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This paper develops a new approach to band subset selection (BSS) for hyperspectral image classification (HSIC) which selects multiple bands simultaneously as a band subset, referred to as simultaneous multiple band selection (SMMBS), rather than one band at a time sequentially, referred to as sequential multiple band selection (SQMBS), as most traditional band selection methods do. In doing so, a criterion is particularly developed for BSS that can be used for HSIC. It is a linearly constrained minimum variance (LCMV) derived from adaptive beamforming in array signal processing which can be used to model misclassification errors as the minimum variance. To avoid an exhaustive search for all possible band subsets, two numerical algorithms, referred to as sequential (SQ) and successive (SC) algorithms are also developed for LCMV-based SMMBS, called SQ LCMV-BSS and SC LCMV-BSS. Experimental results demonstrate that LCMV-based BSS has advantages over SQMBS.

Tim-3 is highly expressed on a subset of T cells during T cell exhaustion in settings of chronic viral infection and tumors. Using lymphocytic choriomeningitis virus (LCMV) Clone 13, a model for chronic infection, we found that Tim-3 was neither necessary nor sufficient for the development of T cell exhaustion. Nonetheless, expression of Tim-3 was sufficient to drive resistance to PDL1 blockade therapy during chronic infection. Strikingly, expression of Tim-3 promoted the development of short-lived effector T cells, at the expense of memory precursor development, after acute LCMV infection. These effects were accompanied by increased Akt/mTOR signaling in T cells expressing endogenous or ectopic Tim-3. Conversely, Akt/mTOR signaling was reduced in effector T cells from Tim-3–deficient mice. Thus, Tim-3 is essential for optimal effector T cell responses, and may also contribute to exhaustion by restricting the development of long-lived memory T cells. Taken together, our results suggest that Tim-3 is actually more similar to costimulatory receptors that are up-regulated after T cell activation than to a dominant inhibitory protein like PD-1. These findings have significant implications for the development of anti–Tim-3 antibodies as therapeutic agents.

Mycobacterium tuberculosis (Mtb) represents a major burden to global health, and refined vaccines are needed. Replication-deficient lymphocytic choriomeningitis virus (rLCMV)-based vaccine vectors against cytomegalovirus have proven safe for human use and elicited robust T cell responses in a large proportion of vaccine recipients. Here, we developed an rLCMV vaccine expressing the Mtb antigens TB10.4 and Ag85B. In mice, rLCMV elicited high frequencies of polyfunctional Mtb-specific CD8 and CD4 T cell responses. CD8 but not CD4 T cells were efficiently boosted upon vector re-vaccination. High-frequency responses were also observed in neonatally vaccinated mice, and co-administration of rLCMV with Expanded Program of Immunization (EPI) vaccines did not result in substantial reciprocal interference. Importantly, rLCMV immunization significantly reduced the lung Mtb burden upon aerosol challenge, resulting in improved lung ventilation. Protection was associated with increased CD8 T cell recruitment but reduced CD4 T cell infiltration upon Mtb challenge. When combining rLCMV with BCG vaccination in a heterologous prime-boost regimen, responses to the rLCMV-encoded Mtb antigens were further augmented, but protection was not significantly different from rLCMV or BCG vaccination alone. This work suggests that rLCMV may show utility for neonatal and/or adult vaccination efforts against pulmonary tuberculosis.

Response inhibition is an important instance of cognitive control and can be complicated by perceptual conflict. The neurophysiological mechanisms underlying these processes are still not understood. Especially the relationship between neural processes directly preceding cognitive control (proactive control) and processes underlying cognitive control (reactive control) has not been examined although there should be close links. In the current study, we investigate these aspects in a sample of N  = 50 healthy adults. Time‐frequency and beamforming approaches were applied to analyze the interrelation of brain states before (pre‐trial) and during (within‐trial) cognitive control. The behavioral data replicate a perceptual conflict‐dependent modulation of response inhibition. During the pre‐trial period, insular, inferior frontal, superior temporal, and precentral alpha activity was positively correlated with theta activity in the same regions and the superior frontal gyrus. Additionally, participants with a stronger pre‐trial alpha activity in the primary motor cortex showed a stronger (within‐trial) conflict effect in the theta band in the primary motor cortex. This theta conflict effect was further related to a stronger theta conflict effect in the midcingulate cortex until the end of the trial. The temporal cascade of these processes suggests that successful proactive preparation (anticipatory information gating) entails a stronger reactive processing of the conflicting stimulus information likely resulting in a realization of the need to adapt the current action plan. The results indicate that theta and alpha band activity share and transfer aspects of information when it comes to the interrelationship between proactive and reactive control during conflict‐modulated motor inhibition.

Information flow between brain areas is difficult to estimate from EEG measurements due to the presence of noise as well as due to volume conduction. We here test the ability of popular measures of effective connectivity to detect an underlying neuronal interaction from simulated EEG data, as well as the ability of commonly used inverse source reconstruction techniques to improve the connectivity estimation. We find that volume conduction severely limits the neurophysiological interpretability of sensor-space connectivity analyses. Moreover, it may generally lead to conflicting results depending on the connectivity measure and statistical testing approach used. In particular, we note that the application of Granger-causal (GC) measures combined with standard significance testing leads to the detection of spurious connectivity regardless of whether the analysis is performed on sensor-space data or on sources estimated using three different established inverse methods. This empirical result follows from the definition of GC. The phase-slope index (PSI) does not suffer from this theoretical limitation and therefore performs well on our simulated data. We develop a theoretical framework to characterize artifacts of volume conduction, which may still be present even in reconstructed source time series as zero-lag correlations, and to distinguish their time-delayed brain interaction. Based on this theory we derive a procedure which suppresses the influence of volume conduction, but preserves effects related to time-lagged brain interaction in connectivity estimates. This is achieved by using time-reversed data as surrogates for statistical testing. We demonstrate that this robustification makes Granger-causal connectivity measures applicable to EEG data, achieving similar results as PSI. Integrating the insights of our study, we provide a guidance for measuring brain interaction from EEG data. Software for generating benchmark data is made available. ► We assess methods for EEG-based connectivity analysis on realistically simulated data. ► We demonstrate a number of pitfalls occurring depending on the method used. ► Granger-causal approaches are obscured by so-called weak data asymmetries. ► We propose a simple strategy for alleviating the impact of weak asymmetries. ► Code for data generation and analysis is provided for benchmarking purposes.

CD8⁺ T cells are crucial for the clearance of viral infections. During the acute phase, proinflammatory conditions increase the amount of circulating phosphatidylserine⁺ (PS) extracellular vesicles (EVs). These EVs interact especially with CD8⁺ T cells; however, it remains unclear whether they can actively modulate CD8⁺ T cell responses. In this study, we have developed a method to analyze cell-bound PS⁺ EVs and their target cells in vivo. We show that EV⁺ cell abundance increases during viral infection and that EVs preferentially bind to activated, but not naive, CD8⁺ T cells. Superresolution imaging revealed that PS⁺ EVs attach to clusters of CD8 molecules on the T cell surface. Furthermore, EV-binding induces antigen (Ag)-specific TCR signaling and increased nuclear translocation of the transcription factor Nuclear factor of activated T-cells (NFATc1) in vivo. EV-decorated but not EV-free CD8⁺ T cells are enriched for gene signatures associated with T-cell receptor signaling, early effector differentiation, and proliferation. Our data thus demonstrate that PS⁺ EVs provide Ag-specific adjuvant effects to activated CD8⁺ T cells in vivo.

Beamformers are applied for estimating spatiotemporal characteristics of neuronal sources underlying measured MEG/EEG signals. Several MEG analysis toolboxes include an implementation of a linearly constrained minimum-variance (LCMV) beamformer. However, differences in implementations and in their results complicate the selection and application of beamformers and may hinder their wider adoption in research and clinical use. Additionally, combinations of different MEG sensor types (such as magnetometers and planar gradiometers) and application of preprocessing methods for interference suppression, such as signal space separation (SSS), can affect the results in different ways for different implementations. So far, a systematic evaluation of the different implementations has not been performed. Here, we compared the localization performance of the LCMV beamformer pipelines in four widely used open-source toolboxes (MNE-Python, FieldTrip, DAiSS (SPM12), and Brainstorm) using datasets both with and without SSS interference suppression. We analyzed MEG data that were i) simulated, ii) recorded from a static and moving phantom, and iii) recorded from a healthy volunteer receiving auditory, visual, and somatosensory stimulation. We also investigated the effects of SSS and the combination of the magnetometer and gradiometer signals. We quantified how localization error and point-spread volume vary with the signal-to-noise ratio (SNR) in all four toolboxes. When applied carefully to MEG data with a typical SNR (3–15 ​dB), all four toolboxes localized the sources reliably; however, they differed in their sensitivity to preprocessing parameters. As expected, localizations were highly unreliable at very low SNR, but we found high localization error also at very high SNRs for the first three toolboxes while Brainstorm showed greater robustness but with lower spatial resolution. We also found that the SNR improvement offered by SSS led to more accurate localization. •Different beamformer implementations are reported to sometimes yield differing source estimates for the same MEG data.•We compared beamformers in four major open-source MEG analysis toolboxes.•All toolboxes provide consistent and accurate results with 3–15-dB input SNR.•However, localization errors are high at very high input SNR for the tested scalar beamformers.•We discuss the critical differences between the implementations.

During an acute viral infection, CD8 T cells encounter a myriad of antigenic and inflammatory signals of variable strength, which sets off individual T cells on their own differentiation trajectories. However, the developmental path for each of these cells will ultimately lead to one of only two potential outcomes after clearance of the infection—death or survival and development into memory CD8 T cells. How this cell fate decision is made remains incompletely understood. In this study, we explore the transcriptional changes during effector and memory CD8 T cell differentiation at the single-cell level. Using single-cell, transcriptome-derived gene regulatory network analysis, we identified two main groups of regulons that govern this differentiation process. These regulons function in concert with changes in the enhancer landscape to confer the establishment of the regulatory modules underlying the cell fate decision of CD8 T cells. Furthermore, we found that memory precursor effector cells maintain chromatin accessibility at enhancers for key memory-related genes and that these enhancers are highly enriched for E2A binding sites. Finally, we show that E2A directly regulates accessibility of enhancers of many memory-related genes and that its overexpression increases the frequency of memory precursor effector cells and accelerates memory cell formation while decreasing the frequency of short-lived effector cells. Overall, our results suggest that effector and memory CD8 T cell differentiation is largely regulated by two transcriptional circuits, with E2A serving as an important epigenetic regulator of the memory circuit.

A compact millimeter-wave radar system operating at 33 GHz is presented for integration on small unmanned aerial systems (UAS) and for ground-based counter-UAS reconnaissance. The design is specifically motivated by civil-sector agricultural applications, where large-payload crop-dusting and precision-spraying drones operating under FAA 14 CFR Part 137 require lightweight sense-and-avoid radar that conforms aerodynamically to existing aircraft or ground vehicles. The system is based on a 36-element hemispherical conformal phased array of crossed half-wave dipole radiators that generate right-hand circular polarization (RHCP) on transmit and selectively receives left-hand circular polarization (LHCP) echoes from targets, providing passive first-stage suppression of co-polarized rain and ground clutter. A Linearly Constrained Minimum Variance (LCMV) digital beamformer, applied to per-element analog-to-digital converter (ADC) outputs, delivers closed-form beam weights that enforce a distortionless response at each scan direction while globally minimizing sidelobe power. The formulation resolves the main-beam drift caused by the ill-conditioned re-scaling step in iterative Chebyshev tapering, achieving sidelobe levels below −20 dB with main-beam peaks within 0.1° of their commanded angles across all evaluated positions. Mutual coupling between array elements is modeled analytically using the induced-EMF method, yielding a 36×36 impedance matrix whose off-diagonal entries are at most 8.2% of the element self-impedance at the minimum inter-element separation of 2.70 λ. A closed-form decoupling matrix is applied to the receive manifold prior to LCMV weight computation. Seven simultaneous independent receive beams covering 0°–60° elevation are formed from a single data snapshot. A Scaled Conjugate Gradient neural network classifier, trained on radar-equation-scaled micro-Doppler features following Swerling I–IV radar cross-section (RCS) fluctuation statistics, achieves overall classification accuracy above 85% across five target classes. The five classes comprise two bird-signature classes (SW-I and SW-II), two UAV-signature classes (SW-III and SW-IV), and a clutter class. The design is entirely simulation-based; experimental validation using a sub-array prototype is identified as the primary direction for future work.