Combining sensory inputs over space and time is fundamental to vision. Population receptive field models have been successful in characterizing spatial encoding throughout the human visual pathways. A parallel question, how visual areas in the human brain process information distributed over time, has received less attention. One challenge is that the most widely used neuroimaging method, fMRI, has coarse temporal resolution compared with the time-scale of neural dynamics. Here, via carefully controlled temporally modulated stimuli, we show that information about temporal processing can be readily derived from fMRI signal amplitudes in male and female subjects. We find that all visual areas exhibit subadditive summation, whereby responses to longer stimuli are less than the linear prediction from briefer stimuli. We also find fMRI evidence that the neural response to two stimuli is reduced for brief interstimulus intervals (indicating adaptation). These effects are more pronounced in visual areas anterior to V1-V3. Finally, we develop a general model that shows how these effects can be captured with two simple operations: temporal summation followed by a compressive nonlinearity. This model operates for arbitrary temporal stimulation patterns and provides a simple and interpretable set of computations that can be used to characterize neural response properties across the visual hierarchy. Importantly, compressive temporal summation directly parallels earlier findings of compressive spatial summation in visual cortex describing responses to stimuli distributed across space. This indicates that, for space and time, cortex uses a similar processing strategy to achieve higher-level and increasingly invariant representations of the visual world. Combining sensory inputs over time is fundamental to seeing. Two important temporal phenomena are summation, the accumulation of sensory inputs over time, and adaptation, a response reduction for repeated or sustained stimuli. We investigated these phenomena in the human visual system using fMRI. We built predictive models that operate on arbitrary temporal patterns of stimulation using two simple computations: temporal summation followed by a compressive nonlinearity. Our new temporal compressive summation model captures (1) subadditive temporal summation, and (2) adaptation. We show that the model accounts for systematic differences in these phenomena across visual areas. Finally, we show that for space and time, the visual system uses a similar strategy to achieve increasingly invariant representations of the visual world.

BackgroundRecent toxicological and epidemiological studies have shown associations between particulate matter (PM) and adverse health effects, but which PM components are most influential is less well known.ObjectivesIn this study, we used time-series analyses to determine the associations between daily fine PM [PM ≤ 2.5 μm in aerodynamic diameter (PM2.5)] concentrations and daily mortality in two U.S. cities—Seattle, Washington, and Detroit, Michigan.MethodsWe obtained daily PM2.5 filters for the years of 2002–2004 and analyzed trace elements using X-ray fluorescence and black carbon using light reflectance as a surrogate measure of elemental carbon. We used Poisson regression and distributed lag models to estimate excess deaths for all causes and for cardiovascular and respiratory diseases adjusting for time-varying covariates. We computed the excess risks for interquartile range increases of each pollutant at lags of 0 through 3 days for both warm and cold seasons.ResultsThe cardiovascular and respiratory mortality series exhibited different source and seasonal patterns in each city. The PM2.5 components and gaseous pollutants associated with mortality in Detroit were most associated with warm season secondary aerosols and traffic markers. In Seattle, the component species most closely associated with mortality included those for cold season traffic and other combustion sources, such as residual oil and wood burning.ConclusionsThe effects of PM2.5 on daily mortality vary with source, season, and locale, consistent with the hypothesis that PM composition has an appreciable influence on the health effects attributable to PM.

Most cancers are resistant to anti-PD-1/PD-L1 and chemotherapy. Herein we identify PDLIM2 as a tumor suppressor particularly important for lung cancer therapeutic responses. While PDLIM2 is epigenetically repressed in human lung cancer, associating with therapeutic resistance and poor prognosis, its global or lung epithelial-specific deletion in mice causes increased lung cancer development, chemoresistance, and complete resistance to anti-PD-1 and epigenetic drugs. PDLIM2 epigenetic restoration or ectopic expression shows antitumor activity, and synergizes with anti-PD-1, notably, with chemotherapy for complete remission of most lung cancers. Mechanistically, through repressing NF-κB/RelA and STAT3, PDLIM2 increases expression of genes involved in antigen presentation and T-cell activation while repressing multidrug resistance genes and cancer-related genes, thereby rendering cancer cells vulnerable to immune attacks and therapies. We identify PDLIM2-independent PD-L1 induction by chemotherapeutic and epigenetic drugs as another mechanism for their synergy with anti-PD-1. These findings establish a rationale to use combination therapies for cancer treatment.

Signal transducer and activator of transcription 3 (STAT3) is linked to multiple cancers, including pulmonary adenocarcinoma. However, the role of STAT3 in lung cancer pathogenesis has not been determined. Using lung epithelial-specific inducible knockout strategies, we demonstrate that STAT3 plays contrasting roles in the initiation and growth of both chemically and genetically induced lung cancers. Selective deletion of lung epithelial STAT3 in mice prior to cancer induction by the smoke carcinogen, urethane, resulted in increased lung tissue damage and inflammation, K-Ras oncogenic mutations, and tumorigenesis. Deletion of lung epithelial STAT3 after establishment of lung cancer inhibited cancer cell proliferation. Simultaneous deletion of STAT3 and expression of oncogenic K-Ras in mouse lung elevated pulmonary injury, inflammation, and tumorigenesis, but reduced tumor growth. These studies indicate that STAT3 prevents lung cancer initiation by maintaining pulmonary homeostasis under oncogenic stress, whereas it facilitates lung cancer progression by promoting cancer cell growth. These studies also provide a mechanistic basis for targeting STAT3 to lung cancer therapy.

Visual neurons respond to static images with specific dynamics: neuronal responses sum sub-additively over time, reduce in amplitude with repeated or sustained stimuli (neuronal adaptation), and are slower at low stimulus contrast. Here, we propose a simple model that predicts these seemingly disparate response patterns observed in a diverse set of measurements–intracranial electrodes in patients, fMRI, and macaque single unit spiking. The model takes a time-varying contrast time course of a stimulus as input, and produces predicted neuronal dynamics as output. Model computation consists of linear filtering, expansive exponentiation, and a divisive gain control. The gain control signal relates to but is slower than the linear signal, and this delay is critical in giving rise to predictions matched to the observed dynamics. Our model is simpler than previously proposed related models, and fitting the model to intracranial EEG data uncovers two regularities across human visual field maps: estimated linear filters (temporal receptive fields) systematically differ across and within visual field maps, and later areas exhibit more rapid and substantial gain control. The model is further generalizable to account for dynamics of contrast-dependent spike rates in macaque V1, and amplitudes of fMRI BOLD in human V1.

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Combining sensory inputs over space and time is fundamental to vision. Population receptive field models have been successful in characterizing spatial encoding throughout the human visual pathways. A parallel question-how visual areas in the human brain process information distributed over timehas received less attention. One challenge is that the most widely used neuroimaging method-fMRIhas coarse temporal resolution compared to the time-scale of neural dynamics. Here, via carefully controlled temporally modulated stimuli, we show that information about temporal processing can be readily derived from fMRI signal amplitudes in male and female subjects. We find that all visual areas exhibit sub-additive summation, whereby responses to longer stimuli are less than the linear prediction from briefer stimuli. We also find fMRI evidence that the neural response to two stimuli is reduced for brief interstimulus intervals (indicating adaptation). These effects are more pronounced in visual areas anterior to V1-V3. Finally, we develop a general model that shows how these effects can be captured with two simple operations: temporal summation followed by a compressive nonlinearity. This model operates for arbitrary temporal stimulation patterns and provides a simple and interpretable set of computations that can be used to characterize neural response properties across the visual hierarchy. Importantly, compressive temporal summation directly parallels earlier findings of compressive spatial summation in visual cortex describing responses to stimuli distributed across space. This indicates that for space and time, cortex uses a similar processing strategy to achieve higher-level and increasingly invariant representations of the visual world. Significance statementCombining sensory inputs over time is fundamental to seeing. Two important temporal phenomena are summation, the accumulation of sensory inputs over time, and adaptation, a response reduction for repeated or sustained stimuli. We investigated these phenomena in the human visual system using fMRI. We built predictive models that operate on arbitrary temporal patterns of stimulation using two simple computations: temporal summation followed by a compressive nonlinearity. Our new temporal compressive summation model captures (1) subadditive temporal summation, and (2) adaptation. We show that the model accounts for systematic differences in these phenomena across visual areas. Finally, we show that for space and time, the visual system uses a similar strategy to achieve increasingly invariant representations of the visual world.

One of the most fundamental and challenging questions in the cancer field is how immunity in cancer patients is transformed from tumor immunosurveillance to tumor-promoting inflammation. Here, we identify the transcription factor STAT3 as the culprit responsible for this pathogenic event in lung cancer development. We found that antitumor type 1 CD4+ T helper (Th1) cells and CD8+ T cells were directly counter-balanced in lung cancer development with tumor-promoting myeloid-derived suppressor cells (MDSCs) and suppressive macrophages, and that activation of STAT3 in MDSCs and macrophages promoted tumorigenesis through pulmonary recruitment and increased resistance of suppressive cells to CD8+ T cells, enhancement of cytotoxicity towards CD4+ and CD8+ T cells, induction of regulatory T cell (Treg), inhibition of dendritic cells (DCs), and polarization of macrophages toward the M2 phenotype. The deletion of myeloid STAT3 boosted antitumor immunity and suppressed lung tumorigenesis. These findings increase our understanding of immune programming in lung tumorigenesis and provide a mechanistic basis for developing STAT3-based immunotherapy against this and other solid tumors.