BackgroundMany technological, biological, social, and information networks fall into the broad class of ‘small-world’ networks: they have tightly interconnected clusters of nodes, and a shortest mean path length that is similar to a matched random graph (same number of nodes and edges). This semi-quantitative definition leads to a categorical distinction (‘small/not-small’) rather than a quantitative, continuous grading of networks, and can lead to uncertainty about a network's small-world status. Moreover, systems described by small-world networks are often studied using an equivalent canonical network model – the Watts-Strogatz (WS) model. However, the process of establishing an equivalent WS model is imprecise and there is a pressing need to discover ways in which this equivalence may be quantified.Methodology/Principal FindingsWe defined a precise measure of ‘small-world-ness’ S based on the trade off between high local clustering and short path length. A network is now deemed a ‘small-world’ if S>1 - an assertion which may be tested statistically. We then examined the behavior of S on a large data-set of real-world systems. We found that all these systems were linked by a linear relationship between their S values and the network size n. Moreover, we show a method for assigning a unique Watts-Strogatz (WS) model to any real-world network, and show analytically that the WS models associated with our sample of networks also show linearity between S and n. Linearity between S and n is not, however, inevitable, and neither is S maximal for an arbitrary network of given size. Linearity may, however, be explained by a common limiting growth process.Conclusions/SignificanceWe have shown how the notion of a small-world network may be quantified. Several key properties of the metric are described and the use of WS canonical models is placed on a more secure footing.

Project Overview Adolescence is a critical period for political development. Different political attitudes, political behaviors, and political interests tend to develop during adolescence and persist into adulthood. Welfare participation is associated with lower political participation and pessimistic views of politics among adults, yet we have not uncovered the extent to which welfare participation in adolescence affects political outcomes in adulthood. This project aims to address the disconnect in the literature between what we know about the effects of welfare program experiences and what we know about individual political development. Data and Data Collection Overview The broader project relied on both qualitative and quantitative data, including secondary data from the American National Election Studies (ANES), the National Longitudinal Survey of Youth 1997 (NLSY97) cohort, and the National Longitudinal Study of Adolescent Health (Add Health), which are not included here. The original data collected by the depositing researcher are included, as described below. The qualitative data included a focus group with seven participants and individual interviews with 30 other individuals recruited by the researcher. Interviews were chosen so that participants could be more comfortable sharing personal experiences in a private setting. This data collection technique also allowed the researcher to keep conversations on topic and to ask probing and follow-up questions more easily. The focus group technique was chosen to provide for interactions among the participants involved, thus allowing participants to react to each other’s experiences and comments, and going beyond top-of-mind themes for any one participant. Participants in Round 1 (including those in the focus group and individual interviews) and Round 2 were recruited from the undergraduate student body at a large midwestern public university (N=7), as well as from a local community college (N=13). They were recruited through IRB-approved mass emails to the undergraduate student bodies. Participants in the Round 3 data collection (N=10) were recruited from the sample of Qualtrics panel respondents who completed the Adolescent Hardship and Politics Attitudes Survey (AHPAS; more detail below). Among the ten individuals interviewed in Round 3, five were on welfare during their adolescence, and the other five were not on welfare but grew up in poverty. The Round 1 and Round 2 questionnaire data include the pseudonyms that were selected by participants from a list. The participants in Round 3 chose any name they wanted as a pseudonym. A list of Round 3 names chosen is included as documentation, so that they can be paired with the unique ID code that was used as part of the AHPAS survey. There were two key original quantitative data sources. First, the quantitative data included national-level survey data called the Adolescent Hardship and Political Attitudes Survey (AHPAS), fielded by the researcher via Qualtrics Research Services ( https://www.qualtrics.com/support/survey-platform/distributions-module/online-panels/ ). The AHPAS sample consisted of 1,137 respondents recruited by Qualtrics, who were surveyed in January 2025. About half of the sample had experienced means-tested welfare programs during adolescence, while the other half had not been on welfare, but was in poverty during the period. Second, quantitative data were separately derived from a questionnaire about political attitudes and demographic factors that interview participants from the Round 1 and Round 2 qualitative data collection also completed. After receiving IRB approval, a recruitment email was distributed with a screener survey to identify individuals with adolescent welfare program experience. Participants were selected based on the extent of their program experience (indexed in terms of number of programs used), as well as their availability to participate in the focus group or an interview. Participants were offered a $25 gift card incentive for their participation. To protect confidentiality and privacy, participants selected a pseudonym to use in the subsequent focus group The focus group and interview transcripts were analyzed using Atlas.ti. The transcripts were coded by combining deductive and inductive coding approaches. Selection and Organization of Shared Data Data files shared in this deposit include: The de-identified transcripts from the focus group discussion and the three rounds of individual interviews, all labeled with participants’ chosen pseudonyms, along with the researcher-collected questionnaire data from the same participants. The original national-level quantitative data from the AHPAS used for analysis are also shared, in a raw and clean version, in .dta and .csv formats. The Original version has the uncoded variables in it, while in the Clean version, the variables are coded/labeled, although there is no separate codebook. Secondary users who want to...

The Teen & Dog Study is a longitudinal research project aimed at understanding the impact of youth-dog relationships on youth coping with social anxiety. The study will follow 514 United States adolescents (ages 13–17) with high social anxiety who live with dogs and their families, collecting longitudinal assessments of their physiological, emotional, and social well-being. With a focus on identifying the mechanisms by which youth-dog interactions may support adaptive coping, the study has three primary aims: (1) assess how the youth-dog relationship contributes to coping with social anxiety over time, factoring in individual, family, and peer influences; (2) investigate family-level processes that enhance youth-dog relationships and identify barriers to optimization; and (3) examine how dog interactions influence adolescents’ physiological responses, particularly in relation to anxiety. The study integrates quantitative and qualitative data, including surveys, interviews, ecological momentary activity, and continuous physiological monitoring, to assess strategies for optimizing youth-dog interactions in the context of social anxiety. This paper outlines the study protocol and presents characteristics of the study sample at baseline. Ultimately, the Teen & Dog Study seeks to inform interventions that harness the benefits of youth-dog relationships to improve mental health outcomes.

Despite strong interest in how noise affects marine mammals, little is known about the most abundant and commonly exposed taxa. Social delphinids occur in groups of hundreds of individuals that travel quickly, change behavior ephemerally, and are not amenable to conventional tagging methods, posing challenges in quantifying noise impacts. We integrated drone-based photogrammetry, strategically-placed acoustic recorders, and broad-scale visual observations to provide complimentary measurements of different aspects of behavior for short- and long-beaked common dolphins. We measured behavioral responses during controlled exposure experiments (CEEs) of military mid-frequency (3-4 kHz) active sonar (MFAS) using simulated and actual Navy sonar sources. We used latent-state Bayesian models to evaluate response probability and persistence in exposure and post-exposure phases. Changes in sub-group movement and aggregation parameters were commonly detected during different phases of MFAS CEEs but not control CEEs. Responses were more evident in short-beaked common dolphins (n=14 CEEs), and a direct relationship between response probability and received level was observed. Long-beaked common dolphins (n=20) showed less consistent responses, although contextual differences may have limited which movement responses could be detected. These are the first experimental behavioral response data for these abundant dolphins to directly inform impact assessments for military sonars. Methods We used complementary visual and acoustic sampling methods at variable spatial scales to measure different aspects of common dolphin behavior in known and controlled MFAS exposure and non-exposure contexts. Three fundamentally different data collection systems were used to sample group behavior. A broad-scale visual sampling of subgroup movement was conducted using theodolite tracking from shore-based stations. Assessments of whole-group and sub-group sizes, movement, and behavior were conducted at 2-minute intervals from shore-based and vessel platforms using high-powered binoculars and standardized sampling regimes. Aerial UAS-based photogrammetry quantified the movement of a single focal subgroup. The UAS consisted of a large (1.07 m diameter) custom-built octocopter drone launched and retrieved by hand from vessel platforms. The drone carried a vertically gimballed camera (at least 16MP) and sensors that allowed precise spatial positioning, allowing spatially explicit photogrammetry to infer movement speed and directionality. Remote-deployed (drifting) passive acoustic monitoring (PAM) sensors were strategically deployed around focal groups to examine both basic aspects of subspecies-specific common dolphin acoustic (whistling) behavior and potential group responses in whistling to MFAS on variable temporal scales (Casey et al., in press). This integration allowed us to evaluate potential changes in movement, social cohesion, and acoustic behavior and their covariance associated with the absence or occurrence of exposure to MFAS. The collective raw data set consists of several GB of continuous broadband acoustic data and hundreds of thousands of photogrammetry images. Three sets of quantitative response variables were analyzed from the different data streams: directional persistence and variation in speed of the focal subgroup from UAS photogrammetry; group vocal activity (whistle counts) from passive acoustic records; and number of sub-groups within a larger group being tracked by the shore station overlook. We fit separate Bayesian hidden Markov models (HMMs) to each set of response data, with the HMM assumed to have two states: a baseline state and an enhanced state that was estimated in sequential 5-s blocks throughout each CEE. The number of subgroups was recorded during periodic observations every 2 minutes and assumed constant across time blocks between observations. The number of subgroups was treated as missing data 30 seconds before each change was noted to introduce prior uncertainty about the precise timing of the change. For movement, two parameters relating to directional persistence and variation in speed were estimated by fitting a continuous time-correlated random walk model to spatially explicit photogrammetry data in the form of location tracks for focal individuals that were sequentially tracked throughout each CEE as a proxy for subgroup movement. Movement parameters were assumed to be normally distributed. Whistle counts were treated as normally distributed but truncated as positive because negative count data is not possible. Subgroup counts were assumed to be Poisson distributed as they were distinct, small values. In all cases, the response variable mean was modeled as a function of the HMM with a log link: log(Responset) = l0 + l1Z t where at each 5-s time block t, the hidden state took values of Zt = 0 to identify one state with a baseline response level l0, or Zt = 1 to identify an “enhanced” state, with l1 representing the enhancement of the quantitative value of the response variable. A flat uniform (-30,30) prior distribution was used for l0 in each response model, and a uniform (0,30) prior distribution was adopted for each l1 to constrain enhancements to be positive. For whistle and subgroup counts, the enhanced state indicated increased vocal activity and more subgroups. A common indicator variable was estimated for the latent state for both the movement parameters, such that switching to the enhanced state described less directional persistence and more variation in velocity. Speed was derived as a function of these two parameters and was used here as a proxy for their joint responses, representing directional displacement over time.
To assess differences in the behavior states between experimental phases, the block-specific latent states were modeled as a function of phase-specific probabilities, Z t ~ Bernoulli (pphaset), to learn about the probability pphase of being in an enhanced state during each phase. For each pre-exposure, exposure, and post-exposure phase, this probability was assigned a flat uniform (0,1) prior probability. The model was programmed in R (R version 3.6.1; The R Foundation for Statistical Computing) with the nimble package (de Valpine et al. 2020) to estimate posterior distributions of model parameters using Markov Chain Monte Carlo (MCMC) sampling. Inference was based on 100,000 MCMC samples following a burn-in of 100,000, with chain convergence determined by visual inspection of three MCMC chains and corroborated by convergence diagnostics (Brooks and Gelman, 1998). To compare behavior across phases, we compared the posterior distribution of the pphase parameters for each response variable, specifically by monitoring the MCMC output to assess the “probability of response” as the proportion of iterations for which pexposure was greater or less than ppre-exposure and the “probability of persistence” as the proportion of iterations for which ppost-exposre was greater or less than ppre-exposure. These probabilities of response and persistence thus estimated the extent of separation (non-overlap) between the distributions of pairs of pphase parameters: if the two distributions of interest were identical, then p=0.5, and if the two were non-overlapping, then p=1. Similarly, we estimated the average values of the response variables in each phase by predicting phase-specific functions of the parameters: Mean.responsephase = exp(l0 + l1pphase) and simply derived average speed as the mean of the speed estimates for 5-second blocks in each phase.

The Teen & Dog Study is a longitudinal research project aimed at understanding the impact of youth-dog relationships on youth coping with social anxiety. The study will follow 514 United States adolescents (ages 13–17) with high social anxiety who live with dogs and their families, collecting longitudinal assessments of their physiological, emotional, and social well-being. With a focus on identifying the mechanisms by which youth-dog interactions may support adaptive coping, the study has three primary aims: (1) assess how the youth-dog relationship contributes to coping with social anxiety over time, factoring in individual, family, and peer influences; (2) investigate family-level processes that enhance youth-dog relationships and identify barriers to optimization; and (3) examine how dog interactions influence adolescents’ physiological responses, particularly in relation to anxiety. The study integrates quantitative and qualitative data, including surveys, interviews, ecological momentary activity, and continuous physiological monitoring, to assess strategies for optimizing youth-dog interactions in the context of social anxiety. This paper outlines the study protocol and presents characteristics of the study sample at baseline. Ultimately, the Teen & Dog Study seeks to inform interventions that harness the benefits of youth-dog relationships to improve mental health outcomes.

Screening survey measures: Eligibility criteria and socio demographics.

An R script which provides a user made function to implement the Q-Gen methodology. The original QuSAGE package is all that is needed. Example code of how to implement the function is provided as well. (TXT 2.61 KB)

BackgroundAdvance care planning has been advocated as a way for people to have their wishes recorded and respected in relation to types of treatment and place of care. However, uptake in England remains low.AimsTo examine the views of older, well, adults towards Advance Care Plans (ACPs) and planning for end-of-life care, in order to inform national policy decisions.MethodsA mixed methods approach was adopted, involving individual and mini-group qualitative interviews (n = 76, ages 45–85), followed by a quantitative survey (n = 2294, age 55+). The quantitative sample was based on quotas in age, gender, region, socio-economic grade, and ethnicity, combined with light weighting to ensure the findings were representative of England.ResultsKnowledge and understanding of advance care planning was low, with only 1% of survey respondents reporting they had completed an ACP for themselves. Common reasons for not putting wishes into writing were not wanting/needing to think about it now, the unpredictability of the future, trusting family/friends to make decisions, and financial resources limiting real choice.ConclusionWhilst advance care planning is seen as a good idea in theory by older, well, adults living in the community, there is considerable reticence in practice. This raises questions over the current, national policy position in England, on the importance of written ACPs. We propose that policy should instead focus on encouraging ongoing conversations between individuals and all those (potentially) involved in their care, about what is important to them, and on ensuring there are adequate resources in community networks and health and social care systems, to be responsive to changing needs.

Molecular-stream separation (MSS), for example, by free flow electrophoresis or continuous annular chromatography, has great potential for applications that require continuous downstream separation such continuous flow synthesis. Despite its potential, MSS still needs to be greatly advanced, which requires currently lacking tools for quantitative characterization of streams in MSS. We developed and introduce here an analytical toolbox for this task. The first tool is a method to convolute 3D raw MSS data into a 2D “angulagram” via signal integration over the whole separation zone using a polar coordinate system. The second tool is three quantitative parameters characterizing stream width, linearity, and deflection, which are determined from an angulagram. The third tool is the analysis of the three parameters in relation to physicochemical characteristics of MSS which reveals deficiencies and guides improvements in MSS devices and methods. Examples of toolbox application to validation of previously published MSS data are provided.

Main characteristics of the studies included in meta-analysis.

Studies included in the systematic review, grouped according to the different leadership style investigated.

Blockchain technology is widely used in almost every domain of life nowadays including healthcare sector. Although there are existing frameworks to govern healthcare data but they have certain limitations in effectiveness of data governance to ensure security and privacy. This study aimed to evaluate effectiveness of health care data governance frameworks, examining security and privacy concerns and limitations within the existing frameworks of ISO Standards, GDPR, and HIPAA. In this study quantitative research approach was followed. A sample of 250 participants from Islamabad, Lahore and Karachi based healthcare experts, IT specialist, blockchain research and developer, administrator was selected. The collected data was analyzed though frequencies and descriptive statistical tests with the help of SPSS. The results revealed un-satisfaction for data governance frameworks, i.e., ISO standards, GDPR, and HIPAA in terms of security concerns, i.e., data encryption, access controls, audit trails, interoperability and standards, smart contracts for compliance, data integrity, regulatory compliance monitoring and privacy concerns, i.e., consent management, anonymization and pseudonymization, data minimization. The participants agreed that there is a need of integration of reliable data governance framework in health care data management. Various personalized governance techniques, targeted security upgrades, and continuous improvement in the specific customized data governance framework has been presented based on the findings of the study. An implementation of blockchain-based systems is recommended in order to ensure and expand the security and privacy of healthcare data management.

Test of normality for the five categories of continuous scale variables.

A method for quantitative characterization of grain size in thin sections has been established for mantle spinel peridotite xenoliths, using optical scanning of large areas of thin sections, skeletonization of grain-section outlines and computerized measurement of individual grain-section areas. Measurements range from 218 for the coarsest example to more than 3000 in the finest grained. Variability of the samples has been examined in relation to size and number of grain-section areas measured by using multiple and orthogonal sections from several xenoliths. The results show a linear relationship of arithmetic mean against additive standard deviation, including data from coarse-grained protogranular, through porphyroclastic to the finer-grained equigranular examples. This suggests that peridotite textures form a continuous series rather than discrete groups, as suggested by qualitative (subjective) assessment. The observed distributions of grain-section areas have been explored in relation to their description and possible mechanistic origin. By direct measurement and comparison of cumulative number and area distribution curves, we show that qualitatively assessed ‘typical grain sizes’ are influenced by a small number of larger grain sections. Although the arithmetic mean and standard deviation provide a convenient method for comparison, in practice grain-section area distributions show marked positive skewness more consistent with log-normal or power-law functions. Linear log-probability curves also support the existence of a continuous series of peridotite textures, suggesting that the shallow lithospheric mantle has been subject to processes of comminution and/or grain growth dependent on the Law of Proportionate Effect.

Pearson correlations for quantitative sample (N = 151).

Herbaceous plants: Marta ChilinaAbundance Native Plants:Abundance of native plants, the number of native plants within a 1m2 quadrat, was collected through a biological survey method. Native species comprised of flowering plants (like goldenrod), shamrocks/clovers, and several other unidentifiable species. Species were counted over the entire 1m2 quadrat, rather than a quarter. This variable is quantitative and discrete. Data was collected near a pond and a nearby impermeable area (baseball diamond). Sampling at pond area began at 3:00 pm and sampling at impermeable area began at 4:00 pm.Abundance Exotic Plants: Abundance of exotic plants, the number of exotic plants within a 1m2 quadrat, was collected through a biological survey method. Grass was categorized as an exotic species, and surveyed over one quarter of the 1m2 quadrat, which was then multiplied by four to get a total estimate of colonies in the entire quadrat. This variable is quantitative and discrete. Data was collected near a pond and a nearby impermeable area (baseball diamond). Sampling at pond area began at 3:00 pm and sampling at impermeable area began at 4:00 pm.Total Number of Flowers:

Total number of flowers, the number of flowers on plants within a 1m2 quadrat, was collected through a biological survey method. Flowers counts were obtained from one plant, and then multiplied by the abundance of the same plant in the 1m2 quadrat to record the total number of flowers present on that plant species in that quadrat. When counting goldenrod flowers, only stems were counted and multiplied by the total number of goldenrods in the quadrat, not individual flower buds. Only flower blossoms were considered in this experiment. This variable is quantitative and discrete. Data was collected near a pond and a nearby impermeable area (baseball diamond). Sampling at pond area began at 3:00 pm and sampling at impermeable area began at 4:00 pm.Woody plants: Noah MalaquiA 50 metre transect was placed near the shore of a pond and in an impermeable area. Data for the abundance of woody plants, canopy coverage, vegetation/ground coverage, and abundance of flowers was measured every 2 metres--starting at one end of the transect--in each location through visual inspection. For the abundance of woody plants, the number of trees in a 0.5m radius from a spot, only trees that had a height of at least 1.5 metres or more side were considered woody plants. This variable is a quantitative discrete variable. Canopy coverage, the percent of the sky covered by canopy from trees above a point of observation, was measured by remaining in place and looking up at the sky. This variable is a quantitative, continuous variable. Similarly, vegetation/ground coverage was measured by standing still and looking down at the ground; data was measured as a percentage of how much bare ground was not visible in that distinct spot. This variable is a quantitative continuous variable. For the abundance of flowers, the number of flowers within 1m on each side of the transect, only plants that had visible flowers sprouting were considered and recorded. This variable is a quantitative discrete variable. A 50 metre transect yielded a total of 25 replicates since data was measured and recorded every 2 metres.Vertebrates & Invertebrates: Mihails DitmansVertebrate Abundance:The vertebrate abundance is the number of vertebrates seen within a 50m radius of the observation point over a course of 15 minutes. Vertebrates were counted on trees, on the ground, in the air, and in the pond. This is a quantitative discrete variable. Data collection began in the pond area at 3:00 for the vertebrate observation and 3:15 for the invertebrate observations and in the impermeable area began at 3:45 for vertebrate and 4:00 for invertebrate.Vertebrate Diversity:The vertebrate diversity is the number of different types of vertebrates found within a 50m radius of the observation point over a course of 15 minutes. Birds of observably different morphology were counted separate in terms of diversity, for example seagulls, small black birds and large black birds. Vertebrates were counted on trees, on the ground, and in the air. This is a quantitative discrete variable. Data collection began in the pond area at 3:00 for the vertebrate observation and 3:15 for the invertebrate observations and in the impermeable area began at 3:45 for vertebrate and 4:00 for invertebrate.Human Abundance:The human abundance is the number of humans seen within a 50m radius of the observation point over a course of 15 minutes. Cars were counted as 1 person unless more could be seen through the windows. One person passing through multiple times was only counted once. This is a quantitative discrete variable. Data collection began in the pond area at 3:00 for the vertebrate observation and 3:15 for the invertebrate observations and in the impermeable area began at 3:45 for vertebrate and 4:00 for invertebrate.Observed Invertebrate Abundance:The observed abundance of invertebrates is the number of invertebrates seen in a 5m radius of the observation point over a course of 15 minutes. Both land invertebrates and flying invertebrates were included. If a fast moving invertebrate was seen multiple times it was counted as multiple individuals. This is a quantitative discrete variable. Data collection began in the pond area at 3:00 for the vertebrate observation and 3:15 for the invertebrate observations and in the impermeable area began at 3:45 for vertebrate and 4:00 for invertebrate.Invertebrates: Miriam BastawrousA total of 6 pan traps were distributed 3 metres apart in alternating colours of yellow, white, and blue. They were filled with soapy water from 3:00pm and left out until 3:30pm near the shore of a pond (total duration of 30 minutes) and from 3:40pm to 4:00pm in the impermeable area (total duration of 20 minutes). At the end of each period the abundance of invertebrates, which is the number of invertebrates seen inside the liquid of the bowl, were recorded. This variable is a quantitative discrete variable. Next, a sweep net was used along the 50 metre length of the transect 10 separate times. Each time the sweep net reached the end of the transect, the abundance of invertebrates, which is the number of invertebrates within any part of the net, found in the net was recorded. This variable is a quantitative discrete variable. This was done in the pond area at 3:10 and in the impermeable area at 3:45.Methods:A 50 metre transect was used to measure several variables throughout the data collecting process. Quadrats were placed every 2 metres alternating left and right starting at one end of the transect for a total of 25 replicates. Total abundance of native and exotic plants were recorded as well as total number of flowers within each quadrat. Additionally, the transect was used to measure the abundance of woody plants, canopy coverage, vegetation/ground coverage, and abundance of flowers. Here, observations were made after stopping every 2 metres and observing the immediate surroundings for all variables. Canopy coverage was estimated by looking straight up while vegetation/ground coverage and abundance of flowers was estimated by looking down. This process was also repeated for a total of 25 replicates. The abundance of vertebrates and number of vertebrate species within a 50 metre radius at one end of the transect was measured and recorded through observation for at least 15 minutes. In another 15 minute interval, the abundance of invertebrates was recorded using the same methods. Finally, sweep nets were used to collect the abundance of invertebrates by walking along the transect for a total of 10 replicates. Subsequently, 6 pan traps placed 3 metres apart were used to also collect the abundance of invertebrates at one end of the transect. The traps alternated in colours of yellow, white, and blue. Each pan trap was left to collect invertebrates for at least 30 minutes. All of the methods listed above were conducted in both a pond area and impermeable area at the Keele Campus of York University on a foggy and humid afternoon from 3pm-5pm.Hypothesis:Plant and animal species would be more abundant in pond areas compared to impermeable areas. This would be due to plants having soft soil for nutrients to grow and consequently the animals having plants to eat.Predictions:1) There will be a greater abundance of native, flowering, and exotic plants in the pond area compared to the impermeable area.2) Canopy coverage and vegetation coverage will be greater in the pond area than in the impermeable area.3) Abundance of invertebrates, abundance of vertebrates, and diversity of vertebrates will be greater in the pond area than in the impermeable area.

This study protocol aims to explore the application of mechanical quantitative techniques in the postoperative rehabilitation assessment following anterior cruciate ligament reconstruction (ACLR). Anterior cruciate ligament injuries are prevalent among athletes and pose a considerable threat to their careers. While ACLR remains the primary therapeutic intervention for such injuries, the assessment of postoperative rehabilitation still encounters significant challenges. Traditional assessment methods are limited by subjectivity and the absence of quantitative data. Mechanical quantitative techniques present a novel approach, offering objective and precise quantitative metrics for knee functional rehabilitation through the analysis of soft tissue mechanical properties. Methods: An observational methodology will be employed to gather clinical data from patients with ACL injuries. A soft tissue mechanical quantitative measurement device will be utilized for knee functional assessment. The anticipated sample size is 66 cases, with inclusion criteria encompassing specific age ranges, injury durations, and other pertinent factors. Exclusion criteria will exclude participants with other injuries or diseases that may impact rehabilitation. The assessment protocol will consist of traditional knee functional assessments, including knee muscle strength, range of motion, and Lysholm scores, as well as mechanical quantitative assessments of the quadriceps and hamstrings. Blinding: Due to the inherent challenges in clinical controlled trials, a strict double-blind design is infeasible. Therefore, participants will be aware of their group assignments, whereas assessors will remain blinded to the specific research objectives and group allocations. Data entry and statistical grouping will be managed by independent personnel. Statistical Analysis: Data analysis is conducted using SPSS 20.0 software. Categorical data are represented as frequencies (percentages), with intergroup comparisons made using chi-square tests or exact probability methods; continuous data are represented as means ± standard deviations, with intergroup comparisons made using t-tests, analysis of variance, or rank sum tests, and correlation analysis is performed using Spearman’s correlation coefficient. A P-value

File List FDind.R -- R source code for indices computation Description The FDind.R R program computes the 3 functional diversity indices presented in Villéger et al. 2008 (Ecology 89:2290–2301), and the functional specialization index proposed by Bellwood et al. 2006 (Proc. R. Soc. B. 273:101–107). Computation requires R libraries « ape » and « geometry » and two matrices as inputs:

 « traits »
  (S*T): T traits values for the S species (at least 2 quantitative
  continuous traits, i.e., numeric values)


 « abundances »
  (C*S) : abundances for the S species in C communities (NA allowed)

The function returns a dataframe (C*5) with Species richness (Nbsp), functional richness (FRic), functional evenness (FEve), functional divergence (FDiv) and functional specialization (FSpe) values for each of the C communities. A basic example is provided at the end of the script. Details on indices computation are presented in Appendix B.

Descriptive Statistics of Respondents (Sample 1 and Sample 2).

Themes are presented with example quotations from each round.