Disease Delineation for Multiple Sclerosis, Friedreich Ataxia, and Healthy Controls Using Supervised Machine Learning on Speech Acoustics.

Neurodegenerative disease often affects speech. Speech acoustics can be used as objective clinical markers of pathology. Previous investigations of pathological speech have primarily compared controls with one specific condition and excluded comorbidities. We broaden the utility of speech markers by examining how multiple acoustic features can delineate diseases. We used supervised machine learning with gradient boosting (CatBoost) to delineate healthy speech from speech of people with multiple sclerosis or Friedreich ataxia. Participants performed a diadochokinetic task where they repeated alternating syllables. We subjected 74 spectral and temporal prosodic features from the speech recordings to machine learning. Results showed that Friedreich ataxia, multiple sclerosis and healthy controls were all identified with high accuracy (over 82%). Twenty-one acoustic features were strong markers of neurodegenerative diseases, falling under the categories of spectral qualia, spectral power, and speech rate. We demonstrated that speech markers can delineate neurodegenerative diseases and distinguish healthy speech from pathological speech with high accuracy. Findings emphasize the importance of examining speech outcomes when assessing indicators of neurodegenerative disease. We propose large-scale initiatives to broaden the scope for differentiating other neurological diseases and affective disorders.

Metaphor-A workflow for streamlined assembly and binning of metagenomes.

Recent advances in bioinformatics and high-throughput sequencing have enabled the large-scale recovery of genomes from metagenomes. This has the potential to bring important insights as researchers can bypass cultivation and analyze genomes sourced directly from environmental samples. There are, however, technical challenges associated with this process, most notably the complexity of computational workflows required to process metagenomic data, which include dozens of bioinformatics software tools, each with their own set of customizable parameters that affect the final output of the workflow. At the core of these workflows are the processes of assembly-combining the short-input reads into longer, contiguous fragments (contigs)-and binning, clustering these contigs into individual genome bins. The limitations of assembly and binning algorithms also pose different challenges depending on the selected strategy to execute them. Both of these processes can be done for each sample separately or by pooling together multiple samples to leverage information from a combination of samples. Here we present Metaphor, a fully automated workflow for genome-resolved metagenomics (GRM). Metaphor differs from existing GRM workflows by offering flexible approaches for the assembly and binning of the input data and by combining multiple binning algorithms with a bin refinement step to achieve high-quality genome bins. Moreover, Metaphor generates reports to evaluate the performance of the workflow. We showcase the functionality of Metaphor on different synthetic datasets and the impact of available assembly and binning strategies on the final results.

The cost and impact of distributing naloxone to people who are prescribed opioids to prevent opioid-related deaths: findings from a modelling study.

BACKGROUND AND AIMS: Although most opioid-related mortality in Australia involves prescription opioids, most research to understand the impact of naloxone supply on opioid-related mortality has focused upon people who inject heroin. We aimed to examine the cost and probable impact of up-scaling naloxone supply to people who are prescribed opioids. DESIGN: Decision-tree model. Four scenarios were compared with a baseline scenario (the current status quo): naloxone scale-up between 2020 and 2030 to reach 30 or 90% coverage by 2030, among the subgroups of people prescribed either ≥ 50 or ≥ 100 mg of oral morphine equivalents (OME). SETTING: Australia. PARTICIPANTS: People who are prescribed opioids. MEASUREMENTS: Possible deaths averted, costs (ambulance and naloxone distribution) and cost per life saved for different scenarios of naloxone scale-up. FINDINGS: Maintaining the status quo, there would be an estimated 7478 [uncertainty interval (UI) = 6868-8275] prescription opioid overdose deaths between 2020 and 2030, resulting in Australian dollars (A$)51.9 million (49.4, 56.0) in ambulance costs. If naloxone were scaled-up to 90% of people prescribed > 50 mg OME, an estimated 657 (UI = 245, 1489) deaths could be averted between 2020 and 2030 (a 20% reduction in the final year of the model compared with the no naloxone scenario), with a cost of A$43 600 (20 800-110 500) per life saved. If naloxone were scaled-up to 30% of people prescribed > 50 mg OME an estimated 219 (82-496) deaths could be averted with the same cost per live saved. If naloxone were restricted to those prescribed > 100 mg OME, an estimated 130 (UI = 44-289) deaths would be averted if scaled-up to 30% or 390 (UI = 131-866) deaths averted if scaled-up to 90%, with the cost per life saved for both scenarios A$38 200 (UI = 12 400-97 400). CONCLUSION: In Australia, scaling-up take-home naloxone by 2030 to reach 90% of people prescribed daily doses of ≥ 50 mg of oral morphine equivalents would be cost-effective and save more than 650 lives.

Short-Term Attractive Tilt Aftereffects Predicted by a Recurrent Network Model of Primary Visual Cortex.

Adaptation is a multi-faceted phenomenon that is of interest in terms of both its function and its potential to reveal underlying neural processing. Many behavioral studies have shown that after exposure to an oriented adapter the perceived orientation of a subsequent test is repulsed away from the orientation of the adapter. This is the well-known Tilt Aftereffect (TAE). Recently, we showed that the dynamics of recurrently connected networks may contribute substantially to the neural changes induced by adaptation, especially on short time scales. Here we extended the network model and made the novel behavioral prediction that the TAE should be attractive, not repulsive, on a time scale of a few 100 ms. Our experiments, using a novel adaptation protocol that specifically targeted adaptation on a short time scale, confirmed this prediction. These results support our hypothesis that recurrent network dynamics may contribute to short-term adaptation. More broadly, they show that understanding the neural processing of visual inputs that change on the time scale of a typical fixation requires a detailed analysis of not only the intrinsic properties of neurons, but also the slow and complex dynamics that emerge from their recurrent connectivity. We argue that this is but one example of how even simple recurrent networks can underlie surprisingly complex information processing, and are involved in rudimentary forms of memory, spatio-temporal integration, and signal amplification.

A virtual experiment improved students' understanding of physiological experimental processes ahead of a live inquiry-based practical class.

Physiology is commonly taught through direct experience and observation of scientific phenomena in "hands-on" practical laboratory classes. The value of such classes is limited by students' lack of understanding of the underlying theoretical concepts and their lack of confidence with the experimental techniques. In our experience, students follow experimental steps as if following a recipe, without giving thought to the underlying theory and the relationship between the experimental procedure and the research hypotheses. To address this issue, and to enhance student learning, we developed an online virtual experiment for students to complete before an inquiry-based practical. The virtual experiment and "live" practical laboratory were an investigation of how autonomic nerves control contractions in the isolated rabbit ileum. We hypothesized that the virtual experiment would support students' understanding of the physiological concepts, as well as the experimental design associated with the practical. Anonymous survey data and usage analytics showed that most students engaged with the virtual experiment. Students thought that it helped them to understand the practical physiological concepts and experimental design, with self-reported time spent on the virtual experiment (and not on lectures or practical class notes) a significant predictor of their understanding. This novel finding provides evidence that virtual experiments can contribute to students' research skills development. Our results indicate that self-paced online virtual experiments are an effective way to enhance student understanding of physiological concepts and experimental processes, allowing for a more realistic experience of the scientific method and a more effective use of time in practical classes.

Topography of claustrum and insula projections to medial prefrontal and anterior cingulate cortices of the common marmoset (Callithrix jacchus).

The claustrum has been the subject of intense research interest in recent years, driven in large part by its extensive connections with various regions of the cerebral cortex and by hypotheses surrounding its possible role in multimodal sensory and/or sensory-emotional integration. Here we employed neuroanatomical tracers to map projections from the claustrum-insular region to the medial prefrontal and anterior cingulate cortex of the common marmoset (Callithrx jacchus). These areas were selected based on their identification as "hub" areas of the default mode and cortical salience networks, respectively. Microinjections of fluorescent tracers, along with gold-nanoparticle-conjugated cholera toxin B-subunit and biotinylated dextran amine, were placed in subdivisions of the anterior cingulate area 24b/c and in medial prefrontal areas 32 and 32V. The resulting distribution of transported label showed rostral-caudal and dorsal-ventral topographic arrangement of claustrum connections and clear rostral-caudal topography of insular projections. Medial prefrontal connections were restricted mainly to a ventromedial strip located in the rostral half of the claustrum, with a second, smaller patch of cells in the caudal, ventrolateral portion. In contrast, injections into area 24 yielded dense, widespread connections from the dorsal claustrum, extending along its entire rostral-caudal length. Projections from the "classical" agranular, disgranular, and granular insular areas were sparse or nonexistent in areas 32 and 32V, with progressively increasing connections observed in more caudal tracer injections (i.e., in subdivisions of area 24). Transported label was observed in rostral peri-insular areas orbital periallocortex, orbital proisocortex, and insular proisocortex following all prefrontal injections. These data provide a structural connectivity foundation for interpretation of functional imaging studies, which often indicate activity in the "anterior insula" that may arise, in part, from claustrum and/or peri-insular projections to the anterior cingulate and medial prefrontal cortices. J. Comp. Neurol. 525:1421-1441, 2017. © 2016 Wiley Periodicals, Inc.

Adaptation without Plasticity.

Sensory adaptation is a phenomenon in which neurons are affected not only by their immediate input but also by the sequence of preceding inputs. In visual cortex, for example, neurons shift their preferred orientation after exposure to an oriented stimulus. This adaptation is traditionally attributed to plasticity. We show that a recurrent network generates tuning curve shifts observed in cat and macaque visual cortex, even when all synaptic weights and intrinsic properties in the model are fixed. This demonstrates that, in a recurrent network, adaptation on timescales of hundreds of milliseconds does not require plasticity. Given the ubiquity of recurrent connections, this phenomenon likely contributes to responses observed across cortex and shows that plasticity cannot be inferred solely from changes in tuning on these timescales. More broadly, our findings show that recurrent connections can endow a network with a powerful mechanism to store and integrate recent contextual information.