Validity-Preserving Delta Debugging via Generator

Reducing test inputs that trigger bugs is crucial for efficient debugging. Delta debugging is the most popular approach for this purpose. When test inputs need to conform to certain specifications, existing delta debugging practice encounters a validity problem: it blindly applies reduction rules, producing a large number of invalid test inputs that do not satisfy the required specifications. This overall diminishing effectiveness and efficiency becomes even more pronounced when the specifications extend beyond syntactical structures. Our key insight is that we should leverage input generators, which are aware of these specifications, to generate valid reduced inputs, rather than straightforwardly performing reduction on test inputs. In this paper, we propose a generator-based delta debugging method, namely GReduce, which derives validity-preserving reducers. Specifically, given a generator and its execution, demonstrating how the bug-inducing test input is generated, GReduce searches for other executions on the generator that yield reduced, valid test inputs. To evaluate the effectiveness, efficiency, and versatility of GReduce, we apply GReduce and the state-of-the-art reducer Perses in three domains: graphs, deep learning models, and JavaScript programs. The results of GReduce are 28.5%, 34.6%, 75.6% in size of those from Perses, and GReduce takes 17.5%, 0.6%, 65.4% time taken by Perses

Facilitating Self-monitored Physical Rehabilitation with Virtual Reality and Haptic feedback

Physical rehabilitation is essential to recovery from joint replacement operations. As a representation, total knee arthroplasty (TKA) requires patients to conduct intensive physical exercises to regain the knee's range of motion and muscle strength. However, current joint replacement physical rehabilitation methods rely highly on therapists for supervision, and existing computer-assisted systems lack consideration for enabling self-monitoring, making at-home physical rehabilitation difficult. In this paper, we investigated design recommendations that would enable self-monitored rehabilitation through clinical observations and focus group interviews with doctors and therapists. With this knowledge, we further explored Virtual Reality(VR)-based visual presentation and supplemental haptic motion guidance features in our implementation VReHab, a self-monitored and multimodal physical rehabilitation system with VR and vibrotactile and pneumatic feedback in a TKA rehabilitation context. We found that the third point of view real-time reconstructed motion on a virtual avatar overlaid with the target pose effectively provides motion awareness and guidance while haptic feedback helps enhance users' motion accuracy and stability. Finally, we implemented \systemname to facilitate self-monitored post-operative exercises and validated its effectiveness through a clinical study with 10 patients

Deep quantum neural networks equipped with backpropagation on a superconducting processor

Deep learning and quantum computing have achieved dramatic progresses in recent years. The interplay between these two fast-growing fields gives rise to a new research frontier of quantum machine learning. In this work, we report the first experimental demonstration of training deep quantum neural networks via the backpropagation algorithm with a six-qubit programmable superconducting processor. In particular, we show that three-layer deep quantum neural networks can be trained efficiently to learn two-qubit quantum channels with a mean fidelity up to 96.0% and the ground state energy of molecular hydrogen with an accuracy up to 93.3% compared to the theoretical value. In addition, six-layer deep quantum neural networks can be trained in a similar fashion to achieve a mean fidelity up to 94.8% for learning single-qubit quantum channels. Our experimental results explicitly showcase the advantages of deep quantum neural networks, including quantum analogue of the backpropagation algorithm and less stringent coherence-time requirement for their constituting physical qubits, thus providing a valuable guide for quantum machine learning applications with both near-term and future quantum devices.Comment: 7 pages (main text) + 11 pages (Supplementary Information), 10 figure

State-of-the-art methods for exposure-health studies: Results from the exposome data challenge event

The exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating high-throughput omics layers. To tackle these challenges, the Barcelona Institute for Global Health (ISGlobal) held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P > 100 exposure variables) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes in 1301 mother–child pairs. Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss features of the statistical models used and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share state-of-the-art analytical methods, setting a new standard for open science in the exposome and environmental health field

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Data_Sheet_1_Intramammary infusion of matrine-chitosan hydrogels for treating subclinical bovine mastitis —effects on milk microbiome and metabolites.docx

BackgroundBovine metabolism undergoes significant changes during subclinical mastitis, but the relevant molecular mechanisms have not been elucidated. In this study we investigated the changes in milk microbiota and metabolites after intramammary infusion of matrine-chitosan hydrogels (MCHs) in cows with subclinical mastitis.MethodsInfusions were continued for 7 days, and milk samples were collected on days 1 and 7 for microbiome analysis by 16S rRNA gene sequencing and metabolite profiling by liquid chromatography-mass spectrometry.ResultsMCHs significantly decreased the somatic cell count on day 7 compared to day 1, and the Simpson index indicated that microbial diversity was significantly lower on day 7. The relative abundance of Aerococcus, Corynebacterium_1, Staphylococcus and Firmicutes was significantly decreased on day 7, while Proteobacteria increased. In the milk samples, we identified 74 differentially expressed metabolites. The MCHs infusion group had the most significantly upregulated metabolites including sphingolipids, glycerophospholipids, flavonoids and fatty acyls. The mammary gland metabolic pathways identified after MCHs treatment were consistent with the known antimicrobial and anti-inflammatory properties of matrine that are associated with glycerophospholipid metabolism and the sphingolipid metabolic signaling pathways.ConclusionThese insights into the immunoregulatory mechanisms and the corresponding biological responses to matrine demonstrate its potential activity in mitigating the harmful effects of bovine mastitis.</p

Diagnostic value of applying preoperative breast ultrasound and clinicopathologic features to predict axillary lymph node burden in early invasive breast cancer: a study of 1247 patients

Abstract Background Since the Z0011 trial, the assessment of axillary lymph node status has been redirected from the previous assessment of the occurrence of lymph node metastasis alone to the assessment of the degree of lymph node loading. Our aim was to apply preoperative breast ultrasound and clinicopathological features to predict the diagnostic value of axillary lymph node load in early invasive breast cancer. Methods The 1247 lesions were divided into a high lymph node burden group and a limited lymph node burden group according to axillary lymph node status. Univariate and multifactorial analyses were used to predict the differences in clinicopathological characteristics and breast ultrasound characteristics between the two groups with high and limited lymph node burden. Pathological findings were used as the gold standard. Results Univariate analysis showed significant differences in ki-67, maximum diameter (MD), lesion distance from the nipple, lesion distance from the skin, MS, and some characteristic ultrasound features (P < 0.05). In multifactorial analysis, the ultrasound features of breast tumors that were associated with a high lymph node burden at the axilla included MD (odds ratio [OR], 1.043; P < 0.001), shape (OR, 2.422; P = 0.0018), hyperechoic halo (OR, 2.546; P < 0.001), shadowing in posterior features (OR, 2.155; P = 0.007), and suspicious lymph nodes on axillary ultrasound (OR, 1.418; P = 0.031). The five risk factors were used to build the predictive model, and it achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.702. Conclusion Breast ultrasound features and clinicopathological features are better predictors of high lymph node burden in early invasive breast cancer, and this prediction helps to develop more effective treatment plans

A novel TOX3-WDR5-ABCG2 signaling axis regulates the progression of colorectal cancer by accelerating stem-like traits and chemoresistance.

The eradication of cancer stem cells (CSCs) with drug resistance confers the probability of local tumor control after chemotherapy or targeted therapy. As the main drug resistance marker, ABCG2 is also critical for colorectal cancer (CRC) evolution, in particular cancer stem-like traits expansion. Hitherto, the knowledge about the expression regulation of ABCG2, in particular its upstream transcriptional regulatory mechanisms, remains limited in cancer, including CRC. Here, ABCG2 was found to be markedly up-regulated in CRC CSCs (cCSCs) expansion and chemo-resistant CRC tissues and closely associated with CRC recurrence. Mechanistically, TOX3 was identified as a specific transcriptional factor to drive ABCG2 expression and subsequent cCSCs expansion and chemoresistance by binding to -261 to -141 segments of the ABCG2 promoter region. Moreover, we found that TOX3 recruited WDR5 to promote tri-methylation of H3K4 at the ABCG2 promoter in cCSCs, which further confers stem-like traits and chemoresistance to CRC by co-regulating the transcription of ABCG2. In line with this observation, TOX3, WDR5, and ABCG2 showed abnormal activation in chemo-resistant tumor tissues of in situ CRC mouse model and clinical investigation further demonstrated the comprehensive assessment of TOX3, WDR5, and ABCG2 could be a more efficient strategy for survival prediction of CRC patients with recurrence or metastasis. Thus, our study found that TOX3-WDR5/ABCG2 signaling axis plays a critical role in regulating CRC stem-like traits and chemoresistance, and a combination of chemotherapy with WDR5 inhibitors may induce synthetic lethality in ABCG2-deregulated tumors

Lumbrokinase Extracted from <i>Earthworms</i> Synergizes with Bevacizumab and Chemotherapeutics in Treating Non-Small Cell Lung Cancer by Targeted Inactivation of BPTF/VEGF and NF-κB/COX-2 Signaling

As a kind of proteolytic enzyme extracted from earthworms, lumbrokinase has been used as an antithrombotic drug clinically. Nevertheless, its potential in anti-cancer, especially in anti-non-small cell lung cancer (NSCLC), as a single form of treatment or in combination with other therapies, is still poorly understood. In this study, we explored the anti-tumor role and the responsive molecular mechanisms of lumbrokinase in suppressing tumor angiogenesis and chemoresistance development in NSCLC and its clinical potential in combination with bevacizumab and chemotherapeutics. Lumbrokinase was found to inhibit cell proliferation in a concentration-dependent manner and caused metastasis suppression and apoptosis induction to varying degrees in NSCLC cells. Lumbrokinase enhanced the anti-angiogenesis efficiency of bevacizumab by down-regulating BPTF expression, decreasing its anchoring at the VEGF promoter region and subsequent VEGF expression and secretion. Furthermore, lumbrokinase treatment reduced IC50 values of chemotherapeutics and improved their cytotoxicity in parental and chemo-resistant NSCLC cells via inactivating the NF-κB pathway, inhibiting the expression of COX-2 and subsequent secretion of PGE2. LPS-induced NF-κB activation reversed its inhibition on NSCLC cell proliferation and its synergy with chemotherapeutic cytotoxicity, while COX-2 inhibitor celecoxib treatment boosted such effects. Lumbrokinase combined with bevacizumab, paclitaxel, or vincristine inhibited the xenograft growth of NSCLC cells in mice more significantly than a single treatment. In conclusion, lumbrokinase inhibited NSCLC survival and sensitized NSCLC cells to bevacizumab or chemotherapeutics treatment by targeted down-regulation of BPTF/VEGF signaling and inactivation of NF-κB/COX-2 signaling, respectively. The combinational applications of lumbrokinase with bevacizumab or chemotherapeutics are expected to be developed as promising candidate therapeutic strategies to improve the efficacy of the original monotherapy in anti-NSCLC