Suppression of electron spin decoherence in a quantum dot

The dominant source of decoherence for an electron spin in a quantum dot is the hyperfine interaction with the surrounding bath of nuclear spins. The decoherence process may be slowed down by subjecting the electron spin to suitable sequences of external control pulses. We investigate the performance of a variety of dynamical decoupling protocols using exact numerical simulation. Emphasis is given to realistic pulse delays and the long-time limit, beyond the domain where available analytical approaches are guaranteed to work. Our results show that both deterministic and randomized protocols are capable to significantly prolong the electron coherence time, even when using control pulse separations substantially larger than what expected from the {\em upper cutoff} frequency of the coupling spectrum between the electron and the nuclear spins. In a realistic parameter range, the {\em total width} of such a coupling spectrum appears to be the physically relevant frequency scale affecting the overall quality of the decoupling.Comment: 8 pages, 3 figures. Invited talk at the XXXVII Winter Colloquium on the Physics of Quantum Electronics, Snowbird, Jan 2007. Submitted to J. Mod. Op

Imaging fast electrical activity in the brain with electrical impedance tomography.

Imaging of neuronal depolarization in the brain is a major goal in neuroscience, but no technique currently exists that could image neural activity over milliseconds throughout the whole brain. Electrical impedance tomography (EIT) is an emerging medical imaging technique which can produce tomographic images of impedance changes with non-invasive surface electrodes. We report EIT imaging of impedance changes in rat somatosensory cerebral cortex with a resolution of 2ms and <200μm during evoked potentials using epicortical arrays with 30 electrodes. Images were validated with local field potential recordings and current source-sink density analysis. Our results demonstrate that EIT can image neural activity in a volume 7×5×2mm in somatosensory cerebral cortex with reduced invasiveness, greater resolution and imaging volume than other methods. Modeling indicates similar resolutions are feasible throughout the entire brain so this technique, uniquely, has the potential to image functional connectivity of cortical and subcortical structures

Cadmium Induces the Expression of Grp78, an Endoplasmic Reticulum Molecular Chaperone, in LLC-PK1 Renal Epithelial Cells

To reveal the effects of cadmium exposure on the endoplasmic reticulum (ER) stress response, we examined the expression and function of 78-kDa glucose-regulated protein (Grp78), an ER-resident molecular chaperone, in LLC-PK1 cells. In cells treated with 10 μM cadmium chloride, Grp78 protein levels increased after 6 hr and remained elevated at 24 hr. When cells were incubated with 1–20 μM CdCl(2) for 6 hr, Grp78 increased in a dose-dependent manner. In addition, Grp78 mRNA levels were elevated in response to CdCl(2) exposure. After exposure to 10 μM CdCl(2), the levels of activating transcription factor 4 (ATF4) were increased at 2 hr, with a further enhancement after that; this accumulation followed the transient but marked phosphorylation of the α subunit of eukaryotic translation initiation factor 2 (eIF2α) on serine 51. Although ATF4 mRNA levels increased mildly by CdCl(2) exposure, treatment with actinomycin D did not suppress CdCl(2)-induced accumulation of ATF4 protein, suggesting the involvement of posttranscriptional and, in part, transcriptional mechanisms. Compared with other heavy-metal compounds such as manganese chloride, zinc chloride, mercuric chloride, and lead chloride, CdCl(2) could increase the levels of Grp78, ATF4, and the phosphorylated form of eIF2α more markedly without definite cellular damage. The silencing of Grp78 expression using short-interference RNA enhanced CdCl(2)-induced cellular damage. These results show that cadmium induces the expression of Grp78 probably via phosphorylation of eIF2α and resultant translation of ATF4, and this ER stress response plays a role in protection against cadmium cytotoxicity in this renal epithelial cell

MAMMALS IN PORTUGAL: A data set of terrestrial, volant, and marine mammal occurrences in Portugal

Mammals are threatened worldwide, with ca. 26% of all species being included in the IUCN threatened categories. This overall pattern is primarily associated to habitat loss or degradation, and human persecution for terrestrial mammals, and pollution, open net fishing, climate change and prey depletion for marine mammals. Mammals play a key role in maintaining ecosystems functionality and resilience, and therefore information on their distribution is crucial to delineate and support conservation actions. MAMMALS IN PORTUGAL is a publicly available data set compiling unpublished geo-referenced occurrence records of 92 terrestrial, volant, and marine mammals in mainland Portugal and archipelagos of Azores and Madeira that includes 107,852 data entries between 1873 and 2021 (72% of the data occurring in 2000 and 2021). The methods used to collect the data were: live observations/captures (42%), sign surveys (38%), camera trapping (16%), bioacoustics surveys (4%) and radio-tracking and inquiries that represent less than 1% of the records. The data set includes 13 types of records: 1) burrows | soil mounds | tunnel, 2) capture, 3) colony, 4) dead animal | hair | skulls | jaws, 5) genetic confirmation, 6) inquiries, 7) observation of live animal, 8), observation in shelters, 9) photo trapping | video, 10), predators diet | pellets | pine cones/nuts, 11) scat | track | ditch, 12) telemetry and 13) vocalization | echolocation. The spatial uncertainty of most records ranges between 0 and 100 m (76%). Rodentia (n = 34,754) has the highest number of records followed by Chiroptera (n = 18,858), Carnivora (n = 18,594), Lagomorpha (n = 17,679), Cetartiodactyla (n = 11,568) and Eulipotyphla (n = 6400). The data set includes records of species classified by the IUCN as threatened (e.g., Oryctolagus cuniculus (n = 12,407), Monachus monachus (n = 1512), and Lynx pardinus (n = 197)]. We believe that this data set may stimulate the publication of other European countries data sets which would certainly contribute to ecology and conservation-related research, and therefore assisting on the development of more accurate and tailored conservation management strategies for each species. There are no copyright restrictions; please cite this data paper when the data are used in publications

A method for reconstructing tomographic images of evoked neural activity with electrical impedance tomography using intracranial planar arrays

A method is presented for reconstructing images of fast neural evoked activity in rat cerebral cortex recorded with electrical impedance tomography (EIT) and a 6 × 5 mm(2) epicortical planar 30 electrode array. A finite element model of the rat brain and inverse solution with Tikhonov regularization were optimized in order to improve spatial resolution and accuracy. The optimized FEM mesh had 7 M tetrahedral elements, with finer resolution (0.05 mm) near the electrodes. A novel noise-based image processing technique based on t-test significance improved depth localization accuracy from 0.5 to 0.1 mm. With the improvements, a simulated perturbation 0.5 mm in diameter could be localized in a region 4 × 5 mm(2) under the centre of the array to a depth of 1.4 mm, thus covering all six layers of the cerebral cortex with an accuracy of <0.1 mm. Simulated deep brain hippocampal or thalamic activity could be localized with an accuracy of 0.5 mm with a 256 electrode array covering the brain. Parallel studies have achieved a temporal resolution of 2 ms for imaging fast neural activity by EIT during evoked activity; this encourages the view that fast neural EIT can now resolve the propagation of depolarization-related fast impedance changes in cerebral cortex and deeper in the brain with a resolution equal or greater to the dimension of a cortical column

Hip fracture risk assessment: Artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study

Copyright @ 2013 Tseng et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background - Osteoporotic hip fractures with a significant morbidity and excess mortality among the elderly have imposed huge health and economic burdens on societies worldwide. In this age- and sex-matched case control study, we examined the risk factors of hip fractures and assessed the fracture risk by conditional logistic regression (CLR) and ensemble artificial neural network (ANN). The performances of these two classifiers were compared. Methods - The study population consisted of 217 pairs (149 women and 68 men) of fractures and controls with an age older than 60 years. All the participants were interviewed with the same standardized questionnaire including questions on 66 risk factors in 12 categories. Univariate CLR analysis was initially conducted to examine the unadjusted odds ratio of all potential risk factors. The significant risk factors were then tested by multivariate analyses. For fracture risk assessment, the participants were randomly divided into modeling and testing datasets for 10-fold cross validation analyses. The predicting models built by CLR and ANN in modeling datasets were applied to testing datasets for generalization study. The performances, including discrimination and calibration, were compared with non-parametric Wilcoxon tests. Results - In univariate CLR analyses, 16 variables achieved significant level, and six of them remained significant in multivariate analyses, including low T score, low BMI, low MMSE score, milk intake, walking difficulty, and significant fall at home. For discrimination, ANN outperformed CLR in both 16- and 6-variable analyses in modeling and testing datasets (p?<?0.005). For calibration, ANN outperformed CLR only in 16-variable analyses in modeling and testing datasets (p?=?0.013 and 0.047, respectively). Conclusions - The risk factors of hip fracture are more personal than environmental. With adequate model construction, ANN may outperform CLR in both discrimination and calibration. ANN seems to have not been developed to its full potential and efforts should be made to improve its performance.National Health Research Institutes in Taiwa

Ultracold dense gas of deeply bound heteronuclear molecules

Recently, the quest for an ultracold and dense ensemble of polar molecules has attracted strong interest. Polar molecules have bright prospects for novel quantum gases with long-range and anisotropic interactions, for quantum information science, and for precision measurements. However, high-density clouds of ultracold polar molecules have so far not been produced. Here, we report a key step towards this goal. Starting from an ultracold dense gas of heteronuclear 40K-87Rb Feshbach molecules with typical binding energies of a few hundred kHz and a negligible dipole moment, we coherently transfer these molecules into a vibrational level of the ground-state molecular potential bound by >10 GHz. We thereby increase the binding energy and the expected dipole moment of the 40K-87Rb molecules by more than four orders of magnitude in a single transfer step. Starting with a single initial state prepared with Feshbach association, we achieve a transfer efficiency of 84%. While dipolar effects are not yet observable, the presented technique can be extended to access much more deeply bound vibrational levels and ultimately those exhibiting a significant dipole moment. The preparation of an ultracold quantum gas of polar molecules might therefore come within experimental reach.Comment: 5 pages, 5 figure

Maternal social environment affects offspring cognition through behavioral and immune pathways in rats

The social environment of lactation is a key etiological factor for the occurrence of postpartum disorders affecting women and their children. Postpartum depression and anxiety disorders are highly prevalent in new mothers and negatively affect offspring's cognitive development through mechanisms which are still unclear. Here, using a rat model, we manipulated the maternal social environment during lactation and explored the pathways through which social isolation (vs. the opportunity for limited social interaction with another lactating female, from 1 day before parturition to postpartum day 16) and chronic social conflict (daily exposure to a male intruder from postpartum day 2 to day 16) affect offspring learning and memory, measured at 40 to 60 days of age. We specifically explored the consequences of these social treatments on two main hypothesized mediators likely to affect offspring neurophysiological development: the quality of maternal care and maternal inflammation factors (BDNF, GM‐CSF, ICAM‐1, TIMP‐1 and VEGF) likely to influence offspring development through lactation. Maternal rats which had the opportunity to interact with another lactating female spent more time with their pups which, in turn, displayed improved working and reference memory. Social stress affected maternal plasma levels of cytokines that were associated with cognitive deficits in their offspring. However, females subjected to social stress were protected from these stress‐induced immune changes and associated offspring cognitive impairment by increased social affiliation. These results underscore the effects of social interaction for new mothers and their offspring and can be used to inform the development of clinical preventative measures and interventions

Integrable models: from dynamical solutions to string theory

We review the status of integrable models from the point of view of their dynamics and integrability conditions. Some integrable models are discussed in detail. We comment on the use it is made of them in string theory. We also discuss the Bethe Ansatz solution of the SO(6) symmetric Hamiltonian with SO(6) boundary. This work is especially prepared for the seventieth anniversaries of Andr\'{e} Swieca (in memoriam) and Roland K\"{o}berle.Comment: 24 pages, to appear in Brazilian Journal of Physic

Cortical microstructure in primary progressive aphasia: a multicenter study

Cortical mean diffusivity is a novel imaging metric sensitive to early changes in neurodegenerative syndromes. Higher cortical mean diffusivity values reflect microstructural disorganization and have been proposed as a sensitive biomarker that might antedate macroscopic cortical changes. We aimed to test the hypothesis that cortical mean diffusivity is more sensitive than cortical thickness to detect cortical changes in primary progressive aphasia (PPA).In this multicenter, case-control study, we recruited 120 patients with PPA (52 non-fluent, 31 semantic, and 32 logopenic variants; and 5 GRN-related PPA) as well as 89 controls from three centers. The 3-Tesla MRI protocol included structural and diffusion-weighted sequences. Disease severity was assessed with the Clinical Dementia Rating scale. Cortical thickness and cortical mean diffusivity were computed using a surface-based approach.The comparison between each PPA variant and controls revealed cortical mean diffusivity increases and cortical thinning in overlapping regions, reflecting the canonical loci of neurodegeneration of each variant. Importantly, cortical mean diffusivity increases also expanded to other PPA-related areas and correlated with disease severity in all PPA groups. Cortical mean diffusivity was also increased in patients with very mild PPA when only minimal cortical thinning was observed and showed a good correlation with measures of disease severity.Cortical mean diffusivity shows promise as a sensitive biomarker for the study of the neurodegeneration-related microstructural changes in PPA.© 2022. The Author(s)