We performed a multicenter survey using a semistructured interview in 1,072 consecutive patients with Parkinson's disease (PD) enrolled during 12 months in 55 Italian centers to assess the prevalence of nonmotor symptoms (NMSs), their association with cognitive impairment, and the impact on patients' quality of life (QoL). We found that 98.6% of patients with PD reported the presence of NMSs. The most common were as follows: fatigue (58%), anxiety (56%), leg pain (38%), insomnia (37%), urgency and nocturia (35%), drooling of saliva and difficulties in maintaining concentration (31%). The mean number of NMS per patient was 7.8 (range, 0-32). NMS in the psychiatric domain were the most frequent (67%). Frequency of NMS increased along with the disease duration and severity. Patients with cognitive impairment reported more frequently apathy, attention/memory deficit, and psychiatric symptoms. Apathy was the symptom associated with worse PDQ-39 score but also presence of fatigue, attention/memory, and psychiatric symptoms had a negative impact on QoL. These findings further support a key role for NMS in the clinical frame of PD and the need to address them specifically in clinical trials using dedicated scales.

The accuracy and the performance of three two-dimensional compressible flow codes at freestream Mach numbers as low as 0.001 are examined. Two of the codes employ a finite volume discretization scheme along with a multistage time-stepping algorithm to solve the Euler equations. The two codes differ in their respective use of cell-centered and node-centered differencing schemes. The third code uses an implicit finite difference procedure to solve the unsteady Navier-Stokes equations. Computational test cases are the inviscid steady flow over a circular cylinder and the impulsively started viscous flow over a cylinder. Errors in the numerical results are presented as functions of mesh size and computational Mach number. It is shown that for certain classes of problems, the compressible codes can be sufficiently accurate to predict flow features at essentially incompressible speeds and that they can be adequately efficient since there is no need to lower excessively the computational Mach number in order to extract these results.

No abstract

No abstract

No abstract