Miracle-dz/UniFAR-FTUs huggingface dataset:
Polygenic Risk Score in NAFLD/NASH-Associated Hepatocellular Carcinoma Along With Multifactorial Process

The prevalence and incidence of Non-Alcoholic Fatty Liver Disease (NAFLD) is increasing due to the epidemics of obesity and Type 2 Diabetes Mellitus (T2DM). Recently, a link between NAFLD and hypertension along with new genetic expression has been identified. NAFLD is a multisystem disease and is associated with hepatic and extrahepatic diseases. The author previously described that in addition to the epidemiological and single genetic factors, the study of combined effect of the variants may be attributed to the risk stratification in NAFLD/NASHrelated hepatocellular carcinoma (HCC) and also suggested that the stratification of the risk of NAFLD related HCC especially non-cirrhotic HCC based on the characteristic clinical and genetic evidence may contribute to the prevention, prediction, and surveillance. The risk factors and multifactorial process include obesity, T2DM, hypertension, ethnicity, genetic polymorphism PNPLA3, TM6SF2, GCKR, MBOAT7, and HSD17B13, epigenetic factors, transcriptional factors, post-transcriptional modification, and hepatic lipogenesis carcinogenesis in NAFLD. In this article, the author reviewed the current knowledge of risk factors, multifactorial process, and polygenic risk score (PRS) in NAFLD-related HCC. Although several risk factors and complex and multifactorial process are present in the progression of NAFLD-related HCC, the author suggests that comprehensive determination using epidemiological factor and PRS including PNPLA3, TM6SF2, GCKR, MBOAT7, and HSD17B13 may be attributed to the risk stratification, prognosis, and therapeutic strategy in cirrhosis and non-cirrhosis patients with NAFLD-
A Large-Scale Comparison of Tetrahedral and Hexahedral Elements for Solving Elliptic PDEs with the Finite Element Method

The Finite Element Method (FEM) is widely used to solve discrete Partial Differential Equations (PDEs) in engineering and graphics applications. The popularity of FEM led to the development of a large family of variants, most of which require a tetrahedral or hexahedral mesh to construct the basis. While the theoretical properties of FEM basis (such as convergence rate, stability, etc.) are well understood under specific assumptions on the mesh quality, their practical performance, influenced both by the choice of the basis construction and quality of mesh generation, have not been systematically documented for large collections of automatically meshed 3D geometries. We introduce a set of benchmark problems involving most commonly solved elliptic PDEs, starting from simple cases with an analytical solution, moving to commonly used test problem setups, and using manufactured solutions for thousands of real-world, automatically meshed geometries. For all these cases, we use state-of-the-art meshing tools to create both tetrahedral and hexahedral meshes, and compare the performance of different element types for common elliptic PDEs. The goal of this benchmark is to enable comparison of complete FEM pipelines, from mesh generation to algebraic solver, and exploration of relative impact of different factors on the overall system performance. As a specific application of our geometry and benchmark dataset, we explore the question of relative advantages of unstructured (triangular/ tetrahedral) and structured (quadrilateral/hexahedral) discretizations. We observe that for Lagrange-type elements, while linear tetrahedral elements perform poorly, quadratic tetrahedral elements perform equally well or outperform hexahedral elements for our set of problems and currently available mesh generation algorithms. This observation suggests that for common problems in structural analysis, thermal analysis, and low Reynolds number flows, high-quality results can be obtained with unstructured tetrahedral meshes, which can be created robustly and automatically. We release the description of the benchmark problems, meshes, and reference implementation of our testing infrastructure to enable statistically significant comparisons between different FE methods, which we hope will be helpful in the development of new meshing and FEA techniques.
Effects of rainfall variability on river discharge in Dawa river basin, Southern Ethiopia

Understanding the link between river flow and rainfall is crucial particularly in dryland areas where there is high spatial and temporal rainfall variability. The purpose of this study is to show the link between rainfall variability and river discharge in Dawa river basin in southern Ethiopia. In order to analyze the surface hydrological effect of rainfall variability, historical rainfall and river flow data from 1972 and 2017 were used. The rainfall data were obtained from the Ethiopian Meteorological Agency, whereas the river flow data was obtained from the Ethiopian Water Resource Agency. The link between rainfall variability and river flow was modeled using linear regression model. During the 1972 2017 period, in all seasons, the temporal trends of the rainfall flow and the river flow show decreasing trends. In summer, winter, autumn and springs seasons the rainfall show decreasing trends with annual decrease of. 0.04 mm, 0.064 mm, 0.05 mm and 0.15 mm respectively. While the river flow show decreasing trends in summer, winter, autumn and springs seasons with annual decrease of 2.89 m 3 s -1 , 0.26m 3 s -1 , 1.08 m 3 s -1 and 0.41 m 3 s -1 respectively. The result shows that rainfall variability resulted in diminish river flow discharge in annual from the year1972 to 2017 in the study area. Thus, it is recommended that strategies should be designed by the agricultural experts for the agricultural sector for the impacts of declining and erratic nature of rainfall and decreasing trends of river flow in to consideration in their routine activities in the study area.
Evaluation of neural code compression techniques for image retrieval

—In its paper babenko et al. proved that the activations invoked by an image within the top layers of a large convolutional neural network provide a high-level descriptor of the visual content of the image. While its paper provided evidence that compressed neural codes outperform compressed stated of the art descriptors, they didnt explore alternate deep neural network (DNN) architectures. In this paper, I evaluate the improvement in the retrieval performance of three states of the art DNN architectures when the network is retrained on a dataset of images that are similar to images encountered at test time. Furthermore, I examine these models and provide visualization in an effort to better understand their convolutional kernels and the relationship between the quality of the neural codes and the DNN convolutional layers. As in the original, we compressed neural codes and show that a simple PCA compression provides very good short codes that give state-of-the-art accuracy on both the Pasis6k dataset as well as the oxford5k dataset. An attempt was made to evaluate neural codes using Linear Discriminant Analysis (LDA), but due to some technical limitations, we were unable to compress all the features using LDA.
Peirce on Complex

In a world of ever growing specialization, the issue of complexity attracts a good amount of attention from cross-disciplinary points of view. The size of this huge Congress confirms this. Indeed, it would seem that the idea of the unity of science, which is commonly discarded as an impossible ideal, is enjoying a strong comeback. But, lest we fall back into some sort of positivistic utopia, it may be useful to listen to some of the suggestions which were made by Charles S. Peirce. It is my conviction that Peirce's thought may help us not only to reassume our philosophical responsibility which has been largely abdicated by much of 20th century philosophy, but also to tackle some of the most stubborn contemporary problems (Debrock 1992: 1). More specifically, the founder of pragmatism not only identified most of these problems one century ago, but he also mapped out some paths that we could follow to overcome the poverty of contemporary scientistic reductionism. One of these paths is related to the issue of complexity, that lies at the heart of all his thinking, and which forms the topic of my paper.
Chondrites-CladiChnus ICHNOCOENOSIS FROM THE DEEP-SEA DEPOSITS OF PIERFRANCESCO (CRETACEOUS; ITALY): OXYGEN- OR NUTRIENT-LIMITED?

The Italian Northern Apennines are acknowledged as the place where ichnology was born, but there is comparatively little work about their ichnological record. This study bridges this gap by describing two new ichnosites from the locality of Pierfrancesco, which preserve an abundant, low-disparity trace-fossil assemblage within the Late Cretaceous beds of the M. Cassio Flysch. Results show that lithofacies and ichnotaxa are rhythmically organized. The base of each cycle consists of Megagrapton-bearing calciclastic turbidites, which are overlain by marlstone beds with an abundant, low-disparity assemblage of trace fossils. This includes Chondrites intricatus, C. patulus, C. targionii, C. recurvus and Cladichnus fischeri. The cycle top consists of mudstones with no distinct burrows. The rhythmic pattern of Pierfrancesco reflects a deep-sea ecological succession, in which species and behaviour changed as turbidite-related disturbances altered the seafloor. This study opens the question of whether the Chondrites-Cladichnus ichnocoenosis represents low-oxygen or nutrient-poor settings. Received: October 29, 2020; accepted: September 03, 2021
Intradialytic Exercise on Changes in Blood Pressure in Chronic Kidney Failure Patients during Hemodialysis Therapy

BACKGROUND: Chronic kidney disease (CKD) is a chronic decline in kidney function that causes irreversible and progressive kidney tissue damage. Blood pressure is a major factor that can cause death up to 20 times in CKD patients undergoing hemodialysis therapy. AIM: This study aims to analyze the effect of intradialytic exercise that will benefit the cardiovascular system on changes in blood pressure in CKD patients undergoing hemodialysis therapy. METHODS: The research method used is a quasi-experimental pre-post design with a control group design. The study population was 97 dialysis patients. The sampling technique used purposive sampling as many as 30 people. Inclusion criteria: Patients with systolic blood pressure not >180 mmHg and diastolic 120 mmHg were included in the study, exclusion criteria: Patients with comorbidities, namely, asthma and heart disease were excluded from the study. Data analysis using paired t-test. RESULTS: The results showed that the average blood pressure in the control group, before exercise was: Systolic 168.93 mmHg, diastolic 106.20 mmHg, after exercise: Systolic 173.13 mmHg, diastolic 107 mmHg. The results of the average blood pressure in the intervention group, before exercise was: Systolic 162.20 mmHg, diastolic 104.27 mmHg, after exercise: Systolic 153.13 mmHg, diastolic 94.33 mmHg. CONCLUSION: The p value in the control group was found to be 0.321 or p > 0.05, while in the intervention group, p = 0.025 or p < 0.05. Intradialytic exercise can lower blood pressure in CKD patients undergoing hemodialysis therapy. Edited by: Ksenija Bogoeva-Kostovska Citation: Agustin WR, Safitri W, Kurniasari D, Setiyawan S, Murharyati A, Fitriana RN. Intradialytic Exercise on Changes in Blood Pressure in Chronic Kidney Failure Patients during Hemodialysis Therapy. Open-Access Maced J Med Sci. 2022 Jan 01; 10(G):1-5. https://doi.org/10.3889/oamjms.2022. 7271
Executive Functions’ Impact on Vocabulary and Verbal Fluency among Mono- and Bilingual Preschool-Aged Children

Background The phenomenon of multilingualism and its impact on child development are in the spotlight of latter-day psychology, and of utmost importance both for theory and practice. Language development is a strong predictor of psychological readiness for school and further academic success. At the same time, children’s mastery of written and oral speech in school education in a multilingual environment has several distinctive features. This study was dedicated to examining the influence of executive functions on the development of the vocabulary aspects of speech (both active and passive vocabulary) of mono- and bilingual children growing up in a bilingual environment. Objective We aimed to analyze the relationship between bilingualism and language development (vocabulary and verbal fluency) and determine which executive functions may help overcome the resulting difficulties at preschool age. Design Both monolingual and bilingual children participated in the study (n = 137 and n = 81, respectively). The children’s ages ranged from 6 to 7 years (M = 78.7 months, SD = 5.87). Two independent General Linear Models (GLM) were built to define which executive functions influenced the vocabulary and verbal fluency of the mono- and bilingual subjects (controlling for age, gender, and non-verbal intelligence as well). Results The results confirmed that bilingualism is negatively related to language development, but showed that verbal working memory significantly helps bilinguals compensate for difficulties in developing vocabulary and verbal fluency. Conclusion The study demonstrated that the ability to preserve and reproduce verbal information was of more significance for children’s vocabulary and verbal fluency than their language group (mono- or bilingual).
Responses of onion growth and yield to different planting dates and land management practices

Local varieties of onion (Allium cepa L.) are believed to be well adopted to agroecological conditions of their respective growing region but their lower productivity is a concern to be addressed. The variety ‘Safid e Paisaye’ was selected for this investigation due to its long storability and higher market demand. The present study was carried out at Agriculture Research Farm of Kabul University to study the influence of land management practices and planting dates on growth and yield of onion. Different agronomic traits including number of leaves per plant, leaf length, leaf area per plant, leaf area index, normalized difference vegetation index (NDVI), maturity period, marketable yield and total yield were studied in these trials. The recorded data were statistically analysed with R software. The planting dates had significant influence on growth and yield of onions. At 90 days after sowing, the highest number of leaves per plant (7.18), leaf length (30.07 cm), leaf area per plant (277.43 cm), leaf area index (0.93) and NDVI (0.29) were recorded for the first planting date (10 May). Similarly, the longest maturity period (176.44 days) and highest marketable yield (37.01 t ha) and total yield (40.08 t ha) were also observed under the first planting date. Land management practices did not have significant effect on growth and maturity period of onions. However, the marketable yield was influenced by tillage depth and land preparation. The deep tillage and flatbed recorded highest marketable yield of 38.58 t ha and 26.9 t ha, respectively. The results of the study indicate that, early planting was more appropriate to get vigorous and high yielding onions. The deep ploughed flatbeds were appropriate to get higher marketable yield of onions.
Available iron distribution in Nigerian soils-A review

Enhancement and maintenance of soil available iron (Fe) in a continuous cropping system such as practiced in Southeast Nigeria is critical to producing iron rich food crops and sustaining agricultural productivity. This review focused on the distribution of Fe in Nigerian soils and factors affecting its availability. It is understood that the amount of Fe in soils depends on soil texture, pH, calcium carbonate (CaCO3) level, organic matter content, and other soil characteristics. However, soil organic matter and pH have the strongest influence on iron levels. Since both major factors could be enhanced through proper soil management, Fe availability for improved crop production could be achieved.
The role of enhancer RNA in IFN-γ priming effect on TNF induction by lipopolysaccharide

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A Designerly Approach to Exploring Disruptions in Service: Insights from Employing a Systems Perspective

Failure in any human endeavor is inevitable and service is no exception (Pina e Cunha et al., 2009). When a customer experiences service failure, it is often frontline employees (FLEs), supported by organizational recovery procedures, who swoop in to save the day. Service encompasses multiple interaction processes among several actors (Holmlid & Björndal, 2016; Sampson, 2012) who contribute with their resources, competences, and capacities (Holmlid, 2012; Sangiorgi & Clark, 2004) to systemic value creation. Correspondingly, service research has shifted its focus from dyadic interactions to multi-actor perspectives, with designing and developing service systems being listed high on research priorities (Ostrom et al., 2015). However, extant service recovery literature has developed by distinctly focusing either on the FLEs or the organizational perspective (Van Vaerenbergh & Orsingher, 2016). Real-word service systems require a unified approach that cuts across system levels and addresses the accompanying complexities and interdependencies. It is important to understand how actors empower or disenfranchise themselves, and others, by acting with and through their (in)formal connections in systemic contexts. A reductionist view of service design that ignores systemic interdependencies and institutional arrangements governing such multi-actor systems can impact the realization of long-term change (Vink et al., 2020). Against this backdrop, we argue that a systemic perspective can offer an alternative understanding by foregrounding feedback coordination, requisite variety, continuous adaptation, ordering, and boundary framing. In this context, there is a lot of potential in putting generative, material, and embodied design tools and methods to use when investigating human interactions and activity and rendering the intangible tangible. The purpose of this paper is to put forth a designerly approach to understand employees’ responses to service disruptions through a systemic lens. Accordingly, the guiding research question is “What can we learn about how actors respond to service disruptions when applying a systems perspective? ”. For this paper, service failure is understood as a concept more oriented to the customer outcomes. Service disruption as a concept denotes a deviation or interruption in service that may or may not result in service failure. This exploratory qualitative study undertaken in a healthcare context utilizes an interview study complemented by a generative design activity (Sanders, 2000), to explore how service ORIGINAL ARTICLE
Probabilistic Risk Models for Multiple Disturbances: Probabilistic Risk Models for Multiple Disturbances:

Building probabilistic risk models for highly random forest disturbances like wildfire and forest insect outbreaks is a challenging. Modeling the interactions among natural disturbances is even more difficult. In the case of wildfire and forest insects, we looked at the probability of a large fire given an insect outbreak and also the incidence of insect outbreaks following wildfire. We developed and used a probabilistic model framework for estimating (1) the probability that a wildfire, at a given location and time, reaches a given size class under the conditions at the site—including history of insect outbreaks; and (2) the probability of an insect infestation at a given location and year under the conditions at the site—including history of fire occurrence and size. The study used historical data (1980 through 2004) on fire occurrence and forest insect outbreaks collected in Oregon and Washington. Spatial data on insect activity was obtained from aerial sketch maps created by the Forest Service Forest Health Protection program. Federal wildfire data obtained from the Desert Research Institute included information on the date, location, and size of the fire. Average monthly temperature and Palmer Drought Severity Indices were obtained from the National Climatic Data Center’s climate division data set Web page. The methods employed provide an objective tool for modeling complex hybrid processes and estimating associated probability maps.
OBEY AT ANY COST

If someone in a position of authority ordered you to deliver an electrical shock of 350 volts to another person because the other person answered a question incorrectly, would you obey? Neither would I. If you met someone who was willing to do such a thing, you would probably think of him or her as cruel and sadistic. This study. by Stanley Milgram of Yale University set out to examine the idea of obedience and produced some shocking and disturbing findings.
Literature Review of Physiotherapy Interventions for Lateral Elbow Tendinopathy

Initial Management Strategies The NICE Clinical Knowledge Summary suggests that initial management should recommend the use of analgesia such as paracetamol or topical non-steroidal anti-inflammatory drugs (NSAIDs), with a subsequent prescription of oral NSAIDs if ineffective. It is recommended to give advice to avoid heavy lifting, avoid forceful gripping and twisting activities, favour palm-up lifting rather than palmdown, and modify work by taking more rest breaks, alter work patterns and change practice regarding lifting.
Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin Machine

In this article, we introduce a novel variant of the Tsetlin machine (TM) that randomly drops clauses, the key learning elements of a TM. In effect, TM with drop clause ignores a random selection of the clauses in each epoch, selected according to a predefined probability. In this way, additional stochasticity is introduced in the learning phase of TM. To explore the effects drop clause has on accuracy, training time, interpretability and robustness, we conduct extensive experiments on nine benchmark datasets in natural language processing~(NLP) (IMDb, R8, R52, MR and TREC) and image classification (MNIST, Fashion MNIST, CIFAR-10 and CIFAR-100). Our proposed model outperforms baseline machine learning algorithms by a wide margin and achieves competitive performance in comparison with recent deep learning model such as BERT and AlexNET-DFA. In brief, we observe up to +10% increase in accuracy and 2x to 4x faster learning compared with standard TM. We further employ the Convolutional TM to document interpretable results on the CIFAR datasets, visualizing how the heatmaps produced by the TM become more interpretable with drop clause. We also evaluate how drop clause affects learning robustness by introducing corruptions and alterations in the image/language test data. Our results show that drop clause makes TM more robust towards such changes.
Multitime structure functions and the Lagrangian scaling of turbulence

We deﬁne and characterize multi-time Lagrangian structure functions using data stem-ming from two swirling ﬂows with mean ﬂow and turbulent ﬂuctuations: A Taylor-Green numerical ﬂow, and a von K´arm´an laboratory experiment. Data is obtained from numerical integration of tracers in the former case, and from three-dimensional particle tracking velocimetry measurements in the latter. Multi-time statistics are shown to decrease the contamination of large scales in the inertial range scaling. A time scale at which contamination from the mean ﬂow becomes dominant is identiﬁed, with this scale separating two different Lagrangian scaling ranges. The results from the multi-time structure functions also indicate that Lagrangian intermittency is not a result of large-scale ﬂow effects. The multi-time Lagrangian structure functions can be used without prior knowledge of the forcing mechanisms or boundary conditions, allowing their application in different ﬂow geometries.