Microlensing optical depth towards the Galactic bulge from MOA observations during 2000 with Difference Image Analysis

We analyze the data of the gravitational microlensing survey carried out by by the MOA group during 2000 towards the Galactic Bulge (GB). Our observations are designed to detect efficiently high magnification events with faint source stars and short timescale events, by increasing the the sampling rate up to 6 times per night and using Difference Image Analysis (DIA). We detect 28 microlensing candidates in 12 GB fields corresponding to 16 deg^2. We use Monte Carlo simulations to estimate our microlensing event detection efficiency, where we construct the I-band extinction map of our GB fields in order to find dereddened magnitudes. We find a systematic bias and large uncertainty in the measured value of the timescale $t_{\rm Eout}$ in our simulations. They are associated with blending and unresolved sources, and are allowed for in our measurements. We compute an optical depth tau = 2.59_{-0.64}^{+0.84} \times 10^{-6} towards the GB for events with timescales 0.3<t_E<200 days. We consider disk-disk lensing, and obtain an optical depth tau_{bulge} = 3.36_{-0.81}^{+1.11} \times 10^{-6}[0.77/(1-f_{disk})] for the bulge component assuming a 23% stellar contribution from disk stars. These observed optical depths are consistent with previous measurements by the MACHO and OGLE groups, and still higher than those predicted by existing Galactic models. We present the timescale distribution of the observed events, and find there are no significant short events of a few days, in spite of our high detection efficiency for short timescale events down to t_E = 0.3 days. We find that half of all our detected events have high magnification (>10). These events are useful for studies of extra-solar planets.Comment: 65 pages and 30 figures, accepted for publication in ApJ. A systematic bias and uncertainty in the optical depth measurement has been quantified by simulation

Characterisation of PduS, the pdu Metabolosome Corrin Reductase, and Evidence of Substructural Organisation within the Bacterial Microcompartment

PduS is a corrin reductase and is required for the reactivation of the cobalamin-dependent diol dehydratase. It is one component encoded within the large propanediol utilisation (pdu) operon, which is responsible for the catabolism of 1,2-propanediol within a self-assembled proteinaceous bacterial microcompartment. The enzyme is responsible for the reactivation of the cobalamin coenzyme required by the diol dehydratase. The gene for the cobalamin reductase from Citrobacter freundii (pduS) has been cloned to allow the protein to be overproduced recombinantly in E. coli with an N-terminal His-tag. Purified recombinant PduS is shown to be a flavoprotein with a non-covalently bound FMN that also contains two coupled [4Fe-4S] centres. It is an NADH-dependent flavin reductase that is able to mediate the one-electron reductions of cob(III)alamin to cob(II)alamin and cob(II)alamin to cob(I)alamin. The [4Fe-4S] centres are labile to oxygen and their presence affects the midpoint redox potential of flavin. Evidence is presented that PduS is able to bind cobalamin, which is inconsistent with the view that PduS is merely a flavin reductase. PduS is also shown to interact with one of the shell proteins of the metabolosome, PduT, which is also thought to contain an [Fe-S] cluster. PduS is shown to act as a corrin reductase and its interaction with a shell protein could allow for electron passage out of the bacterial microcompartment

Feasibility of large-scale deployment of multiple wearable sensors in Parkinson’s disease

Wearable devices can capture objective day-to-day data about Parkinson’s Disease (PD). This study aims to assess the feasibility of implementing wearable technology to collect data from multiple sensors during the daily lives of PD patients. The Parkinson@home study is an observational, two-cohort (North America, NAM; The Netherlands, NL) study. To recruit participants, different strategies were used between sites. Main enrolment criteria were self-reported diagnosis of PD, possession of a smartphone and age ≥18 years. Participants used the Fox Wearable Companion app on a smartwatch and smartphone for a minimum of 6 weeks (NAM) or 13 weeks (NL). Sensor-derived measures estimated information about movement. Additionally, medication intake and symptoms were collected via self-reports in the app. A total of 953 participants were included (NL: 304, NAM: 649). Enrolment rate was 88% in the NL (n = 304) and 51% (n = 649) in NAM. Overall, 84% (n = 805) of participants contributed sensor data. Participants were compliant for 68% (16.3 hours/participant/day) of the study period in NL and for 62% (14.8 hours/participant/day) in NAM. Daily accelerometer data collection decreased 23% in the NL after 13 weeks, and 27% in NAM after 6 weeks. Data contribution was not affected by demographics, clinical characteristics or attitude towards technology, but was by the platform usability score in the NL (χ2 (2) = 32.014, p<0.001), and self-reported depression in NAM (χ2(2) = 6.397, p = .04). The Parkinson@home study shows that it is feasible to collect objective data using multiple wearable sensors in PD during daily life in a large cohort

A review of Parkinson’s disease cardinal and dyskinetic motor symptoms assessment methods using sensor systems

This paper is reviewing objective assessments of Parkinson’s disease(PD) motor symptoms, cardinal, and dyskinesia, using sensor systems. It surveys the manifestation of PD symptoms, sensors that were used for their detection, types of signals (measures) as well as their signal processing (data analysis) methods. A summary of this review’s finding is represented in a table including devices (sensors), measures and methods that were used in each reviewed motor symptom assessment study. In the gathered studies among sensors, accelerometers and touch screen devices are the most widely used to detect PD symptoms and among symptoms, bradykinesia and tremor were found to be mostly evaluated. In general, machine learning methods are potentially promising for this. PD is a complex disease that requires continuous monitoring and multidimensional symptom analysis. Combining existing technologies to develop new sensor platforms may assist in assessing the overall symptom profile more accurately to develop useful tools towards supporting better treatment process