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Heme oxygenase-1 (HO-1, HMOX1) degrades heme protecting cells from heme-induced oxidative damage. Beyond its well-established cellular functions, heme has emerged as a stabilizer of G-quadruplexes. These secondary DNA structures interfere with DNA replication. We recently revealed that nuclear HO-1 colocalizes with DNA G-quadruplexes and promotes their removal. Here, we investigate whether HO-1 safeguards cells against replication stress.Experiments were conducted in control and HMOX1-deficient HEK293T cell lines. Immunostaining unveiled that DNA G-quadruplexes accumulated in the absence of HO-1, the effect that was further enhanced in response to δ-aminolevulinic acid (ALA), a substrate in heme synthesis. This was associated with replication stress, as evidenced by an elevated proportion of stalled forks analyzed by fiber assay. We observed the same effects in hematopoietic stem cells isolated from Hmox1 knockout mice and in a lymphoblastoid cell line from an HMOX1-deficient patient. Interestingly, in the absence of HO-1, the speed of fork progression was higher, and the response to DNA conformational hindrance less stringent, indicating dysfunction of the PARP1-p53-p21 axis. PARP1 activity was not decreased in the absence of HO-1. Instead, we observed that HO-1 deficiency impairs the nuclear import and accumulation of p53, an effect dependent on the removal of excess heme. We also demonstrated that administering ALA is a more specific method for increasing intracellular free heme compared to treatment with hemin, which in turn induces strong lipid peroxidation.Our results indicate that protection against replication stress is a universal feature of HO-1, presumably contributing to its widely recognized cytoprotective activity.

Purpose – This paper analyzes individual subjective well-being using a survey database from the Strasbourg metropolitan development council (France). The authors focus on the effects of externalities generated by public services (transport, culture and sport), environmental quality and feeling of security in the Strasbourg metropolitan area (Eurométropole de Strasbourg, EMS). Results show that EMS specificities (public facilities, environmental quality, safety and security) and individual features like opportunities to laugh or live with children significantly influence individual well-being. These findings are robust when using three subjective measures: feeling of well-being, environmental satisfaction and social life satisfaction. The authors also show that income may affect the perceived well-being of individuals belonging to a low-income group, while individuals belonging to a high-income group tend to be unsatisfied with environmental quality but satisfied with their social life. Besides, social comparison in terms of income does not matter for individual well-being in the Strasbourg metropolitan area. Design/methodology/approach – Theoretical and empirical paper —Utility theory in economics—Econometric modeling using an ordered probit model. Findings – Specificities of the Strasbourg metropolitan area-France (public services related to transport, culture and sport, environmental quality perceived as convenient for individual health, sense of security) significantly impact individual subjective well-being. Income does not substantially impact the individual subjective perception of happiness: income may matter for the feeling of well-being only for individuals belonging to a low-income group. Wealthy individuals tend to be unsatisfied with environmental quality but satisfied with their social life. Social comparison in terms of income does not matter for individual well-being in the Strasbourg metropolitan area. Research limitations/implications – Cross-sectional data, but it is the only available database from a survey conducted by EMS in 2017 to collect information on potential elements relative to individual well-being in the Strasbourg metropolitan area. Practical implications – Results shed light on the role of territorial policies in improving individual well-being and might provide some guidelines for policy-makers concerned about the population’s welfare. Policy-makers should give strong attention to public facilities (an essential element of local public action) and improve environmental quality. If they care about the population’s happiness, they have to reorient current policies in this direction. Of course, through the inquiry in 2017 giving this database, the Strasbourg agglomeration development council aimed to provide such evidence to the local administration. Nevertheless, the results were a bit upsetting for many people in the administrative and political circles, who generally prioritize economic and demographic development, while the citizens’ responses to the inquiry have revealed a strong focus on the quality of everyday life in their neighborhood. Originality/value – The present study contributes to the literature on subjective well-being, with a focus on the role of local characteristics and living environment. The authors’ starting point is related to the standard utility theory, indicating that environmental quality and public services are positive externalities. The authors investigate whether the local living environment and public facilities are crucial elements explaining individual well-being. To do this, we consider three subjective measures: feeling of well-being, environmental satisfaction and social life satisfaction, which are used as proxies of individual utility. The authors consider different explicative variables representing specificities of EMS in terms of public services (transport, culture and sport), environmental quality perceived as convenient for individual health, safety and security, etc. The authors also provide a test for relative standing by including the median monthly household income at the municipality level.

Purpose – The authors estimated the hidden overhead (capital diversion or wasteful use of capital) of Vietnam state-owned enterprises (SOEs). Design/methodology/approach – The authors used a panel data set of 10,200 Vietnam SOEs observed over the period 2010–2018. The authors modeled and estimated the hidden overhead by using a stochastic production frontier. The hidden overhead parameter is modelled as the technical inefficiency in the production function. Findings – Vietnam SOEs are very capital intensive. The hidden overhead (or the wasteful use of capital) is very high with an average rate of 69%. Research limitations/implications – Alternative estimation methods should be used to account for endogeneity in production inputs. Lack of comparison with the Vietnam private firms. Originality/value – The paper proposes an original way to quantify hidden overhead (or capital diversion) in the Vietnam SOEs. The finding (a capital diversion rate of 69% on average) is astonishing. It calls for an urgent and profound reform of the Vietnam SOEs.

Curcuma sahuynhensis Škorničk. & N.S. Lý has been discovered recently whose antiviral potential is unknown, thus deserved for discovery-phase screening. A combination of experimental characterization, quantum calculation, molecular docking simulation, physicochemical analysis, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) was designed for the theoretical argument on the potentiality of oil-based components (1−27) against H5 hemagglutinin in influenza A virus (PDB-5E32), wild-variant SARS-CoV-2 main protease (PDB-6LU7), and SARS-CoV-2 Omicron spike protein (PDB-7T9J). Theoretical arguments based on various computational platforms specify the most promising bio-inhibitors, i.e. 23 (bio-compatibility: ground energy −966.73 a.u., dipole moment 3.708 Debye; bio-inhibitability: DS̅\bar{{\rm{DS}}} −12.5 kcal mol−1; drug-likeness: mass 304.7 amu, log P 1.31; polar-interactability: polarizability 32.8 Å3) and 26 (bio-compatibility: ground energy −1393.66109 a.u., dipole moment 5.087 Debye; bio-inhibitability: DS̅\bar{{\rm{DS}}} −11.9 kcal mol−1; drug-likeness: mass 437.5 amu, log P 4.28; polar-interactability: polarizability 45.7 Å3). The pkCSM-ADMET model confirms their favorable pharmacokinetics and pharmacology. The total essence is unsuitable for use as an antiviral source in its pure form since the most bioactive candidates are accountable for the small content. Particularly, 23 (7β-hydroxydehydroepiandrosterone) and 26 (ethyl cholate) are recommended for further experimental efforts of isolation and bioassaying trials.

In the past decade, Saudi Arabia has witnessed a surge in flash floods, resulting in significant losses of lives and property. This raises a need for accurate near-real-time precipitation estimates. Satellite products offer precipitation data with high spatial and temporal resolutions. Among these, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Dynamic Infrared Rain Rate near-real-time (PDIR-Now) stands out as a novel, global, and long-term resource. In this study, a rigorous comparative analysis was conducted from 2017 to 2022, contrasting PDIR-Now with rain gauge data. This analysis employs six metrics to assess the accuracy of PDIR-Now across various daily rainfall rates and four yearly extreme precipitation indices. The findings reveal that PDIR-Now slightly underestimates light precipitation but significantly underestimates heavy precipitation. Challenges arise in regions characterized by orographic rainfall patterns in the southwestern area of Saudi Arabia, emphasizing the importance of spatial resolution and topographical considerations. While PDIR-Now successfully captures annual maximum 1-day and 5-day precipitation measurements across rain gauge locations, it exhibits limitations in the length of wet and dry spells. This research highlights the potential of PDIR-Now as a valuable tool for precipitation estimation, offering valuable insights for hydrological, climatological, and water resource management studies.

Locked thumb metacarpophalangeal (MCP) joint has been known as a relatively rare condition. We present a case of a 25-year-old male presented with a traumatic locked thumb MCP joint of the right hand. Clinical presentation showed the joint was in a hyperextention position and resistant to active flexion and extension. The radial sesamoid was distally displaced on X-ray film and entrapped into the joint on CT scan. The mechanism of the locked MCP joint was thought as a “pull-hook lock” between the pointed proximal edge of the sesamoid and the deformed volar groove on the cartilaginous surface of the metacarpal head. Surgical treatment included partially releasing the insertion of the abductor pollicis brevis (APB) and flexor pollicis brevis (FPB) muscles to move the entrapped radial sesamoid, and resurfacing the deformed metacarpal head.

Abstract Background Renal hypoperfusion is one of the most common causes of acute kidney injury (AKI), especially in shock and perioperative patients. An optimal blood pressure (BP) target to prevent AKI remains undetermined. We conducted a systematic review and meta-analysis of available randomized clinical trial (RCT) results to address this knowledge gap. Methods From inception to May 13, 2022, we searched Ovid Medline, EMBASE, Cochrane Library, SCOPUS, clinicaltrials.gov, and WHO ICTRP for RCTs comparing higher BP target versus normotension in hemodynamically unstable patients (shock, post-cardiac arrest, or surgery patients). The outcomes of interest were post-intervention AKI rate and renal replacement therapy (RRT) rate. Two investigators independently screened the citations and reviewed the full texts for eligible studies according to a predefined form. Results Twelve trials were included, enrolling a total of 5759 participants, with shock, non-cardiac, and cardiac surgery patients accounting for 3282 (57.0%), 1687 (29.3%) and 790 (13.7%) patients, respectively. Compared to lower mean arterial blood pressure (MAP) targets that served as normotension, targeting higher MAP had no significant effect on AKI rates in shock (RR [95% CI] = 1.10 [0.93, 1.29]), in cardiac-surgery (RR [95% CI] = 0.87 [0.73, 1.03]) and non-cardiac surgery patients (RR [95% CI] = 1.25 [0.98, 1.60]) using random-effects meta-analyses. In shock patients with premorbid hypertension, however, targeting MAP above 70 mmHg resulted in significantly lower RRT risks, RR [95%CI] = 1.20 [1.03, 1.41], p

Abstract Increases in precipitation rates and volumes from tropical cyclones (TCs) caused by anthropogenic warming are predicted by climate modeling studies and have been identified in several high intensity storms occurring over the last half decade. However, it has been difficult to detect historical trends in TC precipitation at time scales long enough to overcome natural climate variability because of limitations in existing precipitation observations. We introduce an experimental global high-resolution climate data record of precipitation produced using infrared satellite imagery and corrected at the monthly scale by a gauge-derived product that shows generally good performance during two hurricane case studies but estimates higher mean precipitation rates in the tropics than the evaluation datasets. General increases in mean and extreme rainfall rates during the study period of 1980–2019 are identified, culminating in a 12–18%/40-year increase in global rainfall rates. Overall, all basins have experienced intensification in precipitation rates. Increases in rainfall rates have boosted the mean precipitation volume of global TCs by 7–15%/year, with the starkest rises seen in the North Atlantic, South Indian, and South Pacific basins (maximum 59–64% over 40 years). In terms of inland rainfall totals, year-by-year trends are generally positive due to increasing TC frequency, slower decay over land, and more intense rainfall, with an alarming increase of 81–85% seen from the strongest global TCs. As the global trend in precipitation rates follows expectations from warming sea surface temperatures (11.1%/°C), we hypothesize that the observed trends could be a result of anthropogenic warming creating greater concentrations of water vapor in the atmosphere, though retrospective studies of TC dynamics over the period are needed to confirm.

This paper uses Molecular Dynamics (MD) method to study the influence of high temperature (T) and high pressure (P) on the structure and phase transition of Fe2O3 materials. The results show that, when increasing the temperature from T = 300 K to T = 7000 K, P = 0.0 GPa, the size (1) of the Fe2O3 materials increases, the energy (E) increases, the length link (r) decreased, the number of structural units FeO4, FeO5 increased, and FeO6 decreased. Similarly, as the pressure (P) is increased, from P = 0 GPa to P = 360 GPa at temperatures T, l decreases, E increases, r decreases, FeO4 decreases and disappears, FeO5 decreases, and FeO6 increases at high P with P ≥ 150 GPa, FeO5 disappeared at P ≥ 250 GPa and only FeO6 appeared at T = 2300, 7000 K. In addition, when increasing T, P, the bond angle of Fe–O–Fe, O–Fe–O decreases, E increases, r decreases, l increases when T increases and l decreases when P increases, leading to the number of structural units FeO4, FeO5 increasing and FeO6 decreasing when T increases and vice versa when P increases. In addition, the phase transition temperature (Tm), Tm = 2300 K was determined. All the obtained results will be the basis for future experimental studies of amorphous Fe2O3 materials

Near-real-time satellite precipitation estimation is indispensable in areas where ground-based measurements are not available. In this study, an evaluation of two near-real-time products from the Center for Hydrometeorology and Remote Sensing at the University of California, Irvine—PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Cloud Classification System) and PDIR-Now (PERSIANN-Dynamic Infrared Rain Rate near-real-time)—were compared to each other and evaluated against IMERG Final (Integrated Multi-satellite Retrievals for Global Precipitation Measurement—Final Run) from 2015 to 2020 over the Mekong River Basin and Delta (MRB) using a spatial resolution of 0.1∘ by 0.1∘ and at a daily scale. PERSIANN-CDR (PERSIANN-Climate Data Record) was also included in the evaluation but was not compared against the real-time products. In this evaluation, PDIR-Now exhibited a superior performance to that of PERSIANN-CCS, and the performance of PERSIANN-CDR was deemed satisfactory. The second part of the study entailed performing a Mann–Kendall trend test of extreme precipitation indices using 38 years of PERSIANN-CDR data over the MRB. This annual trend analysis showed that extreme precipitation over the 95th and 99th percentiles has decreased over the Upper Mekong River Basin, and the consecutive number of wet days has increased over the Lower Mekong River Basin.

In this report, we develop a low-cost cyclic voltammetry platform for electrochemical analyses based on the LMP91000 integrated circuit (IC). The proposed system can be suitable to detect chemicals of interest in food safety or biology. The system includes three main blocks: the electrodes, electrochemical measuring circuit, and data acquisition. An algorithm was also built to process and display the data on the screen. Experiments are carried out with a standard redox solution containing Fe(II)/Fe(III) 5 mM and KCl 0.1 mM to evaluate system performance and make comparisons to the commercial potentiostat. The results show that the obtained signals from the proposed system and the commercial potentiostat device are similar. With applications of the miniaturised potentiostat, a non-enzymatic electrochemical biosensor for urea detection was fabricated on cobalt sulphide materials (CoS) synthesized via a microwave assisted method. The sensing performance for urea was determined by changing the oxidation potential peak of 100 mV. The sensor had a linear range from 1-8 mM corresponding to the urea concentration in blood. The sensor sensitivity was comparable (7.5 µAmM-1cm-2 ) to some previous publications. With the achieved results, the proposed system demonstrates potential in urea detection and can be a prototype to develop a commercial device for point-of-care testing in the future.

Abstract Due to the mixed distribution of buildings and vegetation, wildland-urban interface (WUI) areas are characterized by complex fuel distributions and geographical environments. The behavior of wildfires occurring in the WUI often leads to severe hazards and significant damage to man-made structures. Therefore, WUI areas warrant more attention during the wildfire season. Due to the ever-changing dynamic nature of California’s population and housing, the update frequency and resolution of WUI maps that are currently used can no longer meet the needs and challenges of wildfire management and resource allocation for suppression and mitigation efforts. Recent developments in remote sensing technology and data analysis algorithms pose new opportunities for improving WUI mapping methods. WUI areas in California were directly mapped using building footprints extracted from remote sensing data by Microsoft along with the fuel vegetation cover from the LANDFIRE dataset in this study. To accommodate the new type of datasets, we developed a threshold criteria for mapping WUI based on statistical analysis, as opposed to using more ad-hoc criteria as used in previous mapping approaches. This method removes the reliance on census data in WUI mapping, and does not require the calculation of housing density. Moreover, this approach designates the adjacent areas of each building with large and dense parcels of vegetation as WUI, which can not only refine the scope and resolution of the WUI areas to individual buildings, but also avoids zoning issues and uncertainties in housing density calculation. Besides, the new method has the capability of updating the WUI map in real-time according to the operational needs. Therefore, this method is suitable for local governments to map local WUI areas, as well as formulating detailed wildfire emergency plans, evacuation routes, and management measures.

Previous studies have demonstrated the ability of osseointegration of porous titanium implants in cancellous bone. Our study was designed to (i) investigate the ability of bone ingrowth into 3D-printed porous titanium alloy implant on the cortical bone of rabbits using CT-scan and histology, and (ii) to identify the consistency of the radiology information between clinical Cone Beam Computed Tomography (CBCT) and Micro Computed Tomography (μCT) in the evaluation of bone ingrowth. The porous titanium alloy implants were 3D-printed employing the Electron Beam Melting (EBM) technology with an intended pore size of 600 μm and porosity of approximately 50 percent. Each implant was inserted into tibial diaphysis in one rabbit and its pores were classified as contacting bone or non-contacting bone. Depending on the time of explantation, the rabbits were divided into two groups: group 1 consisting of 6 rabbits between 13 and 20 weeks and group 2 consisting of 6 rabbits between 26 and 32 weeks. Tissue ingrowth into the non-bone contacting pores were evaluated by CBCT and histology. μCT was used to further investigate the bone ingrowth into four implants (two from each group were randomly chosen). The CBCT detected the present of tissue with bone-like density in both bone-contacting pores and non-bone-contacting pores of all implants. The μCT analysis also supported this result. All the bone-like tissues were then histologically confirmed to be mature bone. The analysis of CBCT data to assess bone ingrowth in porous implants had the sensitivity, specificity, positive and negative predictive values of 85, 84, 93 and 70 percent, respectively, when considering μCT assessment as the gold standard. Fully porous titanium alloy implant has great potential to reconstruct diaphyseal bone defect due to its good ability of osseointegration. CBCT is a promising method for evaluation of bone ingrowth into porous implants.

Abstract Past studies reported a drastic growth in the wildland–urban interface (WUI), the location where man‐made structures meet or overlap wildland vegetation. Fighting fire is difficult in the WUI due to the combination of wildland and structural fuels, and therefore, WUI areas are characterized by frequent damage and loss of structures from wildfires. Recent wildland fire policy has targeted fire prevention, evacuation planning, fuel treatment, and home hardening in WUI areas. Therefore, it is important to understand the occurrence of wildfire events relative to the location of the WUI. In this work, we have reported the occurrences of wildfires with respect to the WUI and quantified how much of the WUI is on complex topography in California, which intensifies fire behavior and complicates fire suppression. We have additionally analyzed the relative importance of WUI‐related parameters, such as housing density, vegetation density, and distance to wildfires, as well as topographic factors, such as slope, elevation, aspect, and surface roughness, on the occurrence of large and small wildfires and the burned area of large wildfires near the WUI. We found that a very small percentage of wildfire ignition points and large wildfire‐burned areas (>400 ha or 1000 acres) were located in the WUI areas. A small percentage of large wildfires were encountered in WUI (3%), and the WUI area accounted for only 4% of the area burned, which increased to 5% and 56%, respectively, outside WUI (5‐km buffer from WUI). Similarly, 66% of fires ignited outside WUI, whereas only 3.6% ignited within WUI. Results from this study have implications for fuel management and infrastructure hardening, as well as for fire suppression and community response.

Papiliotrema huenov was previously reported to be highly tolerant of a range of extremely toxic heavy metals. This study aimed to identify the potential of P. huenov to remove manganese and copper from aqueous solution. Physical conditions which affect removal of Mn(II) and Cu(II) were determined. Optimal temperature for adsorption of both metal ions was 30 °C, and optimal pH for maximum uptake of Mn(II) and Cu(II) were 5 and 6, respectively. Under these conditions, living cells of P. huenov accumulated up to 75.58% of 110 mg/L Mn(II) and 70.5% of 128 mg/L Cu(II) over 120 h, whereas, the removal efficiency of metal ions by dead cells over 1 h was 60.3% and 56.5%, respectively. These results indicate that living cells are more effective than dead biomass for bioremediation, but that greater time is required. The experimental data extends the potential use of P. huenov in biosorption and bioaccumulation of toxic heavy metals to copper and manganese, two of the most common industrial contaminants.

Measurement(s) volume of hydrological precipitation • spatial resolution • spatial coverage • temporal resolution • temporal coverage Technology Type(s) digital curation Sample Characteristic - Location 60°S - 60°N Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14656452

Variability and spatiotemporal changes in precipitation characteristics can have profound socioenvironmental impacts. Several studies have shown that the frequency and/or magnitude of precipitation events have changed over the contiguous United States (CONUS) in the past decades. Most previous studies used only one precipitation dataset and only investigated mean or extreme precipitation. Here, using 6 gridded daily precipitation datasets, we show that there are substantial discrepancies in the changes in characteristics of both extreme and non-extreme precipitation events from 1983 to 2017. Our results highlight that using a single record to study precipitation changes can potentially lead to biased results. Using different datasets enables examining the overall agreements and discrepancies in precipitation characteristics. For example, we show that almost all datasets agree that some areas show statistically significant changes in the annual precipitation maxima; however, the locations and signs of changes are not consistent across datasets. There is a relative agreement between datasets on changes in the total annual precipitation. When examining other percentiles of the precipitation distribution, including non-extreme values, however, we find widespread discrepancies among different precipitation products (e.g., what part of the precipitation distribution is changing). In fact, depending on the source of data, there exist opposing trends and patterns of change in precipitation characteristics. This highlights the need to further investigate non-extreme precipitation events to unravel potential non-extreme but “unexpected” or “unusual” patterns. Finally, we argue that protocols for data selection are needed to address the issue of inter-data variability and to ensure reliability of statistical analysis.

In this paper, the structure and phase transition temperature of bulk silicate materials are studied by the simulation method (SM) of molecular dynamics (MD). In this research, all samples are prepared on the same nanoscale material model with the atomic number of 3000 atoms, for which the SM of MD is performed with Beest-Kramer-van Santen and van Santen pair interaction potentials under cyclic boundary conditions. The obtained results show that both the model size (l) and the total energy of the system (Etot) increase slowly in the low temperature (T) region (negative T values) at pressure (P), P = 0 GPa. However, the increase of l determines the Etot value with very large values in the high T region. It is found that l decreases greatly in the high T region with increasing P, and vice versa. In addition, when P increases, the decrease in the Etot value is small in the low T region, but large in the high T region. As a consequence, a change appears in the lengths of the Si-Si, Si-O, and O-O bonds, which are very large in the high T and high P regions, but insignificant in the low T and low P regions. Furthermore, the structural unit number of SiO7 appears at T > 2974 K in the high P region. The obtained results will serve as the basis for future experimental studies to exploit the stored energy used in semiconductor devices.

This study investigates the application of precipitation estimation from remote sensing information using artificial neural networks (PERSIANN) for hydrological modeling over the Russian River catchment in California in the United States as a case study. We evaluate two new PERSIANN products including the PERSIANN-Cloud Classification System–Climate Data Record (CCS–CDR), a climatology dataset, and PERSIANN–Dynamic Infrared Rain Rate (PDIR), a near-real-time precipitation dataset. We also include older PERSIANN products, PERSIANN-Climate Data Record (CDR) and PERSIANN-Cloud Classification System (CCS) as the benchmarks. First, we evaluate these PERSIANN datasets against observations from the Climate Prediction Center (CPC) dataset as a reference. The results showed that CCS–CDR has the least bias among all PERSIANN family datasets. Comparing the two near-real-time datasets, PDIR performs significantly more accurately than CCS. In simulating streamflow using the nontransformed calibration process, EKGE values (Kling–Gupta efficiency) for CCS–CDR (CDR) during the calibration and validation periods were 0.42 (0.34) and 0.45 (0.24), respectively. In the second calibration process, PDIR was considerably better than CCS (EKGE for calibration and validation periods ~ 0.83, 0.82 for PDIR vs. 0.12 and 0.14 for CCS). The results demonstrate the capability of the two newly developed datasets (CCS–CDR and PDIR) of accurately estimating precipitation as well as hydrological simulations.

Based on a meta-analysis, this paper highlights the strength and relevance of several social incentive factors concerning pro-environmental behaviors, including social influence, network factors (like network size, network connection and leadership), trust in others, and trust in institutions. Firstly, our results suggest that social influence is necessary for the emergence of pro-environmental behaviors. More specifically, an internal social influence (i.e., motivating people to change their perceptions and attitudes) is essential to promote pro-environmental behaviors. Secondly, network connection encourages pro-environmental behaviors, meaning that the effectiveness of a conservation policy can be improved if connections among individuals are increased. Finally, trust in institutions can dictate individual behaviors to shape policy design and generate desired policy outcomes.

Introduction: Pulmonary artery sling is a rare congenital vascular anomaly in which the left pulmonary artery originates from the right pulmonary artery, encircles the distal trachea and right main stem bronchus, and requiring early surgical interventions. Case presentation: We report a 8-month old girl who presented with 3 consecutive episodes within 2 months of recurrent respiratory symptoms and signs masquerading pneumonia. CT thorax and echocardiography showed an anomalous origin of the left pulmonary artery from the right pulmonary artery passing behind the trachea. A left pulmonary artery reimplantation without tracheoplasty was performed. The child recovered slowly but uneventfully after surgery, and was discharged home in good health after 2-month-stay in the hospital. Conclusion: Although rare, pulmonary artery sling should not be overlooked in the differential diagnosis of children with recurrent respiratory symptoms. Prognosis after surgical repair without tracheoplasty seems to be good, and follow-up needs to be done routinely to identify the long-term consequences.

Ionic liquids (ILs) have great potential for application in energy storage and conversion devices. They have been identified as promising electrolytes candidates in various battery systems. However, the practical application of many ionic liquids remains limited due to the unfavorable melting points (Tm) which constrain the operating temperatures of the batteries and exhibit unfavorable transport property. To fine tune the Tm of ILs, a systematic study and accurate prediction of Tm of ILs is highly desirable. However, the Tm of an IL can change considerably depending on the molecular structures of the anion and cation and their combination. Thus, a fine control in Tm of ILs can be challenging. In this study, we employed a deep-learning model to predict the Tm of various ILs that consist of different cation and anion classes. Based on this model, a prediction of the melting point of ILs can be made with a reasonably high accuracy, achieving an R2 score of 0.90 with RMSE of ~32 K, and the Tm of ILs are mostly dictated by some important molecular descriptors, which can be used as a set of useful design rules to fine tune the Tm of ILs.

This paper experimentally investigates the impact of different information sharing mechanisms in a common-pool resource game, with a view to finding a mechanism that is both efficient and inexpensive for the managing agency. More precisely, we compare the observed extraction levels produced as a result of three mechanisms: a mandatory information sharing mechanism and two voluntary information sharing mechanisms that differ in the degree of freedom given to the players. Our main result is that a voluntary information sharing mechanism could help in reaching a lower average extraction level than that observed with the mandatory mechanism.

In the current market economy, alliances play a key role in developing strategies across fields. In order to have a good partner, managers have used both qualitative and quantitative methodologies. This paper proposes a mathematical model to figure out the most suitable strategic partners. With input data from published financial reports, the authors use the data envelopment analysis (DEA) to evaluate the business efficiency of the steel companies in the period of 2011–2019. Then, Grey system theory is applied to predict their performance in the future period. The findings recommend the two leading steel manufactures but having ineffective performance, the Hoa Sen Group, and the Pomina Steel Corporation, as the most feasible beneficial partnership. Managers and the government can take advantages of the model in order to implement and have overall plans of steel enterprise in the future.

Satellite precipitation estimates (SPEs) are promising alternatives to gauge observations for hydrological applications (e.g., streamflow simulation), especially in remote areas with sparse observation networks. However, the existing SPEs products are still biased due to imperfections in retrieval algorithms, data sources and post-processing, which makes the effective use of SPEs a challenge, especially at different spatial and temporal scales. In this study, we used a distributed hydrological model to evaluate the simulated discharge from eight quasi-global SPEs at different spatial scales and explored their potential scale effects of SPEs on a cascade of basins ranging from approximately 100 to 130,000 km2. The results indicate that, regardless of the difference in the accuracy of various SPEs, there is indeed a scale effect in their application in discharge simulation. Specifically, when the catchment area is larger than 20,000 km2, the overall performance of discharge simulation emerges an ascending trend with the increase of catchment area due to the river routing and spatial averaging. Whereas below 20,000 km2, the discharge simulation capability of the SPEs is more randomized and relies heavily on local precipitation accuracy. Our study also highlights the need to evaluate SPEs or other precipitation products (e.g., merge product or reanalysis data) not only at the limited station scale, but also at a finer scale depending on the practical application requirements. Here we have verified that the existing SPEs are scale-dependent in hydrological simulation, and they are not enough to be directly used in very fine scale distributed hydrological simulations (e.g., flash flood). More advanced retrieval algorithms, data sources and bias correction methods are needed to further improve the overall quality of SPEs.

Spatiotemporal precipitation trend analysis provides valuable information for water management decision-making. Satellite-based precipitation products with high spatial and temporal resolution and long records, as opposed to temporally and spatially sparse rain gauge networks, are a suitable alternative to analyze precipitation trends over Iran. This study analyzes the trends in annual, seasonal, and monthly precipitation along with the contribution of each season and month in the annual precipitation over Iran for the 1983–2018 period. For the analyses, the Mann–Kendall test is applied to the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) estimates. The results of annual, seasonal, and monthly precipitation trends indicate that the significant decreases in the monthly precipitation trends in February over the western (March over the western and central-eastern) regions of Iran cause significant effects on winter (spring) and total annual precipitation. Moreover, the increases in the amounts of precipitation during November in the south and south-east regions lead to a remarkable increase in the amount of precipitation during the fall season. The analysis of the contribution of each season and month to annual precipitation in wet and dry years shows that dry years have critical impacts on decreasing monthly precipitation over a particular region. For instance, a remarkable decrease in precipitation amounts is detectable during dry years over the eastern, northeastern, and southwestern regions of Iran during March, April, and December, respectively. The results of this study show that PERSIANN-CDR is a valuable source of information in low-density gauge network areas, capturing spatiotemporal variation of precipitation.

Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation. The Cloud Profiling Radar (CPR) on the Polar Orbiting CloudSat satellite has provided a unique dataset to characterize cloud types. However, data from this nadir-looking radar offers limited capability for estimating precipitation because of the narrow satellite swath coverage and low temporal frequency. We use these high-quality observations to build a Deep Neural Network Cloud-Type Classification (DeepCTC) model to estimate cloud types from multispectral data from the Advanced Baseline Imager (ABI) onboard the GOES-16 platform. The DeepCTC model is trained and tested using coincident data from both CloudSat and ABI over the CONUS region. Evaluations of DeepCTC indicate that the model performs well for a variety of cloud types including Altostratus, Altocumulus, Cumulus, Nimbostratus, Deep Convective and High clouds. However, capturing low-level clouds remains a challenge for the model. Results from simulated GOES-16 ABI imageries of the Hurricane Harvey event show a large-scale perspective of the rapid and consistent cloud-type monitoring is possible using the DeepCTC model. Additionally, assessments using half-hourly Multi-Radar/Multi-Sensor (MRMS) precipitation rate data (for Hurricane Harvey as a case study) show the ability of DeepCTC in identifying rainy clouds, including Deep Convective and Nimbostratus and their precipitation potential. We also use DeepCTC to evaluate the performance of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) product over different cloud types with respect to MRMS referenced at a half-hourly time scale for July 2018. Our analysis suggests that DeepCTC provides supplementary insights into the variability of cloud types to diagnose the weakness and strength of near real-time GEO-based precipitation retrievals. With additional training and testing, we believe DeepCTC has the potential to augment the widely used PERSIANN-CCS algorithm for estimating precipitation.

RutR is a member of the large family of TetR transcriptional regulators inEscherichiacoli. It was originally discovered as the regulator of therutABCDEFG operon encoding a novel pathway for pyrimidine utilization, but its highest affinity target is the control region of thecarAB operon, encoding carbamoylphosphate synthase. Unlike most other TetR‐like regulators, RutR exerts both positive and negative effects on promoter activity. Furthermore, RutR exhibits a very narrow ligand binding specificity, unlike the broad effector specificity that characterizes some of the well‐studied multidrug resistance regulators of the family. Here we focus on ligand binding and ligand specificity of RutR. We construct single alanine substitution mutants of amino acid residues of the ligand‐binding pocket, study their effect onin vitro DNA binding in absence and presence of potential ligands, and analyse their effect on positive regulation of thecarP1 promoter and negative autoregulationin vivo. Although RutR structures have been determined previously, they were deposited in the Protein Data Bank without accompanying publications. All of them have uracil bound in the effector‐binding site, representing the inactive form of the regulator. We determined the crystal structure of an unliganded mutant RutR protein and provide a structural basis for the use of uracil as sole effector molecule and the exclusion of the very similar thymine from the ligand‐binding pocket.

In this paper, we present a state-of-the-art precipitation estimation framework which leverages advances in satellite remote sensing as well as Deep Learning (DL). The framework takes advantage of the improvements in spatial, spectral and temporal resolutions of the Advanced Baseline Imager (ABI) onboard the GOES-16 platform along with elevation information to improve the precipitation estimates. The procedure begins by first deriving a Rain/No Rain (R/NR) binary mask through classification of the pixels and then applying regression to estimate the amount of rainfall for rainy pixels. A Fully Convolutional Network is used as a regressor to predict precipitation estimates. The network is trained using the non-saturating conditional Generative Adversarial Network (cGAN) and Mean Squared Error (MSE) loss terms to generate results that better learn the complex distribution of precipitation in the observed data. Common verification metrics such as Probability Of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), Bias, Correlation and MSE are used to evaluate the accuracy of both R/NR classification and real-valued precipitation estimates. Statistics and visualizations of the evaluation measures show improvements in the precipitation retrieval accuracy in the proposed framework compared to the baseline models trained using conventional MSE loss terms. This framework is proposed as an augmentation for PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network- Cloud Classification System) algorithm for estimating global precipitation.

Current software applications for densitometric analysis, such as ImageJ, QuantityOne (BioRad) and the Intelligent or Advanced Quantifier (Bio Image) do not allow to take the non-linearity of autoradiographic films into account during calibration. As a consequence, quantification of autoradiographs is often regarded as problematic, and phosphorimaging is the preferred alternative. However, the non-linear behaviour of autoradiographs can be described mathematically, so it can be accounted for. Therefore, the 'Densitometric Image Analysis Software' has been developed, which allows to quantify electrophoretic bands in autoradiographs, as well as in gels and phosphorimages, while providing optimized band selection support to the user. Moreover, the program can determine protein-DNA binding constants from Electrophoretic Mobility Shift Assays (EMSAs). For this purpose, the software calculates a chosen stepwise equilibrium constant for each migration lane within the EMSA, and estimates the errors due to non-uniformity of the background noise, smear caused by complex dissociation or denaturation of double-stranded DNA, and technical errors such as pipetting inaccuracies. Thereby, the program helps the user to optimize experimental parameters and to choose the best lanes for estimating an average equilibrium constant. This process can reduce the inaccuracy of equilibrium constants from the usual factor of 2 to about 20%, which is particularly useful when determining position weight matrices and cooperative binding constants to predict genomic binding sites. The MATLAB source code, platform-dependent software and installation instructions are available via the website http://micr.vub.ac.be.