Miracle-dz/UniFAR-FTUs huggingface dataset:
Decision-theoretic inspection planning using imperfect and incomplete data

Abstract Attempts to formalize inspection and monitoring strategies in industry have struggled to combine evidence from multiple sources (including subject matter expertise) in a mathematically coherent way. The perceived requirement for large amounts of data are often cited as the reason that quantitative risk-based inspection is incompatible with the sparse and imperfect information that is typically available to structural integrity engineers. Current industrial guidance is also limited in its methods of distinguishing quality of inspections, as this is typically based on simplified (qualitative) heuristics. In this paper, Bayesian multi-level (partial pooling) models are proposed as a flexible and transparent method of combining imperfect and incomplete information, to support decision-making regarding the integrity management of in-service structures. This work builds on the established theoretical framework for computing the expected value of information, by allowing for partial pooling between inspection measurements (or groups of measurements). This method is demonstrated for a simulated example of a structure with active corrosion in multiple locations, which acknowledges that the data will be associated with some precision, bias, and reliability. Quantifying the extent to which an inspection of one location can reduce uncertainty in damage models at remote locations has been shown to influence many aspects of the expected value of an inspection. These results are considered in the context of the current challenges in risk based structural integrity management.
Shoe-fit is correlated with exercise tolerance in community-dwelling elderly people

Purpose: Maintenance of physical activity significantly affects quality of life, and the frequency of physical activity depends upon exercise tolerance. However, there is minimal information on the external factors that contribute to exercise tolerance. The aim of this study was to examine the association between exercise tolerance and shoe-fit in community-dwelling elderly people. Methods: Subjects were 155 elderly, healthy, community-dwelling Japanese volunteers. Exercise tolerance (Shuttle Walk Test [SWT]), 10-m walking time (10mWT), and forced expiratory volume in 1 second (FEV1) were measured. Shoe-fit was assessed and participants were divided into three groups according to the heel-fit of their shoes (Too Loose, Loose, Fit). Group scores in the above variables were compared. Further, a multivariate logistic regression model using a stepwise method was performed to investigate which shoe-fit factors were independently associated with SWT. Results: No significant differences in age, gender, body mass index, 10mWT, FEV1, or presence or absence of pain sites were observed between the three groups. The Fit (p = 0.001) and Loose (p = 0.008) groups had significantly higher SWT score than the Too Loose group. Multivariate logistic regression analysis showed that poor heel-fit was significantly correlated with a low SWT score, even following adjustments for age, gender, 10mWT and FEV1 (odds ratio: 0.25, 95% confidence interval: 0.07–0.95, p = 0.04). Conclusions: This study demonstrates that heel-fit is associated with exercise tolerance in community-dwelling elderly people. It is important for elderly people to wear adequate fit shoes in order to enhance physical functions and prevent declining physical functions.
International Journal of Emerging Electric Power Systems Power System Disturbance Recognition Using Wavelet and S-Transform Techniques

Power quality issues have been a source of major concern in recent years due to extensive use of power electronic devices and non-linear loads in electrical power system and consequently sensitive detection and accurate classification of power disturbances have become very much necessary. To monitor electrical power quality disturbances, short time discrete Fourier transform (STFT) is most often used. But for nonstationary signals, the STFT does not track the signal dynamics properly due to the limitations of a fixed window width chosen a priori. This paper presents a new approach for power quality analysis using a modified wavelet transform, known as S–transform and the analysis of several power quality problems using both S–transform as well as discrete wavelet transform validates the superiority of S–transform.
From the Literature

In the most recently published National Nosocomial Infections Surveillance System report [1], the incidence of SSI after colorectal surgery ranged from 3.98% in patients in risk index category 1 to 11.25% in those in risk index category 3. (These were adjusted results, taking into account that the incidence of infection is lower after laparoscopic procedures than after open procedures). The report, however, does not make a distinction between surgery involving the colon and surgery involving the rectum; this lack of distinction turns out to be an important problem. Konishi and colleagues prospectively determined the incidence of incisional SSIs among all recipients of 556 elective, open colorectal resections performed by a single surgeon over a 46-month period at a Tokyo, Japan, hospital. All patients had mechanical preoperative bowel preparation, and some also received orally administered metronidazole and kanamycin. A second-generation cephalosporin (the specific agent was not stated) was given perioperatively. The overall incidence of SSI detected within 30 days after the procedure was 12.8%. The incidence after resections limited to the colon, however, was only 9.4% (32 of 332 procedures), whereas it was 18.0% (39 of 217 procedures) after rectal surgery. Independent risk factors for SSI after colonic surgery were lack of receipt of oral antibiotics and performance of an ostomy closure, whereas the risk factors identified for SSI after rectal surgery were preoperative use of steroids, ostomy creation, and preoperative radiation therapy. It is difficult to compare these overall results to those observed in the United States, because it is likely that a number of perioperative procedures differ in the 2 countries. Nonetheless, the observation of a significantly greater risk of SSI after rectal surgery is likely to apply to this country as well. The fact that this difference is not accounted for in the National Nosocomial Infections Surveillance System data is likely to have important consequences for US hospitals, because public reporting of nosocomial infection is increasingly required and because “pay for performance” measures are progressively being instituted. A hospital with an adverse mix of cases with a greater-than-expected proportion of patients undergoing rectal surgery—especially those with the risk factors for SSI identified by Konishi and colleagues—may suffer as a consequence of their ability and willingness to deal with the most difficult cases.
A comparison of hierarchically paged and scrolling displays for fault finding

The selection of test points when fault finding with a hierarchically paged display is more efficient than when using a scrolling display. However, this difference is not found with more able subjects. An unexpected finding is that both displays seem to facilitate more consistent performance than that obtained when the operator's view of the system is unrestricted. The effect of restricting system information by a ‘window’, as both types of display do, is discussed in terms of strategies operators may adopt in consequence, in particular strategies to cope with memory load.
Developing Capacity Models for Local Roundabouts

Modern roundabouts are gaining popularity in many parts of the United States. However, because of a lack of well-established capacity models, many transportation professionals rely on international experience when analyzing the capacity of U.S. roundabouts. From a comprehensive evaluation of U.S. roundabouts, the authors of NCHRP Report 572 developed capacity models for single-lane and multilane roundabouts. NCHRP Report 572 suggests that these models can be calibrated for local conditions by adjustment of two key parameters: critical headway and follow-up headway. However, there is little direct connection between such calibrated models and the actual flow rates achieved in the field. For communities with congested roundabouts, this paper presents a streamlined process to develop capacity models. The process consists of video data collection, data processing and verification, and model development. The process was applied to a case study of three roundabouts in Carmel, Indiana. The single-lane roundabout capacity model developed in the case study is substantially different than the model in NCHRP Report 572. Overall, the streamlined process to develop capacity models for local roundabouts proved valuable, and it can be scalable according to available resources. The capacity models developed by this process are based on actual flow rates at local congested roundabouts and will provide an accurate capacity estimation for planning new roundabouts or capacity improvements. The difference between the case study's capacity model and the NCHRP Report 572's capacity model may be partially attributed to driver familiarity. The study underscores the importance of using capacity models that are applicable to local conditions.
ANKLE1 cleaves mitochondrial DNA and contributes to cancer risk by driving the Warburg effect and apoptosis resistance

Alleles within the chr19p13.1 locus are associated with increased risk of both ovarian and breast cancer and increased expression of the ANKLE1 gene. ANKLE1 is molecularly characterized as an endonuclease that efficiently cuts branched DNA and shuttles between the nucleus and cytoplasm. However, the role of ANKLE1 in mammalian development and homeostasis remains unknown. In normal development ANKLE1 expression is limited to the erythroblast lineage and we found that ANKLE1’s role is to cleave the mitochondrial genome during erythropoiesis. We show that ectopic expression of ANKLE1 in breast epithelial-derived cells leads to genome instability and mitochondrial DNA (mtDNA) cleavage. mtDNA degradation then leads to mitophagy and causes a shift from oxidative phosphorylation to glycolysis (Warburg effect). Moreover, mtDNA degradation activates STAT1 and expression of epithelial-mesenchymal transition (EMT) genes. Reduction in mitochondrial content contributes to apoptosis resistance, which may allow precancerous cells to avoid apoptotic checkpoints and proliferate. These findings provide evidence that ANKLE1 is the causal cancer susceptibility gene in the chr19p13.1 locus and describe mechanisms by which higher ANKLE1 expression promotes cancer risk.
Detection of Selenium/Sulphur substitution in the heterologous expression of Thioredoxin isoform 1 (Trx1) from Saccharomyces cerevisiae

Thioredoxins are ubiquous proteins with 2 cysteines at the active site. The isoform 1 of Thioredoxin from Saccharomyces cerevisae (Trx1) has six sulphur aminoacids, two cysteines and four methionines. In this work we performed the replacement of cysteines by selenocysteines by growth of a transformed celular expression vector E. coli BL21-DE3 in selenocysteine containing culture medium. The Maldi-TOF spectra of Seleno/Sulphur substituted Trx1 revealed six component peaks with 46-48 Da range between them, that is the isotopic Seleno-Sulfur difference, showing the replacement of the Cysteines and Methionines to Selenocysteines and Selenomethionines. The Maldi-TOF spectra of the peptides derived from Trypsin digestion of the purified Thioredoxin (peptide mass fingerprint) show Selenocysteine and Selenomethionine containing peptides. Therefore we are demonstrating that cystein can be replaced by selenocystein and be metabolically converted to selenomethionine during Trx1 heterologous translation. Furthermore, the Maldi-TOF spectra are showing the presence of the most abundant isotopes of selenium inserted in the peptides containing cysteine and methionine, derived from the Trx1 digestion. The one dimensional 77Se–1H heteronuclear multiple quantum coherence NMR spectroscopy (1D-HMQC) for reduced Seleno substituted Trx1 (Se-Trx1), revealed three ressonance lines for 1Hβ1 from Selenocysteines 30 and 33, between 1.6 and 2,0 ppm. The bidimensional HMQC spectra (2D-HMQC) of the reduced Se-Trx1 show the 77 Se ressonance signal in 178 ppm, coupled with 1Hβ1 and 1Hβ2 lines between 2.1 and 1.8 ppm. The 1D-HMQC for oxidized Trx1 revealed the only one broad resonance in 2.6 ppm probably relative to the 1Hβ1 prótons. The 2D-HMQC spectrum of oxidized protein shows a higher chemical shift of selenocysteine 77Se (832 ppm) if compared to reduced state (178 ppm). Together these data are showing that the protocol of Se – S substitution developed here is a efficient method to label the active site of Thioredoxin 1 with a broad band chemical shift atom 77Se. Furthermore the large spectral window of the 77Se NMR detected between reduced and oxidized states of the Thioredoxin 1 shows that this atom is an excellent probe for accessing oxidative states and probably the conformational dynamics of the active site of the Se-Trx1.
β-glucan imprinting remodels macrophage function in response to environmental cues

In vitro, exposure of human primary monocytes to the fungal β-glucan enhances their pro-inflammatory responsiveness towards several pathogens. Yet, the role of environmental condition of this process remains unclear. Here we found that β-glucan-induced innate immune memory counteract the anti-inflammatory status of the macrophages. In response to β-glucan imprinting, M-CSF-(M2-like-) macrophages increase their pro-inflammatory responsiveness to secondary stimuli associated with decrease of the M-CSF differentiation hallmarks. In contrast, in GM-CSF-(M1-like-) environment, β-glucan imprinting reduced the pro-inflammatory canonical feature of the macrophages, together with their terminal differentiation marks. Comparing M-CSF and GM-CSF environment, we observed that β-glucan-imprinted macrophages present comparable functions in terms of cytokine responses, phagocytosis, oxidative burst, and angiogenesis. This effect is mediated through Dectin-1 and associated with altered expression of the master regulators of macrophage terminal differentiation, IRF5 and IRF3. β-glucan-induced innate immune memory skews the commitment of the macrophages in complex environment towards similar and less terminally differentiated cells. Together, these observations suggest a potential therapeutic role for β-glucan-induced modulation of innate memory in different pathological contexts.
Microstructural development from 9-14 years: evidence from the ABCD Study

During late childhood behavioral changes, such as increased risk-taking and emotional reactivity, have been associated with the maturation of cortico-cortico and cortico-subcortical circuits. Understanding microstructural changes in both white matter and subcortical regions may aid our understanding of how individual differences in these behaviors emerge. Restriction spectrum imaging (RSI) is a framework for modelling diffusion-weighted imaging that decomposes the diffusion signal from a voxel into hindered, restricted, and free compartments. This yields greater specificity than conventional methods of characterizing diffusion. Using RSI, we quantified voxelwise restricted diffusion across the brain and measured age associations in a large sample (n=8,086) from the Adolescent Brain and Cognitive Development (ABCD) study aged 9-14 years. Older participants showed a higher restricted signal fraction across the brain, with the largest associations in subcortical regions, particularly the basal ganglia and ventral diencephalon. Importantly, age associations varied with respect to the cytoarchitecture within white matter fiber tracts and subcortical structures, for example age associations differed across thalamic nuclei. This suggests that age-related changes may map onto specific cell populations or circuits and highlights the utility of voxelwise compared to ROI-wise analyses. Future analyses will aim to understand the relevance of this microstructural developmental for behavioral outcomes.
Loss of histone methyltransferase SETD1B in oogenesis results in the redistribution of genomic histone 3 lysine 4 trimethylation

Histone 3 lysine 4 trimethylation (H3K4me3) is an epigenetic mark found at gene promoters and CpG islands. H3K4me3 is essential for mammalian development, yet mechanisms underlying its genomic targeting are poorly understood. H3K4me3 methyltransferases SETD1B and MLL2 are essential for oogenesis. We investigated changes in H3K4me3 in Setd1b conditional knockout (cKO) oocytes using ultra-low input ChIP-seq, with comparisons to DNA methylation and gene expression analyses. H3K4me3 was redistributed in Setd1b cKO oocytes showing losses at active gene promoters associated with downregulated gene expression. Remarkably, many regions also gained H3K4me3, in particular those that were DNA hypomethylated, transcriptionally inactive and CpG-rich, which are hallmarks of MLL2 targets. Consequently, loss of SETD1B disrupts the balance between MLL2 and de novo DNA methyltransferases in determining the epigenetic landscape during oogenesis. Our work reveals two distinct, complementary mechanisms of genomic targeting of H3K4me3 in oogenesis, with SETD1B linked to gene expression and MLL2 to CpG content. Graphical Abstract In oogenesis, SETD1B and CXXC1 target H3K4me3 to actively transcribed gene promoters, while MLL2 targets transcriptionally inactive regions based on underlying CpG composition (upper panel). When SETD1B is ablated, H3K4me3 is lost at a subset of active promoters, resulting in downregulation of transcription (lower panel). Loss of SETD1B alters the activity of MLL2, permitting MLL2 to deposit H3K4me3 at CpG-rich regions, many of which should otherwise be DNA methylated. Thus, it is evident that MLL2 and de novo DNMTs compete for genomic occupancy late in oogenesis, and loss of SETD1B disrupts the balance of these mechanisms.
Self-Supervised Quality Estimation for Machine Translation

Quality estimation (QE) of machine translation (MT) aims to evaluate the quality of machine-translated sentences without references and is important in practical applications of MT. Training QE models require massive parallel data with hand-crafted quality annotations, which are time-consuming and labor-intensive to obtain. To address the issue of the absence of annotated training data, previous studies attempt to develop unsupervised QE methods. However, very few of them can be applied to both sentence- and word-level QE tasks, and they may suffer from noises in the synthetic data. To reduce the negative impact of noises, we propose a self-supervised method for both sentence- and word-level QE, which performs quality estimation by recovering the masked target words. Experimental results show that our method outperforms previous unsupervised methods on several QE tasks in different language pairs and domains.
A Fine-Grained Domain Adaption Model for Joint Word Segmentation and POS Tagging

Domain adaption for word segmentation and POS tagging is a challenging problem for Chinese lexical processing. Self-training is one promising solution for it, which struggles to construct a set of high-quality pseudo training instances for the target domain. Previous work usually assumes a universal source-to-target adaption to collect such pseudo corpus, ignoring the different gaps from the target sentences to the source domain. In this work, we start from joint word segmentation and POS tagging, presenting a fine-grained domain adaption method to model the gaps accurately. We measure the gaps by one simple and intuitive metric, and adopt it to develop a pseudo target domain corpus based on fine-grained subdomains incrementally. A novel domain-mixed representation learning model is proposed accordingly to encode the multiple subdomains effectively. The whole process is performed progressively for both corpus construction and model training. Experimental results on a benchmark dataset show that our method can gain significant improvements over a vary of baselines. Extensive analyses are performed to show the advantages of our final domain adaption model as well.
Translation-based Supervision for Policy Generation in Simultaneous Neural Machine Translation

In simultaneous machine translation, finding an agent with the optimal action sequence of reads and writes that maintain a high level of translation quality while minimizing the average lag in producing target tokens remains an extremely challenging problem. We propose a novel supervised learning approach for training an agent that can detect the minimum number of reads required for generating each target token by comparing simultaneous translations against full-sentence translations during training to generate oracle action sequences. These oracle sequences can then be used to train a supervised model for action generation at inference time. Our approach provides an alternative to current heuristic methods in simultaneous translation by introducing a new training objective, which is easier to train than previous attempts at training the agent using reinforcement learning techniques for this task. Our experimental results show that our novel training method for action generation produces much higher quality translations while minimizing the average lag in simultaneous translation.
Considering Nested Tree Structure in Sentence Extractive Summarization with Pre-trained Transformer

Sentence extractive summarization shortens a document by selecting sentences for a summary while preserving its important contents. However, constructing a coherent and informative summary is difficult using a pre-trained BERT-based encoder since it is not explicitly trained for representing the information of sentences in a document. We propose a nested tree-based extractive summarization model on RoBERTa (NeRoBERTa), where nested tree structures consist of syntactic and discourse trees in a given document. Experimental results on the CNN/DailyMail dataset showed that NeRoBERTa outperforms baseline models in ROUGE. Human evaluation results also showed that NeRoBERTa achieves significantly better scores than the baselines in terms of coherence and yields comparable scores to the state-of-the-art models.
Improving Stance Detection with Multi-Dataset Learning and Knowledge Distillation

Stance detection determines whether the author of a text is in favor of, against or neutral to a specific target and provides valuable insights into important events such as legalization of abortion. Despite significant progress on this task, one of the remaining challenges is the scarcity of annotations. Besides, most previous works focused on a hard-label training in which meaningful similarities among categories are discarded during training. To address these challenges, first, we evaluate a multi-target and a multi-dataset training settings by training one model on each dataset and datasets of different domains, respectively. We show that models can learn more universal representations with respect to targets in these settings. Second, we investigate the knowledge distillation in stance detection and observe that transferring knowledge from a teacher model to a student model can be beneficial in our proposed training settings. Moreover, we propose an Adaptive Knowledge Distillation (AKD) method that applies instance-specific temperature scaling to the teacher and student predictions. Results show that the multi-dataset model performs best on all datasets and it can be further improved by the proposed AKD, outperforming the state-of-the-art by a large margin. We publicly release our code.