Spatiotemporal response integration across the neural receptive field (RF) is a general feature of sensory coding and has an important role in shaping responses to naturalistic stimuli. In the primary somatosensory cortex of the rat vibrissa pathway, such integration across the vibrissa array strongly shapes the coding of spatiotemporally distributed deflections. Using a spatiotemporal paired-pulse paradigm, this study revealed that fundamentally different types of pairwise interactions have similar qualitative behavior but that the magnitude, latency, and precision of the neural responses depend on the specific RF components being engaged. In all cases, however, increase in the suppression of response magnitude accompanied a lengthening of latency and a decrease in response precision. Furthermore, nonlinear interactions evoked by stimulation of multiple RF subregions strongly influence both response magnitude and timing to more complex sequences. Despite their complexity, such response interactions are highly predictable from elementary pairwise interactions. To understand the functional role of spatiotemporal interactions in coding, we developed a response model that incorporated the experimentally measured modulations in response magnitude, latency, and precision induced by cross-vibrissa interactions. Simulations of a simplified textural discrimination task indicate that spatiotemporal interactions enhance discrimination under certain stimulus time scales. This improvement follows from a nonlinear response property that acts to restore the neural response in the face of suppression. Together, the present findings highlight the role of response integration in shaping single-cell responses and provide predictions about how changes in response parameters influence coding accuracy.
BackgroundHyperglycemia following solid organ transplant is common among patients without pre-existing diabetes mellitus (DM). Post-transplant hyperglycemia can occur once or multiple times, which if continued, causes new-onset diabetes after transplantation (NODAT).ObjectiveTo study if the first and recurrent incidence of hyperglycemia are affected differently by immunosuppressive regimens, demographic and medical-related risk factors, and inpatient hyperglycemic conditions (i.e., an emphasis on the time course of post-transplant complications).MethodsWe conducted a retrospective analysis of 407 patients who underwent kidney transplantation at Mayo Clinic Arizona. Among these, there were 292 patients with no signs of DM prior to transplant. For this category of patients, we evaluated the impact of (1) immunosuppressive drugs (e.g., tacrolimus, sirolimus, and steroid), (2) demographic and medical-related risk factors, and (3) inpatient hyperglycemic conditions on the first and recurrent incidence of hyperglycemia in one year post-transplant. We employed two versions of Cox regression analyses: (1) a time-dependent model to analyze the recurrent cases of hyperglycemia and (2) a time-independent model to analyze the first incidence of hyperglycemia.ResultsAge (P = 0.018), HDL cholesterol (P = 0.010), and the average trough level of tacrolimus (P<0.0001) are significant risk factors associated with the first incidence of hyperglycemia, while age (P<0.0001), non-White race (P = 0.002), BMI (P = 0.002), HDL cholesterol (P = 0.003), uric acid (P = 0.012), and using steroid (P = 0.007) are the significant risk factors for the recurrent cases of hyperglycemia.DiscussionThis study draws attention to the importance of analyzing the risk factors associated with a disease (specially a chronic one) with respect to both its first and recurrent incidence, as well as carefully differentiating these two perspectives: a fact that is currently overlooked in the literature.
During behavior, rats and other rodents use their facial vibrissae to actively explore surfaces through whisking and head/body movement, resulting in complex sensory inputs that vary over a large range of angular velocities and temporal scales. How these complex sensory inputs manifest in the patterns of cortical firing events that ultimately form the perceptual experience is not well understood. Through single-unit cortical recordings of layer 4 neurons in S1 of the anesthetized rat, we systematically quantified the interactions between instantaneous velocity and timing of vibrissa motion, finding a strong interaction between angular velocity and timing of contacts on the tens of milliseconds time scale. From the quantification of these joint tuning properties, a detailed nonlinear encoding model was formulated that was highly predictive of firing probabilityandtimingcharacteristicsofthesparsecorticalrepresentationofcomplexpatternedtactileinputs.WithinaBayesianframework,the encoding model was then used to decode tactile patterns under simple transformations of the stimulus along dimensions of velocity and timing, as a demonstration of the lower bound of the idealized perceptual capabilities of the animal.
Problem definition: Organ-transplanted patients typically receive high amounts of immunosuppressive drugs (e.g., tacrolimus) as a mechanism to reduce their risk of organ rejection. However, because of the diabetogenic effect of these drugs, this practice exposes them to a greater risk of new-onset diabetes after transplantation (NODAT), and hence, becoming insulin dependent. We study and develop effective medication management strategies to address the common conundrum of balancing the risk of organ rejection versus that of NODAT. Academic/practical relevance: Our research contributes to the healthcare operations management literature by developing a robust stochastic decision-making framework that allows for incorporating (1) false-positive and false-negative errors of medical tests, (2) inevitable estimation errors when data sets are used, (3) variability among physician’ attitudes toward ambiguous outcomes, and (4) dynamic and patient risk-profile-dependent progression of health conditions. Methodology: We apply an ambiguous partially observable Markov decision process (APOMDP) approach where dynamic optimization with respect to a “cloud” of possible models allows us to make decisions that are robust to potential misspecifications of risks. Results: We first provide various structural results that facilitate characterizing the optimal medication policies. Utilizing a clinical data set, we then compare the performance of the optimal medication policies obtained from our APOMDP model with the policies currently used in the medical practice. We observe that, in one year after transplant, our proposed policies can improve the life expectancy of each patient up to 4.58%, while reducing the medical expenditures up to 11.57%. Managerial implications: Balancing the risks of organ rejection and diabetes complications and considering factors such as physicians’ attitudes toward ambiguous outcomes, partial observability of medical tests, and patient-specific risk factors are shown to result in more cost-effective strategies for management of post-transplant medications compared with the current medical practice. Finally, simultaneous management of medications can facilitate the care coordination process between transplantation/nephrology and endocrinology departments of a hospital that are typically in charge of administering such medications.
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