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Brain-computer interfaces (BCI) are used in stroke rehabilitation to translate brain signals into intended movements of the paralyzed limb. However, the efficacy and mechanisms of BCI-based therapies remain unclear. Here we show that BCI coupled to functional electrical stimulation (FES) elicits significant, clinically relevant, and lasting motor recovery in chronic stroke survivors more effectively than sham FES. Such recovery is associated to quantitative signatures of functional neuroplasticity. BCI patients exhibit a significant functional recovery after the intervention, which remains 6–12 months after the end of therapy. Electroencephalography analysis pinpoints significant differences in favor of the BCI group, mainly consisting in an increase in functional connectivity between motor areas in the affected hemisphere. This increase is significantly correlated with functional improvement. Results illustrate how a BCI–FES therapy can drive significant functional recovery and purposeful plasticity thanks to contingent activation of body natural efferent and afferent pathways. Brain-computer interface (BCI) can improve motor skills on stroke patients. This study shows that BCI-controlled neuromuscular electrical stimulation therapy can cause cortical reorganization due to activation of efferent and afferent pathways, and this effect can be long lasting in a brain region specific manner.

There is a general lack of consensus on both anatomic definition and function of Broca’s area, often localized to the pars triangularis (pT) and pars opercularis (pOp) of the left inferior frontal gyrus (IFG). Given the belief that this region plays a critical role in expressive language functions, resective surgery is often avoided to preserve function. However, the putative role of Broca’s area in speech production has been recently challenged. The current study aims to investigate the plausibility of glioma resection and neurological outcomes in “Broca’s area”. We report a single-surgeon, consecutive case series feasibility study describing the resection of gliomas within the IFG. Presentation, mapping, functional outcome, and extent of resection variables were considered for analysis. All included patients had tumors located in the traditional “Broca’s area”, eight (53.33 %) additionally extending into the insular and subinsular regions. All patients except for one, presented with speech-language deficits preoperatively. Awake brain surgery for tumor resection with direct cortical and subcortical stimulation and intraoperative neuropsychological evaluation was carried out in all individuals. During stimulation, positive speech-language sites within the IFG were identified in ten patients. Two patients (13.33 %) experienced a decline in naming during intraoperative cognitive monitoring and thirteen (86.66 %) had a stable performance throughout surgery. At two-week follow-up, all patients had recovery of language functions compared to initial presentation. Overall extent of resection (EOR) was 60.35 % ( ± 29.60) with residual tumor being the greatest within the insular and subinsular areas. EOR was stratified in anatomical regions within the IFG, being the pOr the area with the greatest EOR (97.4 %), followed by the pT (84.1 %), pOp (83.8 %), and vPMC (80 %). The belief that Broca’s area is not safe to resect is challenged. Adequate mapping and careful patient selection allow maximum safe resection of tumors located in the traditional “Broca’s area”, with low risk of postoperative morbidity. •Tumor surgery in the dominant inferior frontal gyrus is feasible in a carefully selected subset of patients.•Awake brain surgery with speech and language mapping are fundamental during surgery of the dominant inferior frontal gyrus.•Extent of resection in the cortical component of tumors within the IFG, ranged between 80 % and 97 %, which validated the notion that surgery within Broca’s area is feasible.•Extent of resection was limited by functional speech areas detected intraoperatively.

Individual variability has clear effects upon the outcome of therapies and treatment approaches. The customization of healthcare options to the individual patient should accordingly improve treatment results. We propose a novel approach to brain interventions based on personalized brain network models derived from non-invasive structural data of individual patients. Along the example of a patient with bitemporal epilepsy, we show step by step how to develop a Virtual Epileptic Patient (VEP) brain model and integrate patient-specific information such as brain connectivity, epileptogenic zone and MRI lesions. Using high-performance computing, we systematically carry out parameter space explorations, fit and validate the brain model against the patient's empirical stereotactic EEG (SEEG) data and demonstrate how to develop novel personalized strategies towards therapy and intervention. •A novel approach to brain interventions is proposed based on personalized large-scale brain network models.•The approach relies on the fusion of structural data of individual patients and mathematical modeling of brain activations.•Personalization is achieved by integrating patient specific brain connectivity, epileptogenic zone and MRI lesions.•High-performance computing enables systematic parameter space explorations, fitting and validation of the brain model.•Large-scale brain models foster the development of personalized strategies towards therapy and intervention.

Ecological dynamics are strongly influenced by the relationship between prey density and predator feeding behavior—that is, the predatory functional response. A useful understanding of this relationship requires us to distinguish between competing models of the functional response, and to robustly estimate the model parameters. Recent advances in this topic have revealed bias in model comparison, as well as in model parameter estimation in functional response studies, mainly attributed to the quality of data. Here, we propose that an adaptive experimental design framework can mitigate these challenges. We then present the first practical demonstration of the improvements it offers over standard experimental design. Our results reveal that adaptive design can efficiently identify the preferred functional response model among the competing models, and can produce much more precise posterior distributions for the estimated functional response parameters. By increasing the efficiency of experimentation, adaptive experimental design will lead to reduced logistical burden.

Abdominal pain and liver injury have been frequently reported during coronavirus disease-2019 (COVID-19). Our aim was to investigate characteristics of abdominal pain in COVID-19 patients and their association with disease severity and liver injury. Data of all COVID-19 patients hospitalized during the first wave in one hospital were retrieved. Patients admitted exclusively for other pathologies and/or recovered from COVID-19, as well as pregnant women were excluded. Patients whose abdominal pain was related to alternative diagnosis were also excluded. Among the 1026 included patients, 200 (19.5%) exhibited spontaneous abdominal pain and 165 (16.2%) after abdomen palpation. Spontaneous pain was most frequently localized in the epigastric (42.7%) and right upper quadrant (25.5%) regions. Tenderness in the right upper region was associated with severe COVID-19 (hospital mortality and/or admission to intensive/intermediate care unit) with an adjusted odds ratio of 2.81 (95% CI 1.27–6.21, p = 0.010). Patients with history of lower abdomen pain experimented less frequently dyspnea compared to patients with history of upper abdominal pain (25.8 versus 63.0%, p < 0.001). Baseline transaminases elevation was associated with history of pain in epigastric and right upper region and AST elevation was strongly associated with severe COVID-19 with an odds ratio of 16.03 (95% CI 1.95–131.63 p = 0.010). More than one fifth of patients admitted for COVID-19 presented abdominal pain. Those with pain located in the upper abdomen were more at risk of dyspnea, demonstrated more altered transaminases, and presented a higher risk of adverse outcomes.

This work aims at corroborating the importance and efficacy of mutual learning in motor imagery (MI) brain-computer interface (BCI) by leveraging the insights obtained through our participation in the BCI race of the Cybathlon event. We hypothesized that, contrary to the popular trend of focusing mostly on the machine learning aspects of MI BCI training, a comprehensive mutual learning methodology that reinstates the three learning pillars (at the machine, subject, and application level) as equally significant could lead to a BCI-user symbiotic system able to succeed in real-world scenarios such as the Cybathlon event. Two severely impaired participants with chronic spinal cord injury (SCI), were trained following our mutual learning approach to control their avatar in a virtual BCI race game. The competition outcomes substantiate the effectiveness of this type of training. Most importantly, the present study is one among very few to provide multifaceted evidence on the efficacy of subject learning during BCI training. Learning correlates could be derived at all levels of the interface-application, BCI output, and electroencephalography (EEG) neuroimaging-with two end-users, sufficiently longitudinal evaluation, and, importantly, under real-world and even adverse conditions.

Simulations of large-scale brain dynamics are often impacted by overexcitation resulting from heavy-tailed structural network distributions, leading to biologically implausible simulation results. We implement a homeodynamic plasticity mechanism, known from other modeling work, in the widely used Jansen-Rit neural mass model for The Virtual Brain (TVB) simulation framework. We aim at heterogeneously adjusting the inhibitory coupling weights to reach desired dynamic regimes in each brain region. We show that, by using this dynamic approach, we can control the target activity level to obtain biologically plausible brain simulations, including post-synaptic potentials and blood-oxygen-level-dependent functional magnetic resonance imaging (fMRI) activity. We demonstrate that the derived dynamic Feedback Inhibitory Control (dFIC) can be used to enable increased variability of model dynamics. We derive the conditions under which the simulated brain activity converges to a predefined target level analytically and via simulations. We highlight the benefits of dFIC in the context of fitting the TVB model to static and dynamic measures of fMRI empirical data, accounting for global synchronization across the whole brain. The proposed novel method helps computational neuroscientists, especially TVB users, to easily “tune” brain models to desired dynamical regimes depending on the specific requirements of each study. The presented method is a steppingstone towards increased biological realism in brain network models and a valuable tool to better understand their underlying behavior.

This article describes how technology—i.e. the infrastructure of tools, systems, platforms—enhances knowledge transfer. The effect of tools on the relationship between knowledge characteristics and knowledge transfer effectiveness is under-researched. This article attempts to address the interplay of knowledge characteristics and transfer tools within multinational corporations (MNCs). Based on the structural equation modelling, this research proposes and tests a basic model that captures knowledge characteristics and transfer tools at 125 Japanese subsidiaries operating in China. Drawing on the literature, this article argues that the role of knowledge characteristics and transfer tools need to be considered for effective knowledge transfer between MNCs and their subsidiaries. Knowledge characteristics and transfer tools play differing roles in knowledge transfer. This article also extends the existing studies by focusing on knowledge characteristics and transfer tool constructs simultaneously in a model to understand the notion of knowledge transfer effectiveness in the global business context.

Double-stranded RNA (dsRNA) acts as the elicitor molecule of the RNA silencing (RNA interference, RNAi), the endogenous and evolutionary conserved surveillance system present in all eukaryotes. DsRNAs and their subsequent degradation products, namely the small interfering RNAs (siRNAs), act in a sequence-specific manner to control gene expression. Exogenous application of dsRNAs onto plants elicits resistance against plant viruses. In the present work, exogenously applied dsRNA molecules, derived from Zucchini yellow mosaic virus (ZYMV) HC-Pro region, onto tomato plants were detected in aphids (Myzus persicae), whiteflies (Trialeurodes vaporariorum) and mites (Tetranychus urticae) that were fed on treated as well as systemic tomato leaves. Furthermore, four siRNAs, deriving from the dsRNA applied, were detected in tomato and the agricultural pests fed on treated tomato plants. More specifically, dsRNA was detected in agricultural pests at 3 and 10 dpt (days post treatment) in dsRNA-treated leaves and at 14 dpt in systemic leaves. In addition, using stem-loop RT-PCR, siRNAs were detected in agricultural pests at 3 and 10 dpt in aphids and mites. Surprisingly, in whiteflies carrying the applied dsRNA, siRNAs were not molecularly detected. Our results showed that, upon exogenous application of dsRNAs molecules, these moved rapidly within tomato and were uptaken by agricultural pests fed on treated tomato. As a result, this non-transgenic method has the potential to control important crop pests via RNA silencing of vital genes of the respective pests.

Apples depend on insect pollination but intensification of agriculture jeopardizes pollination services in agroecosystems. Concerns about the dependency of crop pollination exclusively on honey bees increase the interest in agricultural practices that safeguard wild pollinators in agroecosystems. The purpose of the study was to assess the potential of floral resource provision in apple orchards to enhance the conservation of hymenopterous pollinating insects and potentially the pollination service to the crop. For this reason, flowering plant mixtures sown in patches inside apple orchards were tested against wild plant patches. Pollinator taxa recorded on the sown and wild plant patches were honey bees, wild bees (Andrena, Anthophora, Eucera, Halictus, Lasioglossum, Megachilidae on both; Systropha only on wild plants; Bombus, Hylaeus, Sphecodes, Nomada, Xylocopa only on sown mixture), syrphids, bee flies. The most abundant pollinator of apple was A. mellifera but wild bees were also recorded (Andrena, Anthophora, Bombus, Xylocopa, Lasioglossum, Megachilidae). The sown mixture attracted a more diverse taxa of pollinators and in greater numbers compared to the weed flora, but it did not have an effect on pollinators visiting apple flowers. Groundcover management with patches of suitable flowering mixtures can enhance pollinator conservation in apple orchards.