Proton Shell Gaps in N=28 Nuclei from the First Complete Spectroscopy Study with FRIB Decay Station Initiator

The first complete measurement of the β-decay strength distribution of _{17}^{45}Cl_{28} was performed at the Facility for Rare Isotope Beams (FRIB) with the FRIB Decay Station Initiator during the second FRIB experiment. The measurement involved the detection of neutrons and γ rays in two focal planes of the FRIB Decay Station Initiator in a single experiment for the first time. This enabled an analytical consistency in extracting the β-decay strength distribution over the large range of excitation energies, including neutron unbound states. We observe a rapid increase in the β-decay strength distribution above the neutron separation energy in _{18}^{45}Ar_{27}. This was interpreted to be caused by the transitioning of neutrons into protons excited across the Z=20 shell gap. The SDPF-MU interaction with reduced shell gap best reproduced the data. The measurement demonstrates a new approach that is sensitive to the proton shell gap in neutron rich nuclei according to SDPF-MU calculations.

Observation of New Isotopes in the Fragmentation of ^{198}Pt at FRIB

Five previously unknown isotopes (^{182,183}Tm, ^{186,187}Yb, ^{190}Lu) were produced, separated, and identified for the first time at the Facility for Rare Isotope Beams (FRIB) using the Advanced Rare Isotope Separator (ARIS). The new isotopes were formed through the interaction of a ^{198}Pt beam with a carbon target at an energy of 186  MeV/u and with a primary beam power of 1.5 kW. Event-by-event particle identification of A, Z, and q for the reaction products was performed by combining measurements of the energy loss, time of flight, magnetic rigidity Bρ, and total kinetic energy. The ARIS separator has a novel two-stage design with high resolving power to strongly suppress contaminant beams. This successful new isotope search was performed less than one year after FRIB operations began and demonstrates the discovery potential of the facility which will ultimately provide 400 kW of primary beam power.

Linking functional and structural brain organisation with behaviour in autism: a multimodal EU-AIMS Longitudinal European Autism Project (LEAP) study

Neuroimaging analyses of brain structure and function in autism have typically been conducted in isolation, missing the sensitivity gains of linking data across modalities. Here we focus on the integration of structural and functional organisational properties of brain regions. We aim to identify novel brain-organisation phenotypes of autism. We utilised multimodal MRI (T1-, diffusion-weighted and resting state functional), behavioural and clinical data from the EU AIMS Longitudinal European Autism Project (LEAP) from autistic (n = 206) and non-autistic (n = 196) participants. Of these, 97 had data from 2 timepoints resulting in a total scan number of 466. Grey matter density maps, probabilistic tractography connectivity matrices and connectopic maps were extracted from respective MRI modalities and were then integrated with Linked Independent Component Analysis. Linear mixed-effects models were used to evaluate the relationship between components and group while accounting for covariates and non-independence of participants with longitudinal data. Additional models were run to investigate associations with dimensional measures of behaviour. We identified one component that differed significantly between groups (coefficient = 0.33, padj = 0.02). This was driven (99%) by variance of the right fusiform gyrus connectopic map 2. While there were multiple nominal (uncorrected p < 0.05) associations with behavioural measures, none were significant following multiple comparison correction. Our analysis considered the relative contributions of both structural and functional brain phenotypes simultaneously, finding that functional phenotypes drive associations with autism. These findings expanded on previous unimodal studies by revealing the topographic organisation of functional connectivity patterns specific to autism and warrant further investigation.

Protocol of the Healthy Brain Study: An accessible resource for understanding the human brain and how it dynamically and individually operates in its bio-social context

The endeavor to understand the human brain has seen more progress in the last few decades than in the previous two millennia. Still, our understanding of how the human brain relates to behavior in the real world and how this link is modulated by biological, social, and environmental factors is limited. To address this, we designed the Healthy Brain Study (HBS), an interdisciplinary, longitudinal, cohort study based on multidimensional, dynamic assessments in both the laboratory and the real world. Here, we describe the rationale and design of the currently ongoing HBS. The HBS is examining a population-based sample of 1,000 healthy participants (age 30–39) who are thoroughly studied across an entire year. Data are collected through cognitive, affective, behavioral, and physiological testing, neuroimaging, bio-sampling, questionnaires, ecological momentary assessment, and real-world assessments using wearable devices. These data will become an accessible resource for the scientific community enabling the next step in understanding the human brain and how it dynamically and individually operates in its bio-social context. An access procedure to the collected data and bio-samples is in place and published on https://www.healthybrainstudy.nl/en/data-and-methods/access.

Trail registration:https://www.trialregister.nl/trial/7955.

Handwritten-word spotting using biologically inspired features

For quick access to new handwritten collections, current handwriting recognition methods are too cumbersome. They cannot deal with the lack of labeled data and would require extensive laboratory training for each individual script, style, language and collection. We propose a biologically inspired whole-word recognition method which is used to incrementally elicit word labels in a live, web-based annotation system, named Monk. Since human labor should be minimized given the massive amount of image data, it becomes important to rely on robust perceptual mechanisms in the machine. Recent computational models of the neuro-physiology of vision are applied to isolated word classification. A primate cortex-like mechanism allows to classify text-images that have a low frequency of occurrence. Typically these images are the most difficult to retrieve and often contain named entities and are regarded as the most important to people. Usually standard pattern-recognition technology cannot deal with these text-images if there are not enough labeled instances. The results of this retrieval system are compared to normalized word-image matching and appear to be very promising.