Miracle-dz/UniFAR-FTUs huggingface dataset:
Reciprocal self-disclosure: Although respondents are reluctant to steal the spotlight, self-disclosers feel validated, understood, and cared for when respondents share comparable experiences

People often self-disclose news about themselves. Respondents may have similar news and feel conflicted about whether to share that news in the moment – that is, to reciprocally self-disclose. On the one hand, reciprocally self-disclosing could enable them to convey that they understand the recipient’s experience. On the other hand, reciprocally self-disclosing could dishonor the recipient’s contribution in a manner akin to stealing the spotlight. What contextual variables do respondents consider when navigating this conflict? How do recipients perceive reciprocal self-disclosures (RSDs) relative to non-disclosures – and what contextual variables moderate recipients’ perceptions? In this paper, we report four studies (N = 1200 Reddit conversations, 733 MTurk and 701 Prolific users) examining how people use and perceive RSD across a variety of contexts. We focus principally on context valence (positive vs. negative) and news similarity (similar vs. more extreme). Across experimental and naturalistic studies, respondents were highly context-sensitive and particularly reluctant to reciprocally self-disclose in positive contexts. Yet, recipients were less context-sensitive. Recipients evaluated similar RSDs as comparably responsive as non-disclosures in positive contexts and more responsive than non-disclosures in negative contexts. Furthermore, recipients perceived more extreme RSDs as more responsive than non-disclosures in negative contexts and less responsive than non-disclosures in positive contexts. Exploratory analyses suggested privacy, gender of respondent, and closeness predicted whether respondents used RSD but not whether recipients perceived RSD as responsive. In short, people often refrain from RSD to avoid committing communication errors – but our research suggests RSD can be highly responsive and incurs few costs relative to non-disclosure.
Study of conservation laws with the Local Ensemble Transform Kalman Filter

Numerical discretization schemes have a long history of incorporating the most important conservation properties of the continuous system in order to improve the prediction of the nonlinear flow. The question arises whether data assimilation algorithms should follow a similar approach. To address this issue, we explore the conservation properties during data assimilation using perfect model experiments with a 2D shallow‐water model preserving important properties of the true nonlinear flow. The data assimilation scheme used here is the Local Ensemble Transform Kalman Filter with varying observed variables, inflation, localization radius and thinning interval. It is found that, during the assimilation, the total energy of the analysis ensemble mean converges with time towards the nature run value. However, enstrophy, divergence and the energy spectra are strongly affected by the data assimilation settings. Having in mind that the conservation of both the kinetic energy and enstrophy by the momentum advection schemes in the case of non‐divergent flow prevents a systematic and unrealistic energy cascade towards the high wave numbers, we test the effects on the prediction depending on the type of error in the initial condition. During the assimilation, we assess the downward nonlinear energy cascade through a scalar, domain‐averaged noise measure. We show that the accumulated noise during assimilation and the error of analysis are good indicators of the quality of the prediction.
A Multi-semantic Audio Classication Method Based on Tensor Space ⋆

Researches on the audio classification have involved in various types of semantic information represented as vectors. However, multi-semantics of audio information not only have their own properties, but also have correlations among them, whereas, to a certain extent, the simple vector representation can not represent multi-semantics and ignores their relations. In order to realize multi-semantics representation, fully consider the relations and find the content interested by users in different music resources. Tensor Uniform Content Locator (TUCL) is brought forward to express the semantic information of audio, and construct a 3-order Tensor Semantic Space (TES). Tensor Semantic Dispersion (TSD) can aggregate some audio resources with the same semantics, and the different audio resources are determined which type they belong to by calculating their TSD. So as to evaluate the performance of TSD classification, the precision are used to test the results under the premise of a certain recall. For the problem of multi-semantic audio classification, experiment results show that our method can significantly improve the classification precision by comparing with the typical method of GMM.
Novel Biotinylated Cu(II)-Phenanthroline Complexes: 2D and 3D Cytotoxic Activity and Mechanistic Insight

The interest in the use of copper as a metal scaffold for the development of novel chemotherapeutics has considerably grown in recent years. This is mainly due to the relatively lower toxicity of copper complexes with respect to platinum drugs (i.e., cisplatin), the different mechanisms of action, and the cheaper cost. In the last decades, hundreds of copper-based complexes were developed and screened as anticancer agents, with the antesignanus of all compounds being copper bis-phenanthroline [Cu(phen)2]2+ developed by D.S. Sigman in the late 1990s. In particular, copper(phen) derivatives have been shown high interest in their capacity to interact with DNA by nucleobase intercalation. Here, we report the synthesis and chemical characterization of four novel copper(II) complexes functionalised with phenanthroline derivatives containing biotin. Biotin, also known as Vitamin B7, is involved in a series of metabolic processes, and its receptors are often overexpressed in many tumour cells. A detailed biological analysis including cytotoxicity in 2D and 3D, cellular drug uptake, DNA interaction, and morphological studies are discussed.
Influenza A Virus Weakens the Immune Response of Mice to Toxoplasma gondii, Thereby Aggravating T. gondii Infection

Simple Summary To investigate whether the influenza virus infection affects the reproduction and transmission of Toxoplasma gondii, we attempted to identify the specific influencing factors. In this study, we found that decreased expression levels of IL-1β, IL-6, and IL-12 in the co-infected group were associated with the early immune responses of the host against T. gondii type II strain (Pru), which affected the division of T. gondii (Pru). Moreover, the significant decrease in the CD4+/CD8+ ratio indicated a weakened long-term immune killing ability of the host against T. gondii (Pru) after IAV infection. In conclusion, our study showed that a mixed infection of IAV (influenza A virus) virus and T. gondii (Pru) co-infection was lethal in mice and that the two pathogens may act synergistically. Abstract This study aimed to investigate the relationship between the T. gondii type II strain (Pru) and respiratory viral infections, specifically focusing on the co-infection with PR8 (influenza A/Puerto Rico/8/34). In this study, we found that the number of T. gondii (Pru) in the lungs of co-infected mice was significantly higher and lesions were more severe than those in the group infected with T. gondii (Pru) alone, whereas IAV (influenza A virus) copy numbers of co-infected and PR8 alone infected groups were negligible, suggesting that infection with IAV increased the pathogenicity of T. gondii (Pru) in mice. The invasion and proliferation assays demonstrated no significant effect of co-infection on T. gondii (Pru) infection or replication in vitro. To further explore the factors causing the altered pathogenicity of T. gondii (Pru) caused by co-infection, we found that decreased expression levels of IL-1β, IL-6, and IL-12 in the co-infected group were associated with the early immune responses against T. gondii (Pru), which affected the division of T. gondii (Pru). Moreover, the significant decrease in the CD4+/CD8+ ratio indicated a weakened long-term immune killing ability of the host against T. gondii (Pru) following IAV infection. In conclusion, a T. gondii type II strain (Pru) could not be properly cleared by the host immune system after IAV infection, resulting in toxoplasmosis and even death in mice.
Does Indonesia's Inclusive Curriculum Education Sustainability in Privat Madrasah Program?

Private madrasah in Indonesia is dominant, but the limited number of madrasah managers with high competence in providing inclusive education can be a challenge to the trust of parents and students. The importance of research to get a theoretical approach to inclusive curriculum education in madrasah. This study analyses the sustainability of inclusive curriculum education programs in private madrasah. The research uses a qualitative method with a grounded theory approach. The analysis uses design thinking through literature reviews and interviews with private madrasah managers providing inclusive education in Banten, Indonesia. The research finds that social evolution is empathy, exploration, elaboration, exposure, execution, and expansion supporting sustainable, inclusive madrasah curriculum programs. The design thinking from the six models of social evolution encourages inclusive curriculum education sustainability in private madrasah. Further research needs to examine social evolution quantification in madrasah's inclusive curricula.
Thick Cloud Removal for Multitemporal Remote Sensing Images: When Tensor Ring Decomposition Meets Gradient Domain Fidelity

Thick clouds in remote sensing (RS) images deteriorate the visual quality and hinder subsequent applications. The emerging multitemporal RS images with rich temporal information bring the opportunity for cloud removal. How to effectively exploit the rich temporal information of the multitemporal RS images remains challenging. As multitemporal RS images with the same geographic scene, the spatial gradient of RS images at different time nodes has a resemblance, which can guide the reconstruction of the cloudy region. Motivated by this, we suggest a gradient domain fidelity with respect to the guided gradient for thick cloud removal in multitemporal RS images, which faithfully preserves the fine edges and textures compared with the original pixel domain fidelity. Armed with the gradient domain fidelity, we propose a low-rank tensor ring decomposition model (termed as TRGFid) for the thick cloud removal problem. In the proposed model, the guided gradient of the cloudy region is availably estimated by using the Regression method from the cloud-free region of different time nodes. Moreover, we develop an efficient proximal alternating minimization (PAM)-based algorithm for solving the proposed nonconvex model. Extensive simulated and real experiments show that the proposed method outperforms its competitors and preserves fine edges and textures.
Achieving net-zero emissions in agriculture: a review

Agriculture accounts for 12% of global annual greenhouse gas (GHG) emissions (7.1 Gt CO2 equivalent), primarily through non-CO2 emissions, namely methane (54%), nitrous oxide (28%), and carbon dioxide (18%). Thus, agriculture contributes significantly to climate change and is significantly impacted by its consequences. Here, we present a review of technologies and innovations for reducing GHG emissions in agriculture. These include decarbonizing on-farm energy use, adopting nitrogen fertilizers management technologies, alternative rice cultivation methods, and feeding and breeding technologies for reducing enteric methane. Combined, all these measures can reduce agricultural GHG emissions by up to 45%. However, residual emissions of 3.8 Gt CO2 equivalent per year will require offsets from carbon dioxide removal technologies to make agriculture net-zero. Bioenergy with carbon capture and storage and enhanced rock weathering are particularly promising techniques, as they can be implemented within agriculture and result in permanent carbon sequestration. While net-zero technologies are technically available, they come with a price premium over the status quo and have limited adoption. Further research and development are needed to make such technologies more affordable and scalable and understand their synergies and wider socio-environmental impacts. With support and incentives, agriculture can transition from a significant emitter to a carbon sink. This study may serve as a blueprint to identify areas where further research and investments are needed to support and accelerate a transition to net-zero emissions agriculture.
Extreme Value Meta-Learning for Few-Shot Open-Set Recognition of Hyperspectral Images

Recent advancements in prototype-based few-shot open-set recognition (FSOSR) approaches reject outliers based on the high metric distances from the known class prototypes and fail to distinguish spectrally fine-grained land cover outliers. Learning only the Euclidean distance fit spherical distributions ignores the essential distribution parameters such as shift, shape, and scale. The conventional meta-training of FSOSR also ignores the topological consistency of the known classes impacting reduced closed and open accuracy in the meta-testing phase. Moreover, the existing hyperspectral outlier detection methods do not provide intuition about the rejected outlier’s land cover category. To tackle the aforesaid problems, we introduce extreme value meta-learning (EVML), where we fit Weibull distributions per known class based on the limited support-set distances from the respective prototypes. A newly proposed prototypical OpenMax (P-OpenMax) layer leverages these meta-trained Weibull models and calibrates the query distances to reject fine-grained outliers. Then, to learn the topological consistency, we split all the samples in an episode into four parts, including the prototype and its same known class queries, other known class queries, and the remaining known–unknown queries. A novel open quadruplet loss ensures that a prototype’s same-class queries reside closer than the other known-class and known–unknown queries. Finally, we coarse classify the detected outliers into major land cover categories and perform cross-dataset incremental FSOSR to enhance robustness over unknown geographical regions. We validate the efficacy of EVML over four benchmark hyperspectral datasets.
Bio-Fabrication of Cu/Ag/Zn Nanoparticles and Their Antioxidant and Dye Degradation Activities

The biological synthesis of nanoparticles with copper, silver, and zinc (Cu, Ag, Zn) is reported in this study, adopting a greener, safe, reliable, and eco-friendly approach by using an aqueous leaf extract of Catharanthus roseus. The synthesised trimetallic nanoparticles were characterised using different characterisation techniques. The UV–visible spectroscopic technique was initially used to assess nanoparticle formation, in which absorption bands were observed at 220, 270, and 370 nm for Cu, Zn, and Ag nanocomposites, respectively. XRD revealed that the average crystalline size of the nanocomposites was 34.67 nm. The roles of reducing and capping/stabilising agents in the synthesis of Cu/Ag/Zn nanoparticles were confirmed by FTIR analysis, and the successful biosynthesis of the same was also confirmed by X-ray energy-dispersive spectroscopy (EDX) analysis. Potential applications of these synthesised trimetallic nanoparticles were evaluated by assessing their antioxidant and catalytic dye degradation activities. The antioxidant activity of the synthesised nanomaterial was studied using the DPPH assay. The catalytic breakdown of the harmful dyes phenol red and eosin yellow was examined using NaBH4 as a reducing agent. The results showed that the nanomaterial’s radical scavenging capacity at 1000 ug/mL was 75.76% and the degradation of these dyes was up to 78% in the presence of NaBH4. Furthermore, the biogenic trimetallic nanomaterial exhibited effective catalytic degradation activity against methyl red and phenol red dyes.
Identification and Validation of Anoikis-Related Signatures for Predicting Prognosis in Lung Adenocarcinoma with Machine Learning

Background Lung adenocarcinoma (LUAD) is an aggressive cancer that has an extremely poor prognosis. As well as facilitating the detachment of cancer cells from the primary tumor site, anoikis plays an important role in cancer metastasis. Few studies to date, however, have examined the role of anoikis in LUAD, in patient prognosis. Methods A total of 316 anoikis-related genes (ANRGs) integrated from Genecards and Harmonizome portals. LUAD transcriptome data were retrieved from the Genotype-Tissue Expression Project (GEO) and The Cancer Genome Atlas (TCGA). Anoikis-related prognostic genes (ANRGs) were primarily screened by univariate Cox regression. All ANRGs were included in the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model to construct the powerful prognostic signature. This signature was validated and assessed using the Kaplan-Meier method as well as univariate and multivariate Cox regression analyses. Anoikis-related regulators of risk score were identified using a XG-boost machine learning model. The expression of ITGB4 protein was examined in a ZhengZhou University (ZZU) tissue cohort by immunohistochemistry, and the potential mechanisms of action of ITGB4 in LUAD were explored by GO, KEGG, and ingenuity pathway analyses and by GSEA. Results A risk score signature was constructed based on eight ANRGs, with high risk scores found to closely correlate with unfavorable clinical features. ITGB4 expression may be associated with 5-year over survival, with immunohistochemistry showed that the expression of ITGB4 was higher in LUAD than in nontumor tissues. Enrichment analysis suggested that ITGB4 may promote LUAD development by targeting E2F, MYC, and oxidative phosphorylation signaling pathways. Conclusion Our anoikis-related signature from RNA-seq data may be a novel prognostic biomarker in patients with LUAD. It may help physicians develop personalized LUAD treatments in clinical practice. Moreover, ITGB4 may affect the development of LUAD through the oxidative phosphorylation pathway.
Forensic DNA Analysis of Mixed Mosquito Blood Meals: STR Profiling for Human Identification

Simple Summary Blood-fed mosquitoes found at crime scenes can provide human blood traces for genetic analysis that aid in forensic investigations. Because a mosquito can obtain its blood meal from two or more sources, this study aims to determine whether it is possible to obtain a human DNA profile from the blood meal of a single Culex pipiens mosquito experimentally fed on mixed blood of human male-female or human-mice. Twenty-four human STR loci were amplified over a complete digestion profile post-feeding. Data revealed that full, complete, and partial STR profiles could be obtained from all blood meal types up to 12, 24, and 36 h post-feeding, respectively. The mixed human–animal blood maximized the DNA degradation and, thus, impaired STR identification beyond 36 h until they became poorly detectable at 48 h post-feeding. These results indicate that human DNA can be obtained from mosquito blood meals, even if it is mixed with non-human blood, for up to 36 h post-feeding. Abstract Mosquito vectors captured at a crime scene are forensically valuable since they feed on human blood, and hence, human DNA can be recovered to help identify the victim and/or the suspect. This study investigated the validity of obtaining the human short tandem repeats (STRs) profile from mixed blood meals of the mosquito, Culex pipiens L. (Diptera, Culicidae). Thus, mosquitoes were membrane-feed on blood from six different sources: a human male, a human female, mixed human male-female blood, mixed human male-mouse blood, mixed human female-mouse blood, and mixed human male-female-mouse blood. DNA was extracted from mosquito blood meals at 2 h intervals up to 72 h post-feeding to amplify 24 human STRs. Data showed that full DNA profiles could be obtained for up to 12 h post-feeding, regardless of the type of blood meal. Complete and partial DNA profiles were obtained up to 24 h and 36 h post-feeding, respectively. The frequencies of STR loci decreased over time after feeding on mixed blood until they became weakly detectable at 48 h post-feeding. This may indicate that a blood meal of human blood mixed with animal blood would contribute to maximizing DNA degradation and thus affects STR identification beyond 36 h post-feeding. These results confirm the feasibility of human DNA identification from mosquito blood meals, even if it is mixed with other types of non-human blood, for up to 36 h post-feeding. Therefore, blood-fed mosquitoes found at the crime scene are forensically valuable, as it is possible to obtain intact genetic profiles from their blood meals to identify a victim, a potential offender, and/or exclude a suspect.
Optimization of DSM Sampling Frequency and Interleaving Frequency for Bilevel Quadrature-Modulation EPWM Transmitter

We previously proposed a bilevel quadrature-modulation (QM) envelope-pulse-width-modulation (EPWM) transmitter using half a side of a trilevel EPWM architecture. However, the optimum relationship between the  modulation (DSM) sampling frequency (fs), the interleaving frequency (fi) and the carrier frequency (fc) for a bilevel QM EPWM transmitter has not been investigated. This paper presents this optimum relationship, with the error vector magnitude (EVM) and the coding efficiency (CE) as figures of merit (FOMs).
Engagement and consumption behavior of eSports gamers

Purpose The purpose of this study is to present a conceptual model where consumer electronic sports (eSports) engagement (CeSE) acts a predictor for gamers’ online engagement in eSports-related products/firm either through direct contribution (purchase intention) or indirect contribution (co-production, community engagement, word-of-mouth and recruitment). Design/methodology/approach Data from 262 eSports consumers aged 18–24 years were collected and analyzed through WarpPLS 8.0. Findings The findings of this study confirm that CeSE significantly influences all dimensions of the consumption behaviors (purchase intention, co-production, community engagement, word-of-mouth and recruitment). Originality/value This study provides empirical support for a conceptual framework developed through the social exchange theory and engagement theory. Besides, hierarchical component model approach is applied to estimate the composite model of CeSE.
Iterative Regularization Using an Acceleration Technique for Synthetic Aperture Interferometric Radiometer

The inverse problem of synthetic aperture interferometric radiometers (SAIRs) aims at retrieving the brightness temperature map from the visibility function samples. To efficiently obtain an accurate solution, an accelerated iterative regularization technique is proposed to reconstruct the SAIR brightness temperature map. The acceleration technique modifies the conventional least square function using a negative penalty term to speed up the initial iterations. A series of decreasing coefficients must be tuned to prevent noise amplification in subsequent iterations. Several numerical experiments were performed on one-dimensional (1-D) and (2-D) SAIR systems. The experiment results indicate that the accelerated Landweber method reduces the number of iteration steps by more than 50% and effectively improves the computational speed without reducing the reconstruction accuracy compared to the conventional Landweber method.