Miracle-dz/UniFAR-FTUs huggingface dataset:
Chemoprevention of colorectal cancer in general population and high-risk population: a systematic review and network meta-analysis

Abstract Background: Many nutritional supplements and pharmacological agents have been reported to show preventive effects on colorectal adenoma and colorectal cancer (CRC). We performed a network meta-analysis to summarize such evidence and assess the efficacy and safety of these agents. Methods: We searched PubMed, Embase, and the Cochrane Library for studies published in English until October 31, 2021 that fit our inclusion criteria. We performed a systematic review and network meta-analysis to assess the comparative efficacy and safety of candidate agents (low-dose aspirin [Asp], high-dose Asp, cyclooxygenase-2 inhibitors [coxibs], calcium, vitamin D, folic acid, ursodeoxycholic acid [UDCA], estrogen, and progesterone, alone or in combination) for preventing colorectal adenoma and CRC. Cochrane risk-of-bias assessment tool was employed to evaluate the quality of each included study. Results: Thirty-two randomized controlled trials (278,694 participants) comparing 13 different interventions were included. Coxibs significantly reduced the risk of colorectal adenoma (risk ratio [RR]: 0.59, 95% confidence interval [CI]: 0.44–0.79, six trials involving 5486 participants), advanced adenoma (RR: 0.63, 95% CI: 0.43–0.92, four trials involving 4723 participants), and metachronous adenoma (RR: 0.58, 95% CI: 0.43–0.79, five trials involving 5258 participants) compared with placebo. Coxibs also significantly increased the risk of severe adverse events (RR: 1.29, 95% CI: 1.13–1.47, six trials involving 7109 participants). Other interventions, including Asp, folic acid, UDCA, vitamin D, and calcium, did not reduce the risk of colorectal adenoma in the general and high-risk populations compared with placebo. Conclusions: Considering the balance between benefits and harms, regular use of coxibs for prevention of colorectal adenoma was not supported by the current evidence. Benefit of low-dose Asp for chemoprevention of colorectal adenoma still requires further evidence. Registration: PROSPERO, No. CRD42022296376
Heavy Menstrual Bleeding Treatment With a Levonorgestrel 52-mg Intrauterine Device

The levonorgestrel 52-mg intrauterine device significantly improved heavy menstrual bleeding in a population that included participants with severe obesity and nulliparous participants. OBJECTIVE: To evaluate heavy menstrual bleeding treatment outcomes with levonorgestrel 52-mg intrauterine device (IUD) use in participants without body mass index (BMI) or parity restrictions. METHODS: Investigators included participants aged 18–50 years with no pelvic or systemic pathology causing heavy menstrual bleeding at 29 U.S. centers in a prospective trial. Participants had up to three screening cycles with menstrual product collection for alkaline hematin blood-loss measurements. Investigators enrolled those with two menses with blood loss of 80 mL or more (values averaged for baseline blood loss), placed the IUD, and followed participants for up to six 28-day cycles. Participants collected any menstrual products used during cycles 3 and 6 for blood-loss measurement. We evaluated outcomes in participants with at least one follow-up assessment for the primary outcome of median absolute blood-loss change and, secondarily, treatment success, defined as the proportion with a final measured blood loss less than 80 mL and at least 50% reduction from baseline. We evaluated exploratory outcomes of differences in blood-loss changes by BMI and parity using Wilcoxon rank sum test. RESULTS: Of 105 enrolled participants, 47 (44.8%) had obesity (BMI 30.0 or higher) and 29 (27.6%) were nulliparous. Baseline mean blood loss ranged from 73 to 520 mL (median 143 mL, interquartile range 112–196 mL). Eighty-nine (84.8%) had at least one evaluable follow-up evaluation. Participants had median (interquartile range) absolute blood-loss decreases at cycles 3 (n=86) and 6 (n=81) of 93.3% (86.1–97.7%) and 97.6% (90.4–100%), respectively. At cycle 6, participants without obesity (n=43) and with obesity (n=38) had similar median [interquartile range] decreases (97.6% [91.8–100%] and 97.5% [90.3–100%], respectively; P=.89), with comparable findings for nulliparous (n=25) and parous (n=56) participants (97.0% [91.7–99.1%] and 98.1% [89.9–100%], respectively; P=.43). Treatment success occurred in 81.8% (95% CI 74.2–89.4%) of 99 participants, excluding those with no outcomes due to lost to follow-up or consent withdrawal, and did not vary by BMI or parity. The most common adverse events leading to discontinuation were bleeding or cramping (n=6 [5.7%]) and expulsion (n=5 [4.8%]). CONCLUSION: This levonorgestrel 52-mg IUD reduces blood loss by more than 90% over 6 months compared with baseline for most users with heavy menstrual bleeding. FUNDING SOURCE: Medicines360. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov, NCT03642210.
Cisheteronormativity and its influence on the psychosocial experience of LGBTQ+ people with cancer: A qualitative systematic review

Cisheteronormativity refers to the relationship of heterosexual and cisgender privilege stemming from patriarchy. Although studies have shown that cisheteronormativity can impact health outcomes for lesbian, gay, bisexual, transgender, queer and other sexual, gender diverse, and gender nonconforming (LGBTQ+) people, the specific impact on cancer care has not been described. We synthesized the qualitative evidence on how cisheteronormativity impacts the psychosocial experience of LGBTQ+ people with cancer.
A randomized controlled study evaluating outcomes of intense pulsed light and low-level light therapy for treating meibomian gland dysfunction and evaporative dry eye

Purpose: This randomized, controlled, blinded study evaluates the efficacy of intense pulsed light (IPL) therapy with low-level light therapy (LLLT) in the treatment of meibomian gland dysfunction (MGD) and evaporative dry eye (EDE) compared to a control group. Methods: Hundred patients with MGD and EDE were randomized into control (50 subjects, 100 eyes) and study group (50 subjects, 100 eyes). The study group underwent three sittings of IPL with LLLT 15 days apart and were followed up 1 month and 2 months after the last treatment sitting. The control group underwent sham treatment and was followed up at the same intervals. The patients were evaluated at baseline and 1 month and 3 months (post 1st treatment) for dry eye. Schirmer's test and tear breakup time (TBUT), OSDI, meibomian gland expression, and meibography. Results: The study group showed significant improvement in OSDI scores (P < 0.0001) compared to the control group and a significant improvement in TBUT (P < 0.005) compared to the control group. There was no change in schirmer's test and an improvement in the meibomian gland expression but not significant. Conclusion: The results show that a combined therapy of IPL with LLT is effective in treating MGD with EDE compared to controls, and repeated treatment sessions have a cumulative effect on the disease outcomes.
Mathematical Models of Economic Growth.

textabstractEconomics Handbook Series, edited by Seymour E. Harris. In Spanish: Modelos Matematicos del Crecimiento Economico, Series ‘Biblioteca de Ciencias Sociales’, Aguilar, Madrid, 1966, XVI + 165 p. In French: Modeles Mathematiques de Croissance Economique. Series ‘Techniques Economiques Modernes’, Gauthier, Paris, 1969, VI + 145 p. In Russian: Matematiceskie Modeli Ekonomiceskogo Rosta, Progress, Moscow, 1967, 172 p.
Non-regular maximum likelihood problems

Four non-regular estimation problems are reviewed and discussed. One (the unbounded likelihood problem) involves distributions with infinite spikes, for which maximum likelihood can fail to give consistent estimators. A comparison is made with modified likelihood and spacings methods which do give efficient estimators in this case. An application to the Box-Cox shifted power transform is given. The other three problems occur when the true parameter lies in some special subregion. In one (the constrained parameter problem) the subregion is a boundary. The other two (the embedded model and the indeterminate parameters problems) occur when the model takes on a special form in the subregion. These last two problems have previously been investigated separately. We show that they are equivalent in some situations. Both often arise in non-linear models and we give a directed graph approach which allows for their occurrence in nested model building. It is argued that many non-regular problems can be handled systematically without having to resort to elaborate technical assumptions. Relatively uncomplicated methods may be used provided that the underlying nature of the non-regularity is understood
Thoracolumbar transitional vertebrae: Quantitative differentiation and associated numeric variation in the vertebral column using skeletal remains

Transitional vertebrae at the thoracolumbar region are called thoracolumbar transitional vertebrae (TLTV) and retain physical features from the thoracic and lumbar regions. Since TLTV were first classified 40 years ago, there has been much discrepancy regarding its features, identification and clinical relevance. Vertebral body levels are used in the medical field as a frame of reference to locate specific organs, vessels, nerves or landmarks. Any numeric variation or deviation in the vertebral column may lead to clinical errors. Previous findings have suggested a high association between numeric variation and the presence of TLTV. Therefore, the aim of this study was to identify the types of TLTV observed and to identify any possible associated numeric variation in the vertebral column. This study also aimed to validate the established technique to quantitatively differentiate TLTV from T12 and L1 at the thoracolumbar junction using skeletal remains from a South African population group. Skeletal remains (n = 187) remains from the Pretoria bone collection were assessed. Measurements were taken of the angle of the superior zygapophyseal processes of the last thoracic vertebra (T12), the first lumbar (L1), and identified TLTV. The results indicate a TLTV prevalence of 35% (n = 66/187). The results show that each vertebral type (T12, L1, TLTV) fall into independent confidence intervals: T12 is 188° ± 9.22 (CI: 187° < μ < 189.6°), 110° ± 7.52 (CI: 109.2° < μ < 111.3°) in L1, and 135° ± 24.51 (CI: 130.4° < μ < 139.1°) in the TLTV. This study observed that 70% of cases with TLTV was associated with numeric variation in the spine, both homeotic and meristic and that TLTV has a 35% prevalence. The results clearly show that quantitative morphometric analysis can effectively differentiate TLTV from other vertebral types at the thoracolumbar junction in skeletal remains.
The Escherichia coli Fur pan-regulon has few conserved but many unique regulatory targets

Abstract While global transcription factors (TFs) have been studied extensively in Escherichia coli model strains, conservation and diversity in TF regulation between strains is still unknown. Here we use a combination of ChIP-exo–to define ferric uptake regulator (Fur) binding sites–and differential gene expression–to define the Fur regulon in nine E. coli strains. We then define a pan-regulon consisting of 469 target genes that includes all Fur target genes in all nine strains. The pan-regulon is then divided into the core regulon (target genes found in all the strains, n = 36), the accessory regulon (target found in two to eight strains, n = 158) and the unique regulon (target genes found in one strain, n = 275). Thus, there is a small set of Fur regulated genes common to all nine strains, but a large number of regulatory targets unique to a particular strain. Many of the unique regulatory targets are genes unique to that strain. This first-established pan-regulon reveals a common core of conserved regulatory targets and significant diversity in transcriptional regulation amongst E. coli strains, reflecting diverse niche specification and strain history.
Insplico: effective computational tool for studying splicing order of adjacent introns genome-wide with short and long RNA-seq reads

Abstract Although splicing occurs largely co-transcriptionally, the order by which introns are removed does not necessarily follow the order in which they are transcribed. Whereas several genomic features are known to influence whether or not an intron is spliced before its downstream neighbor, multiple questions related to adjacent introns' splicing order (AISO) remain unanswered. Here, we present Insplico, the first standalone software for quantifying AISO that works with both short and long read sequencing technologies. We first demonstrate its applicability and effectiveness using simulated reads and by recapitulating previously reported AISO patterns, which unveiled overlooked biases associated with long read sequencing. We next show that AISO around individual exons is remarkably constant across cell and tissue types and even upon major spliceosomal disruption, and it is evolutionarily conserved between human and mouse brains. We also establish a set of universal features associated with AISO patterns across various animal and plant species. Finally, we used Insplico to investigate AISO in the context of tissue-specific exons, particularly focusing on SRRM4-dependent microexons. We found that the majority of such microexons have non-canonical AISO, in which the downstream intron is spliced first, and we suggest two potential modes of SRRM4 regulation of microexons related to their AISO and various splicing-related features. Insplico is available on gitlab.com/aghr/insplico.
Smile Recognition Based on Comprehensive Dataset Construction and Bayesian Neural Architecture Search

Smile recognition is a very important topic in image processing and deep learning, and has a certain practical significance. At present, existing datasets tend to have simple backgrounds or imbalanced ethnicities. And researchers need to build convolutional neural networks empirically. To address these problems, we first construct a comprehensive dataset which contains 10,392 images of four major races (i.e., White, Asian, American Native or Pacific Islander, and Black or African American) in complex backgrounds. Our dataset exhibits larger variations in background, illumination, and ethnicity. And then, we apply Bayesian neural architecture search algorithm to automatically build the optimal convolutional network architecture and perform model training. The network architecture can be repeatedly searched and optimized through Bayesian optimizer in the specified time. Through the comparisons of experimental data, the performance of our network is better than those of the classical networks.
Secondary Structure Detection Through Direct Nanopore RNA Sequencing

Techniques that can directly sequence RNAs and determine secondary structures are essential to establish how an RNA molecule folds and how folding affects its function. We describe the development of a direct RNA nanopore sequencing technique using an engineered Bombyx mori retroelement reverse transcriptase (RT) to thread RNA through the Mycobacterium smegmatis porin A (MspA) nanopore in single-nucleotide steps. First, we establish the map that correlates RNA sequence and MspA ion current. We find that during sequencing the RT can sense the strength of RNA secondary structures 11-12 nt downstream of its front boundary, modifying its stepping dynamics accordingly. Using this feature, we achieve simultaneous RNA nanopore sequencing and structure detection, without the need of prior conversion to complementary DNA (cDNA) or RNA modifications. The sequence-dependent ion currents open the way to utilize the MspA nanopore to investigate the single-molecule activity of other processive RNA translocases such as the ribosome.
Distinct roles of Bdnf I and Bdnf IV transcript variant expression in hippocampal neurons

Brain-derived neurotrophic factor (Bdnf) plays a critical role in brain development, dendritic growth, synaptic plasticity, as well as learning and memory. The rodent Bdnf gene contains nine 5′ non-coding exons (I-IXa), which are spliced to a common 3′ coding exon (IX). Transcription of individual Bdnf variants, which all encode the same BDNF protein, is initiated at unique promoters upstream of each non-coding exon, enabling precise spatiotemporal and activity-dependent regulation of Bdnf expression. Although prior evidence suggests that Bdnf transcripts containing exon I (Bdnf I) or exon IV (Bdnf IV) are uniquely regulated by neuronal activity, the functional significance of different Bdnf transcript variants remains unclear. To investigate functional roles of activity-dependent Bdnf I and IV transcripts, we used a CRISPR activation (CRISPRa) system in which catalytically-dead Cas9 (dCas9) fused to a transcriptional activator (VPR) is targeted to individual Bdnf promoters with single guide RNAs (sgRNAs), resulting in transcript-specific Bdnf upregulation. Bdnf I upregulation is associated with gene expression changes linked to dendritic growth, while Bdnf IV upregulation is associated with genes that regulate protein catabolism. Upregulation of Bdnf I, but not Bdnf IV, increased mushroom spine density, volume, length, and head diameter, and also produced more complex dendritic arbors in cultured rat hippocampal neurons. In contrast, upregulation of Bdnf IV, but not Bdnf I, in the rat hippocampus attenuated contextual fear expression. Our data suggest that while Bdnf I and IV are both activity-dependent, BDNF produced from these promoters may serve unique cellular, synaptic, and behavioral functions.
Penalized regression with a combination of the L1 norm and the correlation based penalty.

La selection de variables peut etre difficile, en particulier dans les situations ou un grand nombre de variables explicatives est disponible, avec la presence possible de correlations elevees comme dans le cas des donnees d'expression genetique. Dans cette note, nous proposons une nouvelle methode de regression lineaire penalisee, appelee elastic corr-net, pour simultanement estimer les parametres inconnus et selectionner les variables importantes. De plus, elle encourage un effet de groupe:les variables fortement correlees ont tendance a etre toutes incluses ou toutes exclues du modele. La methode est fondee sur les moindres carres penalises avec une penalite qui, comme la penalite $L_1$, retrecit certains coefficients exactement vers zero. En outre, cette penalite contient un terme qui lie explicitement la force de penalisation a la correlation entre les variables explicatives. Pour montrer les avantages de notre approche par rapport aux methodes les plus concurrentes, une etude sur des donnees simulees est realisee en moyenne et grande dimension. Enfin, nous appliquons la methodologie a un exemple de donnees reelles. Si $p>> n,$ notre methode reste competitive et elle permet aussi de selectionner plus que $n$ variables.
Effects of hydraulic traits on the species abundance distribution pattern of desert plant communities

: Plant death caused by drought is an important cause of species composition changes in terrestrial ecosystems. Revealing the influence of hydraulic triats on species distribution, which is of great theoretical significance for understanding plant drought adaptation, restoration and management of desert vegetation. In this study, the woody plants in the Ebinur Lake Wetland Nature Reserve were selected as subject to compare the variation characteristics of species abundance and hydraulic traits under soil drought gradient and analyze the relationship between hydraulic traits and species abundance distribution. Results showed that the sum of abundance (community density) of all species did not change significantly. However, the responses of different species abundance to drought stress were significantly different. With the increase in soil drought, the specific conductivity, quasi-steady conductivity, and Huber value increased; the water potential of branches and leaves decreased continuously, and the stem wood density did not change significantly. The difference of hydraulic triats among species changed with the degree of drought. Redundancy analysis and generalized additive model showed that 92.8% of the variation of desert woody plant abundance distribution pattern can be explained by the measured hydraulic triats. In addition, plant abundance increased with the quasi- steady conductivity, specific conductivity, Huber value, and water potential of branches and leaves but decreased with the increase in stem wood density. Therefore, the changes of the woody plant abundance distribution can be predicted using the changes of hydraulic traits.
Detecting Fire and Smoke Using Faster RCNN

With the constant increase in forest fires and the huge losses being experienced because of fire, having the information about a fire starting at the earliest has become a necessity. To cater to this, we have researched and created a system that detects fire and smoke in outdoor and indoor environments using faster RCNN. We have received lesser false positives when compared to fire detection done using color space analysis.
Interpretable Movie Review Analysis Using Machine Learning and Transformer Models Leveraging XAI

Text classification has been a common topic of interest for many years. A lot of advanced models has been developed so far in this area. But it is very difficult to understand how the models behave while predicting the class of the text. In our work, we utilized some models to classify the sentiment of movie reviews from text data and observed how the models behaved using Explainable Artificial Intelligence (XAI). At first dataset was collected and pre-processed. Then the processed dataset was separated into different train and test sets. The train set was used to classify using different different machine learning and neural network based models. The test set was used after training to evaluate the trained classifiers. Finally, the performance of the classifiers were compared and evaluated. After different variations of pre-processing and training steps, the best accuracy score of 91% was obtained using Roberta LSTM model. In the trained models, we sent texts that are correctly classified by RoBERTa models but misclassified by other models. Finally we figured out the reasons of misclassification with the help of LIME Algorithm.
Stochastic models of economic optimization

This paper deals with some stochastic models of economic optimization. Due to the value in practice, the models are quite attractive. But our knowledge on them is still very limited, some fundamental problems remain open. We begin with a short review of the study on some global economic models (or economy in large scale), the well-known input-output method and L. K. Hua’s fundamental theorem for the stability of economy. Then, we show that it is necessary to study the stochastic models. A collapse theorem for a non-controlling stochastic economic system is introduced. In the analysis of the system, the products of random matrices play a critical role. Especially, the first eigenvalue, the corresponding eigenfunctions and an ergodic theorem of Markov chains play a nice role here. Partial proofs are included. Some challenge open problems are also mentioned. AMS 2000 Subject classification: 60-02, 60F99, 62P20.