Miracle-dz/UniFAR-FTUs huggingface dataset:
Nitrogen-Containing Compounds Enhance Light Absorption of Aromatic-Derived Brown Carbon.

The formation of secondary brown carbon (BrC) is chemically complex, leading to an unclear relationship between its molecular composition and optical properties. Here, we present an in-depth investigation of molecular-specific optical properties and aging of secondary BrC produced from the photooxidation of ethylbenzene at varied NOx levels for the first time. Due to the pronounced formation of unsaturated products, the mass absorption coefficient (MAC) of ethylbenzene secondary organic aerosols (ESOA) at 365 nm was higher than that of biogenic SOA by a factor of 10. A high NOx level ([ethylbenzene]0/[NOx]0 < 10 ppbC ppb-1) was found to significantly increase the average MAC300-700nm of ESOA by 0.29 m2 g-1. The data from two complementary high-resolution mass spectrometers and quantum chemical calculations suggested that nitrogen-containing compounds were largely responsible for the enhanced light absorption of high-NOx ESOA, and multifunctional nitroaromatic compounds (such as C8H9NO3 and C8H9NO4) were identified as important BrC chromophores. High-NOx ESOA underwent photobleaching upon direct exposure to ultraviolet light. Photolysis did not lead to the significant decomposition of C8H9NO3 and C8H9NO4, indicating that nitroaromatic compounds may serve as relatively stable nitrogen reservoirs and would effectively absorb solar radiation during the daytime.
Brain-Derived neurotrophic factor (BDNF) plasma level increases in patients with resistant schizophrenia treated with electroconvulsive therapy (ECT)

Abstract Objectives The study aimed to assess the effect of Electroconvulsive Therapy (ECT) on plasma BDNF levels in patients with resistant schizophrenia. Methods It was a cohort study that included 60 patients with resistant schizophrenia fulfilling the DSM-5 criteria of schizophrenia and APA criteria of resistant schizophrenia. They were divided into two groups, followed over 4 weeks, and compared to their baseline assessment. Group (A) included 45 patients who received 4–10 sessions of ECT while Group (B) included 15 patients who received the usual treatment with antipsychotics without ECT. The assessment included the severity of psychotic symptoms assessed by the Positive and Negative Symptom Scale (PANSS) in addition to plasma BDNF level. Results Patients in Group (A) had an increased level of BDNF after treatment with a statistically significant difference in comparison to their baseline BDNF level (P = 0.027). Meanwhile, patients in group (B) showed a non-significant increase in BDNF. Patients in both groups improved significantly in all PANSS subscales after treatment. Conclusions It was concluded that plasma BDNF levels in patients with resistant schizophrenia increase after electroconvulsive therapy in association with clinical improvement. Key points BDNF increases after ECT treatment of resistant schizophrenia. BDNF is not correlated with the severity of psychotic symptoms Patients treated with ECT showed a better response.
Drug resistance profiles and influencing factors among HIV-infected children and adolescents receiving long-term ART: a multicentre observational study in China.

OBJECTIVES To analyse the characteristics and determinants of drug resistance mutations (DRMs) in HIV-infected children and adolescents on long-term ART in China. METHODS An observational cohort study was conducted in five centres. All participants younger than 15 years at ART initiation were screened, and those identified as having virological failure (VF) with viral load (VL) ≥ 400 copies/mL were included for genotypic resistance testing. Logistic regression analysis was performed and the accumulation of major mutations was analysed in a subgroup of resistant individuals with complete VL results since HIV diagnosis. RESULTS Among 562 eligible participants, protease and RT regions were successfully amplified for 93 who failed treatment with a median of 10.0 years ART. Sixty-eight (73.1%) harboured ≥1 major mutations. NRTI, NNRTI and dual-class resistance accounted for 48.4%, 63.4% and 38.7%, respectively. Only 3.2% were resistant to PIs. Age at ART initiation [adjusted OR (aOR) = 0.813, 95% CI 0.690-0.957], subtype B (aOR = 4.378, 95% CI 1.414-13.560) and an initial NNRTI-based regimen (aOR = 3.331, 95% CI 1.180-9.402) were independently associated with DRMs. Among 40 resistant participants with additional VL data, 55.0% had continued VF on a suboptimal regimen and the estimated duration of VF was positively correlated with the total number of major mutations (r = 0.504, P = 0.001). CONCLUSIONS The development of DRMs was common in children and adolescents receiving long-term treatment, and continued VF was prevalent in those with resistance. Timely genotypic testing and new child-friendly formulations are therefore urgently required.
Feature-specific retrieval of the knowledge of having lied before: Persons and questions independently retrieve truth-related information

Previous research on event coding has shown that, by default, bindings are binary and elemental, that is, individual objects or single features of these objects can retrieve responses separately and independently. In our study, we applied these findings to the automatic retrieval of former deceptions. Specifically, we investigated whether the person or the question to which one has answered deceptively can retrieve this knowledge independently, or whether there is also evidence for configural retrieval processes that use a combination of person and question information to retrieve the truth status of former episodes. We found evidence for retrieval based on single cues (i.e., person or question), supporting that the elementary retrieval of episodes by independent cues also holds in the context of retrieving knowledge about former lies.
A Novel Artificial Intelligence–Powered Method for Prediction of Early Recurrence of Prostate Cancer After Prostatectomy and Cancer Drivers

PURPOSE To develop a novel artificial intelligence (AI)–powered method for the prediction of prostate cancer (PCa) early recurrence and identification of driver regions in PCa of all Gleason Grade Group (GGG). MATERIALS AND METHODS Deep convolutional neural networks were used to develop the AI model. The AI model was trained on The Cancer Genome Atlas Prostatic Adenocarcinoma (TCGA-PRAD) whole slide images (WSI) and data set (n = 243) to predict 3-year biochemical recurrence after radical prostatectomy (RP) and was subsequently validated on WSI from patients with PCa (n = 173) from the University of Wisconsin-Madison. RESULTS Our AI-powered platform can extract visual and subvisual morphologic features from WSI to identify driver regions predictive of early recurrence of PCa (regions of interest [ROIs]) after RP. The ROIs were ranked with AI-morphometric scores, which were prognostic for 3-year biochemical recurrence (area under the curve [AUC], 0.78), which is significantly better than the GGG overall (AUC, 0.62). The AI-morphometric scores also showed high accuracy in the prediction of recurrence for low- or intermediate-risk PCa—AUC, 0.76, 0.84, and 0.81 for GGG1, GGG2, and GGG3, respectively. These patients could benefit the most from timely adjuvant therapy after RP. The predictive value of the high-scored ROIs was validated by known PCa biomarkers studied. With this focused biomarker analysis, a potentially new STING pathway–related PCa biomarker—TMEM173—was identified. CONCLUSION Our study introduces a novel approach for identifying patients with PCa at risk for early recurrence regardless of their GGG status and for identifying cancer drivers for focused evolution-aware novel biomarker discovery. AI-powered platform can extract morphologic features from digital slide images to predict prostate cancer outcomes.
Barriers to healthcare and self-reported adverse outcomes for autistic adults: a cross-sectional study

Objectives Autistic people experience poor physical and mental health along with reduced life expectancy compared with non-autistic people. Our aim was to identify self-reported barriers to primary care access by autistic adults compared with non-autistic adults and to link these barriers to self-reported adverse health consequences. Design Following consultation with the autistic community at an autistic conference, Autscape, we developed a self-report survey, which we administered online through social media platforms. Setting A 52-item, international, online survey. Participants 507 autistic adults and 157 non-autistic adults. Primary and secondary outcome measures Self-reported barriers to accessing healthcare and associated adverse health outcomes. Results Eighty per cent of autistic adults and 37% of non-autistic respondents reported difficulty visiting a general practitioner (GP). The highest-rated barriers by autistic adults were deciding if symptoms warrant a GP visit (72%), difficulty making appointments by telephone (62%), not feeling understood (56%), difficulty communicating with their doctor (53%) and the waiting room environment (51%). Autistic adults reported a preference for online or text-based appointment booking, facility to email in advance the reason for consultation, the first or last clinic appointment and a quiet place to wait. Self-reported adverse health outcomes experienced by autistic adults were associated with barriers to accessing healthcare. Adverse outcomes included untreated physical and mental health conditions, not attending specialist referral or screening programmes, requiring more extensive treatment or surgery due to late presentations and untreated potentially life-threatening conditions. There were no significant differences in difficulty attending, barriers experienced or adverse outcomes between formally diagnosed and self-identified autistic respondents. Conclusions Reduction of healthcare inequalities for autistic people requires that healthcare providers understand autistic perspectives, communication needs and sensory sensitivities. Adjustments for autism-specific needs are as necessary as ramps for wheelchair users.
Efficacy and Safety of Sintilimab Plus Anlotinib for PD-L1–Positive Recurrent or Metastatic Cervical Cancer: A Multicenter, Single-Arm, Prospective Phase II Trial

PURPOSE No combined immunotherapy and antiangiogenic therapy have been investigated in exclusively programmed death-ligand 1 (PD-L1)–positive advanced cervical cancer (CA). We investigated the efficacy and safety of sintilimab plus anlotinib as second-line or later therapy for PD-L1–positive recurrent or metastatic (R/M) CA. PATIENTS AND METHODS Patients with PD-L1–positive (Combined Positive Score ≥ 1) R/M CA who progressed after at least one prior systemic chemotherapeutic regimen or could not tolerate chemotherapy were eligible for the phase II trial. The patients received 200 mg sintilimab once on day 1 and 10 mg anlotinib once daily on days 1-14 every 3 weeks. The primary end point was investigator-confirmed objective response rate (ORR) per RECIST v1.1. Secondary end points included progression-free survival (PFS), overall survival, and disease control rate. Biomarkers were explored. RESULTS Forty-two patients were enrolled. The ORR was 54.8% (95% CI, 38.7 to 70.2). In 39 efficacy-evaluable patients, the ORR was 59.0% (95% CI, 42.1 to 74.4); the disease control rate was 94.9% (95% CI, 82.7 to 99.4). The median PFS was 9.4 months (95% CI, 8.0 to 14.6). The median overall survival was not reached. Furthermore, 85.8% of the patients experienced treatment-related adverse events. The most frequent treatment-related adverse events were hypothyroidism (33.3%), elevated aspartate aminotransferase levels (21.4%), and hypertension (19.0%). Patients with altered PIK3CA, PI3K-AKT signaling, or KMT2D had a higher ORR, whereas those with altered STK11 and/or JAK2 had a significantly shorter PFS. CONCLUSION Sintilimab plus anlotinib as second-line or later therapy is efficacious and safe for patients with advanced CA who have failed prior chemotherapy.
Branching Brownian motion in a periodic environment and existence of pulsating traveling waves

We study the limits of the additive and derivative martingales of one-dimensional branching Brownian motion in a periodic environment. Then we prove the existence of pulsating travelling wave solutions of the corresponding F-KPP equation in the supercritical and critical cases by representing the solutions probabilistically in terms of the limits of the additive and derivative martingales. We also prove that there is no pulsating travelling wave solution in the subcritical case. Our main tools are the spine decomposition and martingale change of measures.
MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset

Misinformation is becoming increasingly prevalent on social media and in news articles. It has become so widespread that we require algorithmic assistance utilising machine learning to detect such content. Training these machine learning models require datasets of sufficient scale, diversity and quality. However, datasets in the field of automatic misinformation detection are predominantly monolingual, include a limited amount of modalities and are not of sufficient scale and quality. Addressing this, we develop a data collection and linking system (MuMiN-trawl), to build a public misinformation graph dataset (MuMiN), containing rich social media data (tweets, replies, users, images, articles, hashtags) spanning 21 million tweets belonging to 26 thousand Twitter threads, each of which have been semantically linked to 13 thousand fact-checked claims across dozens of topics, events and domains, in 41 different languages, spanning more than a decade. The dataset is made available as a heterogeneous graph via a Python package (mumin). We provide baseline results for two node classification tasks related to the veracity of a claim involving social media, and demonstrate that these are challenging tasks, with the highest macro-average F1-score being 62.55% and 61.45% for the two tasks, respectively. The MuMiN ecosystem is available at https://mumin-dataset.github. io/, including the data, documentation, tutorials and leaderboards.
Residual Bootstrap Exploration for Stochastic Linear Bandit

We propose a new bootstrap-based online algorithm for stochastic linear bandit problems. The key idea is to adopt residual bootstrap exploration, in which the agent estimates the next step reward by re-sampling the residuals of mean reward estimate. Our algorithm, residual bootstrap exploration for stochastic linear bandit (\texttt{LinReBoot}), estimates the linear reward from its re-sampling distribution and pulls the arm with the highest reward estimate. In particular, we contribute a theoretical framework to demystify residual bootstrap-based exploration mechanisms in stochastic linear bandit problems. The key insight is that the strength of bootstrap exploration is based on collaborated optimism between the online-learned model and the re-sampling distribution of residuals. Such observation enables us to show that the proposed \texttt{LinReBoot} secure a high-probability $\tilde{O}(d \sqrt{n})$ sub-linear regret under mild conditions. Our experiments support the easy generalizability of the \texttt{ReBoot} principle in the various formulations of linear bandit problems and show the significant computational efficiency of \texttt{LinReBoot}.
Exposure theory for learning complex networks with random walks

Random walks are a common model for the exploration and discovery of complex networks. While numerous algorithms have been proposed to map out an unknown network, a complementary question arises: in a known network, which nodes and edges are most likely to be discovered by a random walker in finite time? Here, we introduce exposure theory, a statistical mechanics framework that predicts the learning of nodes and edges across several types of networks, including weighted and temporal, and show that edge learning follows a universal trajectory. While the learning of individual nodes and edges is noisy, exposure theory produces a highly accurate prediction of aggregate exploration statistics.
MeqSilhouette v2: Spectrally-resolved polarimetric synthetic data generation for the Event Horizon Telescope

We present MeqSilhouette v2.0 (MeqSv2), a fully polarimetric, time-and frequency-resolved synthetic data generation software for simulating millimetre (mm) wavelength very long baseline interferometry (VLBI) observations with heterogeneous arrays. Synthetic data are a critical component in understanding real observations, testing calibration and imaging algorithms, and predicting performance metrics of existing or proposed sites. MeqSv2 applies physics-based instrumental and atmospheric signal corruptions constrained by empirically-derived site and station parameters to the data. The new version is capable of applying instrumental polarization effects and various other spectrally-resolved effects using the Radio Interferometry Measurement Equation (RIME) formalism and produces synthetic data compatible with calibration pipelines designed to process real data. We demonstrate the various corruption capabilities of MeqSv2 using different arrays, with a focus on the effect of complex bandpass gains on closure quantities for the EHT at 230 GHz. We validate the frequency-dependent polarization leakage implementation by performing polarization self-calibration of synthetic EHT data using PolSolve. We also note the potential applications for cm-wavelength VLBI array analysis and design and future directions.
On the existence of infinitely many realization functions of non-global local minima in the training of artificial neural networks with ReLU activation

Gradient descent (GD) type optimization schemes are the standard instruments to train fully connected feedforward artificial neural networks (ANNs) with rectified linear unit (ReLU) activation and can be considered as temporal discretizations of solutions of gradient flow (GF) differential equations. It has recently been proved that the risk of every bounded GF trajectory converges in the training of ANNs with one hidden layer and ReLU activation to the risk of a critical point. Taking this into account it is one of the key research issues in the mathematical convergence analysis of GF trajectories and GD type optimization schemes, respectively, to study sufficient and necessary conditions for critical points of the risk function and, thereby, to obtain an understanding about the appearance of critical points in dependence of the problem parameters such as the target function. In the first main result of this work we prove in the training of ANNs with one hidden layer and ReLU activation that for every $ a, b \in \mathbb{R} $ with $ a0 $ we have that there exists a Lipschitz continuous target function $ f \colon [a,b] \to \mathbb{R} $ such that for every number $ H>1 $ of neurons on the hidden layer we have that the risk function has uncountably many different realization functions of non-global local minimum points whose risks are strictly larger than the sum of the risk of the global minimum points and the arbitrarily large $ \delta $. In the second main result of this work we show in the training of ANNs with one hidden layer and ReLU activation in the special situation where there is only one neuron on the hidden layer and where the target function is continuous and piecewise polynomial that there exist at most finitely many different realization functions of critical points.
Reliable Inlier Evaluation for Unsupervised Point Cloud Registration

Unsupervised point cloud registration algorithm usually suffers from the unsatisfied registration precision in the partially overlapping problem due to the lack of effective inlier evaluation. In this paper, we propose a neighborhood consensus based reliable inlier evaluation method for robust unsupervised point cloud registration. It is expected to capture the discriminative geometric difference between the source neighborhood and the corresponding pseudo target neighborhood for effective inlier distinction. Specifically, our model consists of a matching map refinement module and an inlier evaluation module. In our matching map refinement module, we improve the point-wise matching map estimation by integrating the matching scores of neighbors into it. The aggregated neighborhood information potentially facilitates the discriminative map construction so that high-quality correspondences can be provided for generating the pseudo target point cloud. Based on the observation that the outlier has the significant structure-wise difference between its source neighborhood and corresponding pseudo target neighborhood while this difference for inlier is small, the inlier evaluation module exploits this difference to score the inlier confidence for each estimated correspondence. In particular, we construct an effective graph representation for capturing this geometric difference between the neighborhoods. Finally, with the learned correspondences and the corresponding inlier confidence, we use the weighted SVD algorithm for transformation estimation.Under the unsupervised setting, we exploit the Huber function based global alignment loss, the local neighborhood consensus loss and spatial consistency loss for model optimization. The experimental results on extensive datasets demonstrate that our unsupervised point cloud registration method can yield comparable performance.
Criterion of Bari basis property for 2 × 2 Dirac‐type operators with strictly regular boundary conditions

The paper is concerned with the Bari basis property of a boundary value problem associated in L2([0,1];C2)$L^2([0,1]; \mathbb {C}^2)$ with the following 2 × 2 Dirac‐type equation for y=col(y1,y2)$y = \operatorname{col}(y_1, y_2)$ : LU(Q)y=−iB−1y′+Q(x)y=λy,B=b100b2,b1<0
Distributed Online Optimization in Time-Varying Unbalanced Networks Without Explicit Subgradients

This paper studies a distributed online constrained optimization problem over time-varying unbalanced digraphs without explicit subgradients. In sharp contrast to the existing algorithms, we design a novel consensus-based distributed online algorithm with a local randomized zeroth-order oracle and then rescale the oracle by constructing row-stochastic matrices, which aims to address the unbalancedness of time-varying digraphs. Under mild conditions, the average dynamic regret over a time horizon is shown to asymptotically converge at a sublinear rate provided that the accumulated variation grows sublinearly with a specific order. Moreover, the counterpart of the proposed algorithm when subgradients are available is also provided, along with its dynamic regret bound, which reflects that the convergence of our algorithm is essentially not affected by the zeroth-order oracle. Simulations on distributed targets tracking problem and dynamic sparse signal recovery problem in sensor networks are employed to demonstrate the effectiveness of the proposed algorithm.