Applications of turning point analysis in behavioral medicine research: A systematic scoping review.

BACKGROUND: Turning Point Analysis (TPA) is a methodological approach that allows scholars to retrospectively capture change over time by identifying instances of critical change (i.e., turning points) encountered across a given time period. While TPA has been used to examine time as a variable in health and illness experiences, the use of the method in behavioral medicine scholarship may be limited by the lack of understanding of TPA procedures and applicability. OBJECTIVE: To describe how TPA has been used and enhance its accessibility by identifying and synthesizing methods of TPA data collection and analysis. METHODS: A systematic search of electronic databases, including Academic Source Premier, Psychology and Behavioral Sciences Collection, APA PsycInfo, and ProQuest Dissertation and Theses Database, was conducted in May 2020. In July 2020, we used hand searching to identify additional articles, including forward and back tracking seminal articles on TPA. Studies were screened in duplicate. RESULTS: Of the 1184 studies screened for this review, we included 52. Studies used TPA to examine relational (k = 40), organizational (k = 6), and individual (k = 6) variables and included an analysis of either turning points (k = 28), the trajectories of change over time created by the turning points (k = 3), or both (k = 21). Turning points and trajectories were captured and analyzed using qualitative and quantitative analytic approaches, with most studies using either purely qualitative (k = 26) or mixed methods (k = 21). CONCLUSIONS: The findings of this review provide insight into the varied applications of TPA and suggest the potential value of this methodological approach in better understanding health experiences across time. By synthesizing the procedural and analytic steps to conducting a TPA, this review could also increase the accessibility and use of TPA in behavioral medicine research.

Specificity and valence of adolescents' turning point memory narratives: Relationships with depressive symptoms over time.

Although recent research suggests that, for community youth, greater specific (episodic) detail in self-relevant turning point memory narratives predicts depressive symptoms over time, no research has investigated whether the narratives' specificity similarly predicts depression. Therefore, we investigated whether recalling a specific (unique, 24-hour or less) turning point narrative predicted youth depressive symptoms concurrently and across 6 months (Study 1), and, for a subset of participants, three years (Study 2). We also examined whether the valence of the implication of the experience for self (the resolution) explained additional variance and interacted with memory specificity. For Study 1 (N = 320, M = 16.9 years, 81% female), a specific (rather than a non-specific) turning point predicted greater depressive symptoms concurrently but not longitudinally, whereas a negative resolution predicted both concurrent and longitudinal depressive symptoms. The moderation result showed that a specific turning point predicted escalating depressive symptoms across six months when the resolution was negative. Study 2 (N = 68) additionally showed that a specific turning point predicted increased depressive symptoms three years later. These findings contrast with research suggesting that specific memories are related to better mental health and highlight the complexity of the role of memory in emerging youth depression.

Facing the challenge of NDVI dataset consistency for improved characterization of vegetation response to climate variability.

Non-linear trend detection in Earth observation time series has become a standard method to characterize changes in terrestrial ecosystems. However, results are largely dependent on the quality and consistency of the input data, and only few studies have addressed the impact of data artifacts on the interpretation of detected abrupt changes. Here we study non-linear dynamics and turning points (TPs) of temperate grasslands in East Eurasia using two independent state-of-the-art satellite NDVI datasets (CGLS v3 and MODIS C6) and explore the impact of water availability on observed vegetation changes during 2001-2019. By applying the Break For Additive Season and Trend (BFAST01) method, we conducted a classification typology based on vegetation dynamics which was spatially consistent between the datasets for 40.86 % (459,669 km2) of the study area. When considering also the timing of the TPs, 27.09 % of the pixels showed consistent results between datasets, suggesting that careful interpretation was needed for most of the areas of detected vegetation dynamics when applying BFAST to a single dataset. Notably, for these areas showing identical typology we found that interrupted decreases in vegetation productivity were dominant in the transition zone between desert and steppes. Here, a strong link with changes in water availability was found for >80 % of the area, indicating that increasing drought stress had regulated vegetation productivity in recent years. This study shows the necessity of a cautious interpretation of the results when conducting advanced characterization of vegetation response to climate variability, but at the same time also the opportunities of going beyond the use of single dataset in advanced time-series approaches to better understanding dryland vegetation dynamics for improved anthropogenic interventions to combat vegetation productivity decrease.

Short-term restoration effects of ecological projects detected using the turning point method in the Three River Headwater Region, China.

The Three River Headwater Region (TRHR) is an important river source area providing important ecological functions. Decades ago, climate change and human activities severely degraded the ecosystem in the TRHR. To restore vegetation, a series of ecological projects have been implemented since 1989. Using net primary productivity (NPP) data from 1988 to 2012, a sequential Mann-Kendall trend test (SQ-MK) method was applied to identify the turning point of vegetation NPP. This approach was able to represent the critical response time of the vegetation to important disturbances. A 3-year time window was set after the implementation of one ecological project to detect and analyze its short-term effects. The ecological projects included the Yangtze River Basin Shelterbelt System Construction Project (YRCP), the TRHR Nature Reserve Construction Project (TNR), the Returning Grazing Land to Grassland Project (RGLGP), and the first phase of the Ecological Conservation and Restoration Project of the TRHR (ECRP). Our results showed that the vegetation in the TRHR responded positively to restoration: 89% of pixels showed an increasing trend and 54% of pixels underwent an abrupt change. The accelerated growth type accounted for the highest proportion among all types of detected turning points. In the ECRP's window, the positive turns rose rapidly, from 41% in 2005 to 86% in 2008, and it showed the most balanced restoration effects across grasslands. The alpine meadow and montane meadow restoration was largely influenced by the ECRP and the RGLGP (both >40%). The alpine steppe restoration was mainly attributed to the ECRP (68%). On the county scale, the positive turns in Yushu at the source of the Yangtze River mainly benefited from the RGLGP (56%), while the positive turns in Maduo at the source of the Yellow River benefited from the ECRP (77%). Nangqian, Tanggula and Zaduo County were still in need of intervention for restoration (< 3%). The results of the study can enhance our understanding of the spatio-temporal distribution of the short-term ecological benefits of different ecological projects, thus provide a scientific and timely reference for future planning and adjustment of the conservation and restoration projects.

Ensemble learning model identifies adaptation classification and turning points of river microbial communities in response to heatwaves.

Heatwaves are a global issue that threaten microbial populations and deteriorate ecosystems. However, how river microbial communities respond to heatwaves and whether and how high temperatures exceed microbial adaptation remain unclear. In this study, we proposed four types of pulse temperature-induced microbial responses and predicted the possibility of microbial adaptation to high temperature in global rivers using ensemble machine learning models. Our findings suggest that microbial communities in parts of South American (e.g., Brazil and Chile) and Southeast Asian (e.g., Vietnam) countries are likely to change due to heatwave disturbance from 25 to 37°C for consecutive days. Furthermore, the microbial communities in approximately 48.4% of the global river gauge stations are prone to fast stress inadaptation, with approximately 76.9% of these stations expected to exceed microbial adaptation after heatwave disturbances. If emissions of particulate matter with sizes not more than 2.5 µm (PM2.5, an indicator of human activities) increase by twofold, the number of global rivers associated with the fast stress adaptation type will decrease by ~13.7% after heatwave disturbances. Understanding microbial responses is crucially important for effective ecosystem management, especially for fragile and sensitive rivers facing heatwave events.

Dispersion Turning Attenuation Microfiber for Flowrate Sensing.

We demonstrated a new optical fiber modal interferometer (MI) for airflow sensing; the novelty of the proposed structure is that an MI is fabricated based on a piece of HAF, which makes the sensitive MI itself also a hotwire. The interferometer is made by applying arc-discharge tapering and then flame tapering on a 10 mm length high attenuation fiber (HAF, 2 dB/cm) with both ends spliced to a normal single mode fiber. When the diameter of the fiber in the processing region is reduced to about 2 µm, the near-infrared dispersion turning point (DTP) can be observed in the interferometer's transmission spectrum. Due to the absorption of the HAF, the interferometer will have a large temperature increase under the action of a pump laser. At the same time, the spectrum of the interferometer with a DTP is very sensitive to the change in ambient temperature. Since airflow will significantly affect the temperature around the fiber, this thermosensitive interferometer with an integrated heat source is suitable for airflow sensing. Such an airflow sensor sample with a 31.2 mm length was made and pumped by a 980 nm laser with power up to 200 mW. In the comparative experiment with an electrical anemometer, this sensor exhibits a very high air-flow sensitivity of -2.69 nm/(m/s) at a flowrate of about 1.0 m/s. The sensitivity can be further improved by enlarging the waist length, increasing the pump power, etc. The optical anemometer with an extremely high sensitivity and a compact size has the potential to measure a low flowrate in constrained microfluidic channels.

My Turning Point Tells the Story: A Longitudinal Examination of Greater Episodic Detail and Youth Depressive Symptoms.

Although research findings show that the personal memories of people who are depressed are characterized by sparse episodic detail, under some circumstances, the opposite pattern emerges. Specifically, a recent study (Salmon et al., 2021) has shown that for community youth, greater episodic detail in a highly self-relevant narrative (a life turning point) predicted increased depressive symptoms concurrently and one year later. In a new longitudinal study of young people (N = 320 at Time 1, M = 16.9 years; 81% female) followed up over six months, we aimed to replicate and extend this finding. In Study A, we compared the turning point with a narrative about a conflict event, to establish whether the detail in a turning point memory uniquely predicted depressive symptoms. Supporting the first hypothesis, at both time-points, greater episodic detail was concurrently positively associated with depressive symptoms for turning point narratives only. Contrary to our second hypothesis, greater detail did not predict increased depressive symptoms longitudinally. The reverse pattern was significant, however, in that greater initial depressive symptoms predicted greater detail uniquely in the turning point narrative six months later. In Study B, we determined that the concurrent association between episodic detail and depressive symptoms in turning points (but not conflict events) was exacerbated by linguistic markers of self-focus (greater I-talk and lower distancing language). These findings suggest that greater detail in a turning point narrative may uniquely signify risk of psychological distress when youth narrate the experience with heightened self-focus.

Fabrication of High-Sensitivity Optical Fiber Sensor by an Improved Arc-Discharge Heating System.

We proposed a high-sensitivity optical fiber sensor based on a dual-resonance helical long-period fiber grating (HLPG). The grating is fabricated in a single-mode fiber (SMF) by using an improved arc-discharge heating system. The transmission spectra and the dual-resonance characteristics of the SMF-HLPG near the dispersion turning point (DTP) were studied through simulation. In the experiment, a four-electrode arc-discharge heating system was developed. The system can keep the surface temperature of optical fiber relatively constant during the grating preparation process, which shows an advantage in preparing high-quality triple- and single-helix HLPGs. In particular, benefiting from this manufacturing system, the SMF-HLPG operating near the DTP was successfully prepared directly by arc-discharge technology, without secondary processing of the grating. As a typical application example of the proposed SMF-HLPG, physical parameters such as temperature, torsion, curvature and strain can be measured with high sensitivity by monitoring the variation of the wavelength separation in the transmission spectrum. Therefore, the proposed sensor and its fabrication technology have potential application prospects in practical sensing measurement.

Curvature and turning point of the environmental Kuznets curve in a global economy: the role of governance.

This study explores the role of governance in shaping the environmental Kuznets curve (EKC), especially focusing on its curvature and turning point. The study highlights the role of various governance indicators in the formulation, implementation, and enforcement of environmental regulations. However, the study asserts that since in developing countries poverty, infrastructure, and human development are valued above a clean environment, good governance is less likely to contribute to mitigating pollution in developing countries. Using a panel of 160 countries, the study finds that better governance helps bring down the critical level of per capita income at which the relationship between income and pollution turns negative. Furthermore, the EKC can be shifted downward by improving governance in the respective countries. The study, however, reveals that the dividends of better governance are more pronounced at higher income levels. Since good governance works only when the desired regulations are in place, it is recommended that for poor countries aid packages for governance reforms need to emphasize the enactment of specific environmental regulations. Investment in institutions is expected to yield maximum dividends in such countries that have gained high-income status but are still lacking in institutional development.

Ultrasensitive Optical Fiber Sensors Working at Dispersion Turning Point: Review.

Optical fiber sensors working at the dispersion turning point (DTP) have served as promising candidates for various sensing applications due to their ultrahigh sensitivity. In this review, recently developed ultrasensitive fiber sensors at the DTP, including fiber couplers, fiber gratings, and interferometers, are comprehensively analyzed. These three schemes are outlined in terms of operation principles, device structures, and sensing applications. We focus on sensitivity enhancement and optical transducers, we evaluate each sensing scheme based on the DTP principle, and we discuss relevant challenges, aiming to provide some clues for future research.

Reversal of soil moisture constraint on vegetation growth in North China.

The response of vegetation growth to soil moisture varies greatly from space and time under climate change and anthropogenic activities. As an important grain producer in China, the vegetation growth and grain production of North China are constrained by the region's water resources. With the significant increase in vegetation greenness in North China over the last 40 years, it is essential to explore the changes in soil moisture constraints on vegetation growth to water management. However, to what degree vegetation growth responds to soil moisture and how the response varies spatiotemporally in North China remain unclear. In this study, the response patterns of vegetation growth to soil moisture at different depths and the spatiotemporal trend patterns of their relationships were explored thoroughly based on long time series remote sensing data in North China over the past 40 years. The results showed that compared to forests, the growth of grasslands and crops with one maturity per year and two maturity per year in North China was more constrained by soil moisture. Due to the combined effects of climatic conditions and human activities, vegetation growth in North China has been significantly less constrained by soil moisture over the last 40 years. This was especially seen in one maturity per year crop and natural vegetation in Shanxi and central Shandong. However, with the significant increase in temperature, potential evapotranspiration and water demand of the crop, the moisture constraints on vegetation growth in North China have begun to show an increasing trend since the early 2000s, especially for irrigated crop in central and southern North China. These findings highlight a comprehensive understanding of the vegetation response to soil moisture from the time-varying perspective and provide a theoretical basis for water management and appropriate planning of agricultural water use in North China.

EMT and Tumor Turning Point Analysis in 3D Spheroid Culture of HNSCC and Mesenchymal Stem Cells.

The prognosis, metastasis, and behavior of head and neck squamous cancer cells are influenced by numerous factors concerning the tumor microenvironment, intercellular communication, and epithelial-to-mesenchymal transition (EMT). The aim of this study was to examine the codependent interaction of the mesenchymal stroma with head and neck squamous cell carcinoma (HNSCC) in a 3D spheroid structure. To simulate stroma-rich and -poor 3D tumor microenvironments, cells of the established cell SCC-040 were cultured with human mesenchymal stromal cells (MSCs), forming 3D stroma-tumor spheroids (STSs). STSs were compared to uniform spheroids of SCC-040 and MSC, respectively. The expressions of CD24, ß-catenin, SNAI2, and ZEB2 were analyzed via RT-qPCR. The immunohistochemical expressions of E-cadherin, connexin 43, vimentin, and emmprin were analyzed, and protein expression pathways as well as Akt signaling were assessed via protein analysis. A promotive effect on the expressions of EMT markers ZEB2 (p = 0.0099), SNAI2 (p = 0.0352), and ß-catenin (p = 0.0031) was demonstrated in STSs, as was the expression of Akt pathway proteins mTOR (p = 0.007), Erk1/2 (p = 0.0045), and p70 S6 Kinase (p = 0.0016). Our study demonstrated a change in genetic expression patterns early on in tumor development, indicating a tumor turning point.

Highly Sensitive Temperature Sensor Based on Cascaded Polymer-Infiltrated Fiber Mach-Zehnder Interferometers Operating near the Dispersion Turning Point.

High-accuracy temperature measurement plays a vital role in biomedical, oceanographic, and photovoltaic industries. Here, a highly sensitive temperature sensor is proposed and demonstrated based on cascaded polymer-infiltrated Mach-Zehnder interferometers (MZIs), operating near the dispersion turning point. The MZI was constructed by splicing a half-pitch graded index fiber (GIF) and two sections of single-mode fiber and creating an inner air cavity based on femtosecond laser micromachining. The UV-curable polymer-infiltrated air cavity functioned as one of the interference arms of MZI, and the residual GIF core functioned as the other. Two MZIs with different cavity lengths and infiltrated with the UV-curable polymers, having the refractive indexes on the different sides of the turning point, were created. Moreover, the effects of the length and the bending way of transmission SMF between the first and the second MZI were studied. As a result, the cascaded MZI temperature sensor exhibits a greatly enhanced temperature sensitivity of -24.86 nm/°C based on wavelength differential detection. The aforementioned result makes it promising for high-accuracy temperature measurements in biomedical, oceanographic, and photovoltaic applications.

Broadband Acoustic Sensing with Optical Nanofiber Couplers Working at the Dispersion Turning Point.

Herein, a broadband ultrasensitive acoustic sensor based on an optical nanofiber coupler (ONC) attached to a diaphragm is designed and experimentally demonstrated. The ONC is sensitive to axial strain and works as the core transducing element to monitor the deformation of the diaphragm driven by acoustic waves. We first theoretically studied the sensing property of the ONC to axial strain and the deformation of the diaphragm. The results reveal that ONC working at the dispersion turning point (DTP) shows improved ultra-sensitivity towards axial strain, and the largest deformation of the circular diaphragm occurs at the center. Guided by the theoretical results, we fabricated an ONC with a DPT at 1550 nm, and we fixed one end of the ONC to the center of the diaphragm and the other end to the edge to construct the acoustic sensor. Finally, the experimental results show that the sensor can achieve accurate measurement in the broadband acoustic wave range of 30~20,000 Hz with good linearity. Specifically, when the input acoustic wave frequency is 120 Hz, the sensitivity reaches 1923 mV/Pa, the signal-to-noise ratio is 42.45 dB, and the minimum detectable sound pressure is 330 µPa/Hz1/2. The sensor has the merits of simple structure, low cost, and high performance, and it provides a new method for acoustic wave detection.

Changes of narrative meaning-making markers during the different phases of breast cancer treatment for women below 50 years old.

BACKGROUND: The onset of breast cancer is considered a potential traumatic event associated with physical and psychological effects, in particular when it occurs at the age below 50. The literature lacks a longitudinal narrative exploration of breast cancer experience of young women. PARTICIPANTS AND PROCEDURE: Using the narrative device as a diachronic tool aimed at promoting semiotic connection processes during the different phases of the therapeutic path, the authors constructed an ad hoc narrative interview to explore the meaning-making processes of 10 breast cancer patients below 50 years old during three turning-point phases: pre-hospitalization (T1); postoperative counseling (T2); and adjuvant therapy (T3). The research took place at National Cancer Institute Pascale of Naples. Through an ad hoc qualitative methodology, this study identifies the prevailing modes with which the five narrative meaning-making functions are articulated in the repeated narrative during the three phases. RESULTS: The findings capture the patterns of change of narrative meaning-making markers during the illness experience, highlighting both integrated modes of connections between aspects of experience over time and moments of block in specific critical phases for women under 50. CONCLUSIONS: From a clinical health psychology point of view the results suggest the promotion of integration between the synchrony of the medical path and the diachrony of the subjective experience of women to support resources for adapting to experience.

Long-term predictions of current confirmed and dead cases of COVID-19 in China by the non-autonomous delayed epidemic models.

In this paper, we make long-term predictions based on numbers of current confirmed cases, accumulative dead cases of COVID-19 in different regions in China by modeling approach. Firstly, we use the SIRD epidemic model (S-Susceptible, I-Infected, R-Recovered, D-Dead) which is a non-autonomous dynamic system with incubation time delay to study the evolution of the COVID-19 in Wuhan City, Hubei Province and China Mainland. According to the data in the early stage issued by the National Health Commission of China, we can accurately estimate the parameters of the model, and then accurately predict the evolution of the COVID-19 there. From the analysis of the issued data, we find that the cure rates in Wuhan City, Hubei Province and China Mainland are the approximately linear increasing functions of time t and their death rates are the piecewisely decreasing functions. These can be estimated by finite difference method. Secondly, we use the delayed SIRD epidemic model to study the evolution of the COVID-19 in the Hubei Province outside Wuhan City. We find that its cure rate is an approximately linear increasing function and its death rate is nearly a constant. Thirdly, we use the delayed SIR epidemic model (S-Susceptible, I-Infected, R-Removed) to predict those of Beijing, Shanghai, Zhejiang and Anhui Provinces. We find that their cure rates are the approximately linear increasing functions and their death rates are the small constants. The results indicate that it is possible to make accurate long-term predictions for numbers of current confirmed, accumulative dead cases of COVID-19 by modeling. In this paper the results indicate we can accurately obtain and predict the turning points, the end time and the maximum numbers of the current infected and dead cases of the COVID-19 in China. In spite of our simple method and small data, it is rather effective in the long-term prediction of the COVID-19.

A turning point prediction method of stock price based on RVFL-GMDH and chaotic time series analysis.

Stock market prediction is extremely important for investors because knowing the future trend of stock prices will reduce the risk of investing capital for profit. Therefore, seeking an accurate, fast, and effective approach to identify the stock market movement is of great practical significance. This study proposes a novel turning point prediction method for the time series analysis of stock price. Through the chaos theory analysis and application, we put forward a new modeling approach for the nonlinear dynamic system. The turning indicator of time series is computed firstly; then, by applying the RVFL-GMDH model, we perform the turning point prediction of the stock price, which is based on the fractal characteristic of a strange attractor with an infinite self-similar structure. The experimental findings confirm the efficacy of the proposed procedure and have become successful for the intelligent decision support of the stock trading strategy. Supplementary Information: The online version contains supplementary material available at 10.1007/s10115-021-01602-3.

The heterogeneous time and income effects in Kuznets curves of municipal solid waste generation: comparing developed and developing economies.

In rapidly developing countries, it is imperative to study the changes in municipal solid waste (MSW) generation for planning waste management and treatment. This study took the largest 11 economies in the world as cases, comprising half of the global population, analyzed the variations of definition of MSW among these economies. Based on the Environmental Kuznets Curve (EKC) hypothesis and using parametric model, Feasible General Least Squares (FGLS) regression, and nonparametric models, Generalized Additive Mixed Models (GAMMs), it was expected that the change features and its socioeconomic drivers of total MSW generation and per capita MSW (PCMSW) since the 1960s would be determined. Efforts were also made to find the turning/stabilizing point in the relationship between PCMSW and per capita gross domestic product (PCGDP) in each economy. It shows that population has the most important impact on total MSW, however, the economic indicators might be ignored. The United States and Germany have the highest PCMSW generation, while China and India indicate the lowest. The turning/stabilizing point in the relationship between PCMSW and PCGDP perfermed in most developed economies, Singapore and Korea reached the turning point around 1990, while for other developed economies it was 2000. Germany came to a stabilizing point in 1990, and with some arbitrary, so did the United States. The developing economies seem to be still in their early stage of the potential EKC. In developed economies, heterogeneous time effects on PCMSW seem to be more significant than heterogeneous income effects, which is contrary to developing economies.

The Effect of Vaccination Rates on the Infection of COVID-19 under the Vaccination Rate below the Herd Immunity Threshold.

Although vaccination is carried out worldwide, the vaccination rate varies greatly. As of 24 May 2021, in some countries, the proportion of the population fully vaccinated against COVID-19 has exceeded 50%, but in many countries, this proportion is still very low, less than 1%. This article aims to explore the impact of vaccination on the spread of the COVID-19 pandemic. As the herd immunity of almost all countries in the world has not been reached, several countries were selected as sample cases by employing the following criteria: more than 60 vaccine doses per 100 people and a population of more than one million people. In the end, a total of eight countries/regions were selected, including Israel, the UAE, Chile, the United Kingdom, the United States, Hungary, and Qatar. The results find that vaccination has a major impact on reducing infection rates in all countries. However, the infection rate after vaccination showed two trends. One is an inverted U-shaped trend, and the other is an L-shaped trend. For those countries with an inverted U-shaped trend, the infection rate begins to decline when the vaccination rate reaches 1.46-50.91 doses per 100 people.