Nonlinear study of indolamines: A hidden property that might have possible implications in neurodegeneration

Indolamines (e.g., serotonin and melatonin) are tryptophan-derived class of neurotransmitters and neuromodulators that play crucial roles in mood regulation, sleep-wake cycles, and gastrointestinal functions. These biogenic amines exert their effects by binding to specific receptors in the central nervous system, influencing neuronal activity and signalling cascades. Indolamines are vital in maintaining homeostasis, and imbalances in their levels have been implicated in various neurological and psychiatric disorders. Hence, in the present study, we have investigated the nonlinear properties of indolamines under a continuous wave (CW) and pulsed laser excitation using the closed-aperture (CA) Z-scan technique. The CA Z-scan is a cost-effective and sensitive analytical tool for investigating nonlinear properties. It is observed that indolamines show negative refractive and positive absorptive nonlinearity under in vitro physiological conditions. The origin of nonlinearity is ascribed to the thermo-optical effect governed by the saturated atomic absorption and molecular orientation mechanisms under CW and pulsed laser excitation, respectively. The strength of nonlinearity is found to vary linearly with the concentration of indolamines. Overall, serotonin possesses stronger nonlinearity than melatonin. The maximum nonlinearity (refractive index (n2) & absorption coefficient (β)) for melatonin under CW and pulsed laser excitations are (-1.266 × 10-12 m2W-1 and -1.883 × 10-17 m2W-1) & (8.046 × 10-8 mW-1 and 1.516 × 10-13 mW-1), respectively. Meanwhile, the maximum n2 and β under pulsed laser excitation for serotonin are obtained as -3.195 × 10-17 m2W-1 and 6.149 × 10-12 mW-1, respectively. The outcome of the results may be utilized in understanding processes mediated by indolamines and designing therapeutic interventions.

A nonlinear relationship between the triglycerides to high-density lipoprotein cholesterol ratio and stroke risk: an analysis based on data from the China Health and Retirement Longitudinal Study

OBJECTIVE

The connection between triglycerides to high-density lipoprotein cholesterol (TG/HDL-C) ratio and stroke risk is controversial. Our goal was to explore this relationship in individuals aged 45 and older enrolled in the China Health and Retirement Longitudinal Study (CHARLS).

METHODS

Our analysis encompassed 10,164 participants from the CHARLS cohorts. We applied the Cox proportional-hazards regression model to evaluate the potential correlation between the TG/HDL-C ratio and stroke incidence. Using a cubic spline function and smooth curve fitting within the Cox model allowed us to unearth a possible non-linear pattern in this relationship. We also conducted thorough sensitivity and subgroup analyses to deepen our understanding of the TG/HDL-C ratio's impact on stroke risk.

RESULTS

Adjusting for various risk factors, we observed a significant link between the TG/HDL-C ratio and increased stroke risk in individuals aged 45 and above (HR: 1.03, 95% CI 1.00-1.05, P = 0.0426). The relationship appeared non-linear, with an inflection at a TG/HDL-C ratio of 1.85. Ratios below this threshold indicated a heightened stroke risk (HR: 1.28, 95% CI 1.06-1.54, P = 0.0089), while ratios above it did not show a significant risk increase (HR: 1.01, 95% CI 0.98-1.04, P = 0.6738). Sensitivity analysis confirmed the robustness of these findings. Notably, non-smokers exhibited a stronger correlation between the TG/HDL-C ratio and stroke risk compared to past and current smokers.

CONCLUSION

Our investigation revealed a significant, yet non-linear, association between the TG/HDL-C ratio and the incidence of stroke among individuals aged 45 and above. Specifically, we found that stroke risk increased in correlation with TG/HDL-C ratio below the threshold of 1.85. These insights may guide healthcare providers in advising and developing more effective strategies for stroke prevention in this demographic.

Hematin anhydride (β-hematin): An analogue to malaria pigment hemozoin possesses nonlinearity

Hematin anhydride (β-hematin), the synthetic analogue of the malaria pigment, "hemozoin", is a heme dimer produced by reciprocal covalent bonds among carboxylic acid groups on the protoporphyrin-IX ring and the iron atom present in the two adjacent heme molecules. Hemozoin is a disposal product formed from the digestion of hemoglobin present in the red blood cells infected with hematophagous malaria parasites. Besides, as the parasites invade red blood cells, hemozoin crystals are eventually released into the bloodstream, where they accumulate over time in tissues. Severe malaria infection leads to significant dysfunction in vital organs such as the liver, spleen, and brain in part due to the autoimmune response to the excessive accumulation of hemozoin in these tissues. Also, the amount of these crystals in the vasculature correlates with disease progression. Thus, hemozoin is a unique indicator of infection used as a malaria biomarker and hence, used as a target for the development of antimalarial drugs. Hence, exploring various properties of hemozoin is extremely useful in the direction of diagnosis and cure. The present study focuses on finding one of the unknown properties of β-hematin in physiological conditions by using the Z-scan technique, which is simple, sensitive, and economical. It is observed that hemozoin possesses one of the unique material properties, i.e., nonlinearity with a detection limit of ∼ 15 µM. The self-defocusing action causes β-hematin to exhibit negative refractive nonlinearity. The observed data is analyzed with a thermal lensing model. We strongly believe that our simple and reliable approach to probing the nonlinearity of β-hematin will provide fresh opportunities for malaria diagnostics & cure in the near future.

A non-linear connection between the total cholesterol to high-density lipoprotein cholesterol ratio and stroke risk: a retrospective cohort study from the China Health and Retirement Longitudinal Study

OBJECTIVE

The connection between total cholesterol to high-density lipoprotein cholesterol (TC/HDL-C) ratio and stroke risk is controversial. This study aims to examine the connection between the TC/HDL-C ratio and stroke in middle-aged and older individuals who are part of the China Health and Retirement Longitudinal Study (CHARLS).

METHODS

This study conducted a retrospective cohort analysis, enrolling a total of 10,184 participants who met the designated criteria from CHARLS between 2011 and 2012. We then used the Cox proportional-hazards regression model to analyze the relationship between the TC/HDL-C ratio and stroke risk. Using a Cox proportional hazards regression model with cubic spline functions and smooth curve fitting, we were able to identify the non-linear relationship between the TC/HDL-C ratio and stroke occurrence. The sensitivity and subgroup analyses were also performed to investigate the connection between TC/HDL-C ratio and stroke.

RESULTS

This study revealed a statistically significant association between the TC/HDL-C ratio and stroke risk in subjects aged 45 years or older after adjusting for risk factors (HR: 1.05, 95%CI 1.00-1.10, P = 0.0410). Furthermore, a non-linear connection between the TC/HDL-C ratio and stroke risk was detected, with a TC/HDL-C ratio inflection point of 3.71. We identified a significant positive connection between the TC/HDL-C ratio and stroke risk, when the TC/HDL-C ratio was less than 3.71 (HR: 1.25, 95%CI 1.07-1.45, P = 0.0039). However, their connection was not significant when the TC/HDL-C ratio exceeded 3.71 (HR: 1.00, 95%CI 0.94-1.06, P = 0.9232). The sensitivity analysis and subgroup analyses revealed that our findings were well-robust.

CONCLUSION

Our study demonstrated a positive, non-linear connection between the TC/HDL-C ratio and stroke risk in middle-aged and older individuals. There was a significant positive connection between the TC/HDL-C ratio and stroke risk, when the TC/HDL-C ratio was less than 3.71. The current research can be used as a guideline to support clinician consultation and optimize stroke prevention measures for middle-aged and older adults.

Development of a hierarchical support vector regression-based in silico model for the prediction of the cysteine depletion in DPRA

Topical and transdermal treatments have been dramatically growing recently and it is crucial to consider skin sensitization during the drug discovery and development process for these administration routes. Various tests, including animal and non-animal approaches, have been devised to assess the potential for skin sensitization. Furthermore, numerous in silico models have been created, providing swift and cost-effective alternatives to traditional methods such as in vivo, in vitro, and in chemico methods for categorizing compounds. In this study, a quantitative structure-activity relationship (QSAR) model was developed using the innovative hierarchical support vector regression (HSVR) scheme. The aim was to quantitatively predict the potential for skin sensitization by analyzing the percent of cysteine depletion in Direct Peptide Reactivity Assay (DPRA). The results demonstrated accurate, consistent, and robust predictions in the training set, test set, and outlier set. Consequently, this model can be employed to estimate skin sensitization potential of novel or virtual compounds.

Association of spontaneous abortion with bipolar disorder and major depression based on inverse probability treatment weighting of multigroup propensity scores: Evidence from the UK Biobank

BACKGROUND

Few studies have explored the association between the number of SAs and bipolar disorder and major depression (BDMD). This study aims to investigate the association between SA and BDMD, and the possible dose-response relationship between them.

METHODS

We conducted a cross-sectional study of 13,200 female UK Biobank participants. Participants were classified into BDMD and no-BDMD groups based on their BDMD status. The number of SAs was grouped into non-SA, occasional SA (OSA), and recurrent SA (RSA). Baseline characteristics of the three groups were balanced using inverse probability treatment weighting (IPTW) based on propensity scores. The three-knots restricted cubic spline regression model was utilized to assess the dose-response relationship between the number of SAs and BDMD.

RESULTS

The IPTW-adjusted multivariate logistic regression revealed that SA was an independent risk factor for BDMD, with adjusted OR of 1.12 (95 % CI: 1.07-1.19) and 1.32 (95 % CI: 1.25-1.40) in the OSA and RSA groups, respectively. The strength of this association amplified as the number of SAs (P for trend <0.001). There was a nonlinear relationship between the number of SAs and the risk of BDMD, with an approximately inverted L-shaped curve.

LIMITATIONS

The information of the SA and BDMD status relied on self-reported by volunteers, and the study sample was mostly of European descent.

CONCLUSIONS

Women who reported experiencing multiple SAs are more likely to have BDMD. Therefore, it is imperative to provide psychological care and interventions for women in the postpartum period.

The Association Between Anemia and Depressive Symptoms in Non-White Male Adults: National Health and Nutrition Examination Survey (2005-2018)

BACKGROUND

The relationship between anemia and depression remains controversial. This study aimed to investigate the association between hemoglobin (Hb) levels and depressive symptoms. Study Design: A cross-sectional study.

METHODS

This study was conducted using National Health and Nutrition Examination Survey data from 2005-2018. Hb levels were obtained from laboratory files, and depressive symptoms were assessed using the Patient Health Questionnaire (PHQ-9). Multivariable logistic regression analysis and smoothing plots were performed to examine the relationship between anemia and depression, including potential nonlinear associations.

RESULTS

The study included 6008 male adults. Multivariable analysis revealed that anemia was associated with an increased odds ratio for mild (OR=1.49, 95% CI: 1.06, 2.10) and moderate (OR=2.05, 95% CI: 1.14-3.70) anemia. Additionally, each additional g/dL of Hb was significantly inversely associated with developing depression (OR=0.91, 95% CI: 0.85, 0.96). A nonlinear relationship was detected between Hb and depression, with an inflection point at 15 g/dL. Below this threshold, there was a significantly negative association between Hb and depression (OR=0.88, 95% CI: 0.79, 0.98); no significant relationship was observed above it (OR=1.05, 95% CI: 0.84, 1.31).

CONCLUSION

Anemia was positively associated with depression in non-White American men. A nonlinear relationship between Hb and depression was detected, and it had a saturation effect. A significant negative correlation with depression was observed when the Hb level was below 15 g/dL.

Methodologies for modeling and identification of breathing crack: A review

Swift development of technology for monitoring complex structures demands major attention on the precision of damage detection methods. The early detection of any type of deterioration or degradation of structures is of paramount importance to avoid sudden catastrophic failure. It warns users about the impending state of the system. At the initiation of a crack or some other system faults, the system may generate a time-varying state of crack under ambient vibration. It represents the nonlinear breathing phenomena of crack. An assessment of this degree of nonlinearity can be utilized for the detection, localization, and quantification of breathing cracks. Appropriate modeling of such cracks is thus necessary to capture distinctive nonlinear features. Recognizing this importance, various methods of modeling and nonlinear system identification which have been employed in the past for the detection of breathing crack are reviewed. The present study also explores some of the available vibration as well as acoustic-based damage identification techniques, chronologically connecting their evolutions. It summarizes the advantages and limitations of the methods to inspect potential future applications. The future scopes drawn from this review are highlighted to pave the path of wide-spread applications of nonlinear features of crack.

The impact of urbanization on ecosystem services: Both time and space are important to identify driving forces

Rapid urbanization is one of the key factors in threatening regional ecological security and undermining human well-being. Understanding of the impacts of urbanization on ecosystem services (ESs) could provide comprehensive information for policy making to support ecological governance. In this study, the spatial and temporal distributions of four ESs, namely water yield (WY), soil conservation (SC), nitrogen export (NE), and habitat quality (HQ), and four factors of urbanization, namely construction land percentage, economic density, population density, and nighttime lighting, were analyzed in the Xiangjiang River Basin (XJRB) from 1990 to 2020. The impacts of urbanization on ESs at the sub-watershed and county level were investigated using the space-for-time and change-over-time methods. The results showed that: (1) WY, SC, and NE fluctuated throughout the study period, while HQ significantly decreased and urbanization factors significantly increased. (2) Each urbanization factor had a significant influence on the spatial heterogeneity of ESs, with the contribution at the county level being 2.88%-56.11% higher than that at the sub-watershed level. Moreover, there were enhanced interactions between factors in general, although spatial heterogeneity effects on NE and HQ were weaker at the county level. (3) Urbanization and ESs had a significant nonlinear relationship, and there was a threshold of relationship change between them, with the impact of urbanization on ESs showing evident spatial heterogeneity in terms of both the driving direction and intensity of change over time. (4) The change-over-time method identified 1992-1995 and 2008-2013 as key periods of change in the relationship between urbanization and ESs in the XJRB, and the method had the advantage of revealing the spatial heterogeneity of the effects of driving factors. These findings provide a reference for decision making related to urban planning.

Nonlinear fourth-order elastic characterization of the cornea using torsional wave elastography

Measuring the mechanical nonlinear properties of the cornea remains challenging due to the lack of consensus in the methodology and in the models that effectively predict its behaviour. This study proposed developing a procedure to reconstruct nonlinear fourth-order elastic properties of the cornea based on a mathematical model derived from the theory of Hamilton et al. and using the torsional wave elastography (TWE) technique. In order to validate its diagnostic capability of simulated pathological conditions, two different groups were studied, non-treated cornea samples (n=7), and ammonium hydroxide ([Formula: see text]) treated samples (n=7). All the samples were measured in-plane by a torsional wave device by increasing IOP from 5 to 25 mmHg with 5 mmHg steps. The results show a nonlinear variation of the shear wave speed with the IOP, with higher values for higher IOPs. Moreover, the shear wave speed values of the control group were higher than those of the treated group. The study also revealed significant differences between the control and treated groups for the Lamé parameter [Formula: see text] (25.9-6.52 kPa), third-order elastic constant A (215.09-44.85 kPa), and fourth-order elastic constant D (523.5-129.63 kPa), with p-values of 0.010, 0.024, and 0.032, respectively. These findings demonstrate that the proposed procedure can distinguish between healthy and damaged corneas, making it a promising technique for detecting diseases associated with IOP alteration, such as corneal burns, glaucoma, or ocular hypertension.

New concentration-response functions for seven morbidity endpoints associated with short-term PM2.5 exposure and their implications for health impact assessment

BACKGROUND

Morbidity burdens from ambient air pollution are associated with market and non-market costs and are therefore important for policymaking. The estimation of morbidity burdens is based on concentration-response functions (CRFs). Most existing CRFs for short-term exposures to PM2.5 assume a fixed risk estimate as a log-linear function over an extrapolated exposure range, based on evidence primarily from Europe and North America.

OBJECTIVES

We revisit these CRFs by performing a systematic review for seven morbidity endpoints previously assessed by the World Health Organization, including data from all available regions. These endpoints include all cardiovascular hospital admission, all respiratory hospital admission, asthma hospital admission and emergency room visit, along with the outcomes that stem from morbidity, such as lost work days, respiratory restricted activity days, and child bronchitis symptom days.

METHODS

We estimate CRFs for each endpoint, using both a log-linear model and a nonlinear model that includes additional parameters to better fit evidence from high-exposure regions. We quantify uncertainties associated with these CRFs through randomization and Monte Carlo simulations.

RESULTS

The CRFs in this study show reduced model uncertainty compared with previous CRFs in all endpoints. The nonlinear CRFs produce more than doubled global estimates on average, depending on the endpoint. Overall, we assess that our CRFs can be used to provide policy analysis of air pollution impacts at the global scale. It is however important to note that improvement of CRFs requires observations over a wide range of conditions, and current available literature is still limited.

DISCUSSION

The higher estimates produced by the nonlinear CRFs indicates the possibility of a large underestimation in current assessments of the morbidity impacts attributable to air pollution. Further studies should be pursued to better constrain the CRFs studied here, and to better characterize the causal relationship between exposures to PM2.5 and morbidity outcomes.

Nonlinear association between body mass index and overall survival in advanced NSCLC patients treated with immune checkpoint blockade

BACKGROUND

We investigated the association of body mass index (BMI) modeled as a continuous variable with survival outcomes in advanced non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICI).

METHODS

We performed a single-institution retrospective analysis of consecutively diagnosed locally advanced or metastatic NSCLC patients treated with single-agent ICI in the first line or recurrent setting. The primary outcome was overall survival (OS). Secondary outcomes were progression-free survival (PFS) and objective response rate (ORR). BMI was modeled using a four-knot restricted cubic spline. Multiple Cox regression was used for survival analysis.

RESULTS

Two hundred patients were included (female 54%; never smoker 12%). Adenocarcinoma was the most common histology (61%). Median age was 67 years, median BMI was 25.9 kg/m2, and 65% of patients had Eastern Cooperative Oncology Group performance status (ECOG PS) of 0-1. On multivariable analysis, only BMI and ECOG PS were independently associated with OS (p < 0.01). Mortality risk decreased as the BMI increased from 20 to 30 kg/m2 (HR 0.49, 95% CI 0.28-0.84); however, it was reversed as the BMI surpassed ~ 30 kg/m2. Compared to ECOG PS ≥ 2, patients with ECOG PS of 0-1 had a longer OS (HR 0.42, 95% CI 0.28-0.63). Similar trends were observed with PFS and ORR, but the strength of the association was weaker.

CONCLUSION

We observed a nonlinear association between BMI and OS following treatment with ICI in advanced NSCLC. Risk of death increases at both extremes of BMI with a nadir that exists around 30 kg/m2.

Reframing Cognitive Science as a Complexity Science

Complexity science is an investigative framework that stems from a number of tried and tested disciplines-including systems theory, nonlinear dynamical systems theory, and synergetics-and extends a common set of concepts, methods, and principles to understand how natural systems operate. By quantitatively employing concepts, such as emergence, nonlinearity, and self-organization, complexity science offers a way to understand the structures and operations of natural cognitive systems in a manner that is conceptually compelling and mathematically rigorous. Thus, complexity science both transforms understandings of cognition and reframes more traditional approaches. Consequently, if cognitive systems are indeed complex systems, then cognitive science ought to consider complexity science as a centerpiece of the discipline.

On the nonlinear dynamics of a piezoresistive based mass switch based on catastrophic bifurcation

This research investigates the feasibility of mass sensing in piezoresistive MEMS devices based on catastrophic bifurcation and sensitivity enhancement due to the orientation adjustment of the device with respect to the crystallographic orientation of the silicon wafer. The model studied is a cantilever microbeam at the end of which an electrostatically actuated tip mass is attached. The piezoresistive layers are bonded to the vicinity of the clamped end of the cantilever and the device is set to operate in the resonance regime by means of harmonic electrostatic excitation. The nonlinearities due to curvature, shortening and electrostatic excitation have been considered in the modelling process. It is shown that once the mass is deposited on the tip mass, the system undergoes a cyclic fold bifurcation in the frequency domain, which yields a sudden jump in the output voltage of the piezoresistive layers; this bifurcation is attributed to the nonlinearities governing the dynamics of the response. The partial differential equations of the motion are derived and discretized to give a finite degree of freedom model based on the Galerkin method, and the limit cycles are captured in the frequency domain by using the shooting method. The effect of the orientation of the device with respect to the crystallographic coordinates of the silicon and the effect of the orientation of the piezoresistive layers with respect to the microbeam length on the sensitivity of the device is also investigated. Thanks to the nonlinearity and the orientation adjustment of the device and piezoresistive layers, a twofold sensitivity enhancement due to the added mass was achieved. This achievement is due to the combined amplification of the sensitivity in the vicinity of the bifurcation point, which is attributed to the nonlinearity and maximizing the sensitivity by orientation adjustment of the anisotropic piezoresistive coefficients.

Stochastic convergence of ecological footprint: new insights from a unit root test based on smooth transitions and nonlinear adjustment

The ecological footprint has currently become a highly popular environmental performance indicator. It provides the basis for setting goals, identifying options for action, and tracking progress toward stated goals. Because the examination of the existence of convergence is important for the climate change protection of the earth, the convergence of ecological footprint and its subcomponents are a major concern for scholars and policymakers. To this end, this study aims to investigate the stochastic convergence of ecological footprint and its subcomponents. We employ the recently developed Hepsag (2021) unit root test that allows nonlinearity and smooth structural change simultaneously to study stochastic convergence in per-capita ecological footprint over the period 1961-2018 for the most polluting countries. The results provide mixed evidence of the presence of stochastic convergence in conventional unit root tests such as ADF, KPSS and Fourier KPSS. According to the Hepsag (2021) unit root test results for all countries, built-up land footprint converges except Australia, Malaysia, Poland, and Turkey. Carbon footprint converges for Indonesia, Malaysia, Mexico, South Africa, Thailand, Turkey, the UK, and the USA. Cropland footprint converges for Australia, Canada, China, France, Indonesia, Italy, Japan, Korea, Malaysia, Mexico, Poland, South Africa, the UK, and Vietnam. Fishing grounds footprint converges in Brazil, France, Germany, Indonesia, Italy, Mexico, South Africa, and Vietnam. Forest product footprint converges in Australia, Canada, France, Germany, India, Korea, Mexico, Poland, Turkey, and Vietnam. Grazing land footprint converges in Canada, France, India, Indonesia, Japan, Korea, Poland, South Africa, Thailand, and Vietnam. And lastly, the total ecological footprint converges in Canada, France, Korea, Malaysia, Mexico, South Africa, the UK, and the USA.

Nonlinear Stiffness and Nonlinear Damping in Atomically Thin MoS2 Nanomechanical Resonators

We report on experimental measurements and quantitative analyses of nonlinear dynamic characteristics in ultimately thin nanomechanical resonators built upon single-layer, bilayer, and trilayer (1L, 2L, and 3L) molybdenum disulfide (MoS₂) vibrating drumhead membranes. This synergistic study with calibrated measurements and analytical modeling on observed nonlinear responses has led to the determination of nonlinear damping and stiffness coefficients at cubic and quintic orders for these two-dimensional (2D) resonators operating in the very high frequency (VHF) band (up to ∼90 MHz). We find that the quintic force can be ∼20% of the Duffing force at larger amplitudes, and thus, it generally cannot be ignored in a nonlinear dynamics analysis. This study provides the first quantification of nonlinear damping and frequency detuning characteristics in 2D semiconductor nanomechanical resonators and elucidates their origins and dependency on engineerable parameters, setting a foundation for future exploration and utilization of the rich nonlinear dynamics in 2D nanomechanical systems.

Assessing the longitudinal association between the GGT/HDL-C ratio and NAFLD: a cohort study in a non-obese Chinese population

BACKGROUND

A cross-sectional association between the combination indicator of high-density lipoprotein cholesterol (HDL-C) and gamma-glutamyl transferase (GGT) and fatty liver has been described in several recent studies, and this study aims to further evaluate the longitudinal relationship between the ratio of GGT to HDL-C (GGT/HDL-C ratio) and nonalcoholic fatty liver disease (NAFLD).

METHODS

This cohort study included 12,126 individuals without NAFLD at baseline, followed prospectively for 5 years, and the endpoint of interest was new-onset NAFLD. The relationship of the GGT/HDL-C ratio with new-onset NAFLD and the shape of the association was assessed by Cox regression models and restricted cubic spline (RCS) regression, respectively. Time-dependent receiver operator characteristics (ROC) curves were constructed to evaluate the predictive value of GGT, HDL-C, GGT/HDL-C ratio and BMI for the occurrence of NAFLD at different time points in the future.

RESULTS

The prevalence of NAFLD was 72.46/1000 person-years during the 5-year follow-up period. Results of multivariate Cox regression analysis showed a positive association of the GGT/HDL-C ratio with new-onset NAFLD after adequate adjustment of the related confounding factors, and the degree of correlation was slightly higher than that of GGT, and further subgroup analysis found that this association was more significant in the population with elevated systolic blood pressure (SBP). In addition, we also found a nonlinear relationship of the GGT/HDL-C ratio with the risk of new-onset NAFLD using the RCS regression, where the saturation threshold was about 31.79 U/mmol. Time-dependent ROC analysis results showed that the GGT/HDL-C ratio was increasingly valuable in predicting NAFLD over time, and was better than HDL-C in predicting NAFLD in the early stage (1-3 years), but was not superior to BMI and GGT.

CONCLUSIONS

In this large longitudinal cohort study based on a Chinese population, our results supported that the GGT/HDL-C ratio was positively and nonlinearly associated with the risk of new-onset NAFLD in a non-obese population. In the assessment of future NAFLD risk, the GGT/HDL-C ratio was slightly better than GGT alone; However, the GGT/HDL-C ratio did not appear to have a significant advantage over GGT and BMI alone in predicting NAFLD.

Effect of hyaluronic acid on microscale deformations of collagen gels

As fibrous collagen is the most abundant protein in mammalian tissues, gels of collagen fibers have been extensively used as an extracellular matrix scaffold to study how cells sense and respond to cues from their microenvironment. Other components of native tissues, such as glycosaminoglycans like hyaluronic acid, can affect cell behavior in part by changing the mechanical properties of the collagen gel. Prior studies have quantified the effects of hyaluronic acid on the mechanical properties of collagen gels in experiments of uniform shear or compression at the macroscale. However, there remains a lack of experimental studies of how hyaluronic acid changes the mechanical properties of collagen gels at the scale of a cell. Here, we studied how addition of hyaluronic acid to gels of collagen fibers affects the local field of displacements in response to contractile loads applied on length scales similar to those of a contracting cell. Using spherical poly(N-isopropylacrylamide) particles, which contract when heated, we induced displacement in gels of collagen and collagen with hyaluronic acid. Displacement fields were quantified using a combination of confocal microscopy and digital image correlation. Results showed that hyaluronic acid suppressed the distance over which displacements propagated, suggesting that it caused the network to become more linear. Additionally, hyaluronic acid had no statistical effect on heterogeneity of the displacement fields, but it did make the gels more elastic by substantially reducing the magnitude of permanent deformations. Lastly, we examined the effect of hyaluronic acid on fiber remodeling due to localized forces and found that hyaluronic acid partially - but not fully - inhibited remodeling. This result is consistent with prior studies suggesting that fiber remodeling is associated with a phase transition resulting from an instability caused by nonlinearity of the collagen gel.

Convergence in ecological footprint across the member states of ECOWAS: evidence from a novel panel unit root test

In this paper, we investigate the convergence in ecological footprint per capita across the ECOWAS (Economic Community of West African States) countries from 1968 to 2017. For this aim, we apply a panel Fourier threshold unit root test recently proposed by Yilanci et al. (2021). This methodology considers both multiple smooth structural changes and nonlinearity, which allows us to get more reliable results. The results of the study support the evidence of nonlinearity for the series, so we test the null of divergence in a nonlinear framework. Since test statistics are statistically significant, we conclude that there is global convergence for the sample. Besides, we find that the type of convergence is absolute, which supports the view that the ecological footprint of the ECOWAS countries will equalize in the long run, and the emission allocation policies should be given priority by policymakers.

Dynamical adaptation in photoreceptors with gain control

The retina hosts all processes needed to convert external visual stimuli into a neural code. Light phototransduction and its conversion into an electrical signal involve biochemical cascades, ionic regulations, and different kinds of coupling, among other relevant processes. These create a nonlinear processing scheme and light-dependent adaptive responses. The dynamical adaptation model formulated in recent years is an excellent phenomenological candidate to resume all these phenomena into a single feedforward processing scheme. In this work, we analyze this description in highly nonlinear conditions and find that responses do not match those resulting from a very detailed microscopic model, developed to reproduce electrophysiological recordings on horizontal cells. When a delayed light-dependent gain factor incorporates into the description, responses are in excellent agreement, even when spanning several orders of magnitude in light intensity, contrast, and duration, for simple and complex stimuli. This extended model may be instrumental for studies of the retinal function, enabling the linking of the microscopic domain to the understanding of signal processing properties, and further incorporated in spatially extended retinal networks.

Eigenproperties of perception (dynamic touch) and action (phase dynamic) out of diversities

This study explores perception-action heuristics from a fundamental theoretical perspective to describe the comprehensive frameworks of movement as a process within a system dynamic. We address issues related to the identification of dynamics by using a nonrepresentational perspective, namely, functional nonlinearity. Experimental-based tools and calculation procedures for perception (dynamic touch) and action (inter-limb synchrony) revealed a basic pattern of response. The applied models and analyses strongly reflect the invariant principles of a collective structure, which could be the key to understanding complex behavioral processes with simple underlying properties. Our results provide an empirical perspective on dynamic systems and may potentially lead to a set of interconnected elements whose interactions lead to various syntheses.

Adaptive Weighted Neighbors Method for Sensitivity Analysis

Identifying key factors from observational data is important for understanding complex phenomena in many disciplines, including biomedical sciences and biology. However, there are still some limitations in practical applications, such as severely nonlinear input-output relationships and highly skewed output distributions. To acquire more reliable sensitivity analysis (SA) results in these extreme cases, inspired by the weighted k-nearest neighbors algorithm, we propose a new method called adaptive weighted neighbors (AWN). AWN makes full use of the information contained in all training samples instead of limited samples and automatically gives more weight to nearby samples. Then, the bootstrap technique and Jansen's method are used to obtain reliable SA results based on AWN. We demonstrate the performance and accuracy of AWN by analyzing various biological and biomedical data sets, three simulated examples and two case studies, showing that it can effectively overcome the above limitations. We therefore expect it to be a complementary approach for SA.

Application of multi-wavelength dual-position absorption spectrum to improve the accuracy of leukocyte spectral quantitative analysis based on "M + N" theory

Leukocytes are the most important immune cells in human body, which are very important to maintain the immune function of human body. They can phagocytose foreign bodies and produce antibodies to resist the invasion of pathogens. Nowadays, the abuse of antibiotics is widespread, and the detection and analysis of leukocytes is very important for clinical diagnosis. It is of great medical significance to use chemical quantitative analysis method based on spectrum to realize the rapid and trace detection of leukocytes in clinic. It is the development direction of clinical detection in the future and provides a new way to improve the abuse of antibiotics. However, due to the influence of nonlinearity introduced by the measurement, the relationship between absorbance and concentration deviates from Lambert-Beer law, which leads to low measurement accuracy and restricts its development in clinical application. In order to improve the accuracy of spectral analysis, this paper with the guidance of "M + N" theory measured the transmission spectra of 392 whole blood samples under two different optical path lengths, and subtracts them to obtain the multi-band differential absorption spectra for modeling and prediction of leukocyte concentration. The following experiments were designed: using the transmission spectra measured at one position and the absorption spectra obtained by subtracting the transmission spectra measured at position 1 and position 2 as the input of modeling. Partial least squares (PLS) method was proposed in this paper for modeling and predicting the concentration of leukocyte. The experimental results show that the modeling results of dual-position absorption spectrum have been significantly improved and promoted compared with the modeling results of transmission spectrum in one position, and the calibration set correlation coefficient (R_C) values has increased by 57.92% to 0.864904, where the prediction set correlation coefficient (R_P) increased by 106.81% to 0.8502. The root mean square error of the calibration set (RMSEC) decreased by 40.01% from 3.1149 to 1.8686. The results suggest that modelling and analysing leukocytes with a multi-band dual-position absorption spectrum may reduce the influence of nonlinearity to a certain amount, significantly increase the model's prediction precision and accuracy, and obtain satisfactory results. This paper provides the possibility for rapid clinical micro-detection of leukocytes, as well as the ideas and directions for improving the accuracy of spectral quantitative analysis of components in complex solutions.

Testing the animal spirits theory for ethical investments: further evidence from aggregated and disaggregated data

This study aims to test the animal spirits theory by Akerlof and Shiller (Animal spirits - how human psychology drives the economy, and why it matters for global capitalism? Princeton University Press) for ethical stock markets using Islamic and sustainable stock indexes during calm and crisis periods. This question helps determine whether ethical finance is driven more by its specific rules or determined by animal spirits. We used data covering January 1996-September 2021, which includes both calm periods and crisis periods (dot-com bubble of 2000, subprime crisis of 2007, global financial crisis of 2008-2009, and COVID-19 recession). Accordingly, we applied different time series tests, ran a quantile regression, and built an econometric framework to empirically test the animal spirits theory. We provide two key findings. First, investor sentiment and consumer confidence significantly affect the dynamics of both ethical stock returns, suggesting further evidence of animal spirits. This finding supports the assumption that investors' emotions and sentiments affect their behaviors and related feelings, for example, spontaneous instinctive that urge to action than inaction, optimism, and so forth, might help to apprehend some investment actions. Second, and interestingly, animal spirit effects enter asymmetrically and nonlinearly as their effects on ethical stock returns are time-varying and vary with the quantile under consideration.

Exploring the type and strength of nonlinearity in water quality responses to nutrient loading reduction in shallow eutrophic water bodies: Insights from a large number of numerical simulations

Reducing the load of nutrients is essential to improve water quality while water quality may not respond to the load reduction in a linear way. Despite nonlinear water quality responses being widely mentioned by studies, there is a lack of comprehensive assessment on the extent and type of nonlinear responses considering the seasonal changes. This study aimed to measure the strength of nonlinearity of theoretically possible water quality responses and explore their potential types in shallow eutrophic water bodies. Hereto, we generated 14,710 numerical water body cases that describe the water quality processes using the Environmental Fluid Dynamics Code (EFDC) and applied eight load reduction scenarios on each water body case. Inflows are simplified from Lake Dianchi. The climate conditions consider three cases: Lake Dianchi, Wissahickon Creek, and Famosa Slough. We then developed a nonlinearity strength indicator to quantify the strength and frequency of nonlinear water quality responses. Based on the quantification of nonlinearity, we clustered all the samples of water quality responses using K-Means, an unsupervised Machine Learning algorithm, to find the potential types of nonlinear water quality responses for TN (total nitrogen), TP (total phosphorus), and Chla (chlorophyll a). Results show linear or near-linear response types account for 90%, 69%, and 20% of TN, TP, and Chla samples respectively. TP and Chla could perform more types of nonlinearity. Representative nonlinear water quality responses include disproportional improvement, peak change (disappear, move forwards or afterward), and seasonal deterioration of TN after load reduction. This study would contribute to the current understanding of nonlinear water quality responses to load reduction and provide a basis to study under which conditions the nonlinear responses may emerge.

Non-linear relationship between high-density lipoprotein cholesterol and incident diabetes mellitus: a secondary retrospective analysis based on a Japanese cohort study

BACKGROUND AND OBJECTIVE

High-density lipoprotein cholesterol (HDL-C) may be directly involved in glucose metabolism by enhancing insulin sensitivity and insulin secretion. This current study aimed to explore the association between HDL-C and the risk of diabetes mellitus (DM) in Japanese population.

METHODS

This retrospective cohort study was based on a publicly available DRYAD dataset. We enrolled 15,388 Japanese participants who received medical examinations from 2004 to 2015 at Murakami Memorial Hospital. Our study selected HDL-C at baseline and incident DM during follow-up as the target independent variable and the dependent variable, respectively. Cox proportional-hazards regression was used to investigate the association between HDL-C and DM, generalized additive models to identify non-linear relationships.

RESULTS

After adjusting for the demographic and clinical covariates, the result showed low HDL-C levels were associated with increased risk for diabetes (HR = 0.54, 95%CI (0.35, 0.82)). The results remained robust in a series of sensitive analysis. A non-linear relationship was detected between HDL-C and incident DM with an inflection point of HDL-C at 1.72 mmol/L (Log-likelihood ratio test P = 0.005). Subgroup analysis showed that a stronger association could be found in ex-smokers and current-smokers. The same trend was also seen in the community with hypertension (P for interaction = 0.010, HR = 1.324).

CONCLUSION

This study demonstrates a negative and non-linear relationship between HDL-C and diabetes in the Japanese population. There is a threshold effect between HDL-C and diabetes. When HDL-C is lower than 1.72 mmol/L, the decreased HDL-C levels were associated with an increased risk for diabetes.

Kernel principal component analysis (PCA) control chart for monitoring mixed non-linear variable and attribute quality characteristics

The products are commonly measured by two types of quality characteristics. The variable characteristics measure the numerical scale. Meanwhile, the attribute characteristics measure the categorical data. Furthermore, in monitoring processes, the multivariate variable quality characteristics may have a nonlinear relationship. In this paper, the Kernel PCA control chart is applied to monitor the mixed (attribute and variable) characteristics with the nonlinear relationship. First, the Average Run Length (ARL) is utilized to evaluate the performance of the proposed chart. The simulation studies show that the proposed chart can detect the shift in process. For this case, the Radial Basis Function (RBF) kernel demonstrates the consistent performance for several cases studied. Second, the performance comparison between the proposed chart and the conventional PCA Mix chart is performed. Based on the results, it is known that the proposed chart performs better in detecting the small shift in process. Finally, the proposed chart is applied to monitor the well-known NSL KDD dataset. The proposed chart shows good accuracy in detecting intrusion in the network. However, it still produces more False Negatives (FN).

Z-scan analysis and theoretical studies of dopamine under physiological conditions

Dopamine (DA) is a widely researched catecholamine best known for its role in motor, motivation, addiction, and reward. Disruption in dopamine homeostasis and signaling within the central nervous system (CNS) can lead to disorders such as attention deficit hyperactivity disorder (ADHD), schizophrenia, Parkinson's disease, and obsessive-compulsive disorder. In the periphery, circulating DA is stored in blood platelets, and its disruption correlates with pathological conditions such as head and neck paragangliomas, Huntington's chorea, and schizophrenia. Various methods to sensitively and selectively detect dopamine have been reported, but sparse attempts have been made to exploit its intrinsic properties. Previously, we have harnessed dopamine's natural mid-ultraviolet auto-fluorescence to carry out its label-free imaging in live brain tissues. Recently, we used the closed-aperture (CA) Z-scan method to provide the first line of evidence on the existence of dopamine nonlinearity. Here, we utilized this simple, sensitive, and straightforward CA Z-scan technique and coupled this with theoretical simulations to further investigate the nonlinear photophysical properties of DA under physiological conditions. Our combined approach revealed that the nonlinear property of dopamine is governed by the thermo-optical effects, and the CA Z-scan profiles can be modulated by parameters such as phase-shift, orders of absorption, and time dependency. Simple and physiologically relevant systems, such as the platelets, are amenable to Z-scan analysis, thereby empowering us to scrutinize in the future if nonlinearity and its alterations, if any, have a direct bearing on DA homeostasis and associated diseases.

Human-Designed Filters May Outperform Machine-Learned Filters

Machine-learned image processing systems in medical imaging have shown better results than those obtained by traditional human-designed techniques. The success of machine learning techniques inspires humans to design better systems. The convolutional neural network (CNN) has a multi-channel architecture, which the conventional filters do not have. This paper proposes that by borrowing the multi-channel architecture, the human-designed denoising filter can have better performance than the machined-learned version. We illustrate the feasibility of this idea with a toy example in a sinogram denoising task in the area of tomography.

A hierarchical integrative group least absolute shrinkage and selection operator for analyzing environmental mixtures

Environmental health studies are increasingly measuring multiple pollutants to characterize the joint health effects attributable to exposure mixtures. However, the underlying dose-response relationship between toxicants and health outcomes of interest may be highly nonlinear, with possible nonlinear interaction effects. Existing penalized regression methods that account for exposure interactions either cannot accommodate nonlinear interactions while maintaining strong heredity or are computationally unstable in applications with limited sample size. In this paper, we propose a general shrinkage and selection framework to identify noteworthy nonlinear main and interaction effects among a set of exposures. We design hierarchical integrative group least absolute shrinkage and selection operator (HiGLASSO) to (a) impose strong heredity constraints on two-way interaction effects (hierarchical), (b) incorporate adaptive weights without necessitating initial coefficient estimates (integrative), and (c) induce sparsity for variable selection while respecting group structure (group LASSO). We prove sparsistency of the proposed method and apply HiGLASSO to an environmental toxicants dataset from the LIFECODES birth cohort, where the investigators are interested in understanding the joint effects of 21 urinary toxicant biomarkers on urinary 8-isoprostane, a measure of oxidative stress. An implementation of HiGLASSO is available in the higlasso R package, accessible through the Comprehensive R Archive Network.

Deep IDA: A Deep Learning Method for Integrative Discriminant Analysis of Multi-View Data with Feature Ranking-An Application to COVID-19 severity

COVID-19 severity is due to complications from SARS-Cov-2 but the clinical course of the infection varies for individuals, emphasizing the need to better understand the disease at the molecular level. We use clinical and multiple molecular data (or views) obtained from patients with and without COVID-19 who were (or not) admitted to the intensive care unit to shed light on COVID-19 severity. Methods for jointly associating the views and separating the COVID-19 groups (i.e., one-step methods) have focused on linear relationships. The relationships between the views and COVID-19 patient groups, however, are too complex to be understood solely by linear methods. Existing nonlinear one-step methods cannot be used to identify signatures to aid in our understanding of the complexity of the disease. We propose Deep IDA (Integrative Discriminant Analysis) to address analytical challenges in our problem of interest. Deep IDA learns nonlinear projections of two or more views that maximally associate the views and separate the classes in each view, and permits feature ranking for interpretable findings. Our applications demonstrate that Deep IDA has competitive classification rates compared to other state-of-the-art methods and is able to identify molecular signatures that facilitate an understanding of COVID-19 severity.

Elevated gamma-glutamyl transferase has a non-linear association with incident non-alcoholic fatty liver disease in the non-obese Chinese population: a secondary retrospective study

BACKGROUND

Effective and applicable predictors of non-alcoholic fatty liver disease (NAFLD) are needed for the non-obese Chinese population. This study was undertaken to investigate: whether serum gamma-glutamyl transferase (GGT) was associated with incident NAFLD in the non-obese Chinese population.

METHODS

This was a retrospective cohort study that enrolled 33,153 initially NAFLD-free individuals who underwent a health examination in Wenzhou Medical Center of Wenzhou People's Hospital from January 2010 to December 2014. Serum GGT levels at the time of enrollment were evaluated in 11,906 persons who follow-up. The relationship between GGT levels and incident NAFLD was analyzed using Cox regression and generalized additive models after adjusting for demographic and clinical variables. In addition, Subgroup analysis was conducted, which was explored by Cox proportional hazard models. It was stated that the data had been downloaded from the DATADRYAD website.

RESULTS

Multivariable Cox regression models were used to estimate the hazard ratio (HR) for GGT with incident NAFLD after adjusted demographic and clinical variables (HR, 1.010; 95% CI, 1.007-1.012; P < 0.001). The incident NAFLD in the highest quartile of GGT levels was 3.653 times as high (95% confidence interval, 2.915 to 4.579) as that the lowest quartile. A non-linear relationship was firstly detected between GGT and incidence of NAFLD, which had an inflection point of GGT was 26 U/L. The effect sizes and the confidence intervals on the left and right sides of the inflection point were 1.104 (1.089-1.120) and 1.001 (0.999-1.004), respectively. In subgroup analyses, the hazard ratio for incident NAFLD remained consistent across subgroups.

CONCLUSIONS

In conclusion, the GGT level in the non-obese Chinese population was statistically significantly associated with incident NAFLD. The relationship between GGT level and incident NAFLD is non-linear. When GGT level is less than 26 U/L, GGT was strong positively with incident NAFLD.

Investigating mechanisms of fast BOLD responses: The effects of stimulus intensity and of spatial heterogeneity of hemodynamics

Recent studies have demonstrated that fast fMRI can track neural activity well above the temporal limit predicted by the canonical hemodynamic response model. While these findings are promising, the biophysical mechanisms underlying these fast fMRI phenomena remain underexplored. In this study, we discuss two aspects of the hemodynamic response, complementary to several existing hypotheses, that can accommodate faster fMRI dynamics beyond those predicted by the canonical model. First, we demonstrate, using both visual and somatosensory paradigms, that the timing and shape of hemodynamic response functions (HRFs) vary across graded levels of stimulus intensity-with lower-intensity stimulation eliciting faster and narrower HRFs. Second, we show that as the spatial resolution of fMRI increases, voxel-wise HRFs begin to deviate from the canonical model, with a considerable portion of voxels exhibiting faster temporal dynamics than predicted by the canonical HRF. Collectively, both stimulus/task intensity and image resolution can affect the sensitivity of fMRI to fast brain activity, which may partly explain recent observations of fast fMRI signals. It is further noteworthy that, while the present investigations focus on fast neural responses, our findings suggest that a revised hemodynamic model may benefit the many fMRI studies using paradigms with wide ranges of contrast levels (e.g., resting or naturalistic conditions) or with modern, high-resolution MR acquisitions.

Conversion of amplitude modulation to phase modulation in the human cochlea

When an amplitude modulated signal with a constant-frequency carrier is fed into a generic nonlinear amplifier, the phase of the carrier of the output signal is also modulated. This phenomenon is referred to as amplitude-modulation-to-phase-modulation (AM-to-PM) conversion and regarded as an unwanted signal distortion in the field of electro-communication engineering. Herein, we offer evidence that AM-to-PM conversion also occurs in the human cochlea and that listeners can use the PM information effectively to process the AM of sounds. We recorded otoacoustic emissions (OAEs) evoked by AM signals. The results showed that the OAE phase was modulated at the same rate as the stimulus modulation. The magnitude of the AM-induced PM of the OAE peaked generally around the stimulus level corresponding to the compression point of individual cochlear input-output functions, as estimated using a psychoacoustic method. A computational cochlear model incorporating a nonlinear active process replicates the abovementioned key features of the AM-induced PM observed in OAEs. These results indicate that AM-induced PM occurring at the cochlear partition can be estimated by measuring OAEs. Psychophysical experiments further revealed that, for individuals with higher sensitivity to PM, the PM magnitude is correlated with AM-detection performance. This result implies that the AM-induced PM information cannot be a dominant cue for AM detection, but listeners with higher sensitivity may partly rely on the AM-induced PM cue.

Nonlinear mechanisms in photoacoustics-Powerful tools in photoacoustic imaging

Many nonlinear effects have been discovered and developed in photoacoustic imaging. These nonlinear mechanisms have been explored for different utilizations, such as enhancing imaging contrast, measuring tissue temperature, achieving super-resolution imaging, enabling functional imaging, and extracting important physical parameters. This review aims to introduce different nonlinear mechanisms in photoacoustics, underline the fundamental principles, highlight their representative applications, and outline the occurrence conditions and applicable range of each nonlinear mechanism. Furthermore, this review thoroughly discusses the nonlinearity rule concerning how the mathematical structure of the nonlinear dependence is correlated to its practical applications. This summarization is useful for identifying and guiding the potential applications of nonlinearity based on their mathematical expressions, and is helpful for new nonlinear mechanism discovery or implementation in the future, which facilitates further breakthroughs in nonlinear photoacoustics.

Nonlinear mechanisms in photoacoustics-Powerful tools in photoacoustic imaging

Many nonlinear effects have been discovered and developed in photoacoustic imaging. These nonlinear mechanisms have been explored for different utilizations, such as enhancing imaging contrast, measuring tissue temperature, achieving super-resolution imaging, enabling functional imaging, and extracting important physical parameters. This review aims to introduce different nonlinear mechanisms in photoacoustics, underline the fundamental principles, highlight their representative applications, and outline the occurrence conditions and applicable range of each nonlinear mechanism. Furthermore, this review thoroughly discusses the nonlinearity rule concerning how the mathematical structure of the nonlinear dependence is correlated to its practical applications. This summarization is useful for identifying and guiding the potential applications of nonlinearity based on their mathematical expressions, and is helpful for new nonlinear mechanism discovery or implementation in the future, which facilitates further breakthroughs in nonlinear photoacoustics.

Entropy modulation of electroencephalographic signals in physiological aging

Aging is a multifactorial physiological process characterized by the accumulation of degenerative processes impacting on different brain functions, including the cognitive one. A tool largely employed in the investigation of brain networks is the electroencephalogram (EEG). Given the cerebral complexity and dynamism, many non-linear approaches have been applied to explore age-related brain electrical activity modulation detected by the EEG: one of them is the entropy, which measures the disorder of a system. The present study had the aim to investigate aging influence on brain dynamics applying Approximate Entropy (ApEn) parameter to resting state EEG data of 68 healthy adult participants, divided with respect to their age in two groups, focusing on several specialized brain regions. Results showed that elderly participants present higher ApEn values than younger participants in the central, parietal and occipital areas, confirming the hypothesis that aging is characterized by an evolution of brain dynamics. Such findings may reflect a reduced synchronization of the neural networks cyclic activity, due to the reduction of cerebral connections typically found in aging process. Understanding the dynamics of brain networks by applying the entropy parameter could be useful for developing appropriate and personalized rehabilitation programs and for future studies on neurodegenerative diseases.

Effect of ambient temperatures on category C notifiable infectious diarrhea in China: An analysis of national surveillance data

BACKGROUND

Many studies have explored the association between meteorological factors and infectious diarrhea (ID) transmission but with inconsistent results, in particular the roles from temperatures. We aimed to explore the effects of temperatures on the transmission of category C ID, to identify its potential heterogeneity in different climate zones of China, and to provide scientific evidence to health authorities and local communities for necessary public health actions.

METHODS

Daily category C ID counts and meteorological variables were collected from 270 cities in China over the period of 2014-16. Distributed lag non-linear models (DLNMs) were applied in each city to obtain the city-specific temperature-disease associations, then a multivariate meta-analysis was implemented to pool the city-specific effects. Multivariate meta-regression was conducted to explore the potential effect modifiers. Attributable fraction was calculated for both low and high temperatures, defined as temperatures below the 5th percentile of temperature or above the 95th percentile of temperature.

RESULTS

A total of 2,715,544 category C ID cases were reported during the study period. Overall, a M-shaped curve relationship was observed between temperature and category C ID, with a peak at the 81st percentile of temperatures (RR = 1.723, 95% CI: 1.579-1.881) compared to 50th percentile of temperatures. The pooled associations were generally stronger at high temperatures compared to low ambient temperatures, and the attributable fraction due to heat was higher than cold. Latitude was identified as a possible effect modifier.

CONCLUSIONS

The overall positive pooled associations between temperature and category C ID in China suggest the increasing temperature could bring about more category C infectious diarrhea cases, which warrants further public health measurements.

Modelling COVID-19 incidence in the African sub-region using smooth transition autoregressive model

Prediction of COVID-19 incidence and transmissibility rates are essential to inform disease control policy and allocation of limited resources (especially to hotspots), and also to prepare towards healthcare facilities demand. This study demonstrates the capabilities of nonlinear smooth transition autoregressive (STAR) model for improved forecasting of COVID-19 incidence in the Africa sub-region were investigated. Data used in the study were daily confirmed new cases of COVID-19 from February 25 to August 31, 2020. The results from the study showed the nonlinear STAR-type model with logistic transition function aptly captured the nonlinear dynamics in the data and provided a better fit for the data than the linear model. The nonlinear STAR-type model further outperformed the linear autoregressive model for predicting both in-sample and out-of-sample incidence.

Multisine frequency modulation of intra-epidermal electric pulse sequences: A novel tool to study nociceptive processing

A sustained sensory stimulus with a periodic variation of intensity creates an electrophysiological brain response at associated frequencies, referred to as the steady-state evoked potential (SSEP). The SSEPs elicited by the periodic stimulation of nociceptors in the skin may represent activity of a brain network that is primarily involved in nociceptive processing. Exploring the behavior of this network could lead to valuable insights regarding the pathway from nociceptive stimulus to pain perception. We present a method to directly modulate the pulse rate of nociceptive afferents in the skin with a multisine waveform through intra-epidermal electric stimulation. The technique was demonstrated in healthy volunteers. Each subject was stimulated using a pulse sequence modulated by a multisine waveform of 3, 7 and 13 Hz. The EEG was analyzed for the presence of the base frequencies and associated (sub)harmonics. Topographies showed significant central and contralateral SSEP responses at 3, 7 and 13 Hz in respectively 7, 4 and 3 out of the 9 participants included for analysis. As such, we found that intra-epidermal stimulation with a multisine frequency modulated pulse sequence can generate nociceptive SSEPs. The possibility to stimulate the nociceptive system using multisine frequency modulated pulses offers novel opportunities to study the temporal dynamics of nociceptive processing.

Existence and global asymptotic stability criteria for nonlinear neutral-type neural networks involving multiple time delays using a quadratic-integral Lyapunov functional

In this paper we consider a standard class of the neural networks and propose an investigation of the global asymptotic stability of these neural systems. The main aim of this investigation is to define a novel Lyapunov functional having quadratic-integral form and use it to reach a stability criterion for the under study neural networks. Since some fundamental characteristics, such as nonlinearity, including time-delays and neutrality, help us design a more realistic and applicable model of neural systems, we will use all of these factors in our neural dynamical systems. At the end, some numerical simulations are presented to illustrate the obtained stability criterion and show the essential role of the time-delays in appearance of the oscillations and stability in the neural networks.

Examining spatiotemporal changing patterns of bike-sharing usage during COVID-19 pandemic

The COVID-19 pandemic has led to a globally unprecedented change in human mobility. Leveraging two-year bike-sharing trips from the largest bike-sharing program in Chicago, this study examines the spatiotemporal evolution of bike-sharing usage across the pandemic and compares it with other modes of transport. A set of generalized additive (mixed) models are fitted to identify relationships and delineate nonlinear temporal interactions between station-level daily bike-sharing usage and various independent variables including socio-demographics, land use, transportation features, station characteristics, and COVID-19 infections. Results show: 1) the proportion of commuting trips is substantially lower during the pandemic; 2) the trend of bike-sharing usage follows an "increase-decrease-rebound" pattern; 3) bike-sharing presents as a more resilient option compared with transit, driving, and walking; 4) regions with more white, Asian, and fewer African-American residents are found to become less dependent on bike-sharing; 5) open space and residential areas exhibit less decrease and earlier start-to-recover time; 6) stations near the city center, with more docks, or located in high-income areas go from more increase before the pandemic to more decrease during the pandemic. Findings provide a timely understanding of bike-sharing usage changes and offer suggestions on how different stakeholders should respond to this unprecedented crisis.

Identification of nonlinear features in cortical and subcortical signals of Parkinson's Disease patients via a novel efficient measure

This study offers a novel and efficient measure based on a higher order version of autocorrelative signal memory that can identify nonlinearities in a single time series. The suggested method was applied to simultaneously recorded subthalamic nucleus (STN) local field potentials (LFP) and magnetoencephalography (MEG) from fourteen Parkinson's Disease (PD) patients who underwent surgery for deep brain stimulation. Recordings were obtained during rest for both OFF and ON dopaminergic medication states. We analyzed the bilateral LFP channels that had the maximum beta power in the OFF state and the cortical sources that had the maximum coherence with the selected LFP channels in the alpha band. Our findings revealed the inherent nonlinearity in the PD data as subcortical high beta (20-30 Hz) band and cortical alpha (8-12 Hz) band activities. While the former was discernible without medication (p=0.015), the latter was induced upon the dopaminergic medication (p<6.10-4). The degree of subthalamic nonlinearity was correlated with contralateral tremor severity (r=0.45, p=0.02). Conversely, for the cortical signals nonlinearity was present for the ON medication state with a peak in the alpha band and correlated with contralateral akinesia and rigidity (r=0.46, p=0.02). This correlation appeared to be independent from that of alpha power and the two measures combined explained 34 % of the variance in contralateral akinesia scores. Our findings suggest that particular frequency bands and brain regions display nonlinear features closely associated with distinct motor symptoms and functions.

Lake water-level fluctuation forecasting using machine learning models: a systematic review

Lake water-level fluctuation is a complex and dynamic process, characterized by high stochasticity and nonlinearity, and difficult to model and forecast. In recent years, applications of machine learning (ML) models have yielded substantial progress in forecasting lake water-level fluctuations. This paper presents a comprehensive review of the applications of ML models for modeling water-level dynamics in lakes. Among the many existing ML models, seven popular ML model types are reviewed: (1) artificial neural network (ANN); (2) support vector machine (SVM); (3) artificial neuro-fuzzy inference system (ANFIS); (4) hybrid models, such as hybrid wavelet-artificial neural network (WA-ANN) model, hybrid wavelet-artificial neuro-fuzzy inference system (WA-ANFIS) model, and hybrid wavelet-support vector machine (WA-SVM) model; (5) evolutionary models, such as gene expression programming (GEP) and genetic programming (GP); (6) extreme learning machine (ELM); and (7) deep learning (DL). Model inputs, data split, model performance criteria, and model inter-comparison as well as the associated issues are discussed. The advantages and limitations of the established ML models are also discussed. Some specific directions for future research are also offered. This review provides a new vision for hydrologists and water resources planners for sustainable management of lakes.

Association between triglyceride-glucose index and risk of incident diabetes: a secondary analysis based on a Chinese cohort study : TyG index and incident diabetes

Background

Recent studies have suggested the triglyceride-glucose index (TyG index) may serve as a suitable substitute for insulin resistance. However, evidence for the relationship between TyG index and risk of diabetes remains limited. This study sought to explore the association of baseline TyG index with risk of developing diabetes in Chinese adults.

Methods

This retrospective cohort study was conducted using data from the health screening program in China. A total of 201,298 non-diabetic individuals were included. TyG index was calculated as Ln [fasting plasma glucose (mg/dL) × fasting triglyceride level (mg/dL) / 2]. Diabetes was defined as fasting plasma glucose ≥126 mg/dL and/or self-reported diabetes. Cox proportion-hazard model was employed to evaluate the independent impact of baseline TyG index on future diabetes risk. Sensitivity and subgroup analyses were implemented to verify the reliability of results. Notably, data were downloaded from the DATADRYAD website, and used only for secondary analyses.

Results

During an average follow-up of 3.12 years, among 201,298 individuals aged ≥20 years, 3389 subjects developed diabetes. After adjusting for potential confounders, elevated TyG index were independently correlated with greater risk of incident diabetes (hazard ratio (HR), 3.34; 95% confidence interval (CI), 3.11–3.60). Compared with the lowest quartile (Q1), increasing TyG index (Q2, Q3, and Q4) was related to increased HR estimates of incident diabetes [HR (95% CI), 1.83 (1.49–2.26); 3.29 (2.70–4.01), and 6.26 (5.15–7.60), respectively]. Moreover, a nonlinear relationship was observed between TyG index and risk of diabetes and the slope of the curve increased accompanying the rise of TyG index. Subgroup analysis revealed the positive association was stronger among subjects with age < 40 years, body mass index ≥18.5 kg/m² and < 24 kg/m², or systolic blood pressure < 140 mmHg, or in females.

Conclusions

Elevated TyG index is independently correlated with increased risk of incident diabetes in Chinese adults, indicating it may represent a reliable predictor of diabetes in high-risk populations.

Supplementary information

Supplementary information accompanies this paper at 10.1186/s12944-020-01403-7.

Deeply digging the interaction effect in multiple linear regressions using a fractional-power interaction term

In multiple regression Y ~ β₀ + β₁X₁ + β₂X₂ + β₃X₁ X₂ + ɛ., the interaction term is quantified as the product of X₁ and X₂. We developed fractional-power interaction regression (FPIR), using βX₁^M X₂^N as the interaction term. The rationale of FPIR is that the slopes of Y-X₁ regression along the X₂ gradient are modeled using the nonlinear function (Slope = β₁ + β₃MX₁^M-1 X₂^N), instead of the linear function (Slope = β₁ + β₃X₂) that regular regressions normally implement. The ranges of M and N are from -56 to 56 with 550 candidate values, respectively. We applied FPIR using a well-studied dataset, nest sites of the crested ibis (Nipponia nippon).We further tested FPIR by other 4692 regression models. FPIRs have lower AIC values (-302 ± 5003.5) than regular regressions (-168.4 ± 4561.6), and the effect size of AIC values between FPIR and regular regression is 0.07 (95% CI: 0.04–0.10). We also compared FPIR with complex models such as polynomial regression, generalized additive model, and random forest. FPIR is flexible and interpretable, using a minimum number of degrees of freedom to maximize variance explained. We have provided a new R package, interactionFPIR, to estimate the values of M and N, and suggest using FPIR whenever the interaction term is likely to be significant.

• Introduced fractional-power interaction regression (FPIR) as Y ~ β₀ + β₁X₁ + β₂X₂ + β₃X₁^M X₂^N + ɛ to replace the current regression model Y ~ β₀ + β₁X₁ + β₂X₂ + β₃X₁ X₂ + ɛ;

• Clarified the rationale of FPIR, and compared it with regular regression model, polynomial regression, generalized additive model, and random forest using regression models for 4692 species;

• Provided an R package, interactionFPIR, to calculate the values of M and N, and other model parameters.

No goal is an island: the implications of systems theory for the Sustainable Development Goals

The Sustainable Development Goals (SDGs) have now been in place for 4 years, as the center-piece of the sustainable development program of the United Nations. This paper argues that the Earth system fundamentally represents the organizational framework of the planet and, therefore, any attempt at avoiding the existential threat to humanity that our activities are creating must be integrated within this system. We examine how complex systems function in order to identify the key characteristics that any sustainability policy must possess in order to deliver successful, long-term coexistence of humanity within the biosphere. We then examine what this means in terms of the SDGs, currently the dominant policy document on global sustainability and lying at the heart of Agenda 30. The paper explores what a sustainable program of actions, aimed at properly integrating within the Earth system, should look like, and what changes are needed if humanity is to address the multiple challenges facing us, based on systems theory. Central to this is the acknowledgement of shortcomings in current policy and the urgent need to address these in practice.

Ca2+ channel dynamics explain the nonlinear neuroplasticity induction by cathodal transcranial direct current stimulation over the primary motor cortex

Transcranial direct current stimulation (tDCS) induces polarity-dependent neuroplasticity: with conventional protocols, anodal tDCS results in excitability enhancement while cathodal stimulation reduces excitability. However, partially non-linear responses are observed with increased stimulation intensity and/or duration. Cathodal tDCS with 2 mA for 20 min reverses the excitability-diminishing plasticity induced by stimulation with 1 mA into excitation, while cathodal tDCS with 3 mA again results in excitability diminution. Since tDCS generates NMDA receptor-dependent neuroplasticity, such non-linearity could be explained by different levels of calcium concentration changes, which have been demonstrated in animal models to control for the directionality of plasticity. In this study, we tested the calcium dependency of non-linear cortical plasticity induced by cathodal tDCS in human subjects in a placebo controlled, double-blind and randomized design. The calcium channel blocker flunarizine was applied in low (2.5 mg), medium (5 mg) or high (10 mg) dosages before 20 min cathodal motor cortex tDCS with 3 mA in 12 young healthy subjects. After-effects of stimulation were monitored with TMS-induced motor evoked potentials (MEPs) until 2 h after stimulation. The results show that motor cortical excitability-diminishing after-effects of stimulation were unchanged, diminished, or converted to excitability enhancement with low, medium and high dosages of flunarizine. These results suggest a calcium-dependency of the directionality of tDCS-induced neuroplasticity, which may have relevant implications for future basic and clinical research.

Frequency doubling by nonlinearity of TiO2 nanomaterial

We reported titanium dioxide nanoparticles solution (TiO₂ NPS) preparation by the sol-gel method. The produced NPS was employed as a liquid crystal to generate the second harmonic (SH) of one part of the pumping Nd: YAG laser at different pumping intensities. The remaining part of the pumping laser was focused on a stander nonlinear material (NPP63) to produce another SH pulse (to be used as a reference). Then the two SH pulses (one from TiO₂ and one from NPP63) were used to calculate the nonlinear coefficient (β) of the synthesized TiO₂ Nano solution. The suggested method represents a simple and inexpensive setup for calculation β by excluding the very expensive femtosecond laser used in previous studies. The results reveal that β for the prepared TiO₂ nanoparticles is very large (5.3×10⁻²⁶ esu).

The nonlinear structure of linkage disequilibrium

The allele frequency dependence of the ranges of all measures of linkage disequilibrium is well-known. The maximum values of commonly used parameters such as r² and D vary depending on the allele frequencies at each locus. However, though this phenomenon is recognized and accounted for in many studies, the comprehensive mathematical framework underlying the limits of linkage disequilibrium measures at various frequency combinations is often heuristic or empirical. Here, it is demonstrated that underlying this behavior is the fundamental shift between linear and nonlinear dependence in the linkage disequilibrium structure between loci. The proportion of linear and nonlinear dependence can be estimated and it demonstrates how even the same values of r² can have different implications for the nature of the overall dependence. One result of this is the value of D^', when defined as only a positive number, has a minimum value of |r|. Understanding this dependence is crucial to making correct inferences about the relationships between two loci in linkage disequilibrium.