Miracle-dz/UniFAR-FTUs huggingface dataset:
AGPAT1 as a Novel Colonic Biomarker for Discriminating Between Ulcerative Colitis With and Without Primary Sclerosing Cholangitis

INTRODUCTION: Ulcerative colitis (UC) associated with primary sclerosing cholangitis (PSC-UC) is considered a unique inflammatory bowel disease (IBD) entity. PSC diagnosis in an IBD individual entails a significantly higher risk of gastrointestinal cancer; however, biomarkers for identifying patients with UC at risk for PSC are lacking. We, therefore, performed a thorough PSC-UC biomarker study, starting from archived colonic tissue. METHODS: Proteins were extracted out of formalin-fixed paraffin-embedded proximal colon samples from PSC-UC (n = 9), UC (n = 7), and healthy controls (n = 7). Patients with IBD were in clinical and histological remission, and all patients with UC had a history of pancolitis. Samples were processed by the multienzyme digestion FASP and subsequently analyzed by liquid chromatography–tandem mass spectrometry. Candidate proteins were replicated in an independent cohort (n: PSC-UC = 16 and UC = 21) and further validated by immunohistochemistry. RESULTS: In the discovery step, 7,279 unique proteins were detected. The top 5 most differentiating proteins (PSC-UC vs UC) based on linear regression analysis were selected for replication. Of these, 1-acetylglycerol-3-phosphate O-acyltransferase 1 (AGPAT1) was verified as higher in PSC-UC than UC (P = 0.009) in the replication cohort. A difference on the group level was also confirmed by immunohistochemistry, showing more intense AGPAT1 staining in patients with PSC-UC compared with UC. DISCUSSION: We present AGPAT1 as a potential colonic biomarker for differentiating PSC-UC from UC. Our findings have possible implication for future PSC-IBD diagnostics and surveillance.
Frailty Is Associated With Cognitive Decline Independent of Cerebral Small Vessel Disease and Brain Atrophy

Abstract Background To examine the effect of frailty on cognitive decline independent of cerebral small vessel disease (cSVD) and brain atrophy, and whether associations between neuropathology and cognition differed depending on frailty status. Methods The Tasmanian Study of Cognition and Gait was a population-based longitudinal cohort study with data collected at 3 phases from 2005 to 2012. Participants aged 60–85 were randomly selected from the electoral roll. Various data were used to operationalize a 36-item frailty index (FI) at baseline. Brain MRI was undertaken to obtain baseline measures of neuropathology. A neuropsychological battery was used to assess cognition at each time point. Generalized linear mixed models were used to examine the effect of frailty and MRI measures on cognition over time. The associations between MRI measures and cognition were explored after stratifying the sample by baseline frailty status. All analyses were adjusted for age, sex, and education. Results A total of 385 participants were included at baseline. The mean age was 72.5 years (standard deviation [SD] 7.0), 44% were female (n = 171). In fully adjusted linear mixed models, frailty (FI × time β −0.001, 95% confidence interval [CI] −0.003, −0.001, p = .03) was associated with decline in global cognition, independent of brain atrophy, and cSVD. The association between cSVD and global cognition was significant only in those with low levels of frailty (p = .03). Conclusion These findings suggest that frailty is an important factor in early cognitive dysfunction, and measuring frailty may prove useful to help identify future risk of cognitive decline.
Beyond 3DMM: Learning to Capture High-fidelity 3D Face Shape

3D Morphable Model (3DMM) fitting has widely benefited face analysis due to its strong 3D priori. However, previous reconstructed 3D faces suffer from degraded visual verisimilitude due to the loss of fine-grained geometry, which is attributed to insufficient ground-truth 3D shapes, unreliable training strategies and limited representation power of 3DMM. To alleviate this issue, this paper proposes a complete solution to capture the personalized shape so that the reconstructed shape looks identical to the corresponding person. Specifically, given a 2D image as the input, we virtually render the image in several calibrated views to normalize pose variations while preserving the original image geometry. A many-to-one hourglass network serves as the encode-decoder to fuse multiview features and generate vertex displacements as the fine-grained geometry. Besides, the neural network is trained by directly optimizing the visual effect, where two 3D shapes are compared by measuring the similarity between the multiview images rendered from the shapes. Finally, we propose to generate the ground-truth 3D shapes by registering RGB-D images followed by pose and shape augmentation, providing sufficient data for network training. Experiments on several challenging protocols demonstrate the superior reconstruction accuracy of our proposal on the face shape.
Impact of Effortful Word Recognition on Supportive Neural Systems Measured by Alpha and Theta Power

Objectives: The goal of this study was to use theta and alpha electroencephalography (EEG) frequency power and self-report measures to examine performance monitoring, cognitive inhibition, and perceived effort required for speech understanding in noise. It was hypothesized that with a linear increase in word recognition task difficulty, there would be a linear increase in listening effort and word recognition performance would decrease in the challenging conditions. In addition, theta and alpha power would have an inverted U-shape across easy to challenging listening conditions. The inverted U-shape would reflect the neural underpinnings of listening effort that cannot be measured by task performance alone. Design: EEG data were collected in 34 normal-hearing adults (18 to 33 years old) during the Words-In-Noise (WIN) test, which was presented in sound field. EEG frequency data were averaged and analyzed at three frontal channels for theta power (4 to 8 Hz), which is thought to reflect performance monitoring, and three parietal channels for alpha power (8 to 12 Hz), which is thought to reflect cognitive inhibition. A ten-point visual analog scale was administered after each WIN signal-to-noise ratio (SNR) condition to capture self-reported required and invested listening effort (RLE and ILE, respectively). The WIN SNR conditions were presented in descending and random order. Results: The SNR presentation (descending or random SNR) had a null effect on word recognition performance; however, presentation did have an effect on theta power, alpha power, and ILE. When controlling for presentation, there were significant effects of SNR and presentation on both theta and alpha frequency power. Theta and alpha power had an inverted U-shape as a function of SNR from easy to challenging, with peak power in the moderate SNR conditions. RLE and ILE both significantly increased as task difficulty increased as expected; however, RLE showed a stronger relation to task performance than ILE. Alpha power was a significant predictor of RLE, ILE, and WIN performance when controlling for SNR. Conclusions: The elevated theta and alpha power in the easy to moderate SNRs and alpha power predicting self-reported listening effort suggest the activation of supportive neural systems during word recognition that could be considered a marker of listening effort. Moreover, the measures of neural support systems and listening effort were independent from task performance, which is a key element to further understanding the neural bases for listening effort. In the context of the broader literature, these results are consistent with (1) a parietal alpha role in supporting inhibitory control to suppress irrelevant information and (2) a frontal theta role in supporting performance monitoring in difficult listening conditions where speech recognition is feasible.
Increased Lean Body Mass After Bodyweight-Based High Intensity Interval Training in Overweight and Obese Men

ABSTRACT Purpose: The effects of 8 weeks of bodyweight exercise-based, high-intensity interval training (BWHIIT) on body composition and blood-based markers of metabolic health were investigated in overweight and obese, sedentary young men. Methods: In a parallel group, PRE-POST design, n = 30 men (age, 25.7 ± 4.3 y; body mass index, 27.7 ± 2.1 kg m−2; 26.1 ± 5.2% body fat) were randomized to BWHIIT (n = 20) or a control group (CON; n = 10). BWHIIT consisted of supervised, group-based training sessions (~30 minutes) performed 3 times weekly. Each session consisted of 6 high-intensity bodyweight-based exercises, with each exercise being performed for 4 minutes in the manner of 8 sets of 20 seconds of exercise, 10 seconds of rest. Prior to commencing training (PRE), and 36 h after the final training session (POST), an overnight fasted blood sample was drawn, and body composition was assessed by dual-energy X-ray absorptiometry. Eighteen participants completed the intervention (CON, n = 9; BWHIIT, n = 9). Results: Lean body mass (LBM) was increased at POST in BWHIIT compared to CON (P = .011, η2 p = .359), with the mean (95% confidence limits) increase in LBM from PRE to POST within BWHIIT being 1.23 (0.55, 1.92) kg. Body mass and fat mass were unchanged in both groups from PRE to POST. BWHIIT had no effect on serum concentrations of total cholesterol, HDL-C, LDL-C, triglycerides, NEFA, hsCRP, or glucose. Conclusion: Eight weeks of bodyweight exercise-based high intensity interval training by overweight and obese sedentary young men increased LBM by ~2%, but fat mass and blood-based markers of metabolic health were unchanged.
Enabling Things to Talk: Designing IoT solutions with the IoT Architectural Reference Model

The Internet of Things (IoT) is an emerging network superstructure that will connect physical resources and actual users. It will support an ecosystem of smart applications and services bringing hyper-connectivity to our society by using augmented and rich interfaces. Whereas in the beginning IoT referred to the advent of barcodes and Radio Frequency Identification (RFID), which helped to automate inventory, tracking and basic identification, today IoT is characterized by a dynamic trend toward connecting smart sensors, objects, devices, data and applications. The next step will be cognitive IoT, facilitating object and data re-use across application domains and leveraging hyper-connectivity, interoperability solutions and semantically enriched information distribution. The Architectural Reference Model (ARM), presented in this book by the members of the IoT-A project team driving this harmonization effort, makes it possible to connect vertically closed systems, architectures and application areas so as to create open interoperable systems and integrated environments and platforms. It constitutes a foundation from which software companies can capitalize on the benefits of developing consumer-oriented platforms including hardware, software and services. The material is structured in two parts. Part A introduces the general concepts developed for and applied in the ARM. It is aimed at end users who want to use IoT technologies, managers interested in understanding the opportunities generated by these novel technologies, and system architects who are interested in an overview of the underlying basic models. It also includes several case studies to illustrate how the ARM has been used in real-life scenarios. Part B then addresses the topic at a more detailed technical level and is targeted at readers with a more scientific or technical background. It provides in-depth guidance on the ARM, including a detailed description of a process for generating concrete architectures, as well as reference manuals with guidelines on how to use the various models and perspectives presented to create a concrete architecture. Furthermore, best practices and tips on how system engineers can use the ARM to develop specific IoT architectures for dedicated IoT solutions are illustrated and exemplified in reverse mapping exercises of existing standards and platforms.
Effect of wax deposit area in waxy hot oil pipeline on whole running conditions

A mathematical model of the wax deposit distribution and running conditions for waxy hot oil pipeline which have no floating between the two stations is built. The factors including outlet oil temperature, environmental temperature and flow determine the location of wax deposition in waxy hot oil pipeline. When there is wax deposition, running conditions such as waxy sections' heat transfer coefficient, flow velocity, pipeline friction and length of each flow pattern along the pipeline will change. The examples analysis shows that the heat radiation capacity decreases due to the existence of the wax along the pipeline. However, there is no obvious increase of the inlet oil temperature when increasing the outlet oil temperature. With the increase of the distance between the starting point of wax deposition and the outlet of station, the pipeline friction decreases in a linear manner with high slope. In addition, the length of different flow pattern of the oil in the pipe has a basic linear relationship with the location of wax along the pipe. (3 Figures, 3 Tables, 11 References)
The first-principles study of BC3 nanosheet as the delivery vehicle for 6-mercaptopurine drug

In this study, the potential applications of perfect boron carbide (BC3) nanosheet and single-vacancy defect BC3 nanosheets (SVBC3-B and SVBC3-C), as the drug delivery vehicle for 6-mercaptopurine (6-MP), are investigated based on the calculation of the density functional theory (DFT). The interactions of the 6-MP drug with the BC3 nanosheet, SVBC3-B and SVBC3-C are simulated in the gas phase, water solvent and acidic solvent, respectively. The results demonstrated that the 6-MP drug tends to adsorb parallel on the surface of the BC3 nanosheet, and the adsorption energy on SVBC3-B is the largest, followed by the perfect BC3. The interactions between the 6-MP drug and the perfect BC3 nanosheet, SVBC3-B and SVBC3-C will be weakened in a water solvent. Furthermore, the interaction force between the 6-MP drug and the perfect BC3 nanosheet or SVBC3-B nanosheet in an acidic solvent is more significant than that in a water solvent, which is not conducive to separating the 6-MP drug from the two kinds of BC3 nanosheets. But, the adsorption energy of the 6-MP drug on SVBC3-C in the acidic solvent is much lesser than that in the water solvent, which indicates that the 6-MP drug can be easily separated from SVBC3-C in the acidic environment. The PH value of cancer tissue is just acidic, which provides an ideal environment for separating 6-MP. Generally, the results confirmed the possibility of SVBC3-C as a delivery vehicle for the 6-MP drug. GRAPHICAL ABSTRACT
DC and RF analysis of vertical 3D p-type silicon nanotube FET for low power applications

ABSTRACT A vertical p-type 3D silicon nanotube field effect transistor (p-type Si-NTFET) is designed and simulated by using the tool Sentaurus Technology computer-aided design (TCAD). To improve the performance with reduced short channel effects, Si-NTFET device is created with inner and outer gate structures. The performance of the proposed device is analysed in terms of outer gate length. The vertical 3D p-type Si-NTFET gives an on state current (I ON) of −2.084 × 10−3 A/µm, off state current of (I OFF) of −2.457 × 10−12A/µm and subthreshold swing of the p-type Si-NTFET device is 53 mV/dec at V DS of −0.3 V. The obtained I ON/I OFF ratio of the device is 109. The capacitances (C GD, C GG) and the transconductance (g m) and cutoff frequency (f t) of the device are also analysed. The simulated p-type Si-NTFET device has proved that the device is suitable for low-power applications.
Quality Assurance Standards for Forensic DNA Testing Laboratories

The standards describe the quality assurance requirements that laboratories performing forensic DNA testing or utilizing the Combined DNA Index System (CODIS) shall follow to ensure the quality and integrity of the data generated by the laboratory. These standards also apply to vendor laboratories that perform forensic DNA testing in accordance with Standard 17. These standards do not preclude the participation of a laboratory, by itself or in collaboration with others, in research and development, on procedures that have not yet been validated.
Modeling and Prediction of Grain Size and Hardness of ZE41/ ZrO 2 Nano-surface Composite Using Multiple Regression, Power Law and Artiﬁcial Intelligence Techniques

In this work, nano-ZrO 2 particles were incor-porated in cast rare earth ZE41 magnesium alloy by friction stir processing (FSP) and a surface composite layer of ZE41/ZrO 2 was fabricated. The relationship of FSP parameters with the grain size and hardness was estab-lished using multiple regression and artiﬁcial neural network (ANN) techniques. The experimental results showed that ZrO 2 particles were not uniformly distributed in the stir zone (SZ) and SZ was divided into two regions. For modelling by these techniques, the input parameters used were tool rotational speed, tool traverse speed and the type of SZ region and outputs were grain size and hardness. The models can be used to predict grain size and hardness as a function of rotational speed, traverse speed and type of region. Moreover, a sensitivity analysis was done to study the impact of the parametric inputs on the model output using the power law model. The mean square error (MSE) for grain size was found to be 0.3418 (regression model) and 0.000121 (ANN), whereas MSE for hardness was found to be 1.0211 (regression model) and 0.00018 (ANN). The value of coefﬁcient of correlation (R 2 ) for training and testing for grain size and hardness was found close to 1. These values of MSE and R 2 indicate a good agreement between predicted and experimental results. Hence, in this work, the possibility of using multiple regression, power law and ANN for the calculation of grain size and hardness of ZE41/ZrO 2 surface composite produced by FSP without carrying out the experiments has thus been established.
OUTLIER REMOVAL AND PATTERN REDUCTION TECHNIQUES USING ERROR SIGN TESTING

Most real data sets are noisy in nature. A number of techniques for outlier detection and removal have been proposed recently. This paper reviews the issues on the effects of outliers in training a back-propagation neural network. A new technique to detect and remove the outliers using error sign testing is proposed, and the method is applied to classify a set of wireline log data obtained from an oil well in Australia into different rock types. The results on this example show that the reduced training set improves generalisation on a validation test data set
Final Report: Analysing the social context to transforming construction through digital innovation and offsite construction

This report summarises the key contributions from the research project ‘ Analysing the social context to transforming construction through digital innovation and offsite construction ’ . This research is part of the Centre for Digital Built Britain’s (CDBB) work at the University of Cambridge within the Construction Innovation Hub (CIH). The study contributes to achieving CIH impacts related to information management, as well as to the development of skills and capabilities and to fostering the effective collaboration needed for the effective adoption and use of digital technologies and modern methods of construction.
Instantaneous Tracking of Muscular Activation

–For the purposes of instantaneously tracking speciﬁc muscle activity, the following electrical circuit was created that gains and ﬁlters a traditional EMG signal in to a DC signal that can then be used to activate various LED orientations. The LED’s practically convey if the muscles respective to an attached electrode are contracting or at rest. The electrical circuit was simulated on falstad.com and processes the EMG signal as desired resulting in a visual activation when the speciﬁc voltage thresholds are met. Clinical Relevance –The design will allow experts with professions in, but not limited to, personal training, physical therapy, and chiropractic adjust-ment additional knowledge and understanding on the activities and behaviors of muscles that could potentially be causing pain, discomfort, limited range of motion, injury, neuromuscular disorders, etc. The practical application and usefulness of this design is two-fold because it can also be used to help and protect people with physically demanding occupations that many of our communities are dependent on.
Bias-aware Fair Neural Ranking for Addressing Stereotypical Gender Biases

Research has shown that neural rankers can pick up and intensify gender biases. The expression of stereotypical gender biases in retrieval systems can lead to their reinforcement in users’ beliefs. As such, the objective of this paper is to propose a bias-aware fair ranker that explicitly incorporates a notion of gender bias and hence controls how bias is expressed in documents that are retrieved. The proposed approach is designed such that it learns the notion of relevance between the document and the query from the relevant sampled documents while incorporating the notion of gender bias by penalizing irrelevant biased sampled documents. We show that unlike the state of the art, our approach reduces bias while maintaining retrieval effectiveness over different query sets.
Gamma Probabilistic Databases: Learning from Exchangeable Query-Answers

In this paper we propose a novel knowledge compilation technique that compiles Bayesian inference procedures , starting from probabilistic programs expressed in terms of probabilistic query-answers. To do so, we extend the framework of Dirichlet Proba-bilistic Databases with the ability to process exchangeable observations of query-answers. We show that the resulting framework can encode non-trivial models, like Latent Dirichlet Allocation and the Ising model, and generate high-performance Gibbs sam-plers for both models.
FPGA Acceleration of Structured-Mesh-Based Explicit and Implicit Numerical Solvers using SYCL

We explore the design and development of structured-mesh-based solvers on Intel FPGA hardware using the SYCL programming model. Two classes of applications are targeted : (1) stencil applications based on explicit numerical methods and (2) multi-dimensional tridiagonal solvers based on implicit methods. Both classes of solvers appear as core modules in a variety of real-world applications ranging from computational fluid dynamics to financial computing. A general, unified workflow is formulated for synthesizing these applications on Intel FPGAs together with predictive analytic models to explore the design space to obtain optimized performance. Performance of synthesized designs, using the above techniques, for two non-trivial applications on an Intel PAC D5005 FPGA card is benchmarked. Results are compared to the performance of optimized parallel implementations of the same applications on a Nvidia V100 GPU. Observed runtime results indicate the FPGA providing comparable or improved performance to the V100 GPU. However, more importantly the FPGA solutions consume 59%–76% less energy for their largest configurations. Our performance model predicts the runtime of designs with high accuracy with less than 5% error for all cases tested, demonstrating significant utility for design space exploration. With these tools and techniques, we discuss determinants for a given structured-mesh code to be amenable to FPGA implementation, providing insights into the feasibility and profitability FPGA implementation, how to code designs using SYCL, and the resulting performance.