Dynamic fading memory and expectancy effects in the monkey primary visual cortex.

In order to investigate the involvement of the primary visual cortex (V1) in working memory (WM), parallel, multisite recordings of multi-unit activity were obtained from monkey V1 while the animals performed a delayed match-to-sample (DMS) task. During the delay period, V1 population firing rate vectors maintained a lingering trace of the sample stimulus that could be reactivated by intervening impulse stimuli that enhanced neuronal firing. This fading trace of the sample did not require active engagement of the monkeys in the DMS task and likely reflects the intrinsic dynamics of recurrent cortical networks in lower visual areas. This renders an active, attention-dependent involvement of V1 in the maintenance of WM contents unlikely. By contrast, population responses to the test stimulus depended on the probabilistic contingencies between sample and test stimuli. Responses to tests that matched expectations were reduced which agrees with concepts of predictive coding.

Visual exposure enhances stimulus encoding and persistence in primary cortex.

The brain adapts to the sensory environment. For example, simple sensory exposure can modify the response properties of early sensory neurons. How these changes affect the overall encoding and maintenance of stimulus information across neuronal populations remains unclear. We perform parallel recordings in the primary visual cortex of anesthetized cats and find that brief, repetitive exposure to structured visual stimuli enhances stimulus encoding by decreasing the selectivity and increasing the range of the neuronal responses that persist after stimulus presentation. Low-dimensional projection methods and simple classifiers demonstrate that visual exposure increases the segregation of persistent neuronal population responses into stimulus-specific clusters. These observed refinements preserve the representational details required for stimulus reconstruction and are detectable in postexposure spontaneous activity. Assuming response facilitation and recurrent network interactions as the core mechanisms underlying stimulus persistence, we show that the exposure-driven segregation of stimulus responses can arise through strictly local plasticity mechanisms, also in the absence of firing rate changes. Our findings provide evidence for the existence of an automatic, unguided optimization process that enhances the encoding power of neuronal populations in early visual cortex, thus potentially benefiting simple readouts at higher stages of visual processing.

Comparative Expression Profiling Reveals Molecular Markers Involved in the Progression of Cutaneous Melanoma towards Metastasis.

Cutaneous melanoma is one of the most aggressive types of cancer and often proves fatal in metastatic stages. Few treatment options are available, and its global incidence is quickly increasing. In order to gain an improved understanding of the molecular features regarding melanoma progression, we have compared gene and small non-coding RNA expression profiles from cell lines derived from primary melanoma (MelJuSo), lymph node metastasis (MNT-1) and brain metastasis (VMM1), representing distinct stages of malignant progression. Our preliminary results highlighted the aberrant regulation of molecular markers involved in several processes that aid melanoma progression and metastasis development, including extracellular matrix remodeling, migratory potential and angiogenesis. Moreover, bioinformatic analysis revealed potential targets of the microRNAs of interest. Confocal microscopy and immunohistochemistry analysis were used for validation at the protein level. Exploring the molecular landscape of melanoma may contribute to the achievement of future efficient targeted therapy, as well as better prevention, diagnosis and clinical management.

Stimulus complexity shapes response correlations in primary visual cortex.

Spike count correlations (SCCs) are ubiquitous in sensory cortices, are characterized by rich structure, and arise from structured internal dynamics. However, most theories of visual perception treat contributions of neurons to the representation of stimuli independently and focus on mean responses. Here, we argue that, in a functional model of visual perception, featuring probabilistic inference over a hierarchy of features, inferences about high-level features modulate inferences about low-level features ultimately introducing structured internal dynamics and patterns in SCCs. Specifically, high-level inferences for complex stimuli establish the local context in which neurons in the primary visual cortex (V1) interpret stimuli. Since the local context differentially affects multiple neurons, this conjecture predicts specific modulations in the fine structure of SCCs as stimulus identity and, more importantly, stimulus complexity varies. We designed experiments with natural and synthetic stimuli to measure the fine structure of SCCs in V1 of awake behaving macaques and assessed their dependence on stimulus identity and stimulus statistics. We show that the fine structure of SCCs is specific to the identity of natural stimuli and changes in SCCs are independent of changes in response mean. Critically, we demonstrate that stimulus specificity of SCCs in V1 can be directly manipulated by altering the amount of high-order structure in synthetic stimuli. Finally, we show that simple phenomenological models of V1 activity cannot account for the observed SCC patterns and conclude that the stimulus dependence of SCCs is a natural consequence of structured internal dynamics in a hierarchical probabilistic model of natural images.

Reward-Related Suppression of Neural Activity in Macaque Visual Area V4.

In order for organisms to survive, they need to detect rewarding stimuli, for example, food or a mate, in a complex environment with many competing stimuli. These rewarding stimuli should be detected even if they are nonsalient or irrelevant to the current goal. The value-driven theory of attentional selection proposes that this detection takes place through reward-associated stimuli automatically engaging attentional mechanisms. But how this is achieved in the brain is not very well understood. Here, we investigate the effect of differential reward on the multiunit activity in visual area V4 of monkeys performing a perceptual judgment task. Surprisingly, instead of finding reward-related increases in neural responses to the perceptual target, we observed a large suppression at the onset of the reward indicating cues. Therefore, while previous research showed that reward increases neural activity, here we report a decrease. More suppression was caused by cues associated with higher reward than with lower reward, although neither cue was informative about the perceptually correct choice. This finding of reward-associated neural suppression further highlights normalization as a general cortical mechanism and is consistent with predictions of the value-driven attention theory.

Versatile Biomaterial Platform Enriched with Graphene Oxide and Carbon Nanotubes for Multiple Tissue Engineering Applications.

Carbon-based nanomaterials, such as graphene oxide (GO) or carbon nanotubes (CNTs) are currently used in various medical applications due to their positive influence on biocompatibility, adhesion, proliferation, and differentiation, as well as their contribution to modulating cell behavior in response to nanomaterial substrates. In this context, in this study, novel flexible membranes based on cellulose acetate (CA) enriched with CNT and GO in different percentages were tested for their versatility to be used as substrates for soft or hard tissue engineering (TE), namely, for their ability to support human adipose-derived stem cells (hASCs) adhesion during adipogenic or osteogenic differentiation. For this purpose, differentiation markers were assessed both at gene and protein levels, while histological staining was performed to show the evolution of the processes in response to CA-CNT-GO substrates. Micro-CT analysis indicated porous morphologies with open and interconnected voids. A slightly lower total porosity was obtained for the samples filled with the highest amount of GO and CNTs, but thicker walls, larger and more uniform pores were obtained, providing beneficial effects on cell behavior and increased mechanical stability. The addition of 1 wt% GO and CNT to the biocomposites enhanced hASCs adhesion and cytoskeleton formation. The evolution of both adipogenic and osteogenic differentiation processes was found to be augmented proportionally to the GO-CNT concentration. In conclusion, CA-CNT-GO biomaterials displayed good properties and versatility as platforms for cell differentiation with potential as future implantable materials in TE applications.

Where's the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network.

Even in the absence of sensory stimulation the brain is spontaneously active. This background "noise" seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network's spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network's behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms.

Untangling perceptual memory: hysteresis and adaptation map into separate cortical networks.

Perception is an active inferential process in which prior knowledge is combined with sensory input, the result of which determines the contents of awareness. Accordingly, previous experience is known to help the brain "decide" what to perceive. However, a critical aspect that has not been addressed is that previous experience can exert 2 opposing effects on perception: An attractive effect, sensitizing the brain to perceive the same again (hysteresis), or a repulsive effect, making it more likely to perceive something else (adaptation). We used functional magnetic resonance imaging and modeling to elucidate how the brain entertains these 2 opposing processes, and what determines the direction of such experience-dependent perceptual effects. We found that although affecting our perception concurrently, hysteresis and adaptation map into distinct cortical networks: a widespread network of higher-order visual and fronto-parietal areas was involved in perceptual stabilization, while adaptation was confined to early visual areas. This areal and hierarchical segregation may explain how the brain maintains the balance between exploiting redundancies and staying sensitive to new information. We provide a Bayesian model that accounts for the coexistence of hysteresis and adaptation by separating their causes into 2 distinct terms: Hysteresis alters the prior, whereas adaptation changes the sensory evidence (the likelihood function).

Paying attention to natural scenes in area V1.

Natural scene responses in the primary visual cortex are modulated simultaneously by attention and by contextual signals about scene statistics stored across the connectivity of the visual processing hierarchy. We hypothesized that attentional and contextual signals interact in V1 in a manner that primarily benefits the representation of natural stimuli, rich in high-order statistical structure. Recording from two macaques engaged in a spatial attention task, we found that attention enhanced the decodability of stimulus identity from population responses evoked by natural scenes, but not by synthetic stimuli lacking higher-order statistical regularities. Population analysis revealed that neuronal responses converged to a low-dimensional subspace only for natural stimuli. Critically, we determined that the attentional enhancement in stimulus decodability was captured by the natural-scene subspace, indicating an alignment between the attentional and natural stimulus variance. These results suggest that attentional and contextual signals interact in V1 in a manner optimized for natural vision.

Robust encoding of natural stimuli by neuronal response sequences in monkey visual cortex.

Parallel multisite recordings in the visual cortex of trained monkeys revealed that the responses of spatially distributed neurons to natural scenes are ordered in sequences. The rank order of these sequences is stimulus-specific and maintained even if the absolute timing of the responses is modified by manipulating stimulus parameters. The stimulus specificity of these sequences was highest when they were evoked by natural stimuli and deteriorated for stimulus versions in which certain statistical regularities were removed. This suggests that the response sequences result from a matching operation between sensory evidence and priors stored in the cortical network. Decoders trained on sequence order performed as well as decoders trained on rate vectors but the former could decode stimulus identity from considerably shorter response intervals than the latter. A simulated recurrent network reproduced similarly structured stimulus-specific response sequences, particularly once it was familiarized with the stimuli through non-supervised Hebbian learning. We propose that recurrent processing transforms signals from stationary visual scenes into sequential responses whose rank order is the result of a Bayesian matching operation. If this temporal code were used by the visual system it would allow for ultrafast processing of visual scenes.

Graphene-Related Nanomaterials for Biomedical Applications.

This paper builds on the context and recent progress on the control, reproducibility, and limitations of using graphene and graphene-related materials (GRMs) in biomedical applications. The review describes the human hazard assessment of GRMs in in vitro and in vivo studies, highlights the composition-structure-activity relationships that cause toxicity for these substances, and identifies the key parameters that determine the activation of their biological effects. GRMs are designed to offer the advantage of facilitating unique biomedical applications that impact different techniques in medicine, especially in neuroscience. Due to the increasing utilization of GRMs, there is a need to comprehensively assess the potential impact of these materials on human health. Various outcomes associated with GRMs, including biocompatibility, biodegradability, beneficial effects on cell proliferation, differentiation rates, apoptosis, necrosis, autophagy, oxidative stress, physical destruction, DNA damage, and inflammatory responses, have led to an increasing interest in these regenerative nanostructured materials. Considering the existence of graphene-related nanomaterials with different physicochemical properties, the materials are expected to exhibit unique modes of interactions with biomolecules, cells, and tissues depending on their size, chemical composition, and hydrophil-to-hydrophobe ratio. Understanding such interactions is crucial from two perspectives, namely, from the perspectives of their toxicity and biological uses. The main aim of this study is to assess and tune the diverse properties that must be considered when planning biomedical applications. These properties include flexibility, transparency, surface chemistry (hydrophil-hydrophobe ratio), thermoelectrical conductibility, loading and release capacity, and biocompatibility.

Predictive coding of natural images by V1 firing rates and rhythmic synchronization.

Predictive coding is an important candidate theory of self-supervised learning in the brain. Its central idea is that sensory responses result from comparisons between bottom-up inputs and contextual predictions, a process in which rates and synchronization may play distinct roles. We recorded from awake macaque V1 and developed a technique to quantify stimulus predictability for natural images based on self-supervised, generative neural networks. We find that neuronal firing rates were mainly modulated by the contextual predictability of higher-order image features, which correlated strongly with human perceptual similarity judgments. By contrast, V1 gamma (γ)-synchronization increased monotonically with the contextual predictability of low-level image features and emerged exclusively for larger stimuli. Consequently, γ-synchronization was induced by natural images that are highly compressible and low-dimensional. Natural stimuli with low predictability induced prominent, late-onset beta (ß)-synchronization, likely reflecting cortical feedback. Our findings reveal distinct roles of synchronization and firing rates in the predictive coding of natural images.

Cortical gamma-band resonance preferentially transmits coherent input.

Synchronization has been implicated in neuronal communication, but causal evidence remains indirect. We use optogenetics to generate depolarizing currents in pyramidal neurons of the cat visual cortex, emulating excitatory synaptic inputs under precise temporal control, while measuring spike output. The cortex transforms constant excitation into strong gamma-band synchronization, revealing the well-known cortical resonance. Increasing excitation with ramps increases the strength and frequency of synchronization. Slow, symmetric excitation profiles reveal hysteresis of power and frequency. White-noise input sequences enable causal analysis of network transmission, establishing that the cortical gamma-band resonance preferentially transmits coherent input components. Models composed of recurrently coupled excitatory and inhibitory units uncover a crucial role of feedback inhibition and suggest that hysteresis can arise through spike-frequency adaptation. The presented approach provides a powerful means to investigate the resonance properties of local circuits and probe how these properties transform input and shape transmission.

New Understanding of the Difference in Filtration Performance between Anatase and Rutile TiO2 Nanoparticles through Blending into Ultrafiltration PSF Membranes.

The blending of nanomaterials into a polymeric matrix is a method known for its ability, under certain circumstances, to lead to an improvement in membrane properties. TiO2 nanoparticles have been used in membrane research for the last 20 years and have continuously shown promise in this field of research. Polysulfone (PSf) membranes were obtained through the phase inversion method, with different TiO2 nanoparticle concentrations (0, 0.1, 0.5, and 1 wt.%) and two types of TiO2 crystalline structure (anatase and rutile), via the addition of commercially available nanopowders. Research showed improvement in all studied properties. In particular, the 0.5 wt.% TiO2 rutile membrane recorded an increase in permeability of 139.7% compared to the control membrane. In terms of overall performance, the best nanocomposite membrane demonstrated a performance index increase of 71.1% compared with the control membrane.

The Non-Coding Landscape of Cutaneous Malignant Melanoma: A Possible Route to Efficient Targeted Therapy.

Considered to be highly lethal if not diagnosed in early stages, cutaneous malignant melanoma is among the most aggressive and treatment-resistant human cancers, and its incidence continues to rise, largely due to ultraviolet radiation exposure, which is the main carcinogenic factor. Over the years, researchers have started to unveil the molecular mechanisms by which malignant melanoma can be triggered and sustained, in order to establish specific, reliable biomarkers that could aid the prognosis and diagnosis of this fatal disease, and serve as targets for development of novel efficient therapies. The high mutational burden and heterogeneous nature of melanoma shifted the main focus from the genetic landscape to epigenetic and epitranscriptomic modifications, aiming at elucidating the role of non-coding RNA molecules in the fine tuning of melanoma progression. Here we review the contribution of microRNAs and lncRNAs to melanoma invasion, metastasis and acquired drug resistance, highlighting their potential for clinical applications as biomarkers and therapeutic targets.

Deciphering the Molecular Landscape of Cutaneous Squamous Cell Carcinoma for Better Diagnosis and Treatment.

Cutaneous squamous cell carcinoma (cSCC) is a common type of neoplasia, representing a terrible burden on patients' life and clinical management. Although it seldom metastasizes, and most cases can be effectively treated with surgical intervention, once metastatic cSCC displays considerable aggressiveness leading to the death of affected individuals. No consensus has been reached as to which features better characterize the aggressive behavior of cSCC, an achievement hindered by the high mutational burden caused by chronic ultraviolet light exposure. Even though some subtypes have been recognized as high risk variants, depending on certain tumor features, cSCC that are normally thought of as low risk could pose an increased danger to the patients. In light of this, specific genetic and epigenetic markers for cutaneous SCC, which could serve as reliable diagnostic markers and possible targets for novel treatment development, have been searched for. This review aims to give an overview of the mutational landscape of cSCC, pointing out established biomarkers, as well as novel candidates, and future possible molecular therapies for cSCC.

Release of the Non-Steroidal Anti-Inflammatory Drug Flufenamic Acid by Multiparticulate Delivery Systems Promotes Adipogenic Differentiation of Adipose-Derived Stem Cells.

Engineered tissue-like structures often instigate an inflammatory response in the host that can inhibit wound healing and ultimately lead to the rejection of the implant. In our previous study, we have characterized the properties and biocompatibility of novel multiparticulate drug delivery systems (MDDS), based on collagen matrix with gradual release of anti-inflammatory drug flufenamic acid, we evaluated their anti-inflammatory potential and demonstrated their efficiency against burns and soft tissue lesions. In addition to these results, FA was previously described as a stimulant for adipogenesis, therefore we hypothesized that MDDS might also be appropriate for adipose tissue engineering. After the cell-scaffold constructs were obtained, cell morphology, adhesion and spreading on the systems were highlighted by scanning electron microscopy, immunostaining and confocal microscopy. The effect of FA-enriched materials on adipogenesis was evaluated at gene and protein level, by RT-qPCR, confocal microscopy and immunohistochemistry. Our current work indicates that flufenamic acid plays a beneficial role in adipocyte differentiation, with a direct effect upon the gene and protein expression of important early and late markers of adipogenesis, such as PPARγ2 and perilipin.

Epitranscriptomic Signatures in lncRNAs and Their Possible Roles in Cancer.

In contrast to the amazing exponential growth in knowledge related to long non-coding RNAs (lncRNAs) involved in cell homeostasis or dysregulated pathological states, little is known so far about the links between the chemical modifications occurring in lncRNAs and their function. Generally, ncRNAs are post-transcriptional regulators of gene expression, but RNA modifications occurring in lncRNAs generate an additional layer of gene expression control. Chemical modifications that have been reported in correlation with lncRNAs include m6A, m5C and pseudouridylation. Up to date, several chemically modified long non-coding transcripts have been identified and associated with different pathologies, including cancers. This review presents the current level of knowledge on the most studied cancer-related lncRNAs, such as the metastasis associated lung adenocarcinoma transcript 1 (MALAT1), the Hox transcript antisense intergenic RNA (HOTAIR), or the X-inactive specific transcript (XIST), as well as more recently discovered forms, and their potential roles in different types of cancer. Understanding how these RNA modifications occur, and the correlation between lncRNA changes in structure and function, may open up new therapeutic possibilities in cancer.

Nanocomposite foams based on flexible biobased thermoplastic polyurethane and ZnO nanoparticles as potential wound dressing materials.

In the present study biobased and soft thermoplastic polyurethane (TPU), obtained by polymerization from fatty acids, was used to produce TPU/ZnO nanocomposite foams by thermally induced phase separation method (TIPS). The produced foams were characterized and evaluated regarding their potential uses as wound dressing materials. The structure and morphology of the prepared flexible polymer foams with different content of ZnO nanofiller (1, 2, 5 and 10 wt% related to the polymer) were studied by Fourier transform infrared spectroscopy (FTIR) and Scanning electron microscopy (SEM). Highly porous nanocomposite structure made of interconnected pores with dimensions between 10 and 60 µm was created allowing water vapor transmission rate (WVTR) up to 8.9 mg/cm2·h. The TPU/ZnO foams, tested for their ability to support cells and their growth, showed highest cell proliferation for TPU nanocomposite foams with 2 and 5 wt% ZnO. Overall, the nanocomposite foams displayed a low cytotoxic potential (varied proportionally to the ZnO content) and good biocompatibility. All tested nanocomposite foams were found to be significantly active against biofilms formed by different Gram-positive (Enterococcus faecalis and Staphylococcus aureus) and Gram-negative (Escherichia coli and Pseudomonas aeruginosa) bacteria. Based on their behaviors, flexible TPU/ZnO nanocomposite foams can be considered for biomedical applications such as potential active wound dressing.