Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal Proximity Links Unrelated News Events in Memory

Some memories are linked such that recalling one can trigger the retrieval of another. What determines which memories are linked? Some models predict that simply occurring close together in time is sufficient for links to form between memories. A competing theory suggests that temporal proximity is generally not sufficient, and existing evidence for such links is an artifact of using chainlike lists of items in artificial laboratory tasks. To test these competing accounts, we asked subjects to recall news stories that they had encountered over the past 2 years (Experiment 1) or 4 months (Experiment 2). In both experiments, subjects showed a strong bias to successively recall stories that appeared in the news within days of each other—even after accounting for the fact that stories that occur close in time tend to be semantically related. By moving beyond laboratory tasks, this research solidifies the foundation of contemporary memory theory.
A comprehensive review of EEG-based brain–computer interface paradigms

Advances in brain science and computer technology in the past decade have led to exciting developments in brain–computer interface (BCI), thereby making BCI a top research area in applied science. The renaissance of BCI opens new methods of neurorehabilitation for physically disabled people (e.g. paralyzed patients and amputees) and patients with brain injuries (e.g. stroke patients). Recent technological advances such as wireless recording, machine learning analysis, and real-time temporal resolution have increased interest in electroencephalographic (EEG) based BCI approaches. Many BCI studies have focused on decoding EEG signals associated with whole-body kinematics/kinetics, motor imagery, and various senses. Thus, there is a need to understand the various experimental paradigms used in EEG-based BCI systems. Moreover, given that there are many available options, it is essential to choose the most appropriate BCI application to properly manipulate a neuroprosthetic or neurorehabilitation device. The current review evaluates EEG-based BCI paradigms regarding their advantages and disadvantages from a variety of perspectives. For each paradigm, various EEG decoding algorithms and classification methods are evaluated. The applications of these paradigms with targeted patients are summarized. Finally, potential problems with EEG-based BCI systems are discussed, and possible solutions are proposed.
Mediation of associations between adiposity and colorectal cancer risk by inflammatory and metabolic biomarkers

Inflammation and hyperinsulinemia may drive associations between adiposity and colorectal cancer (CRC) risk, but few studies have examined this hypothesis using mediation analysis. We used inverse odds ratio weighting and logistic regression to calculate odds ratios (OR) and 95% confidence intervals (CI) for estimated total effects (ORTE) of body mass index, waist circumference, and adult weight gain on CRC risk, and estimated effects operating through seven inflammatory and metabolic biomarkers (natural indirect effect; ORNIE) or through paths independent of these biomarkers (natural direct effect; ORNDE) among 209 CRC cases and 382 matched controls nested within the Health Professionals Follow‐up Study, a prospective cohort of male health professionals. A one‐interquartile range (IQR) increase in body mass index (3.6 kg/m2) was associated with an ORTE of 1.40 (95% CI: 1.13, 1.73), which decomposed into an ORNIE of 1.26 (95% CI: 0.97, 1.52) and an ORNDE of 1.11 (0.87, 1.42), with possibly stronger mediation by these biomarkers for adult weight gain (IQR = 10.4 kg; ORTE = 1.32 [95% CI: 1.06, 1.64]; ORNIE = 1.47 [95% CI: 1.01, 1.81]; ORNDE = 0.89 [95% CI: 0.72, 1.11]), but no mediation for waist circumference. Mediation appeared to be stronger for the metabolic biomarkers than the inflammatory biomarkers. Inflammatory and metabolic mechanisms may mediate associations between both body mass index and adult weight gain with CRC risk.
MRI-based radiotherapy planning method using rigid image registration technique combined with outer body correction scheme: a feasibility study

An alternative pseudo CT generation method for magnetic resonance image (MRI)-based radiotherapy planning was investigated in the work. A pseudo CT was initially generated using the rigid image registration between the planning MRI and previously acquired diagnostic CT scan. The pseudo CT generated was then refined to have the same morphology with that of the referenced planning image scan by applying the outer body correction scheme. This method was applied to some sample of brain image data and the feasibility of the method was assessed by comparing dosimetry results with those from the current gold standard CT-based calculations. Validation showed that nearly the entire pixel doses calculated from pseudo CT were agreed well with those from actual planning CT within 2% in dosimetric and 1mm in geometric uncertainty ranges. The results demonstrated that the method suggested in the study was sufficiently accurate, and thus could be applicable to MRI-based brain radiotherapy planning.
Adaptation, chance, and history in experimental evolution reversals to unicellularity

Evolution is often deemed irreversible. The evolution of complex traits that require many mutations makes their reversal unlikely. Even in simpler traits, reversals might become less likely as neutral or beneficial mutations, with deleterious effects in the ancestral context, become fixed in the novel background. This is especially true in changes that involve large reorganizations of the organism and its interactions with the environment. The evolution of multicellularity involves the reorganization of previously autonomous cells into a more complex organism; despite the complexity of this change, single cells have repeatedly evolved from multicellular ancestors. These repeated reversals to unicellularity undermine the generality of Dollo's law. In this article, we evaluated the dynamics of reversals to unicellularity from recently evolved multicellular phenotypes of the brewers yeast Saccharomyces cerevisae. Even though multicellularity in this system evolved recently, it involves the evolution of new levels of selection. Strong selective pressures against multicellularity lead to rapid reversibility to single cells in all of our replicate lines, whereas counterselection favoring multicellularity led to minimal reductions to the rates of reversal. History and chance played an important role in the tempo and mode of reversibility, highlighting the interplay of deterministic and stochastic events in evolutionary reversals.
Practical Model Selection for Prospective Virtual Screening

Virtual (computational) high-throughput screening provides a strategy for prioritizing compounds for experimental screens, but the choice of virtual screening algorithm depends on the dataset and evaluation strategy. We consider a wide range of ligand-based machine learning and docking-based approaches for virtual screening on two protein-protein interactions, PriA-SSB and RMI-FANCM, and present a strategy for choosing which algorithm is best for prospective compound prioritization. Our workflow identifies a random forest as the best algorithm for these targets over more sophisticated neural network-based models. The top 250 predictions from our selected random forest recover 37 of the 54 active compounds from a library of 22,434 new molecules assayed on PriA-SSB. We show that virtual screening methods that perform well in public datasets and synthetic benchmarks, like multi-task neural networks, may not always translate to prospective screening performance on a specific assay of interest.
β-Catenin is essential for Müllerian duct regression during male sexual differentiation

During male sexual differentiation, the transforming growth factor-β (TGF-β) signaling molecule anti-Müllerian hormone (AMH; also known as Müllerian inhibiting substance, MIS) is secreted by the fetal testes and induces regression of the Müllerian ducts, the primordia of the female reproductive tract organs. Currently, the molecular identity of downstream events regulated by the AMH signaling pathway remains unclear. We found that male-specific Wnt4 expression in mouse Müllerian duct mesenchyme depends upon AMH signaling, implicating the WNT pathway as a downstream mediator of Müllerian duct regression. Inactivation of β-catenin, a mediator of the canonical WNT pathway, did not affect AMH signaling activation in the Müllerian duct mesenchyme, but did block Müllerian duct regression. These data suggest that β-catenin mediates AMH signaling for Müllerian duct regression during male sexual differentiation.
Dockerization Impacts in Database Performance Benchmarking

Docker seems to be an attractive solution for cloud database benchmarking as it simplifies the setup process through pre-built images that are portable and simple to maintain. However, the usage of Docker for benchmarking is only valid if there is no effect on measurement results. Existing work has so far only focused on the performance overheads that Docker directly induces for specific applications. In this paper, we have studied indirect effects of dockerization on the results of database benchmarking. Among others, our results clearly show that containerization has a measurable and non-constant influence on measurement results and should, hence, only be used after careful analysis.
Development of unsupervised feature selection methods for high dimensional biomedical data in regression domain

In line with technological developments, there is almost no limit to collect data of high dimension in various fields including bioinformatics. In most cases, these high dimensional datasets contain many irrelevant or noisy features which need to be filtered out to find a small but biologically meaningful set of attributes. Although there have been various attempts to select predictive feature sets from high dimensional data in classification and clustering, there have only been limited attempts to do this for regression problems. Since supervised feature selection methods tend to identify noisy features in addition to discriminative variables, unsupervised feature selection methods (USFSMs) are generally regarded as more unbiased approaches. The aim of this thesis is, therefore, to provide (i) a comprehensive overview of feature selection methods for regression problems where feature selection methods are shown along with their types, references, sources, and code repositories (ii) a taxonomy of feature selection methods for regression problems to assist researchers to select appropriate feature selection methods for their research (iii) a deep learning based unsupervised feature selection framework, DFSFR (iv) a K-means based unsupervised feature selection method, KBFS. To the best of our knowledge, DFSFR is the first deep learning based method to be designed particularly for regression tasks. In addition, a hybrid USFSM, DKBFS, is proposed which combines KBFS and DFSFR to select discriminative features from very high dimensional data. The proposed frameworks are compared with the state-of-the-art USFSMs, including Multi Cluster Feature Selection (MCFS), Embedded Unsupervised Feature Selection (EUFS), Infinite Feature Selection (InFS), Spectral Regression Feature Selection (SPFS), Laplacian Score Feature Selection (LapFS), and Term Variance Feature Selection (TV) along with the entire feature sets as well as the methods used in previous studies. To evaluate the effectiveness of proposed methods, four different case studies are considered: (i) a low dimensional RV144 vaccine dataset; (ii) three different high dimensional peptide binding affinity datasets; (iii) a very high dimensional GSE44763 dataset; (iv) a very high dimensional GSE40279 dataset. Experimental results from these data sets are used to validate the effectiveness of the proposed methods. Compared to state-of-the-art feature selection methods, the proposed methods achieve improvements in prediction accuracy of as much as 9% for the RV144 Vaccine dataset, 75% for the peptide binding affinity datasets, 3% for the GSE44763 dataset, and 55% for the GSE40279 dataset.
Interpreting results of field surveys using probability calculators

Field practitioners in humanitarian settings often face challenges analyzing and interpreting the results of nutrition surveys. Most key variables of interest in field surveys are categorical, i.e., expressed as discrete categories, for example yes/no, or normal/moderate/severe. Examples of categorical variables commonly measured in emergency surveys include prevalence of Global Acute Malnutrition (GAM), stunting, underweight, anemia, coverage of measles immunization and vitamin A distribution programs, and several others.
sST2 as a novel biomarker for the prediction of in-hospital mortality after coronary artery bypass grafting

Abstract Objectives: Soluble suppression of tumorigenicity 2 (sST2) biomarker is an emerging predictor of adverse clinical outcomes, but its prognostic value for in-hospital mortality after coronary artery bypass grafting (CABG) is not well understood. This study measured the association between operative sST2 levels and in-hospital mortality after CABG. Methods: A prospective cohort of 1560 CABG patients were analyzed from the Northern New England Cardiovascular Disease Study Group Biomarker Study. The primary outcome was in-hospital mortality after CABG surgery (n = 32). Results: After risk adjustment, patients in the third tercile of pre-, post- and pre-to-postoperative sST2 values experienced significantly greater odds of in-hospital death compared to patients in the first tercile of sST2 values. The addition of both postoperative and pre-to-postoperative sST2 biomarker significantly improved ability to predict in-hospital mortality status following CABG surgery, compared to using the EuroSCORE II mortality model alone, (c-statistic: 0.83 [95% CI: 0.75, 0.92], p value 0.0213) and (c-statistic: 0.83 [95% CI: 0.75, 0.92], p value 0.0215), respectively. Conclusion: sST2 values are associated with in-hospital mortality after CABG surgery and postoperative and pre-to-post operative sST2 values improve prediction. Our findings suggest that sST2 can be used as a biomarker to identify adult patients at greatest risk of in-hospital death after CABG surgery.
Dynamics of EGFR mutations in plasma recapitulates the clinical response to EGFR-TKIs in NSCLC patients

Objectives Genomic profiling using plasma cell-free DNA (cfDNA) represents a non-invasive alternative to tumor re-biopsy, which is challenging in clinical practice. The feasibility of dynamically monitoring epidermal growth factor receptor (EGFR) mutation status using serial plasma samples from non-small cell lung cancer (NSCLC) patients treated by tyrosine kinase inhibitors (TKIs) and its application in tracking clinical response and detection of resistance were investigated. Patients and methods Forty-five NSCLC patients with EGFR mutation-positive pre-TKI plasma and at least two post-TKI plasma collections were recruited to this study. EGFR mutations including L858R, exon 19 deletion (19-del) and T790M were analyzed using droplet digital PCR (ddPCR) in longitudinally collected plasma samples. Results We observed a significant reduction in plasma EGFR mutation abundance during the first two-month of TKI treatment. Acquiring of secondary T790M gatekeeper mutation or completed “loss” of EGFR mutations represented two major categories of resistance profiles. Moreover, we demonstrated that levels of plasma EGFR mutations highly correlated with changes of tumor diameter as determined by radiographic imaging, or development of new lesions. In a subset of patients, we further showed that reappearance of EGFR mutations could be detected in plasma up to 5 months ahead of progressive disease (PD), suggesting an early detection of drug resistance. Conclusions Our findings suggest that genomic analysis using plasma cfDNA may offer an effective approach to monitor clinical response and emergence of resistance.
Identification of CD14 as a potential biomarker of hepatocellular carcinoma using iTRAQ quantitative proteomics

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors without effective diagnostic biomarkers. This study intended to dynamically analyze serum proteomics in different pathological stages of liver diseases, and discover potential diagnostic biomarkers for early HCC. Patients with hepatitis B virus (HBV) infection, liver cirrhosis (LC), or HCC together with healthy controls (HC) were enrolled. Proteins differentially expressed between groups were screened using isobaric tagging for relative and absolute quantitation (iTRAQ), and promising HCC biomarker candidates were subjected to bioinformatics analysis, including K-means clustering, gene ontology (GO) and string network analysis. Potential biomarkers were validated by Western blotting and enzyme-linked immunosorbent assay (ELISA), and their diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis. Finally, 93 differentially expressed proteins were identified, of which 43 differed between HBV and HC, 70 between LC and HC, and 51 between HCC and HC. Expression levels of gelsolin (GELS) and sulfhydryl oxidase 1 (QSOX1) varied with disease state as follows: HC < HBV < LC < HCC. The reverse trend was observed with CD14. These iTRAQ results were confirmed by Western blotting and ELISA. Logistic regression and ROC curve analysis identified the optimal cut-off for alpha-fetoprotein (AFP), CD14 and AFP/CD14 was 191.4 ng/mL (AUC 0.646, 95%CI 0.467-0.825, sensitivity 31.6%, specificity 94.4%), 3.16 ng/mL (AUC 0.760, 95%CI 0.604-0.917, sensitivity 94.7%, specificity 50%) and 0.197 ng/mL (AUC 0.889, 95%CI 0.785-0.993, sensitivity 84.2%, specificity 83.3%) respectively. In conclusion, Assaying CD14 levels may complement AFP measurement for early detection of HCC.
A Care Coordination Model Can Facilitate Interagency Collaboration When Designing Recovery-Oriented Services.

The purpose of the current article is to highlight the potential of a care coordination model in promoting interagency collaboration when designing recovery-oriented services. The authors argue the case using exemplars from the literature and lessons learned from Australia's Partners in Recovery initiative. Interagency collaboration is paramount when designing a recovery-oriented service system. A care coordination model has the potential to overcome most challenges that preclude implementation of service system integration. Although the care coordination model is relatively new in recovery-oriented services and effectiveness studies of this model have yet to be undertaken, the model has the potential to be a viable alternative to service system integration. [Journal of Psychosocial Nursing and Mental Health Services, 57(5), 38-43. ].
Automatic Feature Weight Determination using Indexing and Pseudo-Relevance Feedback for Multi-feature Content-Based Image Retrieval

Content-based image retrieval (CBIR) is one of the most active research areas in multimedia information retrieval. Given a query image, the task is to search relevant images in a repository. Low level features like color, texture, and shape feature vectors of an image are always considered to be an important attribute in CBIR system. Thus the performance of the CBIR system can be enhanced by combining these feature vectors. In this paper, we propose a novel CBIR framework by applying to index using multiclass SVM and finding the appropriate weights of the individual features automatically using the relevance ratio and mean difference. We have taken four feature descriptors to represent color, texture and shape features. During retrieval, feature vectors of query image are combined, weighted and compared with feature vectors of images in the database to rank order the results. Experiments were performed on four benchmark datasets and performance is compared with existing techniques to validate the superiority of our proposed framework.
Reflex Regulation of Arterial Pressure during Sleep in Man: A Quantitative Method of Assessing Baroreflex Sensitivity

The control of arterial pressure during sleep was studied in 13 untreated, unsedated subjects aged 20 to 46, including 7 with hypertension. Arterial pressure was measured directly. A transient rise of arterial pressure up to 30 mm Hg was produced by the sudden intravenous injection of 0.25 to 2 μg of angiotensin. Linear plots were obtained in 10 of 13 subjects when the systolic pressures of successive pulses during the pressure rise were plotted against the pulse intervals which began the next beat. The relationship was disturbed by movement or arousal, and was better when pulse intervals falling in inspiration were discarded. The slope of the line (milliseconds of cardiac slowing per millimeter rise in systolic pressure) in the awake subject ranged from 2 to 15.5 msec/mm Hg, and from 4.5 to 28.9 during sleep. Reflex sensitivity was highest in dreaming sleep. In 7 of 10 subjects, baroreflex sensitivity increased significantly during sleep; in 6, the prevailing arterial pressure was inversely correlated with the baroreflex sensitivity. The pressure appeared to be the dependent variable. It is concluded that the baroreceptor reflex are can be rapidly reset, particularly during sleep. The lower arterial pressures during sleep may be actively maintained in some subjects by increased baroreflex sensitivity.
Structural Patching Fosters Divergence of Mitochondrial Ribosomes

Abstract Mitochondrial ribosomes (mitoribosomes) are essential components of all mitochondria that synthesize proteins encoded by the mitochondrial genome. Unlike other ribosomes, mitoribosomes are highly variable across species. The basis for this diversity is not known. Here, we examine the composition and evolutionary history of mitoribosomes across the phylogenetic tree by combining three-dimensional structural information with a comparative analysis of the secondary structures of mitochondrial rRNAs (mt-rRNAs) and available proteomic data. We generate a map of the acquisition of structural variation and reconstruct the fundamental stages that shaped the evolution of the mitoribosomal large subunit and led to this diversity. Our analysis suggests a critical role for ablation and expansion of rapidly evolving mt-rRNA. These changes cause structural instabilities that are “patched” by the acquisition of pre-existing compensatory elements, thus providing opportunities for rapid evolution. This mechanism underlies the incorporation of mt-tRNA into the central protuberance of the mammalian mitoribosome, and the altered path of the polypeptide exit tunnel of the yeast mitoribosome. We propose that since the toolkits of elements utilized for structural patching differ between mitochondria of different species, it fosters the growing divergence of mitoribosomes.