Evaluating the dynamic interplay of social distancing policies regarding airborne pathogens through a temporal interaction-driven model that uses real-world and synthetic data.

OBJECTIVE: The recent SARS-CoV-2 pandemic has exhibited diverse patterns of spread across countries and communities, emphasizing the need to consider the underlying population dynamics in modeling its progression and the importance of evaluating the effectiveness of non-pharmaceutical intervention strategies in combating viral transmission within human communities. Such an understanding requires accurate modeling of the interplay between the community dynamics and the disease propagation dynamics within the community. METHODS: We build on an interaction-driven model of an airborne disease over contact networks that we have defined. Using the model, we evaluate the effectiveness of temporal, spatial, and spatiotemporal social distancing policies. Temporal social distancing involves a pure dilation of the timeline while preserving individual activity potential and thus prolonging the period of interaction; spatial distancing corresponds to social distancing pods; and spatiotemporal distancing pertains to the situation in which fixed subgroups of the overall group meet at alternate times. We evaluate these social distancing policies over real-world interactions' data and over history-preserving synthetic temporal random networks. Furthermore, we evaluate the policies for the disease's with different number of initial patients, corresponding to either the phase in the progression of the infection through a community or the number of patients infected together at the initial infection event. We expand our model to consider the exposure to viral load, which we correlate with the meetings' duration. RESULTS: Our results demonstrate the superiority of decreasing social interactions (i.e., time dilation) within the community over partial isolation strategies, such as the spatial distancing pods and the spatiotemporal distancing strategy. In addition, we found that slow-spreading pathogens (i.e., pathogens that require a longer exposure to infect) spread roughly at the same rate as fast-spreading ones in highly active communities. This result is surprising since the pathogens may follow different paths. However, we demonstrate that the dilation of the timeline considerably slows the spread of the slower pathogens. CONCLUSIONS: Our results demonstrate that the temporal dynamics of a community have a more significant effect on the spread of the disease than the characteristics of the spreading processes.

Multi-Dimensional Validation of the Integration of Syntactic and Semantic Distance Measures for Clustering Fibromyalgia Patients in the Rheumatic Monitor Big Data Study.

This study primarily aimed at developing a novel multi-dimensional methodology to discover and validate the optimal number of clusters. The secondary objective was to deploy it for the task of clustering fibromyalgia patients. We present a comprehensive methodology that includes the use of several different clustering algorithms, quality assessment using several syntactic distance measures (the Silhouette Index (SI), Calinski-Harabasz index (CHI), and Davies-Bouldin index (DBI)), stability assessment using the adjusted Rand index (ARI), and the validation of the internal semantic consistency of each clustering option via the performance of multiple clustering iterations after the repeated bagging of the data to select multiple partial data sets. Then, we perform a statistical analysis of the (clinical) semantics of the most stable clustering options using the full data set. Finally, the results are validated through a supervised machine learning (ML) model that classifies the patients back into the discovered clusters and is interpreted by calculating the Shapley additive explanations (SHAP) values of the model. Thus, we refer to our methodology as the clustering, distance measures and iterative statistical and semantic validation (CDI-SSV) methodology. We applied our method to the analysis of a comprehensive data set acquired from 1370 fibromyalgia patients. The results demonstrate that the K-means was highly robust in the syntactic and the internal consistent semantics analysis phases and was therefore followed by a semantic assessment to determine the optimal number of clusters (k), which suggested k = 3 as a more clinically meaningful solution, representing three distinct severity levels. the random forest model validated the results by classification into the discovered clusters with high accuracy (AUC: 0.994; accuracy: 0.946). SHAP analysis emphasized the clinical relevance of "functional problems" in distinguishing the most severe condition. In conclusion, the CDI-SSV methodology offers significant potential for improving the classification of complex patients. Our findings suggest a classification system for different profiles of fibromyalgia patients, which has the potential to improve clinical care, by providing clinical markers for the evidence-based personalized diagnosis, management, and prognosis of fibromyalgia patients.

Treatment Prediction in the ICU Using a Partitioned, Sequential, Deep Time Series Analysis.

We have developed a time-oriented machine-learning tool to predict the binary decision of administering a medication and the quantitative decision regarding the specific dose. We evaluated our tool on the MIMIC-IV ICU database, for three common medical scenarios. We use an LSTM based neural network, and considerably extend its use by introducing several new concepts. We partition the common 12-hour prediction horizon into three sub-windows. Partitioning models the treatment dynamics better, and allows the use of previous sub-windows' data as additional training data with improved performance. We also introduce a sequential prediction process, composed of a binary treatment-decision model, followed, when relevant, by a quantitative dose-decision model, with improved accuracy. Finally, we examined two methods for including non-temporal features, such as age, within the temporal network. Our results provide additional treatment-prediction tools, and thus another step towards a reliable and trustworthy decision-support system that reduces the clinicians' cognitive load.

Automated Process Mining and Learning of Therapeutic Actions in the Intensive Care Unit.

In this study, we implemented a hybrid approach, incorporating temporal data mining, machine learning, and process mining for modeling and predicting the course of treatment of Intensive Care Unit (ICU) patients. We used process mining algorithms to construct models of management of ICU patients. Then, we extracted the decision points from the mined models and used temporal data mining of the periods preceding the decision points to create temporal-pattern features. We trained classifiers to predict the next actions expected for each point. The methodology was evaluated on medical ICU data from the hypokalemia and hypoglycemia domains. The study's contributions include the representation of medical treatment trajectories of ICU patients using process models, and the integration of Temporal Data Mining and Machine Learning with Process Mining, to predict the next therapeutic actions in the ICU.

Assessing individual risk and the latent transmission of COVID-19 in a population with an interaction-driven temporal model.

Interaction-driven modeling of diseases over real-world contact data has been shown to promote the understanding of the spread of diseases in communities. This temporal modeling follows the path-preserving order and timing of the contacts, which are essential for accurate modeling. Yet, other important aspects were overlooked. Various airborne pathogens differ in the duration of exposure needed for infection. Also, from the individual perspective, Covid-19 progression differs between individuals, and its severity is statistically correlated with age. Here, we enrich an interaction-driven model of Covid-19 and similar airborne viral diseases with (a) meetings duration and (b) personal disease progression. The enriched model enables predicting outcomes at both the population and the individual levels. It further allows predicting individual risk of engaging in social interactions as a function of the virus characteristics and its prevalence in the population. We further showed that the enigmatic nature of asymptomatic transmission stems from the latent effect of the network density on this transmission and that asymptomatic transmission has a substantial impact only in sparse communities.

Provision of Decision Support Through Continuous Prediction of Recurring Clinical Actions.

We propose a framework for provision of decision support through the continuous prediction of recurring targets, in particular clinical actions, which can potentially occur more than once in the patient's longitudinal clinical record. We first perform an abstraction of the patient's raw time-stamped data into intervals. Then, we partition the patient's timeline into time windows, and perform frequent temporal patterns mining in the features' window. Finally, we use the discovered patterns as features for a prediction model. We demonstrate the framework on the task of treatment prediction in the Intensive Care Unit, in the domains of Hypoglycemia, Hypokalemia and Hypotension.

Stimuli Response of Eu3+-Based Metallo-Supramolecular Polymers toward Pharmaceutical Amines.

Metallo-supramolecular polymers offer a highly controllable platform for sensing. Their modular characteristics obtained by the ability of varying both building blocks, the metal ion and the organic ligand, provide tunability of their optical and chemical properties. Specifically, polymers based on lanthanide ions and conjugated aromatic ligands exhibit enhanced luminescence properties that can be altered by external stimulation. Herein, using europium-based polymers, we demonstrate the ability to detect different pharmaceutical amines, including in complex biological media, based on their luminescence quenching efficiency as a result of their polymer dissociation capacity. A combination of absorption, luminescence, and NMR measurements reveals combined static and dynamic quenching mechanisms that enable selective sensing of strong basic amines with high pKa values.

Rich Landscape of Colloidal Semiconductor-Metal Hybrid Nanostructures: Synthesis, Synergetic Characteristics, and Emerging Applications.

Nanochemistry provides powerful synthetic tools allowing one to combine different materials on a single nanostructure, thus unfolding numerous possibilities to tailor their properties toward diverse functionalities. Herein, we review the progress in the field of semiconductor-metal hybrid nanoparticles (HNPs) focusing on metal-chalcogenides-metal combined systems. The fundamental principles of their synthesis are discussed, leading to a myriad of possible hybrid architectures including Janus zero-dimensional quantum dot-based systems and anisotropic quasi 1D nanorods and quasi-2D platelets. The properties of HNPs are described with particular focus on emergent synergetic characteristics. Of these, the light-induced charge-separation effect across the semiconductor-metal nanojunction is of particular interest as a basis for the utilization of HNPs in photocatalytic applications. The extensive studies on the charge-separation behavior and its dependence on the HNPs structural characteristics, environmental and chemical conditions, and light excitation regime are surveyed. Combining the advanced synthetic control with the charge-separation effect has led to demonstration of various applications of HNPs in different fields. A particular promise lies in their functionality as photocatalysts for a variety of uses, including solar-to-fuel conversion, as a new type of photoinitiator for photopolymerization and 3D printing, and in novel chemical and biomedical uses.

A statistical model for early estimation of the prevalence and severity of an epidemic or pandemic from simple tests for infection confirmation.

Epidemics and pandemics require an early estimate of the cumulative infection prevalence, sometimes referred to as the infection "Iceberg," whose tip are the known cases. Accurate early estimates support better disease monitoring, more accurate estimation of infection fatality rate, and an assessment of the risks from asymptomatic individuals. We find the Pivot group, the population sub-group with the highest probability of being detected and confirmed as positively infected. We differentiate infection susceptibility, assumed to be almost uniform across all population sub-groups at this early stage, from the probability of being confirmed positive. The latter is often related to the likelihood of developing symptoms and complications, which differs between sub-groups (e.g., by age, in the case of the COVID-19 pandemic). A key assumption in our method is the almost-random subgroup infection assumption: The risk of initial infection is either almost uniform across all population sub-groups or not higher in the Pivot sub-group. We then present an algorithm that, using the lift value of the pivot sub-group, finds a lower bound for the cumulative infection prevalence in the population, that is, gives a lower bound on the size of the entire infection "Iceberg." We demonstrate our method by applying it to the case of the COVID-19 pandemic. We use UK and Spain serological surveys of COVID-19 in its first year to demonstrate that the data are consistent with our key assumption, at least for the chosen pivot sub-group. Overall, we applied our methods to nine countries or large regions whose data, mainly during the early COVID-19 pandemic phase, were available: Spain, the UK at two different time points, New York State, New York City, Italy, Norway, Sweden, Belgium, and Israel. We established an estimate of the lower bound of the cumulative infection prevalence for each of them. We have also computed the corresponding upper bounds on the infection fatality rates in each country or region. Using our methodology, we have demonstrated that estimating a lower bound for an epidemic's infection prevalence at its early phase is feasible and that the assumptions underlying that estimate are valid. Our methodology is especially helpful when serological data are not yet available to gain an initial assessment on the prevalence scale, and more so for pandemics with an asymptomatic transmission, as is the case with Covid-19.

Treatment Prediction in the ICU Setting Using a Partitioned, Sequential Deep Time Series Analysis.

We developed a neural network architecture to evaluate the patient's state using temporal data, patient's demographics and comorbidities. We examined the model's ability to predict both a binary medication-treatment decision and its specific dose in three common scenarios: hypokalemia, hypoglycemia and hypotension. We partition the common 12-hours horizon window into three sub-windows, examining how patterns of treatment evolve following a key clinical event or state. This partitioned analysis also helps in alleviating the problem of small data sets, by utilizing previous sub-windows' data as additional training data. We also propose a solution to the problem of the relative inability of dose-prediction models to output a "no treatment" classification, through the use of sequential prediction.

Distributed application of guideline-based decision support through mobile devices: Implementation and evaluation.

BACKGROUND: Traditionally guideline (GL)-based Decision Support Systems (DSSs) use a centralized infrastructure to generate recommendations to care providers, rather than to patients at home. However, managing patients at home is often preferable, reducing costs and empowering patients. Thus, we wanted to explore an option in which patients, in particular chronic patients, might be assisted by a local DSS, which interacts as needed with the central DSS engine, to manage their disease outside the standard clinical settings. OBJECTIVES: To design, implement, and demonstrate the technical and clinical feasibility of a new architecture for a distributed DSS that provides patients with evidence-based guidance, offered through applications running on the patients' mobile devices, monitoring and reacting to changes in the patient's personal environment, and providing the patients with appropriate GL-based alerts and personalized recommendations; and increase the overall robustness of the distributed application of the GL. METHODS: We have designed and implemented a novel projection-callback (PCB) model, in which small portions of the evidence-based guideline's procedural knowledge are projected from a projection engine within the central DSS server, to a local DSS that resides on each patient's mobile device. The local DSS applies the knowledge using the mobile device's local resources. The GL projections generated by the projection engine are adapted to the patient's previously defined preferences and, implicitly, to the patient's current context, in a manner that is embodied in the projected therapy plans. When appropriate, as defined by a temporal pattern within the projected plan, the local DSS calls back the central DSS, requesting further assistance, possibly another projection. To support the new model, the initial specification of the GL includes two levels: one for the central DSS, and one for the local DSS. We have implemented a distributed GL-based DSS using the projection-callback model within the MobiGuide EU project, which automatically manages chronic patients at home using sensors on the patients and their mobile phone. We assessed the new GL specification process, by specifying two very different, complex GLs: for Gestational Diabetes Mellitus, and for Atrial Fibrillation. Then, we evaluated the new computational architecture by applying the two GLs to the automated clinical management, at real time, of patients in two different countries: Spain and Italy, respectively. RESULTS: The specification using the new projection-callback model was found to be quite feasible. We found significant differences between the distributed versions of the two GLs, suggesting further research directions and possibly additional ways to analyze and characterize GLs. Applying the two GLs to the two patient populations proved highly feasible as well. The mean time between the central and local interactions was quite different for the two GLs: 3.95 ± 1.95 days in the case of the gestational diabetes domain, and 23.80 ± 12.47 days, in the case of the atrial fibrillation domain, probably corresponding to the difference in the distributed specifications of the two GLs. Most of the interaction types were due to projections to the local DSS (83%); others were data notifications, mostly to change context (17%). Some of the data notifications were triggered due to technical errors. The robustness of the distributed architecture was demonstrated through the successful recovery from multiple crashes of the local DSS. CONCLUSIONS: The new projection-callback model has been demonstrated to be feasible, from specification to distributed application. Different GLs might significantly differ, however, in their distributed specification and application characteristics. Distributed medical DSSs can facilitate the remote management of chronic patients by enabling the central DSSs to delegate, in a dynamic fashion, determined by the patient's context, much of the monitoring and treatment management decisions to the mobile device. Patients can be kept in their home environment, while still maintaining, through the projection-callback mechanism, several of the advantages of a central DSS, such as access to the patient's longitudinal record, and to an up-to-date evidence-based GL repository.

Analysis of the competition among viral strains using a temporal interaction-driven contagion model.

The temporal dynamics of social interactions were shown to influence the spread of disease. Here, we model the conditions of progression and competition for several viral strains, exploring various levels of cross-immunity over temporal networks. We use our interaction-driven contagion model and characterize, using it, several viral variants. Our results, obtained on temporal random networks and on real-world interaction data, demonstrate that temporal dynamics are crucial to determining the competition results. We consider two and three competing pathogens and show the conditions under which a slower pathogen will remain active and create a second wave infecting most of the population. We then show that when the duration of the encounters is considered, the spreading dynamics change significantly. Our results indicate that when considering airborne diseases, it might be crucial to consider the duration of temporal meetings to model the spread of pathogens in a population.

The Impact of US County-Level Factors on COVID-19 Morbidity and Mortality.

The effect of socio-economic factors, ethnicity, and other factors, on the morbidity and mortality of COVID-19 at the sub-population-level, rather than at the individual level, and their temporal dynamics, is only partially understood. Fifty-three county-level features were collected between 4/2020 and 11/2020 from 3,071 US counties from publicly available data of various American government and news websites: ethnicity, socio-economic factors, educational attainment, mask usage, population density, age distribution, COVID-19 morbidity and mortality, presidential election results, and ICU beds. We trained machine learning models that predict COVID-19 mortality and morbidity using county-level features and then performed a SHAP value game theoretic importance analysis of the predictive features for each model. The classifiers produced an AUROC of 0.863 for morbidity prediction and an AUROC of 0.812 for mortality prediction. A SHAP value-based analysis indicated that poverty rate, obesity rate, mean commute time, and mask usage statistics significantly affected morbidity rates, while ethnicity, median income, poverty rate, and education levels heavily influenced mortality rates. Surprisingly, the correlation between several of these factors and COVID-19 morbidity and mortality gradually shifted and even reversed during the study period; our analysis suggests that this phenomenon was probably due to COVID-19 being initially associated with more urbanized areas and, then, from 9/2020, with less urbanized ones. Thus, socio-economic features such as ethnicity, education, and economic disparity are the major factors for predicting county-level COVID-19 mortality rates. Between counties, low variance factors (e.g., age) are not meaningful predictors. The inversion of some correlations over time can be explained by COVID-19 spreading from urban to rural areas.

A Cyber-Security Risk Assessment Methodology for Medical Imaging Devices: the Radiologists' Perspective.

Medical imaging devices (MIDs) are exposed to cyber-security threats. Currently, a comprehensive, efficient methodology dedicated to MID cyber-security risk assessment is lacking. We propose the Threat identification, ontology-based Likelihood, severity Decomposition, and Risk assessment (TLDR) methodology and demonstrate its feasibility and consistency with existing methodologies, while being more efficient, providing details regarding the severity components, and supporting organizational prioritization and customization. Using our methodology, the impact of 23 MIDs attacks (that were previously identified) was decomposed into six severity aspects. Four Radiology Medical Experts (RMEs) were asked to assess these six aspects for each attack. The TLDR methodology's external consistency was demonstrated by calculating paired T-tests between TLDR severity assessments and those of existing methodologies (and between the respective overall risk assessments, using attack likelihood estimates by four healthcare cyber-security experts); the differences were insignificant, implying externally consistent risk assessment. The TLDR methodology's internal consistency was evaluated by calculating the pairwise Spearman rank correlations between the severity assessments of different groups of two to four RMEs and each of their individual group members, showing that the correlations between the severity rankings, using the TLDR methodology, were significant (P < 0.05), demonstrating that the severity rankings were internally consistent for all groups of RMEs. Using existing methodologies, however, the internal correlations were insignificant for groups of less than four RMEs. Furthermore, compared to standard risk assessment techniques, the TLDR methodology is also sensitive to local radiologists' preferences, supports a greater level of flexibility regarding risk prioritization, and produces more transparent risk assessments.

A dual-layer context-based architecture for the detection of anomalous instructions sent to medical devices.

Complex medical devices are controlled by instructions sent from a host personal computer (PC) to the device. Anomalous instructions can introduce many potentially harmful threats to patients (e.g., radiation overexposure), to physical device components (e.g., manipulation of device motors), or to functionality (e.g., manipulation of medical images). Threats can occur due to cyber-attacks, human error (e.g., using the wrong protocol, or misconfiguring the protocol's parameters by a technician), or host PC software bugs. Thus, anomalous instructions might represent an intentional threat to the patient or to the device, a human error, or simply a non-optimal operation of the device. To protect medical devices, we propose a new dual-layer architecture. The architecture analyzes the instructions sent from the host PC to the physical components of the device, to detect anomalous instructions using two detection layers: (1) an unsupervised context-free (CF) layer that detects anomalies based solely on the instruction's content and inter-correlations; and (2) a supervised context-sensitive (CS) layer that detects anomalies in both the clinical objective and patient contexts using a set of supervised classifiers pre-trained for each specific context. The proposed dual-layer architecture was evaluated in the computed tomography (CT) domain, using 4842 CT instructions that we recorded, including two types of CF anomalous instructions, four types of clinical objective context instructions and four types of patient context instructions. The CF layer was evaluated using 14 unsupervised anomaly detection algorithms. The CS layer was evaluated using six supervised classification algorithms applied to each context (i.e., clinical objective or patient). Adding the second CS supervised layer to the architecture improved the overall anomaly detection performance (by improving the detection of CS anomalous instructions [when they were not also CF anomalous]) from an F1 score baseline of 72.6%, to an improved F1 score of 79.1% to 99.5% (depending on the clinical objective or patient context used). Adding, the semantics-oriented CS layer enables the detection of CS anomalies using the semantics of the device's procedure, which is not possible when using just the purely syntactic CF layer. However, adding the CS layer also introduced a somewhat increased false positive rate (FPR), and thus reduced somewhat the specificity of the overall process. We conclude that by using both the CF and CS layers, a dual-layer architecture can better detect anomalous instructions to medical devices. The increased FPR might be reduced, in the future, through the use of stronger models, and by training them on more data. The improved accuracy, and the potential capability of adding explanations to both layers, might be useful for creating decision support systems for medical device technicians.

Implementation and evaluation of a multivariate abstraction-based, interval-based dynamic time-warping method as a similarity measure for longitudinal medical records.

OBJECTIVES: A common prerequisite for tasks such as classification, prediction, clustering and retrieval of longitudinal medical records is a clinically meaningful similarity measure that considers both [multiple] variable (concept) values and their time. Currently, most similarity measures focus on raw, time-stamped data as these are stored in a medical record. However, clinicians think in terms of clinically meaningful temporal abstractions, such as "decreasing renal functions", enabling them to ignore minor time and value variations and focus on similarities among the clinical trajectories of different patients. Our objective was to define an abstraction- and interval-based methodology for matching longitudinal, multivariate medical records, and rigorously assess its value, versus the option of using just the raw, time-stamped data. METHODS: We have developed a new methodology for determination of the relative distance between a pair of longitudinal records, by extending the known dynamic time warping (DTW) method into an interval-based dynamic time warping (iDTW) methodology. The iDTW methodology includes (A): A three-steps interval-based representation (iRep) method: [1] abstracting the raw, time-stamped data of the longitudinal records into clinically meaningful interval-based abstractions, using a domain-specific knowledge base, [2] scoping the period of comparison of the records, [3] creating from the intervals a symbolic time series, by partitioning them into a predetermined temporal granularity; (B) An interval-based matching (iMatch) method to match each relevant pair of multivariate longitudinal records, each represented as multiple series of short symbolic intervals in the determined temporal granularity, using a modified DTW version. EVALUATION: Three classification or prediction tasks were defined: (1) classifying 161 records of oncology patients as having had autologous versus allogenic bone-marrow transplantation; (2) classifying the longitudinal records of 125 hepatitis patients as having B or C hepatitis; and (3) predicting micro- or macro-albuminuria in the second year, for 151 diabetes patients who were followed for five years. The raw, time-stamped, multivariate data within each medical record, for one, two, or three concepts out of four or five concepts judged as relevant in each medical domain, were abstracted into clinically meaningful intervals using the Knowledge-Based Temporal-Abstraction method, using previously acquired knowledge. We focused on two temporal-abstraction types: (1) State abstractions, which discretize a concept's raw value into a predetermined range (e.g., LOW or HIGH Hemoglobin); and (2) Gradient abstractions, which indicate the trend of the concept's value (e.g., INCREASING, DECREASING Hemoglobin value). We created all of the combinations of either uni-dimensional (State or Gradient) or multi-dimensional (State and Gradient) abstractions, of all of the concepts used. Classification of a record was determined by using a majority of the k-Nearest-Neighbors (KNN) of the given record, k ranging over the odd numbers (to break ties) from 1 to N, N being the size of the training set. We have experimented with all possible configurations of the parameters that our method uses. Overall, a total of 75,936 experiments were performed: 33,600 in the Oncology domain, 28,800 in the Hepatitis domain, and 13,536 in the Diabetes domain. Each experiment involved the performance of a 10-fold Cross Validation to compute the mean performance of a particular iDTW method-configuration set of settings, for a specific subset of one, two, or three concepts out of all of the domain-specific concepts relevant to the classification or prediction task on which the experiment focuses. We measured for each such experimental combination the Area Under the Curve (AUC) and the optimal Specificity/Sensitivity ratio using Youden's Index. We then aggregated the experiments by the types of unidimensional or multidimensional abstractions used in them (including the use of only raw concepts as a special case); for example, two state abstractions of different concepts, and one gradient abstraction of a third concept. We compared the mean AUC when using each such feature representation, or combination of abstractions, across all possible method-setting configurations, to the mean AUC when using as a feature representation, for the same task, only raw concepts, also across all possible method-setting configurations. Finally, we applied a paired t-test, to determine whether the mean difference between the accuracy of each temporal-abstraction representation, across all concept and configuration combinations, and the respective raw-concept combinations, across all concept subset and configuration combinations, is significant (P < 0.05). RESULTS: The mean performance of the classification and prediction tasks when using, as a feature representation, the various temporal-abstraction combinations, was significantly higher than that performance when using only raw data. Furthermore, in each domain and task, there existed at least one representation using interval-based abstractions whose use led, on average (over all concept subset combinations and method configurations) to a significantly better performance than the use of only subsets of the raw time-stamped data. In seven of nine combinations of domain type (out of three) and number of concepts used (one, two, or three), the variance of the AUCs (for all representations and configurations) was considerably higher across all raw-concept subsets, compared to all abstract combinations. Increasing the number of features used by the matching task enhanced performance. Using multi-dimensional abstractions of the same concept further enhanced the performance. When using only raw data, increasing the number of neighbors monotonically increased the mean performance (over all concept combinations and method configurations) until reaching an optimal saddle-point aroundN; when using abstractions, however, optimal mean performance was often reached after matching only five nearest neighbors. CONCLUSIONS: Using multivariate and multidimensional interval-based, abstraction-based similarity measures is feasible, and consistently and significantly improved the mean classification and prediction performance in time-oriented domains, using DTW-inspired methods, compared to the use of only raw, time-stamped data. It also made the KNN classification more effective. Nevertheless, although the mean performance for the abstract representations was higher than the mean performance when using only raw-data concepts, the actual optimal classification performance in each domain and task depends on the choice of the specific raw or abstract concepts used as features.

Visualizing Ultrafast Electron Transfer Processes in Semiconductor-Metal Hybrid Nanoparticles: Toward Excitonic-Plasmonic Light Harvesting.

Recently, it was demonstrated that charge separation in hybrid metal-semiconductor nanoparticles (HNPs) can be obtained following photoexcitation of either the semiconductor or of the localized surface plasmon resonance (LSPR) of the metal. This suggests the intriguing possibility of photocatalytic systems benefiting from both plasmon and exciton excitation, the main challenge being to outcompete other ultrafast relaxation processes. Here we study CdSe-Au HNPs using ultrafast spectroscopy with high temporal resolution. We describe the complete pathways of electron transfer for both semiconductor and LSPR excitation. In the former, we distinguish hot and band gap electron transfer processes in the first few hundred fs. Excitation of the LSPR reveals an ultrafast (<30 fs) electron transfer to CdSe, followed by back-transfer from the semiconductor to the metal within 210 fs. This study establishes the requirements for utilization of the combined excitonic-plasmonic contribution in HNPs for diverse photocatalytic applications.

Charge Carrier Dynamics in Photocatalytic Hybrid Semiconductor-Metal Nanorods: Crossover from Auger Recombination to Charge Transfer.

Hybrid semiconductor-metal nanoparticles (HNPs) manifest unique, synergistic electronic and optical properties as a result of combining semiconductor and metal physics via a controlled interface. These structures can exhibit spatial charge separation across the semiconductor-metal junction upon light absorption, enabling their use as photocatalysts. The combination of the photocatalytic activity of the metal domain with the ability to generate and accommodate multiple excitons in the semiconducting domain can lead to improved photocatalytic performance because injecting multiple charge carriers into the active catalytic sites can increase the quantum yield. Herein, we show a significant metal domain size dependence of the charge carrier dynamics as well as the photocatalytic hydrogen generation efficiencies under nonlinear excitation conditions. An understanding of this size dependence allows one to control the charge carrier dynamics following the absorption of light. Using a model hybrid semiconductor-metal CdS-Au nanorod system and combining transient absorption and hydrogen evolution kinetics, we reveal faster and more efficient charge separation and transfer under multiexciton excitation conditions for large metal domains compared to small ones. Theoretical modeling uncovers a competition between the kinetics of Auger recombination and charge separation. A crossover in the dominant process from Auger recombination to charge separation as the metal domain size increases allows for effective multiexciton dissociation and harvesting in large metal domain HNPs. This was also found to lead to relative improvement of their photocatalytic activity under nonlinear excitation conditions.

Photocatalytic Hybrid Semiconductor-Metal Nanoparticles; from Synergistic Properties to Emerging Applications.

Hybrid semiconductor-metal nanoparticles (HNPs) manifest unique combined and often synergetic properties stemming from the materials combination. These structures exhibit spatial charge separation across the semiconductor-metal junction upon light absorption, enabling their use as photocatalysts. So far, the main impetus of photocatalysis research in HNPs addresses their functionality in solar fuel generation. Recently, it was discovered that HNPs are functional in efficient photocatalytic generation of reactive oxygen species (ROS). This has opened the path for their implementation in diverse biomedical and industrial applications where high spatially temporally resolved ROS formation is essential. Here, the latest studies on the synergistic characteristics of HNPs are summarized, including their optical, electrical, and chemical properties and their photocatalytic function in the field of solar fuel generation is briefly discussed. Recent studies are then focused concerning photocatalytic ROS formation with HNPs under aerobic conditions. The emergent applications of this capacity are then highlighted, including light-induced modulation of enzymatic activity, photodynamic therapy, antifouling, wound healing, and as novel photoinitiators for 3D-printing. The superb photophysical and photocatalytic properties of HNPs offer already clear advantages for their utility in scenarios requiring on-demand light-induced radical formation and the full potential of HNPs in this context is yet to be revealed.