Miracle-dz/UniFAR-FTUs huggingface dataset:
Galactic 26Al traces metal loss through hot chimneys

Radioactive 26Al is an excellent tracer for metal ejection in the Milky Way, and can provide a direct constraint on the modelling of supernova feedback in galaxy evolution. Gamma-ray observations of the 26Al decay line have found high velocities and hence require a significant fraction of the Galactic 26Al in the hot component. At the same time, meteoritic data combined with simulation results suggest that a significant amount of 26Al makes its way into stars before decay. We investigated the distribution into hot and cold channels with a simulation of a Milky-Way-like galaxy with massive-star feedback in superbubbles and with ejecta traced by 26Al. About 30-40 per cent of the ejecta remain hot, with typical cooling times of the order Gyr. 26Al traces the footpoints of a chimney-fed outflow that mixes metals turbulently into the halo of the model galaxy on a scale of at least 50 kpc. The rest diffuses into cold gas with temperatures less than about 10,000 K, and may therefore be quickly available for star formation. We discuss the robustness of the result by comparison to a simulation with a different global flow pattern. The branching ratio into hot and cold components is comparable to that of longer term average results from chemical evolution modelling of galaxies, clusters and the intracluster medium.
Machine Learning and Soil Humidity Sensing: Signal Strength Approach

The Internet-of-Things vision of ubiquitous and pervasive computing gives rise to future smart irrigation systems comprising the physical and digital worlds. A smart irrigation ecosystem combined with Machine Learning can provide solutions that successfully solve the soil humidity sensing task in order to ensure optimal water usage. Existing solutions are based on data received from the power hungry/expensive sensors that are transmitting the sensed data over the wireless channel. Over time, the systems become difficult to maintain, especially in remote areas due to the battery replacement issues with a large number of devices. Therefore, a novel solution must provide an alternative, cost- and energy-effective device that has unique advantage over the existing solutions. This work explores the concept of a novel, low-power, LoRa-based, cost-effective system that achieves humidity sensing using Deep Learning techniques that can be employed to sense soil humidity with high accuracy simply by measuring the signal strength of the given underground beacon device.
Field dependence of the Yukawa coupling in the three flavor quark-meson model

We investigate the renormalization group flow of the field dependent Yukawa coupling in the framework of the three flavor quark--meson model. In a conventional perturbative calculation, given that the field rescaling is trivial, the Yukawa coupling does not get renormalized at the one loop level if it is coupled to an equal number of scalar and pseudoscalar fields. Its field dependent version, however, does flow with respect to the scale. Using the functional renormalization group technique, we show that it is highly nontrivial how to extract the actual flow of the Yukawa coupling as there are several new chirally invariant operators that get generated by quantum fluctuations in the effective action, which need to be distinguished from that of the Yukawa interaction.
A Time-Frequency Based Suspicious Activity Detection for Anti-Money Laundering

Money laundering is the crucial mechanism utilized by criminals to inject proceeds of crime into the financial system. The primary responsibility of the detection of suspicious activity related to money laundering is with the financial institutions. Most of the current systems in these institutions are rule-based and ineffective (over 90 % false positives). The available data science-based anti-money laundering (AML) models to replace the existing rule-based systems work on customer relationship management (CRM) features and time characteristics of transaction behaviour. Due to thousands of possible account features, customer features, and their combinations, it is challenging to perform feature engineering to achieve reasonable accuracy. Aiming to improve the detection performance of suspicious transaction monitoring systems for AML systems, in this article, we introduce a novel feature set based on time-frequency analysis, that uses 2-D representations of financial transactions. Random forest is utilized as a machine learning method, and simulated annealing is adopted for hyperparameter tuning. The designed algorithm is tested on real banking data, proving the results’ efficacy in practically relevant environments. It is shown that the time-frequency characteristics are discriminatory features for suspicious and non-suspicious entities. Therefore, these features substantially improve the area under curve results (over 1%) of the existing data science-based transaction monitoring systems. Using time-frequency features alone, a false positive rate of 14.9% has been achieved, with an F-score of 59.05%. When combined with transaction and CRM features, the false positive rate is 11.85%, and the F-Score is improved to 74.06%.
Demonstrations of Cooperative Perception: Safety and Robustness in Connected and Automated Vehicle Operations

Cooperative perception, or collective perception (CP), is an emerging and promising technology for intelligent transportation systems (ITS). It enables an ITS station (ITS-S) to share its local perception information with others by means of vehicle-to-X (V2X) communication, thereby achieving improved efficiency and safety in road transportation. In this paper, we present our recent progress on the development of a connected and automated vehicle (CAV) and intelligent roadside unit (IRSU). The main contribution of the work lies in investigating and demonstrating the use of CP service within intelligent infrastructure to improve awareness of vulnerable road users (VRU) and thus safety for CAVs in various traffic scenarios. We demonstrate in experiments that a connected vehicle (CV) can “see” a pedestrian around the corners. More importantly, we demonstrate how CAVs can autonomously and safely interact with walking and running pedestrians, relying only on the CP information from the IRSU through vehicle-to-infrastructure (V2I) communication. This is one of the first demonstrations of urban vehicle automation using only CP information. We also address in the paper the handling of collective perception messages (CPMs) received from the IRSU, and passing them through a pipeline of CP information coordinate transformation with uncertainty, multiple road user tracking, and eventually path planning/decision-making within the CAV. The experimental results were obtained with manually driven CV, fully autonomous CAV, and an IRSU retrofitted with vision and laser sensors and a road user tracking system.
Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz

We use a neural-network ansatz originally designed for the variational optimization of quantum systems to study dynamical large deviations in classical ones. We use recurrent neural networks to describe the large deviations of the dynamical activity of model glasses, kinetically constrained models in two dimensions. We present the first finite size-scaling analysis of the large-deviation functions of the two-dimensional Fredrickson-Andersen model, and explore the spatial structure of the high-activity sector of the South-or-East model. These results provide a new route to the study of dynamical large-deviation functions, and highlight the broad applicability of the neural-network state ansatz across domains in physics.
Modelling heterogeneous dust particles: an application to cometary polarization

In this work, we introduce a comet dust model that incorporates multiple dust morphologies along with inhomogeneous mixture of silicate minerals and carbonaceous materials under power-law size distribution, to replicate the standard polarization-phase curve observed in several comets in the narrow-band continuum. Following the results from Rosetta/MIDAS and COSIMA, we create high porosity Hierarchical Aggregates (HA) and low porosity (< 10$\%$) Solids in the form of agglomerated debris. We also introduce a moderate porosity structure with solids in the core, surrounded by fluffy aggregates called Fluffy Solids (FS). We study the mixing combinations, (HA and Solids), (HA and FS) and (HA, FS and Solids) for a range of power-law index n=2.0 to 3.0 for different sets of mixing percentage of silicate minerals and carbonaceous materials. Polarimetry of the short period comets 1P/Halley and 67P/Churyumov-Gerasimenko match best with the polarisation resulting from the combination of HA and Solids while the combinations (HA and FS) and (HA, FS and Solids) provide the best fit results for the long period comets C/1995 O1 (Hale-Bopp) and C/1996 B2 (Hyakutake). The best fit model results also recreate the observed wavelength dependence of polarization. Our dust model agree with the idea that the long period comets may have high percentage of loose particles (HA and FS) compared to those in the case of short period comets as the short period comets experience more frequent and/or higher magnitude of weathering.
A More Comprehensive Method for Using The Target-side Monolingual Data to Improve Low Resource Neural Machine Translation.

Improving neural machine translation (NMT) models using the back-translations of the monolingual target data (synthetic parallel data) is currently the state-of-the-art approach for training improved translation systems. The target-side side monolingual data has been used in the back-translation approach to improve the forward (target) translation model. Whereas the success of the approach heavily relies on the additional parallel data generating model -- the backward model -- the aim of the approach is only targeted at improving the forward model. The back-translation approach was designed primarily to benefit from an additional data whose source-side is synthetic. But research works have shown that translation models can also benefit from additional data whose target-side is synthetic. This work proposes the use of the target-side data throughout the back-translation approach to improve both the backward and forward models. We explored using only the target-side monolingual data to improve the backward model through forward translation and the forward model through back-translation. Experimental results on English-German and English-Vietnamese low resource neural machine translation showed that the proposed approach outperforms baselines that use the traditional back-translation method.
Deep Template-based Object Instance Detection

Much of the focus in the object detection literature has been on the problem of identifying the bounding box of a particular class of object in an image. Yet, in contexts such as robotics and augmented reality, it is often necessary to find a specific object instance—a unique toy or a custom industrial part for example—rather than a generic object class. Here, applications can require a rapid shift from one object instance to another, thus requiring fast turnaround which affords little-to-no training time. What is more, gathering a dataset and training a model for every new object instance to be detected can be an expensive and time-consuming process. In this context, we propose a generic 2D object instance detection approach that uses example viewpoints of the target object at test time to retrieve its 2D location in RGB images, without requiring any additional training (i.e. fine-tuning) step. To this end, we present an end-to-end architecture that extracts global and local information of the object from its viewpoints. The global information is used to tune early filters in the backbone while local viewpoints are correlated with the input image. Our method offers an improvement of almost 30 mAP over the previous template matching methods on the challenging Occluded Linemod [3] dataset (overall mAP of 50.7). Our experiments also show that our single generic model (not trained on any of the test objects) yields detection results that are on par with approaches that are trained specifically on the target objects.
MEK inhibitors in non-V600 BRAF mutations and fusions

Mutations in BRAF at the 600th codon have proven sensitive to combination BRAF and MEK inhibition. Mutations outside this codon, however, are approximately as common but do not have approved targeted therapy approaches. Herein, we discuss targeting these non-V600 mutation and fusions in BRAF with MEK inhibitors.
Inflammation and diabetic retinopathy

Purpose To investigate the relationship between inflammation in the vitreous and diabetic retinopathy. Methods Vitreous samples from 21 patients with proliferative diabetic retinopathy (PDR), 21 patients with nonproliferative diabetic retinopathy (NPDR), and 21 nondiabetic patients with idiopathic epiretinal membranes (control) were studied. The interferon-gamma (IFN-γ), tumor necrosis factor-alpha (TNF-α), matrix metalloproteinase (MMP)-2, MMP-9, and adiponectin levels in the vitreous were detected in all samples with enzyme-linked immunosorbent assay (ELISA). Samples were stored at −80 °C until analyzed. Results The TNF-α levels in the vitreous were not statistically significant between all groups (p>0.005). The mean IFN-γ levels were statistically significantly higher in patients with PDR (70.98 pg/ml) and patients with NPDR (46.61 pg/ml) than in nondiabetic patients (22.02 pg/ml). There was a difference in the IFN-γ levels in the vitreous between patients with PDR and patients with NPDR (p<0.005). The MMP-2 and MMP-9 concentrations in the vitreous were not different between all groups (p>0.05). There was a correlation between the IFN-γ and TNF-α levels. We investigated the statistically significantly decreased levels of adiponectin in the proliferative (p<0.05) and nonproliferative (p<0.05) diabetic eyes compared to the nondiabetic eyes. Conclusions Increased levels of IFN-γ and TNF-α in the vitreous were found in patients with diabetes compared to nondiabetic patients. Decreased levels of adiponectin in the vitreous were found in patients with diabetes compared to nondiabetic patients. The data support the hypothesis that inflammation is associated with diabetic retinopathy.
iTRAQ-Based Quantitative Proteomic Analysis of Intestines in Murine Polymicrobial Sepsis with Hydrogen Gas Treatment

Objective Sepsis-associated intestinal injury has a higher morbidity and mortality in patients with sepsis, but there is still no effective treatment. Our research team has proven that inhaling 2% hydrogen gas (H2) can effectively improve sepsis and related organ damage, but the specific molecular mechanism of its role is not clear. In this study, isobaric tags for relative and absolute quantitation (iTRAQ)-based quantitative proteomics analysis was used for studying the effect of H2 on intestinal injury in sepsis. Methods Male C57BL/6J mice were used to prepare a sepsis model by cecal ligation and puncture (CLP). The 7-day survival rates of mice were measured. 4-kd fluorescein isothiocyanate-conjugated Dextran (FITC-dextran) blood concentration measurement, combined with hematoxylin-eosinstain (HE) staining and Western blotting, was used to study the effect of H2 on sepsis-related intestinal damage. iTRAQ-based liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis was used for studying the proteomics associated with H2 for the treatment of intestinal injury. Results H2 can significantly improve the 7-day survival rates of sepsis mice. The load of blood and peritoneal lavage bacteria was increased, and H2 treatment can significantly reduce it. CLP mice had significant intestinal damage, and inhalation of 2% hydrogen could significantly reduce this damage. All 4194 proteins were quantified, of which 199 differentially expressed proteins were associated with the positive effect of H2 on sepsis. Functional enrichment analysis indicated that H2 may reduce intestinal injury in septic mice through the effects of thyroid hormone synthesis and nitrogen metabolism signaling pathway. Western blot showed that H2 was reduced by down-regulating the expressions of deleted in malignant brain tumors 1 protein (DMBT1), insulin receptor substrate 2 (IRS2), N-myc downregulated gene 1 (NDRG1) and serum amyloid A-1 protein (SAA1) intestinal damage in sepsis mice. Conclusion A total of 199 differential proteins were related with H2 in the intestinal protection of sepsis. H2-related differential proteins were notably enriched in the following signaling pathways, including thyroid hormone synthesis signaling pathway, nitrogen metabolism signaling pathways, digestion and absorption signaling pathways (vitamins, proteins and fats). H2 reduced intestinal injury in septic mice by down-regulating the expressions of SAA1, NDRG1, DMBT1 and IRS2.
Prevalence and factors associated with poor self-rated health among communitydwelling older adults in Lomé (Togo) in 2019

The number of older adults is increasing worldwide, including in sub-Saharan Africa (SSA). However, there is a paucity of data on the overall health status of older adults living in SSA. To assess the prevalence and factors associated with poor Self-Rated Health (SRH) among community-dwelling older adults in Lomé, Togo, we conducted a cross-sectional study from January to June 2019 in Lomé among community-dwelling older adults aged 50 years and older. A 30- minute questionnaire was used to collect socio-demographic characteristics, medical history, patterns of medication use and use of herbal products and dietary supplements during a face-to-face interview. SRH was assessed using a single item: Overall, you would say that your health is… (1) excellent, (2) very good, (3) good, (4) fair and (5) poor with response fair or poor defining poor SRH. A total of 344 respondents with median age 63 years, (IQR: 55-72) were enrolled in the study. Women represented 57.6% of the sample. Overall prevalence of poor SRH was 56.4% (95%CI: 51.0-61.9) and was the highest among females (62.6% vs 47.9%; P=0.007) and participants ³60 years (61.5% vs 51.1%; P=0.021). Female sex, aged ≥60 years, osteoarthritis, hospitalization within the 12 months preceding the survey, polypharmacy, and the use of herbal products were factors associated with poor SRH (P<0.05). More than half of community- dwelling older adults had poor SRH in Lomé. Further studies are needed to guide policymakers in their efforts to design and implement meaningful policies to improve older adults health conditions.
The selective blockade of metabotropic glutamate receptor-5 attenuates fat accumulation in an in vitro model of benign steatosis

It has been previously found that the blockade of metabotropic glutamate receptor type 5 (mGluR5) protects against hepatic ischemia/reperfusion injury and acetaminophen toxicity. The role of mGluR5 in NAFLD has not yet been elucidated. Here, we evaluated the effects of mGluR5 blockade in an in vitro model of steatosis. HepG2 cells were pre-incubated for 12 h with an mGluR5 agonist, a negative allosteric modulator (DHPG and MPEP, respectively) or vehicle, then treated with 1.5 mM oleate/palmitate (O/P) for another 12 h. Cell viability was evaluated with the MTT assay; fat accumulation was measured using the fluorescent dye Nile red; SREBP- 1, PPAR-α, iNOS and Caspase-3 protein expression were evaluated by Western blot; NFkB activity was evaluated as pNFkB/NFkB ratio. mGluR5 modulation did not alter cell viability in O/P-incubated cells; MPEP prevented intracellular lipid accumulation in O/P treated cells; MPEP administration was also associated with a reversion of O/P-induced changes in SREBP-1 and PPAR-α expression, involved in free fatty acid metabolism and uptake. No changes were observed in iNOS and caspase-3 expression, or in NFkB activity. In conclusion, mGluR5 pharmacological blockade reduced fat accumulation in HepG2 cells incubated with O/P, probably by modulating the expression of SREBP-1 and PPAR-α.
Functional Connectivity Density with Frequency-Dependent Changes in Patients with Diffuse Axonal Injury: A Resting-State Functional Magnetic Resonance Imaging Study

Purpose We explored changes in spontaneous brain connectivity in patients with diffuse axonal injury (DAI), assessed via functional connectivity density (FCD) tests using different frequency bands. Patients and Methods In all, 23 patients with DAI (17 males and 6 females) and 23 healthy controls (HCs; 17 males and 6 females) were included. Functional magnetic resonance imaging scans were performed when the participants were in a resting state and the FCD levels in three frequency bands (slow-4: 0.027–0.073 Hz, slow-5: 0.01–0.027 Hz, and typical: 0.01–0.08 Hz) were measured. In addition, Pearson’s correlation coefficient was used to explore the relationship between clinical indices and brain regions with abnormal FCD values. Results Compared to HCs, DAI patients had significantly greater FCD values in the right extranuclear/limbic lobe/cingulate gyrus and left limbic lobe/hippocampus/parahippocampal gyrus, and significantly lower FCD values in the left precuneus/posterior cingulate gyrus, in the slow-4 band. In the slow-5 band, the DAI patients had higher FCD values in the left inferior temporal gyrus/superior temporal gyrus, left parahippocampal gyrus/limbic lobe, left extranuclear/cingulate gyrus, and right medial frontal gyrus, and lower values in the right inferior frontal gyrus, right inferior parietal lobule, and left cingulate gyrus/limbic lobe. Moreover, compared to HCs, the values in the typical band were higher in the right extranuclear/limbic lobe/hippocampus/parahippocampal gyrus, but were significantly lower in the right precuneus/posterior cingulate gyrus and right inferior parietal lobule/supramarginal gyrus. The abnormal FCD values of these brain regions were linearly correlated with different clinical scale scores. Conclusion DAI patients had abnormal FCD values in various brain regions, indicating disruption to the brain functional network. Moreover, the values were frequency dependent. Our results provide new evidence for the pathogenesis of functional impairment and may explain the neuropathological or compensatory mechanism of the disease.
Small molecule CDS-3078 induces G2/M phase arrest and mitochondria-mediated apoptosis in HeLa cells

The tumor suppressor p53 serves important roles in cell cycle arrest and apoptosis, and its activation increases the sensitivity of cancer cells to radiotherapy or chemotherapy. In the present study, the small molecule 2-[1-(4-(benzyloxy)phenyl)-3-oxoisoindolin-2-yl)-2-(4-methoxyphenyl)] acetic acid (CDS-3078) significantly increased p53 mRNA expression levels in a dose-dependent manner. Treatment with CDS-3078 increased p53 expression levels and p53-mediated activation of its downstream target genes in HeLa cells. Additionally, p53+/+ HeLa cells treated with CDS-3078 presented with dysfunctional mitochondria, as indicated by the decrease in Bcl-2 levels, the increase in Bcl-2 homologous antagonist killer and the increase in cytochrome c release from the mitochondria to the cytoplasm. The present results suggested that CDS-3078 treatment significantly induced G2/M phase cell cycle arrest. Therefore, CDS-3078 administration induced apoptosis via p53-mediated cell cycle arrest, causing mitochondrial dysfunction and resulting in apoptotic cell death in cervical cancer cells. Collectively, the present results suggested that CDS-3078 may be a potential anticancer agent.