Cued spatial attention modulates functionally relevant alpha rhythms in visual cortices in humans. Here, we present evidence for analogous phenomena in primary somatosensory neocortex (SI). Using magnetoencephalography, we measured changes in the SI mu rhythm containing mu-alpha (7-14 Hz) and mu-beta (15-29 Hz) components. We found that cued attention impacted mu-alpha in the somatopically localized hand representation in SI, showing decreased power after attention was cued to the hand and increased power after attention was cued to the foot, with significant differences observed 500 -1100 ms after cue. Mu-beta showed differences in a time window 800 -850 ms after cue. The visual cue also drove an early evoked response beginning ϳ70 ms after cue with distinct peaks modulated with cued attention. Distinct components of the tactile stimulus-evoked response were also modulated with cued attention. Analysis of a second dataset showed that, on a trial-by-trial basis, tactile detection probabilities decreased linearly with prestimulus mu-alpha and mu-beta power. These results support the growing consensus that cue-induced alpha modulation is a functionally relevant sensory gating mechanism deployed by attention. Further, while cued attention had a weaker effect on the allocation of mu-beta, oscillations in this band also predicted tactile detection.

Cell-based drug delivery systems have shown promising capability for tumor-targeted therapy owing to the intrinsic tumor-homing and drug-carrying property of some living cells. However, imaging tracking of their migration and bio-effects is urgently needed for clinical application, especially for glioma. Here, we report the inflammation-activatable engineered neutrophils by internalizing doxorubicin-loaded magnetic mesoporous silica nanoparticles (ND-MMSNs) which can provide the potential for magnetic resonance (MR) imaging tracking of the drug-loaded cells to actively target inflamed brain tumor after surgical resection of primary tumor. The phagocytized D-MMSNs possess high drug loading efficiency and do not affect the host neutrophils’ viability, thus remarkably improving intratumoral drug concentration and delaying relapse of surgically treated glioma. Our study offers a new strategy in targeted cancer theranostics through combining the merits of living cells and nanoparticle carriers.

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Fig. 1. DexPilot enabled teleoperation across a wide variety of tasks, e.g., rectifying a Pringles can and placing it inside the red bowl (upper-left), inserting cups (upper-right), concurrently picking two cubes with four fingers (lower-left), and extracting money from a wallet (lower-right). Videos are available at https://sites.google.com/view/dex-pilot.

The right inferior frontal cortex (rIFC) is specifically associated with attentional control via the inhibition of behaviorally irrelevant stimuli and motor responses. Similarly, recent evidence has shown that alpha (7-14 Hz) and beta (15-29 Hz) oscillations in primary sensory neocortical areas are enhanced in the representation of non-attended stimuli, leading to the hypothesis that allocation of these rhythms plays an active role in optimal inattention. Here, we tested the hypothesis that selective synchronization between rIFC and primary sensory neocortex occurs in these frequency bands during inattention. We used magnetoencephalography to investigate phase synchrony between primary somatosensory (SI) and rIFC regions during a cued-attention tactile detection task that required suppression of response to uncertain distractor stimuli. Attentional modulation of synchrony between SI and rIFC was found in both the alpha and beta frequency bands. This synchrony manifested as an increase in the alpha-band early after cue between non-attended SI representations and rIFC, and as a subsequent increase in beta-band synchrony closer to stimulus processing. Differences in phase synchrony were not found in several proximal control regions. These results are the first to reveal distinct interactions between primary sensory cortex and rIFC in humans and suggest that synchrony between rIFC and primary sensory representations plays a role in the inhibition of irrelevant sensory stimuli and motor responses.

We consider the problem of predicting sensitivity of cancer cell lines to new drugs based on supervised learning on genomic profiles. The genetic and epigenetic characterization of a cell line provides observations on various aspects of regulation including DNA copy number variations, gene expression, DNA methylation and protein abundance. To extract relevant information from the various data types, we applied a random forest based approach to generate sensitivity predictions from each type of data and combined the predictions in a linear regression model to generate the final drug sensitivity prediction. Our approach when applied to the NCI-DREAM drug sensitivity prediction challenge was a top performer among 47 teams and produced high accuracy predictions. Our results show that the incorporation of multiple genomic characterizations lowered the mean and variance of the estimated bootstrap prediction error. We also applied our approach to the Cancer Cell Line Encyclopedia database for sensitivity prediction and the ability to extract the top targets of an anti-cancer drug. The results illustrate the effectiveness of our approach in predicting drug sensitivity from heterogeneous genomic datasets.

A framework for design of personalized cancer therapy requires the ability to predict the sensitivity of a tumor to anticancer drugs. The predictive modeling of tumor sensitivity to anti-cancer drugs has primarily focused on generating functions that map gene expressions and genetic mutation profiles to drug sensitivity. In this paper, we present a new approach for drug sensitivity prediction and combination therapy design based on integrated functional and genomic characterizations. The modeling approach when applied to data from the Cancer Cell Line Encyclopedia shows a significant gain in prediction accuracy as compared to elastic net and random forest techniques based on genomic characterizations. Utilizing a Mouse Embryonal Rhabdomyosarcoma cell culture and a drug screen of 60 targeted drugs, we show that predictive modeling based on functional data alone can also produce high accuracy predictions. The framework also allows us to generate personalized tumor proliferation circuits to gain further insights on the individualized biological pathway.