Systematic review | EPFL Graph Search

A systematic review is a scholarly synthesis of the evidence on a clearly presented topic using critical methods to identify, define and assess research on the topic. A systematic review extracts and interprets data from published studies on the topic, then analyzes, describes, and summarizes interpretations into a refined conclusion. For example, a systematic review of randomized controlled trials is a way of summarizing and implementing evidence-based medicine. While a systematic review may be applied in the biomedical or health care context, it may also be used where an assessment of a precisely defined subject can advance understanding in a field of research. A systematic review may examine clinical tests, public health interventions, environmental interventions, social interventions, adverse effects, qualitative evidence syntheses, methodological reviews, policy reviews, and economic evaluations. An understanding of systematic reviews and how to implement the

Les mathématiques financières robustes, le maritime et les FFA

Jérôme Reboulleau

Powerful mathematical tools have been developed for trading in stocks and bonds, but other markets that are equally important for the globalized world have to some extent been neglected. We decided to study the shipping market as an new area of development in mathematical finance. The market in shipping derivatives (FFA and FOSVA) has only been developed after 2000 and now exhibits impressive growth. Financial actors have entered the field, but it is still largely undiscovered by institutional investors. The first part of the work was to identify the characteristics of the market in shipping, i.e. the segmentation and the volatility. Because the shipping business is old-fashioned, even the leading actors on the world stage (ship owners and banks) are using macro-economic models to forecast the rates. If the macro-economic models are logical and make sense, they fail to predict. For example, the factor port congestion has been much cited during the last few years, but it is clearly very difficult to control and is simply an indicator of traffic. From our own experience it appears that most ship owners are in fact market driven and rather bad at anticipating trends. Due to their ability to capture large moves, we chose to consider Lévy processes for the underlying price process. Compared with the macro-economic approach, the main advantage is the uniform and systematic structure this imposed on the models. We get in each case a favorable result for our technology and a gain in forecasting accuracy of around 10% depending on the maturity. The global distribution is more effectively modelled and the tails of the distribution are particularly well represented. This model can be used to forecast the market but also to evaluate the risk, for example, by computing the VaR. An important limitation is the non-robustness in the estimation of the Lévy processes. The use of robust estimators reinforces the information obtained from the observed data. Because maximum likelihood estimation is not easy to compute with complex processes, we only consider some very general robust score functions to manage the technical problems. Two new class of robust estimators are suggested. These are based on the work of F. Hampel ([29]) and P. Huber ([30]) using influence functions. The main idea is to bound the maximum likelihood score function. By doing this a bias is created in the parameters estimation, which can be corrected by using a modification of the following type and as proposed by F. Hampel. The procedure for finding a robust estimating equation is thus decomposed into two consecutive steps : Subtract the bias correction and then Bound the score function. In the case of complex Lévy processes, the bias correction is difficult to compute and generally unknown. We have developed a pragmatic solution by inverting the Hampel's procedure. Bound the score function and then Correct for the bias. The price is a loss of the theoretical properties of our estimators, besides the procedure converges to maximum likelihood estimate. A second solution to for achieving robust estimation is presented. It considers the limiting case when the upper and lower bounds tend to zero and leads to B-robust estimators. Because of the complexity of the Lévy distributions, this leads to identification problems.

EPFL2008

Sensor-based control strategies for integration of epidural electrical stimulation in rehabilitation after spinal cord injury

Miroslav Caban

Spinal Cord Injury (SCI) results in damage to neural circuitry connecting the brain to the periphery. Consequently, sensory and motor function is lost, to varying degree, depending on the site and severity of the lesion. More than half of spinal cord injuries result in paraplegia, a partial or complete paralysis of the lower limbs. Despite the emergence of therapeutic interventions in recent decades, outcomes have been incremental, and few have transformed SCI rehabilitation.Epidural electrical stimulation (EES) stimulates paralyzed muscles. Anatomically intact neural circuits below the lesion can be reactivated to a functional state by stimulating afferent fibers in the dorsal roots. Recently, EES enabled independent stepping and improved functional recovery during activity-based rehabilitation. Neuroplasticity, the formation of new functional pathways based on neural activity above and below the lesion, allows for motor control recovery. However, it took months of individualized rehabilitation training to see functional recovery, making widespread application difficult.For the first time in humans, we use biomimetic EES to deliver temporal sequences of spatially selective stimulation trains to the spinal cord. During rehabilitation exercises, we target specific muscle groups to mimic a natural afferent firing pattern. We improve simultaneous descending and ascending activity by using closed-loop motor-intent sensors. We propose three goals to drive clinical translation of biomimetic EES.We first enable EES for locomotor training and outside of a dedicated research environment. Following a systematic review of gait-detection algorithms, we developed a novel neurostimulation platform the functions in closed-loop using inertial measurement units. We further designed a platform intended for independent use of stimulation by the patient. We investigated clinical outcomes of biomimetic EES applied during locomotor rehabilitation and found that performance improved with stimulation and after a few months, participants regained voluntary control over previously paralysed muscles without stimulation.Then we extend EES to patients with trunk impairments and complete leg motor loss. We modified our closed-loop paradigms for people with motor complete SCI and created an electrode lead that covers the thoracic spine. We evaluated biomimetic EES in complete SCI patients during locomotor and trunk rehabilitation. Three participants could walk and control trunk movements after neurorehabilitation.Finally, we incorporate EES into rehabilitation activities across the continuum-of-care. We created a graphical user interface for clinicians to easily create activity-specific simulation programs and synchronized stimulation with common robotic devices. Then we evaluated EES's clinical benefit in conventional and robotic rehab. Individuals could immediately stand, walk, cycle, swim, and move their trunks. Using EES and rehabilitation robotics restored physiologically relevant muscle activity and allowed for more challenging training. Recreational rehabilitation activities, that were otherwise inaccessible, could be performed.Biomimetic EES optimizes conditions for neuroplasticity and shows promise of delivering clinically relevant outcomes in a short timeframe. We thus bring the intervention closer to a clinical reality, offering a new hope to the SCI community.

EPFL2022

Systematic analysis of low-affinity transcription factor binding site clusters in vitro and in vivo establishes their functional relevance

Shiyu Cheng, Ivan Istomin, Maria Lopez Malo, Sebastian Maerkl, Evan James Olson, Amir Shahein

Binding to binding site clusters has yet to be characterized in depth, and the functional relevance of low-affinity clusters remains uncertain. We characterized transcription factor binding to low-affinity clusters in vitro and found that transcription factors can bind concurrently to overlapping sites, challenging the notion of binding exclusivity. Furthermore, small clusters with binding sites an order of magnitude lower in affinity give rise to high mean occupancies at physiologically-relevant transcription factor concentrations. To assess whether the observed in vitro occupancies translate to transcriptional activation in vivo, we tested low-affinity binding site clusters in a synthetic and native gene regulatory network in S. cerevisiae. In both systems, clusters of low-affinity binding sites generated transcriptional output comparable to single or even multiple consensus sites. This systematic characterization demonstrates that clusters of low-affinity binding sites achieve substantial occupancies, and that this occupancy can drive expression in eukaryotic promoters.

NATURE PORTFOLIO2022