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Atmospheric waves excited by strong surface explosions, both natural and anthropogenic, often disturb upper atmosphere. In this letter, we report an N-shaped pulse with period ~ 1.3 min propagating southward at ~ 0.8 km/s, observed as changes in ionospheric total electron content using continuous GNSS stations in Israel and Palestine, ~ 10 min after the August 4, 2020 chemical explosion in Beirut, Lebanon. The peak-to-peak amplitude of the disturbance reached ~ 2% of the background electrons, comparable to recently recorded volcanic explosions in the Japanese Islands. We also succeeded in reproducing the observed disturbances assuming acoustic waves propagating upward and their interaction with geomagnetic fields.Several root-colonizing bacterial species can simultaneously promote plant growth and induce systemic resistance. How these rhizobacteria modulate plant metabolism to accommodate the carbon and energy demand from these two competing processes is largely unknown. Here, we show that strains of three Paraburkholderia species, P. graminis PHS1 (Pbg), P. hospita mHSR1 (Pbh), and P. terricola mHS1 (Pbt), upon colonization of the roots of two Broccoli cultivars led to cultivar-dependent increases in biomass, changes in primary and secondary metabolism and induced resistance against the bacterial leaf pathogen Xanthomonas campestris. Strains that promoted growth led to greater accumulation of soluble sugars in the shoot and particularly fructose levels showed an increase of up to 280-fold relative to the non-treated control plants. Similarly, a number of secondary metabolites constituting chemical and structural defense, including flavonoids, hydroxycinnamates, stilbenoids, coumarins and lignins, showed greater accumulation while other resource-competing metabolite pathways were depleted. High soluble sugar generation, efficient sugar utilization, and suppression or remobilization of resource-competing metabolites potentially contributed to curb the tradeoff between the carbon and energy demanding processes induced by Paraburkholderia-Broccoli interaction. Collectively, our results provide a comprehensive and integrated view of the temporal changes in plant metabolome associated with rhizobacteria-mediated plant growth promotion and induced resistance.The molecular basis underlying fetal programming in response to maternal nutrition remains unclear. Herein, we investigated the regulatory relationships between genes in fetal cerebrum, liver, and muscle tissues to shed light on the putative mechanisms that underlie the effects of early maternal nutrient restriction on bovine developmental programming. To this end, cerebrum, liver, and muscle gene expression were measured with RNA-Seq in 14 fetuses collected on day 50 of gestation from dams fed a diet initiated at breeding to either achieve 60% (RES, n = 7) or 100% (CON, n = 7) of energy requirements. To build a tissue-to-tissue gene network, we prioritized tissue-specific genes, transcription factors, and differentially expressed genes. Furthermore, we built condition-specific networks to identify differentially co-expressed or connected genes. Nutrient restriction led to differential tissue regulation between the treatments. Myogenic factors differentially regulated by ZBTB33 and ZNF131 may negatively affect myogenesis. Additionally, nutrient-sensing pathways, such as mTOR and PI3K/Akt, were affected by gene expression changes in response to nutrient restriction. By unveiling the network properties, we identified major regulators driving gene expression. However, further research is still needed to determine the impact of early maternal nutrition and strategic supplementation on pre- and post-natal performance.As courts strive to simultaneously remain self-consistent and adapt to new legal challenges, a complex network of of citations between decided cases is established. Using network science methods to analyze the underlying patterns of citations between cases can help us understand the large-scale mechanisms which shape the judicial system. Here, we use the case-to-case citation structure of the Court of Justice of the European Union to examine this question. Using a link-prediction model, we show that over time the complex network of citations evolves in a way which improves our ability to predict new citations. Investigating the factors which enable prediction over time, we find that the content of the case documents plays a decreasing role, whereas both the predictive power and significance of the citation network structure itself show a consistent increase over time. Finally, our analysis enables us to validate existing citations and recommend potential citations for future cases within the court.Early-stage diagnosis of pancreatic ductal adenocarcinoma (PDAC) is difficult due to non-specific symptoms. Circulating miRNAs in body fluids have been emerging as potential non-invasive biomarkers for diagnosis of many cancers. Thus, this study aimed to assess a panel of miRNAs for their ability to differentiate PDAC from chronic pancreatitis (CP), a benign inflammatory condition of the pancreas. Next-generation sequencing was performed to identify miRNAs present in 60 FFPE tissue samples (27 PDAC, 23 CP and 10 normal pancreatic tissues). Four up-regulated miRNAs (miR-215-5p, miR-122-5p, miR-192-5p, and miR-181a-2-3p) and four down-regulated miRNAs (miR-30b-5p, miR-216b-5p, miR-320b, and miR-214-5p) in PDAC compared to CP were selected based on next-generation sequencing results. The levels of these 8 differentially expressed miRNAs were measured by qRT-PCR in 125 serum samples (50 PDAC, 50 CP, and 25 healthy controls (HC)). The results showed significant upregulation of miR-215-5p, miR-122-5p, and miR-192-5p in PDAC serum samples. In contrast, levels of miR-30b-5p and miR-320b were significantly lower in PDAC as compared to CP and HC. ROC analysis showed that these 5 miRNAs can distinguish PDAC from both CP and HC. Hence, this panel can serve as a non-invasive biomarker for the early detection of PDAC.Previous randomized trials, performed decades ago, showed no survival benefit of intensive screening for distant metastasis in breast cancer. However, recent improvements in targeted therapies and diagnostic accuracy of imaging have again raised the question of the clinical benefit of screening for distant metastasis. Therefore, we investigated the association between the use of modern imaging and survival of patients with breast cancer who eventually developed distant metastasis. We retrospectively reviewed data of 398 patients who developed distant metastasis after their initial curative treatment between January 2000 and December 2015. Patients in the less-intensive surveillance group (LSG) had significantly longer relapse-free survival than did patients in the intensive surveillance group (ISG) (8.7 vs. 22.8 months; p = 0.002). While the ISG showed worse overall survival than the LSG did (50.2 vs. 59.9 months; p = 0.015), the difference was insignificant after adjusting for other prognostic factors. Among the 225 asymptomatic patients whose metastases were detected on imaging, the intensity of screening did not affect overall survival. A small subgroup of patients showed poor survival outcomes when they underwent intensive screening. Patients with HR-/HER2 + tumors and patients who developed lung metastasis in the LSG had better overall survival than those in the ISG did. Highly intensive screening for distant metastasis in disease-free patients with breast cancer was not associated with significant survival benefits, despite the recent improvements in therapeutic options and diagnostic techniques.What determines why some birds migrate and others do not? This question is fundamental to understanding how migratory systems are responding to environmental changes, but the causes of individual migratory behaviours have proven difficult to isolate. We show that, in a partially migratory population of Eurasian oystercatchers (Haematopus ostralegus), the migratory behaviour of progeny follows paternal but not maternal behaviour, and is unrelated to timing of hatching or fledging. These findings highlight the key role of social interactions in shaping the migratory behaviour of new generations, and thus the spatio-temporal distribution of migratory populations.Mild traumatic brain injury (mTBI), or concussion, accounts for 85% of all TBIs. Yet survivors anticipate full cognitive recovery within several months of injury, if not sooner, dependent upon the specific outcome/measure. Recovery is variable and deficits in executive function, e.g., working memory (WM) can persist years post-mTBI. selleck chemical We tested whether cognitive deficits persist in otherwise healthy undergraduates, as a conservative indicator for mTBI survivors at large. We collected WM performance (change detection, n-back tasks) using various stimuli (shapes, locations, letters; aurally presented numbers and letters), and wide-ranging cognitive assessments (e.g., RBANS). We replicated the observation of a general visual WM deficit, with preserved auditory WM. Surprisingly, visual WM deficits were equivalent in participants with a history of mTBI (mean 4.3 years post-injury) and in undergraduates with recent sports-related mTBI (mean 17 days post-injury). In seeking the underlying mechanism of these behavioral deficits, we collected resting state fMRI (rsfMRI) and EEG (rsEEG). RsfMRI revealed significantly reduced connectivity within WM-relevant networks (default mode, central executive, dorsal attention, salience), whereas rsEEG identified no differences (modularity, global efficiency, local efficiency). In summary, otherwise healthy current undergraduates with a history of mTBI present behavioral deficits with evidence of persistent disconnection long after full recovery is expected.The intrinsic temporality of learning demands the adoption of methodologies capable of exploiting time-series information. In this study we leverage the sequence data framework and show how data-driven analysis of temporal sequences of task completion in online courses can be used to characterise personal and group learners' behaviors, and to identify critical tasks and course sessions in a given course design. We also introduce a recently developed probabilistic Bayesian model to learn sequential behaviours of students and predict student performance. The application of our data-driven sequence-based analyses to data from learners undertaking an on-line Business Management course reveals distinct behaviors within the cohort of learners, identifying learners or groups of learners that deviate from the nominal order expected in the course. Using course grades a posteriori, we explore differences in behavior between high and low performing learners. We find that high performing learners follow the progression between weekly sessions more regularly than low performing learners, yet within each weekly session high performing learners are less tied to the nominal task order. We then model the sequences of high and low performance students using the probablistic Bayesian model and show that we can learn engagement behaviors associated with performance. We also show that the data sequence framework can be used for task-centric analysis; we identify critical junctures and differences among types of tasks within the course design. We find that non-rote learning tasks, such as interactive tasks or discussion posts, are correlated with higher performance. We discuss the application of such analytical techniques as an aid to course design, intervention, and student supervision.