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Experimental studies of charge transport through single molecules often rely on break junction setups, where molecular junctions are repeatedly formed and broken while measuring the conductance, leading to a statistical distribution of conductance values. Modeling this experimental situation and the resulting conductance histograms is challenging for theoretical methods, as computations need to capture structural changes in experiments, including the statistics of junction formation and rupture. This type of extensive structural sampling implies that even when evaluating conductance from computationally efficient electronic structure methods, which typically are of reduced accuracy, the evaluation of conductance histograms is too expensive to be a routine task. Highly accurate quantum transport computations are only computationally feasible for a few selected conformations and thus necessarily ignore the rich conformational space probed in experiments. To overcome these limitations, we investigate the potential of machine learning for modeling conductance histograms, in particular by Gaussian process regression. We show that by selecting specific structural parameters as features, Gaussian process regression can be used to efficiently predict the zero-bias conductance from molecular structures, reducing the computational cost of simulating conductance histograms by an order of magnitude. This enables the efficient calculation of conductance histograms even on the basis of computationally expensive first-principles approaches by effectively reducing the number of necessary charge transport calculations, paving the way toward their routine evaluation.

After natalizumab (NAT) cessation, some multiple sclerosis (MS) patients experience a severe disease rebound. The rebound pathophysiology is still unclear; however, it has been linked to interleukin-17-producing T-helper (Th17) cells. We demonstrate that during NAT treatment, MCAM+CCR6+Th17 cells gradually acquire a pathogenic profile, including proinflammatory cytokine production, pathogenic transcriptional signatures, brain endothelial barrier impairment, and oligodendrocyte damage via induction of apoptotic pathways. This is accompanied by an increase in Th17 cell frequencies in the cerebrospinal fluid of NAT-treated patients. Notably, Th17 cells derived from NAT-treated patients, who later developed a disease rebound upon treatment cessation, displayed a distinct transcriptional pathogenicity profile associated with altered migratory properties. Accordingly, increased brain infiltration of patient Th17 cells was illustrated in a humanized mouse model and brain histology from a rebound patient. Therefore, peripheral blood-accumulated MCAM+CCR6+Th17 cells might be involved in rebound pathophysiology, and monitoring of changes in Th17 cell pathogenicity in patients before/during NAT treatment cessation might enable rebound risk assessment in the future.

Peripheral central nervous system (CNS)-infiltrating lymphocytes are a hallmark of relapsing-remitting multiple sclerosis. Tissue-resident memory T cells (T RM ) not only populate the healthy CNS parenchyma but also are suspected to contribute to multiple sclerosis pathology. Because cerebrospinal fluid (CSF), unlike CNS parenchyma, is accessible for diagnostics, we evaluated whether human CSF, apart from infiltrating cells, also contains T RM cells and CNS-resident myeloid cells draining from the parenchyma or border tissues. Using deep generative models, we integrated 41 CSF and 14 CNS parenchyma single-cell RNA sequencing (scRNAseq) samples from eight independent studies, encompassing 120,629 cells. By comparing CSF immune cells collected during multiple sclerosis relapse with cells collected during therapeutic very late antigen-4 blockade, we could identify immune subsets with tissue provenance across multiple lineages, including CNS border-associated macrophages, CD8 and CD4 T RM cells, and tissue-resident natural killer cells. All lymphocytic CNS-resident cells shared expression of CXCR6 but showed differential ITGAE expression (encoding CD103). A common signature defined CD4 and CD8 T RM cells by expression of ZFP36L2 , DUSP1 , and ID2 . We further developed a user interface-driven application based on this analysis framework for atlas-level cell identity transfer onto new CSF scRNAseq data. Together, these results define CNS-resident immune cells involved in multiple sclerosis pathology that can be detected and monitored in CSF. Targeting these cell populations might be promising to modulate immunopathology in progressive multiple sclerosis and other neuroinflammatory diseases.

We report transport measurements on tunable single-molecule junctions of the organic perchlorotrityl radical molecule, contacted with gold electrodes at low temperature. The current-voltage characteristics of a subset of junctions shows zero-bias anomalies due to the Kondo effect and in addition elevated magnetoresistance (MR). Junctions without Kondo resonance reveal a much stronger MR. Furthermore, we show that the amplitude of the MR can be tuned by mechanically stretching the junction. On the basis of these findings, we attribute the high MR to an interference effect involving spin-dependent scattering at the metal-molecule interface and assign the Kondo effect to the unpaired spin located in the center of the molecule in asymmetric junctions.

Mobile charge carriers in heterostructured nanoparticles are relevant for applications requiring charge separation and extraction. We investigate the benchmark systems CdSe-CdS core-shell quantum dots and quantum dots in quantum rods by optical and THz pump-probe spectroscopy. We relate photoconductivity and carrier location and observe that only shell-located electrons in quantum rods contribute to an observable photoconductivity. Despite the shallow electron confinement in the quasi-type II heterostructures, core-located carriers are bound into immobile excitons that respond on external electrical fields by polarization.