Cellular Plasticity of CD4+ T Cells in the Intestine

Barrier sites such as the gastrointestinal tract are in constant contact with the environment which contains both beneficial and harmful components. The immune system at the epithelia must make the distinction between these components to balance tolerance, protection and immunopathology. This is achieved via multifaceted immune recognition, highly organised lymphoid structures and the interaction of many types of immune cells. The adaptive immune response in the gut is orchestrated by CD4+ helper T (Th) cells which are integral to gut immunity. In recent years it has become apparent that the functional identity of these Th cells is not as fixed as initially thought. Plasticity in differentiated T cell subsets has now been firmly established, in both health and disease. The gut, in particular, utilises CD4+ T cell plasticity to mould CD4+ T cell phenotypes to maintain its finely poised balance of tolerance and inflammation and to encourage biodiversity within the enteric microbiome. In this review we will discuss intestinal helper T cell plasticity and our current understanding of its mechanisms, including our growing knowledge of an evolutionarily ancient symbiosis between microbiota and malleable CD4+ T cell effectors

Tbet or Continued RORγt Expression Is Not Required for Th17-Associated Immunopathology

The discovery of Th17 cell plasticity, in which CD4 + IL-17-producing Th17 cells give rise to IL-17/IFN-γ double-producing cells and Th1-like IFNγ+ ex-Th17 lymphocytes, has raised questions regarding which of these cell types contribute to immunopathology during inflammatory diseases. In this study, we show using Helicobacter hepaticus-induced intestinal inflammation that IL-17ACre- or Rag1Cre-mediated deletion of Tbx21 has no effect on the generation of IL-17/IFN-g double-producing cells, but leads to a marked absence of Th1-like IFNγ+ ex-Th17 cells. Despite the lack of Th1-like ex-Th17 cells, the degree of H. hepaticus-Triggered intestinal inflammation in mice in which Tbx21 was excised in IL-17-producing or Rag1-expressing cells is indistinguishable from that observed in control mice. In stark contrast, using experimental autoimmune encephalomyelitis, we show that IL-17ACre-mediated deletion of Tbx21 prevents the conversion of Th17 cells to IL-17A/IFN-γ double-producing cells as well as Th1-like IFN-γ+ ex-Th17 cells. However, IL-17ACre-mediated deletion of Tbx21 has only limited effects on disease course in this model and is not compensated by Ag-specific Th1 cells. IL-17ACre-mediated deletion of Rorc reveals that RORγt is essential for the maintenance of the Th17 cell lineage, but not immunopathology during experimental autoimmune encephalomyelitis. These results show that neither the single Th17 subset, nor its progeny, is solely responsible for immunopathology or autoimmunity

Review of the BCI Competition IV

The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students. The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) session-to-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.BMBF, 01IB001A, LOKI - Lernen zur Organisation komplexer Systeme der Informationsverarbeitung - Lernen im Kontext der SzenenanalyseBMBF, 01GQ0850, Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine InteraktionEC/FP7/224631/EU/Tools for Brain-Computer Interaction/TOBIEC/FP7/216886/EU/Pattern Analysis, Statistical Modelling and Computational Learning 2/PASCAL2BMBF, 01GQ0420, Verbundprojekt: Bernstein-Zentrum für Neural Dynamics, Freiburg - CNDFBMBF, 01GQ0761, Bewegungsassoziierte Aktivierung - Dekodierung bewegungsassoziierter GehirnsignaleBMBF, 01GQ0762, Bewegungsassoziierte Aktivierung - Gehirn- und Maschinenlerne

Surveillance for the Management of Small Renal Masses

Surveillance is a new management option for small renal masses (SRMs) in aged and infirm patients with short-life expectancy. The current literature on surveillance of SRM contains mostly small, retrospective studies with limited data. Imaging alone is inadequate for suggesting the aggressive potential of SRM for both diagnosis and followup. Current data suggest that a computed tomography (CT) or magnetic resonance imaging (MRI) every 3 months in the 1st year, every 6 months in the next 2 years, and every year thereafter, is appropriate for observation. The authors rather believe in active surveillance with mandatory initial and followup renal tumor biopsies than classical observation. Since not all SRMs are harmless, selection criteria for active surveillance need to be improved. In addition, there is need for larger studies in order to better outline oncological outcome and followup protocols

A simulation study on the effects of neuronal ensemble properties on decoding algorithms for intracortical brain-machine interfaces

Background: Intracortical brain-machine interfaces (BMIs) harness movement information by sensing neuronal activities using chronic microelectrode implants to restore lost functions to patients with paralysis. However, neuronal signals often vary over time, even within a day, forcing one to rebuild a BMI every time they operate it. The term "rebuild" means overall procedures for operating a BMI, such as decoder selection, decoder training, and decoder testing. It gives rise to a practical issue of what decoder should be built for a given neuronal ensemble. This study aims to address it by exploring how decoders' performance varies with the neuronal properties. To extensively explore a range of neuronal properties, we conduct a simulation study. Methods: Focusing on movement direction, we examine several basic neuronal properties, including the signal-to-noise ratio of neurons, the proportion of well-tuned neurons, the uniformity of their preferred directions (PDs), and the non-stationarity of PDs. We investigate the performance of three popular BMI decoders: Kalman filter, optimal linear estimator, and population vector algorithm. Results: Our simulation results showed that decoding performance of all the decoders was affected more by the proportion of well-tuned neurons that their uniformity. Conclusions: Our study suggests a simulated scenario of how to choose a decoder for intracortical BMIs in various neuronal conditions

Structure Learning in a Sensorimotor Association Task

Learning is often understood as an organism's gradual acquisition of the association between a given sensory stimulus and the correct motor response. Mathematically, this corresponds to regressing a mapping between the set of observations and the set of actions. Recently, however, it has been shown both in cognitive and motor neuroscience that humans are not only able to learn particular stimulus-response mappings, but are also able to extract abstract structural invariants that facilitate generalization to novel tasks. Here we show how such structure learning can enhance facilitation in a sensorimotor association task performed by human subjects. Using regression and reinforcement learning models we show that the observed facilitation cannot be explained by these basic models of learning stimulus-response associations. We show, however, that the observed data can be explained by a hierarchical Bayesian model that performs structure learning. In line with previous results from cognitive tasks, this suggests that hierarchical Bayesian inference might provide a common framework to explain both the learning of specific stimulus-response associations and the learning of abstract structures that are shared by different task environments

Event prediction and causality inference despite incomplete information

We explored the challenge of predicting and explaining the occurrence of events within sequences of data points. Our focus was particularly on scenarios in which unknown triggers causing the occurrence of events may consist of non-consecutive, masked, noisy data points. This scenario is akin to an agent tasked with learning to predict and explain the occurrence of events without understanding the underlying processes or having access to crucial information. Such scenarios are encountered across various fields, such as genomics, hardware and software verification, and financial time series prediction. We combined analytical, simulation, and machine learning (ML) approaches to investigate, quantify, and provide solutions to this challenge. We deduced and validated equations generally applicable to any variation of the underlying challenge. Using these equations, we (1) described how the level of complexity changes with various parameters (e.g., number of apparent and hidden states, trigger length, confidence, etc.) and (2) quantified the data needed to successfully train an ML model. We then (3) proved our ML solution learns and subsequently identifies unknown triggers and predicts the occurrence of events. If the complexity of the challenge is too high, our ML solution can identify trigger candidates to be used to interactively probe the system under investigation to determine the true trigger in a way considerably more efficient than brute force methods. By sharing our findings, we aim to assist others grappling with similar challenges, enabling estimates on the complexity of their problem, the data required and a solution to solve it.Comment: 16 pages, 8 figures, 1 tabl

Ohmic cooking of carrots: Limitations in the use of power input and cooking value for process characterization

Ohmic cooking is considered a fast and homogeneous process. However, achieving heating uniformity depends on several process parameters and intrinsic product characteristics. Furthermore, reference indicators for evaluating the ohmic process and generating reliable comparisons with conventional cooking are still lacking. The objective of this study was to investigate the reliability of the use of power input and cooking value as process indicators. The results showed that the specific ohmic power did affect only the heating rate but not the heating uniformity and the tissue softening rate. Therefore, the power input as process acceleration tool is not sufficient as stand-alone process indicator because other critical parameters (i.e., electrical conductivity) need to be taken into account to display the complex product-process-interactions. The cooking value was proven to be not valid as indicator for ohmic heating, as it does not take into account additional effects not attributable to only thermal exposure

Implantable Neural Probes for Brain-Machine Interfaces - Current Developments and Future Prospects

A Brain-Machine interface (BMI) allows for direct communication between the brain and machines. Neural probes for recording neural signals are among the essential components of a BMI system. In this report, we review research regarding implantable neural probes and their applications to BMIs. We first discuss conventional neural probes such as the tetrode, Utah array, Michigan probe, and electroencephalography (ECoG), following which we cover advancements in next-generation neural probes. These next-generation probes are associated with improvements in electrical properties, mechanical durability, biocompatibility, and offer a high degree of freedom in practical settings. Specifically, we focus on three key topics: (1) novel implantable neural probes that decrease the level of invasiveness without sacrificing performance, (2) multi-modal neural probes that measure both electrical and optical signals, (3) and neural probes developed using advanced materials. Because safety and precision are critical for practical applications of BMI systems, future studies should aim to enhance these properties when developing next-generation neural probes