Critical Index Research Articles

The impact of procedural justice on employee turnover intentions and the role of two mediators

Purpose While there is ample discussion in management studies and organizational behavior textbooks about the factors that impact organizational outcomes, such as employee retention, this research is focused on exploring the previously unexplored question of how procedural justice, job characteristics and meaningful work influence employees' intentions to leave their organizations. As such, this study aims to investigate the impact of procedural justice on employees' intentions to leave, both independently and in conjunction with job characteristics and meaningful work as mediators. Design/methodology/approach This study uses partial least squares structural equation modeling (PLS-SEM) to develop the research model and for hypothesis testing. The path model is assessed using critical model fit indices and measures of goodness of fit. Findings The results reveal a negative relationship between procedural justice and employees’ intentions to leave. This negative relationship persists and is strengthened when both job characteristics and meaningful work act as mediators. Although job characteristics only exerted a significant effect through indirect effects, meaningful work demonstrated a significant negative impact on the intentions to leave through both direct and indirect effects. Originality/value This study presents a new perspective on employee retention by proposing an original mediation-based path model. Through the testing of eleven hypotheses, the study reveals the intricate relationships between the four constructs examined. The findings provide valuable insights that can serve as a basis for future research in management studies and organizational behavior.

Development of a Full-Scale Connected U-Net for Reflectivity Inpainting in Spaceborne Radar Blind Zones

Abstract CloudSat’s Cloud Profiling Radar is a valuable tool for remotely monitoring high-latitude snowfall, but its ability to observe hydrometeor activity near the Earth’s surface is limited by a radar blind zone caused by ground clutter contamination. This study presents the development of a deeply supervised U-Net-style convolutional neural network to predict cold season reflectivity profiles within the blind zone at two Arctic locations. The network learns to predict the presence and intensity of near-surface hydrometeors by coupling latent features encoded in blind zone-aloft clouds with additional context from collocated atmospheric state variables (i.e., temperature, specific humidity, and wind speed). Results show that the U-Net predictions outperform traditional linear extrapolation methods, with low mean absolute error, a 38% higher Sørensen–Dice coefficient, and vertical reflectivity distributions 60% closer to observed values. The U-Net is also able to detect the presence of near-surface cloud with a critical success index (CSI) of 72% and cases of shallow cumuliform snowfall and virga with 18% higher CSI values compared to linear methods. An explainability analysis shows that reflectivity information throughout the scene, especially at cloud edges and at the 1.2-km blind zone threshold, along with atmospheric state variables near the tropopause, are the most significant contributors to model skill. This surface-trained generative inpainting technique has the potential to enhance current and future remote sensing precipitation missions by providing a better understanding of the nonlinear relationship between blind zone reflectivity values and the surrounding atmospheric state. Significance Statement Snowfall is a critical contributor to the global water–energy budget, with important connections to water resource management, flood mitigation, and ecosystem sustainability. However, traditional spaceborne remote monitoring of snowfall faces challenges due to a near-surface radar blind zone, which masks a portion of the atmosphere. In this study, a deep learning model was developed to fill in missing data across these regions using surface radar and atmospheric state variables. The model accurately predicts reflectivity, with significant improvements over conventional methods. This innovative approach enhances our understanding of reflectivity patterns and atmospheric interactions, bolstering advances in remote snowfall prediction.

Analyzing interrelationships among factors affecting hygiene and sanitization in India: key insights and policy recommendations

PurposeDespite the fact that hygiene and sanitation are becoming more critical for improving the present situation in developing nations, the factors that affect them are not well covered in the present research. This paper investigates the quality of the hygiene and sanitization factors and identifies the interrelations between the identified factors.Design/methodology/approachA graph theory-based approach is proposed to assess the factors influencing the practice, and a critical service index (CSI) is used to quantify the same.FindingsTwo Indian villages are used to illustrate the implementation of the suggested approach. This represents the validation of the suggested method, as well as assisting in the development of essential suggestions for increasing the quality of hygiene and sanitization in the Indian context. In spite of the increasing importance of hygiene and sanitation for improving the current situation in developing countries, the factors that influence them are not well-researched.Research limitations/implicationsThis study contributes in two ways. First, it provides an organized methodology for quantifying hygiene and sanitation factors and a critical service index that incorporates the findings. The suggested approach may also be used to evaluate and classify other sectors. Second, it shows how the methodology was used to create key recommendations for two Indian villages, which may be considered the first effort in India’s hygiene and sanitation initiatives.Originality/valueThis research discussed improvements in sanitation and hygiene habits among Indian households, which have not been achieved as expected under the Swachh Bharat Mission.

A tuning-free moderate-scale burned area detection algorithm — a case study in chornobyl-contaminated region

ABSTRACT The predefined threshold is one of the main fundamental and methodological challenges underlying the uncertainty and delayed release of existing burned area (BA) products. To improve the accuracy and timeliness of BA mapping, a tuning-free moderate-scale BA detection algorithm (AT&RF algorithm) was proposed to carry out rapid BA mapping for the immediate post-fire assessment. A case study was carried out at the Chornobyl Exclusion Zone (ChEZ) to assess the performance of the algorithm. The evaluation results indicated that the algorithm successfully detected the BA in 2015 and 2020 in ChEZ, which outperforms the existing BA products by significantly decreasing the omission error by 18.6% and 15.1% and improving the Kappa coefficient and critical success index by 9.4% and 4.0% on average for MCD64A1 and FireCCI51 products, respectively. It is also possible to transfer the algorithm quickly to other regions without complicated adjustments and significantly improve the timeliness and accuracy compared to the existing global BA products for assessing environmental impact and scope.

Threshold-based flood early warning in an urbanizing catchment through multi-source data integration: Satellite and citizen science contribution

An effective flood early warning system is vital to take action to save lives and protect properties in urban areas which are increasingly prone to flooding. Despite substantial progress in flood early warning systems, limited available and accessible data often impede their advancement and reliability. Engaging communities affected by flooding can help address data and information gaps in flood early warning systems, facilitated by appropriate methods. This study developed and evaluated a flood threshold combination method to support a community-based flood early warning system in the Akaki catchment, home to Addis Ababa, the capital city of Ethiopia. Various flood threshold combinations were formulated, calibrated and validated by integrating multiple sources of data: rainfall, antecedent precipitation index estimates, Sentinel-1 Synthetic Aperture Radar satellite time series of flood extent, long-term simulated streamflow, citizen science data, river water level and three days lead-time numerical weather prediction rainfall forecast. During validation, the rainfall and river water level threshold combination outperformed other threshold combinations with probability of detection, false alarm ratio, and critical success index estimates of 0.74, 0.18 and 0.63, respectively. The flood threshold combination showed high detection performance for most flooding conditions. Flood forecasts with a 1-day lead-time exhibited a high likelihood in detecting historical severe flood events. The study provides a tested methodology for selecting suitable flood threshold-combinations, enhance the engagement of citizen scientists in a community–based flood early warning system in urban communities.

Evaluation of Retrospective National Water Model Soil Moisture and Streamflow for Drought‐Monitoring Applications

AbstractThe National Oceanic and Atmospheric Administration (NOAA)’s National Water Model (NWM) provides analyses and predictions of hydrologic variables relevant to drought monitoring and forecasts at fine time and space scales (hourly, 0.25–1 km). We present results exploring the potential for NWM soil moisture and streamflow analyses to inform operational drought monitoring. Both agricultural and hydrologic drought monitoring rely either explicitly or implicitly on an accurate representation of anomalous soil moisture values. Much of our analysis focuses on comparisons of soil moisture anomalies in the NWM to those from in‐situ observations. To establish benchmarks for NWM soil moisture skill, we also include other gridded data sets currently used to inform the US Drought Monitor, specifically those from the North American Land Data Assimilation System phase 2 (NLDAS‐2) land surface models. We then compare NWM streamflow low flows with ∼500 stream gauges from the United States Geological Survey (USGS) Hydro‐Climatic Data Network of undisturbed basins. The NWM soil moisture simulation’s skill parallels that from NLDAS‐2. The accuracy of drought condition identification from NWM streamflow exceeds that based on soil moisture as determined by Critical Success Index scores for extreme dry percentiles. Different meteorological forcings are used in the operational NWM cycles than those used in this retrospective analysis. This forcing disconnect, together with concerns about current‐generation land surface model soil moisture‐transport schemes, inhibit its current operational use for drought monitoring.

Measurement-Induced Phase Transition for Free Fermions above One Dimension.

A theory of the measurement-induced entanglement phase transition for free-fermion models in d>1 dimensions is developed. The critical point separates a gapless phase with ℓ^{d-1}lnℓ scaling of the second cumulant of the particle number and of the entanglement entropy and an area-law phase with ℓ^{d-1} scaling, where ℓ is a size of the subsystem. The problem is mapped onto an SU(R) replica nonlinear sigma model in d+1 dimensions, with R→1. Using renormalization-group analysis, we calculate critical indices in one-loop approximation justified for d=1+ε with ε≪1. Further, we carry out a numerical study of the transition for a d=2 model on a square lattice, determine numerically the critical point, and estimate the critical index of the correlation length, ν≈1.4.

Bloom weighted bounds for sparse forms associated to commutators

In this paper we consider bilinear sparse forms intimately related to iterated commutators of a rather general class of operators. We establish Bloom weighted estimates for these forms in the full range of exponents, both in the diagonal and off-diagonal cases. As an application, we obtain new Bloom bounds for commutators of (maximal) rough homogeneous singular integrals and the Bochner–Riesz operator at the critical index. We also raise the question about the sharpness of our estimates. In particular we obtain the surprising fact that even in the case of Calderón–Zygmund operators, the previously known quantitative Bloom bounds are not sharp for the second and higher order commutators.

A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara Basin

Abstract. Precipitation is a vital key element in various studies of hydrology, flood prediction, drought monitoring, and water resource management. The main challenge in conducting studies over remote regions with rugged topography is that weather stations are usually scarce and unevenly distributed. However, open-source satellite-based precipitation products (SPPs) with a suitable resolution provide alternative options in these data-scarce regions, which are typically associated with high uncertainty. To reduce the uncertainty of individual satellite products, we have proposed a D-vine copula-based quantile regression (DVQR) model to merge multiple SPPs with rain gauges (RGs). The DVQR model was employed during the 2001–2017 summer monsoon seasons and compared with two other quantile regression methods based on the multivariate linear (MLQR) and the Bayesian model averaging (BMAQ) techniques, respectively, and with two traditional merging methods – the simple modeling average (SMA) and the one-outlier-removed average (OORA) – using descriptive and categorical statistics. Four SPPs have been considered in this study, namely, Tropical Applications of Meteorology using SATellite (TAMSAT v3.1), the Climate Prediction Center MORPHing Product Climate Data Record (CMORPH-CDR), Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG v06), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR). The bilinear (BIL) interpolation technique was applied to downscale SPPs from a coarse to a fine spatial resolution (1 km). The rugged-topography region of the upper Tekeze–Atbara Basin (UTAB) in Ethiopia was selected as the study area. The results indicate that the precipitation data estimates with the DVQR, MLQR, and BMAQ models and with traditional merging methods outperform the downscaled SPPs. Monthly evaluations reveal that all products perform better in July and September than in June and August due to precipitation variability. The DVQR, MLQR, and BMAQ models exhibit higher accuracy than the traditional merging methods over the UTAB. The DVQR model substantially improved all of the statistical metrics (CC = 0.80, NSE = 0.615, KGE = 0.785, MAE = 1.97 mm d−1, RMSE = 2.86 mm d−1, and PBIAS = 0.96 %) considered compared with the BMAQ and MLQR models. However, the DVQR model did not outperform the BMAQ and MLQR models with respect to the probability of detection (POD) and false-alarm ratio (FAR), although it had the best frequency bias index (FBI) and critical success index (CSI) among all of the employed models. Overall, the newly proposed merging approach improves the quality of SPPs and demonstrates the value of the proposed DVQR model in merging multiple SPPs over regions with rugged topography such as the UTAB.

MR-guided stereotactic radiation therapy for head and neck cancers

PurposeMR-guided radiotherapy (MRgRT) has the advantage of utilizing high soft tissue contrast imaging to track daily changes in target and critical organs throughout the entire radiation treatment course. Head and neck (HN) stereotactic body radiation therapy (SBRT) has been increasingly used to treat localized lesions within a shorter timeframe. The purpose of this study is to examine the dosimetric difference between the step-and-shot intensity modulated radiation therapy (IMRT) plans on Elekta Unity and our clinical volumetric modulated arc therapy (VMAT) plans on Varian TrueBeam for HN SBRT. MethodFourteen patients treated on TrueBeam sTx with VMAT treatment plans were re-planned in the Monaco treatment planning system for Elekta Unity MR-Linac (MRL). The plan qualities, including target coverage, conformity, homogeneity, nearby critical organ doses, gradient index and low dose bath volume, were compared between VMAT and Monaco IMRT plans. Additionally, we evaluated the Unity adaptive plans of adapt-to-position (ATP) and adapt-to-shape (ATS) workflows using simulated setup errors for five patients and assessed the outcomes of our treated patients. ResultsMonaco IMRT plans achieved comparable results to VMAT plans in terms of target coverage, uniformity and homogeneity, with slightly higher target maximum and mean doses. The critical organ doses in Monaco IMRT plans all met clinical goals; however, the mean doses and low dose bath volumes were higher than in VMAT plans. The adaptive plans demonstrated that the ATP workflow may result in degraded target coverage and OAR doses for HN SBRT, while the ATS workflow can maintain the plan quality. ConclusionThe use of Monaco treatment planning and online adaptation can achieve dosimetric results comparable to VMAT plans, with the additional benefits of real-time tracking of target volume and nearby critical structures. This offers the potential to treat aggressive and variable tumors in HN SBRT and improve local control and treatment toxicity.

Improving Precipitation Forecasting through Early Fusion and Spatiotemporal Prediction: A Case Study Using the MultiPred Model

Due to the complexity and uncertainty of meteorological systems, traditional precipitation forecasting methods have certain limitations. Therefore, based on the common characteristics of meteorological data, a precipitation forecasting model named MultiPred is proposed, with the goal of continuously predicting precipitation for 4 h in a specific region. This model combines the multimodal fusion method with recursive spatiotemporal prediction models. The training and testing process of the model roughly involves using spatial feature extraction networks and temporal feature extraction networks to generate preliminary predictions for multimodal data. Subsequently, a modal fusion layer is employed to further extract and fuse the spatial features of the preliminary predictions from the previous step, outputting the predicted precipitation values for the target area. Experimental tests and training were conducted using ERA5 multi-meteorological modal data and GPM satellite precipitation data from 2017 to 2020, covering longitudes from 110° to 122° and latitudes from 20° to 32°. The training set used data from the first three years, while the validation set and test set each comprised 50% of the data from the fourth year. The initial learning rate for the experiment was set to 1 × 10−4, and training was performed for 1000 epochs. Additionally, the training process utilized a loss function composed of Mean Absolute Error (MAE), Mean Squared Error (MSE), and Structural Similarity Index (SSIM). The model was evaluated using the Critical Success Index (CSI), Probability of Detection (POD), and the Heidke Skill Score (HSS). Experimental results demonstrate that MultiPred excels in precipitation forecasting, particularly for light precipitation events with amounts greater than or equal to 0.1 mm and less than 2 mm. It achieves optimal performance in both light and heavy precipitation forecasting tasks.

The influence of Urmia Lake desiccation on an extreme snowfall event: A case study using the WRF-Lake model

Urmia Lake in the northwest Iran has undergone significant desiccation in recent decades, shrinking to a much smaller size than its original. This study implements the WRF-Lake coupled modeling system to examine the impacts of the observed physical changes over the Lake during a heavy snowfall event occurred February 1–2, 2017. Modifications are introduced to represent the shrunken, salt-encrusted lake surface and surrounding arid environment through updating land use, soil characteristics, lake size and depth, and albedo data. Experiments incorporating lake-atmosphere coupling and land surface refinements (Lake_CTL, Lake_LULC, Lake_AL) outperform simulations without a lake (WRF_noLake) in reproducing observed precipitation patterns at stations surrounding the Urmia Lake. All simulations underestimate the extreme 290 mm snowfall recorded at the Urmia station; however, the Lake_LULC and Lake_AL improve snowfall representation at other stations around the lake. Regarding snow cover validation, Lake_LULC and Lake_AL exhibit a higher correlation with satellite observations and show a significant improvement in snow detection near Urmia Lake. They have a Probability of Detection (POD) of 0.94 and a Critical Success Index (CSI) of 0.91, compared to a POD of 0.79–0.80 and a CSIs of ∼0.77 for the default lake model (Lake_CTL) and the WRF_noLake experiments, respectively. Additionally, adjusting the initial lake surface temperature to match observations substantially reduces cold biases in near-surface air temperatures over Urmia Lake. The lack of lake temperature updating in WRF-Lake poses ongoing challenges in the model, though. In summary, this study demonstrates that refining the representations of desiccated lakes and their surroundings in high-resolution coupled models would improve simulations of meteorological processes influenced by lake-atmosphere interactions.

On the Dependence of the Critical Success Index (CSI) on Prevalence.

The critical success index (CSI) is an established metric used in meteorology to verify the accuracy of weather forecasts. It is defined as the ratio of hits to the sum of hits, false alarms, and misses. Translationally, CSI has gained popularity as a unitary outcome measure in various clinical situations where large numbers of true negatives may influence the interpretation of other, more traditional, outcome measures, such as specificity (Spec) and negative predictive value (NPV), or when unified interpretation of positive predictive value (PPV) and sensitivity (Sens) is needed. The derivation of CSI from measures including PPV has prompted questions as to whether and how CSI values may vary with disease prevalence (P), just as PPV estimates are dependent on P, and hence whether CSI values are generalizable between studies with differing prevalences. As no detailed study of the relation of CSI to prevalence has been undertaken hitherto, the dataset of a previously published test accuracy study of a cognitive screening instrument was interrogated to address this question. Three different methods were used to examine the change in CSI across a range of prevalences, using both the Bayes formula and equations directly relating CSI to Sens, PPV, P, and the test threshold (Q). These approaches showed that, as expected, CSI does vary with prevalence, but the dependence differs according to the method of calculation that is adopted. Bayesian rescaling of both Sens and PPV generates a concave curve, suggesting that CSI will be maximal at a particular prevalence, which may vary according to the particular dataset.

Toxins profiles, toxicological properties, and histological alteration potentiality of Trimeresurus erythrurus venom: In vitro and in vivo experiments

ObjectivesTrimeresurus erythrurus (Red-tailed bamboo pit viper) contributes significantly to snakebite envenoming cases in Bangladesh, India, Myanmar, and Nepal. However, the absence of specific antivenom and challenging socio-economic conditions in these regions necessitate the development of symptom-based medication to mitigate the severity of T. erythrurus envenoming. Therefore, documenting the effects of T. erythrurus venom on mammalian organs can establish a foundation for further research on symptom-based medication. MethodsWe profiled T. erythrurus venom toxins using SDS-PAGE. Additionally, we assessed the venom's toxicological properties through lethality, hemorrhagic, edematogenic, hemolytic, and phospholytic assays. Furthermore, we examined the histological alterations induced by crude venom on various organs of mice. ResultsSDS-PAGE profiling confirmed the presence of Snake Venom Metalloproteases (SVMPs), Snake Venom Serine Proteases (SVSPs), Phospholipase A2 (PLA2), L-amino Acid Oxidases (LAAOs), Myotoxins, and Disintegrins as toxic components in T. erythrurus venom. The venom exhibited a Median Lethal Dose (LD50) of 1.131 mg/kg, Minimum Hemorrhagic Dose (MHD) of 0.21 mg/kg, Minimum Edematogenic Dose (MED) of 0.17 mg/kg, and Median Hemolytic Dose (HD50) of 2.527 mg/L. Moreover, the venom induced significant pathological changes in mammalian skin, skeletal muscle, lung, heart, kidney, and intestine. ConclusionsThis study elucidates the major toxins, critical toxicological indices, and histopathological effects of T. erythrurus venom on mammalian organs. These findings provide valuable insights on the development of effective treatment strategies for pathophysiological complications that could manifest post T. erythrurus envenoming.

Detection of limit cycle signatures of El Niño in models and observations using reservoir computing

While the physics of the El Niño–Southern Oscillation (ENSO) phenomenon in the Tropical Pacific is quite well understood, there is still debate on several more fundamental aspects. The focus of this paper is on one of these issues that deals with whether ENSO variability, within the recharge-discharge oscillator theory arising from a stochastic Hopf bifurcation, is subcritical or supercritical. Using a Reservoir Computing method, we develop a criticality index as an indicator for the presence of a limit cycle in noisy time series. The utility of this index is shown in three members of a hierarchy of ENSO models: a conceptual box model, the classical Zebiak-Cane model and a state-of-the-art Global Climate Model. Finally, the criticality index is determined from observations, leading to the result that ENSO variability appears to be subcritical.

Validating GOES Radar Estimation via Machine Learning to Inform NWP (GREMLIN) Product over CONUS

Abstract Geostationary Operational Environmental Satellites (GOES) Radar Estimation via Machine Learning to Inform NWP (GREMLIN) is a machine learning model that outputs composite reflectivity using GOES-R Series Advanced Baseline Imager (ABI) and Geostationary Lightning Mapper (GLM) input data. GREMLIN is useful for observing severe weather and initializing convection for short-term forecasts, especially over regions without ground-based radars. This study expands the evaluation of GREMLIN’s accuracy against the Multi-Radar Multi-Sensor (MRMS) System to the entire contiguous United States (CONUS) for the entire annual cycle. Regional and temporal variation of validation metrics are examined over CONUS by season, day of year, and time of day. Since GREMLIN was trained with data in spring and summer, root-mean-square difference (RMSD) and bias are lowest in the order of summer, spring, fall, and winter. In summer, diurnal patterns of RMSD follow those of precipitation occurrence. Winter has the highest RMSD because of cold surfaces mistaken as precipitating clouds, but some of these errors can be removed by applying the ABI clear-sky mask product and correcting biases using a lookup table. In GREMLIN, strong echoes are closely related to the existence of lightning and corresponding low brightness temperatures, which result in different error distributions over different regions of CONUS. This leads to negative biases in cold seasons over Washington State, lower 30-dBZ critical success index caused by high misses over the Northeast, and higher false alarms over Florida that are due to higher frequency of lightning.

Improved Diagnosis of Precipitation Type with LightGBM Machine Learning

Abstract Existing precipitation-type algorithms have difficulty discerning the occurrence of freezing rain and ice pellets. These inherent biases are not only problematic in operational forecasting but also complicate the development of model-based precipitation-type climatologies. To address these issues, this paper introduces a novel light gradient-boosting machine (LightGBM)-based machine learning precipitation-type algorithm that utilizes reanalysis and surface observations. By comparing it with the Bourgouin precipitation-type algorithm as a baseline, we demonstrate that our algorithm improves the critical success index (CSI) for all examined precipitation types. Moreover, when compared with the precipitation-type diagnosis in reanalysis, our algorithm exhibits increased F1 scores for snow, freezing rain, and ice pellets. Subsequently, we utilize the algorithm to compute a freezing-rain climatology over the eastern United States. The resulting climatology pattern aligns well with observations; however, a significant mean bias is observed. We interpret this bias to be influenced by both the algorithm itself and assumptions regarding precipitation processes, which include biases associated with freezing drizzle, precipitation occurrence, and regional synoptic weather patterns. To mitigate the overall bias, we propose increasing the precipitation cutoff from 0.04 to 0.25 mm h−1, as it better reflects the precision of precipitation observations. This adjustment yields a substantial reduction in the overall bias. Finally, given the strong performance of LightGBM in predicting mixed precipitation episodes, we anticipate that the algorithm can be effectively utilized in operational settings and for diagnosing precipitation types in climate model outputs. Significance Statement Freezing rain can have significant impacts on transportation and infrastructure, making accurate prediction of precipitation types crucial. In this study, we use a machine learning method known as LightGBM to predict precipitation types. We show that the new algorithm performs better than the existing methods for all precipitation types examined. Additionally, we compute a freezing-rain climatology over the eastern United States. Although the resulting climatology pattern corresponds well to observations, the algorithm overpredicts freezing-rain occurrence. We argue that this bias can be substantially reduced by increasing the precipitation cutoff from 0.04 to 0.25 mm h−1. Overall, this work highlights the potential of the LightGBM algorithm for both weather forecasting and diagnosing precipitation types in climate models.

MALAT1 expression in granulosa cells in PCOS patients with different phenotypes

Polycystic ovary syndrome (PCOS) is one of the most common reproductive endocrine metabolic disorders. The lncRNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) reportedly can regulate the reproductive system. Despite studies, the alteration of MALAT1 expression in granulosa cells (GCs) from PCOS patients was inconsistent. To evaluate MALAT1 expression in GCs in different PCOS subgroups and its association with PCOS phenotypes, we collected GCs from 110 PCOS cases and 71 controls, and examined MALAT1 expression by quantitative PCR. The results showed MALAT1 expression was upregulated in PCOS cases, especially in insulin resistant (IR) PCOS subgroup, obese PCOS subgroup and non-hyperandrogenic (NHA) PCOS subgroup. MALAT1 expression was positively correlated with BMI and several metabolic parameters in controls. Interestingly, MALAT1 expression was notably associated with some critical endocrine indexes for PCOS, including E2, FSH, LH and LH/FSH ratio. In different PCOS subgroups, we found significant positive correlations with LH/FSH ratio in IR-PCOS and PCOS with normal weight, and with serum T and LH level in NHA-PCOS subgroup. Integrated analysis with lncRNA target databases and PCOS-related databases revealed MALAT1 could participate in PCOS by influencing immune response and lipids metabolism in GCs. In conclusion, MALAT1 was differently expressed in GCs in PCOS, especially in IR, obese and NHA PCOS subgroups. MALAT1 was likely involved in metabolism and immune response in GCs in PCOS. However, more studies are necessary to establish this concept.

Prior-Guided gated convolutional networks for rainstorm forecasting

Accurate rainstorm forecasting is crucial for the sustainable development of human society. Recently, machine learning-based rainstorm prediction methods have shown promising results. However, these methods often fail to adequately consider the prior knowledge of rainstorms and do not explicitly account for the dynamic spatio-temporal patterns of rainstorm events. This study introduces a novel end-to-end prior-informed rainstorm forecasting model that incorporates both fundamental physical priors and the spatio-temporal development patterns of rainstorms. The model utilizes a gated convolutional encoder-decoder network to effectively represent the spatio-temporal patterns of rainstorm events. A key component of the representation network is the Substantial Derivative-GuIded gated convolutional Unit (SDGiU), which updates latent states under the constraints of physical priors. Additionally, an integrated loss function is designed to minimize reconstruction errors on multiple scales and facilitate the generation of forecasts that reproduce the actual spatio-temporal patterns of rainstorm formation, development and dissipation. Experimental results on two reanalysis datasets show that the proposed forecasting model outperforms competing state-of-the-art baselines by at least 19.7% (15.0%) in overall Critical Success Index (Heidke Skill Score). Qualitative analysis indicates that the proposed model can generate predictions that are both physically consistent and spatially-temporally coherent.

Advanced prescription of injectable anticancer drugs: Safety assessment in a European Comprehensive Cancer Centre using the risk matrix approach.

The purpose of this work was to assess failures in the advanced prescription of parenteral anticancer agents in an adult day oncology care unit with more than 100 patients per day. An a priori descriptive analysis was carried out by using the risk matrix approach. After defining the scope in a multidisciplinary meeting, we determined at each step the failure modes (FMs), their effects (E) and their associated causes (C). A severity score (S) was assigned to all effects and a probability of occurrence (O) to all causes. These S and O indicators, were used to obtain a criticality index (CI) matrix. We assessed the risk control (RC) of each failure in order to define a residual criticality index (rCI) matrix. During risk analysis, 14 FMs were detected, and 61 scenarios were identified considering all possible effects and causes. Nine situations (15%) were highlighted with the maximum CI, 18 (30%) with a medium CI, and 34 (55%) with a negligible CI. Nevertheless, among all these critical situations, only three (5%) had an rCI to process (i.e., missed dose adjustment, multiple prescriptions and abnormal biology data); the others required monitoring only. Clinicians' and pharmacists' knowledge of these critical situations enables them to manage the associated risks. Advanced prescription of injectable anticancer drugs appears to be a safe practice for patients when combined with risk management. The major risks identified concerned missed dose adjustment, prescription duplication and lack of consideration for abnormal biology data.