Even though practicing sports has great health benefits, it also entails a risk of developing overuse injuries, which can elicit a negative impact on physical, mental, and financial health. Being ...able to predict the risk of an overuse injury arising is of widespread interest because this may play a vital role in preventing its occurrence. In this paper, we present a machine learning model trained to predict the occurrence of a lower-limb overuse injury (LLOI). This model was trained and evaluated using data from a three-dimensional accelerometer on the lower back, collected during a Cooper test performed by 161 first-year undergraduate students of a movement science program. In this study, gender-specific models performed better than mixed-gender models. The estimated area under the receiving operating characteristic curve of the best-performing male- and female-specific models, trained according to the presented approach, was, respectively, 0.615 and 0.645. In addition, the best-performing models were achieved by combining statistical and sports-specific features. Overall, the results demonstrated that a machine learning injury prediction model is a promising, yet challenging approach.
Zircon U–Pb geochronology results indicate that the John Muir Intrusive Suite of the central Sierra Nevada batholith, California, was assembled over a period of at least 12 Ma between 96 and 84 Ma. ...Bulk mineral thermochronology (U–Pb zircon and titanite,
40
Ar/
39
Ar hornblende and biotite) of rocks from multiple plutons comprising the Muir suite indicates rapid cooling through titanite and hornblende closure following intrusion and subsequent slow cooling through biotite closure. Assembly of intrusive suites in the Sierra Nevada and elsewhere over millions of years favors growth by incremental intrusion. Estimated long-term pluton assembly rates for the John Muir Intrusive Suite are on the order of 0.001 km
3
a
−1
which is inconsistent with the rapid magma fluxes that are necessary to form large-volume magma chambers capable of producing caldera-forming eruptions. If large shallow crustal magma chambers do not typically develop during assembly of large zoned intrusive suites, it is doubtful that the intrusive suites represent cumulates left behind following caldera-forming eruptions.
Relations between molecular design, chemical functionality, and stimulus-triggered response are important for a variety of applications of polymeric systems. Here, reactive amphiphilic block ...copolymers (BCPs) of poly(2-vinylpyridine)-block-poly(2-vinyl-4,4-dimethylazlactone) (PVP-b-PVDMA) were synthesized and assembled into microgels capable of incorporating functional amines. The composition of the PVP-b-PVDMA BCPs was varied to control the number of reactive sites in the spherical aggregates created by self-assembly of PVP-b-PVDMA BCPs in a 2-propanol/THF (v:v = 19:1) solvent mixture, which is selective for PVP. PVDMA and PVP segments were selectively cross-linked by 1,4-diaminobutane (DAB) or 1,4-diiodobutane (DIB) to fabricate core- and corona-cross-linked azlactone-containing microgels, respectively. Non-cross-linked aggregates of PVP-b-PVDMA and DIB-cross-linked microgels dissociate when exposed to THF, which is a good solvent for both blocks. However, the DAB-cross-linked BCP microgels swell in THF, suggesting the formation of a stable, three-dimensional network structure. Because of their ability to be reactively modified in ways that allows their stability or disassembly characteristics to be tailored, these azlactone-containing BCP microgels provide an attractive platform for applications in a wide range of fields, including catalysis, imaging, molecule separation, and guest loading for targeted delivery.
Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain ...both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis and knowledge base completion. This article provides a survey of the current state of the art in PU learning. It proposes seven key research questions that commonly arise in this field and provides a broad overview of how the field has tried to address them.
The aging population has resulted in interest in remote monitoring of elderly individuals' health and well being. This paper describes a simple unsupervised monitoring system that can automatically ...detect if an elderly individual's pattern of presence deviates substantially from the recent past. The proposed system uses a small set of low-cost motion sensors and analyzes the produced data to establish an individual's typical presence pattern. Then, the algorithm uses a distance function to determine whether the individual's observed presence for each day significantly deviates from their typical pattern. Empirically, the algorithm is validated on both synthetic data and data collected by installing our system in the residences of three older individuals. In the real-world setting, the system detected, respectively, five, four, and one deviating days in the three locations. The deviating days detected by the system could result from a health issue that requires attention. The information from the system can aid caregivers in assessing the subject's health status and allows for a targeted intervention. Although the system can be refined, we show that otherwise hidden but relevant events (e.g., fall incident and irregular sleep patterns) are detected and reported to the caregiver.
In this study, we show how both machine learning and alternative data can be successfully leveraged to improve and develop trading strategies. Starting from a trading strategy that harvests the ...EUR/USD volatility risk premium by selling one-week straddles every weekday, we present a machine learning approach to more skillfully time new trades and thus prevent unfavorable ones. To this end, we build probability-calibrated Random Forests on various predictors, extracted from both traditional market data and financial news, to predict the closing Sharpe ratio of short one-week delta-hedged straddles. We then demonstrate how the output of these calibrated machine learning models can be used to engineer intuitive new trading strategies. Ultimately, we show that our proposed strategies outperform the original strategy on risk-based performance measures. Moreover, the features that we derived from financial news articles significantly improve the performance of the approach.
•The future performance of short one-week delta-hedged straddles on EUR/USD can be predicted using Random Forests.•Features extracted from financial news improve prediction performance.•Trading strategies based on probability-calibrated Random Forest predictions significantly outperform the original strategy on risk-based measures.
Osteoarthritis is a common musculoskeletal disorder. Classification models can discriminate an osteoarthritic gait pattern from that of control subjects. However, whether the output of learned models ...(probability of belonging to a class) is usable for monitoring a person's functional recovery status post-total knee arthroplasty (TKA) is largely unexplored. The research question is two-fold: (I) Can a learned classification model's output be used to monitor a person's recovery status post-TKA? (II) Is the output related to patient-reported functioning? We constructed a logistic regression model based on (1) pre-operative IMU-data of level walking, ascending, and descending stairs and (2) 6-week post-operative data of walking, ascending-, and descending stairs. Trained models were deployed on subjects at three, six, and 12 months post-TKA. Patient-reported functioning was assessed by the KOOS-ADL section. We found that the model trained on 6-weeks post-TKA walking data showed a decrease in the probability of belonging to the TKA class over time, with moderate to strong correlations between the model's output and patient-reported functioning. Thus, the LR-model's output can be used as a screening tool to follow-up a person's recovery status post-TKA. Person-specific relationships between the probabilities and patient-reported functioning show that the recovery process varies, favouring individual approaches in rehabilitation.
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