Critical Value Research Articles

Biomechanical Threshold Values for Identifying Clinically Significant Knee-Related Symptoms Six Months Following Anterior Cruciate Ligament Reconstruction.

Slower habitual walking speed and aberrant gait biomechanics are linked to clinically significant knee-related symptoms and articular cartilage composition changes linked to posttraumatic osteoarthritis (PTOA) following anterior cruciate ligament reconstruction (ACLR). To determine specific gait biomechanical variables that can accurately identify individuals with clinically significant knee-related symptoms post-ACLR, and the corresponding threshold values, sensitivity, specificity, and odds ratios for each biomechanical variable. Cross-sectional analysis. Laboratory. Seventy-one individuals (n=38 female; age=21±4 years; height=1.76±0.11 m; mass=75.38±13.79 kg) who were 6 months post-primary unilateral ACLR (6.2±0.4 months). 3D motion capture of 5 overground walking trials was used to calculate discrete gait biomechanical variables of interest during stance phase (1st and 2nd peak vertical ground reaction force [vGRF]; midstance minimum vGRF; peak internal knee abduction and extension moments; and peak knee flexion angle), along with habitual walking speed. Knee Injury and Osteoarthritis Outcome Scores (KOOS) was used to dichotomize patients as symptomatic (n=51) or asymptomatic (n=20) using the Englund et al. 2003 KOOS guidelines for defining clinically significant knee-related symptoms. Separate receiver operating characteristic (ROC) curves and respective areas under the curve (AUC) were used to evaluate the capability of each biomechanical variable of interest for identifying individuals with clinically significant knee-related symptoms. Habitual walking speed (AUC=0.66), vGRF at midstance (AUC=0.69), and 2nd peak vGRF (AUC=0.76), demonstrated low-to-moderate accuracy for identifying individuals with clinically significant knee-related symptoms. Individuals who exhibited habitual walking speeds ≤1.27 m/s, midstance vGRF ≥0.82 BW, and 2nd peak vGRF ≤1.11 BW, demonstrated 3.13, 6.36, and 9.57 times higher odds of experiencing clinically significant knee-related symptoms, respectively. Critical thresholds for gait variables may be utilized to identify individuals with increased odds of clinically significant knee-related symptoms and potential targets for future interventions.

PUM1 and PGK1 are Favorable Housekeeping Genes over Established Biodosimetry-related Housekeeping Genes such as HPRT1, ITFG1, DPM1, MRPS5, 18S rRNA and Others after Radiation Exposure.

In gene expression (GE) studies, housekeeping genes (HKGs) are required for normalization purposes. In large-scale inter-laboratory comparison studies, significant differences in dose estimates are reported and divergent HKGs are employed by the teams. Among them, the 18S rRNA HKG is known for its robustness. However, the high abundance of 18S rRNA copy numbers requires dilution, which is time-consuming and a possible source of errors. This study was conducted to identify the most promising HKGs showing the least radiation-induced GE variance after radiation exposure. In the screening stage of this study, 35 HKGs were analyzed. This included selected HKGs (ITFG1, MRPS5, and DPM1) used in large-scale biodosimetry studies which were not covered on an additionally employed pre-designed 96-well platform comprising another 32 HKGs used for different exposures. Altogether 41 samples were examined, including 27 ex vivo X-ray irradiated blood samples (0, 0.5, 4 Gy), six X-irradiated samples (0, 0.5, 5 Gy) from two cell lines (U118, A549), as well as eight non-irradiated tissue samples to encompass multiple biological entities. In the independent validation stage, the most suitable candidate genes were examined from another 257 blood samples, taking advantage of already stored material originating from three studies. These comprise 100 blood samples from ex vivo X-ray irradiated (0-4 Gy) healthy donors, 68 blood samples from 5.8 Gy irradiated (cobalt-60) Rhesus macaques (RM) (LD29/60) collected 0-60 days postirradiation, and 89 blood samples from chemotherapy-(CTx) treated breast tumor patients. CTx and radiation-induced GE changes in previous studies appeared comparable. RNA was isolated, converted into cDNA, and GE was quantified employing TaqMan assays and quantitative RT-PCR. We calculated the standard deviation (SD) and the interquartile range (IQR) as measures of GE variance using raw cycle threshold (Ct) values and ranked the HKGs accordingly. Dose, time, age, and sex-dependent GE changes were examined employing the parametrical t-test and non-parametrical Kruskal Wallis test, as well as linear regression analysis. Generally, similar ranking results evolved using either SD or IQR GE measures of variance, indicating a tight distribution of GE values. PUM1 and PGK1 showed the lowest variance among the first ten most suitable genes in the screening phase. MRPL19 revealed low variance among the first ten most suitable genes in the screening phase only for blood and cells, but certain comparisons indicated a weak association of MRPL19 with dose (P = 0.02-0.09). In the validation phase, these results could be confirmed. Here, IQR Ct values from, e.g., X-irradiated blood samples were 0.6 raw Ct values for PUM1 and PGK1, which is considered to represent GE differences as expected due to methodological variance. Overall, when compared, the GE variance of both genes was either comparable or lower compared to 18S rRNA. Compared with the IQR GE values of PUM1 and PGKI, twofold-fivefold increased values were calculated for the biodosimetry HKG HPRT1, and comparable values were calculated for biodosimetry HKGs ITFG1, MRPS5, and DPM1. Significant dose-dependent associations were found for ITFG1 and MRPS5 (P = 0.001-0.07) and widely absent or weak (P = 0.02-0.07) for HPRT1 and DPM1. In summary, PUM1 and PGK1 appeared most promising for radiation exposure studies among the 35 HKGs examined, considering GE variance and adverse associations of GE with dose.

Apparent diffusion coefficient values predict response to brachytherapy in bulky cervical cancer

BackgroundDiffusion-weighted magnetic resonance imaging (DWI) provides a measurement of tumor cellularity. We evaluated the potential of apparent diffusion coefficient (ADC) values obtained from post-external beam radiation therapy (EBRT) DWI and prior to brachytherapy (BT) to predict for complete metabolic response (CMR) in bulky cervical cancer.MethodsClinical and DWI (b value = 500 s/mm2) data were obtained from patients undergoing interstitial BT with high-risk clinical target volumes (HR-CTVs) > 30 cc. Volumes were contoured on co-registered T2 weighted images and 90th percentile ADC values were calculated. Patients were stratified by CMR (defined by PET-CT at three months post-BT). Relation of CMR with 90th percentile ADC values and other clinical factors (International Federation of Gynecology and Obstetrics (FIGO) stage, histology, tumor and HR-CTV size, pre-treatment hemoglobin, and age) was assessed both in univariate and multivariate logistic regression analyses. Youden’s J statistic was used to identify a threshold value.ResultsAmong 45 patients, twenty-eight (62%) achieved a CMR. On univariate analysis for CMR, only 90th percentile ADC value was significant (p = 0.029) while other imaging and clinical factors were not. Borderline significant factors were HR-CTV size (p = 0.054) and number of chemotherapy cycles (p = 0.078). On multivariate analysis 90th percentile ADC (p < 0.0001) and HR-CTV size (p < 0.003) were highly significant. Patients with 90th percentile ADC values above 2.10 × 10− 3 mm2/s were 5.33 (95% CI, 1.35–24.4) times more likely to achieve CMR.ConclusionsClinical DWI may serve to risk-stratify patients undergoing interstitial BT for bulky cervical cancer.

Logistic analysis of delayed reporting of emergency blood potassium and comparison of improved outcomes

Potassium testing is an essential test in emergency medicine. Turnaround time (TAT) is the time between specimen receipt by the laboratory and the release of the test report. A brief in-laboratory TAT increases emergency department effectiveness. Optimizing processes to shorten TAT using other tools requires extensive time, resources, training, and support. Therefore, we aimed to find a convenient way to shorten TAT, identify risk factors affecting the timeliness of emergency potassium test reporting, and verify the intervention’s effects. The dependent variable was emergency potassium reporting time > 30 or < 30 min. Logistic analysis was performed on monitorable factors, such as sex, age, potassium results, number of items, specimen processing time (including centrifugation and time before specimen loading), critical value ratio, instrument status, shift where the report was issued, specimen status, and work experience, as independent variables. In the multivariate analysis, work experience, instrument failure rate, and specimen processing time were risk factors for emergency blood potassium reporting exceeding 30 min. Improvement measures were implemented, significantly decreasing the timeout rate for acute potassium reporting. Our study confirms the usefulness of logistics in reducing the time required to report potassium levels in the emergency department, providing a new perspective on quality management.

Programmable Threshold Logic Implementations in a Memristor Crossbar Array.

In this study, we demonstrate the implementation of programmable threshold logics using a 32 × 32 memristor crossbar array. Thanks to forming-free characteristics obtained by the annealing process, its accurate programming characteristics are presented by a 256-level grayscale image. By simultaneous subtraction between weighted sum and threshold values with a differential pair in an opposite way, 3-input and 4-input Boolean logics are implemented in the crossbar without additional reference bias. Also, we verify a full-adder circuit and analyze its fidelity, depending on the device programming accuracy. Lastly, we successfully implement a 4-bit ripple carry adder in the crossbar and achieve reliable operations by read-based logic operations. Compared to stateful logic driven by device switching, a 4-bit ripple carry adder on a memristor crossbar array can perform more reliably in fewer steps thanks to its read-based parallel logic operation.

Are trait-associated genes clustered together in a gene network?

Genome-wide association studies (GWAS) have provided an abundance of information about the genetic variants and their loci that are associated to complex traits and diseases. However, due to linkage disequilibrium (LD)and noncoding regions of loci, it remains a challenge to pinpoint the causal genes. Gene network-based approaches, paired with network diffusion methods, have been proposed to prioritize causal genes and to boost statistical power in GWAS based on the assumption that trait-associated genes are clustered in a gene network. Due to the difficulty in mapping trait-associated variants to genes in GWAS, this assumption has never been directly or rigorously tested empirically. On the other hand, whole exome sequencing (WES)data focuses on the protein-coding regions, directly identifying trait-associated genes. In this study, we tested the assumption by leveraging the recently available exome-based association statistics from the UK Biobank WES data along with two types of networks. We found that almost all trait-associated genes were significantly more proximal to each other than randomly selected genes within both networks. These results support the assumption that trait-associated genes are clustered in gene networks, which can be further leveraged to boost the power of GWAS such as by introducing less stringent p value thresholds.

Multi-objective design optimization of a transonic axial fan stage using sparse active subspaces

In this paper, a multi-objective optimization strategy for efficient design of turbomachinery blades using sparse active subspaces is implemented for a turbofan stage design. The proposed strategy utilized sparse polynomial chaos expansion on a limited dataset to generate a function from which the differential and the covariance matrix can be obtained. Active subspace was used to compute the active variables via singular value decomposition and a hybrid polynomial correlated function expansion was used to construct a surrogate model on the active subspace. Coupled with freeform method and multi-objective genetic algorithm, an automated optimization loop was run at a single operating condition. An improvement in stage efficiency and total pressure ratio of 2.97% and 1.15% was achieved for the optimum design compared with the baseline. Additionally, total pressure loss coefficient decreased by 5.88%, exit flow angle by 34.65% and shock strength by 5.32%. The coupled effect of change in stagger angle, forward sweep, forward lean, and chord length reduced the recirculation at the hub, and blockage at the shroud due the tip leakage flow by decreasing the blade loading. The threshold value hyperparameter was found to be the most influential and must be accurately determined.

The conditional Lyapunov exponents and synchronisation of rotating turbulent flows

The synchronisation between rotating turbulent flows in periodic boxes is investigated numerically. The flows are coupled via a master–slave coupling, taking the Fourier modes with wavenumber below a given value $k_m$ as the master modes. It is found that synchronisation happens when $k_m$ exceeds a threshold value $k_c$ , and $k_c$ depends strongly on the forcing scheme. In rotating Kolmogorov flows, $k_c\eta$ does not change with rotation in the range of rotation rates considered, $\eta$ being the Kolmogorov length scale. Even though the energy spectrum has a steeper slope, the value of $k_c\eta$ is the same as that found in isotropic turbulence. In flows driven by a forcing term maintaining constant energy injection rate, synchronisation becomes easier when rotation is stronger. Here, $k_c\eta$ decreases with rotation, and it is reduced significantly for strong rotations when the slope of the energy spectrum approaches $-3$ . It is shown that the conditional Lyapunov exponent for a given $k_m$ is reduced by rotation in the flows driven by the second type of forcing, but it increases mildly with rotation for the Kolmogorov flows. The local conditional Lyapunov exponents fluctuate more strongly as rotation is increased, although synchronisation occurs as long as the average conditional Lyapunov exponents are negative. We also look for the relationship between $k_c$ and the energy spectra of the Lyapunov vectors. We find that the spectra always seem to peak at approximately $k_c$ , and synchronisation fails when the energy spectra of the conditional Lyapunov vectors have a local maximum in the slaved modes.

Transfer Learning with ResNet3D-101 for Global Prediction of High Aerosol Concentrations

In order to better predict the high aerosol concentrations associated with air pollution and climate change, a machine learning model was developed using transfer learning and the segmentation process of global satellite images. The main concept of transfer learning lies on convolutional neural networks and works by initializing the already trained model weights to better adapt the weights when the network is trained on a different dataset. The transfer learning technique was tested with the ResNet3D-101 model pre-trained from a 2D ImageNet dataset. This model has performed well for contrail detection to assess climate impact. Aerosol distributions can be monitored via satellite remote sensing. Satellites can monitor some aerosol optical properties like aerosol optical thickness. Aerosol optical thickness snapshots were the input dataset for the model and were obtained from NASA’s Terra-Modis satellite; the output images were segmented by comparing the pixel values with a threshold value of 0.8 for aerosol optical thickness. Hyperparameter optimization finds a tuple of hyperparameters that yields an optimal model that minimizes a predefined loss function on given independent data. The model structure was adjusted in order to improve the performance of the model by applying methods and hyperparameter optimization techniques such as grid search, batch size, threshold, and input length. According to the criteria defined by the authors, the distance domain criterion and time domain criterion, the developed model is capable of generating adequate data and finding patterns in the time domain. As observed from the comparison of relative coefficients for the criteria metrics proposed by the authors, ddc and dtc, the deep learning model based on ConvLSTM layers developed in our previous studies has better performance than the model developed in this study with transfer learning.

JUSTIFICATION OF THE STEP OF WRAPPING THE THREAD WITH A MICROWIRE TO AVOID ITS BREAKING WHEN THE COMPLEX THREAD IS STRETCHED

JUSTIFICATION OF THE STEP OF WRAPPING THE THREAD WITH A MICROWIRE TO AVOID ITS BREAKING WHEN THE COMPLEX THREAD IS STRETCHED

Determination of the cycle threshold value of the Xpert Xpress SARS-CoV-2/Flu/RSV test that corresponds to the presence of infectious SARS-CoV-2 in anterior nasal swabs.

Despite having high analytical sensitivities and specificities, qualitative SARS-CoV-2 nucleic acid amplification tests (NAATs) cannot distinguish infectious from non-infectious virus in clinical samples. In this study, we determined the highest cycle threshold (Ct) value of the SARS-CoV-2 targets in the Xpert Xpress SARS-CoV-2/Flu/RSV (Xpert 4plex) test that corresponded to the presence of detectable infectious SARS-CoV-2 in anterior nasal swab samples. A total of 111 individuals with nasopharyngeal swab specimens that were initially tested by the Xpert Xpress SARS-CoV-2 test were enrolled. A healthcare worker subsequently collected anterior nasal swabs from all SARS-CoV-2-positive individuals, and those specimens were tested by the Xpert 4plex test, viral culture, and laboratory-developed assays for SARS-CoV-2 replication intermediates. SARS-CoV-2 Ct values from the Xpert 4plex test were correlated with data from culture and replication intermediate testing to determine the Xpert 4plex assay Ct value that corresponded to the presence of infectious virus. Ninety-eight of the 111 (88.3%) individuals initially tested positive by the Xpert Xpress SARS-CoV-2 test. An anterior nasal swab specimen collected from positive individuals a median of 2 days later (range, 0-9 days) tested positive for SARS-CoV-2 by the Xpert 4plex test in 39.8% (39/98) of cases. Of these samples, 13 (33.3%) were considered to contain infectious virus based on the presence of cultivable virus and replication intermediates, and the highest Ct value observed for the Xpert 4plex test in these instances was 26.3. Specimens that yielded Ct values of ≤26.3 when tested by the Xpert 4plex test had a likelihood of containing infectious SARS-CoV-2; however, no infectious virus was detected in specimens with higher Ct values.IMPORTANCEUnderstanding the correlation between real-time PCR test results and the presence of infectious SARS-CoV-2 may be useful for informing patient management and workforce return-to-work or -duty. Further studies in different patient populations are needed to correlate Ct values or other biomarkers of viral replication along with the presence of infectious virus in clinical samples.

Phase transitions in the fractional three-dimensional Navier–Stokes equations

The fractional Navier–Stokes equations on a periodic domain [0,L]3 differ from their conventional counterpart by the replacement of the −νΔu Laplacian term by νsAsu , where A=−Δ is the Stokes operator and νs=νL2(s−1) is the viscosity parameter. Four critical values of the exponent s⩾0 have been identified where functional properties of solutions of the fractional Navier–Stokes equations change. These values are: s=13 ; s=34 ; s=56 and s=54 . In particular: (i) for s>13 we prove an analogue of one of the Prodi–Serrin regularity criteria; (ii) for s⩾34 we find an equation of local energy balance and; (iii) for s>56 we find an infinite hierarchy of weak solution time averages. The existence of our analogue of the Prodi–Serrin criterion for s>13 suggests the sharpness of the construction using convex integration of Hölder continuous solutions with epochs of regularity in the range 0<s<13 .

Investigation of experimental and numerical analysis of thermal load of WEDM on all metals through process parameters on wire electrode temperature

The thermal load on wire electrode in a wire electrical discharge machining (WEDM) is the most important factor in predicting wire electrode breakage. Thermal failure of wire electrode may be expected when the maximum temperature of the wire crosses a critical value. The maximum temperature of wire electrode is a function of process parameters of the machine and hence the failure depends upon what settings we choose to run WEDM process. In this investigation, simulation is done using finite-difference thermal model for continuous pulse without clustering of the spark. The model is used for determining distribution of the temperature throughout the length of wire electrode and analyzed the significance of various factors on wire breakage. The outcome of the present simulation model is validated by comparing it with the works of other researchers followed by its validation through experiments. A parametric study is also done to determine the effect of input process parameters and their interaction on the temperature of the wire. For finding the interaction effect, analysis of variance analysis is carried out followed by developing an empirical model. The predictive performance of our simulation model and developed regression models is assessed by comparing them with experimental value. The regression model is found to be closer, with an average percentage deviation of −2.55% on the experimental dataset, compared to 2.64% for the simulation model. During the validation test, the minimum and maximum deviations are 0.08% and 0.20% for simulation versus regression model predictions, −3.31% and −1.65% for experimental versus predicted results, and 1.82% and 3.40% for simulation versus experimental results, respectively. It is observed through this study that temperature of wire electrode mainly depends on the intensity of sparks per unit area which is a function of the input parameters of the WEDM.

Intravoxel incoherent motion assessment of liver fibrosis staging in MASLD.

Partial correlation analysis was performed to account for the interference of steatosis changes and inflammatory factors, to determine the true correlation between fibrosis and IVIM parameters (Dfast, Dslow, and F), and to evaluate the diagnostic efficacy of IVIM for liver fibrosis. A total of 106 patients with metabolic dysfunction-associated steatotic liver disease (MASLD) examined by IVIM from November 2016 to November 2023 at our hospital were retrospectively included. Preliminary analysis of each IVIM parameter and correlations with pathological findings were performed using Spearman correlation analysis, and partial correlation analysis was used to exclude the interference of other pathological factors, thus yielding the true correlations between IVIM parameters (Dfast, Dslow, and F) and pathology. The diagnostic efficacy of IVIM parameters for diagnosing MASLDwas assessed via receiver operating characteristic (ROC) curve analysis. Spearman correlation analysis of all the IVIM parameters revealed correlations with steatosis, lobular inflammation, and ballooning. Partial correlation analysis indicated that Dfast was correlated with the pathological fibrosis stage (r = - 0.593, P < 0.001), Dslow was correlated with the pathological steatosis score (r = - 0.313, P < 0.05), and F was correlated with the pathological fibrosis stage and steatosis score (r = - 0.456 and 0.255, P < 0.001 and P < 0.05). In the diagnosis of hepatic fibrosis, significant hepatic fibrosis, advanced liver fibrosis and cirrhosis, Dfast achieved areas under the ROC curve of 0.763, 0.801, 0.853, and 0.897, respectively. The threshold values for diagnosing different fibrosis stages using Dfast (10-3 mm2/s) were 57.613, 54.587, 52.714, and 51.978, respectively. According to our partial correlation analysis, there was a moderate correlation between Dfast and F according to fibrosis stage, and Dfast was not influenced by inflammation or steatosis when diagnosing fibrosis in MASLD patients. A relatively close Dfast threshold is insufficient for accurately and noninvasively assessing various stages of MASLD fibrosis. In clinical practice, this approach can be considered an alternative method for the preliminary assessment of fibrosis in MASLD patients.

Monitoring of Essential and Toxic Elements in Multivitamin/Mineral Effervescent Tablet Supplements and Safety Assessment.

Multivitamin/mineral (MVM) supplements are the most commonly utilized dietary supplements by many populations. However, there is a severe concern about their adverse effects due to elemental impurities. In the present study, it was aimed to determine the levels of 11 elemental impurities (Cd, Pb, As, Hg, Co, V, Ni, Se, Mo, Cu, and Cr) by inductively coupled plasma-mass spectrometry (ICP-MS) and evaluate the human health risk associated with the consumption of 33 MVM effervescent tablet supplements available in Turkey. The precision of the method in terms of relative standard deviation (RSD) was less than 4.6%. The accuracy of the method was tested with recovery experiments, and the results ranged between 86 and 107%. The impurity levels for Cd, Pb, As, Co, V, Ni, Se, Mo, Cu, and Cr were found between 0.011-0.050, 0.025-0.098, 0.018-0.056, 0.010-0.626, 0.027-0.290, 0.026-1.65, 1.92-21.83, 0.034-34.09, 0.140-183.9, and 0.033-13.10µg/g, respectively, and Hg was not detected in any sample. The calculated concentrations for elemental impurities complied with EMA and USP guidelines, except one supplement for Se (21.83µg/g) with a permitted limit of 15µg/g. The hazard quotient (HQ) and hazard index (HI) levels were below 1 for all samples within the ranges of 3.4 × 10-1-1.4 × 10-6 for HQ and 7.8 × 10-1-1.4 × 10-6 for HI indicating that there is no risk for consumption. The carcinogenic risk (CR) of As was between 1.7 × 10-6 and 5.9 × 10-6, below the threshold value of 1 × 10-4. The results showed that there is no risk to human health.

Clinical manifestations and risk factors for COVID-19 and its severity in patients with hematological malignancies

BackgroundPatients with hematological malignancies (HM) were at a high risk of developing severe disease from coronavirus disease 2019 (COVID-19). We aimed to assess the clinical outcome of COVID-19 in hospitalized patients with HM. MethodsAdult patients with HM who were hospitalized with a laboratory-confirmed COVID-19 between May, 2021 and November, 2022 were retrospectively identified. Primary outcome was respiratory failure requiring mechanical ventilation or mortality within 60 days after hospitalization. We also analyzed associated factors for de-isolation (defined as defervescence with a consecutive serial cycle threshold value > 30) within 28 days. ResultsOf 152 eligible patients, 22 (14.5%) developed respiratory failure or mortality in 60 days. Factors associated with developing respiratory failure that required mechanical ventilation or mortality included receipt of allogeneic hematopoietic stem-cell transplantation (allo-HSCT) (adjusted hazards ratio [aHR], 5.10; 95% confidence interval [CI], 1.64–15.85), type 2 diabetes mellitus (aHR, 2.47; 95% CI, 1.04–5.90), lymphopenia at admission (aHR, 6.85; 95% CI, 2.45–19.15), and receiving <2 doses of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines (aHR, 3.00; 95% CI, 1.19–7.60). Ninety-nine (65.1%) patients were de-isolated in 28 days, against which two hazardous factors were identified: receipt of B-cell depletion therapies within one year prior to COVID-19 (aHR, 0.55, 95% CI, 0.35–0.87) and lymphopenia upon admission (aHR, 0.65; 95% CI, 0.43–1.00). ConclusionWe found a high rate of respiratory failure and mortality among patients with HM who contracted the SARS-CoV-2. Factors associated with developing respiratory failure or mortality in 60 days included receipt of allo-HSCT, type 2 diabetes mellitus and lymphopenia upon admission. Having received ≥2 doses of vaccination conferred protection against clinical progression.

Relationship between Polygenic Risk Score and the Hypnotics in Bipolar I Disorder.

Bipolar disorder (BD) is marked by significant change in mood and energy levels with sleep disturbance a common feature, resulting in diminished quality of life and impaired daily functioning. This study assessed the association between BD-polygenic risk scores (PRS) and hypnotics in bipolar I disorder (BD-I) patients. Large-sample data were collected from the genome-wide association study of a multicenter Bipolar Genomic Study, and 1,394 BD-I patients with available medication information were divided into two groups depending on whether they used hypnotics or not. The Diagnostic Interview for Genetic Studies (DIGS) score was used to assess the clinical manifestations and function of the participants and the association between the use of hypnotics and genetic risk was analyzed. Of the 1,394 total participants, 556 (40%) patients received hypnotics, mostly benzodiazepines, administered singly or in combination with other sleeping agents such as, Z-drugs, melatonin-related drugs, and trazodone. The DIGS score was significantly higher for negative categories in the group prescribed hypnotics as was the BD-PRS score, according to the four p value thresholds (p = 0.3, 0.2, 0.1, and 0.05). Logistic regression analysis confirmed a statistically significant association between the BD-PRS and hypnotic use. Our results suggest an association between hypnotic use and genetic susceptibility to BD. Sleep disturbances in participants were based on the prescription status of hypnotics supporting the hypothesis that sleep disturbances may be associated with genetic aspects of BD-I. Further genetic studies on genetic overlaps between BD and specific phenotypes or medication responses are required.

Full-Size Experimental Measurement of Combustion and Destruction Efficiency in Upstream Flares and the Implications for Control of Methane Emissions from Oil and Gas Production

Accurately measuring the combustion and destruction removal efficiency of flaring is important when accounting for methane emissions from oil and gas production. Despite this, the amount of experimental data from full-size flares is limited, especially for flares built without air or steam assistance. The use of a single destruction value of 98% is commonly applied. In this paper, we present new empirical measurements of flare efficiency using three common flare designs employed in upstream applications. Combustion products were analyzed using an extractive sampling method. The results demonstrate that whilst destruction efficiencies in excess of 98% are achievable, if the gas composition falls below a critical heating value of ~300 BTU/scf, the efficiency deteriorates leading to elevated methane emissions. This is further complicated by accurately measuring the flow of combustible gas and the impact of crosswinds. In an operational setting, continuous tracking of flare conditions is therefore a key resource in reducing methane emissions but further work is required to standardize how continuous performance tracking is evaluated if such measurements are to attain full traceability.

Examining the links between household livelihood resilience and vulnerability: disaster resettlement experience from rural China

Livelihood status of rural households is the focus of disaster resettlement research. Influenced by various factors, rural households face multiple environmental and social pressures after relocation, and the study of livelihood resilience and vulnerability provides a new framework for research into the livelihoods of relocated households. There has been a call for more quantitative evidence about the links between livelihood resilience and livelihood vulnerability in the context of disaster resettlement. This study uses data from a sample of 657 individuals from the relocation area of Ankang prefecture in southern Shaanxi, China. The resilience of rural household livelihood systems is quantified in terms of two dimensions of general resilience and specific resilience using the spatial vector method from systems engineering. The IPCC framework was used to measure three dimensions of livelihood vulnerability: exposure, sensitivity, and adaptability, and a threshold regression model was applied to investigate the impacts of livelihood resilience on livelihood vulnerability. Results show that: (i) livelihood vulnerability of relocated households (−0.042) was significantly lower than that of local households (0.091), while relocated households in the process of livelihood reconstruction had a certain degree of vulnerability risk. (ii) there is a significant threshold effect of livelihood resilience on livelihood vulnerability of rural households. (iii) increasing livelihood resilience significantly reduced the degree of vulnerability of rural households, and the marginal effect of general resilience on livelihood vulnerability shows a decreasing trend. Specific resilience has a positive influence on livelihood vulnerability before it exceeds the threshold, but the effect disappears above the threshold of resilience. (iv) The threshold value of livelihood resilience of local households (0.5039) is generally lower than that of relocated households (0.6548), and relocation does improve the ability of rural households to resist uncertainty risks. It is necessary for local governments to formulate more targeted policies to reduce the livelihood vulnerability of rural households and thereby promote sustainable livelihood development.

Testing many constraints in possibly irregular models using incomplete U-statistics

Abstract We consider the problem of testing a null hypothesis defined by equality and inequality constraints on a statistical parameter. Testing such hypotheses can be challenging because the number of relevant constraints may be on the same order or even larger than the number of observed samples. Moreover, standard distributional approximations may be invalid due to irregularities in the null hypothesis. We propose a general testing methodology that aims to circumvent these difficulties. The constraints are estimated by incomplete U-statistics, and we derive critical values by Gaussian multiplier bootstrap. We show that the bootstrap approximation of incomplete U-statistics is valid for kernels that we call mixed degenerate when the number of combinations used to compute the incomplete U-statistic is of the same order as the sample size. It follows that our test controls type I error even in irregular settings. Furthermore, the bootstrap approximation covers high-dimensional settings making our testing strategy applicable for problems with many constraints. The methodology is applicable, in particular, when the constraints to be tested are polynomials in U-estimable parameters. As an application, we consider goodness-of-fit tests of latent-tree models for multivariate data.