Valuable Metrics Research Articles

EVMTracer: Dynamic Analysis of the Parallelization and Redundancy Potential in the Ethereum Virtual Machine

Ethereum is one of the first blockchains executing smart contracts (financial applications directly executed on the ledger using a virtual machine). The performance of the Ethereum virtual machine has become a bottleneck caused by high volumes of transactions due to blockchain applications, including decentralized finance and non-fungible tokens. Hence, there is a need to detail the execution characteristics of the Ethereum virtual machine before its performance can be improved. This work introduces an off-line Ethereum virtual machine tracer called EVMTracer that produces runtime data dependence graphs from executed transactions as an alternative program representation. From the runtime dependence graphs, we can deduce valuable metrics about program execution characteristics, including the degree of parallelization and computational redundancies in smart contracts. Our experiments encompass all blocks up to 12M on the Ethereum mainnet. We found a geometric mean of 1.90× theoretical maximum speedup when executing the smart contracts in parallel and identified 34.97% of SLOAD instructions as redundant.

Assessment of Core Surgical Skills Using a Mixed Reality Headset - The MoTOR Study.

Surgical skill assessment utilises direct observation and feedback by an expert which is potentially subjective, therefore obtaining objective data for hand and eye tracking is essential. Our aim was to evaluate a wearable mixed reality (MR) headset in these domains. Participants with differing levels of surgical expertise [novice (N), intermediate (I) & expert (E)] performed 4 simulated surgical tasks; 2 general dexterity (tasks 1&2) and 2 surgical skills (tasks 3&4) wearing the MR headset capturing their hand and eye movements (median & range). Metrics included hand path length and the speed of each index or thumb tip. Gaze data were also captured. Participant demographics, prior expertise and current experience were captured with an electronic survey. Data were analysed with a Shapiro-Wilk test or ANOVA as appropriate. A p-value of < 0.05 was significant. Thirty-six participants were analysed (N = 18, I = 8, E = 8). Tasks 1&2 revealed 2 speed outcomes (left index and left-hand speed) which were significant. For tasks 3&4, various outcomes were significant: path length for left hand (N:45cm vs. I:31cm vs. E:27cm, p = 0.03) and right hand (N:48cm vs. I:29cm vs. E:28cm, p = 0.01) and total time (N:456s vs. I:292 vs. E: 245, p = 0.0002). With left-hand-tying, average path length (N:61cm vs. I:39 vs. E:36, p = 0.04), average speed (N:11cm/s vs. I:23 vs. E:24, p = 0.03), and total time (N:156s vs. I:43 vs. E:37, p = 0.003) were significant. The gaze-tracking was not statistically significant. The MR headset can be utilised as a valid tool for surgical performance assessment. Outcomes including path length and speed can be valuable metrics captured by the MR Headset during the task completion for detecting surgical proficiency.

Utilizing machine learning algorithms to predict subject genetic mutation class from in silico models of neuronal networks

BackgroundEpilepsy is the fourth-most common neurological disorder, affecting an estimated 50 million patients globally. Nearly 40% of patients have uncontrolled seizures yet incur 80% of the cost. Anti-epileptic drugs commonly result in resistance and reversion to uncontrolled drug-resistant epilepsy and are often associated with significant adverse effects. This has led to a trial-and-error system in which physicians spend months to years attempting to identify the optimal therapeutic approach.ObjectiveTo investigate the potential clinical utility from the context of optimal therapeutic prediction of characterizing cellular electrophysiology. It is well-established that genomic data alone can sometimes be predictive of effective therapeutic approach. Thus, to assess the predictive power of electrophysiological data, machine learning strategies are implemented to predict a subject’s genetically defined class in an in silico model using brief electrophysiological recordings obtained from simulated neuronal networks.MethodsA dynamic network of isogenic neurons is modeled in silico for 1-s for 228 dynamically modeled patients falling into one of three categories: healthy, general sodium channel gain of function, or inhibitory sodium channel loss of function. Data from previous studies investigating the electrophysiological and cellular properties of neurons in vitro are used to define the parameters governing said models. Ninety-two electrophysiological features defining the nature and consistency of network connectivity, activity, waveform shape, and complexity are extracted for each patient network and t-tests are used for feature selection for the following machine learning algorithms: Neural Network, Support Vector Machine, Gaussian Naïve Bayes Classifier, Decision Tree, and Gradient Boosting Decision Tree. Finally, their performance in accurately predicting which genetic category the subjects fall under is assessed.ResultsSeveral machine learning algorithms excel in using electrophysiological data from isogenic neurons to accurately predict genetic class with a Gaussian Naïve Bayes Classifier predicting healthy, gain of function, and overall, with the best accuracy, area under the curve, and F1. The Gradient Boosting Decision Tree performs the best for loss of function models indicated by the same metrics.ConclusionsIt is possible for machine learning algorithms to use electrophysiological data to predict clinically valuable metrics such as optimal therapeutic approach, especially when combining several models.

Pain-autonomic measures reveal nociceptive sensitization in complex regional pain syndrome.

Allodynia and hyperalgesia are common signs in individuals with complex regional pain syndrome (CRPS), mainly attributed to sensitization of the nociceptive system. Appropriate diagnostic tools for the objective assessment of such hypersensitivities are still lacking, which are essential for the development of mechanism-based treatment strategies. This study investigated the use of pain-autonomic readouts to objectively detect sensitization processes in CRPS. Twenty individuals with chronic CRPS were recruited for the study alongside 16 age- and sex-matched healthy controls (HC). All individuals underwent quantitative sensory testing and neurophysiological assessments. Sympathetic skin responses (SSRs) were recorded in response to 15 pinprick and 15 noxious heat stimuli of the affected (CRPS hand/foot) and a control area (contralateral shoulder/hand). Individuals with CRPS showed increased mechanical pain sensitivity and increased SSR amplitudes compared with HC in response to pinprick and heat stimulation of the affected (p< 0.001), but not in the control area (p> 0.05). Habituation of pinprick-induced SSRs was reduced in CRPS compared to HC in both the affected (p= 0.018) and slightly in the control area (p= 0.048). Habituation of heat-induced SSR was reduced in CRPS in the affected (p= 0.008), but not the control area (p= 0.053). This is the first study demonstrating clinical evidence that pain-related autonomic responses may represent objective tools to quantify sensitization processes along the nociceptive neuraxis in CRPS (e.g. widespread hyperexcitability). Pain-autonomic readouts could help scrutinize mechanisms underlying the development and maintenance of chronic pain in CRPS and provide valuable metrics to detect mechanism-based treatment responses in clinical trials. This study provides clinical evidence that autonomic measures to noxious stimuli can objectively detect sensitization processes along the nociceptive neuraxis in complex regional pain syndrome (CRPS) (e.g. widespread hyperexcitability). Pain-autonomic readouts may represent valuable tools to explore pathophysiological mechanisms in a variety of pain patients and offer novel avenues to help guide mechanism-based therapeutic strategies.

S496 A Comparison of Manometric and Functional Lumen Impedance Planimetry (FLIP) Diagnoses of Esophageal Dysmotility

Introduction: Functional lumen impedance planimetry (FLIP) has been utilized extensively as an adjunct to high resolution impedance manometry (HRIM) for evaluating disorders of esophageal dysfunction. HRIM and FLIP provide valuable metrics that provide similar but distinct information about esophageal function, resulting in similar but distinct diagnoses. For both tools to be used in parallel to diagnose esophageal disorders, it is imperative to understand concordance rate of diagnoses between the two studies.The aim of this study is to evaluate the extent of agreement between FLIP and HRIM diagnoses by constructing parallels between diagnoses made by FLIP and HRIM on patients who underwent both studies and evaluating whether the modalities aligned by more than random chance. Methods: A retrospective review of 227 patient charts was performed. Pairs of similar HRIM and FLIP diagnoses pertaining to peristaltic and aperistaltic conditions were formed by comparing and correlating the criteria for each diagnosis by HRIM to criteria by FLIP. Diagnoses without a correlation were designated “other.” The number of patients whose FLIP and HRIM diagnoses were and were not in agreement was recorded for each diagnosis pair. Results: Table shows the specific HRIM and FLIP diagnoses that were correlated, the number of the patients in which their diagnoses by HRIM and FLIP represent the same condition, and the percentage of the patients with each HRIM diagnosis that had the predicted correlating FLIP diagnosis.The overall percentage in agreement among all diagnoses was 26%. Cohen’s Kappa of agreement for all studies was statistically significant at 0.121 (p < 0.0001). When “other” diagnoses without matching diagnoses between HRIM and FLIP were omitted from analysis, agreement increased to 33.5%. Cohen’s Kappa of agreement for paired diagnoses was statistically significant at 0.157 (p < 0.0001). Conclusion: Our study found that FLIP testing agreed with manometric diagnosis more often than random chance would predict, but only 26% of the time. The studies are therefore slightly predictive of one another, though imperfectly. The tools analyze different aspects of esophageal physiology to arrive at their diagnoses. This both complicates direct pairing of diagnoses and also provides each modality a unique role in assessing esophageal dysmotility. These findings support that HRIM and FLIP testing may be considered complementary tools to utilize in parallel for patients with suspected esophageal dysmotility. Table 1. - Correlated HRIM (High Resolution Impedance Manometry) and FLIP (Functional Lumen Impedance Planimetry) Diagnoses and Their Agreement Rates HRIM Diagnosis Correlating FLIP Diagnosis Number in Agreement Percent of Total in Agreement Normal Normal 7 41.2% Diffuse Esophageal Spasm Normal Distensibility with Repetitive Retrograde Contractile Response 0 Undefined Type 1 Achalasia EGJOO with Absent Contractile Response 14 77.8% Type 3 Achalasia EGJOO with Repetitive Retrograde Contractile Response 5 33.3% EGJOO EGJOO with Normal Contractile Response 29 25.7% Aperistalsis Normal Distensibility with Absent Contractile Response 2 33.3% Other (Jack Hammer, Ineffective Esophageal Motility, Type 2 Achalasia, Fragmented Peristalsis) Other (Normal Contractile Response with Increased EGJ Distensibility, Absent Contractile Response with Increased EGJ Distensibility, Diminished or Disordered Contractile Response) 1 1.9% EGJ - Esophagogastrojejunal, EGJOO - Esophagogastrojejunal Outflow Obstruction)

The Intelligent Process Lifecycle of Active Cyber Defenders

“There are a thousand hacking at the branches of evil to one who is striking at the root.” Henry David Thoreau Successful information security requires careful application of tools to goals. The tools must be well used and well configured and require skillful interpretation of results. If problems occur in daily digital forensic workflows, then they must be documented in a systematic manner that aids process improvement. This article is the third in a series of three articles, where the connection between all the articles is displayed in the “Intelligent Process Lifecycle of Active Cyber Defenders” poster [ 1 ]. The poster showcases the main cyber defence disciplines and how, if false-positive events are classified in a systematic manner for each service, this information can afterwards be used to identify improvement areas. It focuses on false positives, error states, and common operation problems that occur when identifying and assessing vulnerabilities, as well as problems that happen when the security operations centre (SOC) tries to add a log source for continuous monitoring. For all these problems it also includes suggestions on how to solve or document them if they cannot be solved. We also demonstrate how to map these practical SOC outcome failures into clear risk management categories. That mapping can be used to optimize workflows, improve strategic cyber risk management, as well as deliver valuable metrics.

Generalization of bi-canonical degrees

We discuss invariants of Cohen-Macaulay local rings that admit a canonical module $$\omega$$ . Attached to each such ring R, when $$\omega$$ is an ideal, there are integers–the type of R, the reduction number of $$\omega$$ –that provide valuable metrics to express the deviation of R from being a Gorenstein ring. In (Ghezzi et al. in JMS 589:506–528, 2017) and (Ghezzi et al. in JMS 571:55–74, 2021) we enlarged this list with the canonical degree and the bi-canonical degree. In this work we extend the bi-canonical degree to rings where $$\omega$$ is not necessarily an ideal. We also discuss generalizations to rings without canonical modules but admitting modules sharing some of their properties.

Technical note: a nose ring sensor system to monitor dairy cow cardiovascular and respiratory metrics.

Monitoring cardiovascular and respiratory measurements corresponds to the precision livestock farming (PLF) objective to continuously monitor and assess dairy cows' welfare and health. Changes in heart rate, breathing rate, and oxygen saturation (SpO2) are valuable metrics in human and veterinary medicine to assess stress, pain, illness, and detect critical conditions. The common way to measure heart rate is either manually or with a stethoscope. Under research conditions, heart rate is usually measured with a sports watch chest belt. Breathing rate is obtained by counting the cow's flank movements which is a time-consuming and labor-intensive method that requires training and is prone to human error. No devices are available on the market that enable practical and easy pulse oximetry in farm animals. This study presents a wireless nose ring sensor system (NoRS) composed of thermal and photoplothysmography sensors that attach to the nostrils of four Holstein dairy cows. The NoRS's thermocouple measured the cow's nasal cavity air temperature; an optic sensor measured the IR (660 nm) and RED (660 nm) signals reflected from the cow's nasal septum. Breathing was calculated from the thermocouple signal's center frequency with a fast Fourier transformation or the signal peak count (i.e., oscillations). The breathing rate was compared to breathing observed by concurrently counting the flank movements. Heart rate and SpO2 were measured by integrated pulse oximetry and heart rate monitor module (MAX30101 TinyCircuit) assembled on the NoRS circuit. Heart rate was also measured with FFT and by counting the number of peaks from the optic sensor's raw IR and RED signals. These measures were compared to an off-the-shelf hand-held pulse oximeter's heart rate and SpO2 readings during the same time. The comparisons revealed highly significant correlations for the heart rate readings where the strength of the correlation was sensitive to the method. The correlation between breathing rate and the veterinarian's visual observations was low, albeit significant. Thus, inhale-exhale cycle counting constitutes a more precise approach than flank movement counts. The hand-held device's 96% SpO2 is compatible with near-saturation values expected in healthy cows. The mean NoRS SpO2 reading was 3% less. After further piloting under field conditions, the NoRS will require no animal restraining to automatically and continuously record cows' breathing rate, heart rate, and SpO2.

ENNGene: an Easy Neural Network model building tool for Genomics

BackgroundThe recent big data revolution in Genomics, coupled with the emergence of Deep Learning as a set of powerful machine learning methods, has shifted the standard practices of machine learning for Genomics. Even though Deep Learning methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are becoming widespread in Genomics, developing and training such models is outside the ability of most researchers in the field.ResultsHere we present ENNGene—Easy Neural Network model building tool for Genomics. This tool simplifies training of custom CNN or hybrid CNN-RNN models on genomic data via an easy-to-use Graphical User Interface. ENNGene allows multiple input branches, including sequence, evolutionary conservation, and secondary structure, and performs all the necessary preprocessing steps, allowing simple input such as genomic coordinates. The network architecture is selected and fully customized by the user, from the number and types of the layers to each layer's precise set-up. ENNGene then deals with all steps of training and evaluation of the model, exporting valuable metrics such as multi-class ROC and precision-recall curve plots or TensorBoard log files. To facilitate interpretation of the predicted results, we deploy Integrated Gradients, providing the user with a graphical representation of an attribution level of each input position. To showcase the usage of ENNGene, we train multiple models on the RBP24 dataset, quickly reaching the state of the art while improving the performance on more than half of the proteins by including the evolutionary conservation score and tuning the network per protein.ConclusionsAs the role of DL in big data analysis in the near future is indisputable, it is important to make it available for a broader range of researchers. We believe that an easy-to-use tool such as ENNGene can allow Genomics researchers without a background in Computational Sciences to harness the power of DL to gain better insights into and extract important information from the large amounts of data available in the field.

Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers

In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed using the Spinal Cord Toolbox to produce normative values as well as inter/intra-site and inter/intra-manufacturer statistics. Reproducibility for the spine generic protocol was high across sites and manufacturers, with an average inter-site coefficient of variation of less than 5% for all the metrics. Full documentation and results can be found at https://spine-generic.rtfd.io/. The datasets and analysis pipeline will help pave the way towards accessible and reproducible quantitative MRI in the spinal cord.

Relationship between pre-existing allergies and anaphylactic reactions post mRNA COVID-19 vaccine administration

Two mRNA vaccines for COVID-19, Pfizer-BioNTech and Moderna, are approved for emergency use in the United States. After their approval and dosing in millions of recipients, reports of anaphylaxis began to appear in the Vaccine Adverse Reporting System (VAERS). Here we provide an analysis of the relationship between prior history of allergy and/or anaphylaxis and anaphylaxis rates following the administration of mRNA COVID-19 vaccines. Overall reported incidence of anaphylaxis was estimated to be rare at 4.2 cases per million doses. It appeared that the relative incidence of anaphylaxis following administration of these COVID-19 vaccines was two and seven times higher for recipients with a prior history of allergies and/or anaphylaxis, respectively. This report provides valuable metrics to make evidence-based decisions for subjects with pre-existing allergic conditions receiving a COVID-19 mRNA vaccine.

Call Admission Control Algorithm with Efficient Handoff for Both 4G and 5G Networks

Recently, many generations of mobile networks have changed from one transition to another transition. The mode of transition from the first generation (1G) to the fifth generation (5G) is characterized by a lot of performance challenges such as delay, speed of the users, mobility, and variety of services. Currently, different from prior generations, 5G is not only concerned with the mobile networks but also, with various applications and different services such as health sector, transportation, energy consumption, safety well as Smart City. All these services are incorporated by 5G. In this paper, we proposed a call admission control (CAC) algorithm with an efficient handoff for both 4G and 5G networks. Simulation is used to evaluate the effectiveness of the proposed algorithm, and the obtained results indicate it considerably performs better than do other algorithms based on valuable metrics such as data throughput, call blocking probability (CBP), and call dropping probability (CDP).

Development of empirical models for the prediction of surface functional parameters in ultrasonic elliptical vibration cutting

Over the past decades, significant improvement has been witnessed in using 3D surface parameter characterization to study machined surfaces’ functional properties. However, the prediction and optimization of these valuable metrics under the influence of various cutting conditions have remained a significant concern. This paper focuses on modeling the effects of cutting speed, feed rate, depth of cut, and vibration amplitude on machining output responses in terms of 3D surface functional parameters, including surface bearing index (Sbi) and core fluid retention index (Sci). Ultrasonic elliptical vibration cutting (UEVC) experiments are performed to evaluate the variation trend of surface functional parameters while adopting the Taguchi analysis and response surface methodology (RSM) approach. The analysis results revealed that the cutting speed has the most significant impact on both (Sbi) and (Sci), followed by feed rate and vibration amplitude in varying rankings between responses; and lastly, the depth of cut, which exhibited the least effect. The validation test result indicated that the developed RSM predictive model was adequate in construction and verification. Finally, the output variables are optimized through the desirability function (CDF) approach. A comparative experimental analysis between conventional cutting (CC) and UEVC was performed under the optimized parameter setting. The comparison result revealed that the functional indices were substantially enhanced under the UEVC method.

Quest for Ranking Excellence Impact Study of Research Metrics

It is a general trend that higher education institutions must undergo one or more ranking agencies evaluation to get reputed in the field of education. Institutions must have a place in the ranking to attract students, faculty and even for successful foreign student exchange programs. Prominence in research is an important parameter to scale the eminence of an institution. A quantum of quality publications is one of the factors judging the prevailing research environment of an institution. While doing an institutional level evaluation on research output, we always go for quantitative measures like total publication, citation and h-index. In the present scenario, the institution must set to have a strategy in achieving ambitious scientific goals. This paper presents some innovative analysis of research data by choosing valuable metrics. The samples selected for the study are the top-ranked institutions in India. The same approach can be applied to any institutions in the world for evaluation and excellence. We have done qualitative-quantitative analysis using different parameters of research output to explain the importance of various metrics. The present study has identified metrics which can be considered seriously to achieve better performance in research output. It is a fused index metrics which explains along with Scholarly Output, the importance of Cited and Uncited Publications and different types of Collaborations and its impact on the overall performance of an institution.

Measuring What Matters: How the Laboratory Contributes Value in the Opioid Crisis.

With over 20 years of the opioid crisis, our collective response has evolved to address the ongoing needs related to the management of opioid use and opioid use disorder. There has been an increasing recognition of the need for standardized metrics to evaluate organizational management and stewardship. The clinical laboratory, with a wealth of objective and quantitative health information, is uniquely poised to support opioid stewardship and drive valuable metrics for opioid prescribing practices and opioid use disorder (OUD) management. To identify laboratory-related insights that support these patient populations, a collection of 5 independent institutions, under the umbrella of the Clinical Laboratory 2.0 movement, developed and prioritized metrics. Using a structured expert panel review, laboratory experts from 5 institutions assessed possible metrics as to their relative importance, usability, feasibility, and scientific acceptability based on the National Quality Forum criteria. A total of 37 metrics spanning the topics of pain and substance use disorder (SUD) management were developed with consideration of how laboratory insights can impact clinical care. Monitoring these metrics, in the form of summative reports, dashboards, or embedded in laboratory reports themselves may support the clinical care teams and health systems in addressing the opioid crisis. The clinical insights and standardized metrics derived from the clinical laboratory during the opioid crisis exemplifies the value proposition of clinical laboratories shifting into a more active role in the healthcare system. This increased participation by the clinical laboratories may improve patient safety and reduce healthcare costs related to OUD and pain management.

Linking particle properties to layer characteristics: Discrete element modelling of cohesive fine powder spreading in additive manufacturing

Abstract Particle properties play a key role in determining the flowability of cohesive fine powders and consequently the quality of parts fabricated by additive manufacturing. However, how the characteristics of a spread powder layer are linked to particle properties remains not well understood, largely due to the limitations of available numerical models and characterisation approaches. This study thus established an efficient discrete element modelling framework to address these issues, combining GPU computing with a novel methodology for particle stiffness scaling. The validity of the DEM model was verified on both packing and spreading of cohesive fine powders. The effect of particle cohesion on the characteristics of a spread powder layer was systematically analysed for non-cohesive and weakly to strongly cohesive powders. The structure of spread powder layer was qualitatively illustrated and quantitatively evaluated by not only commonly used metrics, such as coordination number, packing density and surface profiles but also particle clustering and pore characteristics. While reducing particle cohesion leads to an enhanced powder flowability, different regimes were identified in the relationship between layer characteristics and particle cohesion. The results showed that powder spreadability has a complicated dependence on the strength of particle cohesion, where the underlying mechanisms can be understood in part via a dimensionless inertial length. These findings not only provide valuable metrics to quantitatively evaluate the quality of a spread powder layer, but also enable a better understanding of the physics underlying the spreading of cohesive fine powders.

COVID-19 Pandemic Cases and Deaths in Selected Regions: The Need for Policy Recommendations

Despite the indescribable adversity of COVID-19, which raised deaths in most regions, the pandemic came with a number of good lessons that affect certain value metrics (healthcare, education, productive capacity, and price control mechanisms). In this study, we examined the role played by COVID-19 pandemic cases and deaths in selected regions and the need for policy recommendations. Using a mixed research approach, reported laboratory-confirmed COVID-19 cases and deaths between 12 and 17 March 2020 were obtained across six regions – Western Pacific, Europe, South East Asia, Eastern Mediterranean, America and Africa. Qualitative results showed that the COVID-19 pandemic predominantly affected Western Pacific and European regions. Besides, an analysis of variance revealed an insignificant difference in reported laboratory-confirmed cases and deaths of COVID-19 across the regions during the studied period. These results are, in part, attributable to the homogeneity in the strategies adopted to tackle the COVID-19 pandemic in these regions. Thus, there is a need to strengthen certain valuable metrics, particularly in the healthcare sector by means of upgrading medical equipment (for nations in lack) and recruiting more qualified healthcare workers in view of future events of any pandemic.

The soil microbial carbon pump: From conceptual insights to empirical assessments.

The global soil carbon (C) pool is massive, so relatively small changes in soil organic carbon (SOC) stocks can significantly alter atmospheric C and global climate. The recently proposed concept of the soil microbial carbon pump (MCP) emphasizes the active role of soil microbes in SOC storage by integrating the continual microbial transformation of organic C from labile to persistent anabolic forms. However, the concept has not been evaluated with data. Here, we combine datasets, including microbial necromass biomarker amino sugars and SOC, from two long-term agricultural field studies conducted by large United States bioenergy research programs. We interrogate the soil MCP concept by investigating the asynchronous responses of microbial necromass and SOC to land-use change. Microbial necromass appeared to preferentially accumulate in soil and be the dominant contributor to SOC accrual in diversified perennial bioenergy crops. Specifically, ~92% of the additional SOC enhanced by plant diversity was estimated to be microbial necromass C, and >76% of the additional SOC enhanced by land-use transition from annual to perennial crops was estimated to be microbial necromass. This suggests that the soil MCP was stimulated in diversified perennial agroecosystems. We further delineate and suggest two parameters-soil MCP capacity and efficacy-reflecting the conversion of plant C into microbial necromass and the contribution of microbial necromass to SOC, respectively, that should serve as valuable metrics for future studies evaluating SOC storage under alternative management in changing climates.

Characterizing three dimensional (3-D) morphology of residential buildings by landscape metrics

The key attributes of landscape pattern include composition and configuration, which can be depicted by landscape/spatial metrics. An emerging pathway is leveraging vertical data to advance three-dimensional (3-D) spatial metrics to interpret landscape attributes and quantify 3-D patterns. We introduced a suite of spatial metrics to recognize 3-D morphological characteristics of residential communities and examine their temporal changes. Seventeen 3-D spatial metrics were designed and computed at patch-, class-, and landscape-levels based on building footprints and height information in geographic information system (GIS). These metrics characterized 3-D forms of residential communities, including number, area, height, shape, and diversity. These 3-D features were further used to recognize five typical built types based on the scheme of local climate zone (LCZ) and quantify their 3-D morphological changes with rapid urbanization. The 3-D spatial metrics performed well in describing vertical and volumetric characteristics of residential communities and distinguishing five typical built types in Xiamen, China. Our results indicated that architectural styles of residential communities changed from homo- to mixed-rise buildings and from compact to open arrangement with rapid urbanization. Both 2-D and 3-D features are key attributes of the landscape. Our results showed that 3-D spatial metrics were not only useful tools for quantifying surface patterns but also key complements to vertical feature characterization, offering advantages in representing urbanization over the existing indexes. Growing 3-D datasets have great potential to develop more valuable metrics for characterizing spatial features, capturing ecological processes, and understanding drivers in various landscape contexts.

Force and Rate Metrics Provide Return-to-Sport Criterion after ACL Reconstruction.

Peak vertical ground reaction force and linear loading rate can be valuable metrics for return-to-sport assessment because they represent limb loading dynamics; yet, there is no defined cutoff criterion to differentiate between healthy and altered limb loading. Studies have shown that healthy individuals exhibit strong first-order relationships between gait variables whereas individuals with pathological conditions did not. Thus, this study sought to explore and exploit this first-order relationship to define a region of healthy limb dynamics, which individuals with pathological conditions would reside outside of, to rapidly assess individuals with altered limb loading dynamics for return to sport. We hypothesized that there would be a strong first-order linear relationship between vertical ground reaction force peak force and linear loading rate in healthy controls' limbs, which could be exploited to identify abnormal limb loading dynamics in post-anterior cruciate ligament reconstruction (ACLR) individuals. Thirty-one post-ACLR individuals and 31 healthy controls performed a running protocol. A first-order regression analysis modeled the relationship between peak vertical ground reaction forces and linear vertical ground reaction force loading rate in the healthy control limbs to define a region of healthy dynamics to evaluate post-ACLR reconstructed limb dynamics. A first-order regression model aided in the determination of cutoff criteria to define a region of healthy limb dynamics. Ninety percent of the post-ACLR reconstructed limbs exhibited abnormal limb dynamics based on their location outside of the region of healthy dynamics. This approach successfully delineated between healthy and abnormal limb loadings dynamics in controls and post-ACLR individuals. The findings demonstrate how force and loading rate-dependent metrics can help develop criteria for individualized post-ACLR return-to-sport assessment.