Miracle-dz/UniFAR-FTUs huggingface dataset:
Dose escalation of ripretinib can lead to response in advanced gastrointestinal stromal tumor patients refractory to the standard dose: a report of two cases

Despite an initial response, most metastatic gastrointestinal stromal tumor (GIST) patients will ultimately be refractory to all current therapies, including imatinib, sunitinib, and regorafenib. Ripretinib at 150 mg daily was recently approved as an additional line of treatment. Few options remain after ripretinib. Here we report two cases with responses to dose escalation of ripretinib after progressing on the standard dose. Case 1: A 25-year-old man was diagnosed with a kit exon 9 mutated small intestine GIST after presenting with abdominal pain. The tumor was resected, but a year later, he developed metastases to the liver and pelvis. Over the course of three years, he received multiple lines of therapies as his disease progressed, including imatinib, sunitinib, and regorafenib. He was then placed on ripretinib 150 mg daily. However, he had disease progression after two months. Ripretinib was increased to 150 mg twice a day, which he tolerated well. After three months, he had regression of his disease. Case 2: A 54-year-old male was diagnosed with an unresectable kit exon 11 mutated gastric GISTs after presenting with abdominal pain. Imatinib led to an 80% regression, allowing surgical excision. A year later, his disease recurred. Over the course of 5 years, due to multiple recurrences, he received two additional surgeries, with imatinib, sunitinib, regorafenib, and avapritinib on either the adjuvant or neoadjuvant setting. Ultimately, he developed a metastatic GIST to the left suprarenal region, retroperitoneum, and epigastric region. He was then started on ripretinib 150 mg daily, experiencing progression of his disease in three months. Ripretinib was escalated to 150 mg twice a day, which he tolerated well, and after three months, he had a regression. Dose escalation of ripretinib leads to response in patients that progressed after the standard dose, and it is well-tolerated, being a promising new treatment option in advanced GIST.
STUDY OF KINETICS MODEL OF FLAVONOID TOTAL RELEASE IN PATCH OF ANTIHYPERTENSIVE HERBS

The liquid extract of antihypertensive herbs could reduce blood pressure equivalent to Hydrochlorothiazide 25 mg based on research conducted in “Hortus Medicus” clinic, Tawangmangu, Central Java, but parameters of herbal medicine taste, design, and packaging had the lowest scores. Transdermal patch was chosen as an alternative to resolve those problems. Polymers determined the effectiveness of the active substance release from the formula. Patch was formulated from combination of hydrophilic hydroxy methylcellulose (HPMC) and carboxymethylcellulose natrium (CMC-Na) polymer which would produce a fast release profile. The objective of this research was to study the kinetics models of total flavonoid release and to study the total number of flavonoids released from the antihypertensive herbs patch for 5 hours, as well as to determine the optimum formula for observing the weight, pH and loss on drying. Herbs were infused with distilled water at 90 ° C for 15 minutes, filtered then evaporated. Release kinetics model used a modified type-5 dissolution apparatus equipment with a cellophane membrane. The optimum proportion of HPMC and CMC-Na was 220:180 mg. Patch was dark brown, circle shaped, moist and flexible. It had pH value of 7.29 ± 0.09, folding endurance of >350, and thickness of 0.64 ± 0.05 mm. The average percentage of total flavonoids released from the matrix patch was 37.23% for 5 hours. The release kinetics followed the Higuchi kinetics model with a diffusion mechanism.
Responsible leadership and employee outcomes: a systematic literature review, integration and propositions

PurposeThe dominant view of responsible leadership (RL) has so far lacked adequate testing for employees' motivational outcomes, including presenteeism. Presenteeism, or attending work while being ill and unable to work at full capacity, causes productivity loss and imposes a significant economic burden to businesses and national economies. Applying the social identity theory of leadership (SITL), this paper aims to offer a conceptual framework supporting the relationship between RL and presenteeism and incorporating the mediating roles of organisational commitment and employees' turnover intentions.Design/methodology/approachThis paper conducts a systematic literature review using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart with the existing research on RL, presenteeism, organisational commitment and employee turnover intentions covering the main contributors to this research stream. The proposed model offers eight propositions to promote the examination of RL in more insightful ways.FindingsA shift in focus to the aspect of value-based leadership and presenteeism allows this paper to explore probable employee motivational outcomes, especially with consideration of organisational commitment and turnover intentions. While extant studies about presenteeism have tended to identify negative consequences, this paper explores different contexts in which RL could be crucial and positive. Based on a PRISMA flowchart, this paper provides a conceptual framework and directions that scholars might use to guide organisations and evaluate future research studies in RL and presenteeism.Research limitations/implicationsThe implications of this paper lie first in highlighting the demand for scholars to employ RL when conducting research reviews in organisational leadership and presenteeism. Beyond this broad purpose, this paper will help researchers to develop a holistic and pragmatic research approach more systematically and coherently. It is hoped that this conceptual framework can potentially lead to higher employee productivity and retention.Originality/valueThe systematic literature review offers a novel framework that will allow future researchers to conduct and explore empirical studies in organisational leadership. The suggested propositions will direct future scholars and practitioners to explore solutions in which presenteeism can be recognised at work and managed to achieve practical application of RL within organisational settings.
China’s Pledge to Civilise “All Under Heaven”

With China’s global rise, both its state leadership and key academics have engaged in developing a civilisational discourse for the twenty-first century partly based on ancient cosmological concepts. This article explores the meanings of and intentions behind this discourse, including its promise of a Chinese-led world order, and discusses its intended audience and international appeal. In the backdrop of theoretical debates on empires and their missions, the article claims that without a corresponding cultural appeal, China’s rising economic power and geostrategic clout are insufficient conditions to realise an empire in the classical sense. Growing inconsistencies mar the country’s imperial ambitions, such as those between a global civilising outreach and a toughening domestic embrace. Instead, imperial rhetoric is cautiously integrated in the party-state’s restoration of a Chinese “empire within,” indicating self-centredness and a lurking re-traditionalising of Chinese state power.
Iterative Soft-Input Soft-Output Bit-Level Reed-Solomon Decoder Based on Information Set Decoding

—In this paper, a bit-level decoder is presented for soft-input soft-output iterative decoding of Reed-Solomon (RS) codes. The main aim for the development of the proposed algorithm is to reduce the complexity of the decoding process, while yielding a relatively good error correction performance, for the efﬁcient use of RS codes. The decoder utilises information set decoding techniques to reduce the computational complexity cost by lowering the iterative convergence rate during the decoding process. As opposed to most iterative bit-level soft-decision decoders for RS codes, the proposed algorithm is also able to avoid the use of belief propagation in the iterative decoding of the soft bit information, which also contributes to the reduction in the computational complexity cost of the decoding process. The performance of the proposed decoder is investigated when applied to short RS codes. The error correction simulations show the proposed algorithm is able to yield a similar performance to that of the Adaptive Belief Propagation (ABP) algorithm, while being a less complex decoder.
Speech-Language Pathologists’ Knowledge of Spanish-Influenced English and Dialectical Differences: A Survey

The purpose of this study was to assess the competence of speech-language pathologists (SLPs) in serving bilingual or English-language learning children. Specifically, are SLPs able to identify acceptable English productions of a native Spanish speaker when described and/or given in context? Data were collected through an anonymous 28-question, web-based survey designed for practicing SLPs who worked with children. A total of 99 completed responses were received. The majority of SLPs demonstrated a basic understanding of aspects of Spanish-Influenced English (SIE) and dialectal features associated with native speakers of Spanish; however, SLPs were better able to identify errors in context than features of SIE. To ensure best practice in service provision for children who are English-language learners, SLPs need an understanding of the features of SIE and other English variants. Training programs should therefore provide education on the specific contrasts between languages/dialects while encouraging critical thinking and self-study.
Software defect prediction based on stacked sparse denoising autoencoders and enhanced extreme learning machine

Software defect prediction is an important software quality assurance technique. Nevertheless, the prediction performance of the constructed model is easily susceptible to irrelevant or redundant features in the software projects and is not predominant enough. To address these two issues, a novel defect prediction model called SSEPG based on Stacked Sparse Denoising AutoEncoders (SSDAE) and Extreme Learning Maching (ELM) optimised by Particle Swarm Optimisation (PSO) and another complementary Gravitational Search Algorithm (GSA) are proposed in this paper, which has two main merits: (1) employ a novel deep neural network – SSDAE to extract new combined features, which can effectively learn the robust deep semantic feature representation. (2) integrate strong exploitation capacity of PSO with strong exploration capability of GSA to optimise the input weights and hidden layer biases of ELM, and utilise the superior discriminability of the enhanced ELM to predict the defective modules. The SSDAE is compared with eleven state-of-the-art feature extraction methods in effect and efficiency, and the SSEPG model is compared with multiple baseline models that contain five classic defect predictors and three variants across 24 software defect projects. The experimental results exhibit the superiority of the SSDAE and the SSEPG on six
Efficient Fake News Detection using Machine Learning Algorithm

: The development of online web-based media has prompted an immense expansion in the quantity of clients who need to share and pitch different sorts of data. Twitter, the most well known miniature contributing to a blog stage, has become a hotbed for clients who are associated with various exercises, for example, news distributing, position chasing, enrolling, promoting and exposure. Citing a tweet is a significant activity for communicating a client's views to a large number of other clients. Re-tweet activity enjoys two significant benefits: (i) acquiring speedy openness and (ii) improving probability of acquiring new Twitter supporters consequently. The natural method of acquiring a bigger number of retweets is a tedious cycle, which prompts the formation of out of line strategies to acquire retweets. Subsequently, Twitter clients regularly approach different blackmarket administrations to acquire retweets inorganically in a brief term. Blackmarkets spread their deceitful biological system so that Twitter can't distinguish them even subsequent to dedicating huge exertion to cleanse the stage off bots, savages, and phony records. One significant purpose for the avoidance is that the conniving clients engaged with blackmarket administrations display a blend of natural and inorganic conduct – they naturally rewet some veritable tweets; simultaneously, they norganically retweet tweets submitted to blackmarket administrations.
Density Functional Theory Study of Edge-Induced Atomic-Scale Structural Phase Transitions of MoS2 Nanocrystals: Implications for a High-Performance Catalyst

: Molybdenum disul ﬁ de (MoS 2 ) has attracted much attention as a material to replace the noble-metal-based hydrogen evolution reaction catalyst. Polymorphism is an important factor in improving the catalytic performance of transition-metal dichalcogenides (TMDs) including MoS 2 . Several methods have been proposed to synthesize the 1T/1T ′ phase with high catalytic e ﬃ ciency, and a gas − solid reaction has recently been proposed as one of the alternative methods. However, the atomic-scale reaction mechanism between gas molecules and MoS 2 has not been clari ﬁ ed. Here, we report a detailed atomic-scale mechanism of structural phase transition of MoS 2 nanocrystals under reaction with CO gas molecules using density functional theory calculations. We con ﬁ rm that the evaporation of S atoms at the edge is much faster than the evaporation at the basal plane of MoS 2 nanocrystals. It is found that the S evaporation at the edge induces the structural change from 2H to 1T/1T ′ in the basal plane of nanocrystals. The structural change is also attributed to the chain reaction due to the sequential migration of S atoms to the octahedral sites, which is energetically favorable. The present results provide a guideline for the gas − solid reaction-based phase control of TMDs including MoS 2 to synthesize a high-performance catalyst.
Polar ice core organic matter signatures reveal past atmospheric carbon composition and spatial trends across ancient and modern timescales

Abstract We present polar ice core organic matter (OM) fluorescence signatures to reconstruct ancient and modern atmospheric compositions and relate OM signals to past ecological changes. OM composition from three Arctic ice cores (Canada and Greenland) was characterized by fluorescence spectroscopy and compared to an Antarctic OM record. Diverse OM was measured in ancient and modern ice in both hemispheres and similarities existed across vast spatiotemporal scales. We determined three OM markers, indicating paleoclimate and modern carbon trends: (i) ‘humic-like’, detected in Holocene ice of more complex and aromatic character, supporting trends of higher plant influences in warmer climates, (ii) monolignol- and non-amino acid-like, describing simple, lignin-like OM precursors ubiquitous in the environment and the microbial degradation products of more complex materials from plants/soils, and (iii) amino acid- and tannin-like, indicating microbial degradation of simple OM chemical species, compared to the other markers. Concentration trends were inferred from fluorescence intensities of individual OM types and related to warmer temperatures. No indicators of freshly produced OM by microbes were detected; signals were interpreted as materials externally produced from the ice and transported to polar regions. This marks the first global comparison of atmospheric reconstructions from OM across vast spatiotemporal scales.
A Survey on Various Available Object Detection Models and Application In Automatic License Plate Detection

With the development in technologies right from serial to parallel computing, GPU, AI, and deep learning models a series of tools to process complex images have been developed. The main focus of this research is to compare various algorithms(pre-trained models) and their contributions to process complex images in terms of performance, accuracy, time, and their limitations. The pre-trained models we are using are CNN, R-CNN, R-FCN, and YOLO. These models are python language-based and use libraries like TensorFlow, OpenCV, and free image databases (Microsoft COCO and PAS-CAL VOC 2007/2012). These not only aim at object detection but also on building bounding boxes around appropriate locations. Thus, by this review, we get a better vision of these models and their performance and a good idea of which models are ideal for various situations.
Influence of solvent polarity on reactive extraction of fumaric acid with Amberlite LA-2 from viscous solutions

ABSTRACT Current studies on the reactive extraction of fumaric acid with Amberlite LA-2 from aqueous phases with different viscosities using three solvents highlighted the influences of rheological properties of the fermentation medium on the efficiency of the extraction system. The obtained results showed that increasing the viscosity of the aqueous phase influences both the efficiency of the reactive extraction and the magnitude of the diffusional and kinetic resistances. The negative effect of increasing the viscosity of fumaric acid solutions on the interfacial mass flow of the product resulting from the reaction between acid and extractant was described by the reduction factor. For dichloromethane, by increasing the speed from 200 to 1000 rpm, the R factor increases 1.05 times for 8 cP and 3.56 times for 64 cP, while for n-heptane R factor increases 1.11 times for 8 cP and 9 times for 64 cP. The positive effect of alcohol was quantified using the amplification factor, which reached maximum values for n-heptane, under similar experimental conditions. The influences of studied factors on interfacial mass flow of the complex formed by reaction of fumaric acid with amine extractant have been quantified by mathematical expressions, proposed for extraction system without and with 1-octanol.
Evaluation of Precautionary Measures Taken for COVID-19 in the Hospitality Industry During Pandemic

ABSTRACT The precautionary measures taken against COVID-19 in hotel businesses are important in terms of providing the perception of safe tourism to tourists during the pandemic. Thus, this study aims to determine the dimensional structure of the precautionary measures taken against the pandemic in hotel businesses, and to understand if these measures contribute to tourists’ satisfaction and their dissatisfaction. In this context, through content analysis 1418 online reviews were examined on TripAdvisor between June and August 2020. Ten main categories and twenty eight subcategories were determined as a result of the analysis. While the safety perceptions of tourists regarding the measures taken were high in Disinfection and Hygiene, Temperature Measurement, it was observed that it was low in Social Distance and Mask Usage. The study offers clear theoretical and managerial implications for tourism literature and hotelier.
Active vitamin D increases the risk of hypercalcaemia in non-dialysis chronic kidney disease patients with secondary hyperparathyroidism: a systematic review and meta-analysis

Abstract Background This study evaluates the effects of active (1α-hydroxylated) vitamin D (AVD) therapy on hypercalcaemia in patients with non-dialysis chronic kidney disease (ND-CKD) and secondary hyperparathyroidism (SHPT). Methods A systematic search of the PubMed, Embase and Cochrane Library databases (up to 14 May 2020) was performed to identify randomized, placebo-controlled trials of single-agent, oral AVD therapies in adults with ND-CKD and SHPT. Only studies with ≥30 participants per arm and ≥6 weeks in duration were eligible. The outcome of interest was the number of subjects with an episode of hypercalcaemia. A meta-analysis of eligible studies was conducted using Comprehensive Meta-Analysis software (version 3.0). Results Six studies (five evaluating paricalcitol, one evaluating alfacalcidol) involving 799 patients were identified. Treatment durations ranged from 16 weeks to 2 years. The weekly doses of paricalcitol administered were 7 (three studies) and 14 µg (two studies); the weekly dose of alfacalcidol was 1.75–7.0 µg. Across all studies, rates of hypercalcaemia were 1.1–43.3% with AVD versus 0–3.4% with placebo. Meta-analysis of the six studies showed that AVD was associated with a 6.6-fold greater probability of hypercalcaemia versus placebo (odds ratio: 6.63, 95% confidence interval: 2.37, 18.55; P < 0.001). Two separate sensitivity analyses (one excluded a study identified as having a high risk of bias; the second excluded two studies that accounted for a large proportion of observed hypercalcaemia events) indicated the primary meta-analysis findings were robust. Conclusions Compared with placebo, AVD significantly increased the risk of hypercalcaemia among ND-CKD patients with SHPT.
Multi-target Natural and Nature-Inspired Compounds against Neurodegeneration: A Focus on Dual Cholinesterase and Phosphodiesterase Inhibitors

Alzheimer’s disease is a memory-related neurodegenerative condition leading to cognitive impairment. Cholinergic deficit, together with other underlying mechanisms, leads the to onset and progression of the disease. Consequently, acetylcholinesterase inhibitors are used for the symptomatic treatment of dementia, even if limited efficacy is observed. More recently, some specific phosphodiesterase isoforms emerged as promising, alternative targets for developing inhibitors to contrast neurodegeneration. Phosphodiesterase isoforms 4, 5 and 9 were found to be expressed in brain regions that are relevant for cognition. Given the complex nature of Alzheimer’s disease and the combination of involved biochemical mechanisms, the development of polypharmacological agents acting on more than one pathway is desirable. This review provides an overview of recent reports focused on natural and Nature-inspired small molecules, or plant extracts, acting as dual cholinesterase and phosphodiesterase inhibitors. In the context of the multi-target directed ligand approach, such molecules would pave the way for the development of novel agents against neurodegeneration. More precisely, according to the literature data, xanthines, other alkaloids, flavonoids, coumarins and polyphenolic acids represent promising scaffolds for future optimization.
Exploring the differences between gamer and non-gamer students in the effects of gamification on their motivation and learning

ABSTRACT Studies have shown that the application of gamification has positive effects on motivation and learning. However, various meta-studies indicate that there is high volatility in the published results. The purpose of this study is to analyze whether the student’s activity as gamers is related to these differences. A comparative study was conducted with undergraduates’ students in a database course. Students were divided into two instructional approaches: flipped learning (n = 47), and flipped with gamification (n = 49). Gamers were identified in each class using three methods: game frequency, spending money on videogames and most played game genre. The results showed that students in gamified class obtained significantly better results in learning, but there were no differences in motivation and satisfaction. When comparing gamers with non-gamers in the gamified class, the results using the three methods coincide with respect to motivation but show differences in satisfaction and learning. In the same way, when the effects of gamification on non-gamers were analyzed, there are coincidences in which those of the gamified class have better performance, but there are no coincidences regarding motivation and satisfaction. The results obtained show that the conclusions when comparing between gamers and non-gamers depend on the method used to identify them.