Competitive Intensity Modulates the Pain Empathy Response: An Event-Related Potentials Study

Previous studies have widely reported that competition modulates an individual’s ability to empathize with pain experienced by others. What remains to be clarified, however, is how modulations in the intensity of competition might affect this type of empathy. To investigate this, we first used a Eriksen Flanker task to set different competitive intensity context (high competitive intensity, HCI; medium competitive intensity, MCI; low competitive intensity, LCI). Then we used a recognition task as a competitive task, in which we recorded event-related potentials (ERP) while participants viewed static images of body parts in painful and non-painful situations. Participants were informed that both sets of images depicted an opponent that they were required to play against in the recognition task that varied in levels of competitive intensity according to condition (HCI, MCI, and LCI). We observed an early N2 differentiation between pain and no-pain stimuli over the frontal area under MCI and LCI conditions, but this was not detected under HCI condition. Moreover, we observed a pattern of pain and no-pain differentiation for the late LPP over the frontal and centro-parietal regions under HCI, MCI, and LCI condition. As the pain empathy response is indexed by pain and no-pain differentiation, these results indicate a down-regulation of pain empathy response attributable to a high level of competition. With its very early onset, this effect appears to inhibit bottom-up processing of the ability to perceive pain experienced by an opponent. Our results provide neuroscientific evidence for a deficit in early automatic arousal in response to the pain of the opponent under the influence of high competitive intensity

Adjusting aggregation modes and photophysical and photovoltaic properties of diketopyrrolopyrrole-based small molecules by introducing B←N bonds

The packing mode of small-molecular semiconductors in thin films is an important factor that controls the performance of their optoelectronic devices. Designing and changing the packing mode by molecular engineering is challenging. Three structurally related diketopyrrolopyrrole (DPP)-based compounds were synthesized to study the effect of replacing C−C bonds by isoelectronic dipolar B←N bonds. By replacing one of the bridging C−C bonds on the peripheral fluorene units of the DPP molecules by a coordinative B←N bond and changing the B←N bond orientation, the optical absorption, fluorescence, and excited-state lifetime of the compounds can be tuned. The substitution alters the preferential aggregation of the molecules in the solid state from H-type (for C−C) to J-type (for B←N). Introducing B←N bonds thus provides a subtle way of controlling the packing mode. The photovoltaic properties of the compounds were evaluated in bulk heterojunctions with a fullerene acceptor and showed moderate performance as a consequence of suboptimal morphologies, bimolecular recombination, and triplet-state formation

Improving Performance of All-Polymer Solar Cells Through Backbone Engineering of Both Donors and Acceptors

All-polymer solar cells (APSCs), composed of semiconducting donor and acceptor polymers, have attracted considerable attention due to their unique advantages compared to polymer-fullerene-based devices in terms of enhanced light absorption and morphological stability. To improve the performance of APSCs, the morphology of the active layer must be optimized. By employing a random copolymerization strategy to control the regularity of the backbone of the donor polymers (PTAZ-TPDx) and acceptor polymers (PNDI-Tx) the morphology can be systematically optimized by tuning the polymer packing and crystallinity. To minimize effects of molecular weight, both donor and acceptor polymers have number-average molecular weights in narrow ranges. Experimental and coarse-grained modeling results disclose that systematic backbone engineering greatly affects the polymer crystallinity and ultimately the phase separation and morphology of the all-polymer blends. Decreasing the backbone regularity of either the donor or the acceptor polymer reduces the local crystallinity of the individual phase in blend films, affording reduced short-circuit current densities and fill factors. This two-dimensional crystallinity optimization strategy locates a PCE maximum at highest crystallinity for both donor and acceptor polymers. Overall, this study demonstrates that proper control of both donor and acceptor polymer crystallinity simultaneously is essential to optimize APSC performance

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Cure or Poison? Impact of Identity Verification on the Creation of Fake Posts on Social Media

Identity verification is one of the tools social media platforms use to fight against the creation of fake posts, by making users more responsible for their behaviors on the Internet. However, does identity verification work as expected? With a unique dataset, we find that identity verification would significantly reduce users’ probability of creating fake posts, but only when users cannot obtain privileged credibility after verification. If identity verification is accompanied by privileged credibility, the association between identity verification and the creation of fake posts is significantly positive. Our explanation is that the benefit of privileged credibility may induce malicious users---who can easily cheat the verification system using fake identities---to manipulate their verified status before they create fake posts. An exogenous shock on users’ verification decision caused by a policy change provides us a unique context to find causal evidence

Seeing Is Believing? How Including a Video in Fake News Influences Users’ Reporting of Fake News to Social Media Platforms

Social network data collected from digital sources is increasingly being used to gain insights into human behavior. However, while these observable networks constitute an empirical ground truth, the individuals within the network can perceive the network’s structure differently—and they often act on these perceptions. As such, we argue that there is a distinct gap between the data used to model behaviors in a network, and the data internalized by people when they actually engage in behaviors. We find that statistical analyses of observable network structure do not consistently take these discrepancies into account, and this omission may lead to inaccurate inferences about hypothesized network mechanisms. To remedy this issue, we apply techniques of robust optimization to statistical models for social network analysis. Using robust maximum likelihood, we derive an estimation technique that immunizes inference to errors such as false positives and false negatives, without knowing a priori the source or realized magnitude of the error. We demonstrate the efficacy of our methodology on real social network datasets and simulated data. Our contributions extend beyond the social network context, as perception gaps may exist in many other economic contexts

Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson’s disease and multiple system atrophy

Abstract Objectives This study aims to investigate the potential of radiomics with multiple parameters from conventional T1 weighted imaging (T1WI) and susceptibility weighted imaging (SWI) in distinguishing between idiopathic Parkinson’s disease (PD) and multiple system atrophy (MSA). Methods A total of 201 participants, including 57 patients with PD, 74 with MSA, and 70 healthy control (HCs) individuals, underwent T1WI and SWI scans. From the 12 subcortical nuclei (e.g. red nucleus, substantia nigra, subthalamic nucleus, putamen, globus pallidus, and caudate nucleus), 2640 radiomic features were extracted from both T1WI and SWI scans. Three classification models - logistic regression (LR), support vector machine (SVM), and light gradient boosting machine (LGBM) - were used to distinguish between MSA and PD, as well as among MSA, PD, and HC. These classifications were based on features extracted from T1WI, SWI, and a combination of T1WI and SWI. Five-fold cross-validation was used to evaluate the performance of the models with metrics such as sensitivity, specificity, accuracy, and area under the receiver operating curve (AUC). During each fold, the ANOVA and least absolute shrinkage and selection operator (LASSO) methods were used to identify the most relevant subset of features for the model training process. Results The LGBM model trained by the features combination of T1WI and SWI exhibited the most outstanding differential performance in both the three-class classification task of MSA vs. PD vs. HC and the binary classification task of MSA vs. PD, with an accuracy of 0.814 and 0.854, and an AUC of 0.904 and 0.881, respectively. The texture-based differences (GLCM) of the SN and the shape-based differences of the GP were highly effective in discriminating between the three classes and two classes, respectively. Conclusions Radiomic features combining T1WI and SWI can achieve a satisfactory differential diagnosis for PD, MSA, and HC groups, as well as for PD and MSA groups, thus providing a useful tool for clinical decision-making based on routine MRI sequences

Systematic Assessment of Transcriptomic Biomarkers for Immune Checkpoint Blockade Response in Cancer Immunotherapy

Background: Immune checkpoint blockade (ICB) therapy has yielded successful clinical responses in treatment of a minority of patients in certain cancer types. Substantial efforts were made to establish biomarkers for predicting responsiveness to ICB. However, the systematic assessment of these ICB response biomarkers remains insufficient. Methods: We collected 22 transcriptome-based biomarkers for ICB response and constructed multiple benchmark datasets to evaluate the associations with clinical response, predictive performance, and clinical efficacy of them in pre-treatment patients with distinct ICB agents in diverse cancers. Results: Overall, “Immune-checkpoint molecule” biomarkers PD-L1, PD-L2, CTLA-4 and IMPRES and the “Effector molecule” biomarker CYT showed significant associations with ICB response and clinical outcomes. These immune-checkpoint biomarkers and another immune effector IFN-gamma presented predictive ability in melanoma, urothelial cancer (UC) and clear cell renal-cell cancer (ccRCC). In non-small cell lung cancer (NSCLC), only PD-L2 and CTLA-4 showed preferable correlation with clinical response. Under different ICB therapies, the top-performing biomarkers were usually mutually exclusive in patients with anti-PD-1 and anti-CTLA-4 therapy, and most of biomarkers presented outstanding predictive power in patients with combined anti-PD-1 and anti-CTLA-4 therapy. Conclusions: Our results show these biomarkers had different performance in predicting ICB response across distinct ICB agents in diverse cancers

Alkyl Chain Length Effects of Polymer Donors on the Morphology and Device Performance of Polymer Solar Cells with Different Acceptors

The development of nonfullerene acceptors has brought polymer solar cells into a new era. Maximizing the performance of nonfullerene solar cells needs appropriate polymer donors that match with the acceptors in both electrical and morphological properties. So far, the design rationales for polymer donors are mainly borrowed from fullerene‐based solar cells, which are not necessarily applicable to nonfullerene solar cells. In this work, the influence of side chain length of polymer donors based on a set of random terpolymers PTAZ‐TPD10‐Cn on the device performance of polymer solar cells is investigated with three different acceptor materials, i.e., a fullerene acceptor [70]PCBM, a polymer acceptor N2200, and a fused‐ring molecular acceptor ITIC. Shortening the side chains of polymer donors improves the device performance of [70]PCBM‐based devices, but deteriorates the N2200‐ and ITIC‐based devices. Morphology studies unveil that the miscibility between donor and acceptor in blend films depends on the side chain length of polymer donors. Upon shortening the side chains of the polymer donors, the miscibility between the donor and acceptor increases for the [70]PCBM‐based blends, but decreases for the N2200‐ and ITIC‐based blends. These findings provide new guidelines for the development of polymer donors to match with emerging nonfullerene acceptors