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NRAO and SpaceX have been engaged in coordinated testing efforts since Fall 2021, including conducting experiments on different interference avoidance schemes for the Karl G. Jansky Very Large Array in New Mexico, and the Green Bank Telescope (GBT) inside the National Radio Quiet Zone in West Virginia. The Starlink system is capable of avoiding direct illumination of telescope sites with their adaptive tasking to place downlink beams far away. Nevertheless, even satellites operating in this mode can potentially present strong signals into the telescope’s receiver system if they pass close to the telescope’s main beam at the boresight. For additional protection, Starlink satellites can either momentarily redirect or completely disable their downlink channels while they pass within some minimum angular separation threshold from the telescope’s boresight, methods that are referred to as “telescope boresight avoidance.” In two separate experiments conducted since Fall 2023, NRAO and SpaceX arranged to have the GBT observe a fixed R.A./decl. position in the sky, chosen to have a large number of close-to-boresight Starlink passages. Preliminary analysis from these two experiments illustrates the feasibility of these avoidance methods to significantly reduce, if not eliminate, the negative impact of close-to-boresight satellite passages. Importantly, these experiments demonstrate the value of continuing cooperative efforts between NRAO and SpaceX, and expanding cooperation between the radio astronomy and satellite communities more generally.

In 21 cm experimental cosmology, accurate characterization of a radio telescope’s antenna beam response is essential to measure the 21 cm signal. Computational electromagnetic (CEM) simulations estimate the antenna beam pattern and frequency response by subjecting the EM model to different dependencies, or beam hyperparameters, such as soil dielectric constant or orientation with the environment. However, it is computationally expensive to search all possible parameter spaces to optimize the antenna design or accurately represent the beam to the level required for use as a systematic model in 21 cm cosmology. We therefore present the Model for Emulating Directivities and Electric fields of Antennas ( MEDEA) , an emulator that rapidly and accurately generates far-field radiation patterns over a large hyperparameter space. MEDEA takes a subset of beams simulated by CEM software, spatially decomposes them into coefficients on a complete, linear basis, and then interpolates them to form new beams at arbitrary hyperparameters. We test MEDEA on an analytical dipole and two numerical beams motivated by upcoming lunar lander missions, and then employ MEDEA as a model to fit mock radio spectrometer data to extract covariances on the input beam hyperparameters. We find that the interpolated beams have rms relative errors of at most 10 ^−2 using 20 input beams or less, and that fits to mock data are able to recover the input beam hyperparameters when the model and mock are derived from the same set of beams. When a systematic bias is introduced into the mock data, extracted beam hyperparameters exhibit bias, as expected. We propose several extensions to MEDEA to potentially account for such bias.

The tech sector has been growing at a rapid speed, demanding a higher level of expertise from its labor force. New skills and programming languages are introduced and required by the industry every day, while the university courses are not updated adequately. Finding the high-demand skills and relevant courses to study has become essential to both students and faculty members at tech universities, which leads to a growing research interest in building an intelligence system to support decision making. Leveraging recent development in Natural Language Processing, we built an NLP-based course recommendation system specifically for the computer science (CS) and information technology (IT) fields. In particular, we built (1) a Named Entity Recognition (CSIT-NER) model to extract tech-related skills and entities, then used these skills to build (2) a personalized multi-level course recommendation system using a hybrid model (hybrid CSIT-CRS). Our CSIT-NER model, trained and fine-tuned on a large corpus of text extracted from StackOverflow and GitHub, can accurately extract the relevant skills and entities, outperforming state-of-the-art models across all evaluation metrics. Our hybrid CSIT-CRS can provide recommendations on multiple individualized levels of university courses, career paths with job listings, and industry-required with suitable online courses. The whole system received good ratings and feedback from users from our survey with 201 volunteers who are students and faculty members of tech universities in Australia and Vietnam. This research is beneficial to students, faculty members, universities in CS/IT higher education sector, and stakeholders in tech-related industries.

We present a Bayesian analysis of data from the FIELDS instrument on board the Parker Solar Probe (PSP) spacecraft with the aim of constraining low-frequency (≲6 MHz) sky in preparation for several upcoming lunar-based experiments. We utilize data recorded during PSP’s coning roll maneuvers, in which the axis of the spacecraft is pointed 45° off of the Sun. The spacecraft then rotates about a line between the Sun and the spacecraft with a period of 24 minutes. We reduce the data into two formats: roll-averaged, in which the spectra are averaged over the roll, and phase-binned, in which the spectra are binned according to the phase of the roll. We construct a forward model of the FIELDS observations that includes numerical simulations of the antenna beam, an analytic emissivity function of the galaxy, and estimates of the absorption due to free electrons. Fitting 5 parameters, we find that the roll-averaged data can be fit well by this model, and we obtain posterior parameter constraints that are in general agreement with previous estimates. The model is not, however, able to fit the phase-binned data well, likely due to limitations such as the lack of nonsmooth emission structure at both small and large scales, enforced symmetry between the northern and southern galactic hemispheres, and large uncertainties in the free electron density. This suggests that significant improvement in the low-frequency sky model is needed in order to fully and accurately represent the sky at frequencies below 6 MHz.

One key challenge in detecting a 21 cm cosmological signal at z > 6 is to separate the cosmological signal from foreground emission. This can be studied in a power spectrum space where the foreground is confined to low delay (or, equivalently, k _∥ ) modes, whereas the cosmological signal can spread out to high delay modes. When there is a calibration error, however, the chromaticity of the gain errors propagates to the power spectrum estimate and contaminates the modes for cosmological detection. The Hydrogen Epoch of Reionization Array (HERA) employs a high-precision calibration scheme using redundancy in measurements. In this study, we focus on the gain errors induced by nonredundancies arising from a feed offset relative to HERA’s 14 m parabolic dish element and investigate how to mitigate the chromatic gain errors using three different methods: restricting baseline lengths for calibration, smoothing the antenna gains, and applying a temporal filter prior to calibration. With 2 cm/2° perturbations for translation/tilting motions, a level achievable under normal HERA operating conditions, the combination of the baseline cut and temporal filtering indicates that the spurious gain feature due to nonredundancies is significantly reduced, and the power spectrum recovers the clean foreground-free region. We found that the mitigation technique works even for large feed motions, but in order to keep a stable calibration process, the feed positions need to be constrained to 2 cm for translation motions and 2° for tilting offset relative to the dish’s vertex.

Abstract Objective This study aims to investigate the relationship between segmental longitudinal strain (LS) and quantitative microvascular perfusion (qMVP) in patients with ST-segment elevation myocardial infarction (STEMI), and to explore the prognostic value of the two indicators after STEMI. Methods The retrospective study enrolled 61 patients who underwent primary percutaneous coronary intervention (pPCI) for first STEMI. Microvascular perfusion (MVP) and qMVP were analyzed by myocardial contrast echocardiography (MCE), and segmental LS was analyzed by two-dimensional speckle tracking echocardiography (2D-STE). Myocardial wall perfusion was qualitatively assessed visually. Quantitative myocardial perfusion parameters were analyzed using an 18-segment model. The correlation between segmental LS and qMVP was assessed. The prognostic value of segmental LS and qMVP for major cardiac adverse events were evaluated. Results Among the 314 segments with abnormal wall motion, 44 showed normal microvascular perfusion (nMVP), 87 showed delayed microvascular perfusion (dMVP), and 183 exhibited microvascular obstruction (MVO). Segmental LS was correlated with segmental wall motion (WM) and qMVP. At 12-month follow-up, 19 patients experienced cardiac events. NT-proBNP, regional LS (rLS), and regional qMVP (r-qMVP) were associated with cardiac events. The area under curve (AUC) of combination of rLS and r-qMVP was bigger than single indicator for identifying prognostic value (P

Abstract The limited understanding of the molecular mechanism underlying MYCN-amplified (MNA) neuroblastoma (NB) has hindered the identification of effective therapeutic targets for MNA NB, contributing to its higher mortality rate compared to MYCN non-amplified (non-MNA) NB. Therefore, a comprehensive analysis integrating metabolomics and transcriptomics was conducted to systematically investigate the MNA NB. Metabolomics analysis utilized plasma samples from 28 MNA NB patients and 68 non-MNA NB patients, while transcriptomics analysis employed tissue samples from 15 MNA NB patients and 37 non-MNA NB patients. Notably, joint metabolomics and transcriptomics analysis was performed. A total of 46 metabolites exhibited alterations, with 21 displaying elevated levels and 25 demonstrating reduced levels in MNA NB. In addition, 884 mRNAs in MNA NB showed significant changes, among which 766 mRNAs were higher and 118 mRNAs were lower. Joint-pathway analysis revealed three aberrant pathways involving glycerolipid metabolism, purine metabolism, and lysine degradation. This study highlights the substantial differences in metabolomics and transcriptomics between MNA NB and non-MNA NB, identifying three abnormal metabolic pathways that may serve as potential targets for understanding the molecular mechanisms underlying MNA NB.

Abstract The clinical diagnosis of biliary atresia (BA) poses challenges, particularly in distinguishing it from cholestasis (CS). Moreover, the prognosis for BA is unfavorable and there is a dearth of effective non-invasive diagnostic models for detection. Therefore, the aim of this study is to elucidate the metabolic disparities among children with BA, CS, and normal controls (NC) without any hepatic abnormalities through comprehensive metabolomics analysis. Additionally, our objective is to develop an advanced diagnostic model that enables identification of BA. The plasma samples from 90 children with BA, 48 children with CS, and 47 NC without any liver abnormalities children were subjected to metabolomics analysis, revealing significant differences in metabolite profiles among the 3 groups, particularly between BA and CS. A total of 238 differential metabolites were identified in the positive mode, while 89 differential metabolites were detected in the negative mode. Enrichment analysis revealed 10 distinct metabolic pathways that differed, such as lysine degradation, bile acid biosynthesis. A total of 18 biomarkers were identified through biomarker analysis, and in combination with the exploration of 3 additional biomarkers (LysoPC(18:2(9Z,12Z)), PC (22:5(7Z,10Z,13Z,16Z,19Z)/14:0), and Biliverdin-IX-α), a diagnostic model for BA was constructed using logistic regression analysis. The resulting ROC area under the curve was determined to be 0.968. This study presents an innovative and pioneering approach that utilizes metabolomics analysis to develop a diagnostic model for BA, thereby reducing the need for unnecessary invasive examinations and contributing to advancements in diagnosis and prognosis for patients with BA.

BackgroundIndividuals with high autistic traits exhibit characteristics like those of individuals with autism, including impairments in sociability and communication skills. Whether individuals with high autistic traits exhibit less cooperation remains debated.MethodsThis study employed the prisoner’s dilemma game (PDG) to measure cooperation in 56 dyads, including 27 with high-low (HL) autistic traits and 29 with low-low (LL) autistic traits, using functional near-infrared spectroscopy (fNIRS) hyperscanning technique. Cognitive and emotional empathy were also measured.ResultsIndividuals with high autistic traits had a lower unilateral cooperation rate than did those with low autistic traits; The HL autistic dyads exhibited a lower mutual cooperation rate and reduced interpersonal brain synchronization (IBS) in the right inferior parietal lobule (r-IPL) and right temporoparietal junction (r-TPJ) compared with the LL autistic dyads; Individuals with high autistic traits had significantly lower cognitive empathy scores than did those with low autistic traits, and the cognitive empathy scores had a marginally significant positive correlation with the unilateral cooperation rate and a marginally significant negative correlation with the activation of the right inferior frontal gyrus (r-IFG); Emotional empathy scores did not significantly differ between the high and low autistic groups, and there was a significant positive correlation between emotional empathy scores and the activation of the r-IFG in individuals with high autistic traits.ConclusionThis study revealed abnormal cooperation in individuals with high autistic traits from unilateral and mutual behavior neural perspectives, potentially linked to a disability of cognitive empathy.

The Chinese deciduous fruit Prunus salicina Lindl is loved by people because of its distinctive flavor and significant industrial worth. This study presents the first report of abnormal leaf bud in Prunus salicina Lindl. Malformation of Prunus salicina Lindl leaf buds leads to abnormal development and growth, causing deformities and a decrease in fruit yield. Identifying the underlying causes and causative pathogens is essential for effective disease management. In this experiment, we observed the Malformation Syndrome in Prunus salicina Lindl and used the internal transcribed spacer (ITS) to compare fungal diversity between malformed and healthy leaf buds. These results show a higher level of fungal diversity in diseased leaf buds. Alternaria, Curvibasidium, Symmetrospora, Fonsecazyma, and other genera among the identified fungi are suspected to induce the malformations. Potential contributors to these malformations include Endophyte-plant_Pathogen and Plant_pathogen-Undefined_saprotroph. These findings provide valuable insights for further research on the etiology of plum bud diseases, identifying potential pathogens, and formulating control strategies.

Composting is a quick, effective, and environmentally friendly solution for treating chicken manure. Adding microorganism inoculants to composting materials makes the composting process more effective and improves product quality. This study was carried out to investigate the effect of AINA inoculant on composting of chicken manure. Four treatments (NT1, NT2, NT3, and NT4) were supplemented with AINA microbial inoculant at rates of 0 %, 0.5 %, 1 %, and 1.5 % (v/w). The results showed that the highest temperatures of NT1, NT2, NT3, and NT4 treatments were 46.7 °C, 48.3 °C, 45.5 °C, and 53.7 °C. The final composts' EC, pH, and moisture indicators had 2 - 4 mS/cm, 7.2 – 8.8, and moisture from 53 % - 56 %. The mesophilic and thermophilic bacteria densities were higher than 109 CFU/g and 105 CFU/g. The GI index of final composts in 4 treatments was 83.93, 123.64, 120.75, and 88.78 showing that the compost was mature and nontoxic to plants. The main root length and stem length indicators of NT2 and NT3 were 48.47 mm, 47.33 mm, 60.60 mm, and 56.27 mm being statistically significantly higher than with the remaining treatments (p = 0,05).

Background: Individuals with autism spectrum disorder (ASD) have inhibitory control deficits. The combination of transcranial direct current stimulation (tDCS) and inhibitory control training produces good transfer effects and improves neuroplasticity. However, no studies have explored whether applying tDCS over the dlPFC improves inhibitory control and produces transfer effects in children with ASD. Objective: To explore whether multisession tDCS could enhance inhibitory control training (response inhibition), near-transfer (interference control) and far-transfer effects (sustained attention; stability of attention) in children with ASD and the generalizability of training effects in daily life and the class, as reflected by behavioral performance and neural activity measured by functional near-infrared spectroscopy (fNIRS). Methods: Twenty-eight autistic children were randomly assigned to either the true or sham tDCS group. The experimental group received bifrontal tDCS stimulation at 1.5 mA, administered for 15 min daily across eight consecutive days. tDCS was delivered during a computerized Go/No-go training task. Behavioral performance in terms of inhibitory control (Dog/Monkey and Day/Night Stroop tasks), sustained attention (Continuous Performance and Cancellation tests), prefrontal cortex (PFC) neural activity and inhibitory control and sustained attention in the class and at home were evaluated. Results: Training (response inhibition) and transfer effects (interference control; sustained attention) were significantly greater after receiving tDCS during the Go/No-go training task than after receiving sham tDCS. Changes in oxyhemoglobin (HbO) concentrations in the dlPFC and FPA associated with consistent conditions in the Day/Night Stroop and Continuous Performance test were observed after applying tDCS during the inhibitory control training task. Notably, transfer effects can be generalized to classroom environments. Conclusion: Inhibitory control training combined with tDCS may be a promising, safe, and effective method for improving inhibitory control and sustained attention in children with ASD.