LLukas22/scidocs huggingface dataset:
Indications and outcome of liver transplantation in tyrosinaemia type 1

A retrospective analysis was performed on 17 patients presenting with tyrosinaemia type 1 (TT1) between 1989–1997; 7 pre 1992 prior to the introduction of 2-(2-nitro-4-trifluoromethylbenzoyl)-1,3-cyclohexanedione (NTBC) therapy and 10 post 1992. During this time, eight children (5 males) underwent orthotopic liver transplantation (OLT); six prior to the introduction of NTBC in 1992 and two on NTBC therapy. The primary indications for OLT pre-1992 were risk of hepatocellular carcinoma with evidence of hepatic dysplasia in all, associated with liver failure in two, and rise in α-fetoprotein (AFP) in four. Two of the ten treated with NTBC required OLT. The indications were non-response to NTBC in one child and development of hepatic dysplasia associated with poor quality of life in the second patient. Median age for OLT was 64 months (range 5–127 months) with a median weight of 24 kg (range 6–25 kg). The histology of hepatectomy specimens at transplantation showed: cirrhosis in 8, hepatic dysplasia in 6 and hepatocellular carcinoma in 1. Plasma tyrosine and AFP returned to normal in all cases. Urinary succinylacetone reduced but persisted in small amounts (median 7.7 μmol/mmol creatinine). Hypertrophic cardiomyopathy resolved in 3/3 patients. Hypoglycaemia, not responding to dietary therapy or NTBC treatment, resolved post-transplant in one patient. There were two deaths, one from primary non-function and one from chronic rejection. Late complications in survivors (n=6) include post-transplant lymphoproliferative disease of the iris in one child which resolved and renal dysfunction with a fall in glomerular filtration rate in three (50%). Median follow up post OLT is 6.7 years (range 1–7 years). Quality of life post-transplant in survivors is good with unrestricted diet in all. Conclusion Liver transplantation is an effective treatment for TT1 with good quality of life. The current indications of OLT in TT1 are non-response to NTBC, risk of malignancy and poor quality of life related to dietary restriction and frequency of blood sampling.
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes

Estimating the 6D pose of known objects is important for robots to interact with the real world. The problem is challenging due to the variety of objects as well as the complexity of a scene caused by clutter and occlusions between objects. In this work, we introduce PoseCNN, a new Convolutional Neural Network for 6D object pose estimation. PoseCNN estimates the 3D translation of an object by localizing its center in the image and predicting its distance from the camera. The 3D rotation of the object is estimated by regressing to a quaternion representation. We also introduce a novel loss function that enables PoseCNN to handle symmetric objects. In addition, we contribute a large scale video dataset for 6D object pose estimation named the YCB-Video dataset. Our dataset provides accurate 6D poses of 21 objects from the YCB dataset observed in 92 videos with 133,827 frames. We conduct extensive experiments on our YCBVideo dataset and the OccludedLINEMOD dataset to show that PoseCNN is highly robust to occlusions, can handle symmetric objects, and provide accurate pose estimation using only color images as input. When using depth data to further refine the poses, our approach achieves state-of-the-art results on the challenging OccludedLINEMOD dataset. Our code and dataset are available at https://rse-lab.cs.washington.edu/projects/posecnn/.
Mangroves among the most carbon-rich forests in the tropics

Mangrove forests occur along ocean coastlines throughout the tropics, and support numerous ecosystem services, including fisheries production and nutrient cycling. However, the areal extent of mangrove forests has declined by 30–50% over the past half century as a result of coastal development, aquaculture expansion and over-harvesting1–4. Carbon emissions resulting from mangrove loss are uncertain, owing in part to a lack of broad-scale data on the amount of carbon stored in these ecosystems, particularly below ground5. Here, we quantified whole-ecosystem carbon storage by measuring tree and dead wood biomass, soil carbon content, and soil depth in 25 mangrove forests across a broad area of the Indo-Pacific region—spanning 30 of latitude and 73 of longitude—where mangrove area and diversity are greatest4,6. These data indicate that mangroves are among the most carbon-rich forests in the tropics, containing on average 1,023 Mg carbon per hectare. Organic-rich soils ranged from 0.5 m to more than 3 m in depth and accounted for 49–98% of carbon storage in these systems. Combining our data with other published information, we estimate that mangrove deforestation generates emissions of 0.02–0.12 Pg carbon per year—as much as around 10% of emissions from deforestation globally, despite accounting for just 0.7% of tropical forest area6,7. Deforestation and land-use change currently account for 8–20% of global anthropogenic carbon dioxide (CO2) emissions, second only to fossil fuel combustion7,8. Recent international climate agreements highlight Reduced Emissions from Deforestation and Degradation (REDD+) as a key and relatively cost-effective option for mitigating climate change; the strategy aims to maintain terrestrial carbon (C) stores through financial incentives for forest conservation (for example, carbon credits). REDD+ and similar programs require rigorous monitoring of C pools and emissions8,9, underscoring the importance of robust C storage estimates for various forest types, particularly those with a combination of high C density and widespread land-use change10. Tropical wetland forests (for example, peatlands) contain organic soils up to several metres deep and are among the largest organic C reserves in the terrestrial biosphere11–13. Peatlands’ disproportionate importance in the link between land use and climate change has received significant attention since 1997, when peat fires associated with land clearing in Indonesia increased atmospheric CO2 enrichment by 13–40% over global annual fossil fuel emissions11. This importance has prompted calls to specifically address tropical peatlands in international climate change mitigation strategies7,13.
Analysis of the differences in whole-genome expression related to asthma and obesity.

INTRODUCTION
Concomitant obesity significantly impairs asthma control. Obese asthmatics show more severe symptoms and an increased use of medications.


OBJECTIVES
The primary aim of the study was to identify genes that are differentially expressed in the peripheral blood of asthmatic patients with obesity, asthmatic patients with normal body mass, and obese patients without asthma. Secondly, we investigated whether the analysis of gene expression in peripheral blood may be helpful in the differential diagnosis of obese patients who present with symptoms similar to asthma.


PATIENTS AND METHODS
The study group included 15 patients with asthma (9 obese and 6 normal-weight patients), while the control group-13 obese patients in whom asthma was excluded. The analysis of whole-genome expression was performed on RNA samples isolated from peripheral blood.


RESULTS
The comparison of gene expression profiles between asthmatic patients with obesity and those with normal body mass revealed a significant difference in 6 genes. The comparison of the expression between controls and normal-weight patients with asthma showed a significant difference in 23 genes. The analysis of genes with a different expression revealed a group of transcripts that may be related to an increased body mass (PI3, LOC100008589, RPS6KA3, LOC441763, IFIT1, and LOC100133565). Based on gene expression results, a prediction model was constructed, which allowed to correctly classify 92% of obese controls and 89% of obese asthmatic patients, resulting in the overall accuracy of the model of 90.9%.


CONCLUSIONS
The results of our study showed significant differences in gene expression between obese asthmatic patients compared with asthmatic patients with normal body mass as well as in obese patients without asthma compared with asthmatic patients with normal body mass.
Nasolacrimal duct obstruction as a complication of iodine-131 therapy in patients with thyroid cancer.

PURPOSE
There are few studies evaluating the effect of radioioine therapy on the lacrimal drainage system. This study reports on symptomatic and asymptomatic nasolacrimal duct obstruction (NLDO) as complications of high-dose radioiodine therapy in these patients.


METHODS
Eighty-one cases were randomly selected from a population of radioiodine-treated differentiated thyroid carcinoma patients and were enrolled in a historical cohort study. The patients were categorized into four subgroups on the basis of the received cumulative dose of iodine-131 (I-131). In addition, 17 age-matched and sex-matched individuals were selected as the control group. Using dacryoscintigraphy and a clinical datasheet, the patients and controls were evaluated for partial or complete and symptomatic or asymptomatic NLDO. The data on different subgroups of patients were compared with the data of the control group.


RESULTS
Twenty-nine out of 162 exposed eyes (18%) and three out of 34 control eyes (9%) had evidence of NLDO on scintigraphic images. Among patients treated with less than 11.1 GBq of I-131 (subgroup A), six out of 78 eyes (7.7%) had partial or complete NLDO. This was evident in 23 out of 84 eyes (27.4%) among patients treated with 11.1 GBq or more (subgroup B). The frequency of complete NLDO increases significantly when the cumulative dose of radioiodine exceeds 11.1 GBq (2.9% in the control group, 3.8% in subgroup A, and 23.8% in subgroup B; P=0.006).


CONCLUSION
NLDO may be considered as a side effect of I-131 therapy, especially with a cumulative dose of 11.1 GBq or more.
Genetic determination of islet cell autoimmunity in monozygotic twin, dizygotic twin, and non-twin siblings of patients with type 1 diabetes: prospective twin study.

OBJECTIVE
To test the hypothesis that non-diabetic dizygotic and monozygotic twin siblings of patients with type 1 diabetes have a similar high prevalence of islet cell autoantibodies, thus suggesting that islet cell autoimmunity is mainly environmentally determined.


DESIGN
Prospective twin study.


SETTING
Two specialist centres for diabetes in the United States.


PARTICIPANTS
Non-diabetic monozygotic twin (n=53), dizygotic twin (n=30), and non-twin (n=149) siblings of patients with type 1 diabetes; 101 controls.


MAIN OUTCOME MEASURES
Analysis of progression to diabetes and expression of anti-islet autoantibodies.


RESULTS
Monozygotic twin siblings had a higher risk of progression to diabetes (12/53) than dizygotic twin siblings (0/30; P<0.005). At the last follow up 22 (41.5%) monozygotic twin siblings expressed autoantibodies compared with 6 (20%) dizygotic twin siblings (P<0.05), 16 (10.7%) non-twin siblings (P<0.0001), and 6 (5.9%) controls (P<0.0001). Monozygotic twin siblings expressed multiple (>/=2) antibodies more often than dizygotic twin siblings (10/38 v 1/23; P<0.05). By life table analysis the probability of developing positive autoantibodies was higher among the monozygotic twin siblings bearing the diabetes associated HLA DQ8/DQ2 genotype than in those without this genotype (64.2% (95% confidence interval 32.5% to 96%) v 23.5% (7% to 40%) at 10 years of discordance; P<0.05).


CONCLUSION
Monozygotic and dizygotic twins differ in progression to diabetes and expression of islet cell autoantibodies. Dizygotic twin siblings are similar to non-twin siblings. These two observations suggest that genetic factors play an important part in determination of islet cell autoimmunity, thus rejecting the hypothesis. In addition, there is a high penetrance of islet cell autoimmunity in DQ8/DQ2 monozygotic twin siblings.
OmniClimber: An omnidirectional light weight climbing robot with flexibility to adapt to non-flat surfaces

This paper introduces a novel climbing robot with high maneuverability for inspection of ferromagnetic 3D human made structures. The robot takes advantage of novelties which allows an excellent maneuverability on the structure and can adapt to flat and non-flat structures. In this paper we present the conceptual and detailed design of the robot, the implementation and two tests: laboratory tests on a flat surface; and a set of field tests performed on a wind turbine foundation to prove the applicability of the robot on both structures.
A Review of Financial Accounting Fraud Detection based on Data Mining Techniques

With an upsurge in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries. The failure of internal auditing system of the organization in identifying the accounting frauds has lead to use of specialized procedures to detect financial accounting fraud, collective known as forensic accounting. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. This paper presents a comprehensive review of the literature on the application of data mining techniques for the detection of financial accounting fraud and proposes a framework for data mining techniques based accounting fraud detection. The systematic and comprehensive literature review of the data mining techniques applicable to financial accounting fraud detection may provide a foundation to future research in this field. The findings of this review show that data mining techniques like logistic models, neural networks, Bayesian belief network, and decision trees have been applied most extensively to provide primary solutions to the problems inherent in the detection and classification of fraudulent data.
State-Of-The Art Practices in Bridge Inspection

Abstract—Government reports and published research have flagged and brought to public attention the deteriorating condition of a large percentage of bridges in Canada and the United States. With the increasing number of deteriorated bridges in the US, Canada, and around the globe, condition assessment techniques of concrete bridges are evolving. Investigation for bridges’ defects such as cracks, spalls, and delamination and their level of severity are the main objectives of condition assessment. Inspection and rehabilitation programs are being implemented to monitor and maintain deteriorated bridge infrastructure. This paper highlights the state-of-the art of current practices being performed for concrete bridge inspection. The information is gathered from the literature and through a distributed questionnaire. The current practices in concrete bridge inspection rely on the use of hummer sounding and chain dragging tests. Non-Destructive Testing (NDT) techniques are not being utilized fully in the process. Nonetheless, they are being partially utilized by the recommendation of the bridge inspector after conducting visual inspection. Lanes are usually closed during the performance of visual inspection and bridge inspection in general.
Usefulness of gastroesophageal reflux scintigraphy using the knee-chest position for the diagnosis of gastroesophageal reflux disease.

OBJECTIVES
The aim of this study is to evaluate the usefulness of gastroesophageal reflux (GER) scintigraphy using the knee-chest (KC) position for the diagnosis of gastroesophageal reflux disease (GERD).


METHODS
The study subjects were 37 patients with GERD and 8 healthy volunteers (control group). Endoscopically observed esophageal mucosal breaks were evaluated with the Los Angeles classification. For GER scintigraphy, the subjects ingested liquid yogurt labeled with 99mTc-diethylene triamine pentaacetic acid (99mTc-DTPA) and water. Imaging was performed in the supine and KC position, and GER was graded as 1-4 according to the extent of GER assessed by scintigraphy.


RESULTS
GER scintigraphy revealed no reflux in the control group (specificity: 100%). In the supine position, gastroesophageal reflux was observed in 49% of the patients with GERD, compared to 76% in the KC position. 21 of 23 (91%) patients with erosive esophagitis were shown to have GER with scintigraphy. GER scintigraphy revealed severe reflux (grade 3 or 4) (83%, 10/12) in the patients who had severe mucosal breaks (LA grade C or D). GER scintigraphy detected grade 1 or 2 reflux in 7 of the 14 patients who were endoscopically negative. There was a correlation between the endoscopically determined severity of mucosa and the reflux grade which was determined with GER scintigraphy.


CONCLUSION
GER scintigraphy can detect gastroesophageal reflux with a high sensitivity in the KC position and might be a useful method in the screening and assessment of the severity of this disease. This method would be useful for the diagnosis of GERD in endoscopically negative patients.
Development of Chickpea ( Cicer arietinum L . ) Food Products and Its Benefits to Human Nutrition

--Chickpea locally known as garbanzos is a good source of protein, minerals, and trace elements. Based on the results of the nutrient analysis of chickpea flour, it showed that it is high in dietary fiber, protein, potassium, calcium and iron. Chickpea flour is three times higher in dietary fiber than wheat flour and rice. Chickpea flour has low glycemic index, much lower than wheat flour and milled rice. Various products were developed with the supplementation of wheat flour with chickpea flour. Thirty percent (30%) substitution showed acceptable processed chickpea based food products. Keywords---chickpea, dietary fiber, protein
In-hospital case fatality rates for acute myocardial infarction in Romania.

BACKGROUND
We describe the clinical characteristics, treatments and in-hospital case-fatality rates in an unselected population of patients admitted for acute myocardial infarction.


METHODS
From January 2000 to June 2007, we tracked consecutive patients who were admitted to 7 tertiary referral and 21 county hospitals in Romania for medical treatment of ST-segment elevation acute myocardial infarction. These patients were enrolled in the Romanian Registry for ST-segment Elevation Myocardial Infarction. For this prospective study, we collected data on demographic characteristics, cardiovascular risk factors, various aspects of treatment for myocardial infarction, and in-hospital death.


RESULTS
The 9186 patients in the study group had a mean age of 63.8 years. The median time from onset of symptoms to thrombolysis was 230 (interquartile range 120-510) minutes. Of the 9186 patients, 4986 (54.3%) had hypertension, 1974 (21.5%) had diabetes mellitus, 3545 (38.6%) had lipid disorders and 4653 (50.7%) were smokers. The in-hospital mortality rate was 12.7% (1170 deaths). The study group consisted of 2893 women and 6293 men. The women were older than the men and had higher rates of hypertension and diabetes mellitus but were less likely to be smokers. A smaller proportion of women than men presented within 2 hours after onset of symptoms (23.1% v. 34.4%, p < 0.001). Smaller proportions of women received thrombolytics (40.8% v. 53.5%, p < 0.001), anticoagulants (93.4% v. 95.2%; p = 0.001), antiplatelet agents (88.3% v. 91.2%, p < 0.001) and primary percutaneous coronary interventions (1.5% v. 2.2%, p = 0.030). The risk of in-hospital death was greater for women, even after adjustment for confounders (odds ratio 1.33, 95% confidence interval 1.13-1.56; p < 0.001).


INTERPRETATION
The rates of reperfusion therapy for patients with acute myocardial infarction were low, and in-hospital case-fatality rates were high in this study. Excess in-hospital mortality was more pronounced among women.
Temporal Models for Predicting Student Dropout in Massive Open Online Courses

Over the past few years, the rapid emergence of massive open online courses (MOOCs) has sparked a great deal of research interest in MOOC data analytics. Dropout prediction, or identifying students at risk of dropping out of a course, is an important problem to study due to the high attrition rate commonly found on many MOOC platforms. The methods proposed recently for dropout prediction apply relatively simple machine learning methods like support vector machines and logistic regression, using features that reflect such student activities as lecture video watching and forum activities on a MOOC platform during the study period of a course. Since the features are captured continuously for each student over a period of time, dropout prediction is essentially a time series prediction problem. By regarding dropout prediction as a sequence classification problem, we propose some temporal models for solving it. In particular, based on extensive experiments conducted on two MOOCs offered on Coursera and edX, a recurrent neural network (RNN) model with long short-term memory (LSTM) cells beats the baseline methods as well as our other proposed methods by a large margin.
Blockchain Disruption and Smart Contracts

Blockchain technology provides decentralized consensus and potentially enlarges the contracting space through smart contracts that offer tamper-proofness and algorithmic executions. Meanwhile, generating decentralized consensus entails distributing information that necessarily alters the informational environment. We analyze how decentralization affects consensus effectiveness and how the quintessential features of blockchain reshape industrial organization and the landscape of competition. Smart contracts can mitigate informational asymmetry and improve welfare and consumer surplus through enhanced entry and competition, yet the distribution of information during consensus generation may encourage greater collusion. In general, blockchains can sustain market equilibria with a wider range of economic outcomes. We further discuss the implications for antitrust policies targeted at blockchain applications, such as separating consensus record-keepers from users. (JEL C73, D82, D86, G2, L13, L86)
System-Level Energy-Efficiency for Real-Time Tasks

Dynamic voltage scaling (DVS) has been adopted in many computing systems to reduce the energy consumption of the processor by slowing down the processor speed. However, for system devices without DVS capability, the longer a task executes, the more energy the task consumes in the required system devices. This paper explores energy-efficient scheduling for periodic hard real-time tasks in a system consisted of a DVS processor and multiple non-DVS system devices. We propose an algorithm for static scheduling which minimizes the system energy consumption of a given set of real-time tasks, provided that each task executes in its worst case. For systems in which some tasks might complete earlier than its estimated worst-case execution time, we develop on-line algorithms to reclaim the slack time to reduce the energy consumption. Compared to existing algorithms, our proposed algorithm can reduce the energy consumption both in the CPU and system devices