Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative Multiparametric MRI as an Imaging Biomarker for the Prediction of Breast Cancer Receptor Status and Molecular Subtypes

Objectives To assess breast cancer receptor status and molecular subtypes by using the CAIPIRINHA-Dixon-TWIST-VIBE and readout-segmented echo-planar diffusion weighted imaging techniques. Methods A total of 165 breast cancer patients were retrospectively recruited. Patient age, estrogen receptor, progesterone receptor, human epidermal growth factorreceptor-2 (HER-2) status, and the Ki-67 proliferation index were collected for analysis. Quantitative parameters (Ktrans, Ve, Kep), semiquantitative parameters (W-in, W-out, TTP), and apparent diffusion coefficient (ADC) values were compared in relation to breast cancer receptor status and molecular subtypes. Statistical analysis were performed to compare the parameters in the receptor status and molecular subtype groups.Multivariate analysis was performed to explore confounder-adjusted associations, and receiver operating characteristic curve analysis was used to assess the classification performance and calculate thresholds. Results Younger age (<49.5 years, odds ratio (OR) =0.95, P=0.004), lower Kep (<0.704,OR=0.14, P=0.044),and higher TTP (>0.629 min, OR=24.65, P=0.011) were independently associated with progesterone receptor positivity. A higher TTP (>0.585 min, OR=28.19, P=0.01) was independently associated with estrogen receptor positivity. Higher Kep (>0.892, OR=11.6, P=0.047), lower TTP (<0.582 min, OR<0.001, P=0.004), and lower ADC (<0.719 ×10-3 mm2/s, OR<0.001, P=0.048) had stronger independent associations with triple-negative breast cancer (TNBC) compared to luminal A, and those parameters could differentiate TNBC from luminal A with the highest AUC of 0.811. Conclusions Kep and TTP were independently associated with hormone receptor status. In addition, the Kep, TTP, and ADC values had stronger independent associations with TNBC than with luminal A and could be used as imaging biomarkers for differentiate TNBC from Luminal A.
APICraft: Fuzz Driver Generation for Closed-source SDK Libraries

Fuzz drivers are needed for fuzzing libraries. A fuzz driver is a program which can execute library functions by feeding them with inputs provided by the fuzzer. In practice, fuzz drivers are written by security experts and the drivers’ quality depends on the skill of their authors. To relieve manual efforts and ensure test quality, different techniques have been proposed to automatically generate fuzz drivers. However, existing techniques mostly rely on static analysis of source code, leaving the fuzz driver generation for closed-source SDK libraries an open problem. Fuzz driver generation for closed-source libraries is faced with two major challenges: 1) only limited information can be extracted from the library; 2) the semantic relations among API functions are complex yet their correctness needs to be ensured. To address these challenges, we propose APIC RAFT , an automated fuzz driver generation technique. The core strategy of APIC RAFT is collect – combine . First, APIC RAFT leverages both static and dynamic information (headers, binaries, and traces) to collect control and data dependencies for API functions in a practical manner. Then, it uses a multi-objective genetic algorithm to combine the collected dependencies and build high-quality fuzz drivers. We implemented APIC RAFT as a fuzz driver generation framework and evaluated it with ﬁve attack surfaces from the macOS SDK. In the evaluation, the fuzz drivers generated by APIC RAFT demonstrate superior code coverage than the manually written ones, with an improvement of 64% on average. We further carried out a long-term fuzzing campaign with the fuzz drivers generated by APIC RAFT . After around eight month’s fuzzing, we’ve so far discovered 142 vulnerabilities with 54 assigned CVEs in macOS SDK, which can affect popular Apple products such as Safari , Messages , Preview and so on.
Bilingual Brains Learn to Use L2 Alliterations Covertly like Poets: Brain ERP Evidence

Bilinguals were documented to access their native or first language (L1) during comprehension of their second languages (L2). However, it is uncertain whether they can access L2 when reading their first language. This study used the event-related potential (ERP) technique to demonstrate the implicit and unconscious access to English words when Chinese–English bilinguals read words in Chinese, their native language. The participants were asked to judge whether the Chinese words presented in pairs were semantically related or not, meanwhile unconscious of the occasional alliteration (repetition of the first phoneme) if the Chinese words were translated into English. While the concealed prime in English translations failed to affect the reaction time, the alliteration significantly modulated N400 among advanced English learners, especially for semantically unrelated word pairs. Critically, this modulation effect was discrepant between bilinguals with high-level and normal-level English proficiency. These results indicate that L2 activation is an unconscious correlate of native-language processing depending on L2 proficiency.
Maternal Transmission of Human OGG1 Protects Mice Against Genetically- and Diet-Induced Obesity Through Increased Tissue Mitochondrial Content

Obesity and related metabolic disorders are pressing public health concerns, raising the risk for a multitude of chronic diseases. Obesity is multi-factorial disease, with both diet and lifestyle, as well as genetic and developmental factors leading to alterations in energy balance. In this regard, a novel role for DNA repair glycosylases in modulating risk for obesity has been previously established. Global deletion of either of two different glycosylases with varying substrate specificities, Nei-like endonuclease 1 (NEIL1) or 8-oxoguanine DNA glycosylase-1 (OGG1), both predispose mice to diet-induced obesity (DIO). Conversely, enhanced expression of the human OGG1 gene renders mice resistant to obesity and adiposity. This resistance to DIO is mediated through increases in whole body energy expenditure and increased respiration in adipose tissue. Here, we report that hOGG1 expression also confers resistance to genetically-induced obesity. While Agouti obese (Ay/a) mice are hyperphagic and consequently develop obesity on a chow diet, hOGG1 expression in Ay/a mice (Ay/aTg) prevents increased body weight, without reducing food intake. Instead, obesity resistance in Ay/aTg mice is accompanied by increased whole body energy expenditure and tissue mitochondrial content. We also report for the first time that OGG1-mediated obesity resistance in both the Ay/a model and DIO model requires maternal transmission of the hOGG1 transgene. Maternal, but not paternal, transmission of the hOGG1 transgene is associated with obesity resistance and increased mitochondrial content in adipose tissue. These data demonstrate a critical role for OGG1 in modulating energy balance through changes in adipose tissue function. They also demonstrate the importance of OGG1 in modulating developmental programming of mitochondrial content and quality, thereby determining metabolic outcomes in offspring.
Autism Spectrum Disorder Studies Using fMRI Data and Machine Learning: A Review

Machine learning methods have been frequently applied in the field of cognitive neuroscience in the last decade. A great deal of attention has been attracted to introduce machine learning methods to study the autism spectrum disorder (ASD) in order to find out its neurophysiological underpinnings. In this paper, we presented a comprehensive review about the previous studies since 2011, which applied machine learning methods to analyze the functional magnetic resonance imaging (fMRI) data of autistic individuals and the typical controls (TCs). The all-round process was covered, including feature construction from raw fMRI data, feature selection methods, machine learning methods, factors for high classification accuracy, and critical conclusions. Applying different machine learning methods and fMRI data acquired from different sites, classification accuracies were obtained ranging from 48.3% up to 97%, and informative brain regions and networks were located. Through thorough analysis, high classification accuracies were found to usually occur in the studies which involved task-based fMRI data, single dataset for some selection principle, effective feature selection methods, or advanced machine learning methods. Advanced deep learning together with the multi-site Autism Brain Imaging Data Exchange (ABIDE) dataset became research trends especially in the recent 4 years. In the future, advanced feature selection and machine learning methods combined with multi-site dataset or easily operated task-based fMRI data may appear to have the potentiality to serve as a promising diagnostic tool for ASD.
Prevalence and Risk Factors of Hernia in Patients With Rectus Abdominis Diastasis: A 10-Year Multicenter Retrospective Study

Objectives: Hernias are very common in patients with rectus abdominis diastasis (RAD). This study aimed to identify and compare the risk factors and patterns of hernia between men and women with RAD. Method: We included patients with RAD from six hospitals within the Partners Healthcare System in Massachusetts, USA between 2009 and 2018. Univariate and multivariable binary logistic regression analyses were used to identify risk factors associated with hernia. Results: Of the 1,294 RAD cases, 866 (67%) were women. The risk of RAD in women was 1.9 times greater than that of men. There were 240 men (56.1%) and 310 women (35.8%) having one or more hernia (P < 0.001). Of the 550 hernia cases, 278 men and 175 women had umbilical hernia (28.1 vs. 38.3%, P = 0.085). The distribution of hernia type differed between the two groups (P < 0.0001). Multivariate analysis identified that alcohol use [odd ratio (OR) 1.74 (1.17–2.59); P = 0.006] and depressive disorder [OR 1.90 (1.209–2.998); P = 0.005] were risk factors of coexisting hernia for men with RAD; age [OR 1.51 (1.33–1.72); P = 0.000] and smoking/tobacco use [OR 1.66 (1.13–2.44); P = 0.010] were risk factors of hernia for women. Conclusion: The prevalence and risk factors of hernia in women with RAD significantly differed from that in men with RAD. Umbilical hernia is an important type of hernia. Alcohol use and depressive disorder in men, and age and smoking in women were risk factors of hernias in patients with RAD.
The Mangrove Periwinkle Littoraria angulifera (Mollusca: Littorinidae) in the Urabá Gulf (Colombian Caribbean): Finding Ways in an Urbanizing Coast?

Periwinkles (Littorinidae: genus Littoraria) are one of the very few molluscan clades showing an adaptive radiation closely associated to the mangrove habitat. However, pervasive land use changes associated to urbanization is prompting mangrove loss or degradation, with unknown consequences for mangrove-associated fauna. In the southern Colombian Caribbean, mangrove ecosystems have been encroached by human settlements and different populations of Littoraria angulifera (Lamarck, 1822) now inhabit anthropogenic intertidal substrates in urban areas, but the demographic traits of populations thriving in these novel environments are unknown. We studied the relative abundance and size structure of L. angulifera in remnant mangrove patches, woody debris and anthropogenic substrates (boulder seawalls and built structures) in 13 locations throughout the Urabá Gulf, a human-transformed tropical estuarine system. The abundance of L. angulifera was up to two orders of magnitude higher in anthropogenic than in quasi-natural or natural substrates. Snails also displayed a significant preference for wave protected positions in boulder seawalls and built structures exposed to heavy wave action, which was not previously reported in mangrove forests. Moreover, snail populations in anthropogenic substrate were consistently dominated by individuals of small sizes in comparison with mangroves or driftwood. We argue that the anthropogenic disturbances caused by the expansion of Turbo city during nearly one century in a coast formerly dominated by mangrove forests are providing novel and expanding habitats, whose quality might be good enough as to support high-density populations of L. angulifera. However, we hypothesize that shifted thermal regimes in hard and novel wave-exposed urban seascapes might also be prompting behavioral adjustments and the selection of smaller size ranges than those observed in mangrove forests.
Assessing Antibiotics Biodegradation and Effects at Sub-inhibitory Concentrations by Quantitative Microbial Community Deconvolution

Antibiotics in the environment cause widespread concern as a result of their potent inhibitory action on microbial growth and their role in potentially creating selective conditions for proliferation of antibiotic resistant bacteria. Comprising a carbon skeleton, antibiotics should be amenable to microbial biodegradation, but this is still largely uncharted territory because of their simultaneous strong toxicity. In this study, we estimated potential antibiotics degradation by and effects on mixed microbial communities at concentrations sufficiently high to allow sensitive detection of biomass growth, but simultaneously, low enough to mitigate their toxic action. We used three different mixed inoculum sources freshly derived from freshwater, activated sludge or soil, and tested a series of 15 antibiotics from different classes at 1 mg C-carbon l−1 dosage. Consistent community growth was observed for freshwater and activated sludge with ampicillin, erythromycin and chloramphenicol, and with sulfomethoxazole for activated sludge, which was accompanied by parent compound disappearance. Community growth could be attributed to a few subclasses of recognized cell types by using supervised machine-learning-based classifiers. Most other tested antibiotics resulted in inhibition of community growth on background assimilable organic carbon, concomitant with altered composition of the resulting communities. We conclude that growth-linked biodegradation of antibiotics at low concentrations may be present among typical environmental microbiota, but for a selected subset only, whereas for the majority of antibiotics negative effects prevail without any sign of productive growth.
Machine Learning Applications in Solid Organ Transplantation and Related Complications

The complexity of transplant medicine pushes the boundaries of innate, human reasoning. From networks of immune modulators to dynamic pharmacokinetics to variable postoperative graft survival to equitable allocation of scarce organs, machine learning promises to inform clinical decision making by deciphering prodigious amounts of available data. This paper reviews current research describing how algorithms have the potential to augment clinical practice in solid organ transplantation. We provide a general introduction to different machine learning techniques, describing their strengths, limitations, and barriers to clinical implementation. We summarize emerging evidence that recent advances that allow machine learning algorithms to predict acute post-surgical and long-term outcomes, classify biopsy and radiographic data, augment pharmacologic decision making, and accurately represent the complexity of host immune response. Yet, many of these applications exist in pre-clinical form only, supported primarily by evidence of single-center, retrospective studies. Prospective investigation of these technologies has the potential to unlock the potential of machine learning to augment solid organ transplantation clinical care and health care delivery systems.
Individualized Prediction of Survival Benefits of Pancreatectomy Plus Chemotherapy in Patients With Simultaneous Metastatic Pancreatic Cancer

Background Metastatic pancreatic cancer (mPC) is a highly lethal malignancy with poorer survival. However, chemotherapy alone was unable to maintain long‐term survival. This study aimed to evaluate the individualized survival benefits of pancreatectomy plus chemotherapy (PCT) for mPC. Methods A total of 4546 patients with mPC from 2004 to 2015 were retrieved from the Surveillance, Epidemiology, and End Results database. The survival curve was calculated using the Kaplan-Meier method and differences in survival curves were tested using log-rank tests. Cox proportional hazards regression analyses were performed to evaluate the prognostic value of involved variables. A new nomogram was constructed to predict overall survival based on independent prognosis factors. The performance of the nomogram was measured by concordance index, calibration plot, and area under the receiver operating characteristic curve. Results Compared to pancreatectomy or chemotherapy alone, PCT can significantly improve the prognosis of patients with mPC. In addition, patients with well/moderately differentiated tumors, age ≤66 years, tumor size ≤42 mm, or female patients were more likely to benefit from PCT. Multivariate analysis showed that age at diagnosis, sex, marital status, grade, tumor size, and treatment were independent prognostic factors. The established nomogram has a good ability to distinguish and calibrating. Conclusion PCT can prolong survival in some patients with mPC. Our nomogram can individualize predict OS of pancreatectomy combined with chemotherapy in patients with concurrent mPC.
Socializing Sensorimotor Contingencies

The aim of this review is to highlight the idea of grounding social cognition in sensorimotor interactions shared across agents. We discuss an action-oriented account that emerges from a broader interpretation of the concept of sensorimotor contingencies. We suggest that dynamic informational and sensorimotor coupling across agents can mediate the deployment of action-effect contingencies in social contexts. We propose this concept of socializing sensorimotor contingencies (socSMCs) as a shared framework of analysis for processes within and across brains and bodies, and their physical and social environments. In doing so, we integrate insights from different fields, including neuroscience, psychology, and research on human–robot interaction. We review studies on dynamic embodied interaction and highlight empirical findings that suggest an important role of sensorimotor and informational entrainment in social contexts. Furthermore, we discuss links to closely related concepts, such as enactivism, models of coordination dynamics and others, and clarify differences to approaches that focus on mentalizing and high-level cognitive representations. Moreover, we consider conceptual implications of rethinking cognition as social sensorimotor coupling. The insight that social cognitive phenomena like joint attention, mutual trust or empathy rely heavily on the informational and sensorimotor coupling between agents may provide novel remedies for people with disturbed social cognition and for situations of disturbed social interaction. Furthermore, our proposal has potential applications in the field of human–robot interaction where socSMCs principles might lead to more natural and intuitive interfaces for human users.
PowerCut and Obfuscator: An Exploration of the Design Space for Privacy-Preserving Interventions for Smart Speakers

The pervasive use of smart speakers has raised numerous privacy concerns. While work to date provides an understanding of user perceptions of these threats, limited research focuses on how we can mitigate these concerns, either through redesigning the smart speaker or through dedicated privacy-preserving interventions. In this paper, we present the design and prototyping of two privacy-preserving interventions: `Obfuscator' targeted at disabling recording at the microphones, and `PowerCut' targeted at disabling power to the smart speaker. We present our findings from a technology probe study involving 24 households that interacted with our prototypes; the primary objective was to gain a better understanding of the design space for technological interventions that might address these concerns. Our data and findings reveal complex trade-offs among utility, privacy, and usability and stresses the importance of multi-functionality, aesthetics, ease-of-use, and form factor. We discuss the implications of our findings for the development of subsequent interventions and the future design of smart speakers.
Intrusion Detection in Industrial Internet of Things Network-Based on Deep Learning Model with Rule-Based Feature Selection

The Industrial Internet of Things (IIoT) is a recent research area that links digital equipment and services to physical systems. The IIoT has been used to generate large quantities of data from multiple sensors, and the device has encountered several issues. The IIoT has faced various forms of cyberattacks that jeopardize its capacity to supply organizations with seamless operations. Such risks result in financial and reputational damages for businesses, as well as the theft of sensitive information. Hence, several Network Intrusion Detection Systems (NIDSs) have been developed to fight and protect IIoT systems, but the collections of information that can be used in the development of an intelligent NIDS are a difficult task; thus, there are serious challenges in detecting existing and new attacks. Therefore, the study provides a deep learning-based intrusion detection paradigm for IIoT with hybrid rule-based feature selection to train and verify information captured from TCP/IP packets. The training process was implemented using a hybrid rule-based feature selection and deep feedforward neural network model. The proposed scheme was tested utilizing two well-known network datasets, NSL-KDD and UNSW-NB15. The suggested method beats other relevant methods in terms of accuracy, detection rate, and FPR by 99.0%, 99.0%, and 1.0%, respectively, for the NSL-KDD dataset, and 98.9%, 99.9%, and 1.1%, respectively, for the UNSW-NB15 dataset, according to the results of the performance comparison. Finally, simulation experiments using various evaluation metrics revealed that the suggested method is appropriate for IIOT intrusion network attack classification.