Miracle-dz/UniFAR-FTUs huggingface dataset:
Supplementary material to "Observed Water- and Light-Limitation Across Global Ecosystems"

Abstract. With a changing climate, it is becoming increasingly critical to understand vegetation responses to limiting environmental factors. Here, we investigate the spatial and temporal patterns of light and water limitation on photosynthesis using an observational framework. Our study is unique in characterizing the nonlinear relationships between photosynthesis and water and light, acknowledging approximately two regime behavior (no limitation and varying degree of limitation). It is also unique in using an observational framework instead of using model-derived photosynthesis properties. We combine data from three different satellite sensors, i.e., solar-induced chlorophyll fluorescence (SIF) from TROPOMI, surface soil moisture from SMAP, and above-ground canopy density from MODIS. We find both single-regime and two-regime models describe SIF sensitivity to soil moisture and photosynthetically active radiation (PAR) across the globe. The distribution and strength of soil moisture limitation on SIF are mapped in the water-limited environments while distribution and strength of PAR limitations are mapped in the energy-limited environments. Two-regime behavior is detected in 73 % of the cases for water limitation on photosynthesis, while two-regime detection is much lower at 36 % for light limitation on photosynthesis. SIF sensitivity to PAR strongly increases along moisture gradients, reflecting mesic vegetation’s adaptation to making rapid usage of incoming light availability on the weekly timescales. The transition point detected between the two regimes is connected to soil type and mean annual precipitation for the SIF-soil moisture relationship and for the SIF-PAR relationship. These thresholds have therefore an explicit relation to properties of the landscape, although they may also be related to finer details of the vegetation and soil interactions not resolved by the spatial scales here. The simple functions and thresholds are emergent behaviors capturing the interaction of many processes. The observational thresholds and strength of coupling can be used as benchmark information for land surface and Earth system models, especially those that characterize gross primary production mechanisms and vegetation dynamics.
Urbanization Affects the Occurrence of a Large Stingless Bee Species in a Large City

Insightful studies of habitat fragmentation and bees will consider fragmentation, alteration, and loss of nesting habitats, not just patches of forage plants, as well as the permeability of the surrounding matrix to interpatch movement. Most previous studies on bees defined habitat fragments as remnant patches of floral hosts or forests, overlooking the nesting needs of bees, with their conclusions being that habitat fragmentation is broadly deleterious, but there still remain contrasting results in the literature. We investigated the effects of fragmentation, caused by urbanization, and the permeability of the surrounding matrix on the occurrence of one species of large-sized stingless bee. Individuals of Melipona quadrifasciata were collected at flowering plants in 11 Atlantic Forest fragments (ranging 64–1,406 ha) in southeastern Brazil and analyzed the vegetation structure of the fragments and classified the urbanized matrix. We examined the effects of forest fragment size, fragment isolation and the urban landscape context on the occurrence of M. quadrifasciata. We found the species to be highly dependent on large and tall trees and was found in large fragments with high tree species richness and tree density. The urbanized landscape context was also found to be directly related to the presence of M. quadrifasciata, with the species’ absence from fragments in a highly urbanized landscape. We conclude that habitat quality within a fragment (vegetation structure), as well as matrix quality, are directly related to the occurrence of M. quadrifasciata, as shown by the direct relationship between environmental structural diversity and fragment age. The matrix holding recent fragments, probably due to the sprawl of the city, is more heterogeneous than the one with the oldest fragments. The most heterogeneous matrices possess a certain balance between trees, buildings and bare soil or herbaceous vegetation coverage, making them less impermeable to bees.
Anticancer Activities of Mushrooms: A Neglected Source for Drug Discovery

Approximately 270 species of mushrooms have been reported as potentially useful for human health. However, few mushrooms have been studied for bioactive compounds that can be helpful in treating various diseases. Like other natural regimens, the mushroom treatment appears safe, as could be expected from their long culinary and medicinal use. This review aims to provide a critical discussion on clinical trial evidence for mushrooms to treat patients with diverse types of cancer. In addition, the review also highlights the identified bioactive compounds and corresponding mechanisms of action among the explored mushrooms. Furthermore, it also discusses mushrooms with anticancer properties, demonstrated either in vitro and/or in vivo models, which have never been tested in clinical studies. Several mushrooms have been tested in phase I or II clinical trials, mostly for treating breast cancer (18.6%), followed by colorectal (14%) and prostate cancer (11.6%). The majority of clinical studies were carried out with just 3 species: Lentinula edodes (22.2%), Coriolus versicolor, and Ganoderma lucidum (both 13.9%); followed by two other species: Agaricus bisporus and Grifola frondosa (both 11.1%). Most in vitro cell studies use breast cancer cell lines (43.9%), followed by lung (14%) and colorectal cancer cell lines (13.1%), while most in vivo animal studies are performed in mice tumor models (58.7%). Although 32 species of mushrooms at least show some promise for the treatment of cancer, only 11 species have been tested clinically thus far. Moreover, most clinical studies have investigated fewer numbers of patients, and have been limited to phase III or IV. Therefore, despite the promising preclinical and clinical data publication, more solid scientific efforts are required to clarify the therapeutic value of mushrooms in oncology.
Toward better intelligent learning (iLearning) performance: what makes iLearning work for students in a university setting?

ABSTRACT We explored the critical factors associated with iLearning that impact students’ learning performance and identified the factors with a notable influence to help managers in higher education institutions increase the effectiveness of iLearning for students. We initially synthesised 4 main dimensions (including 26 criteria): performance expectancy, lecturers’ influence, quality of service, and personal innovativeness. Subsequently, we conducted surveys in two stages. First, by studying a group of students with experience using iLearning at Taiwanese universities, we extracted 5 critical dimensions (including 18 criteria) through a factor analysis. Second, by studying a group of senior educators and practitioners in Taiwan, we prioritised the dimensions and criteria through the analytic hierarchy process (AHP). We found that performance expectancy is the top critical dimension, and the top five critical criteria pertain to enhancing the learning performance, increasing the learning participation, altering learning habits, ensuring access at all times, and enabling prompt use of learning resources. Moreover, we recommend several suggestions for the relevant managers to enhance the students’ iLearning performance.
Generic Stability Criteria for Switched Nonlinear Systems With Switching-Signal-Based Lyapunov Functions Using Takagi–Sugeno Fuzzy Model

The aim of this article is to establish generic stability conditions for switched nonlinear systems with mode-dependent average dwell-time (MDADT) switching rules via Takagi–Sugeno (T–S) fuzzy modeling, which cover all unstable modes, all stable modes, and partially unstable modes as special cases. Different from traditional multiple and multiple discontinuous Lyapunov function (MDLF) methods, the proposed novel switching-signal-based multiple discontinuous Lyapunov function (SMDLF) approach divides each mode-running interval dynamically according to the actual switching of the system. The developed approach can not only yield less conservative bounds on the MDADT but can also handle all unstable subsystems, which cannot be done by means of the traditional MDLF. A class of SMDLF is constructed for continuous-time switched T–S fuzzy systems, and relaxed stability conditions are presented for the system with both stable and unstable subsystems, using a larger switching signal space. Then, novel stability conditions are deduced for all stable and unstable subsystems in terms of slow and fast MDADT switching signals, respectively. Finally, comparative simulation examples are provided to verify the advantages and effectiveness of the proposed approach.
Bruceine D Identified as a Drug Candidate against Breast Cancer by a Novel Drug Selection Pipeline and Cell Viability Assay

The multi-target effects of natural products allow us to fight complex diseases like cancer on multiple fronts. Unlike docking techniques, network-based approaches such as genome-scale metabolic modelling can capture multi-target effects. However, the incompleteness of natural product target information reduces the prediction accuracy of in silico gene knockout strategies. Here, we present a drug selection workflow based on context-specific genome-scale metabolic models, built from the expression data of cancer cells treated with natural products, to predict cell viability. The workflow comprises four steps: first, in silico single-drug and drug combination predictions; second, the assessment of the effects of natural products on cancer metabolism via the computation of a dissimilarity score between the treated and control models; third, the identification of natural products with similar effects to the approved drugs; and fourth, the identification of drugs with the predicted effects in pathways of interest, such as the androgen and estrogen pathway. Out of the initial 101 natural products, nine candidates were tested in a 2D cell viability assay. Bruceine D, emodin, and scutellarein showed a dose-dependent inhibition of MCF-7 and Hs 578T cell proliferation with IC50 values between 0.7 to 65 μM, depending on the drug and cell line. Bruceine D, extracted from Brucea javanica seeds, showed the highest potency.
External Intimacy: Community-based Intervention Concerning Crime and the Integral State in Quebec

This article deals with the contradictory way in which community-based intervention concerning crime relates to political domination and to the institutions of the State in Quebec, Canada by exploring the pertinence of the Gramscian conception of civil society. Surprisingly, although Antonio Gramsci offers an interesting set of concepts for the study of the relationship between civil society and government institutions, his ideas have rarely been used to understand community intervention in general and that related to public security in particular. Gramscian concepts such as civil society, political society, hegemony, and the integral State strike us as particularly useful in this regard. In our opinion, they offer a much more comprehensive view of the current relationship between community action (civil society) and the criminal justice system (the government) than narratives that insist on the alleged autonomy of civil society and on the weakening of the State. To show the utility of these concepts, we used them to understand community tensions related to racially discriminatory practices by police officers in the multiethnic borough of Montréal-Nord in Montreal.
Perceptions of Macro- and Microlevel Factors Predict Covid-19 Health and Self-Reported Adherence to Safety Guidelines

Abstract. Introduction: Adherence to Covid-19 safety measures reduces the spread of the pathogen and lowers mortality rates. The present study examines microlevel (including sociodemographics, health risk factors, and mental health) and perceived macrolevel variables (including freedom, justice, and wealth) concerning self-perceived and self-reported Covid-19 safety measure adherence across eight countries. Methods: Self-reported adherence to Covid-19 safety measures and its potential predictors were assessed in representative samples from eight countries (total N = 7,437; Germany, France, Spain, Poland, Russia, Sweden, UK, US) by online surveys (end of May 2020 to the beginning of June 2020). Results: Self-reported adherence was positively predicted by female sex, higher age, higher social status, belonging to a Covid-19 risk group, being personally affected (physically, economically, and mentally), and positive mental health. Adherence was negatively predicted by depression and anxiety. Perceptions of governmental communication as credible and honest, government communication being guided by political interests, the feeling of being well informed, and perceived country-level freedom positively predicted self-reported adherence. Perceived country-level justice negatively predicted adherence, and perceived country-wide wealth was unrelated to it. Conclusions: Self-reported adherence to COVID-19 safety protocols is negatively predicted by depression, anxiety, and perceived justice, is positively predicted by perceived freedom, and not predicted by perceived country-level wealth.
Relationship between Neonatal MRI Findings and Emotional/Behavioral Evaluation in Early Childhood for Extremely Low-Birth-Weight Infants

The aim of this study is to investigate whether it is possible to detect future behavioral and emotional problems in extremely low-birth-weight infants by evaluating the neonatal head magnetic resonance imaging (MRI) using a scoring system. This study included 62 extremely low-birth-weight infants born between April 2015 and March 2017 and those who had undergone MRI at 36 to 42 weeks of gestation. These subjects were administered with the Strength and Difficulties Questionnaire (SDQ) at age 4–5, and the patients who responded to the questionnaire were included in the study. A positive correlation was observed between the Global Brain Abnormality Score and Total Difficulties Score of the SDQ (r = 0.26, p = 0.038). However, no significant difference was observed between the median Global Brain Abnormality Score of the normal and borderline-range group and the Total Difficulties Score of the clinical-range group (p = 0.51). This study demonstrated the relationship between the MRI findings in the newborn period and the emotional and behavioral problems in early childhood, but it is not clinically useful as a predictive marker.
Change Detection in Remote Sensing Image Data Comparing Algebraic and Machine Learning Methods

Remote sensing technology has penetrated all the natural resource segments as it provides precise information in an image mode. Remote sensing satellites are currently the fastest-growing source of geographic area information. With the continuous change in the earth’s surface and the wide application of remote sensing, change detection is very useful for monitoring environmental and human needs. So, it is necessary to develop automatic change detection techniques to improve the quality and reduce the time required by manual image analysis. This work focuses on the improvement of the classification accuracy of the machine learning techniques by reviewing the training samples and comparing the post-classification comparison with the image differencing in the algebraic technique. Landsat data are medium spatial resolution data; that is why pixel-wise computation has been applied. Two change detection techniques have been studied by applying a decision tree algorithm using a separability matrix and image differencing. The first change detection, e.g., the separability matrix, is a post-classification comparison in which individual images are classified by a decision tree algorithm. The second change detection is, e.g., the image differencing change detection technique in which changed and unchanged pixels are determined by applying the corner method to calculate the threshold on the changing image. The performance of the machine learning algorithm has been validated by 10-fold cross-validation. The experimental results show that the change detection using the post-classification method produced better results when compared to the image differencing of the algebraic change detection technique.
Interannual variation of gross primary production detected from optimal convolutional neural network at multi‐timescale water stress

Spatiotemporal patterns of water stress caused by global warming has significantly affected gross primary productivity (GPP). However, its impact is hard to capture as the water stress of different timescales simultaneously influence GPP through the effects of time lag and legacy. As a result, synthetically considering the roles of water stress in the current year and previous years in GPP is very important for accurate modeling of GPP. In this study, we introduced the water stress indicators of standardized precipitation evapotranspiration index (SPEI) at different timescales, GPP observations from global flux network and the remote sensing‐based indexes to models of convolutional neural network (CNN). We built the optimal CNN model which effectively simulated the spatial patterns of GPP and detected their trends in interannual variation. The findings were as follows: (1) The CNN model with introduction of multi‐timescale SPEIs that reflect both current and previous years' water conditions improved the accuracy of modeled GPP. This result indicated that although GPP is most sensitive to current water conditions, it is also clearly affected by previous years' water conditions. (2) The optimal CNN model that considered previous years' water conditions not only aptly simulated the spatial distribution of GPP, but also showed advantages in simulating the interannual fluctuations of GPP and its response to drought. Ignoring the previous water conditions will underestimate the interannual fluctuations of GPP and the impact of drought on GPP.
The Profile of Students’ Mathematical Communication Ability on Statistics Based on Adversity Quotient

Mathematical communication skills have an important role in human life because humans are social creatures who cannot live individually but always relate and need other people. This type of research is descriptive qualitative research. This study aims to determine the profile of students' mathematical communication skills on statistical material in terms of Adversity Quotient (AQ) which are climbers (high), campers (medium), and quitters (low). This research was conducted on students of class XII MIPA 1 SMA Laboratorium UPGRIS. The research subjects were 6 students consisting of 2 subjects with climbers type (high), 2 subjects with campers type (medium), and 2 subjects with quitters type (low). The instrument used in this research is the ARP Adversity Quotient questionnaire and uses a communication skill test. The data analysis technique was carried out in 3 stages, namely data reduction, data display, and conclusion drawing/verification. The validity of the data using source triangulation. The results of this study indicate that (1) Subjects with the quitters type have not met all indicators of communication skills including written text, drawing, and mathematical expression; (2) Subjects with the campers type meet the indicators of mathematical communication skills, namely written text and drawing, but do not meet the indicators of mathematical expression; (3) Subjects with the climbers type fulfills all indicators of mathematical communication skills including written text, drawing, and mathematical expression.
Parametric Investigation on the Essential Flow Factors Commanding Steady Operations of the Second Throat Exhaust Diffuser

The constant area exhaust diffuser (CAED) demands a higher starting pressure than the second throat exhaust diffuser (STED). In the design process, one dimensional (1D) normal shock theory was explored to obtain the optimum starting pressure of STED. Experiments were conducted with a small scale cold gas simulator using nitrogen gas as injectant. The application of 1D normal shock theory for STED design was examined with 20-25% difference in each accuracy in comparison to numerical and experimental evidences. The relation between evacuation quality and essential geometrical parameters such as diffuser– to–nozzle throat area ratio (Ad/At), diffuser–to–the second throat area ratio (Ad/As), and the nozzle expansion ratio (e) is presented for the optimization of STED. The optimum starting pressure increases in proportion to Ad/At and the optimized Ad/As was predicted in the range of 2.2-2.5. After STED starting, the evacuation quality is the same whether the expansion ratio of the nozzle is large or not. However, the range of the transition regime varied according to the nozzle expansion ratio. Nomenclature d A = area of diffuser inlet s A = area of the second throat t A = area of nozzle throat D = diameter e
A stochastic variance reduction algorithm with Bregman distances for structured composite problems

We develop a novel stochastic primal–dual splitting method with Bregman distances for solving structured composite problems involving infimal convolutions in non-Euclidean spaces. The sublinear convergence in expectation of the primal–dual gap is proved under mild conditions on stepsize for the general case. The linear convergence rate is obtained under additional condition like the strong convexity relative to Bregman functions.
Impact of Heatwaves on the Physiology and Retail Meat Quality of Lambs

The experiment investigated the impact of heatwaves (HWs) on the physiology, postmortem muscle metabolism and meat quality of lambs. Seventy-two second-cross lambs (Poll Dorset × (Border Leicester × Merino)) were selected and exposed to either 1, 3 or 5 days of HWs or thermoneutal (TN) (28–38 °C and 40–60% relative humidity, RH; 18–21 °C, 40–55% RH) conditions in climate-controlled chambers. Lambs exposed to 1–5 days of a HW exhibited higher respiration rates (RRs), rectal temperatures (RTs), skin temperatures (STs) and heart rates (HRs) compared to lambs exposed to an equal duration of TN conditions. However, HWs had no significant effects on muscle metabolism (rate and extent of pH decline, muscle glycogen and lactate content) and meat quality (cooking loss and shear force). Similarly, there were limited impacts of 1–5 days of HW on the colour (L*, a*, b* and R630/580) and drip loss of the longissimus thoracis et lumborum (LTL) and semimembranosus (SM) muscles for 4 days’ overwrap retail display. Results suggest that short-duration HWs (1–5 days) had significant negative effects on animal physiology but had no effect on the muscle metabolism and meat quality.
Influence of Online Learning Behavior and Video Playing Questions on Students' Learning Effect

The interactive mode in e-learning can effectively stimulate students' learning motivation, especially the way of video questioning as the mainstream interactive mode of e-learning video, which is conducive to improve students' enthusiasm to participate in learning and enhance learning thinking. This study constructs a theoretical model of the impact of E-learning behavior on Students' learning effect, and analyzes the impact of video bullet problem as an intermediary variable on E-learning behavior on students' learning effect. The results show that the overall Cronbach of the questionnaire α the coefficient is 0.883 and Kaiser – Meyer – Olkin value is 0.822, indicating that the questionnaire has good reliability and validity. The research results of this study provide reference suggestions for exploring the main influencing factors of learning behavior, improving the effect of students' online learning, revealing the optimization strategy of video bullet problem feedback under the network teaching environment, and the production of bullet problem interactive video.