Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Perovskite Solar Cells Fabricated Through CsCl‐Enhanced PbI2 Precursor via Sequential Deposition

The fabrication of high‐quality perovskite film highly relies on chemical composition and the synthesis method of perovskite. So far, sequentially deposited MA0.03FA0. 97Pb(I0.97Br0.03)3 polycrystalline film is adopted to produce high‐performance perovskite solar cells with record power conversion efficiency (PCE). Fewer grain boundaries and incorporation of inorganic cation (e.g., cesium) would further increase device performance via sequential deposition. Here, cesium chloride (CsCl) is introduced into lead iodide (PbI2) precursor solution that beneficially modulates the property of PbI2 film, leading to larger grains with cesium incorporation in the resulting perovskite film. The enlarged crystal grains originate from a slower nucleation process for CsCl‐containing PbI2 film when reacting with formamidine iodide, confirmed by in situ confocal photoluminescence imaging. Photovoltaic devices based on CsCl‐containing PbI2 film demonstrate a higher averaging efficiency of 21.3% than 20.3% of the devices without CsCl additives for reverse scan. More importantly, the device stability is improved by CsCl additives that retain over 90% of their initial PCE value after 4000 min tracking at maximum power point under 1‐sun illumination. This work paves a way to further improve the photovoltaic performance of mixed‐cation‐halide perovskite solar cells via a sequential deposition method.
Editorial of the Special Issue on Following User Pathways: Key Contributions and Future Directions in Cross-Platform Social Media Research

ABSTRACT Social media and the resulting tidal wave of the available data have changed the ways and methods researchers analyze communities at scale. But the full potential for social scientists (and others) is not yet achieved. Despite the popularity of social media analysis in the past decade, few researchers invest in cross-platform analyses. This is a major oversight as a majority of online social media users have multiple social media accounts. Missing are the models and tools necessary to undertake analysis at scale across multiple platforms. Especially promising in support of cross-platform analysis is the mixed-method approach (e.g., qualitative and quantitative methods) in order to better understand how users and society interact online. This special issue “Following User Pathways” addresses methodological, analytical, conceptual, and technological challenges and opportunities of cross-platform analysis in social media ecosystems.
POLLINATION DYNAMICS IN EPILOBIUM CANUM (ONAGRACEAE): CONSEQUENCES FOR GAMETOPHYTIC SELECTION.

Several hand-pollination experiments have shown that pollen tube competition leads to gametophytic selection, and thus affects genotypic and phenotypic characteristics of the next generation. This study is one of the first to quantify natural levels of pollen tube competition. In a population of Epilobium canum, I measured both the amount of pollen deposited on stigmas and the timing of deposition. Approximately 20 tetrads were required for full seed set within fruits. Hummingbirds deposited >20 tetrads at 50-70% of the flowers examined, often in a single load. When pollen arrived in 2 loads, a portion of the ovules within each ovary was probably sired by competing pollen from the second load. Competition may be relatively weak unless at least 30 tetrads compete for ovules. About 20% of the flowers received >30 tetrads in the first load, and 13% acquired > 30 tetrads in 2 loads. The frequency and intensity of pollen tube competition varied among plants. In some styles, 80% of the pollen tubes were excluded from access to ovules, but in others no competition occurred. Further studies of pollination rates and progeny fitness are needed before we can assess the role of pollen tube competition in natural populations. Potential effects of gametophytic selection are discussed.
A Survey on Management Frameworks and Open Challenges in IoT

The new era of technology is a rollback in which massive information comes from interconnected devices. These types of things that have Internet connectivity are called the Internet of Things (IoT). IoT and its applications have enabled novel service provisioning, whereas at the same time it has raised complex challenges of managing such network. Resource limitation such as computational power and energy limitation defines the characteristics of IoT that require a novel design of management framework. Designing a management framework that meets the IoT requirements is a daunting task. Recently, Software-Defined Networking (SDN) paradigm has eased the management of traditional Internet by provisioning centralized view of the network. This makes SDN a potential candidate for managing IoT. However, traditional design of the SDN paradigm needs to be redefined to adapt it for the IoT. Hence, in this paper, we have discussed trends and research of management architecture for IoT based on Software-Defined Networking principles. In addition, we discuss non-SDN-based approaches. In this paper, we compare all of the efforts in the last five years in addressing issues of IoT management that involves security service provisioning, fault tolerance, energy management, and load balancing. We also discuss future research directions that evolved from our detailed survey and taxonomy of the Software-Defined IoT (SDIoT) management frameworks.
Deep convolutional recurrent autoencoders for learning low-dimensional feature dynamics of fluid systems

Model reduction of high-dimensional dynamical systems alleviates computational burdens faced in various tasks from design optimization to model predictive control. One popular model reduction approach is based on projecting the governing equations onto a subspace spanned by basis functions obtained from the compression of a dataset of solution snapshots. However, this method is intrusive since the projection requires access to the system operators. Further, some systems may require special treatment of nonlinearities to ensure computational efficiency or additional modeling to preserve stability. In this work we propose a deep learning-based strategy for nonlinear model reduction that is inspired by projection-based model reduction where the idea is to identify some optimal low-dimensional representation and evolve it in time. Our approach constructs a modular model consisting of a deep convolutional autoencoder and a modified LSTM network. The deep convolutional autoencoder returns a low-dimensional representation in terms of coordinates on some expressive nonlinear data-supporting manifold. The dynamics on this manifold are then modeled by the modified LSTM network in a computationally efficient manner. An offline unsupervised training strategy that exploits the model modularity is also developed. We demonstrate our model on three illustrative examples each highlighting the model's performance in prediction tasks for fluid systems with large parameter-variations and its stability in long-term prediction.
“I Got Confused Reading It”: Using Backchannels to Collaboratively Build Meaning with Texts

This study explores the use of backchannels, real-time online conversations taking place simultaneously with spoken discussions (the front channel), as one approach to meaning-making through discussion. Using transcripts of front and backchannel discussions, we examine how undergraduate preservice teachers utilize backchannels to talk about class-assigned texts. Although previous research has suggested that backchannels can create distractions, our study found that participants within the backchannel groups were able to create meaning through their interactions. We used five types of talk (analytic, personal, intertextual, transparent, and performative) to aid in our analysis. While we found evidence of all types of talk in the transcripts, analytical talk dominated the conversations, suggesting that backchannels can indeed encourage close readings of texts. In addition, we found that the nature of the online environment created a sixth category of talk. This type of talk, which we called negotiation, allowed participants in the backchannel to define and react to the digital space they interacted within. Findings point to increased engagement in class discussion and the potential of backchannels as a way to effectively integrate technology into instruction.
Aspergillus fumigatus conidial metalloprotease Mep1p cleaves host complement proteins

Innate immunity in animals including humans encompasses the complement system, which is considered an important host defense mechanism against Aspergillus fumigatus, one of the most ubiquitous opportunistic human fungal pathogens. Previously, it has been shown that the alkaline protease Alp1p secreted from A. fumigatus mycelia degrades the complement components C3, C4, and C5. However, it remains unclear how the fungal spores (i.e. conidia) defend themselves against the activities of the complement system immediately after inhalation into the lung. Here, we show that A. fumigatus conidia contain a metalloprotease Mep1p, which is released upon conidial contact with collagen and inactivates all three complement pathways. In particular, Mep1p efficiently inactivated the major complement components C3, C4, and C5 and their activation products (C3a, C4a, and C5a) as well as the pattern-recognition molecules MBL and ficolin-1, either by directly cleaving them or by cleaving them to a form that is further broken down by other proteases of the complement system. Moreover, incubation of Mep1p with human serum significantly inhibited the complement hemolytic activity and conidial opsonization by C3b and their subsequent phagocytosis by macrophages. Together, these results indicate that Mep1p associated with and released from A. fumigatus conidia likely facilitates early immune evasion by disarming the complement defense in the human host.
Balancing ​Efficiency and Effectiveness ​Trade-offs in Large Scale Multi-Stage Search Engines

1 ABSTRACT In this talk, we will discuss recent work on managing tradeoffs between efficiency and effectiveness in modern multi-stage ranking architectures which are comprised of a candidate generation stage followed by one or more reranking stages. In such an architecture, the quality of the final ranked list is often sensitive to the quality of initial candidate pool. We will briefly discuss a few recent related papers from my group, and then discuss future directions. First, we will explore dynamic cutoff prediction in early stage retrieval using query difficulty pre-retrieval features. We will then turn our attention to efficiency and effectiveness trade-offs in the later stage cascaded learning-to-rank algorithms. Specifically, we reexamine the importance of tightly integrating feature costs into multi-stage learning-to-rank (LTR) IR systems, and we present a novel approach to optimizing cascaded ranking models which can directly leverage a variety of different state-of-the-art LTR rankers such as LambdaMART and Gradient Boosted Decision Trees. Finally, we discuss interesting future research directions in multi-stage retrieval systems as modern retrieval tasks continue to evolve towards more complex interactive search systems.
The sure thing: The role of integral affect in risky choice framing.

When faced with a decision, certain aspects of the decision itself shape our affective responses to choice options, which, in turn, influence our choices. These integral affective influences manifest as immediate feelings about choice options as well as the feelings that we anticipate we will feel after certain potential outcomes. We examined whether the effect of framing on risk taking can be explained through the mediating roles of immediate and anticipated affect. Two experiments were conducted using a gambling task. On each trial, participants were endowed a sum of money (e.g., $25) then presented with a choice between a sure option (leaving them with a portion of the initial endowment) and a gamble option (that could result in either keeping or losing the entire endowment). The sure option was framed differently across two within-participant conditions: as a gain (keep $20 from $25) or loss (lose $5 from $25). Experiment 1 examined whether immediate feelings toward choice options explain how framing the sure option as a loss versus a gain increases risk taking. Experiment 2 examined whether immediate and/or anticipated affect explain how framing guides risk taking. We found that the tendency to take risks to avoid sure losses was explained by immediate (not anticipated) affective evaluations of the sure option only. Individuals tended to take more risks when faced with sure losses due to greater negative immediate feelings that were evoked by sure losses relative to sure gains. (PsycINFO Database Record
Internet Game Overuse Is Associated With an Alteration of Fronto-Striatal Functional Connectivity During Reward Feedback Processing

Internet gaming disorder is associated with abnormal reward processing in the reward circuit, which is known to interact with other brain regions during feedback learning. Kim et al. (1) observed that individuals with internet game overuse (IGO) exhibit altered behavior and neural activity for non-monetary reward, but not for monetary reward. Here, we extend our analysis of IGO to the functional connectivity of the reward network. Functional MRI data were obtained during a stimulus-response association learning task from 18 young males with IGO and 20 age-matched controls, where either monetary or non-monetary rewards were given as positive feedback for a correct response. Group differences in task-dependent functional connectivity were examined for the ventromedial prefrontal cortex (vmPFC) and ventral striatum (VS), which are known for reward evaluation and hedonic response processing, respectively, using a generalized form of the psychophysiological interaction approach. For non-monetary reward processing, no differences in functional connectivity were found. In contrast, for monetary reward, connectivity of the vmPFC with the left caudate nucleus was weaker for the IGO group relative to controls, while vmPFC connectivity with the right nucleus accumbens (NAcc) was elevated. The strength of vmPFC-NAcc functional connectivity appeared to be behaviorally relevant, because individuals with stronger vmPFC-NAcc connectivity showed lower learning rates for monetary reward. In addition, the IGO group showed weaker ventral striatum functional connectivity with various brain regions, including the right ventrolateral prefrontal cortex, dorsal anterior cingulate regions, and left pallidum. Thus, for monetary reward, the IGO group exhibited stronger functional connectivity within the brain regions involved in motivational salience, whereas they showed reduced functional connectivity the widely distributed brain areas involved in learning or attention. These differences in functional connectivity of reward networks, along with related behavioral impairments of reward learning, suggest that internet gaming disorder is associated with the increased incentive salience or “wanting” of addiction disorders, and may serve as the neurobiological mechanisms underlying the impaired goal-directed behavior.
A Prospective Study on the Fermentation Landscape of Gaseous Substrates to Biorenewables Using Methanosarcina acetivorans Metabolic Model

The abundance of methane in shale gas and of other gases such as carbon monoxide, hydrogen, and carbon dioxide as chemical process byproducts has motivated the use of gas fermentation for bioproduction. Recent advances in metabolic engineering and synthetic biology allow for engineering of microbes metabolizing a variety of chemicals including gaseous feeds into a number of biorenewables and transportation liquid fuels. New computational tools enable the systematic exploration of all feasible conversion alternatives. Here we computationally assessed all thermodynamically feasible ways of co-utilizing CH4, CO, and CO2 using ferric as terminal electron acceptor for the production of all key precursor metabolites. We identified the thermodynamically feasible co-utilization ratio ranges of CH4, CO, and CO2 toward production of the target metabolite(s) as a function of ferric uptake. A revised version of the iMAC868 genome-scale metabolic model of Methanosarcina acetivorans was chosen to assess co-utilization of CH4, CO, and CO2 and their conversion into selected target products using the optStoic pathway design tool. This revised version contains the latest information on electron flow mechanisms by the methanogen while supplied with methane as the sole carbon source. The interplay between different gas co-utilization ratios and the energetics of reverse methanogenesis were also analyzed using the same metabolic model.
Machine learning for bioinformatics and neuroimaging

Machine Learning (ML) is a well‐known paradigm that refers to the ability of systems to learn a specific task from the data and aims to develop computer algorithms that improve with experience. It involves computational methodologies to address complex real‐world problems and promises to enable computers to assist humans in the analysis of large, complex data sets. ML approaches have been widely applied to biomedical fields and a great body of research is devoted to this topic. The purpose of this article is to present the state‐of‐the art in ML applications to bioinformatics and neuroimaging and motivate research in new trend‐setting directions. We show how ML techniques such as clustering, classification, embedding techniques and network‐based approaches can be successfully employed to tackle various problems such as gene expression clustering, patient classification, brain networks analysis, and identification of biomarkers. We also present a short description of deep learning and multiview learning methodologies applied in these contexts. We discuss some representative methods to provide inspiring examples to illustrate how ML can be used to address these problems and how biomedical data can be characterized through ML. Challenges to be addressed and directions for future research are presented and an extensive bibliography is included.
A review of semantic similarity approach for multiple ontologies

Measuring semantic similarity between concepts is an important step in information retrieval and information integration which requires semantic content matching. Semantic similarity has attracted great concern for a long time in artificial intelligence, psychology and cognitive science. Many methods have been proposed. This paper contains a review on the state of art approaches including structure-based approach, information content-based approach, feature-based approach and hybrid-based approach. We also discussed the similarity according to their advantages, disadvantages and issues related to multiple ontologies. Besides that, we also concentrated on methods in feature-based approach which we will be using as a mechanism to measure the similarity for multiple ontologies.
Angiosarcoma: an immunogenic tumour

1 De Verneuil H, Ged C, Moreau-Gaudry F. Congenital erythropoietic porphyria. A0000565C004. In: The Porphyrin Handbook, Vol. 14, Medical Aspects of Porphyrias (Kadisk KM, Smith KM, Guilard R, eds). Amsterdam: Academic Press, 2003; 43–66. 2 Erwin A, Balwani M, Desnick R; Porphyrias Consortium of the NIH-Sponsored Rare Diseases Clinical Research Network. Congenital Erythropoietic Porphyria. 12 Sep 2013 [Updated 7 Apr 2016]. In: Gene Reviews [Internet] (Pagon RA, Adam MP, Ardinger HH et al., eds). Seattle (WA): University of Washington; 1993–2016. Available at: https://www.ncbi. nlm.nih. gov/books/NBK154652/. 3 Phillips JD, Steensma DP, Pulsipher MA et al. Congenital erythropoietic porphyria due to a mutation in GATA1: the first transacting mutation causative for a human porphyria. Blood 2007; 109:2618–21. 4 To-Figueras J, Ducamp S, Clayton J et al. ALAS2 acts as a modifier gene in patients with congenital erythropoietic porphyria. Blood 2011; 118:1443–51. 5 Sarkany RPE, Ibbotson SH, Whatley SD et al. Erythropoietic uroporphyria associated with myeloid malignancy is likely distinct from autosomal recessive congenital erythropoietic porphyria. J Invest Dermatol 2011; 131:1172–5. 6 Podlipnik S, Guijarro F, Combalia A et al. Acquired erythropoietic uroporphyria secondary to myelodysplastic syndrome with chromosome 3 alterations: a case report. Br J Dermatol 2018; 179:486–90.
Long Noncoding RNA XR007793 Regulates Proliferation and Migration of Vascular Smooth Muscle Cell via Suppressing miR-23b

Background Long noncoding RNAs (lncRNAs) were identified as potential regulatory factor in vascular disease. However, the role of XR007793 in the regulation of neointima formation after vascular injury remains largely unknown. Material/Methods LncRNA expression levels were detected using real-time polymerase chain reaction (RT-PCR). In vivo and in vitro assay were performed in Sprague-Dawley rats and VSMCs. Cell Counting Kit-8 (CCK-8) assay, Transwell assay, and scratch wound healing assay were performed to detect cell proliferation and migration. Western blotting was used to detect protein expression. Results The results of qRT-PCR indicated that XR007793 expression was significantly increased in the injured carotid artery of Sprague-Dawley rats and platelet-derived growth factor-BB induced rat aortic smooth muscle cells. Knockdown of XR007793 repressed the proliferation and migration of VSMC in vitro. The expression level of miR-23b was reduced in mouse carotid injured tissues and cell line. Bioinformatics analysis and luciferase reporter assay revealed that XR007793 directly bonds to miR-23b. Pearson correlation analysis showed that XR007793a and miR-23b were negatively correlated in carotid samples. Furthermore, bioinformatics analysis and luciferase assay indicated that miR-23b targeted the Forkhead box O 4 (FOXO4) 3′-UTR to inhibit FOXO4 expression. After transfecting miR-23b inhibitor, the expression both of XR007793 and FOXO4 was increased. The effects on expression were reversed after transfected with miR-23b mimics. Rescue experiments results indicated that miR-23b inhibitor reduced the expression of VSMC marker and promoted proliferation and migration of VSMC. Conclusions This study shows that XR007793 aggravates the loss of function of VSMCs by negatively regulating miR-23b. It does so by targeting FOXO4, which could serve as a novel therapeutic target in post-angioplasty restenosis.