Miracle-dz/UniFAR-FTUs huggingface dataset:
DETECTION AND CHARACTERIZATION OF EXOPLANETS USING PROJECTIONS ON KARHUNEN–LOEVE EIGENIMAGES: FORWARD MODELING

A new class of high-contrast image analysis algorithms that empirically fit and subtract systematic noise has lead to recent discoveries of faint exoplanet/substellar companions and scattered light images of circumstellar disks. These methods are extremely efficient at enhancing the detectability of a faint astrophysical signal, but they generally create systematic biases in their observed properties. This paper provides a general solution for this outstanding problem. We present an analytical derivation of a linear expansion that captures the impact of astrophysical over-subtraction or self-subtraction in current image analysis techniques. We examine the general case for which the reference images of the astrophysical scene move azimuthally and/or radially across the field of view as a result of the observation strategy. Our new method is based on perturbing the covariance matrix underlying any least-squares speckles problem, and propagating this perturbation through the data analysis algorithm. Most of the work in this paper is presented in the Principal Component Analysis framework, but it can be easily generalized to methods relying on the linear combination of images (instead of eigenmodes). Based on this linear expansion, which is obtained in the most general case, we then demonstrate practical applications of this new algorithm. We first consider the spectral extraction of faint point sources in IFS data and illustrate, using public Gemini Planet Imager commissioning data, that our novel perturbation-based Forward Modeling, which we named Karhunen Loeve Image Processing (KLIP-FM), can indeed alleviate algorithmic biases. We then apply KLIP-FM to the detection of point sources and show how it decreases the rate of false negatives while keeping the rate of false positives unchanged when compared to classical KLIP. This can potentially have important consequences on the design of follow-up strategies of ongoing direct imaging surveys.
DISCOVERY OF C iv EMISSION FILAMENTS IN M87

Gas at intermediate temperatures between the hot X-ray-emitting coronal gas in galaxies at the centers of galaxy clusters and the much cooler optical line emitting filaments yields information on transport processes and plausible scenarios for the relationship between X-ray cool cores and other galactic phenomena such as mergers or the onset of an active galactic nucleus. Hitherto, detection of intermediate temperature gas has proven elusive. Here, we present FUV imaging of the “low excitation” emission filaments of M87 and show strong evidence for the presence of C iv 1549 Å emission which arises in gas at temperature ∼105 K co-located with Hα+[N ii] emission from cooler ∼104 K gas. We infer that the hot and cool phases are in thermal communication, and show that quantitatively the emission strength is consistent with thermal conduction, which in turn may account for many of the observed characteristics of cool-core galaxy clusters.
Possible anti-inflammatory, antioxidant, and neuroprotective effects of apigenin in the setting of mild traumatic brain injury: an investigation*

Abstract Objective Apigenin is a plant flavone proven with biological properties such as anti-inflammatory, antioxidant, and antimicrobial effects. This study, it was aimed to examine the possible anti-inflammatory, antioxidant, and neuroprotective effects of apigenin in the setting of the mild traumatic brain injury (TBI) model. Methods Wistar albino male rats were randomly assigned to groups: control (n = 9), TBI (n = 9), TBI + vehicle (n = 8), and TBI + apigenin (20 and 40 mg/kg, immediately after trauma; n = 6 and n = 7). TBI was performed by dropping a 300 g weight from a height of 1 m onto the skull under anesthesia. Neurological examination and tail suspension tests were applied before and 24 h after trauma, as well as Y-maze and object recognition tests, after that rats were decapitated. In brain tissue, luminol- and lucigenin-enhanced chemiluminescence levels and cytokine ELISA levels were measured. Histological damage was scored. Data were analyzed with one-way ANOVA. Results After TBI, luminol (p < .001) and lucigenin (p < .001) levels increased, and luminol and lucigenin levels decreased with apigenin treatments (p < .01–.001). The tail suspension test score increased with trauma (p < .01). According to the pre-traumatic values, the number of entrances to the arms (p < .01) in the Y-maze decreased after trauma (p < .01). In the object recognition test, discrimination (p < .05) and recognition indexes (p < .05) decreased with trauma. There was no significant difference among trauma apigenin groups in behavioral tests. Interleukin (IL)-10 levels, one of the anti-inflammatory cytokines, decreased with trauma (p < .05), and increased with 20 and 40 mg apigenin treatment (p < .001 and p < .01, respectively). The histological damage score in the cortex was decreased in the apigenin 20 mg treatment group significantly (p < .05), but the decrease observed in the apigenin 40 mg group was not significant. Conclusion The results of this study revealed that apigenin 20 and 40 mg treatment may have neuroprotective effects in mild TBI via decreasing the level of luminol and lucigenin and increasing the IL-10 levels. Additionally, apigenin 20 mg treatment ameliorated the trauma-induced cortical tissue damage.
Laryngeal injury following endotracheal intubation: Have you considered reflux?

Laryngotracheal injury is an increasingly common complication of intubation and mechanical ventilation, with an estimated 87% of intubated and ventilated patients developing a laryngotracheal injury often preventing their rehabilitation from acute illness. Laryngotracheal injuries encompass a diverse set of pathologies including inflammation and oedema in addition to vocal cord ulceration and paralysis, granuloma, stenosis, and scarring. The existing literature has identified several factors including intubation duration, endotracheal tube size, type and cuff pressures, and technical factors including the skill and experience of the endoscopist. Despite these associations, a key aspect in the sequelae of laryngotracheal injuries is due to reflux and is not clearly related to iatrogenic and mechanical factors. Laryngopharyngeal reflux is a type of reflux that contaminates the upper aerodigestive tract. The combination of patient positioning and continuous nasogastric tube feeding act to affect the upper aerodigestive tract with acidic and non-acidic refluxate that causes direct and indirect mucosal injury impeding healing. Despite laryngopharyngeal reflux being an established and recognised causative factor of upper aerodigestive tract inflammatory pathology and laryngotracheal injury, it is very understudied in critical care. Further, there is yet to be an agreed pathway to assess, manage and prevent laryngotracheal injury in intubated and ventilated patients. The incidence of laryngopharyngeal reflux in the intubated and mechanically ventilated patient in the intensive care unit is currently unknown. Prospective studies may allow us to understand further potential mechanisms of upper aerodigestive tract injury due to laryngopharyngeal reflux and herald the development of preventative and management strategies of laryngopharyngeal reflux–mediated upper aerodigestive tract injury in critically ill patients.
Pharmacological and Toxicological Study of Coumarinolignoids from Cleome viscosa in Small Animals for the Management of Rheumatoid Arthritis

Abstract This study aims to explore the possible pharmacological potential of Cleome viscosa Linn (Cleomaceae), an annual weed, into therapeutic value-added products. In the present study, we have explored the pharmacological and toxicological profile of coumarinolignoids isolated from Cleome viscose for the management of rheumatoid arthritis and related complications in a small animal model. To avoid the biasness during experiments on animals, we have coded the isolated coumarinolignoids as CLIV-92 to perform the experimental pharmacological study. CLIV-92 was orally administrated (30,100, 300 mg/kg) to animal models of collagen-induced arthritis (CIA), carrageenan-induced acute inflammation, thermal and chemical-induced pain, and Brewerʼs yeast-induced pyrexia. Oral administration of CLIV-92 significantly decreases the arthritis index, arthritis score, and increases the limb withdrawal threshold in the CIA model in experimental rats. The anti-arthritis studies revealed that the anti-inflammatory effect of CLIV-92 was associated with inhibition of the production of inflammatory mediators like TNF- α , IL-6, IL-17A, MMP-1, MMP-9, Nitric oxide, and C-RP in CIA ratʼs serum, and also reduced the NF к B-p65 expression as evidence of immunohistochemistry in knee joint tissue of CIA rats, in a dose-dependent manner. Further individual experiments related to arthritis-related complications in experimental animals demonstrated the analgesic, anti-inflammatory, and antipyretic potential of CLIV-92 in a dose-dependent manner. Further, an in-vivo acute oral toxicity study concluded that CLIV-92 is safe in experimental animals up to 2,000 mg/kg dose. The results of this study suggested that the oral administration of CLIV-92 may be a therapeutic candidate for further investigation in the management of rheumatoid arthritis and related complications.
Effects of a synbiotic as an antibiotic alternative on behavior, production performance, cecal microbial ecology, and jejunal histomorphology of broiler chickens under heat stress

The aim of this study was to examine if synbiotics present similar efficiency to a common antibiotic used in poultry production under heat stress (HS) conditions. Two hundred and forty-one-day-old male Ross 708 broiler chicks were distributed among 3 treatments with 8 pens per treatment of 80 birds each for a 42-day trial. From day 15, birds were heat stressed (32°C for 9 h daily, HS) and fed the basal diet (CONT), the basal diet mixed with an antibiotic (Bactiracin Methylene Disalicylate) (0.05 g/kg of feed, BMD) or a synbiotic (0.5 g/kg of feed, SYN). The treatment effects on bird behavior, production performance, jejunal histomorphology, and cecal microbial ecology were examined. Behavioral observation was recorded by using instantaneous scan sampling technique. Production parameters were measured on day 14, 28, and 42. Cecal microbial populations of Escherichia coli and Lactobacilli and jejunal histomorphological parameters were measured at day 42. The results showed that, SYN birds exhibited more feeding and preening but less drinking and panting behaviors compared with both BMD and CONT birds (P < 0.05). The SYN birds also had higher body weight (BW) at both day 28 and 42 compared to CONT birds (P < 0.05). At the end of the experiment, the counts of Escherichia coli of SYN birds were at the similar levels of BMD but were lower than that of CONT birds (P < 0.05); while there were no treatment effects on the populations of Lactobacilli (P > 0.05). In addition, SYN birds had greater villus height compared with both CONT and BMD birds (P < 0.05). These findings suggest that the dietary synbiotic supplement has significant performance and welfare benefits, with the potential to be used as an alternative to antibiotics for poultry meat production, especially during hot seasons.
In vitro and in silico assessment of new beta amino ketones with antiplasmodial activity

ABSTRACT Background: Based on the current need for new drugs against malaria, our study evaluated eight beta amino ketones in silico and in vitro for potential antimalarial activity. Methods: Using the Brazilian Malaria Molecular Targets (BraMMT) and OCTOPUS® software programs, the pattern of interactions of beta-amino ketones was described against different proteins of P. falciparum and screened to evaluate their physicochemical properties. The in vitro antiplasmodial activities of the compounds were evaluated using a SYBR Green-based assay. In parallel, in vitro cytotoxic data were obtained using the MTT assay. Results: Among the eight compounds, compound 1 was the most active and selective against P. falciparum (IC50 = 0.98 µM; SI > 60). Six targets were identified in BraMMT that interact with compounds exhibiting a stronger binding energy than the crystallographic ligand: P. falciparum triophosphate phosphoglycolate complex (1LYX), P. falciparum reductase (2OK8), PfPK7 (2PML), P. falciparum glutaredoxin (4N0Z), PfATP6, and PfHT. Conclusions: The physicochemical properties of compound 1 were compatible with the set of criteria established by the Lipinski rule and demonstrated its potential as a drug prototype for antiplasmodial activity.
Revealing the Semantics of Data Wrangling Scripts With Comantics

Data workers usually seek to understand the semantics of data wrangling scripts in various scenarios, such as code debugging, reusing, and maintaining. However, the understanding is challenging for novice data workers due to the variety of programming languages, functions, and parameters. Based on the observation that differences between input and output tables highly relate to the type of data transformation, we outline a design space including 103 characteristics to describe table differences. Then, we develop Comantics, a three-step pipeline that automatically detects the semantics of data transformation scripts. The first step focuses on the detection of table differences for each line of wrangling code. Second, we incorporate a characteristic-based component and a Siamese convolutional neural network-based component for the detection of transformation types. Third, we derive the parameters of each data transformation by employing a “slot filling” strategy. We design experiments to evaluate the performance of Comantics. Further, we assess its flexibility using three example applications in different domains.
D-brane width

Loop quantum gravity predicts that there are non-zero minimal area, and non-zero minimal volume in (3+1) dimensions. Given this, one can easily guess that one will have a non-zero minimal 9-volume in (9+1) dimensions. Therefore, in this paper, we argue that not only D9-brane but also Dp-brane for p less than 9 has a 9-volume. This idea is new, as the present view states that such a Dp-brane has p-volume but no 9 volume. To demonstrate this, first, we equate D8-brane action with D9-brane action and show that 9th direction which is perpendicular to D8-brane has non-zero width. We repeat this step for different ps; we equate Dp-brane action with Dp-1 brane action. By this iteration and induction we conclude that Dp-brane has non-zero widths for each of (9-p) directions perpendicular to the Dp-brane, and therefore, non-zero volume. When antisymmetric tensor and field strength are zero, this width is calculated to be 2 pi sqrt(alpha') for all (9-p) directions. For non-vanishing antisymmetric tensor and field strength, the width receives small corrections. In this paper, we only calculate up to the first order correction.
Qualitative Analysis Using Social Maps to Explore Young Women's Experiences With Social Support of their Oral PrEP Use in Kenya and South Africa

Supplemental Digital Content is Available in the Text. Abstract Daily oral pre-exposure prophylaxis (PrEP) adherence is challenging. We explored African adolescent girls and young women's (AGYW) perceptions of the social influencers of their PrEP use and the social influencers' PrEP knowledge and support (six focus group discussions; 33 South African and Kenyan AGYW) in the Prevention Options for Women Evaluation Research demonstration project. Participants completed a social mapping exercise indicating strength and direction of influence of members in their social networks. Mothers and counselors were identified as positive influencers and most influential by >50% of participants, sex partners were labeled negative influencers or both positive and negative, and best friends were mostly positive influencers. HIV- and PrEP-related stigma were the major reasons influencers were identified as negative. Participants wanted their social networks to be better educated about PrEP by someone other than the AGYW themselves (e.g., clinic staff) and to support their PrEP use. To improve PrEP adherence, community- and peer-based PrEP sensitization and delivery interventions should be evaluated.
Determination of Image Aberrations in High-resolution Electron Microscopy using Diffractogram and Cross-correlation Methods

Determination of image aberrations in high-resolution electron microscopy using diffractogram and cross-correlation methods. Methods are discussed to determine the aberration constants of image aberrations in HREM to a high accuracy, as required to obtain ultrahigh resolution. Two methods, designated as diffractogram-tableau (DT) and beam-tilt/cross-correlation BTXC, respectively, are analysed. Both methods utilize sets of images, taken with the beam tilted by several milliradians in different azimuthal directions. Experiments on a CM 200 FEG microscope, using a slow-scan CCD camera and external TEM control, show that both approaches are feasible. In order to correct all relevant aberrations, some additional correction facilities should be provided by the TEM manufacturers.
OA-Bug: An Olfactory-Auditory Augmented Bug Algorithm for Swarm Robots in a Denied Environment

Searching in a denied environment is challenging for swarm robots as no assistance from GNSS, mapping, data sharing, and central processing is allowed. However, using olfactory and auditory signals to cooperate like animals could be an important way to improve the collaboration of swarm robots. In this paper, an Olfactory-Auditory augmented Bug algorithm (OA-Bug) is proposed for a swarm of autonomous robots to explore a denied environment. A simulation environment is built to measure the performance of OA-Bug. The coverage of the search task can reach 96.93% using OA-Bug, which is significantly improved compared with a similar algorithm, SGBA [1]. Furthermore, experiments are conducted on real swarm robots to prove the validity of OA-Bug. Results show that OA-Bug can improve the performance of swarm robots in a denied environment. Video: https://youtu.be/vj9cRiSmgeM.
Optimization-Based Mechanical Perception for Peduncle Localization During Robotic Fruit Harvest

Rising global food demand and harsh working conditions make fruit harvest an important domain to automate. Peduncle localization is an important step for any automated fruit harvesting system, since fruit separation techniques are highly sensitive to peduncle location. Most work on peduncle localization has focused on computer vision, but peduncles can be difficult to visually access due to the cluttered nature of agricultural environments. Our work proposes an alternative method which relies on mechanical -- rather than visual -- perception to localize the peduncle. To estimate the location of this important plant feature, we fit wrench measurements from a wrist force/torque sensor to a physical model of the fruit-plant system, treating the fruit's attachment point as a parameter to be tuned. This method is performed inline as part of the fruit picking procedure. Using our orchard proxy for evaluation, we demonstrate that the technique is able to localize the peduncle within a median distance of 3.8 cm and median orientation error of 16.8 degrees.
ArNLI: Arabic Natural Language Inference for Entailment and Contradiction Detection

Natural Language Inference (NLI) is a hot topic research in natural language processing, contradiction detection between sentences is a special case of NLI. This is considered a difficult NLP task which has a big influence when added as a component in many NLP applications, such as Question Answering Systems, text Summarization. Arabic Language is one of the most challenging low-resources languages in detecting contradictions due to its rich lexical, semantics ambiguity. We have created a dataset of more than 12k sentences and named ArNLI, that will be publicly available. Moreover, we have applied a new model inspired by Stanford contradiction detection proposed solutions on English language. We proposed an approach to detect contradictions between pairs of sentences in Arabic language using contradiction vector combined with language model vector as an input to machine learning model. We analyzed results of different traditional machine learning classifiers and compared their results on our created dataset (ArNLI) and on an automatic translation of both PHEME, SICK English datasets. Best results achieved using Random Forest classifier with an accuracy of 99%, 60%, 75% on PHEME, SICK and ArNLI respectively.
Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure

Most Graph Neural Networks (GNNs) predict the labels of unseen graphs by learning the correlation between the input graphs and labels. However, by presenting a graph classification investigation on the training graphs with severe bias, surprisingly, we discover that GNNs always tend to explore the spurious correlations to make decision, even if the causal correlation always exists. This implies that existing GNNs trained on such biased datasets will suffer from poor generalization capability. By analyzing this problem in a causal view, we find that disentangling and decorrelating the causal and bias latent variables from the biased graphs are both crucial for debiasing. Inspiring by this, we propose a general disentangled GNN framework to learn the causal substructure and bias substructure, respectively. Particularly, we design a parameterized edge mask generator to explicitly split the input graph into causal and bias subgraphs. Then two GNN modules supervised by causal/bias-aware loss functions respectively are trained to encode causal and bias subgraphs into their corresponding representations. With the disentangled representations, we synthesize the counterfactual unbiased training samples to further decorrelate causal and bias variables. Moreover, to better benchmark the severe bias problem, we construct three new graph datasets, which have controllable bias degrees and are easier to visualize and explain. Experimental results well demonstrate that our approach achieves superior generalization performance over existing baselines. Furthermore, owing to the learned edge mask, the proposed model has appealing interpretability and transferability. Code and data are available at: https://github.com/googlebaba/DisC.
From Zero to Production: Baltic-Ukrainian Machine Translation Systems to Aid Refugees

In this paper, we examine the development and usage of six low-resource machine translation systems translating between the Ukrainian language and each of the official languages of the Baltic states. We developed these systems in reaction to the escalating Ukrainian refugee crisis caused by the Russian military aggression in Ukraine in the hope that they might be helpful for refugees and public administrations. Now, two months after MT systems were made public, we analyze their usage patterns and statistics. Our findings show that the Latvian-Ukrainian and Lithuanian-Ukrainian systems are integrated into the public services of Baltic states, leading to more than 127 million translated sentences for the Lithuanian-Ukrainian system. Motivated by these findings, we further enhance our MT systems by better Ukrainian toponym translation and publish an improved version of the Lithuanian-Ukrainian system.
Refined Probabilistic Local Well-Posedness for a Cubic Schrödinger Half-Wave Equation

We obtain probabilistic local well-posedness in quasilinear regimes for the Schr¨odinger half-wave equation with a cubic nonlinearity. We need to use a reﬁned ansatz because of the lack of probabilistic smoothing in the Picard’s iterations, which is due to the high-low-low nonlinear interactions. The proof is an adaptation of the method of Bringmann on the derivative nonlinear wave equation [6] to Schr¨odinger-type equations. In addition, we discuss ill-posedness results for this equation.