Acoustic Pattern Recognition and Courtship Songs: Insights from Insects.

Across the animal kingdom, social interactions rely on sound production and perception. From simple cricket chirps to more elaborate bird songs, animals go to great lengths to communicate information critical for reproduction and survival via acoustic signals. Insects produce a wide array of songs to attract a mate, and the intended receivers must differentiate these calls from competing sounds, analyze the quality of the sender from spectrotemporal signal properties, and then determine how to react. Insects use numerically simple nervous systems to analyze and respond to courtship songs, making them ideal model systems for uncovering the neural mechanisms underlying acoustic pattern recognition. We highlight here how the combination of behavioral studies and neural recordings in three groups of insects-crickets, grasshoppers, and fruit flies-reveals common strategies for extracting ethologically relevant information from acoustic patterns and how these findings might translate to other systems.

Neonatal microglia transplantation at early stage but not late stage after traumatic brain injury shows protective effects in mice.

The transplantation of neonatal microglia suppresses neuroinflammation caused by traumatic brain injury (TBI). This research aimed to explore the optimal time point of neonatal microglia transplantation for the best effect on the improvement of long-term cognitive function and inflammatory response in mouse models. qPCR and immunoblotting showed that the level of Iba1 gradually increased to the highest on day 7 and then gradually declined in TBI mice. Furthermore, it was observed that the level of CD86 and TNF-α increased to the highest after 7 days and subsequently was maintained until day 21, whereas the level of CD206 and IL-10 increased to the highest after 24 h and subsequently decreased until day 21 by qPCR and enzyme-linked immunosorbent assay. Afterward, it was shown that the neonatal microglia transplantation within 1 h significantly attenuated anxiety-like behavior and improved cognitive impairments in TBI mice. Mechanism exploration showed that the neonatal microglia could significantly decrease the level of cleaved caspase-3, M1/M2 polarization, and inflammatory cytokine (TNF-α) while increasing the level of anti-inflammatory factor IL-10 in TBI mice after transplantation within 1 h. Here, our findings demonstrated that neonatal microglia transplantation within 1 h significantly attenuated anxiety-like behavior and cognitive impairments caused by TBI.NEW & NOTEWORTHY The study demonstrated that neonatal microglia transplantation within 1 h significantly inhibited the pathogenesis of traumatic brain injury (TBI) in mouse models through inhibition of M1 polarization and promotion of M2 polarization.

Spatial-temporal patterns of brain disconnectome in Alzheimer's disease.

Mounting evidences have shown that progression of white matter hyperintensities (WMHs) with vascular origin might cause cognitive dysfunction symptoms through their effects on brain networks. However, the vulnerability of specific neural connection related to WMHs in Alzheimer's disease (AD) still remains unclear. In this study, we established an atlas-guided computational framework based on brain disconnectome to assess the spatial-temporal patterns of WMH-related structural disconnectivity within a longitudinal investigation. Alzheimer's Disease Neuroimaging Initiative (ADNI) database was adopted with 91, 90 and 44 subjects including in cognitive normal aging, stable and progressive mild cognitive impairment (MCI), respectively. The parcel-wise disconnectome was computed by indirect mapping of individual WMHs onto population-averaged tractography atlas. By performing chi-square test, we discovered a spatial-temporal pattern of brain disconnectome along AD evolution. When applied such pattern as predictor, our models achieved highest mean accuracy of 0.82, mean sensitivity of 0.86, mean specificity of 0.82 and mean area under the receiver operating characteristic curve (AUC) of 0.91 for predicting conversion from MCI to dementia, which outperformed methods utilizing lesion volume as predictors. Our analysis suggests that brain WMH-related structural disconnectome contributes to AD evolution mainly through attacking connections between: (1) parahippocampal gyrus and superior frontal gyrus, orbital gyrus, and lateral occipital cortex; and (2) hippocampus and cingulate gyrus, which are also vulnerable to Aß and tau confirmed by other researches. All the results further indicate that a synergistic relationship exists between multiple contributors of AD as they attack similar brain connectivity at the prodromal stage of disease.

Explainable Artificial Intelligence Warning Model Using an Ensemble Approach for In-Hospital Cardiac Arrest Prediction: Retrospective Cohort Study.

BACKGROUND: Cardiac arrest (CA) is the leading cause of death in critically ill patients. Clinical research has shown that early identification of CA reduces mortality. Algorithms capable of predicting CA with high sensitivity have been developed using multivariate time series data. However, these algorithms suffer from a high rate of false alarms, and their results are not clinically interpretable. OBJECTIVE: We propose an ensemble approach using multiresolution statistical features and cosine similarity-based features for the timely prediction of CA. Furthermore, this approach provides clinically interpretable results that can be adopted by clinicians. METHODS: Patients were retrospectively analyzed using data from the Medical Information Mart for Intensive Care-IV database and the eICU Collaborative Research Database. Based on the multivariate vital signs of a 24-hour time window for adults diagnosed with heart failure, we extracted multiresolution statistical and cosine similarity-based features. These features were used to construct and develop gradient boosting decision trees. Therefore, we adopted cost-sensitive learning as a solution. Then, 10-fold cross-validation was performed to check the consistency of the model performance, and the Shapley additive explanation algorithm was used to capture the overall interpretability of the proposed model. Next, external validation using the eICU Collaborative Research Database was performed to check the generalization ability. RESULTS: The proposed method yielded an overall area under the receiver operating characteristic curve (AUROC) of 0.86 and area under the precision-recall curve (AUPRC) of 0.58. In terms of the timely prediction of CA, the proposed model achieved an AUROC above 0.80 for predicting CA events up to 6 hours in advance. The proposed method simultaneously improved precision and sensitivity to increase the AUPRC, which reduced the number of false alarms while maintaining high sensitivity. This result indicates that the predictive performance of the proposed model is superior to the performances of the models reported in previous studies. Next, we demonstrated the effect of feature importance on the clinical interpretability of the proposed method and inferred the effect between the non-CA and CA groups. Finally, external validation was performed using the eICU Collaborative Research Database, and an AUROC of 0.74 and AUPRC of 0.44 were obtained in a general intensive care unit population. CONCLUSIONS: The proposed framework can provide clinicians with more accurate CA prediction results and reduce false alarm rates through internal and external validation. In addition, clinically interpretable prediction results can facilitate clinician understanding. Furthermore, the similarity of vital sign changes can provide insights into temporal pattern changes in CA prediction in patients with heart failure-related diagnoses. Therefore, our system is sufficiently feasible for routine clinical use. In addition, regarding the proposed CA prediction system, a clinically mature application has been developed and verified in the future digital health field.

The trade-offs and synergies of the ecological-production-living functions of grassland in the Qilian mountains by ecological priority.

Grassland degradation has caused increasingly prominent conflict between ecological environment conservation and socioeconomic development in the Qilian Mountains, China. How to effectively trade-offs and synergies to ecological and socioeconomic is essential to achieving the sustainable development of the grassland ecosystem. However, few studies have addressed the trade-offs and synergies of grassland ecosystem services in terms of coupling the natural ecosystem and the socioeconomic system. Therefore, we constructed an index of the analyzed trade-offs and synergies of grassland ecosystem services from the perspective of "ecological-production-living" functions (EPLFs) and analyzed the spatial-temporal characteristics of grassland EPLF trade-off and synergy relationships based on the data from the implementation of three conservation policies in the Qilian Mountains from 2003 to 2020. The results showed evident spatial and temporal differentiation of the grassland EPLFs. The ecological function was consistent with the production function, trending upward initially and then decreasing. The living function showed a trend of continuous increase. The spatial pattern of grassland EPLFs showed that the northwest and southeast were more active than the middle of the Qilian Mountains, and the regional gradient difference was apparent. The trade-off and synergy relationships of grassland EPLFs have obvious spatial correlations as well; spatial differences were evident under different conservation policies. With national park construction, the synergistic relationship gradually weakened and the trade-off relationship gradually strengthened. These results suggest that the policy of ecological priority increased trade-offs and reduced synergies among EPLFs was not conducive to coupling and coordinating grassland EPLFs for development in the Qilian Mountains. Our study also demonstrates that maintaining moderate grassland grazing pressure and the appropriate number of herdsmen is crucial to sustainably develop the grassland ecosystem in the Qilian Mountains, and further research into coupling mechanisms for grassland EPLFs is needed to reduce trade-offs and increase synergies with grassland ecosystem services.

Soil inorganic carbon stocks increase non-synergistically with soil organic carbon after ecological restoration practices in drylands.

Ecological restoration practices have been widely adopted to increase soil carbon stocks by improving soil organic carbon (SOC). However, the effects of these practices on the other important soil carbon component, soil inorganic carbon (SIC), remain unclear. To address this, a meta-analysis based on 45 publications and 37 sites was conducted to quantitatively assess the dynamic changes in SIC stocks due to typical restoration practices, including conversion of cropland to forest (C-F), cropland to grassland (C-G), desert to cropland (D-C), conservation agriculture (CA), and desert to forest (D-F). Results showed that, among the restoration practices increasing the SOC stocks, the SIC stocks decreased after the C-F (-34.7%) and C-G (-15.8%) conversions and CA (-6.8%), but increased after the conversion of D-C (2.6%) and D-F (46.9%). Additionally, in terms of recovery duration, the negative effect of C-G on SIC stocks may vanish with increased recovery duration, whereas SIC stocks showed a prominent increase initially after CA and then decreased over time; the response to D-F conversion of SIC stocks remained consistently positive over time. Furthermore, the non-synergistic changes with SIC and SOC could be due to variations in edaphic factors, while the effects edaphic factors on SIC stocks were different under various ecological restoration practices. Among all the impact factors, mean annual temperature, initial SIC stocks, and types of ecological restoration practice, were the most crucial factors explaining the variation in SIC stocks with ecological restoration. Collectively, the results highlight that the change in SIC stocks is asynchronous with the increase in SOC stocks in space and time after ecological restoration, further indicating that changes in SIC stocks should be paid more attention when assessing and predicting carbon sequestration following various ecological restoration practices.

Visualization of frequent temporal patterns in single or two populations.

Temporal knowledge discovery in clinical problems, is crucial to investigate problems in the data science era. Meaningful progress has been made computationally in the discovery of frequent temporal patterns, which may store potentially meaningful knowledge. However, for temporal knowledge discovery and acquisition, effective visualization is essential and still stores much room for contributions. While visualization of frequent temporal patterns was relatively under researched, it stores meaningful opportunities in facilitating usable ways to assist domain experts, or researchers, in exploring and acquiring temporal knowledge. In this paper, a novel approach for the visualization of an enumeration tree of frequent temporal patterns is introduced for, whether mined from a single population, or for the comparison of patterns that were discovered in two separate populations. While this approach is relevant to any sequence-based patterns, we demonstrate its use on the most complex scenario of time intervals related patterns (TIRPs). The interface enables users to browse an enumeration tree of frequent patterns, or search for specific patterns, as well as discover the most discriminating TIRPs among two populations. For that a novel visualization of the temporal patterns is introduced using a bubble chart, in which each bubble represents a temporal pattern, and the chart axes represent the various metrics of the patterns, such as their frequency, reoccurrence, and more, which provides a fast overview of the patterns as a whole, as well as access specific ones. We present a comprehensive and rigorous user study on two real-life datasets, demonstrating the usability advantages of the novel approaches.

Revisiting an ancient legend: influence of the lunar cycle on occurrence of first-ever unprovoked seizures.

BACKGROUND: The mythical effect of the lunar cycle on seizures has been debated over time. Previously healthy individuals presenting with first-ever seizures in whom investigations are negative often invoke questions about potential reasons including a full moon. AIMS: To determine whether there is a temporal relationship between the occurrence of the first-ever unprovoked seizure and the lunar cycle. METHODS: We studied adults who presented with a first-ever unprovoked seizure to two tertiary centres in Australia. Seizure onset time was obtained from the emergency department and ambulance documentations. We used Poisson regression modelling and incidence rate ratios (IRR) to determine whether seizures have a preponderance for a particular lunar phase. We performed further analysis on 'first seizure epilepsy' and 'first seizure not epilepsy' subgroups based on the International League Against Epilepsy criteria for a diagnosis of epilepsy after a single unprovoked seizure. RESULTS: We analysed 1710 patients (38% females; median age 39 years), of whom 18% had epileptiform abnormalities on electroencephalogram (EEG) and potentially epileptogenic lesions were detected on neuroimaging in 28%. Based on the EEG and imaging findings, 684 (40%) patients were categorised as 'first seizure epilepsy' and 1026 (60%) 'first seizure not epilepsy'. The whole cohort and subgroup analysis demonstrated no significant difference in the seizure occurrence among the four lunar quarters. CONCLUSIONS: First unprovoked seizures are not influenced by the lunar cycle. Patients pondering the cause of their first-ever unprovoked seizure can be reassured that the full moon was not responsible.

Evaluating the performance of temporal pattern discovery: new application using statins and rhabdomyolysis in OMOP databases.

BACKGROUND: Temporal pattern discovery (TPD) is a method of signal detection using electronic healthcare databases, serving as an alternative to spontaneous reporting of adverse drug events. Here, we aimed to replicate and optimise a TPD approach previously used to assess temporal signals of statins with rhabdomyolysis (in The Health Improvement Network (THIN) database) by using the OHDSI tools designed for OMOP data sources. METHODS: We used data from the Truven MarketScan US Commercial Claims and the Commercial Claims and Encounters (CCAE). Using an extension of the OHDSI ICTemporalPatternDiscovery package, we ran positive and negative controls through four analytical settings and calculated sensitivity, specificity, bias and AUC to assess performance. RESULTS: Similar to previous findings, we noted an increase in the Information Component (IC) for simvastatin and rhabdomyolysis following initial exposure and throughout the surveillance window. For example, the change in IC was 0.266 for the surveillance period of 1-30 days as compared to the control period of - 180 to - 1 days. Our modification of the existing OHDSI software allowed for faster queries and more efficient generation of chronographs. CONCLUSION: Our OMOP replication matched the we can account forwe can account for of the original THIN study, only simvastatin had a signal. The TPD method is a useful signal detection tool that provides a single statistic on temporal association and a graphical depiction of the temporal pattern of the drug outcome combination. It remains unclear if the method works well for rare adverse events, but it has been shown to be a useful risk identification tool for longitudinal observational databases. Future work should compare the performance of TPD with other pharmacoepidemiology methods and mining techniques of signal detection. In addition, it would be worth investigating the relative TPD performance characteristics using a variety of observational data sources.

Spatio-temporal analysis of land use/land cover change dynamics in Paraguai/Jauquara Basin, Brazil.

Although global climate change is receiving considerable attention, the loss of biodiversity worldwide continues. In this study, dynamics of land use/land cover (LULC) change in the Paraguai/Jauquara Basin, Mato Grosso, Brazil, were investigated. Two analyses were performed using R software. The first was a comparative study of LULC among the LULC classes at the polygon scale, and the second was a spatio-temporal analysis of moving polygons restricted to the agricultural regions in terms of topology, size, distance, and direction of change. The data consisted of Landsat images captured in 1993, 1997, 2001, 2005, 2009, 2013, and 2016 and processed using ArcGIS software. The proposed analytical approach handled complex data structures and allowed for a deeper understanding of LULC change over time. The results showed that there was a statistically significant change from regions of natural vegetation to pastures, agricultural regions, and land for other uses, accompanied by a significant trend of expansion of agricultural regions, appearing to stabilize from 2005. Furthermore, different patterns of LULC change were found according to soil type and elevation. In particular, the purple latosol soil type presented the highest expansion indexes since 2001, and the elevated agricultural areas have been expanding and/or stabilizing since 1997.

Self-organised pattern formation increases local diversity in metacommunities.

Self-organised formation of spatial patterns is known from a variety of different ecosystems, yet little is known about how these patterns affect the diversity of communities. Here, we use a food chain model in which autotroph diversity is described by a continuous distribution of a trait that affects both growth and defence against heterotrophs. On isolated patches, diversity is always lost over time due to stabilising selection, and the local communities settle on one of two alternative stable community states that are characterised by a dominance of either defended or undefended species. In a metacommunity context, dispersal can destabilise these states and complex spatio-temporal patterns in the species' abundances emerge. The resulting biomass-trait feedback increases local diversity by an order of magnitude compared to scenarios without self-organised pattern formation, thereby maintaining the ability of communities to adapt to potential future changes in biotic or abiotic environmental conditions.

Temporal and diurnal variation in social media posts to a suicide support forum.

BACKGROUND: Rates of suicide attempts and deaths are highest on Mondays and these occur more frequently in the morning or early afternoon, suggesting weekly temporal and diurnal variation in suicidal behaviour. It is unknown whether there are similar time trends on social media, of posts relevant to suicide. We aimed to determine temporal and diurnal variation in posting patterns on the Reddit forum SuicideWatch, an online community for individuals who might be at risk of, or who know someone at risk of suicide. METHODS: We used time series analysis to compare date and time stamps of 90,518 SuicideWatch posts from 1st December 2008 to 31st August 2015 to (i) 6,616,431 posts on the most commonly subscribed general subreddit, AskReddit and (ii) 66,934 of these AskReddit posts, which were posted by the SuicideWatch authors. RESULTS: Mondays showed the highest proportion of posts on SuicideWatch. Clear diurnal variation was observed, with a peak in the early morning (2:00-5:00 h), and a subsequent decrease to a trough in late morning/early afternoon (11:00-14:00 h). Conversely, the highest volume of posts in the control data was between 20:00-23:00 h. CONCLUSIONS: Posts on SuicideWatch occurred most frequently on Mondays: the day most associated with suicide risk. The early morning peak in SuicideWatch posts precedes the time of day during which suicide attempts and deaths most commonly occur. Further research of these weekly and diurnal rhythms should help target populations with support and suicide prevention interventions when needed most.

Effects of land use and land cover on soil erosion control in southern China: Implications from a systematic quantitative review.

The degradation of soil resources is accelerating owing to water erosion induced by detrimental land use/land cover (LULC) practices. Although the relationship between affecting factors associated with LULC and soil erosion has been well-documented, a systematic review of LULC for erosion control is lacking. A quantitative synthesis of 1270 runoff and 1302 sediment observations from 79 references covering 57 sites was conducted to assess the effects of LULC on erosion control in the red soil hilly region (RSHR) of southern China. The results indicated that the maximum runoff and soil loss were found in bare land (BL), but the minimum runoff and soil loss were detected in cropland and grassland, respectively. Among the 16 land use subtypes, BL left for natural succession had the highest runoff, but the lowest runoff was found in orchard with contact cover; the soil loss was largest for cropland without conservation measures, while the minimum value was detected in natural shrubland. When the vegetation coverage exceeded 60%, runoff and soil loss both tended to reach a stable rate. Land use with a multi-layer vegetation structure or ground/contact cover or broadleaf plant had less runoff and lower sediment values. In addition, soil loss decreased sharply during the initial 3 y of vegetation restoration, and then slowly declined until reaching a relatively stable rate after 15 y of succession. Our results confirmed that land use (except for BL) has a limited influence on soil erosion provided that suitable land cover is established. This study revealed that sustainable land management measures should be adopted based on local conditions to reduce soil erosion induced by irrational LULC. These findings can serve as a scientific basis for policy makers and land managers with respect to water erosion control in the RSHR.

Low-Frequency Oscillations Code Speech during Verbal Working Memory.

The way the human brain represents speech in memory is still unknown. An obvious characteristic of speech is its evolvement over time. During speech processing, neural oscillations are modulated by the temporal properties of the acoustic speech signal, but also acquired knowledge on the temporal structure of language influences speech perception-related brain activity. This suggests that speech could be represented in the temporal domain, a form of representation that the brain also uses to encode autobiographic memories. Empirical evidence for such a memory code is lacking. We investigated the nature of speech memory representations using direct cortical recordings in the left perisylvian cortex during delayed sentence reproduction in female and male patients undergoing awake tumor surgery. Our results reveal that the brain endogenously represents speech in the temporal domain. Temporal pattern similarity analyses revealed that the phase of frontotemporal low-frequency oscillations, primarily in the beta range, represents sentence identity in working memory. The positive relationship between beta power during working memory and task performance suggests that working memory representations benefit from increased phase separation.SIGNIFICANCE STATEMENT Memory is an endogenous source of information based on experience. While neural oscillations encode autobiographic memories in the temporal domain, little is known on their contribution to memory representations of human speech. Our electrocortical recordings in participants who maintain sentences in memory identify the phase of left frontotemporal beta oscillations as the most prominent information carrier of sentence identity. These observations provide evidence for a theoretical model on speech memory representations and explain why interfering with beta oscillations in the left inferior frontal cortex diminishes verbal working memory capacity. The lack of sentence identity coding at the syllabic rate suggests that sentences are represented in memory in a more abstract form compared with speech coding during speech perception and production.

Detection of Influenza and Other Respiratory Viruses in Air Sampled From a University Campus: A Longitudinal Study.

BACKGROUND: Respiratory virus-laden particles are commonly detected in the exhaled breath of symptomatic patients or in air sampled from healthcare settings. However, the temporal relationship of detecting virus-laden particles at nonhealthcare locations vs surveillance data obtained by conventional means has not been fully assessed. METHODS: From October 2016 to June 2018, air was sampled weekly from a university campus in Hong Kong. Viral genomes were detected and quantified by real-time reverse-transcription polymerase chain reaction. Logistic regression models were fitted to examine the adjusted odds ratios (aORs) of ecological and environmental factors associated with the detection of virus-laden airborne particles. RESULTS: Influenza A (16.9% [117/694]) and influenza B (4.5% [31/694]) viruses were detected at higher frequencies in air than rhinovirus (2.2% [6/270]), respiratory syncytial virus (0.4% [1/270]), or human coronaviruses (0% [0/270]). Multivariate analyses showed that increased crowdedness (aOR, 2.3 [95% confidence interval {CI}, 1.5-3.8]; P < .001) and higher indoor temperature (aOR, 1.2 [95% CI, 1.1-1.3]; P < .001) were associated with detection of influenza airborne particles, but absolute humidity was not (aOR, 0.9 [95% CI, .7-1.1]; P = .213). Higher copies of influenza viral genome were detected from airborne particles >4 µm in spring and <1 µm in autumn. Influenza A(H3N2) and influenza B viruses that caused epidemics during the study period were detected in air prior to observing increased influenza activities in the community. CONCLUSIONS: Air sampling as a surveillance tool for monitoring influenza activity at public locations may provide early detection signals on influenza viruses that circulate in the community.

Rising to the challenge? Inter-individual variation of the androgen response to social interactions in cichlid fish.

The Challenge Hypothesis (Wingfield et al. Am. Nat. 136, 829-846) aims to explain the complex relationship between androgens and social interactions. Despite its well acceptance in the behavioral endocrinology literature, several studies have failed to found an androgen response to staged social interactions. Possible reasons for these inconsistencies are the use of single sampling points that may miss the response peak, and the occurrence of inter-individual variability in the androgen response to social interactions. In this study we addressed these two possible confounding factors by characterizing the temporal pattern of the androgen response to social interactions in the African cichlid, Oreochromis mossambicus, and relating it to inter-individual variation in terms of the individual scope for androgen response (i.e. the difference between baseline and maximum physiological levels for each fish) and behavioral types. We found that the androgen response to territorial intrusions varies between individuals and is related to their scope for response. Individuals that have a lower scope for androgen response did not increase androgens after a territorial intrusion but were more aggressive and exploratory. In contrast males with a higher scope for response had fewer aggressive and exploratory behaviors and exhibited two peaks of KT, an early response 2-15 min after the interaction and a late response at 60-90 min post-interaction. Given that the pharmacological challenge of the Hypothalamic-Pituitary-Gonad axis only elicits the late response, we suggest that these two peaks may be regulated by different physiological mechanisms, with the early response being mediated by direct brain-gonad neural pathways. In summary, we suggest that determining the temporal pattern of the androgen response to social interactions and considering inter-individual variation may be the key to understanding the contradictory results of the Challenge Hypothesis.

Learning hidden patterns from patient multivariate time series data using convolutional neural networks: A case study of healthcare cost prediction.

OBJECTIVE: To develop an effective and scalable individual-level patient cost prediction method by automatically learning hidden temporal patterns from multivariate time series data in patient insurance claims using a convolutional neural network (CNN) architecture. METHODS: We used three years of medical and pharmacy claims data from 2013 to 2016 from a healthcare insurer, where data from the first two years were used to build the model to predict costs in the third year. The data consisted of the multivariate time series of cost, visit and medical features that were shaped as images of patients' health status (i.e., matrices with time windows on one dimension and the medical, visit and cost features on the other dimension). Patients' multivariate time series images were given to a CNN method with a proposed architecture. After hyper-parameter tuning, the proposed architecture consisted of three building blocks of convolution and pooling layers with an LReLU activation function and a customized kernel size at each layer for healthcare data. The proposed CNN learned temporal patterns became inputs to a fully connected layer. We benchmarked the proposed method against three other methods: (1) a spike temporal pattern detection method, as the most accurate method for healthcare cost prediction described to date in the literature; (2) a symbolic temporal pattern detection method, as the most common approach for leveraging healthcare temporal data; and (3) the most commonly used CNN architectures for image pattern detection (i.e., AlexNet, VGGNet and ResNet) (via transfer learning). Moreover, we assessed the contribution of each type of data (i.e., cost, visit and medical). Finally, we externally validated the proposed method against a separate cohort of patients. All prediction performances were measured in terms of mean absolute percentage error (MAPE). RESULTS: The proposed CNN configuration outperformed the spike temporal pattern detection and symbolic temporal pattern detection methods with a MAPE of 1.67 versus 2.02 and 3.66, respectively (p < 0.01). The proposed CNN outperformed ResNet, AlexNet and VGGNet with MAPEs of 4.59, 4.85 and 5.06, respectively (p < 0.01). Removing medical, visit and cost features resulted in MAPEs of 1.98, 1.91 and 2.04, respectively (p < 0.01). CONCLUSIONS: Feature learning through the proposed CNN configuration significantly improved individual-level healthcare cost prediction. The proposed CNN was able to outperform temporal pattern detection methods that look for a pre-defined set of pattern shapes, since it is capable of extracting a variable number of patterns with various shapes. Temporal patterns learned from medical, visit and cost data made significant contributions to the prediction performance. Hyper-parameter tuning showed that considering three-month data patterns has the highest prediction accuracy. Our results showed that patients' images extracted from multivariate time series data are different from regular images, and hence require unique designs of CNN architectures. The proposed method for converting multivariate time series data of patients into images and tuning them for convolutional learning could be applied in many other healthcare applications with multivariate time series data.

Posterior Fusiform and Midfusiform Contribute to Distinct Stages of Facial Expression Processing.

Though the fusiform is well-established as a key node in the face perception network, its role in facial expression processing remains unclear, due to competing models and discrepant findings. To help resolve this debate, we recorded from 17 subjects with intracranial electrodes implanted in face sensitive patches of the fusiform. Multivariate classification analysis showed that facial expression information is represented in fusiform activity and in the same regions that represent identity, though with a smaller effect size. Examination of the spatiotemporal dynamics revealed a functional distinction between posterior fusiform and midfusiform expression coding, with posterior fusiform showing an early peak of facial expression sensitivity at around 180 ms after subjects viewed a face and midfusiform showing a later and extended peak between 230 and 460 ms. These results support the hypothesis that the fusiform plays a role in facial expression perception and highlight a qualitative functional distinction between processing in posterior fusiform and midfusiform, with each contributing to temporally segregated stages of expression perception.

Response to the "letter to the editor" by Sani Rachman Soleman et al., "spatiotemporal association of low birth weight with Cs-137 deposition at the prefecture level in Japan after the Fukushima nuclear power plant accidents".

We thank Sani Rachman Soleman et al. for three specific points of criticism concerning our investigation of the ecological association between low birth weight (LBW) and radioactive contamination in Japan after the Fukushima Daiichi Nuclear Power Plant (FDNPP) accidents: 1. Ecological variables are not justified enough to adjust potential confounding. 2. The spatiotemporal regression model does not consider temporal reduction in radiation dose rate. 3. Dose-response plot between dose rates and odds ratios overestimates R2 and underestimates p-value. This criticism is a good starting point to explain some of the technical backgrounds of our approach in more detail.

Assessment of the spatio-temporal pattern of PM2.5 and its driving factors using a land use regression model in Beijing, China.

With the acceleration of urbanization and industrialization, atmospheric pollution has become a major issue, restricting the sustainable development of the urban environment. Since 2013, Beijing has been among China's most seriously affected regions in terms of haze pollution. Atmospheric pollution is closely linked to land use, particularly the spatial patterns of green and urban land. Therefore, the quantification of the relationship between fine particulate matter (PM2.5) concentration and its driving factors in Beijing is of considerable significance for environmental management and spatial epidemiological studies. A land use regression (LUR) model was constructed to simulate the spatio-temporal distribution of PM2.5 concentration. In this study, the independent variables (driving factors) included land use, meteorological factors, population, roads, the digital elevation model, and the normalized difference vegetation index. The five models had adjusted R2 of 0.887, 0.770, 0.742, 0.877, and 0.798, respectively. Land use and meteorological factors were the main factors affecting PM2.5 concentration. The driving factors of land use on a large scale and roads on a small scale had a significant impact on PM2.5 emissions. Beijing's PM2.5 concentrations in 2015 showed clear spatio-temporal characteristics. The highest (lowest) average PM2.5 concentration was recorded in winter (summer). In terms of spatial distribution, PM2.5 concentrations showed a "low in the northwest and high in the southeast" trend. The most polluted areas were mainly distributed in the central city and the southeastern and southwestern regions. The PM2.5 concentration boundary was essentially consistent with the boundary of land use type. Different land use types promoted or inhibited PM2.5 concentrations, with a difference of more than 20 µg/m3 PM2.5 between the two land use categories. Thus, PM2.5 concentrations should be controlled by optimizing the spatial and temporal patterns of land use.