In vivo imaging of lung inflammation with neutrophil-specific Ga-68 nano-radiotracer

In vivo detection and quantification of inflammation is a major goal in molecular imaging. Furthermore, cell-specific detection of inflammation would be a tremendous advantage in the characterization of many diseases. Here, we show how this goal can be achieved through the synergistic combination of nanotechnology and nuclear imaging. One of the most remarkable features of this hybrid approach is the possibility to tailor the pharmacokinetics of the nanomaterial-incorporated biomolecule and radionuclide. A good example of this approach is the covalent binding of a large amount of a neutrophil-specific, hydrophobic peptide on the surface of Ga-68 core-doped nanoparticles. This new nano-radiotracer has been used for non-invasive in vivo detection of acute inflammation with very high in vivo labelling efficiency, i.e. a large percentage of labelled neutrophils. Furthermore, we demonstrate that the tracer is neutrophil-specific and yields images of neutrophil recruitment of unprecedented quality. Finally, the nano-radiotracer was successfully detected in chronic inflammation in atherosclerosis-prone ApoE(-/-) mice after several weeks on a high-fat diet

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

A Text Mining and Statistical Approach for Assessment of Pedagogical Impact of Students’ Evaluation of Teaching and Learning Outcome in Education

Technology-enhanced learning (TEL) is now at the heart of teaching and learning process in many higher education institutions (HEIs). Today, educators are faced with the challenges of pedagogically specifying what tools, methods, and technologies are used to support the teachers and students, and to help maintain/sustain a continuous education and practices. This study shows that there is an opportunity in the use of (educational) datasets derived about the teaching and learning processes to provide insights for fostering the education process. To this effect, it analyzed the students’ evaluation of teaching (SET) dataset ( $n=471968$ ) collected within a higher education setting to determine prominent factors that influences the students’ performance or the way (TEL-based) education is being delivered, including its didactical impact and implications for practice. Theoretically, the study employed a mixed methodology grounded on integration of the Data-structure approach and Descriptive decision theory to study the rationality behind the students’ evaluation of the teaching and performance. This was done through the Textual data quantification (qualitative) and Statistical (quantitative) analysis. Qualitatively, the study applied the Educational Process and Data Mining (EPDM) model (a text mining method) to extract the different sentiments and emotional valence expressed by the students in the SET, and how those characteristically differ based on the period and type of evaluation they have completed (between 2019 to 2021). For the quantitative analysis, the study used a multivariate analysis of covariance (MANCOVA) and multiple pairwise comparisons post-hoc tests to analyze the quantified information (average sentiment and emotional valence) extracted from the SET data to determine the marginal means of effect the different SET types and evaluation period have on the students’ learning outcomes/perception about the teaching-learning process. In addition, the study empirically discussed and shed light on the implications of the main findings for TEL-based Education, particularly implemented by the HEI during the analyzed periods. The scholastic indicator from the study shows that while the flexible digital models or instructional methods are effective for continuous education, innovative pedagogies, and teaching transformations. It also, on the other hand, serve as an incentive for more robust research that idiosyncratically look into their implications for the students’ learning outcomes and assessment done in this study

Herbage accumulation, growth and structural characteristics of Mombasa grass (Panicum maximum Jacq.) harvested at different cutting intervals

The objective of the present study was to evaluate herbage accumulation, morphological composition, growth rate and structural characteristics in Mombasa grass swards subject to different cutting intervals (3, 5 and 7 wk) during the rainy and dry seasons of the year. Treatments were assigned to experimental units (17.5 m(2)) according to a complete randomised block design, with four replicates. Herbage accumulation was greater in the rainy than in the dry season (83 and 17%, respectively). Herbage accumulation (24,300 kg DM ha(-1)), average growth rate (140 kg DM ha(-1) d(-1)) and sward height (111 cm) were highest in the 7 wk cutting interval, but leaf proportion (56%), leaf:stem (1.6) and leaf:non leaf (1.3) ratios decreased. Herbage accumulation, morphological composition and sward structure of Mombasa grass sward may be manipulated through defoliation frequency. The highest leaf proportion was recorded in the 3-wk cutting interval. Longer cutting intervals affected negatively sward structure, with potential negative effects on utilization efficiency, animal intake and performance

Immune-Related Conditions and Acute Leukemia in Children with Down Syndrome: A Children's Oncology Group Report

BACKGROUND: Children with Down syndrome (DS) have unique immune profiles and increased leukemia susceptibility. METHODS: Mothers of 158 children with DS diagnosed with acute leukemia at 0-19 years in 1997-2002 and 173 children with DS but no leukemia were interviewed. Associations were evaluated via multivariable unconditional logistic regression. RESULTS: No associations were detected for asthma, eczema, allergies, or hypothyroidism. Diabetes mellitus associated with leukemia (odds ratio=9.23, 95% confidence interval: 2.33-36.59), however most instances occurred concurrent with or after the leukemia diagnosis. CONCLUSIONS AND IMPACT: Children with DS who develop leukemia have increased diabetes risk, likely due to treatment and underlying susceptibility factors