Miracle-dz/UniFAR-FTUs huggingface dataset:
Metabolic Contrasts Between Youth and Adults With Impaired Glucose Tolerance or Recently Diagnosed Type 2 Diabetes: I. Observations Using the Hyperglycemic Clamp

OBJECTIVE To compare insulin sensitivity (M/I) and β-cell responses in youth versus adults with impaired glucose tolerance (IGT) or drug-naïve, recently diagnosed type 2 diabetes. RESEARCH DESIGN AND METHODS In 66 youth (80.3% with IGT) and 355 adults (70.7% IGT), hyperglycemic clamps were used to measure 1) M/I, 2) acute (0–10 min [first phase]) C-peptide (ACPRg) and insulin (AIRg) responses to glucose, 3) steady-state C-peptide and insulin concentrations at plasma glucose of 11.1 mmol/L, and 4) arginine-stimulated maximum C-peptide (ACPRmax) and insulin (AIRmax) responses at plasma glucose >25 mmol/L. The fasting C-peptide–to–insulin ratio was used as an estimate of insulin clearance. RESULTS Insulin sensitivity was 46% lower in youth compared with adults (P < 0.001), and youth had greater acute and steady-state C-peptide (2.3- and 1.3-fold, respectively; each P < 0.001) and insulin responses to glucose (AIRg 3.0-fold and steady state 2.2-fold; each P < 0.001). Arginine-stimulated C-peptide and insulin responses were also greater in youth (1.6- and 1.7-fold, respectively; each P < 0.001). After adjustment for insulin sensitivity, all β-cell responses remained significantly greater in youth. Insulin clearance was reduced in youth (P < 0.001). Participants with diabetes had greater insulin sensitivity (P = 0.026), with lesser C-peptide and insulin responses than those with IGT (all P < 0.001) but similar insulin clearance (P = 0.109). CONCLUSIONS In people with IGT or recently diagnosed diabetes, youth have lower insulin sensitivity, hyperresponsive β-cells, and reduced insulin clearance compared with adults. Whether these age-related differences contribute to declining β-cell function and/or impact responses to glucose-lowering interventions remains to be determined.
Evidence of altered depression and dementia‐related proteins in the brains of young rats after ovariectomy

Menopause, a risk factor for brain dysfunction in women, is characterized by neuropsychological symptoms including depression and dementia, which are closely related to alterations in different brain regions after menopause. However, little is known about the variability in pathophysiologic changes associated with menopause in the brain. Here, we observed that menopause in rats induced by bilateral ovariectomy (OVX) showed depressive and dementia‐related behaviors along with neuronal loss in the prefrontal cortex (PFC), hippocampus (HIP), hypothalamus (HYP), and amygdala (AMY) by Nissl staining. Meanwhile, by immunohistochemical staining, increased microglia in the HIP and AMY and increased astrocytes in the PFC, HYP, and AMY were shown. Using quantitative proteomics, we identified 146 differentially expressed proteins in the brains of OVX rats, for example, 20 in the PFC, 41 in the HIP, 17 in the HYP, and 79 in the AMY, and performed further detection by western blotting. A link between neuronal loss and apoptosis was suggested, as evidenced by increases in adenylate kinase 2 (AK2), B‐cell lymphoma 2 associated X (Bax), cleaved caspase 3, and phosphorylated p53 and decreases in Huntingtin‐interacting protein K, hexokinase, and phosphorylated B‐cell lymphoma 2 (Bcl‐2), and apoptosis might be triggered by endoplasmic reticulum stress (probed by increased glucose‐regulated protein 78 (GRP78), cleaved caspase 12, phosphorylated protein kinase R (PKR)‐like endoplasmic reticulum kinase, inositol‐requiring enzyme‐1 and activating transcription factor 6), and mitochondrial dysfunction (probed by increased cytochrome c and cleaved caspase 3 and decreased sideroflexin‐1 (SFXN1) and NADH dehydrogenase (ubiquinone) 1 α subcomplex 11 (NDUFA11)). Activation of autophagy was also indicated by increased autophagy‐related 7, γ‐aminobutyric acid (GABA) receptor‐associated protein‐like 2, and oxysterol‐binding protein‐related protein 1 and confirmed by increased microtubule‐associated protein light chain 3 (LC3II/I), autophagy‐related 5, and Beclin1 in the HIP and AMY. In the AMY, which is important in emotion, higher GABA transporter 3 and lower vesicular glutamate transporter 1 levels indicated an imbalance between excitatory and inhibitory neurotransmission, and the increased calretinin and decreased calbindin levels suggested an adjustment of GABAergic transmission after OVX. In addition, cytoskeletal abnormalities including tau hyperphosphorylation, dysregulated Ca²+ signals, and glutamic synaptic impairments were observed in the brains of OVX rats. Collectively, our study showed the changes in different brain regions related to depression and dementia during menopause.
Constructing Deep Neural Networks by Bayesian Network Structure Learning

We introduce a principled approach for unsupervised structure learning of deep neural networks. We propose a new interpretation for depth and inter-layer connectivity where conditional independencies in the input distribution are encoded hierarchically in the network structure. Thus, the depth of the network is determined inherently. The proposed method casts the problem of neural network structure learning as a problem of Bayesian network structure learning. Then, instead of directly learning the discriminative structure, it learns a generative graph, constructs its stochastic inverse, and then constructs a discriminative graph. We prove that conditional-dependency relations among the latent variables in the generative graph are preserved in the class-conditional discriminative graph. We demonstrate on image classification benchmarks that the deepest layers (convolutional and dense) of common networks can be replaced by significantly smaller learned structures, while maintaining classification accuracy---state-of-the-art on tested benchmarks. Our structure learning algorithm requires a small computational cost and runs efficiently on a standard desktop CPU.
Inferring Routing Preferences of Bicyclists from Sparse Sets of Trajectories

Abstract. Understanding the criteria that bicyclists apply when they choose their routes is crucial for planning new bicycle paths or recommending routes to bicyclists. This is becoming more and more important as city councils are becoming increasingly aware of limitations of the transport infrastructure and problems related to automobile traffic. Since different groups of cyclists have different preferences, however, searching for a single set of criteria is prone to failure. Therefore, in this paper, we present a new approach to classify trajectories recorded and shared by bicyclists into different groups and, for each group, to identify favored and unfavored road types. Based on these results we show how to assign weights to the edges of a graph representing the road network such that minimumweight paths in the graph, which can be computed with standard shortest-path algorithms, correspond to adequate routes. Our method combines known algorithms for machine learning and the analysis of trajectories in an innovative way and, thereby, constitutes a new comprehensive solution for the problem of deriving routing preferences from initially unclassified trajectories. An important property of our method is that it yields reasonable results even if the given set of trajectories is sparse in the sense that it does not cover all segments of the cycle network.
Improving Text-to-SQL Evaluation Methodology

To be informative, an evaluation must measure how well systems generalize to realistic unseen data. We identify limitations of and propose improvements to current evaluations of text-to-SQL systems. First, we compare human-generated and automatically generated questions, characterizing properties of queries necessary for real-world applications. To facilitate evaluation on multiple datasets, we release standardized and improved versions of seven existing datasets and one new text-to-SQL dataset. Second, we show that the current division of data into training and test sets measures robustness to variations in the way questions are asked, but only partially tests how well systems generalize to new queries; therefore, we propose a complementary dataset split for evaluation of future work. Finally, we demonstrate how the common practice of anonymizing variables during evaluation removes an important challenge of the task. Our observations highlight key difficulties, and our methodology enables effective measurement of future development.
Social value and online social shopping intention: the moderating role of experience

Purpose The purpose of this paper is to explore how the shoppers’ social value perception affects their purchase intention in online shopping context through its distinct role and relationships with other value dimensions. The moderating effect of the characteristics of other members on the relationship among value dimensions and the difference of value perception between experienced and inexperienced members were also tested to identify the boundary conditions of the proposed model. Design/methodology/approach The survey included 272 consumers from a well-known social shopping website in China to test the hypotheses. Findings The results indicate that hedonic and utilitarian value fully mediate the relationship between social value and purchase intention. Perceived expertise positively moderates the relationship between social value and the other two values. In particular, the results found that while inexperienced members can acquire both higher utilitarian and hedonic value from social value and their purchase intention relies more on the hedonic value, experienced members place greater emphasis on the utilitarian value. Practical implications The results may help vendors regain confidence in the social shopping business mode and offer specific policy implications on how to leverage shoppers’ social value perception to generate their purchase intention in a social shopping context. Originality/value This study focuses on the legitimacy of the independent role of social value and sheds light on the relationships among social value and other value dimensions based on social capital theory, which was under-explored by previous studies. Besides, this study clarifies the moderating role of experience, which highlights the previously unnoticed changing role of consumers’ value perception.
Genomic characterization of clonal evolution during oropharyngeal carcinogenesis driven by human papillomavirus 16

Secondary prevention via earlier detection would afford the greatest chance for a cure in premalignant lesions. We investigated the exomic profiles of non-malignant and malignant changes in head and neck squamous cell carcinoma (HNSCC) and the genomic blueprint of human papillomavirus (HPV)-driven carcinogenesis in oropharyngeal squamous cell carcinoma (OPSCC). Whole-exome (WES) and whole-genome (WGS) sequencing were performed on peripheral blood and adjacent non-tumor and tumor specimens obtained from eight Korean HNSCC patients from 2013 to 2015. Next-generation sequencing yielded an average coverage of 94.3× for WES and 35.3× for WGS. In comparative genomic analysis of non-tumor and tumor tissue pairs, we were unable to identify common cancer-associated early mutations and copy number alterations (CNA) except in one pair. Interestingly, in this case, we observed that non-tumor tonsillar crypts adjacent to HPV-positive OPSCC appeared normal under a microscope; however, this tissue also showed weak p16 expression. WGS revealed the infection and integration of high-risk type HPV16 in this tissue as well as in the matched tumor. Furthermore, WES identified shared and tumor-specific genomic alterations for this pair. Clonal analysis enabled us to infer the process by which this transitional crypt epithelium (TrCE) evolved into a tumor; this evolution was accompanied by the subsequent accumulation of genomic alterations, including an ERBB3 mutation and large-scale CNAs, such as 3q27-qter amplification and 9p deletion. We suggest that HPV16-driven OPSCC carcinogenesis is a stepwise evolutionary process that is consistent with a multistep carcinogenesis model. Our results highlight the carcinogenic changes driven by HPV16 infection and provide a basis for the secondary prevention of OPSCC.
Defending Malware Classification Networks Against Adversarial Perturbations with Non-Negative Weight Restrictions

There is a growing body of literature showing that deep neural networks are vulnerable to adversarial input modification. Recently this work has been extended from image classification to malware classification over boolean features. In this paper we present several new methods for training restricted networks in this specific domain that are highly effective at preventing adversarial perturbations. We start with a fully adversarially resistant neural network that has hard non-negative weight restrictions and is equivalent to learning a monotonic boolean function and then attempt to relax the constraints to improve classifier accuracy.
Vehicle type recognition based on increasing hierarchical feature convolutional neural networks

To improve the accuracy and reduce convergence rate for vehicle type recognition, this paper proposes a novel method based on IHFCNN (Increasing Hierarchical Feature Convolutional Neural Networks). Firstly, utilizing the characteristics of biological vision, a new network layer based on classical convolutional neural network is presented to simulate the hierarchical processing of information. Secondly, features are extracted from different layers, and then a fully-connected layer is connected. Finally, center loss is introduced to output layer, and Softmax classifier is applied for vehicle type recognition. The experimental results show that, compared with the classic networks as AlexNet, GoogLeNet and VGG16, the proposed method reduces the number of convergent iterations significantly and improves the recognition accuracy up to 97.78% in the database with 321678 training images and 8490 for testing in real application scenarios.
Making Open Data Transparent: Data Discovery on Open Data

Open Data plays a major role in open government initiatives. Governments around the world are adopting Open Data Principles promising to make their Open Data complete, primary, and timely. These properties make this data tremendously valuable. Open Data poses interesting new challenges for data integration research and we take a look at one of those challenges, data discovery . How can we ﬁnd new data sets within this ever expanding sea of Open Data. How do we make this sea transparent?
Sundial: Harmonizing Concurrency Control and Caching in a Distributed OLTP Database Management System

Distributed transactions suffer from poor performance due to two major limiting factors. First, distributed transactions suffer from high latency because each of their accesses to remote data incurs a long network delay. Second, this high latency increases the likelihood of contention among distributed transactions, leading to high abort rates and low performance. We present Sundial, an in-memory distributed optimistic concurrency control protocol that addresses these two limitations. First, to reduce the transaction abort rate, Sundial dynamically determines the logical order among transactions at runtime, based on their data access patterns. Sundial achieves this by applying logical leases to each data element, which allows the database to dynamically calculate a transaction's logical commit timestamp. Second, to reduce the overhead of remote data accesses, Sundial allows the database to cache remote data in a server's local main memory and maintains cache coherence. With logical leases, Sundial integrates concurrency control and cache coherence into a simple unified protocol. We evaluate Sundial against state-of-the-art distributed concurrency control protocols. Sundial outperforms the next-best protocol by up to 57% under high contention. Sundial's caching scheme improves performance by up to 4.6× in workloads with high access skew.
Assault-related self-blame and its association with PTSD in sexually assaulted women: an MRI inquiry

Abstract Sexual assault is a frequent interpersonal trauma, which often leads to post-traumatic stress disorder (PTSD). Among other postassault characteristics, self-blame attributions were suggested to play an important role in sexually assaulted individuals’ coping and were consistently associated with PTSD in this population. The present study aimed to elucidate the neural underpinnings that may associate self-blame and PTSD in women who experienced sexual assault at adulthood, using structural and resting-state functional MRI. Thirty-eight sexually assaulted women and 24 non-exposed matched controls were studied (mean age: 25 years). Among the sexually assaulted participants, assault-related self-blame was negatively correlated with gray matter volume (GMV) bilaterally in the lingual gyrus and adjacent intracalcarine cortex. GMV in this cluster was also predicted by intrusion symptoms and negative social reactions. Resting-state functional connectivity (rs-FC) of this cluster with the left anterior temporal fusiform cortex significantly differed between PTSD and non-PTSD sexually assaulted participants, and was inversely correlated with intrusion symptoms and with peritraumatic dissociation. Finally, lingual cluster’s GMV and rs-FC with the anterior fusiform mediated the association between self-blame and intrusion symptoms across sexually assaulted participants. These findings link assault-related self-blame, disrupted postassault recovery and the neural circuitry involved in the processing of traumatic memories.
Mathematics and Statistics Support Centres: Resources for training postgraduates and others who work in them

This article provides a rationale for the focused training of postgraduates (and others) who work in mathematics and statistics support centres. It outlines a format for training that has been found to work well through workshop-style events run at universities around the country over the last five years. Within any form of training there are important topics for discussion and a wealth of resources freely available for those centre managers who wish to develop training for their own tutors. Tutors are in the front line of tackling the lack of confidence and skill deficits of students who arrive at support centres looking for help; tutors who are well-briefed in terms of the challenges they are likely to face and how they can best respond to students who are finding university mathematics and statistics particularly difficult will help to ensure that the services offered genuinely contribute to enhancing the student experience. While training for new mathematics support tutors is important, of equal, if not greater importance is identifying tutors with the right skills and abilities to work in mathematics and statistics support. We conclude by considering the recruitment of postgraduate tutors and by undertaking a brief discussion about the value of training to the tutors themselves including contribution to professional recognition that is gaining increased importance. Further sources of information can be found within Croft and Grove (2011) which forms a training resource pack, Croft, et al. (2013) and in the resources section of the sigma network website (2016) which includes PowerPoint slides and training activities that can be used in, or adapted for, workshops.
Gate architecture design for enhancement mode p-GaN gate HEMTs for 200 and 650V applications

Enhancement mode p-GaN gate HEMTs with two different gate architectures are compared. The gate is realized by stacked (1-mask) or separate patterning (3-mask) of the p-GaN and gate metal layers. The 3-mask gate architecture, in this work implemented with a novel TiN interlayer, offers the advantage of a low gate resistance, increased flexibility in field plate design and reduced dynamic RDS-ON at high VDS. Both for 200 and 650 V applications excellent device performance is demonstrated on 200 mm substrates using Au-free processing, with a threshold voltage of well above 2 V and a dynamic RDS-ON of below 20%. The 650 V rated device, with a hard breakdown voltage of 1000 V, passes the wafer level HTRB test at 150 °C.
Efficacy of Adalimumab for Treatment of Perianal Fistula in Children with Moderately to Severely Active Crohn’s Disease: Results from IMAgINE 1 and IMAgINE 2

Abstract Background and Aims Adalimumab has been shown to be more effective than placebo in healing fistulae in adults with moderately to severely active Crohn’s disease. The efficacy and safety of adalimumab in healing fistulae in children/adolescents with Crohn’s disease from the 52-week IMAgINE 1 clinical trial, and its open-label extension IMAgINE 2, are reported. Methods Children/adolescents with perianal fistulae at baseline of IMAgINE 1 were assessed for fistula closure and improvement during IMAgINE 1 [Weeks 0–52] and from Week 0 of IMAgINE 2 [Week 52 of IMAgINE 1] through to Week 240 of IMAgINE 2 using non-responder imputation. Results A total of 36 children/adolescents had fistulae at baseline of IMAgINE 1 and were included in the analysis. Fistula closure and improvement were observed in 44.4% and 52.8%, respectively, at Week 12. Rates of closure and improvement were maintained throughout the analysis period to Week 292. No new safety signals were identified. Conclusions In children/adolescents with moderately to severely active, fistulizing Crohn’s disease, adalimumab induced perianal fistula closure and improvement within 12 weeks of treatment, with rates that were sustained for more than 5 years. The safety profile of adalimumab in patients with fistulae at baseline was similar to that of the overall population in IMAgINE 1/2. ClinicalTrials.gov identifiers: IMAgINE 1 (NCT00409682); IMAgINE 2 (NCT00686374).
Feature Selection for Document Flow Segmentation

In this paper, we describe a method to restore a flow of continuous documents. The flow is a collection of consecutive scanned pages without explicit separation marks between documents. Our method is based on contextual and layout descriptors meant to specify the relationship between each pair of consecutive pages. The relationships are represented using vectors of features with boolean values indicating the presence or the absence of descriptors on concerned pages. The segmentation task therefore consists in classifying such vectors into continuities or breaks. The continuity class indicates that pages belong to the same document while the break class ends the ongoing document and starts a new one. The experimental part is based on a large collection of real administrative documents.
An Introduction of Purchase Types and Happiness

Experiential purchases refer to the purchase aiming to get life experience, while material purchases define the purchase made to acquire material possession. Both purchase types have different effects on happiness for the individual and experiential purchases which can make people feel happier and more joyful than material purchase. The reasons exist in the following five areas: firstly, as improved over time, experience is becoming better and better; secondly, experience has closer links with itself; what’s more, experiential purchase, instead of material purchase, will not arouse social comparison; fourthly, experience can meet the basic psychological needs of people, especially the relationship needs; last but not least, experience is easy to share.