Miracle-dz/UniFAR-FTUs huggingface dataset:
Diffuse Oligosymptomatic Caroli’s Disease: Case Report

This article describes a case of Caroli’s disease in a 53-year-old female patient who complained nonspecificabdominal pain without cholestasis or cholangitis. Ultrasound and hepatic magnetic resonanceimaging showed segmental saccular dilations connected to intrahepatic bile ducts without hepaticfibrosis. This clinical case shows the possibility of having oligosymptomatic forms in the diffuse formsof Caroli disease, therefore the interest to consider this diagnosis in case of non-specific abdominalsigns and to request a hepatic ultrasound. Bangladesh J Medicine July 2020; 31(2) : 107-110
RESUPINATE HYDNACEOUS FUNGI OF NORTH AMERICA. I. TYPE STUDIES OF SPECIES DESCRIBED BY PECK

A critical study of types of all species of resupinate hydnaceous fungi described from North America is necessarily the initial phase of a taxonomic study of the group. This paper is concerned with species described by the renowned American mycologist Charles Horton Peck who served as botanist for the State of New York from 1867 to 1915. During this 48-year-period Peck proposed over 2700 new species and varieties of fungi. Included in this total are 25 species of resupinate hydnaceous fungi, all from New York State. Peck (1909) has provided an index of his new species and varieties of fungi described from 1869 to 1908; an index of fungi described by Peck from 1909 to 1915 (Gilbertson, 1962) is also available. Several accounts of Peck's life have appeared in the literature (Atkinson, 1918; Bessey, 1914; Burnham, 1919). These, however, give little information on his field and herbarium procedures, which may be of interest to contemporary mycologists. Peck spent virtually all of the period from May to November each year collecting plants in the field. In his early years as state botanist he dried his collections by the heat of the sun or an open fire. Later he used a portable drier which he called a "tin evaporator. " This was a folding tin box heated from below by a kerosene lantern. He carried a field book and kept data on all collections, including detailed notes on fragile, fleshy specimens that often change greatly on drying. These notebooks are now at the State Museum in Albany. The original labels with Peck's specimens are written on the margin of pieces torn from newspapers and magazines. Information on these labels is usually restricted to the name of the fungus, locality (usually a town name only), substratum, and month of collection. His collections are not numbered and the year of collection is rarely given. It was Peck's habit to spend the evenings while in the field writing descrip-
Multi-modal cyberbullying detection on social networks

Because social networks have become a vital part of people’s lives, cyberbullying becomes the most common risk encountered by young people on social networking platforms and raised serious concerns in society. Over the past few decades, most existing work on cyberbullying has focused on text analysis. Yet, the cyberbullying develops into multi-objective, multi-channel, and multi-form. Traditional text analysis methods cannot satisfy the diversity of bullying data in social networks. To deal with the new type of cyberbullying, we propose a multi-modal detection framework that takes into multi-modal information(e.g., image, video, comments, time) on social networks. Specifically, we not only extract textual features but also use the hierarchical attention networks to capture the session feature in social networks and encode several media information(e.g., video, image). Based on these features, we model the multi-modal cyberbullying detection framework to solve the new form of cyberbullying. Experimental analysis on two real-world datasets shows that our framework outperforms several existing state-of-the-art models.
Machinability of Titanium Grade-5 Alloy (Ti-6Al-4V) During Turning Processes: A Review

1PhD, MSc, (Eng.), Department of Mechanical Eng. Higher Institute of Engineering Professions, Elgubba/Libya. 2MSc, (Eng.), Department of Mechanical Eng. Higher Institute of Engineering Professions, Elgubba/Libya. 3MSc, (Eng.), Department of Mechanical Eng. Higher Institute of Engineering Professions, Elgubba/Libya. ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract Titanium grade-5 alloy Ti-6Al-4V is the most common alloy widely used in Aerospace and Automotive industries, Petrochemical applications and Biomedical parts manufacturing; this is due to its excellent combination of mechanical properties and high corrosion resistance at elevated temperature, but on other hand Ti-6Al-4V alloy shows poor machinability during machining processes because of its low thermal conductivity, low elastic modulus, high strength and high chemical reactivity. The machining characteristics of titanium alloy grade-5 Ti-6Al-4V have not been completely reported so far, this paper focuses on turning process of this alloy in cases of; tool materials used, special techniques employed to improve cutting process machinability, cutting tool failure modes and wear mechanisms, cutting forces, cutting temperature and surface integrity.
Paleosol-Based Inference of Niches for Oligocene and Early Miocene Fossils from the John Day Formation of Oregon

ABSTRACT Over the past decade, we recorded exact locations of in situ fossils and measured calcareous nodules in paleosols of the Oligocene and lower Miocene (Whitneyan–Arikareean) John Day Formation of Oregon. These data enable precise biostratigraphy within an astronomical time scale of Milankovitch obliquity cycles and also provide mean annual precipitation and vegetation for each species. Fossils in paleosols of the John Day Formation alternated between semiarid shrubland and subhumid woodland communities every 41 ka. Land snails ‘Polygyra’ expansa and Monadenia dubiosa were found in semiarid paleosols, but Vespericola dalli and Monadenia marginicola were found in subhumid paleosols. Cicada burrows (Naktodemasis bowni) were found in semiarid paleosols, whereas dung beetle balls (Pallichnus dakotensis) and earthworm castings (Edaphichnium lumbricatum) were found in subhumid paleosols. Among hypertragulids, Hypertragulus hesperius was found in semiarid paleosols and Nanotragulus planiceps in subhumid paleosols. Among glires, the aplodontiid Haplomys liolophus, geomyids Pleurolicus sulcifrons and several species of Entoptychus, castorid Palaeocastor peninsulatus, and leporid Archaeolagus ennisianus were found in semiarid paleosols. Large ungulates were found primarily in subhumid paleosols, including the agriochoere Agriochoerus antiquus, oreodonts Eporeodon occidentalis and Promerycochoerus superbus, equid Miohippus annectens, and rhinos Diceratherium annectens and Diceratherium armatum. The inferred niches of fossil mammals are consistent with interpretations based on their morphology; taxa with adaptations for life in open, arid habitats, such as high-crowned teeth and semifossorial or cursorial limb structure, were mainly in semiarid paleosols, but taxa with arboreal adaptations were only found in subhumid paleosols.
Agent Based Trust Establishment between Vehicle in Vehicular Cloud Networks

— In order to enhance the driving experience with increased security and privacy, a category of MANET has emerged i.e VANET. The nodes are highly mobile, uncoordinated and dynamic in nature. An progressive step in this vision is Vehicular Cloud (VC) the advancement in Intelligent Transport System (ITS). The resources are shared between the vehicle nodes to provide the services at a economical cost. In order to provide them there should be hassle free secure communication link established between the Vehicle nodes, Road side unit (RSU) and the Cloud. Trust establishment in VC between vehicle nodes enhances the security aspects in VC. In this paper we put forth an trust evaluation scheme based on Dempster Shafer theory. The trust evaluation is based on Direct trust and Indirect trust, the priority of which can be accustomed.
DO TARIFFS REDUCE THE TRADE DEFICIT?

Two simple models are used to show that the effect of tariffs on the trade deficit depends on the nature of the deficit and on its persistence. In the first model, a country runs a trade deficit because of a traditional intertemporal motive to borrow in the present to repay in the future, so the deficit is transitory. In this model, a permanent increase in tariffs increases the real interest rate for the country in deficit, leading to less borrowing and to a reduction in the trade deficit. In the second model, the deficit arises because the country acts as a world intermediary, taking gross asset and liability positions towards the rest of the world that lead to a net debtor position. In this second model, we can have a permanent trade deficit. In that case, a tariff increase can actually have a zero effect on the trade deficit.
Pre-trained Language Models with Limited Data for Intent Classification

Intent analysis is capturing the attention of both the industry and academia due to its commercial and noncommercial significance. The rapid growth of unstructured data of micro-blogging platforms, such as Twitter and Facebook, are amongst the important sources for intent analysis. However, the social media data are often noisy and diverse, thus making the task very challenging. Further, the intent analysis frequently suffers from lack of sufficient data because the labeled datasets are often manually annotated. Recently, BERT (Bidirectional Encoder Representation from Transformers), a state-of-the-art language representation model, has attracted attention for accurate language modelling. In this paper, we investigate the application of BERT for its suitability for intent analysis. We study the fine-tuning of the BERT model through inductive transfer learning and investigate methods to overcome the challenges due to limited data availability by proposing a novel semantic data augmentation approach. This technique generates synthetic sentences while preserving the label-compatibility using the semantic meaning of the sentences, to improve the intent classification accuracy. Thus, based on the considerations for finetuning and data augmentation, a systematic and novel step-bystep methodology is presented for applying the linguistic model BERT for intent classification with limited data available. Our results show that the pre-trained language can be effectively used with noisy social media data to achieve state-of-the-art accuracy in intent analysis under low labeled-data regime. Moreover, our results also confirm that the proposed text augmentation technique is effective in eliminating noisy synthetic sentences, thereby achieving further performance improvements.
2 00 6 k-hypermonogenic automorphic forms

In this paper we deal with monogenic and k-hypermonogenic automorphic forms on arithmetic subgroups of the Ahlfors-Vahlen group. Monogenic automorphic forms are exactly the 0-hypermonogenic automorphic forms. In the first part we establish an explicit relation between k-hypermonogenic automorphic forms and Maaß wave forms. In particular, we show how one can construct from any arbitrary non-vanishing monogenic automorphic form a Clifford algebra valued Maaß wave form. In the second part of the paper we compute the Fourier expansion of the k-hypermonogenic Eisenstein series which provide us with the simplest non-vanishing examples of k-hypermonogenic automorphic forms.
Data analysis and tools applied to modeling and simulation of a PV system in Ecuador Análisis de datos y herramientas aplicadas al modelado y simulación de un sistema fotovoltaico en Ecuador

This paper presents a research that was carried out for the management of a photovoltaic system in a Microgrid, with applications and the use of tools applied to modeling and computational simulation in the Microgrid laboratory implanted in the facilities of the University of Cuenca (Ecuador). Additionally, through the use of automatic learning techniques, the behavior of the photovoltaic system has been modeled in the study area based on radiation and temperature with very good results. In addition, several applications can be made in real engineering studies such as feasibility, performance analysis, energy estimation, educational models, etc.
The Photomotor Response - Dynamic Quantification by a Portable Pupillometer

The pupillary light reflex (PLR) is a key component of the physical examination as it reliably tests the functional integrity of the neuromuscular loop between pupil and midbrain. Unlike the traditional manual testing the PLR with a penlight that frequently leads to incorrect interpretation due to its subjectivity, the specialized “pupillometer” tool allows objective testing and trending of pupillary data. We performed quantitative unilateral pupillometry several times in 53 healthy subjects (aged 21-74) in different background illumination levels using the NeurOptics NPi®-200 pupillometer. A number of key parameters describing the PLR were collected and analysed. We found that the individual PLR was very consistent. In general, constriction velocity (the first part of the PLR when the pupil constricts promptly after the onset of a light stimulus) was brisker than the dilation velocity (the second part of the PLR, when the pupil recovers from the constriction). Most importantly, both velocities depend on the initial pupillary resting size. We proved that pupillary parameters depend on environmental light conditions and age, but not gender, and scrutinized the nature and dynamics of anisocoric pupils. Taking together, pupillometry is becoming an important, non-invasive clinical tool for testing the autonomic nervous system. Here, we describe baseline parameters representing the physiological PLR, confirming and extending previously reported data. We thus provide the clinician important criteria to precisely assess the PLR and hence the autonomic nervous system in different pathological conditions such as diabetes, traumatic brain injury or cardiac and other autonomic neuropathies.
Length-Weight relationships of Cyprinus carpio from the Indus River at Chashma Lake, District Mianwali, Punjab, Pakistan

Length-weight relationships (LWRs) and relative condition factor are of great importance in fishery assessment studies since it provides information about the growth of all fishes. The objective of the present study was to develop the model for predicting length-weight relationship (LWR) length relationship (LLR) and condition factor (K) of Cyprinus carpio at Chashma Lake District Mianwali, Punjab, Pakistan. In January 2017, Samples of 43 fish’s range in size of 31.00 cm to 60.80 cm were taken to estimate the LWR and LLR. The length of the fish was ranged in size from 31.00 to 60.80 cm with a mean of 43.50 (± 1.98) in TL and from 25.00 to 50.00 cm with a mean range of 35.20 (± 1.87) in SL. The regressions result for LWR were found highly significant (r = 0.943; P < 0.001) with coefficients of determination, r2 = 0.889 showing a highly significant correlation between logs transformed data of TL and W. Slope value (b) of C. carpio was found 3.01, which is very close to 3.00, hence representing an isometric growth pattern. In LLR, the SL, BG, DFL, PvFL, and AFB were found isometric (b=1) with TL, HL, PtFL, PtFB, PvFB, AFL, CFL, and CFW showed negative allometric growth, while DFB showed positive allometric growth with TL in C. carpio. Condition factor (K) remained insignificant with TL and BW.
Experience-independent transformation of single-cell 3D genome structure and transcriptome during postnatal development of the mammalian brain

Both transcription and 3D organization of the mammalian genome play critical roles in neurodevelopment and its disorders. However, 3D genome structures of single brain cells have not been solved; little is known about the dynamics of single-cell transcriptome and 3D genome after birth. Here we generate a transcriptome atlas of 3,517 cells and a 3D genome atlas of 3,646 cells from the developing mouse cortex and hippocampus, using our high-resolution MALBAC-DT and Dip-C methods. In adults, 3D genome “structure types” delineate all major cell types, with high correlation between A/B compartments and gene expression. During development, both transcriptome and 3D genome are extensively transformed in the first postnatal month. In neurons, 3D genome is rewired across multiple scales, correlated with gene expression modules and independent of sensory experience. Finally, we examine allele-specific structure of imprinted genes, revealing local and chromosome-wide differences. These findings uncover a previously unknown dimension of neurodevelopment. HIGHLIGHTS Transcriptomes and 3D genome structures of single brain cells (both neurons and glia) in the developing mouse forebrain Cell type identity encoded in the 3D wiring of the mammalian genome (“structure types”) Major transformation of both transcriptome and 3D genome during the first month of life, independent of sensory experience Allele-specific 3D structure at 7 imprinted gene loci, including one that spans a whole chromosome
The silage composition and its influence on dairy cows milk yield.

The purpose of the research was to analyze the composition of maize and grass silages and estimate influence of forage: concentrate ratio on milk yield in dairy cows. Data were collected in 2013 and 2014 and consist of 11 samples of maize silage and 15 samples of grass silage. Dry matter (DM) content was not significantly different between years within silage group. Dry matter content of maize silage was 317.2 ± 15.14 g kg-1 in 2013 and 361.5 ± 20.61 g kg-1 in 2014, whereas corresponding DM in grass silage was 303.8 ± 17.58 g kg-1 and 361.5 ± 20.61 g kg-1. Crude protein content of DM was significantly higher in grass silage than in maize silage and ranged from 123.9 ± 8.07 g kg-1 to 137.5 ± 7.12 g kg-1. Net energy for lactation (NEL) was significantly higher in maize silage than in grass silage and ranged from 6.5± 0.09 MJ kg-1 to 6.6 ± 0.06 MJ kg-1 of DM (P<0.05). Neutral detergent fiber (NDF) and acid detergent fiber (ADF) content of DM was lower in maize silage than in grass silage. The range in NDF was from 444.1 ± 12.40 g kg-1 to 487.1± 17.79 g kg-1, whereas corresponding ADF ranged from 252.9 ± 7.76 g kg-1 to 254.4 ± 7.76 g kg-1. The relationship between NEL and DM content was: NEL=0.0007×DM + 6.3317. Highest forage: concentrate dry matter ratio was 1.24 in May, 2013, whereas highest milk yield was observed in May, 2014.
Evaluation of detectability of differential type probe using directional eddy current for fibre waviness in CFRP

This paper describes the detectability of eddy current testing (ECT) using directional eddy current for detection of in-plane fibre waviness in unidirectional carbon fibre reinforced plastic (CFRP) laminate. Three different types of probes, such as circular driving, symmetrical driving and uniform driving probe, were proposed, and the waviness angle was extracted from the contour map of the ECT signal by applying a Canny filter and a Hough transform. By comparing both the waviness angle estimated by ECT and that obtained by an X-ray CT image, the standard deviation (precision) and root mean square error (accuracy) were evaluated to discuss the detectability of these probes. The directional uniform driving probe shows the best detectability and can detect fibre waviness with a waviness angle of more than 2° in unidirectional CFRP. The probe shows a root mean square error of 1.90° and a standard deviation of 4.49° between the actual waviness angle and the angle estimated by ECT. This article is part of the theme issue ‘Advanced electromagnetic non-destructive evaluation and smart monitoring’.
Plasma Metabolomic Profiling in Patients with Rheumatoid Arthritis Identifies Biochemical Features Indicative of Quantitative Disease Activity

Background: Rheumatoid arthritis (RA) is a chronic, autoimmune disorder characterized by joint inflammation and pain. In patients with RA, metabolomic approaches, i.e., high-throughput profiling of small-molecule metabolites, on plasma or serum has thus far enabled the discovery of biomarkers for clinical subgroups, risk factors, and predictors of treatment response. Despite these recent advancements, the identification of blood metabolites that are predictive of quantitative disease activity remains an important challenge in precision medicine for RA. Herein, we use global plasma metabolomic profiling analyses to detect metabolites associated with, and predictive of, quantitative disease activity in patients with RA. Methods: Ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was performed on a discovery cohort consisting of 128 plasma samples from 64 RA patients. The resulting metabolomic profiles were analyzed with two different strategies to find metabolites associated with Disease Activity Score-28 using C-reactive protein (DAS28-CRP). Mixed-effects regression models were used to identify metabolites differentially abundant between two disease activity groups (lower, DAS28-CRP <= 3.2; and higher, DAS28-CRP > 3.2); and to identify metabolites significantly associated with DAS28-CRP scores. A generalized linear model (GLM) was constructed for estimating DAS28-CRP using plasma metabolite abundances. For associating metabolites with CRP (an indicator of inflammation), metabolites differentially abundant between two patient groups (low-CRP, CRP <= 3.0 mg/L; high-CRP, CRP > 3.0 mg/L) were investigated. Results: We identified 33 metabolites differentially abundant between lower and higher disease activity groups (P < 0.05). Additionally, we identified 51 metabolites associated with DAS28-CRP (P < 0.05). A GLM based upon these 51 metabolites resulted in higher prediction accuracy (mean absolute error [MAE]+/-SD): 1.51+/-1.77) compared to a GLM without feature selection (MAE+/-SD: 2.02+/-2.21). The predictive value of this feature set was further demonstrated on a validation cohort of twelve plasma samples, wherein we observed a stronger correlation between predicted vs. actual DAS28-CRP (with feature selection: Spearmans rho = 0.69, 95% CI: [0.18, 0.90]; without feature selection: Spearmans rho = 0.18, 95% CI: [-0.44, 0.68]). Lastly, among all identified metabolites, the abundances of eight were significantly associated with CRP patient groups while controlling for potential confounders (P < 0.05). Conclusion: We demonstrate for the first time the prediction of quantitative disease activity in RA using plasma metabolomes. The metabolites identified herein provide insight into circulating pro-/anti-inflammatory metabolic signatures that can reflect disease activity and inflammatory status in patients with RA.
“Watch out!”: Prediction-Level Intervention for Automated Driving

It seems that autonomous driving systems are substituting human responsibilities in the driving task. However, this does not mean that vehicles should not interact with their driver anymore, even in case of full automation. One reason is that the automation is not yet advanced enough to predict other road user's behavior in complex situations, which can lead to sub-optimal action choices, decrease comfort and user experience. In contrast, a human driver may have a more reliable understanding of other road users’ intentions which could complement that of the automation. We propose a framework that distinguishes between four levels for interaction with automation. Based on the framework, we introduce a concept which allows drivers to provide prediction-level guidance to an automated driving system through gaze-speech interaction. Results of a pilot user study show that people hold a positive attitude towards prediction-level intervention as well as the gaze-based interaction method.