Palladium (II)-Catalyzed C-H Activation with Bifunctional Ligands: From Curiosity to Industrialization.

In 2001, our curiosity to understand the stereochemistry of C-H metalation with Pd prompted our first studies in Pd(II)-catalyzed asymmetric C-H activation (RSC Research appointment: 020 7451 2545, Grant: RG 36873, Dec. 2002). We identified four central challenges: 1.

THEMIS increases TCR signaling in CD4CD8 thymocytes by inhibiting the activity of the tyrosine phosphatase SHP1.

The T cell lineage-restricted protein THEMIS plays a critical role in T cell development at the positive selection stage. In the SHP1 activation model, THEMIS is proposed to enhance the activity of the tyrosine phosphatase SHP1 (encoded by ), thereby dampening T cell antigen receptor (TCR) signaling and preventing the inappropriate negative selection of CD4CD8 thymocytes by positively selecting ligands. In contrast, in the SHP1 inhibition model, THEMIS is proposed to suppress SHP1 activity, rendering CD4CD8 thymocytes more sensitive to TCR signaling initiated by low-affinity ligands to promote positive selection.

GRB2 promotes thymocyte positive selection by facilitating THEMIS-mediated inactivation of SHP1.

The T-lineage restricted protein THEMIS has been shown to play a critical role in T cell development. THEMIS, via its distinctive CABIT domains, inhibits the catalytic activity of the tyrosine phosphatase SHP1 (PTPN6). SHP1 and THEMIS bind to the ubiquitous cytosolic adapter GRB2, and the purported formation of a tri-molecular THEMIS-GRB2-SHP1 complex facilitates inactivation of SHP1 by THEMIS.

Nocardia pseudobrasiliensis Co-infection in SARS-CoV-2 Patients.

During the SARS-CoV-2 pandemic, few cases of Nocar-dia spp. co-infection have been reported during or after a COVID-19 infection. Nocardia spp.

A meta-learning BCI for estimating decision confidence.

We investigated whether a recently introduced transfer-learning technique based on meta-learning could improve the performance of brain-computer interfaces (BCIs) for decision-confidence prediction with respect to more traditional machine learning methods.We adapted the meta-learning by biased regularisation algorithm to the problem of predicting decision confidence from electroencephalography (EEG) and electro-oculogram (EOG) data on a decision-by-decision basis in a difficult target discrimination task based on video feeds. The method exploits previous participants' data to produce a prediction algorithm that is then quickly tuned to new participants.