Miracle-dz/UniFAR-FTUs huggingface dataset:
Construction of a prognostic model for lung squamous cell carcinoma based on seven N6-methylandenosine-related autophagy genes.

OBJECTIVE We aimed to construct a novel prognostic model based on N6-methyladenosine (m6A)-related autophagy genes for predicting the prognosis of lung squamous cell carcinoma (LUSC). METHODS Gene expression profiles and clinical information of Patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) database. In addition, m6A- and autophagy-related gene profiles were obtained from TCGA and Human Autophagy Database, respectively. Pearson correlation analysis was performed to identify the m6A-related autophagy genes, and univariate Cox regression analysis was conducted to screen for genes associated with prognosis. Based on these genes, LASSO Cox regression analysis was used to construct a prognostic model. The corresponding prognostic score (PS) was calculated, and patients with LUSC were assigned to low- and high-risk groups according to the median PS value. An independent dataset (GSE37745) was used to validate the prognostic ability of the model. CIBERSORT was used to calculate the differences in immune cell infiltration between the high- and low-risk groups. RESULTS Seven m6A-related autophagy genes were screened to construct a prognostic model: CASP4, CDKN1A, DLC1, ITGB1, PINK1, TP63, and EIF4EBP1. In the training and validation sets, patients in the high-risk group had worse survival times than those in the low-risk group; the areas under the receiver operating characteristic curves were 0.958 and 0.759, respectively. There were differences in m6A levels and immune cell infiltration between the high- and low-risk groups. CONCLUSIONS Our prognostic model of the seven m6A-related autophagy genes had significant predictive value for LUSC; thus, these genes may serve as autophagy-related therapeutic targets in clinical practice.
Pharmacodynamic Studies of Fluorescent Diamond Carriers of Doxorubicin in Liver Cancer Cells and Colorectal Cancer Organoids

Background We recently reported on preferential deposition of bare fluorescent diamond particles FDP-NV-700/800nm (FDP-NV) in the liver following intravenous administration to rats. The pharmacokinetics of FDP-NV in that species indicated short residency in the circulation by rapid clearance by the liver. Retention of FDP-NV in the liver was not associated with any pathology. These observations suggested that cancer therapeutics, such as doxorubicin, linked to FDP-NV, could potentially serve for anti-cancer treatment while sparing toxicities of peripheral organs. Purpose To generate proof-of-concept (POC) and detail mechanisms of action of doxorubicin-coated FDP-NV-700/800nm (FDP-DOX) as a prospective chemotherapeutic for metastatic liver cancer. Methods FDP-DOX was generated by adsorption chemistry. Experimental design included concentration and time-dependent efficacy studies as compared with naïve (baren) FDP-NV in in vitro liver cancer cells models. Uptake of FDP-NV and FDP-DOX by HepG-2, Hep-3B and hCRC organoids were demonstrated by flow-cytometry and fluorescent microscopy. FDP-DOX pharmacodynamic effects included metabolic as well as cell death biomarkers Annexin V, TUNEL and LDH leakage. DOX desorpted from FDP-DOX was assessed by confocal microscopy and chemical assay of cells fractions. Results FDP-DOX efficacy was dose- and time-dependent and manifested in both liver cancer cell lines and human CRC organoids. FDP-DOX was rapidly internalized into cancer cells/organoids leading to cancer growth inhibition and apoptosis. FDP-DOX disrupted cell membrane integrity as evident by LDH release and suppressing mitochondrial metabolic pathways (AlamarBlue assay). Access of free DOX to the nuclei was confirmed by direct UV-Visible fluorescent assay and confocal microscopy of DOX fluorescence. Conclusion The rapid uptake and profound cancer inhibition observed using FDP-DOX in clinically relevant cancer models, highlight FDP-DOX promise for cancer chemotherapeutics. We also conclude that the in vitro data justify further investment in in vivo POC studies.
Microbial Phylogenetic Context Using Phylogenetic Outlines

Abstract Microbial studies typically involve the sequencing and assembly of draft genomes for individual microbes or whole microbiomes. Given a draft genome, one first task is to determine its phylogenetic context, that is, to place it relative to the set of related reference genomes. We provide a new interactive graphical tool that addresses this task using Mash sketches to compare against all bacterial and archaeal representative genomes in the Genome Taxonomy Database taxonomy, all within the framework of SplitsTree5. The phylogenetic context of the query sequences is then displayed as a phylogenetic outline, a new type of phylogenetic network that is more general than a phylogenetic tree, but significantly less complex than other types of phylogenetic networks. We propose to use such networks, rather than trees, to represent phylogenetic context, because they can express uncertainty in the placement of taxa, whereas a tree must always commit to a specific branching pattern. We illustrate the new method using a number of draft genomes of different assembly quality.
Leucine‐rich repeats and immunoglobulin‐like domains 1 deficiency affects hippocampal dendrite complexity and impairs cognitive function

Leucine‐rich repeat (LRR) transmembrane proteins have been directly linked to neurodevelopmental and cognitive disorders. We have previously shown that the LRR transmembrane protein, leucine‐rich repeats and immunoglobulin‐like domains 1 (Lrig1), is a physiological regulator of dendrite complexity of hippocampal pyramidal neurons and social behavior. In this study, we performed a battery of behavioral tests to evaluate spatial memory and cognitive capabilities in Lrig1 mutant mice. The cognitive assessment demonstrated deficits in recognition and spatial memory, evaluated by novel object recognition and object location tests. Moreover, we found that Lrig1‐deficient mice present specific impairments in the processing of similar but not dissimilar locations in a spatial pattern separation task, which was correlated with an enhanced dendritic growth and branching of Doublecortin‐positive immature granule cells of the dentate gyrus. Altogether, these findings indicate that Lrig1 plays an essential role in controlling morphological and functional plasticity in the hippocampus.
Model-based Head Orientation Estimation for Smart Devices

Voice interaction is friendly and convenient for users. Smart devices such as Amazon Echo allow users to interact with them by voice commands and become increasingly popular in our daily life. In recent years, research works focus on using the microphone array built in smart devices to localize the user's position, which adds additional context information to voice commands. In contrast, few works explore the user's head orientation, which also contains useful context information. For example, when a user says, "turn on the light", the head orientation could infer which light the user is referring to. Existing model-based works require a large number of microphone arrays to form an array network, while machine learning-based approaches need laborious data collection and training workload. The high deployment/usage cost of these methods is unfriendly to users. In this paper, we propose HOE, a model-based system that enables Head Orientation Estimation for smart devices with only two microphone arrays, which requires a lower training overhead than previous approaches. HOE first estimates the user's head orientation candidates by measuring the voice energy radiation pattern. Then, the voice frequency radiation pattern is leveraged to obtain the final result. Real-world experiments are conducted, and the results show that HOE can achieve a median estimation error of 23 degrees. To the best of our knowledge, HOE is the first model-based attempt to estimate the head orientation by only two microphone arrays without the arduous data training overhead.
Ceramides and phospholipids are downregulated with liraglutide treatment: results from the LiraFlame randomized controlled trial

Introduction Treatment with glucagon-like peptide-1 receptor agonists (GLP-1 RAs) can reduce risk of cardiovascular disease (CVD) in persons living with type 2 diabetes, however the mechanisms explaining this cardiovascular benefit are still debated. We investigated changes in the plasma lipidome following treatment with the GLP-1 RA liraglutide. Research design and methods In a double-blind placebo-controlled trial, we randomized 102 persons with type 2 diabetes to liraglutide or placebo for 26 weeks. Fasting blood plasma was collected at baseline and at end-of-treatment. The lipidome was measured using liquid-chromatography-coupled mass-spectrometry as a secondary end point in the study. Treatment response of each lipid was tested with lipid-specific linear mixed-effect models comparing liraglutide with placebo. Bonferroni p<7.1e-03 was employed. The independence of the findings from clinical covariates was evaluated with adjustment for body mass index, HbA1c, fasting status, lipid-lowering treatment and change in lipid-lowering treatment during the trial. Results In total, 260 lipids were identified covering 11 lipid families. We observed significant decreases following liraglutide treatment compared with placebo in 21 lipids (p<7.1e-03) from the following lipid families: ceramides, hexocyl-ceramides, phosphatidylcholines, phosphatidylethanolamines and triglycerides. We confirmed these findings in adjusted models (p≤0.01). In the liraglutide-treated group, the individual lipids were reduced in the range of 14%–61% from baseline level, compared with 19% decrease to 27% increase from baseline level in the placebo group. Conclusions Compared with placebo, liraglutide treatment led to a significant downregulation in ceramides, phospholipids and triglycerides, which all are linked to higher risk of CVD. These findings were independent of relevant clinical covariates. Our findings are hypothesis generating and shed light on the biological mechanisms underlying the cardiovascular benefits observed with GLP-1 RAs in outcome studies, and further strengthen the evidence base for recommending GLP-1 RAs to prevent CVD in type 2 diabetes. Trial registration number NCT03449654.
Dynamic borrowing from a single prior data source using the conditional power prior

ABSTRACT The conditional power prior is a popular method to borrow information from a single prior data source. The amount of borrowing is controlled by the power parameter which is fixed before running the new study. However, fixing this parameter before running a new study is often difficult and may be unwise because if the outcomes in the current study are much different from the prior data outcomes, the power parameter cannot be changed to reflect a more appropriate degree of borrowing. On the other hand, treating the power parameter as a random variable to be updated via Bayes theorem may relinquish control over how much to borrow in cases where regulatory oversight recommends a conservative approach.Previous authors have determined the power parameter at the end of the current study based on “stochastic” similarity in the outcomes between the current study and the prior data. In this paper, we introduce some modifications to those methods. First, we determine the power parameter based on similarity between a percentage of the current study outcome data available at an interim look and the prior outcome data. This may limit potential for operational bias resulting from the determination of the power parameter after the current study is complete. Next, we introduce a new measure of similarity between the current (interim) and prior data that limits similarity by a pre-specified clinical margin. The proposed clinical similarity region may be readily understood by clinicians who need to assess when such borrowing is clinically appropriate. Through simulations, we show that our approach has low bias and good power, while reducing type I error rate in areas outside of the “similarity region”. An example of a hypothetical medical device study illustrates its potential use in practice.
HST/WFC3 Grism Observations of z ∼ 1 Clusters: Evidence for Rapid Outside-in Environmental Quenching from Spatially Resolved Hα Maps

We present and publicly release (www.gclasshst.com) the first spatially resolved Hα maps of star-forming cluster galaxies at z ∼ 1, made possible with the Wide Field Camera 3 (WFC3) G141 grism on the Hubble Space Telescope (HST). Using a similar but updated method to 3D-HST in the field environment, we stack the Hα maps in bins of stellar mass, measure the half-light radius of the Hα distribution, and compare it to the stellar continuum. The ratio of the Hα to stellar continuum half-light radius, R[Hα/C]=Reff,HαReff,Cont , is smaller in the clusters by (6 ± 9)%, but statistically consistent within 1σ uncertainties. A negligible difference in R[Hα/C] with environment is surprising, given the higher quenched fractions in the clusters relative to the field. We postulate that the combination of high quenched fractions and no change in R[Hα/C] with environment can be reconciled if environmental quenching proceeds rapidly. We investigate this hypothesis by performing similar analysis on the spectroscopically confirmed, recently quenched cluster galaxies. 87% have Hα detections, with star formation rates 8 ± 1 times lower than star-forming cluster galaxies of similar stellar mass. Importantly, these galaxies have an R[Hα/C] that is (81 ± 8)% smaller than coeval star-forming field galaxies at fixed stellar mass. This suggests the environmental quenching process occurred outside-in. We conclude that disk truncation due to ram pressure stripping is occurring in cluster galaxies at z ∼ 1, but more rapidly and/or efficiently than in z ≲ 0.5 clusters, such that the effects on R[Hα/C] become observable just after the cluster galaxy has recently quenched.
MicroRNA-221-3p alleviates cell apoptosis and inflammatory response by targeting cyclin dependent kinase inhibitor 1B in chronic obstructive pulmonary disease

ABSTRACT As a chronic bronchitis or emphysema featured by airflow obstruction, chronic obstructive pulmonary disease (COPD) can further develop into respiratory failure and pulmonary heart diseases. MicroRNAs (miRNAs) are crucial mediators in COPD. Nevertheless, the specific role and molecular mechanism of microRNA-221-3p (miR-221-3p) in COPD are unclear. This research aimed to probe into the role of miR-221-3p in COPD. Bioinformatics analysis and a series of assays including western blot, luciferase reporter, reverse transcription quantitative polymerase chain reaction, flow cytometry, cell counting kit-8 and enzyme linked immunosorbent assay were used to explore the functions and mechanism of miR-221-3p in COPD. First, miR-221-3p level was validated to be lowly expressed in the lung tissues of COPD patients and 16HBE cells stimulated by cigarette smoke extract (CSE). Functionally, miR-221-3p overexpression inhibited inflammatory response and apoptosis in CSE-treated 16HBE cells. Moreover, we predicted 5 potential targets of miR-221-3p and found that miR-221-3p shared binding site with cyclin dependent kinase inhibitor 1B (CDKN1B). CDKN1B was targeted by miR-221-3p in CSE-treated 16HBE cells. CDKN1B was negatively modulated by miR-221-3p. Finally, rescue experiments demonstrated that overexpressed CDKN1B counteracted the influences of miR-221-3p on apoptosis and inflammatory response in CSE-treated 16HBE cells. Our data showed that miR-221-3p alleviated cell apoptosis and inflammatory response via targeting CDKN1B in an in vitro model of COPD.
Functional diversity of avian communities increases with canopy height: From individual behavior to continental‐scale patterns

Abstract Vegetation complexity is an important predictor of animal species diversity. Specifically, taller vegetation should provide more potential ecological niches and thus harbor communities with higher species richness and functional diversity (FD). Resource use behavior is an especially important functional trait because it links species to their resource base with direct relevance to niche partitioning. However, it is unclear how exactly the diversity of resource use behavior changes with vegetation complexity. To address this question, we studied avian FD in relation to vegetation complexity along a continental‐scale vegetation gradient. We quantified foraging behavior of passerine birds in terms of foraging method and substrate use at 21 sites (63 transects) spanning 3,000 km of woodlands and forests in Australia. We also quantified vegetation structure on 630 sampling points at the same sites. Additionally, we measured morphological traits for all 111 observed species in museum collections. We calculated individual‐based, abundance‐weighted FD in morphology and foraging behavior and related it to species richness and vegetation complexity (indexed by canopy height) using structural equation modeling, rarefaction analyses, and distance‐based metrics. FD of morphology and foraging methods was best predicted by species richness. However, FD of substrate use was best predicted by canopy height (ranging 10–30 m), but only when substrates were categorized with fine resolution (17 categories), not when categorized coarsely (8 categories). These results suggest that, first, FD might increase with vegetation complexity independently of species richness, but whether it does so depends on the studied functional trait. Second, patterns found might be shaped by how finely we categorize functional traits. More complex vegetation provided larger "ecological space" with more resources, allowing the coexistence of more species with disproportionately more diverse foraging substrate use. We suggest that the latter pattern was driven by nonrandom accumulation of functionally distinct species with increasing canopy height.
Fully resolved simulations of single char particle combustion using a ghost-cell immersed boundary method

A novel ghost-cell immersed boundary method for fully resolved simulation of char particle combustion has been developed. The boundary conditions at the solid particle surface, such as velocity, temperature, density, and chemical species concentration, are well enforced through the present method. Two semiglobal heterogeneous reactions and one homogeneous reaction are used to describe the chemical reactions in the domain, and the Stefan flow caused by the heterogeneous reactions is considered. A satisfactory agreement can be found between the present simulation results and experimental data in the literature. The method is then used to investigate the combustion property of a char particle and the interaction between CO2 gasification and O2 oxidation. Furthermore, combustion effect on the exchange of mass, momentum and energy between gas- and solid- phase is explored. © 2018 American Institute of Chemical Engineers AIChE J, 2018
The elusive effects of incidental anxiety on reinforcement-learning.

Anxiety is a common affective state, characterized by the subjectively unpleasant feelings of dread over an anticipated event. Anxiety is suspected to have important negative consequences on cognition, decision-making, and learning. Yet, despite a recent surge in studies investigating the specific effects of anxiety on reinforcement-learning, no coherent picture has emerged. Here, we investigated the effects of incidental anxiety on instrumental reinforcement-learning, while addressing several issues and defaults identified in a focused literature review. We used a rich experimental design, featuring both a learning and a transfer phase, and a manipulation of outcomes valence (gains vs losses). In two variants (N = 2 × 50) of this experimental paradigm, incidental anxiety was induced with an established threat-of-shock paradigm. Model-free results show that incidental anxiety effects seem limited to a small, but specific increase in postlearning performance measured by a transfer task. A comprehensive modeling effort revealed that, irrespective of the effects of anxiety, individuals give more weight to positive than negative outcomes, and tend to experience the omission of a loss as a gain (and vice versa). However, in line with results from our targeted literature survey, isolating specific computational effects of anxiety on learning per se proved to be challenging. Overall, our results suggest that learning mechanisms are more complex than traditionally presumed, and raise important concerns about the robustness of the effects of anxiety previously identified in simple reinforcement-learning studies. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Selecting a Subset Based on the Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events for Patient-Reported Symptom Monitoring in Lung Cancer Treatment: Mixed Methods Study

Background The Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) item library covers a wide range of symptoms relevant to oncology care. There is a need to select a subset of items relevant to specific patient populations to enable the implementation of PRO-CTCAE–based symptom monitoring in clinical practice. Objective The aim of this study is to develop a PRO-CTCAE–based subset relevant to patients with lung cancer that can be used for monitoring during multidisciplinary clinical practice. Methods The PRO-CTCAE–based subset for patients with lung cancer was generated using a mixed methods approach based on the European Organization for Research and Treatment of Cancer guidelines for developing questionnaires, comprising a literature review and semistructured interviews with both patients with lung cancer and health care practitioners (HCPs). Both patients and HCPs were queried on the relevance and impact of all PRO-CTCAE items. The results were summarized, and after a final round of expert review, a selection of clinically relevant items for patients with lung cancer was made. Results A heterogeneous group of patients with lung cancer (n=25) from different treatment modalities and HCPs (n=22) participated in the study. A final list of eight relevant PRO-CTCAE items was created: decreased appetite, cough, shortness of breath, fatigue, constipation, nausea, sadness, and pain (general). Conclusions On the basis of the literature and both professional and patient input, a subset of PRO-CTCAE items has been identified for use in patients with lung cancer in clinical practice. Future work is needed to confirm the validity and effectiveness of this PRO-CTCAE–based lung cancer subset internationally and in real-world clinical practice settings.
Demystifying the Vetting Process of Voice-controlled Skills on Markets

Smart speakers, such as Google Home and Amazon Echo, have become popular. They execute user voice commands via their built-in functionalities together with various third-party voice-controlled applications, called skills. Malicious skills have brought significant threats to users in terms of security and privacy. As a countermeasure, only skills passing the strict vetting process can be released onto markets. However, malicious skills have been reported to exist on markets, indicating that the vetting process can be bypassed. This paper aims to demystify the vetting process of skills on main markets to discover weaknesses and protect markets better. To probe the vetting process, we carefully design numerous skills, perform the Turing test, a test for machine intelligence, to determine whether humans or machines perform vetting, and leverage natural language processing techniques to analyze their behaviors. Based on our comprehensive experiments, we gain a good understanding of the vetting process (e.g., machine or human testers and skill exploration strategies) and discover some weaknesses. In this paper, we design three types of attacks to verify our results and prove an attacker can embed sensitive behaviors in skills and bypass the strict vetting process. Accordingly, we also propose countermeasures to these attacks and weaknesses.