Miracle-dz/UniFAR-FTUs huggingface dataset:
Outcomes of Infertile Women Without Intrauterine

(1) Background: The aim of this work was to systematically review existing studies on 15 whether hysteroscopy improves the reproductive outcomes of women with infertility even in the 16 absence of intrauterine pathologies when compared to women who did not receive a hysteroscopy. 17 (2) Methods: We established the Participant-Intervention-Comparison-Outcome strategy and used 18 the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement to conduct a 19 systematic review of 11 studies which were retrieved from 3 electronic databases: Ovid-Medline, 20 Ovid-Embase, and the Cochrane Library. Two independent investigators extracted the data from 21 the included studies and used the Cochrane risk-of-bias tool to assess their quality. (3) Results: The 22 primary outcome measures were the clinical pregnancy rates (CPRs) and live birth rates (LBRs) in 23 the in vitro fertilization (IVF)/intracytoplasmic sperm injection (ICSI) cycles. Hysteroscopy in 24 infertile women without intrauterine pathologies showed higher CPRs and LBRs than those in the 25 same population who did not receive hysteroscopy in cases of recurrent implantation failure and 26 IVF (odds ratio: 1.79 and 1.46, 95% confidence interval: 1.46-2.30 and 1.08-1.97 for CPR and LBR, 27 respectively); however, the degree of significance was not as high for LBR. (4) Conclusions: 28 Hysteroscopy before IVF/ICSI in infertile women without intrauterine pathologies may potentially 29 be effective in improving the CPRs and LBRs in patients with RIF. Robust and high-quality 30 randomized trials are warranted to confirm this finding. 31
Ammonia Generation and Utilization in a Passive SCR (TWC+SCR) System on Lean Gasoline Engine

Lean gasoline engines offer greater fuel economy than the common stoichiometric gasoline engine, but the current three-way catalyst (TWC) on stoichiometric engines is unable to control nitrogen oxide (NOX) emissions in the oxygen-rich exhaust. Thus, lean NOX emission control is required to meet existing Tier 2 and upcoming Tier 3 emission regulations set by the U.S. Environmental Protection Agency (EPA). While urea-based selective catalytic reduction (SCR) has proven effective in controlling NOX from diesel engines, the urea storage and delivery components can add significant size and cost. As such, onboard NH3 production via a passive SCR approach is of interest. In a passive SCR system, NH3 is generated over a close-coupled TWC during periodic slightly rich engine operation and subsequently stored on an underfloor SCR catalyst. Upon switching to lean operation, NOX passes through the TWC and is reduced by the stored NH3 on the SCR catalyst. In this work, a passive SCR system was evaluated on a 2.0-liter BMW lean burn gasoline direct injection engine to assess NH3 generation over a Pd-only TWC and utilization over a Cu-based SCR catalyst. System NOX reduction efficiency and fuel efficiency improvement compared to stoichiometric engine operation were measured. A feedback control strategymore » based on cumulative NH3 produced by the TWC during rich operation and NOX emissions during lean operation was implemented on the engine to control lean/rich cycle timing. 15% excess NH3 production over a 1:1 NH3:NOX ratio was required (via longer rich cycle timing) to achieve 99.7% NOX conversion at an SCR average inlet temperature of 350 C. Increasing NH3 generation further resulted in even higher NOX conversion; however, tailpipe NH3 emissions resulted. At higher temperatures, NH3 oxidation becomes important and limits NH3 availability for NOX reduction. At the engine conditions studied here, greater than 99% NOX conversion was achieved with passive SCR while delivering fuel efficiency benefits ranging between 6-11% compared with stoichiometric operation. « less
MetaREVEAL: RL-based Meta-learning from Learning Curves

This paper addresses a cornerstone of Automated Machine Learning: the problem of rapidly uncovering which machine learning algorithm performs best on a new dataset. Our approach leverages performances of such algorithms on datasets to which they have been previously exposed, i.e., implementing a form of meta-learning . More speciﬁcally, the problem is cast as a REVEAL Reinforcement Learning (RL) game: the meta-learning problem is wrapped into a RL environment in which an agent can start, pause, or resume training various machine learning algorithms to progressively “reveal” their learning curves. The learned policy is then applied to quickly uncover the best algorithm on a new dataset. While other similar approaches, such as Freeze-Thaw, were proposed in the past, using Bayesian optimization, our methodology is, to the best of our knowledge, the ﬁrst that trains a RL agent to do this task on previous datasets. Using real and artiﬁcial data, we show that our new RL-based meta-learning paradigm outperforms Free-Thaw and other baseline meth-ods, with respect to the Area under the Learning curve metric, a form of evaluation of Any-time learning (i.e., the capability of interrupting the algorithm at any time while obtaining good performance).
Extremely Well-Differentiated Adenocarcinoma of the Stomach

Predictive biomarker testing plays a critical role in targeted immuno-oncology, including the use of immune checkpoint inhibitors (ICI) for various solid tumors. Molecular advancements in cancers of the breast, kidney and brain have continued to propel tumor classification and precision therapy. GENE FUSION NOMENCLATURE The HUGO Gene Nomenclature Committee has released a new recommendation to denote gene fusion with a double colon, e.g., BCR::ABL [1]. IMMUNE CHECKPOINT INHIBITOR BIOMARKERS • Programmed cell death ligand 1 (PDL1) immunohistochemistry (IHC) uses various scoring systems and cutoffs for particular antibody clones to quantify PDL1 expression as a predictive biomarker for specific FDA approved ICI. For example: The PDL1 SP142 clone is an FDA approved complementary diagnostic to predict response of metastatic NSCLC to atezolizumab. The evaluation is based on either the proportion of tumor area occupied by PDL1 expressing tumor infiltrating immune cells of any intensity or the percentage of PDL1 expressing tumor cells of any intensity. • Deficient mismatch repair (dMMR) / high microsatellite instability (MSI-H) is predictive of ICI efficacy in colorectal cancers and other solid tumors. While it is commonly tested by IHC, assays based on PCR or next generation sequencing (NGS) are available. • High tumor mutational burden (TMB) may also serve as a predictor of immunotherapy response. TMB reflects the number of somatic mutations per megabase of tumor genomic sequence. It can be determined by whole exome sequencing (WES) or targeted sequencing of gene panels (usually ≥ 200 genes). Cutoff values have not yet been unified and may vary in respect to different assay platforms and ICI drug targets. Recently, Foundation Medicine’s FoundationOne CDx, which includes MSI and TMB assessment, was approved as a companion diagnostic for pembrolizumab with a cutoff of ≥ 10 mutations per megabase, regardless of solid tumor type.
A Windowed-Total-Variation Regularization Constraint Model for Blind Image Restoration

Blind restoration for motion-blurred images is always the research hotspot, and the key for the blind restoration is the accurate blur kernel (BK) estimation. Therefore, to achieve high-quality blind image restoration, this thesis presents a novel windowed-total-variation method. The proposed method is based on the spatial scale of edges but not amplitude, and the proposed method thus can extract useful image edges for accurate BK estimation, and then recover high-quality clear images. A large number of experiments prove the superiority.
Dark Energy Survey Year 1 Results: Tomographic cross-correlations between Dark Energy Survey galaxies and CMB lensing from South Pole Telescope plus Planck

We measure the cross-correlation between red M a G i Cgalaxies selected from the Dark Energy Survey (DES) Year-1 data and gravitational lensing of the cosmic microwave background (CMB) reconstructed from South Pole Telescope (SPT) and Planck data over 1289deg 2 . When combining measurements across multiple galaxy redshift bins spanning the redshift range of 0 . 15 < z < 0 . 90, we reject the hypothesis of no correlation at 19.9 σ signiﬁcance. When removing small-scale data points where thermal Sunyaev-Zel’dovich signal and nonlinear galaxy bias could potentially bias our results, the detection signiﬁcance is reduced to 9.9 σ . We perform a joint analysis of galaxy-CMB lensing cross-correlations and galaxy clustering to constrain cosmology, ﬁnding Ω m = 0 . 276 + 0 . 029 − 0 . 030 and S 8 = σ 8 √ Ω m / 0 . 3 = 0 . 800 + 0 . 090 − 0 . 094 . We also perform two alternate analyses aimed at constraining only the growth rate of cosmic structure as a function of redshift, ﬁnding consistency with predictions from the concordance Λ CDM model. The measurements presented here are part of a joint cosmological analysis that combines galaxy clustering, galaxy lensing and CMB lensing using data from DES, SPT and Planck .
CRACK DETECTION ON CONCRETE SURFACES USING IMAGE PROCESSING

Due to environmental changes and poor quality of construction materials, cracks may develop in the walls of the building which is critical for maintenance as well as the continuous exposure will cause the severe damage to the environment. One of the initial signs of the degradation of a concrete surface or a material is cracks. Manual inspection has many drawbacks like invisibility of the crack, time consuming and it completely depends upon specialist’s knowledge and experience. So automatic image-based crack detection is used as replacement for manual inspection which reduces the cost when compared to the manual approach. The proposed algorithm has been tested against various concrete crack images to study how effectively the algorithm is working, and how effectively it is overcoming the drawbacks of the present manual approach. The algorithm is projected to serve the purpose of various people by estimating the parameters of the crack so the bricklayer can take the necessary actions from the degradation of concrete structures. Index terms – Image based detection, Concrete structures etc.
A prediction model for remaiming capacity in batteries based on least square support vector machine

A novel prediction model for remaining capacity of batteries based on least square support vector machine(LS-SVM) was proposed. With battery ending voltage and degree of new as inputs,remaining capacity of resting batteries as output,test was taken to get data samples.Throghout data samples above,LS-SVM model was established,and remaining capacity of batteries can predict by the model. Experimental results show that the construction speed of this LS-SVM model is higher than that of the SVM model,while the prediction errors are less. Moreover,compared with BP model,the accuracy and speed of prediction are much higher than that of the former.
The F-Box Protein ZEITLUPE Confers Dosage-Dependent Control on the Circadian Clock, Photomorphogenesis, and Flowering Time W

As an F-box protein, ZEITLUPE (ZTL) is involved in targeting one or more substrates for ubiquitination and degradation via the proteasome. The initial characterization of ZTL suggested a function limited largely to the regulation of the circadian clock. Here, we show a considerably broader role for ZTL in the control of circadian period and photomorphogenesis. Using a ZTL-speciﬁc antibody, we quantitated and characterized a ZTL dosage series that ranges from a null mutation to a strong ZTL overexpressor. In the dark , ztl null mutations lengthen circadian period, and overexpression causes arrhythmicity, suggesting a more comprehensive role for this protein in the clock than previously suspected. In the light, circadian period becomes increasingly shorter at higher levels of ZTL, to the point of arrhythmicity. By contrast, hypocotyl length increases and ﬂowering time is delayed in direct proportion to the level of ZTL. We propose a novel testable mechanism by which circadian period and amplitude may act together to gate phytochrome B–mediated suppression of hypocotyl. We also demonstrate that ZTL-dependent delay of ﬂowering is mediated through decreases in CONSTANS and FLOWERING LOCUS T message levels, thus directly linking proteasome-dependent proteolysis to ﬂowering.
Impact of digital celebrities on their followers’ purchase intention: Roles of wishful identification, parasocial relationship, and behavioral loyalty

Digital social networking platforms become the mechanism for fans and celebrities to have digital communication and interation. Marketers and celebrities may take advantage of this opportunity to promote merchandise to consumers and fans by leveraging the wishful identification and parasocial relationship between digital celebrities and their followers. This study investigates the effect of wishful identification on parasocial relationships, which then enhance behavioral loyalty and purchase intentions sequentially. An online survey was administered to 300 Vietnamese social network sites (SNS) users, who were following digital celebrities. The statistical results indicate that higher levels of wishful identification positively associated with higher levels of parasocial relationship, which then trigger higher levels of behavioral loyalty and purchase intention sequentially. This study therefore sheds light on fans’ behavioral loyalty with digital celebrities through their purchase identification during Covid-19 time.
Impacts of Discretization and Numerical Propagation on the Ability to Follow Challenging Square Wave Commands

This study determines the threshold for the computational rate of actuator motor controllers for unmanned underwater vehicles necessary to accurately follow discontinuous square wave commands. Motors must track challenging square-wave inputs, and identification of key computational rates permit application of deterministic artificial intelligence (D.A.I.) to achieve tracking to a machine-precision degree of accuracy in direct comparison to other state-of-art approaches. All modeling approaches are validated in MATLAB simulations where the motor process is discretized at varying step-sizes (inversely proportional to computational rate). At a large step-size (fast computational rate), discrete D.A.I. shows a mean error more than three times larger than that of a ubiquitous model-following approach. Yet, at a smaller step size (slower computational rate), the mean error decreases by a factor of 10, only three percent larger than that of continuous D.A.I. Hence, the performance of discrete D.A.I. is critically affected by the sampling period for discretization of the system equations and computational rate. Discrete D.A.I. should be avoided when small step-size discretization is unavailable. In fact, continuous D.A.I. has surpassed all modeling approaches, which makes it the safest and most viable solution to future commercial applications in unmanned underwater vehicles.
EMOTION ANALYSIS USING NEURAL NETWORK

Abstrac t: Feelings structure a truly significant and fundamental part of our lives. Whatever we do, whatever we are stating, by one way or other mirrors some of our feelings, however probably won't be straightforwardly. To comprehend the central conduct of an individual's, we need to inspect these feelings through some information, typically called, the influence information. This information is frequently text, voice, outward appearances and so on Full of feeling figuring is one of the interdisciplinary fields which is utilized for examining the feelings from the feeling dataset. Calculation of feelings might be a difficult assignment and a large part of the work has been done yet more augmentations are likewise conceivable. With the appearance of long-range interpersonal communication destinations, a large number of us will in general inclination pulled in towards dissecting this extremely text accessible on these different locales. Examining this information over the web implies we are crossing across the whole landmass, perusing all the way of life and networks across. This paper sums up crafted by printed feeling examination dependent on different passionate models and computational methodologies utilized alongside accomplishing the general framework precision up-to 96%.
Quantity vs Variety: Non-Cooperative Content Production on Online Knowledge Sharing Platforms

Online question-and-answer platforms allow consumers to learn different perspectives of information and knowledge from content producers. Such platforms’ performance critically depends on both quantity and variety of knowledge contents generated by the crowd. This paper studies how early-stage knowledge productions influence the future crowd’s knowledge production behavior. Using a novel data set from one of the largest question-and-answer platforms, we construct measure of knowledge variety using an unsupervised learning method. Our empirical analysis suggests that early-stage knowledge contents have substantial influences on the quantity and variety of the future knowledge contents. Specifically, we document that (1) lengthier early-stage knowledge contents decrease the quantity of future knowledge contents but increase the variety; (2) higher numbers of upvotes of early-stage knowledge contents lead to more future knowledge contents and do not affect the variety. Moreover, we find that the identity of the early knowledge producer (e.g., expert vs. non-expert) moderates the interrelationship between earlystage knowledge contents and future knowledge contents under the same question on a Q&A platform. Our results shed important managerial insights on how platforms can utilize the early-stage knowledge contents’ influences on the direction of future knowledge content growth and further optimize platform strategies.
PTPRG activates m6A methyltransferase VIRMA to block mitochondrial autophagy mediated neuronal death in Alzheimer's disease

In Alzheimer's disease (AD), neuronal death is one of the key pathology. However, the initiation of neuronal death in AD is far from clear, and new targets are urgently needed to develop effective therapeutic methods. This study analyzed sequencing data from single-cell RNAseq and spatialomics, and revealed the impact of global single-cell mapping and cell spatial distribution relationships in early stage of AD. We found that microglia subpopulation Mic_PTPRG can anchor neurons based on ligand-receptor interaction pairs and cause ectopic expression of PTPRG in neurons during AD progression. PTPRG in neurons can bind and upregulate VIRMA expression, which in turn increases the level of m6A methylation, enhances PRKN transcript degradation and represses translation. Repressed PRKN expression blocks the clearance of damaged mitochondria in neurons, which in turn reprograms neuronal energy and nutrient metabolic pathways and leads to neuronal death during AD progression. This study elucidates novel mechanisms, by which the PTPRG-dependent microglia-synaptic modification may play a role in AD, providing a new scientific basis for potential therapeutic targets for AD.
Genome-by-Trauma Interaction Effects in Depression

Background Self-reported trauma exposure has consistently been found to be a risk factor for Major Depressive Disorder (MDD) and several studies have reported interactions with genetic liability. To date, most studies have examined interaction effects with trauma exposure using genome-wide variants (single nucleotide polymorphisms SNPs) or polygenic scores, both typically capturing less than 3% of phenotypic risk variance. We sought to re-examine genome-by-trauma interaction effects using genetic measures utilising all available genotyped data and thus, maximising accounted variance. Methods Measures of self-reported depression, neuroticism and trauma exposure for 148 129 participants with whole genome SNP data are available from the UK Biobank study. Here, we used a mixed-model statistical approach utilising genetic, trauma exposure and genome-by-trauma exposure interaction similarity matrices to explore sources of variation in depression and neuroticism. Findings Our approach estimated the heritability of MDD to be approximately 0.160 [SE 0.016]. Subtypes of self-reported trauma exposure (catastrophic, adult, childhood and full trauma) accounted for a significant proportion of the variance of each trait, ranging from 0.056 [SE 0.013] to 0.176 [SE 0.025]. The proportion of MDD risk variance accounted for by significant genome-by-trauma interaction ranged from 0.074 [SE 0.006] to 0.201 [SE 0.009]. Results from sex-specific analyses found genome-by-trauma interaction variance estimates approximately 5-fold greater for MDD in males than in females. Interpretation This is the first study to utilise an approach combining all genome-wide SNP data when exploring genome-by-trauma interaction effects in MDD and present evidence that interaction effects are influential in depression manifestation. This effect accounts for greater trait variance within males which points to potential differences in depression aetiology between the sexes. The methodology utilised in this study can be extrapolated to other environmental factors to identify modifiable risk environments and at-risk groups to target with interventions.
Robust Multi-Organ Nucleus Segmentation Using a Locally Rotation Invariant Bispectral U-Net

Locally Rotation Invariant (LRI) operators have shown great potential to robustly identify biomedical textures where discriminative patterns appear at random positions and orientations. We build LRI operators through the local projection of the image on circular harmonics followed by the computation of the bispectrum, which is LRI by design. This formulation allows to avoid the discretization of the orientations and does not require any criterion to locally align the descriptors. This operator is used in a convolutional layer resulting in LRI Convolutional Neural Networks (LRI CNN). To evaluate the relevance of this approach, we used it to segment cellular nuclei in histopathological images. We compared the proposed bispectral LRI layer against a standard convolutional layer in a U-Net architecture. While they performed equally in terms of F-score, the LRI CNN provided more robust segmentation with respect to orientation, even when rotational data augmentation was used. This robustness is essential when the relevant pattern may vary in orientation, which is often the case in medical images.
How Did Air Quality Standards Affect Employment at U.S. Power Plants? The Importance of Stringency, Geography, and Timing

Geographical differences in U.S. Clean Air Act requirements are often used to identify environmental regulatory impacts. The standard approach abstracts from aspects of the law affecting which areas are regulated, how strictly they are regulated, and when regulatory changes occur. We find that omitting these factors can bias results by contaminating the control group, leading to under-estimation of historical employment impacts and overestimation of projected impacts from tightening regulations. Results indicate that 1990 changes to ozone nonattainment provisions reduced power plant employment without significantly affecting generation, suggesting that installation of pollution controls contributed to labor-saving technical change at affected sources.