Environmental Salinity Determines the Specificity and Need for Tat-Dependent Secretion of the YwbN Protein in Bacillus subtilis

Twin-arginine protein translocation (Tat) pathways are required for transport of folded proteins across bacterial, archaeal and chloroplast membranes. Recent studies indicate that Tat has evolved into a mainstream pathway for protein secretion in certain halophilic archaea, which thrive in highly saline environments. Here, we investigated the effects of environmental salinity on Tat-dependent protein secretion by the Gram-positive soil bacterium Bacillus subtilis, which encounters widely differing salt concentrations in its natural habitats. The results show that environmental salinity determines the specificity and need for Tat-dependent secretion of the Dyp-type peroxidase YwbN in B. subtilis. Under high salinity growth conditions, at least three Tat translocase subunits, namely TatAd, TatAy and TatCy, are involved in the secretion of YwbN. Yet, a significant level of Tat-independent YwbN secretion is also observed under these conditions. When B. subtilis is grown in medium with 1% NaCl or without NaCl, the secretion of YwbN depends strictly on the previously described “minimal Tat translocase” consisting of the TatAy and TatCy subunits. Notably, in medium without NaCl, both tatAyCy and ywbN mutants display significantly reduced exponential growth rates and severe cell lysis. This is due to a critical role of secreted YwbN in the acquisition of iron under these conditions. Taken together, our findings show that environmental conditions, such as salinity, can determine the specificity and need for the secretion of a bacterial Tat substrate

Initialization and self-organized optimization of recurrent neural network connectivity

Reservoir computing (RC) is a recent paradigm in the field of recurrent neural networks. Networks in RC have a sparsely and randomly connected fixed hidden layer, and only output connections are trained. RC networks have recently received increased attention as a mathematical model for generic neural microcircuits to investigate and explain computations in neocortical columns. Applied to specific tasks, their fixed random connectivity, however, leads to significant variation in performance. Few problem-specific optimization procedures are known, which would be important for engineering applications, but also in order to understand how networks in biology are shaped to be optimally adapted to requirements of their environment. We study a general network initialization method using permutation matrices and derive a new unsupervised learning rule based on intrinsic plasticity (IP). The IP-based learning uses only local learning, and its aim is to improve network performance in a self-organized way. Using three different benchmarks, we show that networks with permutation matrices for the reservoir connectivity have much more persistent memory than the other methods but are also able to perform highly nonlinear mappings. We also show that IP-based on sigmoid transfer functions is limited concerning the output distributions that can be achieved

Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations

Kühn S, Beyn W-J, Cruse H. Modelling memory functions with recurrent neural networks consisting of input compensation units: I. Static situations. Biological Cybernetics. 2007;96(5):455-470.Humans are able to form internal representations of the information they process?_"a capability which enables them to perform many different memory tasks. Therefore, the neural system has to learn somehow to represent aspects of the environmental situation; this process is assumed to be based on synaptic changes. The situations to be represented are various as for example different types of static patterns but also dynamic scenes. How are neural networks consisting of mutually connected neurons capable of performing such tasks? Here we propose a new neuronal structure for artificial neurons. This structure allows one to disentangle the dynamics of the recurrent connectivity from the dynamics induced by synaptic changes due to the learning processes. The error signal is computed locally within the individual neuron. Thus, online learning is possible without any additional structures. Recurrent neural networks equipped with these computational units cope with different memory tasks. Examples illustrate how information is extracted from environmental situations comprising fixed patterns to produce sustained activity and to deal with simple algebraic relation

Challenges in Neural Computation

Hammer B. Challenges in Neural Computation. Künstliche Intelligenz : KI. 2012;26(4):333-340

Analyzing subcomponents of affective dysregulation in borderline personality disorder in comparison to other clinical groups using multiple e-diary datasets

Background: Affective dysregulation is widely regarded as being the core problem in patients with borderline personality disorder (BPD). Moreover, BPD is the disorder mainly associated with affective dysregulation. However, the empirical confirmation of the specificity of affective dysregulation for BPD is still pending. We used a validated approach from basic affective science that allows for simultaneously analyzing three interdependent components of affective dysregulation that are disturbed in patients with BPD: homebase, variability, and attractor strength (return to baseline). Methods: We applied two types of multilevel models on two e-diary datasets to investigate group differences regarding three subcomponents between BPD patients (n =43; n =51) and patients with posttraumatic stress disorder (PTSD; n= 28) and those with bulimia nervosa (BN; n= 20) as clinical control groups in dataset 1, and patients with panic disorder (PD; n= 26) and those with major depression (MD; n =25) as clinical control groups in dataset 2. In addition, healthy controls (n= 28; n= 40) were included in the analyses. In both studies, e-diaries were used to repeatedly collect data about affective experiences during participants’ daily lives. In study 1 a high-frequency sampling strategy with assessments in 15 min-intervals over 24 h was applied, whereas the assessments occurred every waking hour over 48 h in study 2. The local ethics committees approved both studies, and all participants provided written informed consent. Results: In contradiction to our hypotheses, BPD patients did not consistently show altered affective dysregulation compared to the clinical patient groups. The only differences in affective dynamics in BPD patients emerged with regard to one of three subcomponents, affective homebase. However, these results were not even consistent. Conversely, comparing the patients to healthy controls revealed a pattern of more negative affective homebases, higher levels of affective variability, and (partially) reduced returns to baseline in the patient groups. Conclusions: Our results indicate that affective dysregulation constitutes a transdiagnostic mechanism that manifests in similar ways in several different mental disorders. We point out promising prospects that might help to elucidate the common and distinctive mechanisms that underlie several different disorders and that should be addressed in future studies