Worm infestation as a cause of severe anemia in a frail older adult: a case report.

Introduction: Worm infestations are a common occurrence in low-income countries. Anemia due to iron deficiency can be brought on by human intestinal worms. The authors report a case of an 86-year-old frail older adult with upper gastrointestinal (GI) bleeding caused by a worm infestation most likely to be hookworm. Case presentation: An 86-year-old male, presented to the Emergency Department with complaints of bilateral lower limb swelling and shortness of breath for 4 days associated with melena for 2 months. The authors made a provisional diagnosis of heart failure precipitated by anemia. Upper GI endoscopy revealed multiple whitish exudates, which are resistant to water jets. Multiple worms were noted in the second part of the duodenum. Based on clinical evaluation and endoscopy, the diagnosis of oesophagial candidiasis and iron deficiency anemia secondary to upper GI bleeding due to Hookworm infestation was made. Clinical discussion: In low-income countries, especially those involving the tropical area, worm infestation should be considered as an important cause of obscure acute GI bleeding and severe anemia. Usually, malignancy is suspected in an older adult with severe anemia but hookworm infestation is a treatable disease with a good prognosis and complete recovery. The most commonly used drugs for treatment are mebendazole and albendazole. In a low-income country with a high burden of worm infestations, empirical treatment of iron deficiency anemia with single dose albendazole has been recommended. Conclusion: Usually, severe anemia in an older adult is mostly attributed to an underlying malignancy. Our case serves as a good example of how a treatable condition can improve the quality of life in a frail older adult. Normally, there is a tendency to defer UGI endoscopy in frail elderly due to ageism. However, the diagnosis of a treatable cause of upper GI bleeding can be made by a simple upper GI endoscopy. Severe anemia due to hookworm infestation is treated effectively and quickly with albendazole and iron therapy.

Data-informed reservoir computing for efficient time-series prediction.

We propose a new approach to dynamical system forecasting called data-informed-reservoir computing (DI-RC) that, while solely being based on data, yields increased accuracy, reduced computational cost, and mitigates tedious hyper-parameter optimization of the reservoir computer (RC). Our DI-RC approach is based on the recently proposed hybrid setup where a knowledge-based model is combined with a machine learning prediction system, but it replaces the knowledge-based component by a data-driven model discovery technique. As a result, our approach can be chosen when a suitable knowledge-based model is not available. We demonstrate our approach using a delay-based RC as the machine learning component in conjunction with sparse identification of nonlinear dynamical systems for the data-driven model component. We test the performance on two example systems: the Lorenz system and the Kuramoto-Sivashinsky system. Our results indicate that our proposed technique can yield an improvement in the time-series forecasting capabilities compared with both approaches applied individually, while remaining computationally cheap. The benefit of our proposed approach, compared with pure RC, is most pronounced when the reservoir parameters are not optimized, thereby reducing the need for hyperparameter optimization.

Using machine learning to anticipate tipping points and extrapolate to post-tipping dynamics of non-stationary dynamical systems.

The ability of machine learning (ML) models to "extrapolate" to situations outside of the range spanned by their training data is crucial for predicting the long-term behavior of non-stationary dynamical systems (e.g., prediction of terrestrial climate change), since the future trajectories of such systems may (perhaps after crossing a tipping point) explore regions of state space which were not explored in past time-series measurements used as training data. We investigate the extent to which ML methods can yield useful results by extrapolation of such training data in the task of forecasting non-stationary dynamics, as well as conditions under which such methods fail. In general, we find that ML can be surprisingly effective even in situations that might appear to be extremely challenging, but do (as one would expect) fail when "too much" extrapolation is required. For the latter case, we show that good results can potentially be obtained by combining the ML approach with an available inaccurate conventional model based on scientific knowledge.

Using machine learning to predict statistical properties of non-stationary dynamical processes: System climate,regime transitions, and the effect of stochasticity.

We develop and test machine learning techniques for successfully using past state time series data and knowledge of a time-dependent system parameter to predict the evolution of the "climate" associated with the long-term behavior of a non-stationary dynamical system, where the non-stationary dynamical system is itself unknown. By the term climate, we mean the statistical properties of orbits rather than their precise trajectories in time. By the term non-stationary, we refer to systems that are, themselves, varying with time. We show that our methods perform well on test systems predicting both continuous gradual climate evolution as well as relatively sudden climate changes (which we refer to as "regime transitions"). We consider not only noiseless (i.e., deterministic) non-stationary dynamical systems, but also climate prediction for non-stationary dynamical systems subject to stochastic forcing (i.e., dynamical noise), and we develop a method for handling this latter case. The main conclusion of this paper is that machine learning has great promise as a new and highly effective approach to accomplishing data driven prediction of non-stationary systems.

Simplified single-shot supercontinuum spectral interferometry.

We have experimentally demonstrated a simplified method for performing single-shot supercontinuum spectral interferometry (SSSI) that does not require pre-characterization of the probe pulse. The method, originally proposed by D. T. Vu, D. Jang, and K. Y. Kim, uses a genetic algorithm (GA) and as few as two time-delayed pump-probe shots to retrieve the pump-induced phase shift on the probe [Opt. Express26, 20572 (2018)]. We show that the GA is able to successfully retrieve the transient modulations on the probe, and that the error in the retrieved modulation decreases dramatically with the number of shots used. In addition, we propose and demonstrate a practical method that allows SSSI to be done with a single pump-probe shot (again, without the need for pre-characterization of the probe). This simplified method can prove to be immensely useful when performing SSSI with a low-repetition-rate laser source.