Identification and prediction of Parkinson's disease subtypes and progression using machine learning in two cohorts.

The clinical manifestations of Parkinson's disease (PD) are characterized by heterogeneity in age at onset, disease duration, rate of progression, and the constellation of motor versus non-motor features. There is an unmet need for the characterization of distinct disease subtypes as well as improved, individualized predictions of the disease course. We used unsupervised and supervised machine learning methods on comprehensive, longitudinal clinical data from the Parkinson's Disease Progression Marker Initiative (n = 294 cases) to identify patient subtypes and to predict disease progression. The resulting models were validated in an independent, clinically well-characterized cohort from the Parkinson's Disease Biomarker Program (n = 263 cases). Our analysis distinguished three distinct disease subtypes with highly predictable progression rates, corresponding to slow, moderate, and fast disease progression. We achieved highly accurate projections of disease progression 5 years after initial diagnosis with an average area under the curve (AUC) of 0.92 (95% CI: 0.95 ± 0.01) for the slower progressing group (PDvec1), 0.87 ± 0.03 for moderate progressors, and 0.95 ± 0.02 for the fast-progressing group (PDvec3). We identified serum neurofilament light as a significant indicator of fast disease progression among other key biomarkers of interest. We replicated these findings in an independent cohort, released the analytical code, and developed models in an open science manner. Our data-driven study provides insights to deconstruct PD heterogeneity. This approach could have immediate implications for clinical trials by improving the detection of significant clinical outcomes. We anticipate that machine learning models will improve patient counseling, clinical trial design, and ultimately individualized patient care

Correlates of HIV-1 Genital Shedding in Tanzanian Women

BACKGROUND: Understanding the correlates of HIV shedding is important to inform strategies to reduce HIV infectiousness. We examined correlates of genital HIV-1 RNA in women who were seropositive for both herpes simplex virus (HSV)-2 and HIV-1 and who were enrolled in a randomised controlled trial of HSV suppressive therapy (aciclovir 400 mg b.i.d vs. placebo) in Tanzania. METHODOLOGY: Samples, including a cervico-vaginal lavage, were collected and tested for genital HIV-1 and HSV and reproductive tract infections (RTIs) at randomisation and 6, 12 and 24 months follow-up. Data from all women at randomisation and women in the placebo arm during follow-up were analysed using generalised estimating equations to determine the correlates of cervico-vaginal HIV-1 RNA detection and load. PRINCIPAL FINDINGS: Cervico-vaginal HIV-1 RNA was detected at 52.0% of 971 visits among 482 women, and was independently associated with plasma viral load, presence of genital ulcers, pregnancy, bloody cervical or vaginal discharge, abnormal vaginal discharge, cervical ectopy, Neisseria gonorrhoeae, Chlamydia trachomatis, Trichomonas vaginalis, an intermediate bacterial vaginosis score and HSV DNA detection. Similar factors were associated with genital HIV-1 RNA load. CONCLUSIONS: RTIs were associated with increased presence and quantity of genital HIV-1 RNA in this population. These results highlight the importance of integrating effective RTI treatment into HIV care services

Accessible quantification of multiparticle entanglement

Entanglement is a key ingredient for quantum technologies and a fundamental signature of quantumness in a broad range of phenomena encompassing many-body physics, thermodynamics, cosmology and life sciences. For arbitrary multiparticle systems, entanglement quantification typically involves nontrivial optimisation problems, and it may require demanding tomographical techniques. Here, we develop an experimentally feasible approach to the evaluation of geometric measures of multiparticle entanglement. Our framework provides analytical results for particular classes of mixed states of N qubits, and computable lower bounds to global, partial, or genuine multiparticle entanglement of any general state. For global and partial entanglement, useful bounds are obtained with minimum effort, requiring local measurements in just three settings for any N. For genuine entanglement, a number of measurements scaling linearly with N are required. We demonstrate the power of our approach to estimate and quantify different types of multiparticle entanglement in a variety of N-qubit states useful for uantum information processing and recently engineered in laboratories with quantum optics and trapped ion setups

In Vivo Assessment of Cold Adaptation in Insect Larvae by Magnetic Resonance Imaging and Magnetic Resonance Spectroscopy

Background Temperatures below the freezing point of water and the ensuing ice crystal formation pose serious challenges to cell structure and function. Consequently, species living in seasonally cold environments have evolved a multitude of strategies to reorganize their cellular architecture and metabolism, and the underlying mechanisms are crucial to our understanding of life. In multicellular organisms, and poikilotherm animals in particular, our knowledge about these processes is almost exclusively due to invasive studies, thereby limiting the range of conclusions that can be drawn about intact living systems. Methodology Given that non-destructive techniques like 1H Magnetic Resonance (MR) imaging and spectroscopy have proven useful for in vivo investigations of a wide range of biological systems, we aimed at evaluating their potential to observe cold adaptations in living insect larvae. Specifically, we chose two cold-hardy insect species that frequently serve as cryobiological model systems–the freeze-avoiding gall moth Epiblema scudderiana and the freeze-tolerant gall fly Eurosta solidaginis. Results In vivo MR images were acquired from autumn-collected larvae at temperatures between 0°C and about -70°C and at spatial resolutions down to 27 µm. These images revealed three-dimensional (3D) larval anatomy at a level of detail currently not in reach of other in vivo techniques. Furthermore, they allowed visualization of the 3D distribution of the remaining liquid water and of the endogenous cryoprotectants at subzero temperatures, and temperature-weighted images of these distributions could be derived. Finally, individual fat body cells and their nuclei could be identified in intact frozen Eurosta larvae. Conclusions These findings suggest that high resolution MR techniques provide for interesting methodological options in comparative cryobiological investigations, especially in vivo