Shift from widespread symbiont infection of host tissues to specific colonization of gills in juvenile deep-sea mussels

The deep-sea mussel Bathymodiolus harbors chemosynthetic bacteria in its gills that provide it with nutrition. Symbiont colonization is assumed to occur in early life stages by uptake from the environment, but little is known about this process. In this study, we used fluorescence in situ hybridization to examine symbiont distribution and the specificity of the infection process in juvenile B. azoricus and B. puteoserpentis (4-21 mm). In the smallest juveniles, we observed symbionts, but no other bacteria, in a wide range of epithelial tissues. This suggests that despite the widespread distribution of symbionts in many different juvenile organs, the infection process is highly specific and limited to the symbiotic bacteria. Juveniles >= 9mm only had symbionts in their gills, indicating an ontogenetic shift in symbiont colonization from indiscriminate infection of almost all epithelia in early life stages to spatially restricted colonization of gills in later developmental stages

Biased feedback in brain-computer interfaces

Even though feedback is considered to play an important role in learning how to operate a brain-computer interface (BCI), to date no significant influence of feedback design on BCI-performance has been reported in literature. In this work, we adapt a standard motor-imagery BCI-paradigm to study how BCI-performance is affected by biasing the belief subjects have on their level of control over the BCI system. Our findings indicate that subjects already capable of operating a BCI are impeded by inaccurate feedback, while subjects normally performing on or close to chance level may actually benefit from an incorrect belief on their performance level. Our results imply that optimal feedback design in BCIs should take into account a subject's current skill level

Carbenic nitrile imines: Properties and reactivity

Structures and properties of nitrile imines were investigated computationally at B3LYP and CCSD(T) levels. Whereas NBO analysis at the B3LYP DFT level invariably predicts a propargylic electronic structure, CCSD(T) calculations permit a clear distinction between propargylic, allenic, and carbenic structures. Nitrile imines with strong IR absorptions above ca. 2150 cm-1 have propargylic structures with a CN triple bond (RCNNSiMe 3 and R2BCNNBR2), and those with IR absorptions below ca. 2150 cm-1 are allenic (HCNNH, PhCNNH, and HCNNPh). Nitrile imines lacking significant cumulenic IR absorptions at 1900-2200 cm -1 are carbenic (R-(C:)-N=N-R′). Electronegative but lone pair-donating groups NR2, OR, and F stabilize the carbenic form of nitrile imines in the same way they stabilize "normal" singlet carbenes, including N-heterocyclic carbenes. NBO analyses at the CCSD(T) level confirm the classification into propargylic, allenic, and carbenic reactivity types. Carbenic nitrile imines are predicted to form azoketenes 21 with CO, to form [2+2] and [2+4] cycloadducts and borane adducts, and to cyclize to 1H-diazirenes of the type 24 in mildly exothermic reactions with activation energies in the range 29-38 kcal/mol. Such reactions will be readily accessible photochemically and thermally, e.g., under the conditions of matrix photolysis and flash vacuum thermolysis

Adsorption of 2,2 '-dithiodipyridine as a tool for the assembly of silver nanoparticles

Silver nanostructured thin films stabilized by 2,2’-dithiodipyridine (2dtpy) were prepared. The Ag nanoparticles were obtained by treating the complex [Ag(2dtpy)]NO3 with NaBH4 in a methanol–toluene mixture. The films were transferred to borosilicate glass slips by a dip-coating method and were found to consist of Ag nanoparticles possibly linked via 2dtpy molecules. Surface-enhanced Raman scattering (SERS) studies have offered the possibility of investigating the adsorption modes of 2dtpy at the Ag nanoparticle surfaces in the fil

On the chemistry of stable alpha-oxoketenes

This short review describes the preparation and chemistry of sterically stabilized α-oxoketenes, which can be isolated and handled as true neat compounds. Their reactions with dienophiles afford [4+2] - as well as [2+2] cycloadducts depending on their ability to adopt that conformation suitable for each type of cycloaddition reactions. Addition of nucleophiles leads either to dipivaloylacetic acid derivatives as expected products or to the rare molecular skeleton of mono-or bifunctionalized bridged bisdioxines, which exhibit axial chirality. The bifunctionalized derivatives may serve as novel spacer units in several macrocyclic systems

Predicting mental imagery based BCI performance from personality, cognitive profile and neurophysiological patterns

Mental-Imagery based Brain-Computer Interfaces (MI-BCIs) allow their users to send commands to a computer using their brain-activity alone (typically measured by ElectroEncephaloGraphy— EEG), which is processed while they perform specific mental tasks. While very promising, MI-BCIs remain barely used outside laboratories because of the difficulty encountered by users to control them. Indeed, although some users obtain good control performances after training, a substantial proportion remains unable to reliably control an MI-BCI. This huge variability in user-performance led the community to look for predictors of MI-BCI control ability. However, these predictors were only explored for motor-imagery based BCIs, and mostly for a single training session per subject. In this study, 18 participants were instructed to learn to control an EEG-based MI-BCI by performing 3 MI-tasks, 2 of which were non-motor tasks, across 6 training sessions, on 6 different days. Relationships between the participants’ BCI control performances and their personality, cognitive profile and neurophysiological markers were explored. While no relevant relationships with neurophysiological markers were found, strong correlations between MI-BCI performances and mental-rotation scores (reflecting spatial abilities) were revealed. Also, a predictive model of MI-BCI performance based on psychometric questionnaire scores was proposed. A leave-one-subject-out cross validation process revealed the stability and reliability of this model: it enabled to predict participants’ performance with a mean error of less than 3 points. This study determined how users’ profiles impact their MI-BCI control ability and thus clears the way for designing novel MI-BCI training protocols, adapted to the profile of each user

Improved survival for adolescents and young adults with Hodgkin lymphoma and continued high survival for children in the Netherlands:a population-based study during 1990-2015

Population-based studies that assess long-term patterns of incidence, major aspects of treatment and survival are virtually lacking for Hodgkin lymphoma (HL) at a younger age. This study assessed the progress made for young patients with HL (<25 years at diagnosis) in the Netherlands during 1990–2015. Patient and tumour characteristics were extracted from the population-based Netherlands Cancer Registry. Time trends in incidence and mortality rates were evaluated with average annual percentage change (AAPC) analyses. Stage at diagnosis, initial treatments and site of treatment were studied in relation to observed overall survival (OS). A total of 2619 patients with HL were diagnosed between 1990 and 2015. Incidence rates increased for 18–24-year-old patients (AAPC + 1%, P = 0·01) only. Treatment regimens changed into less radiotherapy and more ‘chemotherapy only’, different for age group and stage. Patients aged 15–17 years were increasingly treated at a paediatric oncology centre. The 5-year OS for children was already high in the early 1990s (93%). For patients aged 15–17 and 18–24 years the 5-year OS improved from 84% and 90% in 1990–1994 to 96% and 97% in 2010–2015, respectively. Survival for patients aged 15–17 years was not affected by site of treatment. Our present data demonstrate that significant progress in HL treatment has been made in the Netherlands since 1990

2021 BEETL competition: advancing transfer learning for subject independence & heterogenous EEG data sets

Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is because regular machine learning methods cannot generalise well across human subjects and handle learning from different, heterogeneously collected data sets, thus limiting the scale of training data available. On the other hand, the many developments in transfer- and meta-learning fields would benefit significantly from a real-world benchmark with immediate practical application. Therefore, we pick electroencephalography (EEG) as an exemplar for all the things that make biosignal data analysis a hard problem. We design two transfer learning challenges around a. clinical diagnostics and b. neurotechnology. These two challenges are designed to probe algorithmic performance with all the challenges of biosignal data, such as low signal-to-noise ratios, major variability among subjects, differences in the data recording sessions and techniques, and even between the specific BCI tasks recorded in the dataset. Task 1 is centred on the field of medical diagnostics, addressing automatic sleep stage annotation across subjects. Task 2 is centred on Brain-Computer Interfacing (BCI), addressing motor imagery decoding across both subjects and data sets. The successful 2021 BEETL competition with its over 30 competing teams and its 3 winning entries brought attention to the potential of deep transfer learning and combinations of set theory and conventional machine learning techniques to overcome the challenges. The results set a new state-of-the-art for the real-world BEETL benchmarks

Single Trial Classification of Motor Imagination Using 6 Dry EEG Electrodes

BACKGROUND: Brain computer interfaces (BCI) based on electro-encephalography (EEG) have been shown to detect mental states accurately and non-invasively, but the equipment required so far is cumbersome and the resulting signal is difficult to analyze. BCI requires accurate classification of small amplitude brain signal components in single trials from recordings which can be compromised by currents induced by muscle activity. METHODOLOGY/PRINCIPAL FINDINGS: A novel EEG cap based on dry electrodes was developed which does not need time-consuming gel application and uses far fewer electrodes than on a standard EEG cap set-up. After optimizing the placement of the 6 dry electrodes through off-line analysis of standard cap experiments, dry cap performance was tested in the context of a well established BCI cursor control paradigm in 5 healthy subjects using analysis methods which do not necessitate user training. The resulting information transfer rate was on average about 30% slower than the standard cap. The potential contribution of involuntary muscle activity artifact to the BCI control signal was found to be inconsequential, while the detected signal was consistent with brain activity originating near the motor cortex. CONCLUSIONS/SIGNIFICANCE: Our study shows that a surprisingly simple and convenient method of brain activity imaging is possible, and that simple and robust analysis techniques exist which discriminate among mental states in single trials. Within 15 minutes the dry BCI device is set-up, calibrated and ready to use. Peak performance matched reported EEG BCI state of the art in one subject. The results promise a practical non-invasive BCI solution for severely paralyzed patients, without the bottleneck of setup effort and limited recording duration that hampers current EEG recording technique. The presented recording method itself, BCI not considered, could significantly widen the use of EEG for emerging applications requiring long-term brain activity and mental state monitoring