsed current mode, alternating pulsed current mode, sinusoidal-ripple current mode, and alternating sinusoidal-ripple current mode on battery performance. Moreover, a comprehensive analysis of the frequency impact on the quality of the current mode is performed. The current modes in this work are evaluated considering the maximum rising temperature, discharging capacity, and charging speed according to experimental results. Furthermore, this work provides guidance for developing pulsed current charging strategies to satisfy future charging requirements.
Methods for monetary valuation of ecosystem services: A scoping review

Adequate assessment of ecosystem services is important for the development of policies and management plans related to forestry activities and the environment. Carefully identified ecosystem values can determine which options policy makers should prioritize to provide the greatest benefit. There are numerous methods used by researchers to evaluate ecosystem services. The most widely applied methods are monetary valuation methods, they are often deemed to be the most pragmatic language when it comes to communication with political and business institutions. The main goal of this review is to analyse available literature using the methodology particular to the scoping review approach in order to identify and describe valuation methods that can be applied for monetary assessment of ecosystem services. As a result of the scoping review, over 20 monetary valuation techniques (including several less common methods such as willingness to sell and Delphi method) were derived from 16 literature sources. In the process of compiling the range of different methods, a few flaws and gaps in the communication of methods were observed such as lack of consistency in the names of different methods and mixing up concepts. In addition, a few areas for future research are suggested.
Respiratory Monitoring at Bedside in COVID-19 Patients

The SARS-CoV-2 (COVID-19) pandemic has forced some reflections to be had surrounding the ventilatory support to be applied to certain types of patients. The model of two phenotypes, set out by Professor Gattinoni and colleagues, suggests that adequate monitoring of respiratory effort may play a key role in the treatment of respiratory failure due to COVID-19. An insufficient control of the patient’s respiratory efforts could lead to an aggravation of lung damage, mainly due to the possibility of generating Patient Self-Inflicted Lung Injury (PSILI) with a consequent aggravation of the pathological picture. Nevertheless, effectively monitoring the patient’s respiratory work, especially in nonintensive settings, is not easy. This article briefly describes some methods that allow the assessment of respiratory effort, such as the use of ultrasound and respiratory tests, which can be performed in nonintensive settings.
How schools contribute to keeping students on track: Narratives from vulnerable students in vocational education and training

This article examines characteristics, practices and conditions within schools that enable vulnerable students to succeed in school. Research suggests that factors related to structure, educational organisation or the climate within schools may play a part in either pushing out or holding on to students whose personal characteristics may put them at risk of dropping out. The study is based on 25 qualitative interviews with students in the second year of vocational education and training. All the students had a low grade point average from lower secondary school, and the majority had an immigrant background from Africa or Asia. The analysis shows that practice-oriented learning from authentic work tasks provided meaningful connections between schoolwork and students’ career choices and opportunities to experience success and mastery. Furthermore, positive expectations and the active support of teachers as well as mutual motivation and support among classmates contributed to students’ sense of belonging at school. The study discusses the importance of schools supporting less school-oriented young people in connecting and identifying with school, both with the social environment and with the educational content and learning methods.
Vaping and COVID-19: Insights for Public Health and Clinical Care from Twitter

This study describes key topics of discussions on Twitter at the intersection of vaping and COVID-19 and documents public reactions to announcements from authoritative health agencies. Twitter posts containing vaping and COVID-19-related terms were collected from 1 December 2019 to 3 May 2020 (n = 23,103 posts). Text classifiers and unsupervised machine learning were used to identify topics in posts. Predominant topics included COVID-19 Respiratory Health (18.87%), COVID-19 Susceptibility (17.53%), Death (10.07%), Other COVID-19 Health Effects (9.62%), and Severity of COVID-19 (7.72%), among others. Public conversations on topics, such as Severity of COVID-19, Transmission, Susceptibility, Health Effects, Death, and Smoking cessation, were shaped by announcements from U.S. and international health agencies. Armed with the insights from this study, medical providers should be prepared to discuss vaping-related health risks with their patients in the era of COVID-19. Misconceptions around vaping as a protective behavior from, and an effective treatment against, COVID-19 should also be corrected.
In vitro induction and identification of polyploid Neolamarckia cadamba plants by colchicine treatment

Polyploidization has played a crucial role in plant breeding and crop improvement. However, studies on the polyploidization of tropical tree species are still very scarce in this region. This paper described the in vitro induction and identification of polyploid plants of Neolamarckia cadamba by colchicine treatment. N. cadamba belongs to the Rubiaceae family is a natural tetraploid plant with 44 chromosomes (2n = 4x = 44). Nodal segments were treated with colchicine (0.1%, 0.3% and 0.5%) for 24 h and 48 h before transferring to shoot regeneration medium. Flow cytometry (FCM) and chromosome count were employed to determine the ploidy level and chromosome number of the regenerants, respectively. Of 180 colchicine-treated nodal segments, 39, 14 and 22 were tetraploids, mixoploids and octoploids, respectively. The highest percentage of polyploidization (20% octoploids; 6.7% mixoploids) was observed after treated with 0.3% colchicine for 48 h. The DNA content of tetraploid (4C) and octoploid (8C) was 2.59 ± 0.09 pg and 5.35 ± 0.24 pg, respectively. Mixoploid plants are made up of mixed tetraploid and octoploid cells. Chromosome count confirmed that tetraploid cell has 44 chromosomes and colchicine-induced octoploid cell has 88 chromosomes. Both octoploids and mixoploids grew slower than tetraploids under in vitro conditions. Morphological characterizations showed that mixoploid and octoploid leaves had thicker leaf blades, thicker midrib, bigger stomata size, lower stomata density, higher SPAD value and smaller pith layer than tetraploids. This indicates that polyploidization has changed and resulted in traits that are predicted to increase photosynthetic capacity of N. cadamba. These novel polyploid plants could be valuable resources for advanced N. cadamba breeding programs to produce improved clones for planted forest development.
Association of FOXO3 Expression with Tumor Pathogenesis, Prognosis and Clinicopathological Features in Hepatocellular Carcinoma: A Systematic Review with Meta-Analysis

Simple Summary Liver cancer, mainly represented by hepatocellular carcinoma (HCC), constitutes the current third leading cause of tumor-associated death worldwide. Therefore, finding new molecules that improve early HCC diagnosis, prognosis and patient outcomes is crucial. Forkhead box O3 (FOXO3), a central factor expressed by hepatocytes, has been related to cancer progression. This novel systematic review, with meta-analysis, aimed to unravel the diagnostic and prognostic value of FOXO3 expression in HCC. We systematically searched Cochrane, Embase, PubMed, Scopus and Web of Science for articles evaluating FOXO3 levels in HCC samples and its association with HCC development, survival or clinicopathological features. After study selection, overall effect and heterogeneity assessment, and subgroup and publication bias analysis were carried out. Based on five studies involving 1059 cases, we found that high FOXO3 expression correlates with tumor development, poor survival and invasion in HCC. Thus, FOXO3 emerges as a novel diagnostic and prognostic biomarker for HCC monitoring. Abstract Forkhead box O3 (FOXO3), an essential transcription factor related to liver disease, has been linked to cancer progression. The most frequent primary liver tumor, hepatocellular carcinoma (HCC), has an elevated mortality rate and patient outcomes remain very poor. Here, we examined the diagnostic, prognostic and clinicopathological significance of FOXO3 expression in HCC. We systematically searched Cochrane, Embase, PubMed, Scopus and Web of Science. Articles analyzing FOXO3 levels in HCC patient samples and its relationship with tumor development, survival or clinicopathological factors were selected. Hazard ratios, odds ratios and 95% confidence intervals were extracted, estimated by Parmar method or calculated and pooled across studies. Heterogeneity was evaluated by chi-square-based Q and I2 tests, while publication bias by funnel plots and Egger’s test. Subgroup analysis was performed when heterogeneity was evident. The study protocol was registered in PROSPERO (CRD42021237321), and data were meta-analyzed employing STATA 16. Five studies involving 1059 HCC cases were finally included in this meta-analysis, finding that high FOXO3 levels significantly correlate with HCC development and shorter overall survival. Moreover, subgroup analysis revealed a significant association between positive FOXO3 expression and the risk of invasion. Thus, FOXO3 could function as a novel biomarker with diagnostic and prognostic value in HCC.
SARIMA – A Model for Forecasting Product order demand

Abstract: Product analysis is the most important part for any working manufacturing. It provides the sales record of their currently manufactured product and also it helps to predict its performance in the future. For this analysis, a SARIMAX model has been used with Time series forecasting. This paper will explain the need of such model instead of using a simple regression model to predict the order demand. This study analyses and presents a forecasting model to predict an order demand for the Product over the time period. Demand in Product is a main component for planning all processes in supply chain, and therefore determining Product demand is a great interest for supply chain. Mean forecasting for product order demand was carried out using SARIMA model, by using the past data from the period of 2011 to 2017. The model with the least value of Akaike Information Criterion (AIC) was selected as the appropriate model for forecasting mean Error. Test for normality of residuals were performed to see the adequacy of the chosen model. SARIMA (1, 1, 1) (0, 1, 1) (12) was selected as the best model for mean product order demand forecast. The results obtained will prove that the model could be utilized to forecast the future demand in the Product manufacturing industry. These results will help the manufacturers for manufacturing reliable guidelines in making decisions. Keywords: ARIMA, AIC, S-ARIMA, Regression
Parameter estimation for models of chemical reaction networks from experimental data of reaction rates

For the purpose of precise mathematical modelling of chemical reaction networks, useful techniques for estimating their parameters from experimental data are necessary. In this manuscript, we propose a new parameter estimation method for enzymatic chemical reaction networks from time-series experimental data of reaction rates. The main idea is based on retrieving time-series data of the species' concentrations from the available experimental data of reaction rates by making use of parametric Bézier curves. The least-squares method is applied to these retrieved data in order to determine the best-fitting values of the parameters in the corresponding mathematical model. Subsequently, we demonstrate the applicability of our parameter estimation method on three examples of enzymatic chemical reaction networks, including a model of ryanodine receptor adaptation and a model of protein kinase cascades. We also address the issue of identifiability of chemical reaction network models from reaction rates.
Ferromagnetic Climbing Robot for Industrial Flaw Detection: An Implementation of Computer Vision

In order to maintain a safe working environment, there is a constant need for the inspection of structures for failure. One common problem faced during inspection is restricted access to the inspection site. Furthermore, inspectors use sensors like cameras and non-destructive testing kits to detect structural failures such as cracks and corrosion. Still, due to the size and complex geometry of most structures, inspection is expensive, time-consuming and potentially unsafe. This paper, therefore, describes the design of a robot that can be used for non-destructive Inspections of various range of structures to improve and automate structural inspections. In order to achieve this aim, different existing robotic designs have been reviewed, with focus on wall-climbing robots in similar environments. The final design combines a set of actuator motors, magnetic tracked wheels to aid vertical movement on magnetic structures, as well as a wireless camera for visual inspection and manoeuvring the robot system for the identification of surface cracks and corrosion using YOLOv4 machine learning algorithm. The structure of the tracks allows the robot to climb over uneven surfaces like bulwark, obstacles etc. which allows inspections in unfriendly and inaccessible environments therefore reducing costs and inspection time considerably.