Origins of choice-related activity in mouse somatosensory cortex.

During perceptual decisions about faint or ambiguous sensory stimuli, even identical stimuli can produce different choices. Spike trains from sensory cortex neurons can predict trial-to-trial variability in choice. Choice-related spiking is widely studied as a way to link cortical activity to perception, but its origins remain unclear. Using imaging and electrophysiology, we found that mouse primary somatosensory cortex neurons showed robust choice-related activity during a tactile detection task. Spike trains from primary mechanoreceptive neurons did not predict choices about identical stimuli. Spike trains from thalamic relay neurons showed highly transient, weak choice-related activity. Intracellular recordings in cortex revealed a prolonged choice-related depolarization in most neurons that was not accounted for by feed-forward thalamic input. Top-down axons projecting from secondary to primary somatosensory cortex signaled choice. An intracellular measure of stimulus sensitivity determined which neurons converted choice-related depolarization into spiking. Our results reveal how choice-related spiking emerges across neural circuits and within single neurons

Case Report: Signal Drop on MRA Imaging of the Internal Carotid Artery after Neuroform Stent Placement

Magnetic resonance angiography (MRA) is an important tool in evaluating the patency of vessels which have previously been stented. Neuroform stents (Boston Scientific, Natick, MA, U.S.A.) are utilized to provide a scaffold across the neck of an aneurysm. These stents are synthesized from Nitinol (nickel and titanium) and thus cause minimal distortion upon imaging with MRA. Patients who have undergone Neuroform stent assisted coiling of aneurysms are routinely followed with MRA to delineate stenosis of the stented segment of vessel as well as recurrence of the aneurysms. While numerous reports show that Neuroform stents do not lead to MRA imaging artifact, we report of a case where the utilization of the Neuroform stent led to a signal drop out at the site of the stent upon evaluation with MRA and thus led to further invasive radiological procedures

Characteristics of Tobacco Users in the Lumber Industry

Cessation interventions for adult smokeless tobacco users may benefit from an improved understanding of the demographic, psychosocial, and tobacco-dependence characteristics of this group. In the current study, 143 employees of the Pacific Lumber Company were interviewed and completed questionnaires about their tobacco use product preference (smokeless tobacco only, cigarettes only, both, and former user), demographic, psychosocial, and tobacco-dependence characteristics. Results of a multivariate discriminant analysis revealed that smokeless-tobacco-only users were younger and reported in engaging in more exercise than did the other three groups; however, they also reported greater dependence on tobacco than did smokers. Formal cessation clinics similar to those that are being used effectively with smokers, and which are age appropriate, may be an effective treatment for adult smokeless tobacco users

Developing a Multi-Dimensional Early Elementary Mathematics Screener and Diagnostic Tool: The Primary Mathematics Assessment

There is a critical need to identify primary level students experiencing difficulties in mathematics to provide immediate and targeted instruction that remediates their deficits. However, most early math screening instruments focus only on the concept of number, resulting in inadequate and incomplete information for teachers to design intervention efforts. We propose a mathematics assessment that screens and provides diagnostic information in six domains that are important to building a strong foundation in mathematics. This article describes the conceptual framework and psychometric qualities of a web-based assessment tool, the Primary Math Assessment (PMA). The PMA includes a screener to identify students at risk for poor math outcomes and a diagnostic tool to provide a more in-depth profile of children’s specific strengths and weaknesses in mathematics. The PMA allows teachers and school personnel to make better instructional decisions by providing more targeted analyses

Oscillatory surface dichroism of an insulating topological insulator Bi2Te2Se

Using circular dichroism-angle resolved photoemission spectroscopy (CD-ARPES), we report a study of the effect of angular momentum transfer between polarized photons and topological surface states on the surface of highly bulk insulating topological insulator Bi2Te2Se. The photoelectron dichroism is found to be strongly modulated by the frequency of the helical photons including a dramatic sign-flip. Our results suggest that the observed dichroism and its sign-flip are consequences of strong coupling between the photon field and the spin-orbit nature of the Dirac modes on the surface. Our studies reveal the intrinsic dichroic behavior of topological surface states and point toward the potential utility of bulk insulating topological insulators in device applications.Comment: 5 pages, 4 figure

Phase Ib study of the combination of pexidartinib (PLX3397), a CSF-1R inhibitor, and paclitaxel in patients with advanced solid tumors.

Purpose:To evaluate the safety, recommended phase II dose (RP2D) and efficacy of pexidartinib, a colony stimulating factor receptor 1 (CSF-1R) inhibitor, in combination with weekly paclitaxel in patients with advanced solid tumors. Patients and Methods:In part 1 of this phase Ib study, 24 patients with advanced solid tumors received escalating doses of pexidartinib with weekly paclitaxel (80 mg/m2). Pexidartinib was administered at 600 mg/day in cohort 1. For subsequent cohorts, the dose was increased by ⩽50% using a standard 3+3 design. In part 2, 30 patients with metastatic solid tumors were enrolled to examine safety, tolerability and efficacy of the RP2D. Pharmacokinetics and biomarkers were also assessed. Results:A total of 51 patients reported ≥1 adverse event(s) (AEs) that were at least possibly related to either study drug. Grade 3-4 AEs, including anemia (26%), neutropenia (22%), lymphopenia (19%), fatigue (15%), and hypertension (11%), were recorded in 38 patients (70%). In part 1, no maximum tolerated dose was achieved and 1600 mg/day was determined to be the RP2D. Of 38 patients evaluable for efficacy, 1 (3%) had complete response, 5 (13%) partial response, 13 (34%) stable disease, and 17 (45%) progressive disease. No drug-drug interactions were found. Plasma CSF-1 levels increased 1.6- to 53-fold, and CD14dim/CD16+ monocyte levels decreased by 57-100%. Conclusions:The combination of pexidartinib and paclitaxel was generally well tolerated. RP2D for pexidartinib was 1600 mg/day. Pexidartinib blocked CSF-1R signaling, indicating potential for mitigating macrophage tumor infiltration

Closed-loop optimization of fast-charging protocols for batteries with machine learning.

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology  to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces