Understanding the long-term storage sink.

Similar to short-term starch storage in the chloroplasts of the leaves that serves to buffer growth of organs from carbohydrate shortages due to diurnal patterns of photosynthesis related to daily patterns of light and darkness, trees also have long-term storage capacity to enable them to supply the minimal respiratory needs of tissues during the winter and resume growth in the spring when trees are still leafless. This long-term storage of carbohydrates and some minerals occurs primarily in the phloem and xylem tissue of the branches, trunk and roots. While active phloem tissue has higher concentrations of stored carbohydrates than xylem tissue, the mass of active xylem storage tissue is many times the mass of the active phloem tissue. Thus, xylem tissue comprises the largest storage compartment of temperate deciduous fruit trees. This chapter deals with understanding the long-term storage sink in fruit trees.

Understanding the shoot sink.

To more fully understand how all the major organs of a tree interact in the semi-autonomous scheme of assimilate distribution and tree functioning, it is important to understand their development and growth behavior. This chapter presents a general description of the development and growth characteristics of the major organs (shoot and leaf) of fruit trees, including shoot structure, morphology and orientation; diurnal patterns of shoot growth; seasonal growth patterns of shoot growth and dormancy; as well as tree aging.

Enabling Automatic Detection of Anomalies in Wastewater: A Highly Simplified Approach to Defining “Normal” in Complex Chemical Mixtures

Wastewater treatment demands management of influent conditions to stabilize biological processes. Generally wastewater collection systems lack advance warning of approaching water parcels with anomalous characteristics, which could then be diverted for testing or pre-treatment. A major challenge in achieving this goal is identifying anomalies against the complex chemical background of wastewaters. This work evaluates unsupervised clustering methods to characterize “normal” wastewater characteristics, using >17 months of 10-min resolution absorbance spectrometry data collected at an operating wastewater treatment facility. Comparison of results using K-means, GMM, Hierarchical, and DBSCAN clustering shows minimal intra-cluster variability achieved using K-means. The four K-means clusters include three representing 99% of samples, with the remaining cluster (<0.3% of samples) representing atypical measurements, demonstrating utility in identifying both underlying modalities of wastewater characteristics and outliers. K-means clustering provides a better separation than grouping based on factors such as month, precipitation, or flow (with 25% overlap at 1-σ level, compared to 93, 93, and 83%, respectively) and enables identification of patterns that are not visible in factor-driven grouping, e.g., shows that summer and November months have a characteristic type of behavior. When evaluated with respect to wastewater influent changes occurring during the SARS-CoV-2 pandemic, the K-means approach shows a distinct change in strength of diurnal patterns when compared to non-pandemic periods during the same season. This method may therefore be useful both as a tool for fast anomaly detection in wastewaters, contributing to improved infrastructure resilience, as well for providing overall analysis of temporal patterns in wastewater characteristics.

Using dynamic time warping self-organizing maps to characterize diurnal patterns in environmental exposures

Advances in measurement technology are producing increasingly time-resolved environmental exposure data. We aim to gain new insights into exposures and their potential health impacts by moving beyond simple summary statistics (e.g., means, maxima) to characterize more detailed features of high-frequency time series data. This study proposes a novel variant of the Self-Organizing Map (SOM) algorithm called Dynamic Time Warping Self-Organizing Map (DTW-SOM) for unsupervised pattern discovery in time series. This algorithm uses DTW, a similarity measure that optimally aligns interior patterns of sequential data, both as the similarity measure and training guide of the neural network. We applied DTW-SOM to a panel study monitoring indoor and outdoor residential temperature and particulate matter air pollution (PM2.5) for 10 patients with asthma from 7 households near Salt Lake City, UT; the patients were followed for up to 373 days each. Compared to previous SOM algorithms using timestamp alignment on time series data, the DTW-SOM algorithm produced fewer quantization errors and more detailed diurnal patterns. DTW-SOM identified the expected typical diurnal patterns in outdoor temperature which varied by season, as well diurnal patterns in PM2.5 which may be related to daily asthma outcomes. In summary, DTW-SOM is an innovative feature engineering method that can be applied to highly time-resolved environmental exposures assessed by sensors to identify typical diurnal (or hourly or monthly) patterns and provide new insights into the health effects of environmental exposures.

Emotional Contagion in the Online Depression Community

Negative emotions are prevalent in the online depression community (ODC), which potentially puts members at risk, according to the theory of emotional contagion. However, emotional contagion in the ODC has not been confirmed. The generalized estimating equation (GEE) was used to verify the extent of emotional contagion using data from 1548 sample users in China’s popular ODC. During interaction, the emotional themes were analyzed according to language use. The diurnal patterns of the interaction behaviors were also analyzed. We identified the susceptible groups and analyzed their characteristics. The results confirmed the occurrence of emotional contagion in ODC, that is, the extent to which the user’s emotion was affected by the received emotion. Our study also found that when positive emotional contagion occurred, the replies contained more hopefulness, and when negative emotional contagion occurred, the replies contained more hopelessness and fear. Second, positive emotions were easier to spread, and people with higher activity in ODC were more susceptible. In addition, nighttime was an active period for user interaction. The results can help community managers and support groups take measures to promote the spread of positive emotions and reduce the spread of negative emotions.

Diurnal fluctuations in musical preference

The rhythm of human life is governed by diurnal cycles, as a result of endogenous circadian processes evolved to maximize biological fitness. Even complex aspects of daily life, such as affective states, exhibit systematic diurnal patterns which in turn influence behaviour. As a result, previous research has identified population-level diurnal patterns in affective preference for music. By analysing audio features from over two billion music streaming events on Spotify, we find that the music people listen to divides into five distinct time blocks corresponding to morning, afternoon, evening, night and late night/early morning. By integrating an artificial neural network with Spotify's API, we show a general awareness of diurnal preference in playlists, which is not present to the same extent for individual tracks. Our results demonstrate how music intertwines with our daily lives and highlight how even something as individual as musical preference is influenced by underlying diurnal patterns.

Chronic Corticosterone Disrupts The Circadian Rhythm of CRH Expression and m6A RNA Methylation In The Chicken Hypothalamus

Background: Corticotropin-releasing hormone (CRH), the major secretagogue of the hypothalamic-pituitary-adrenal (HPA) axis, is intricately intertwined with the clock genes to regulate the circadian rhythm of various body functions. N6-methyladenosine (m6A) RNA methylation is involved in the regulation of circadian rhythm, yet it remains unknown whether CRH expression and m6A modification oscillate with the clock genes in chicken hypothalamus and how the circadian rhythms change under chronic stress. Results: Chronic exposure to corticosterone (CORT) eliminated the diurnal patterns of plasma CORT and melatonin levels in the chicken. The circadian rhythms of clock genes in hippocampus, hypothalamus and pituitary are all disturbed to different extent in CORT-treated chickens. The most striking changes occur in hypothalamus in which the diurnal fluctuation of CRH mRNA is flattened, together with mRNA of other feeding-related neuropeptides. Interestingly, hypothalamic m6A level oscillates in an opposite pattern to CRH mRNA, with lowest m6A level after midnight (ZT18) corresponding to the peak of CRH mRNA before dawn (ZT22). CORT diminished the circadian rhythm of m6A methylation with significantly increased level at night. Further site-specific m6A analysis on 3’UTR of CRH mRNA indicates that higher m6A on 3’UTR of CRH mRNA coincides with lower CRH mRNA at night (ZT18 and ZT22). Conclusions: Our results indicate that chronic stress disrupts the circadian rhythms of CRH expression in hypothalamus, leading to dysfunction of HPA axis in the chicken. RNA m6A modification is involved in the regulation of circadian rhythms in chicken hypothalamus under both basal and chronic stress conditions.