Characterization of the NNVT capillary plate collimators

In this paper, we report the results of the characterization campaign of two prototypes of Micro-Channel Plates (MCPs), designed as the X-ray collimators for the Large Area Detector on board the eXTP mission. The devices were developed ad-hoc by North Night Vision Technology Co., Ltd. (Nanjing, China). Measurements involved the study of the angular response (rocking curve) of each device to X-rays of different energies. The study evidenced how the angular response of a collimator changes with the energy of the incoming photons, with the onset of side lobes at high energy, which enlarge the effective field of view of the device, causing a potential contamination of the on-axis signal. Nevertheless, the magnitude of this effect is proven to be acceptable in most situations of astrophysical interest. On the lower hand of the energy spectrum, photons may also modify the angular response due to grazing reflection on the inner walls of the collimator, a phenomenon strongly dependent on the degree of roughness of the surfaces involved. The whole campaign took place at the INAF/IAPS laboratories in Rome

Children with Moderate Acute Malnutrition with No Access to Supplementary Feeding Programmes Experience High Rates of Deterioration and No Improvement: Results from a Prospective Cohort Study in Rural Ethiopia

Background: Children with moderate acute malnutrition (MAM) have an increased risk of mortality, infections and impaired physical and cognitive development compared to well-nourished children. In parts of Ethiopia not considered chronically food insecure there are no supplementary feeding programmes (SFPs) for treating MAM. The short-term outcomes of children who have MAM in such areas are not currently described, and there remains an urgent need for evidence-based policy recommendations. Methods: We defined MAM as mid-upper arm circumference (MUAC) of ≥11.0cm and <12.5cm with no bilateral pitting oedema to include Ethiopian government and World Health Organisation cut-offs. We prospectively surveyed 884 children aged 6–59 months living with MAM in a rural area of Ethiopia not eligible for a supplementary feeding programme. Weekly home visits were made for seven months (28 weeks), covering the end of peak malnutrition through to the post-harvest period (the most food secure window), collecting anthropometric, socio-demographic and food security data. Results: By the end of the study follow up, 32.5% (287/884) remained with MAM, 9.3% (82/884) experienced at least one episode of SAM (MUAC <11cm and/or bilateral pitting oedema), and 0.9% (8/884) died. Only 54.2% of the children recovered with no episode of SAM by the end of the study. Of those who developed SAM half still had MAM at the end of the follow up period. The median (interquartile range) time to recovery was 9 (4–15) weeks. Children with the lowest MUAC at enrolment had a significantly higher risk of remaining with MAM and a lower chance of recovering. Conclusions: Children with MAM during the post-harvest season in an area not eligible for SFP experience an extremely high incidence of SAM and a low recovery rate. Not having a targeted nutrition-specific intervention to address MAM in this context places children with MAM at excessive risk of adverse outcomes. Further preventive and curative approaches should urgently be considered

Harmonizing Program Induction with Rate-Distortion Theory

Many aspects of human learning have been proposed as a process of constructing mental programs: from acquiring symbolic number representations to intuitive theories about the world. In parallel, there is a long-tradition of using information processing to model human cognition through Rate Distortion Theory (RDT). Yet, it is still poorly understood how to apply RDT when mental representations take the form of programs. In this work, we adapt RDT by proposing a three way trade-off among rate (description length), distortion (error), and computational costs (search budget). We use simulations on a melody task to study the implications of this trade-off, and show that constructing a shared program library across tasks provides global benefits. However, this comes at the cost of sensitivity to curricula, which is also characteristic of human learners. Finally, we use methods from partial information decomposition to generate training curricula that induce more effective libraries and better generalization

Adults tailor their emotional expressions to infants through "emotionese"

In many cultures, adults use simple, slow, and dynamic speech when talking to infants ("parentese," or infant-directed speech) and make expansive, repetitive movements when demonstrating object properties to infants ("motionese," or infant-directed actions). These modifications enhance infants’ attention to and learning about language and goal-directed actions. Adults’ interactions with infants are also full of emotions—do adults also modify their emotional expressions when interacting with infants? Here we showed parents of infants (aged 7 to 14 months; N = 25) emotion-evoking pictures including colorful bubbles, adorable stuffed animals, yummy snacks, broken toys, dangerous fire, and rotten fruits. We asked parents to describe their feelings about these pictures either to their infant or to an adult partner (i.e., an experimenter). While the parents’ use of emotion words did not differ between conditions, their emotional expressions did: Their infant-directed emotional expressions were more positive when they discussed positive pictures and more negative when they discussed negative pictures compared to their adult-directed emotional expressions. These findings suggest that besides "parentese" and "motionese," there is also a unique form of emotional communication in parent-child interaction—"emotionese.

RETRACTED: Wu et al. Preparation and Analysis of Structured Color Janus Droplets Based on Microfluidic 3D Droplet Printing. <i>Micromachines</i> 2023, <i>14</i>, 1911

The Micromachines Editorial Office retracts the article “Preparation and analysis of structured color Janus droplets based on microfluidic 3D droplet printing” [...

Classifying Goliath Grouper (Epinephelus itajara) Behaviors from a Novel, Multi-Sensor Tag

Inertial measurement unit sensors (IMU; i.e., accelerometer, gyroscope and magnetometer combinations) are frequently fitted to animals to better understand their activity patterns and energy expenditure. Capable of recording hundreds of data points a second, these sensors can quickly produce large datasets that require methods to automate behavioral classification. Here, we describe behaviors derived from a custom-built multi-sensor bio-logging tag attached to Atlantic Goliath grouper (Epinephelus itajara) within a simulated ecosystem. We then compared the performance of two commonly applied machine learning approaches (random forest and support vector machine) to a deep learning approach (convolutional neural network, or CNN) for classifying IMU data from this tag. CNNs are frequently used to recognize activities from IMU data obtained from humans but are less commonly considered for other animals. Thirteen behavioral classes were identified during ethogram development, nine of which were classified. For the conventional machine learning approaches, 187 summary statistics were extracted from the data, including time and frequency domain features. The CNN was fed absolute values obtained from fast Fourier transformations of the raw tri-axial accelerometer, gyroscope and magnetometer channels, with a frequency resolution of 512 data points. Five metrics were used to assess classifier performance; the deep learning approach performed better across all metrics (Sensitivity = 0.962; Specificity = 0.996; F1-score = 0.962; Matthew’s Correlation Coefficient = 0.959; Cohen’s Kappa = 0.833) than both conventional machine learning approaches. Generally, the random forest performed better than the support vector machine. In some instances, a conventional learning approach yielded a higher performance metric for particular classes (e.g., the random forest had a F1-score of 0.971 for backward swimming compared to 0.955 for the CNN). Deep learning approaches could potentially improve behavioral classification from IMU data, beyond that obtained from conventional machine learning methods

<p>Interaction between the BDNF rs11030101 genotype and job stress on cognitive empathy</p>

Background: Empathy refers to an individual's ability to experience the emotional and cognitive processes of another person during social interactions. Although many studies have examined the effects of genetic variation on emotional empathy, little is currently known about whether genetic factors may influence cognitive empathy. This study investigated the relationship between BDNF rs11030101 genotype, job stress, and empathy, especially cognitive empathy, in a Chinese Han population.& nbsp;Methods: A cross-sectional design was used and 340 participants were recruited from a university in Beijing. Interpersonal Reactivity Index (IRI) was used to measure empathy. Job stress was measured using House and Rizzo's Job Stress Scale. The BDNF rs11030101 was genotyped in all participants.& nbsp;Results: Gender and age were associated with various IRI subscales (p 0.05). Job stress was negatively associated with Perspective Taking (p = 0.006) and positively associated with Personal Distress (p < 0.001). In addition, the BDNF rs11030101 genotype modulated the relationship between job stress and Fantasy (p = 0.013), indicating that T allele carriers had higher Fantasy scores at higher job stress and lower Fantasy scores at lower job stress than AA homozygotes. This interaction was only present in women. Limitations: The sample size and single-nucleotide polymorphism are limited, and the cross-sectional design should be improved.& nbsp;Conclusions: Female university faculty with the BDNF rs11030101 T allele may utilize higher emotional job demands, thereby fostering their cognitive empathy