Neuronal circuits in the cerebral cortex consist mainly of glutamatergic/excitatory and GABAergic/inhibitory neurons. In the visual cortex, the binocular responsiveness of neurons is modified by monocular visual deprivation during the critical period of postnatal development. Although GABAergic neurons are considered to play a key role in the expression of the critical period, it is not known whether their binocular responsiveness and ocular dominance plasticity are different from those of excitatory neurons. Recently, the end of the critical period was found to be not strict so that cortical neurons in the adult still have some ocular dominance plasticity. It is not known, however, which type of neurons or both maintain such plasticity in adulthood. To address these issues, we applied in vivo two-photon functional Ca 2ϩ imaging to transgenic mice whose GABAergic neurons express a yellow fluorescent protein called Venus. We found that GABAergic neurons are more binocular than excitatory neurons in the normal visual cortex, and both types of neurons show the same degree of modifiability to monocular visual deprivation during the critical period, but the modifiability of GABAergic neurons is stronger than that of excitatory neurons after the end of the critical period.

Two-photon imaging in behaving animals has revealed neuronal activities related to behavioral and cognitive function at single-cell resolution. However, marmosets have posed a challenge due to limited success in training on motor tasks. Here we report the development of protocols to train head-fixed common marmosets to perform upper-limb movement tasks and simultaneously perform two-photon imaging. After 2–5 months of training sessions, head-fixed marmosets can control a manipulandum to move a cursor to a target on a screen. We conduct two-photon calcium imaging of layer 2/3 neurons in the motor cortex during this motor task performance, and detect task-relevant activity from multiple neurons at cellular and subcellular resolutions. In a two-target reaching task, some neurons show direction-selective activity over the training days. In a short-term force-field adaptation task, some neurons change their activity when the force field is on. Two-photon calcium imaging in behaving marmosets may become a fundamental technique for determining the spatial organization of the cortical dynamics underlying action and cognition.

Protein aggregation is a common phenomenon. The preparation of highly concentrated protein samples, typically required for biophysical measurements, often involves a time consuming and tedious testing of solvent conditions for improving protein solubility. Here, in a systematic analysis, we have determined the increase in solubility upon the addition of SEP-tags (solubility enhancement peptide tags) containing, one, three, and five lysines or arginines (or six arginines) to either the N or C terminus of our low solubility model protein, bovine pancreatic trypsin inhibitor variant, BPTI-22 (a BPTI variant containing 22 alanines). As anticipated, the BPTI-22 solubility increased in direct relation to the number of charged residues contained in the SEP-tag, and without altering either the activity or the structure of the protein. The largest solubility increases were of 4.2-, 4.8-, and 6.2-folds produced by the addition, at the C terminus, of five lysine (BPTI-22-C5K), five and six arginine residues (BPTI-22-C5R and BPTI-22-C6R), respectively. The increased solubility of the tagged BPTI-22 yielded higher quality NMR spectra (hetero single quantum correlation HSQC spectra; with respect of the signal-to-noise and line shapes) in a much shorter time than for the untagged BPTI-22. Furthermore, tagged samples remained soluble for over ten days, as observed by their HSQC spectra. We believe that lysine- and arginine-based SEP-tags may provide an effective and versatile method for enhancing protein solubility.

We constructed a support vector machine (SVM)-based domain linker predictor, DROP (Domain linker pRediction using OPtimal features), which was trained with 25 optimal features. The optimal combination of features was identified from a set of 3000 features using a random forest algorithm complemented with a stepwise feature selection. DROP demonstrated a prediction sensitivity and precision of 41.3 and 49.4%, respectively. These values were over 19.9% higher than those of control SVM predictors trained with non-optimized features, strongly suggesting the efficiency of our feature selection method. In addition, the mean NDO-Score of DROP for predicting novel domains in seven CASP8 FM multidomain proteins was 0.760, which was higher than any of the 12 published CASP8 DP servers. Overall, these results indicate that the SVM prediction of domain linkers can be improved by identifying optimal features that best distinguish linker from non-linker regions.