Oral histories document community mobilisation to participate in decision-making regarding a hazardous waste thermal treatment facility.

Colfax, Louisiana hosts a commercial hazardous waste thermal treatment (TT) facility, which treats fireworks, explosives, and military ordnances by open-burn/open-detonation one mile from the edge of the nearest community. Seventy-one percent of Colfax's residents are Black, and forty-six percent live below poverty, indicating the community's structural vulnerability. This community-based study originated at the behest of Colfax community members. We hypothesized that the close relationships among members of this enclave may have enhanced the community's ability to mobilize in opposition to the TT facility. We conducted semi-structured oral history interviews with nineteen community members and examined the social and interorganizational networks used by the Colfax community to claim its role in decision-making regarding the TT facility after years of exclusion from this process. Interview transcripts were analyzed through the lens of community capacity theory to gain insight into how interactions among community members about the environmental hazards led to social mobilization and improved participation in the decision-making process using codes for communication, organization, and outcome. Additionally, we reviewed Louisiana Department of Environmental Quality records for complaints about the facility to gauge public participation. One notable theme across several interviews was exclusion from the initial decision-making process related to the facility. However, interviewees noted a sustained effort was made among community members to educate themselves about the facility, organize a response through neighbor-to-neighbor contact, and take action by submitting formal complaints and participating in public hearings. Through the lens of environmental justice, this study illustrates an evolving condition of procedural justice.

Predictors and Consequences of Cancer and Non-Cancer-Related Pain in Those Diagnosed with Primary and Metastatic Cancers.

OBJECTIVES: The aims of the study were to (1) describe types of pain in cancer patients, (2) examine the predictors and consequences of pain, (3) investigate the association between type of pain and survival, and (4) examine potential biological mediators of pain and survival. METHODS: This was a secondary analysis of baseline data from patients diagnosed with cancer. Patients answered questionnaires that assessed sociodemographic characteristics, pain, depression, sleep, and fatigue. Blood was collected and cytokine assays were performed. Analysis of variance, Kaplan-Meier, and Cox regression survival analyses were used to test the aims. RESULTS: Of the 779 patients diagnosed with cancer, the mean age was 63.5 years, 57.8% male, and 90.6% White. Of those who reported pain (total 70.3%), 46.5% stated their pain was cancer-related while 53.5% stated their pain was non-cancer-related. While both cancer and non-cancer-related pain was associated with depressive symptoms, fatigue, and sleep duration, those with cancer-related pain had significantly higher rates of depressive symptoms (F(1,516) = 21.217, p < 0.001) and fatigue (F(1,516) = 30.973, p < 0.001) but not poorer sleep (F(1,497) = 0.597, p = 0.440). After adjusting for sociodemographic, disease-related characteristics, depression, sleep duration, and morphine milligram equivalent, patient reports of cancer-related pain were significantly associated with poorer survival (HR = 0.646, 95% CI = 0.459-0.910, p = 0.012) compared to those with non-cancer-related pain, which was not associated with survival (HR = 1.022, 95% CI = 0.737-1.418, p = 0.896). Cytokines did not significantly mediate the link between pain and survival. CONCLUSION: While nearly half of the pain reported was cancer-related, both types of pain resulted in greater symptom burden, but only cancer-related pain was associated with survival.

Erratum: Azimuthal Anisotropy of K_{S}

This corrects the article DOI: 10.1103/PhysRevLett.89.132301.

Severity of Chest Imaging is Correlated with Risk of Acute Neuroimaging Findings among Patients with COVID-19.

BACKGROUND AND PURPOSE: Severe respiratory distress in patients with COVID-19 has been associated with higher rate of neurologic manifestations. Our aim was to investigate whether the severity of chest imaging findings among patients with coronavirus disease 2019 (COVID-19) correlates with the risk of acute neuroimaging findings. MATERIALS AND METHODS: This retrospective study included all patients with COVID-19 who received care at our hospital between March 3, 2020, and May 6, 2020, and underwent chest imaging within 10 days of neuroimaging. Chest radiographs were assessed using a previously validated automated neural network algorithm for COVID-19 (Pulmonary X-ray Severity score). Chest CTs were graded using a Chest CT Severity scoring system based on involvement of each lobe. Associations between chest imaging severity scores and acute neuroimaging findings were assessed using multivariable logistic regression. RESULTS: Twenty-four of 93 patients (26%) included in the study had positive acute neuroimaging findings, including intracranial hemorrhage (n = 7), infarction (n = 7), leukoencephalopathy (n = 6), or a combination of findings (n = 4). The average length of hospitalization, prevalence of intensive care unit admission, and proportion of patients requiring intubation were significantly greater in patients with acute neuroimaging findings than in patients without them (P < .05 for all). Compared with patients without acute neuroimaging findings, patients with acute neuroimaging findings had significantly higher mean Pulmonary X-ray Severity scores (5.0 [SD, 2.9] versus 9.2 [SD, 3.4], P < .001) and mean Chest CT Severity scores (9.0 [SD, 5.1] versus 12.1 [SD, 5.0], P = .041). The pulmonary x-ray severity score was a significant predictor of acute neuroimaging findings in patients with COVID-19. CONCLUSIONS: Patients with COVID-19 and acute neuroimaging findings had more severe findings on chest imaging on both radiographs and CT compared with patients with COVID-19 without acute neuroimaging findings. The severity of findings on chest radiography was a strong predictor of acute neuroimaging findings in patients with COVID-19.

Optimal symptom combinations to aid COVID-19 case identification: Analysis from a community-based, prospective, observational cohort.

OBJECTIVES: Diagnostic work-up following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify optimal symptom combinations to capture most cases using fewer tests with implications for COVID-19 vaccine developers across different resource settings and public health. METHODS: UK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms and an RT-PCR test within seven days of symptom onset were included. Sensitivity, specificity, and number of RT-PCR tests needed to identify one case (test per case [TPC]) were calculated for different symptom combinations. A multi-objective evolutionary algorithm was applied to generate combinations with optimal trade-offs between sensitivity and specificity. FINDINGS: UK and US cohorts included 122,305 (1,202 positives) and 3,162 (79 positive) individuals. Within three days of symptom onset, the COVID-19 specific symptom combination (cough, dyspnoea, fever, anosmia/ageusia) identified 69% of cases requiring 47 TPC. The combination with highest sensitivity (fatigue, anosmia/ageusia, cough, diarrhoea, headache, sore throat) identified 96% cases requiring 96 TPC. INTERPRETATION: We confirmed the significance of COVID-19 specific symptoms for triggering RT-PCR and identified additional symptom combinations with optimal trade-offs between sensitivity and specificity that maximize case capture given different resource settings.

Analysis of Stroke Detection during the COVID-19 Pandemic Using Natural Language Processing of Radiology Reports.

BACKGROUND AND PURPOSE: The coronavirus disease 2019 (COVID-19) pandemic has led to decreases in neuroimaging volume. Our aim was to quantify the change in acute or subacute ischemic strokes detected on CT or MR imaging during the pandemic using natural language processing of radiology reports. MATERIALS AND METHODS: We retrospectively analyzed 32,555 radiology reports from brain CTs and MRIs from a comprehensive stroke center, performed from March 1 to April 30 each year from 2017 to 2020, involving 20,414 unique patients. To detect acute or subacute ischemic stroke in free-text reports, we trained a random forest natural language processing classifier using 1987 randomly sampled radiology reports with manual annotation. Natural language processing classifier generalizability was evaluated using 1974 imaging reports from an external dataset. RESULTS: The natural language processing classifier achieved a 5-fold cross-validation classification accuracy of 0.97 and an F1 score of 0.74, with a slight underestimation (-5%) of actual numbers of acute or subacute ischemic strokes in cross-validation. Importantly, cross-validation performance stratified by year was similar. Applying the classifier to the complete study cohort, we found an estimated 24% decrease in patients with acute or subacute ischemic strokes reported on CT or MR imaging from March to April 2020 compared with the average from those months in 2017-2019. Among patients with stroke-related order indications, the estimated proportion who underwent neuroimaging with acute or subacute ischemic stroke detection significantly increased from 16% during 2017-2019 to 21% in 2020 (P = .01). The natural language processing classifier performed worse on external data. CONCLUSIONS: Acute or subacute ischemic stroke cases detected by neuroimaging decreased during the COVID-19 pandemic, though a higher proportion of studies ordered for stroke were positive for acute or subacute ischemic strokes. Natural language processing approaches can help automatically track acute or subacute ischemic stroke numbers for epidemiologic studies, though local classifier training is important due to radiologist reporting style differences.

Optimal symptom combinations to aid COVID-19 case identification: analysis from a community-based, prospective, observational cohort.

OBJECTIVES: Diagnostic work-up following any COVID-19 associated symptom will lead to extensive testing, potentially overwhelming laboratory capacity whilst primarily yielding negative results. We aimed to identify optimal symptom combinations to capture most cases using fewer tests with implications for COVID-19 vaccine developers across different resource settings and public health. METHODS: UK and US users of the COVID-19 Symptom Study app who reported new-onset symptoms and an RT-PCR test within seven days of symptom onset were included. Sensitivity, specificity, and number of RT-PCR tests needed to identify one case (test per case [TPC]) were calculated for different symptom combinations. A multi-objective evolutionary algorithm was applied to generate combinations with optimal trade-offs between sensitivity and specificity. FINDINGS: UK and US cohorts included 122,305 (1,202 positives) and 3,162 (79 positive) individuals. Within three days of symptom onset, the COVID-19 specific symptom combination (cough, dyspnoea, fever, anosmia/ageusia) identified 69% of cases requiring 47 TPC. The combination with highest sensitivity (fatigue, anosmia/ageusia, cough, diarrhoea, headache, sore throat) identified 96% cases requiring 96 TPC. INTERPRETATION: We confirmed the significance of COVID-19 specific symptoms for triggering RT-PCR and identified additional symptom combinations with optimal trade-offs between sensitivity and specificity that maximize case capture given different resource settings.

Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets.

Radiomic features are being increasingly studied for clinical applications. We aimed to assess the agreement among radiomic features when computed by several groups by using different software packages under very tightly controlled conditions, which included standardized feature definitions and common image data sets. Ten sites (9 from the NCI's Quantitative Imaging Network] positron emission tomography-computed tomography working group plus one site from outside that group) participated in this project. Nine common quantitative imaging features were selected for comparison including features that describe morphology, intensity, shape, and texture. The common image data sets were: three 3D digital reference objects (DROs) and 10 patient image scans from the Lung Image Database Consortium data set using a specific lesion in each scan. Each object (DRO or lesion) was accompanied by an already-defined volume of interest, from which the features were calculated. Feature values for each object (DRO or lesion) were reported. The coefficient of variation (CV), expressed as a percentage, was calculated across software packages for each feature on each object. Thirteen sets of results were obtained for the DROs and patient data sets. Five of the 9 features showed excellent agreement with CV < 1%; 1 feature had moderate agreement (CV < 10%), and 3 features had larger variations (CV ≥ 10%) even after attempts at harmonization of feature calculations. This work highlights the value of feature definition standardization as well as the need to further clarify definitions for some features.

Multipartite Entanglement Generation and Contextuality Tests Using Nondestructive Three-Qubit Parity Measurements.

We report on the realization and application of nondestructive three-qubit parity measurements on nuclear spin qubits in diamond. We use high-fidelity quantum logic to map the parity of the joint state of three nuclear spin qubits onto an electronic spin qubit that acts as an ancilla, followed by a single-shot nondestructive readout of the ancilla combined with an electron spin echo to ensure outcome-independent evolution of the nuclear spins. Through the sequential application of three such parity measurements, we demonstrate the generation of genuine multipartite entangled states out of the maximally mixed state. Furthermore, we implement a single-shot version of the Greenberger-Horne-Zeilinger experiment that can generate a quantum versus classical contradiction in each run. Finally, we test a state-independent noncontextuality inequality in eight dimensions. The techniques and insights developed are relevant for fundamental tests as well as for quantum information protocols such as quantum error correction.

Erratum: Observation of D

This corrects the article DOI: 10.1103/PhysRevLett.113.142301.

Tunable long-distance spin transport in a crystalline antiferromagnetic iron oxide.

Spintronics relies on the transport of spins, the intrinsic angular momentum of electrons, as an alternative to the transport of electron charge as in conventional electronics. The long-term goal of spintronics research is to develop spin-based, low-dissipation computing-technology devices. Recently, long-distance transport of a spin current was demonstrated across ferromagnetic insulators1. However, antiferromagnetically ordered materials, the most common class of magnetic materials, have several crucial advantages over ferromagnetic systems for spintronics applications2: antiferromagnets have no net magnetic moment, making them stable and impervious to external fields, and can be operated at terahertz-scale frequencies3. Although the properties of antiferromagnets are desirable for spin transport4-7, indirect observations of such transport indicate that spin transmission through antiferromagnets is limited to only a few nanometres8-10. Here we demonstrate long-distance propagation of spin currents through a single crystal of the antiferromagnetic insulator haematite (α-Fe2O3)11, the most common antiferromagnetic iron oxide, by exploiting the spin Hall effect for spin injection. We control the flow of spin current across a haematite-platinum interface-at which spins accumulate, generating the spin current-by tuning the antiferromagnetic resonance frequency using an external magnetic field12. We find that this simple antiferromagnetic insulator conveys spin information parallel to the antiferromagnetic Néel order over distances of more than tens of micrometres. This mechanism transports spins as efficiently as the most promising complex ferromagnets1. Our results pave the way to electrically tunable, ultrafast, low-power, antiferromagnetic-insulator-based spin-logic devices6,13 that operate without magnetic fields at room temperature.

One-second coherence for a single electron spin coupled to a multi-qubit nuclear-spin environment.

Single electron spins coupled to multiple nuclear spins provide promising multi-qubit registers for quantum sensing and quantum networks. The obtainable level of control is determined by how well the electron spin can be selectively coupled to, and decoupled from, the surrounding nuclear spins. Here we realize a coherence time exceeding a second for a single nitrogen-vacancy electron spin through decoupling sequences tailored to its microscopic nuclear-spin environment. First, we use the electron spin to probe the environment, which is accurately described by seven individual and six pairs of coupled carbon-13 spins. We develop initialization, control and readout of the carbon-13 pairs in order to directly reveal their atomic structure. We then exploit this knowledge to store quantum states in the electron spin for over a second by carefully avoiding unwanted interactions. These results provide a proof-of-principle for quantum sensing of complex multi-spin systems and an opportunity for multi-qubit quantum registers with long coherence times.

C1 Posterior Arch Flare Point: A Useful Landmark for Fluoroscopically Guided C1-2 Puncture.

BACKGROUND AND PURPOSE: The C1-2 intrathecal puncture is routinely performed when lumbar puncture is not feasible. Usage has steadily decreased in part because of the perceived high risk of injury to the cervical cord. Up to this point, vague fluoroscopic guidelines have been used, creating uncertainty about the actual needle location relative to the spinal cord. We present a novel osseous landmark to aid in C1-2 intrathecal puncture, corresponding to the posterior spinal cord margin on lateral fluoroscopic views. This landmark, which we have termed the "flare point," represents the triangular "flaring" of the posterior C1 arch at its junction with the anterior arch. MATERIALS AND METHODS: Cervical spine CT myelograms were reviewed. High-resolution axial images were reformatted into the sagittal plane, and maximum-intensity-projection images were created to simulate a lateral fluoroscopic view. Tangential lines were drawn along the superior cortices of the anterior and posterior C1 arches, with the point of intersection used to approximate the flare point. Chart review was performed for all C1-2 punctures using the flare point technique in the past 3 years. RESULTS: Forty-two cervical myelograms were reviewed. The average flare point was 0.2 ± 0.5 mm posterior to the dorsal spinal cord margin. In 37/42 subjects, the flare point was localized posterior to the spinal cord. Targeting by means of the flare point was used in 16 C1-2 punctures without complications. CONCLUSIONS: The C1 posterior arch flare point accurately approximates the dorsal spinal cord margin on myelography. Targeting between the flare point and the spinolaminar line, at the mid-C1-2 interspace, allows safe and optimal needle positioning.

Multisite Concordance of DSC-MRI Analysis for Brain Tumors: Results of a National Cancer Institute Quantitative Imaging Network Collaborative Project.

BACKGROUND AND PURPOSE: Standard assessment criteria for brain tumors that only include anatomic imaging continue to be insufficient. While numerous studies have demonstrated the value of DSC-MR imaging perfusion metrics for this purpose, they have not been incorporated due to a lack of confidence in the consistency of DSC-MR imaging metrics across sites and platforms. This study addresses this limitation with a comparison of multisite/multiplatform analyses of shared DSC-MR imaging datasets of patients with brain tumors. MATERIALS AND METHODS: DSC-MR imaging data were collected after a preload and during a bolus injection of gadolinium contrast agent using a gradient recalled-echo-EPI sequence (TE/TR = 30/1200 ms; flip angle = 72°). Forty-nine low-grade (n = 13) and high-grade (n = 36) glioma datasets were uploaded to The Cancer Imaging Archive. Datasets included a predetermined arterial input function, enhancing tumor ROIs, and ROIs necessary to create normalized relative CBV and CBF maps. Seven sites computed 20 different perfusion metrics. Pair-wise agreement among sites was assessed with the Lin concordance correlation coefficient. Distinction of low- from high-grade tumors was evaluated with the Wilcoxon rank sum test followed by receiver operating characteristic analysis to identify the optimal thresholds based on sensitivity and specificity. RESULTS: For normalized relative CBV and normalized CBF, 93% and 94% of entries showed good or excellent cross-site agreement (0.8 ≤ Lin concordance correlation coefficient ≤ 1.0). All metrics could distinguish low- from high-grade tumors. Optimum thresholds were determined for pooled data (normalized relative CBV = 1.4, sensitivity/specificity = 90%:77%; normalized CBF = 1.58, sensitivity/specificity = 86%:77%). CONCLUSIONS: By means of DSC-MR imaging data obtained after a preload of contrast agent, substantial consistency resulted across sites for brain tumor perfusion metrics with a common threshold discoverable for distinguishing low- from high-grade tumors.

Bioisosteres of ethyl 8-ethynyl-6-(pyridin-2-yl)-4H-benzo[f]imidazo [1,5-a][1,4]diazepine-3-carboxylate (HZ-166) as novel alpha 2,3 selective potentiators of GABAA receptors: Improved bioavailability enhances anticonvulsant efficacy.

HZ-166 has previously been characterized as an α2,3-selective GABAA receptor modulator with anticonvulsant, anxiolytic, and anti-nociceptive properties but reduced motor effects. We discovered a series of ester bioisosteres with reduced metabolic liabilities, leading to improved efficacy as anxiolytic-like compounds in rats. In the present study, we evaluated the anticonvulsant effects KRM-II-81 across several rodent models. In some models we also evaluated key structural analogs. KRM-II-81 suppressed hyper-excitation in a network of cultured cortical neurons without affecting the basal neuronal activity. KRM-II-81 was active against electroshock-induced convulsions in mice, pentylenetetrazole (PTZ)-induced convulsions in rats, elevations in PTZ-seizure thresholds, and amygdala-kindled seizures in rats with efficacies greater than that of diazepam. KRM-II-81 was also active in the 6 Hz seizure model in mice. Structural analogs of KRM-II-81 but not the ester, HZ-166, were active in all models in which they were evaluated. We further evaluated KRM-II-81 in human cortical epileptic tissue where it was found to significantly-attenuate picrotoxin- and AP-4-induced increases in firing rate across an electrode array. These molecules generally had a wider margin of separation in potencies to produce anticonvulsant effects vs. motor impairment on an inverted screen test than did diazepam. Ester bioisosters of HZ-166 are thus presented as novel agents for the potential treatment of epilepsy acting via selective positive allosteric amplification of GABAA signaling through α2/α3-containing GABA receptors. The in vivo data from the present study can serve as a guide to dosing parameters that predict engagement of central GABAA receptors.

Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches.

Radiomics describes a broad set of computational methods that extract quantitative features from radiographic images. The resulting features can be used to inform imaging diagnosis, prognosis, and therapy response in oncology. However, major challenges remain for methodologic developments to optimize feature extraction and provide rapid information flow in clinical settings. Equally important, to be clinically useful, predictive radiomic properties must be clearly linked to meaningful biologic characteristics and qualitative imaging properties familiar to radiologists. Here we use a cross-disciplinary approach to highlight studies in radiomics. We review brain tumor radiologic studies (eg, imaging interpretation) through computational models (eg, computer vision and machine learning) that provide novel clinical insights. We outline current quantitative image feature extraction and prediction strategies with different levels of available clinical classes for supporting clinical decision-making. We further discuss machine-learning challenges and data opportunities to advance radiomic studies.

Effect of sex on torque, recovery, EMG, and MMG responses to fatigue.

OBJECTIVE: The purpose of the present investigation was to examine the effect of sex on maximal voluntary isometric contraction (MVIC) torque and the EMG and MMG responses as a result of fatiguing, intermittent, submaximal (65% of MVIC), isometric elbow flexion muscle contractions. METHODS: Eighteen men and women performed MVIC trials before (pretest), after (posttest), and 5-min after (5-min recovery) performing 50 intermittent, submaximal isometric muscle contractions. Surface electromyographic (EMG) and mechanomyographic (MMG) signals were simultaneously recorded from the biceps brachii muscle. RESULTS: As a result of the fatiguing workbout torque decreased similarly from pretest to posttest for both the men (24.0%) and women (23.3%). After 5-min of recovery, torque had partially recovered for the men, while torque had returned to pretest levels for the women. For both sexes, from pretest to posttest EMG mean power frequency and MMG amplitude decreased, but returned to pretest levels after 5-min of recovery. CONCLUSIONS: In the present study, there were sex-related differences in muscle fatigue that were not associated with the EMG or MMG responses.

Selection of conformational states in surface self-assembly for a molecule with eight possible pairs of surface enantiomers.

Self-assembly of a molecule with many distinct conformational states, resulting in eight possible pairs of surface enantiomers, is investigated on a Au(111) surface under UHV conditions. The complex molecule is equipped with alkyl and carboxyl moieties to promote controlled self-assembly of lamellae structures. From statistical analysis of Scanning Tunnelling Microscopy (STM) data we observe a clear selection of specific conformational states after self-assembly. Using Density Functional Theory (DFT) calculations we rationalise how this selection is correlated to the orientation of the alkyl moieties in mirror-image domains of the lamellae structures, leading to selection of three out of the eight possible enantiomeric pairs.

Experimental creation of quantum Zeno subspaces by repeated multi-spin projections in diamond.

Repeated observations inhibit the coherent evolution of quantum states through the quantum Zeno effect. In multi-qubit systems this effect provides opportunities to control complex quantum states. Here, we experimentally demonstrate that repeatedly projecting joint observables of multiple spins creates quantum Zeno subspaces and simultaneously suppresses the dephasing caused by a quasi-static environment. We encode up to two logical qubits in these subspaces and show that the enhancement of the dephasing time with increasing number of projections follows a scaling law that is independent of the number of spins involved. These results provide experimental insight into the interplay between frequent multi-spin measurements and slowly varying noise and pave the way for tailoring the dynamics of multi-qubit systems through repeated projections.

Four weeks of high- versus low-load resistance training to failure on the rate of torque development, electromechanical delay, and contractile twitch properties.

The purpose of this study was to investigate the effects of 4-weeks of high- versus low-load resistance training to failure on rate of torque development (RTD), electromechanical delay (EMD), and contractile twitch characteristics. Fifteen men (mean±SD; age=21.7±2.4 yrs) were randomly assigned to either a high- (80% 1RM; n=7) or low-load (30% 1RM; n=8) training group and completed elbow flexion resistance training to failure 3 times per week for 4 weeks. The participants were tested at baseline, 2-, and 4-weeks of training. Peak RTD (pRTDV) and RTD at 0-30 (RTD30V), 0-50 (RTD50V), 0-100 (RTD100V), and 0-200 (RTD200V) ms, integrated EMG amplitude (iEMG) at 0-30, 0-50, and 0-100 ms, and EMD were quantified during maximal voluntary isometric muscle actions. Peak twitch torque, peak RTD, time to peak twitch, 1/2 relaxation time and the peak relaxation rate were quantified during evoked twitches. Four weeks of high-load, but not low-load resistance training, increased RTD200V. There were also increases in iEMG during the first 30 ms of muscle activation for the high- and low-load groups, which may have indirectly indicated increases in early phase motor unit recruitment and/or firing frequency. There were no significant training-induced adaptations in EMD or contractile twitch properties.