Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of color Doppler ultrasound and computed tomography in the radiologic assessment of non-malignant acute splanchnic vein thrombosis

Introduction International guidelines propose color Doppler ultrasound (CDUS) and contrast-enhanced computed tomography (CT) as primary imaging techniques in the diagnosis of acute splanchnic vein thrombosis. However, their reliability in this context is poorly investigated. Therefore, the aim of our study was to validate CDUS and CT in the radiologic assessment of acute splanchnic vein thrombosis, using direct transjugular spleno-portography as gold standard. Materials and methods 49 patients with non-malignant acute splanchnic vein thrombosis were included in a retrospective, multicenter analysis. The thrombosis’ extent in five regions of the splanchnic venous system (right and left intrahepatic portal vein, main trunk of the portal vein, splenic vein, superior mesenteric vein) and the degree of thrombosis (patent, partial thrombosis, complete thrombosis) were assessed by portography, CDUS and CT in a blinded manner. Reliability of CDUS and CT with regard to portography as gold standard was analyzed by calculating Cohen’s kappa. Results Results of CDUS and CT were consistent with portography in 76.6% and 78.4% of examinations, respectively. Cohen’s kappa demonstrated that CDUS and CT delivered almost equally reliable results with regard to the portographic gold standard (k = 0.634 [p < 0.001] vs. k = 0.644 [p < 0.001]). In case of findings non-consistent with portography there was no clear trend to over- or underestimation of the degree of thrombosis in both CDUS (60.0% vs. 40.0%) and CT (59.5% vs. 40.5%). Conclusions CDUS and CT are equally reliable tools in the radiologic assessment of non-malignant acute splanchnic vein thrombosis.
Short-term memory, attention, and temporal orientation as predictors of the cognitive impairment in older adults: A cross-sectional observational study

Late-life cognitive decline ranges from the mildest cases of normal, age-related change to mild cognitive impairment to severe cases of dementia. Dementia is the largest global burden for the 21st century welfare and healthcare systems. The aim of this study was to analyze the neuropsychological constructs (temporal orientation (TO), spatial orientation (SO), fixation memory (FM), attention (A), calculation (C), short-term memory (STM), language (L), and praxis (P)), semantic fluency, level of functionality, and mood that reveal the greatest deficit in the different stages ranging from normal cognition (NC) to cognitive impairment in older adults in a primary healthcare setting. The study included 337 participants (102 men, 235 women), having a mean age of 74 ± 6 years. According to their scores on the Spanish version of the Mini-Mental State Examination (MEC-35), subjects were divided into 4 groups: no deterioration (ND) (score 32–35), subtle cognitive impairment (SCI) (score 28–31), level deterioration (LD) (score 24–27) and moderate deterioration (MD) (score 20–23). The ND group revealed significant differences in TO, STM, C, A, L, P, and S-T as compared to the other groups. The MD group (in all the neuropsychological constructs) and the ND and SCI groups showed significant differences on the Yesavage geriatric depression scale (GDS-15). All except the FM neuropsychological construct were part of the MEC-35 prediction model and all of the regression coefficients were significant for these variables in the model. Furthermore, the highest average percentage of relative deterioration occurs between LD and MD and the greatest deterioration is observed in the STM for all groups, including A and TO for the LD and MD groups. Based on our findings, community programs have been implemented that use cognitive stimulation to prevent cognitive decline and to maintain the neuropsychological constructs.
Is intuition allied with jumping to conclusions in decision-making? An intensive longitudinal study in patients with delusions and in non-clinical individuals

Research suggests that a jumping-to-conclusions (JTC) bias, excessive intuition, and reduced analysis in information processing may favor suboptimal decision-making, both in non-clinical and mentally disordered individuals. The temporal relationship between processing modes and JTC bias, however, remains unexplored. Therefore, using an experience sampling methodology (ESM) approach, this study examines the temporal associations between intuitive/analytical information processing, JTC bias, and delusions in non-clinical individuals and patients with schizophrenia. Specifically, we examine whether a high use of intuitive and/or a low use of analytical processing predicts subsequent JTC bias and paranoid conviction. In a smartphone-based ESM study, participants will be prompted four times per day over three consecutive days to answer questionnaires designed to measure JTC bias, paranoid conviction, and preceding everyday-life intuition/analysis. Our hierarchical data will be analyzed using multilevel modelling for hypothesis testing. Results will further elucidate the role of aberrant human reasoning, particularly intuition, in (non-)clinical delusions and delusion-like experiences, and also inform general information processing models.
Characterization of increased mucus production of HT29-MTX-E12 cells grown under Semi-Wet interface with Mechanical Stimulation

The intestinal mucus layer plays a crucial role in human health. To study intestinal mucus function and structure in vitro, the mucus-producing intestinal cell line HT29-MTX-E12 has been commonly used. However, this cell line produces only low amounts of the intestine-specific MUC2. It has been shown previously that HT29-MTX-E12 cells cultured under Semi-Wet interface with Mechanical Stimulation (SWMS) produced higher amounts of MUC2, concomitant with a thicker mucus layer, compared to cells cultured conventionally. However, it remains unknown which underlying pathways are involved. Therefore, we aimed to further explore the cellular processes underlying the increased MUC2 production by HT29-MTX-E12 cells grown under SWMS conditions. Cells grown on Transwell membranes for 14 days under static and SWMS conditions (after cell seeding and attachment) were subjected to transcriptome analysis to investigate underlying molecular pathways at gene expression level. Caco-2 and LS174T cell lines were included as references. We characterized how SWMS conditions affected HT29-MTX-E12 cells in terms of epithelial barrier integrity, by measuring transepithelial electrical resistance, and cell metabolism, by monitoring pH and lactate production per molecule glucose of the conditioned medium. We confirmed higher MUC2 production under SWMS conditions at gene and protein level and demonstrated that this culturing method primarily stimulated cell growth. In addition, we also found evidence for a more aerobic cell metabolism under SWMS, as shown previously for similar models. In summary, we suggest different mechanisms by which MUC2 production is enhanced under SWMS and propose potential applications of this model in future studies.
CNN-Based Personal Identification System Using Resting State Electroencephalography

As a biometric characteristic, electroencephalography (EEG) signals have the advantages of being hard to steal and easy to detect liveness, which attract researchers to study EEG-based personal identification technique. Among different EEG protocols, resting state signals are the most practical option since it is more convenient to operate than the other protocols. In this paper, a personal identification system based on resting state EEG is proposed, in which data augmentation and convolutional neural network are combined. The cross-validation is performed on a public database of 109 subjects. The experimental results show that when only 14 EEG channels and 0.5 seconds data are employed, the average accuracy and average equal error rate of the system can reach 99.32% and 0.18%, respectively. Compared with some existing representative works, the proposed system has the advantages of short acquisition time, low computational complexity, and rapid deployment using market available low-cost EEG sensors, which further advances the implementation of practical EEG-based identification systems.
Perinuclear positioning of endosomes can affect PS-ASO activities

Abstract Phosphorothioate (PS) modified antisense oligonucleotide (ASO) drugs that act on cellular RNAs must enter cells and be released from endocytic organelles to elicit antisense activity. It has been shown that PS-ASOs are mainly released by late endosomes. However, it is unclear how endosome movement in cells contributes to PS-ASO activity. Here, we show that PS-ASOs in early endosomes display Brownian type motion and migrate only short distances, whereas PS-ASOs in late endosomes (LEs) move linearly along microtubules with substantial distances. In cells with normal microtubules and LE movement, PS-ASO-loaded LEs tend to congregate perinuclearly. Disruption of perinuclear positioning of LEs by reduction of dynein 1 decreased PS-ASO activity, without affecting PS-ASO cellular uptake. Similarly, disruption of perinuclear positioning of PS-ASO-LE foci by reduction of ER tethering proteins RNF26, SQSTM1 and UBE2J1, or by overexpression of P50 all decreased PS-ASO activity. However, enhancing perinuclear positioning through reduction of USP15 or over-expression of RNF26 modestly increased PS-ASO activity, indicating that LE perinuclear positioning is required for ensuring efficient PS-ASO release. Together, these observations suggest that LE movement along microtubules and perinuclear positioning affect PS-ASO productive release.
Inter-individual differences in pain anticipation and pain perception in migraine: Neural correlates of migraine frequency and cortisol-to-dehydroepiandrosterone sulfate (DHEA-S) ratio

Previous studies targeting inter-individual differences in pain processing in migraine mainly focused on the perception of pain. Our main aim was to disentangle pain anticipation and perception using a classical fear conditioning task, and investigate how migraine frequency and pre-scan cortisol-to-dehydroepiandrosterone sulfate (DHEA-S) ratio as an index of neurobiological stress response would relate to neural activation in these two phases. Functional Magnetic Resonance Imaging (fMRI) data of 23 participants (18 females; mean age: 27.61± 5.36) with episodic migraine without aura were analysed. We found that migraine frequency was significantly associated with pain anticipation in brain regions comprising the midcingulate and caudate, whereas pre-scan cortisol-to DHEA-S ratio was related to pain perception in the pre-supplementary motor area (pre-SMA). Both results suggest exaggerated preparatory responses to pain or more general to stressors, which may contribute to the allostatic load caused by stressors and migraine attacks on the brain.
Genome-wide association analysis of anthracnose resistance in sorghum [Sorghum bicolor (L.) Moench]

In warm-humid ago-ecologies of the world, sorghum [Sorghum bicolor (L.) Moench] production is severely affected by anthracnose disease caused by Colletotrichum sublineolum Henn. New sources of anthracnose resistance should be identified to introgress novel genes into susceptible varieties in resistance breeding programs. The objective of this study was to determine genome-wide association of Diversity Arrays Technology Sequencing (DArTseq) based single nucleotide polymorphisms (SNP) markers and anthracnose resistance genes in diverse sorghum populations for resistance breeding. Three hundred sixty-six sorghum populations were assessed for anthracnose resistance in three seasons in western Ethiopia using artificial inoculation. Data on anthracnose severity and the relative area under the disease progress curve were computed. Furthermore, the test populations were genotyped using SNP markers with DArTseq protocol. Population structure analysis and genome-wide association mapping were undertaken based on 11,643 SNPs with <10% missing data. The evaluated population was grouped into eight distinct genetic clusters. A total of eight significant (P < 0.001) marker-trait associations (MTAs) were detected, explaining 4.86–15.9% of the phenotypic variation for anthracnose resistance. Out of which the four markers were above the cutoff point. The significant MTAs in the assessed sorghum population are useful for marker-assisted selection (MAS) in anthracnose resistance breeding programs and for gene and quantitative trait loci (QTL) mapping.
RC-Net: A Convolutional Neural Network for Retinal Vessel Segmentation

Over recent years, increasingly complex approaches based on sophisticated convolutional neural network architectures have been slowly pushing performance on well-established benchmark datasets. In this paper, we take a step back to examine the real need for such complexity. We present RC-Net, a fully convolutional network, where the number of filters per layer is optimized to reduce feature overlapping and complexity. We also used skip connections to keep spatial information loss to a minimum by keeping the number of pooling operations in the network to a minimum. Two publicly available retinal vessel segmentation datasets were used in our experiments. In our experiments, RC-Net is quite competitive, outperforming alternatives vessels segmentation methods with two or even three orders of magnitude less trainable parameters.
Optimized use of the outrigger system to stiffen the coupled shear walls in tall buildings

Based on the conventional yet accurate continuum approach, a general analysis is presented for a pair of coupled shear walls, stiffened by an outrigger and a heavy beam in an arbitrary position on the height. Subsequently, a parametric study is presented to investigate the behavior of the structure. The optimum location of the outrigger and the parameters affecting its position were also investigated. The results showed that the behavior of the structure can be significantly influenced by the location of the outrigger. It was also indicated that in most ordinary cases the best location of the structure to minimize top drift is somewhere between 0·4 to 0·6 of the height of the structure. Though this method is not a substitute for the finite element method, it gives an initial simple solution to determine the size and position of outrigger, stiffening beam and coupled shear walls in the preliminary design stages. Copyright © 2004 John Wiley & Sons, Ltd.
An isomorphism theorem for models of Weak K\"onig's Lemma without primitive recursion

We prove that if ( M, X ) and ( M, Y ) are countable models of the theory WKL ∗ 0 such that IΣ 1 ( A ) fails for some A ∈ X ∩Y , then ( M, X ) and ( M, Y ) are isomorphic. As a consequence, the analytic hierarchy collapses to ∆ 11 provably in WKL ∗ 0 + ¬ IΣ 01 , and WKL is the strongest Π 12 statement that is Π 11 -conservative over RCA ∗ 0 + ¬ IΣ 01 . Applying our results to the ∆ 0 n -deﬁnable sets in models of RCA ∗ 0 + BΣ 0 n + ¬ IΣ 0 n that also satisfy an appropriate relativization of Weak K¨onig’s Lemma, we prove that for each n > 1, the set of Π 12 sentences that are Π 11 -conservative over RCA ∗ 0 + BΣ 0 n + ¬ IΣ 0 n is c.e. In contrast, we prove that the set of Π 12 sentences that are Π 11 -conservative over RCA ∗ 0 + BΣ 0 n is Π 2 -complete. This answers a question of Towsner. We also show that RCA 0 + RT 22 is Π 11 -conservative over BΣ 02 if and only if it is conservative over BΣ 02 with respect to ∀ Π 05 sentences.
Voice Quality and Pitch Features in Transformer-Based Speech Recognition

Jitter and shimmer measurements have shown to be carriers of voice quality and prosodic information which enhance the performance of tasks like speaker recognition, diarization or automatic speech recognition (ASR). However, such features have been seldom used in the context of neural-based ASR, where spectral features often prevail. In this work, we study the effects of incorporating voice quality and pitch features altogether and separately to a Transformer-based ASR model, with the intuition that the attention mechanisms might exploit latent prosodic traits. For doing so, we propose separated convolutional front-ends for prosodic and spectral features, showing that this architectural choice yields better results than simple concatenation of such pitch and voice quality features to mel-spectrogram filterbanks. Furthermore, we find mean Word Error Rate relative reductions of up to 5.6% with the LibriSpeech benchmark. Such findings motivate further research on the application of prosody knowledge for increasing the robustness of Transformer-based ASR.
Interpretable Knowledge Tracing: Simple and Efficient Student Modeling with Causal Relations

Intelligent Tutoring Systems have become critically important in future learning environments. Knowledge Tracing (KT) is a crucial part of that system. It is about inferring the skill mastery of students and predicting their performance to adjust the curriculum accordingly. Deep Learning based models like Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) have shown significant predictive performance compared with traditional models like Bayesian Knowledge Tracing (BKT) and Performance Factors Analysis (PFA). However, it is difficult to extract psychologically meaningful explanations from the tens of thousands of parameters in neural networks, that would relate to cognitive theory. There are several ways to achieve high accuracy in student performance prediction but diagnostic and prognostic reasonings are more critical in learning science. In this work, we present Interpretable Knowledge Tracing (IKT), a simple model that relies on three meaningful features: individual skill mastery, ability profile (learning transfer across skills) and problem difficulty by using data mining techniques. IKT’s prediction of future student performance is made using a Tree Augmented Naive Bayes Classifier (TAN), therefore its predictions are easier to explain than deep learning based student models. IKT also shows better student performance prediction than deep learning based student models without requiring a huge amount of parameters. We conduct ablation studies on each feature to examine their contribution to student performance prediction. Thus, IKT has great potential for providing adaptive and personalized instructions with causal reasoning in real-world educational systems.
Reinforcement Learning based Sequential Batch-sampling for Bayesian Optimal Experimental Design

Engineering problems that are modeled using sophisticated mathematical methods or by expensive experiments, are usually encumbered with limited resources. Moreover, practical scenarios in the industry, impose restrictions, based on logistics and preference, on the manner in which the experiments can be conducted. For example, material supply may enable only a handful of experiments in a single-shot or in the case of computational models one may face significant wait-time based on shared computational resources. In such scenarios, one usually resorts to performing experiments in a manner that allows for maximizing one's state-of-knowledge while satisfying the above mentioned practical constraints. Sequential design of experiments (SDOE) is a popular suite of methods, that has yielded promising results in recent years across different engineering and practical problems. A common strategy, that leverages Bayesian formalism is the Bayesian SDOE, which usually works best in the one-step-ahead or myopic scenario of selecting a single experiment at each step of a sequence of experiments. In this work, we aim to extend the SDOE strategy, to query the information source on a batch of inputs. To this end, we leverage deep reinforcement learning (RL) based policy gradient methods, to propose batches of queries that are selected taking into account entire budget in hand. The algorithm retains the sequential nature while incorporating elements of reward from the domain of deep RL. We demonstrate the performance of the proposed algorithm on a synthetic problem, and a challenging high-dimensional engineering problem.
Study of coordinate measuring machines synthetic dynamic error under different positions and speeds based on dual linear returns

Modern manufacturing increasingly places a high requirement on the speed and accuracy of Coordinate Measuring Machines (CMMs). Measuring speed has become one of the key factors in evaluating the performance of CMMs. In high speed measuring, dynamic error will have a greater influence on accuracy. This paper tests the dynamic error of CMM's measuring system under different measuring positions and speeds using the dual frequency laser interferometer. Based on measured data, the modeling of synthetic dynamic errors is set up adopting the dual linear returns method. Comparing with the measured data, the relative error of modeling is between 15% to 20%, the returns equation is prominent at α=0.01 level, verified by "F". Based on the modeling of synthetic dynamic errors under different measuring positions and speeds, the measuring system dynamic error of CMMs is corrected and reduced.
Understanding software architecture erosion: A systematic mapping study

Architecture erosion (AEr) can adversely affect software development and has received significant attention in the last decade. However, there is an absence of a comprehensive understanding of the state of research about the reasons and consequences of AEr, and the countermeasures to address AEr. This work aims at systematically investigating, identifying, and analyzing the reasons, consequences, and ways of detecting and handling AEr. With 73 studies included, the main results are as follows: (1) AEr manifests not only through architectural violations and structural issues but also causing problems in software quality and during software evolution; (2) non‐technical reasons that cause AEr should receive the same attention as technical reasons, and practitioners should raise awareness of the grave consequences of AEr, thereby taking actions to tackle AEr‐related issues; (3) a spectrum of approaches, tools, and measures has been proposed and employed to detect and tackle AEr; and (4) three categories of difficulties and five categories of lessons learned on tackling AEr were identified. The results can provide researchers a comprehensive understanding of AEr and help practitioners handle AEr and improve the sustainability of their architecture. More empirical studies are required to investigate the practices of detecting and addressing AEr in industrial settings.
Understanding User Perspectives on Prompts for Brief Reflection on Troubling Emotions

We investigate users' perspectives on an online reflective question activity (RQA) that prompts people to externalize their underlying emotions on a troubling situation. Inspired by principles of cognitive behavioral therapy, our 15-minute activity encourages self-reflection without a human or automated conversational partner. A deployment of our RQA on Amazon Mechanical Turk suggests that people perceive several benefits from our RQA, including structured awareness of their thoughts and problem-solving around managing their emotions. Quantitative evidence from a randomized experiment suggests people find that our RQA makes them feel less worried by their selected situation and worth the minimal time investment. A further two-week technology probe deployment with 11 participants indicates that people see benefits to doing this activity repeatedly, although the activity may get monotonous over time. In summary, this work demonstrates the promise of online reflection activities that carefully leverage principles of psychology in their design.