A spatial autocorrelation analysis of road traffic crash by severity using Moran's I spatial statistics: A comparative study of Addis Ababa and Berlin cities

Methodological advancements in road safety research reveal an increasing inclination toward integrating spatial approaches in hot spot identification, spatial pattern analysis, and developing spatially lagged models. Previous studies on hot spot identification and spatial pattern analysis have overlooked crash severities and the spatial autocorrelation of crashes by severity, missing valuable insights into crash patterns and underlying factors. This study investigates the spatial autocorrelation of crash severity by taking two capital cities, Addis Ababa and Berlin, as a case study and compares patterns in low and high-income countries. The study used three-year crash data from each city. It employed the average nearest neighbor distance (ANND) method to determine the significance of spatial clustering of crash data by severity, Global Moran's I to examine the statistical significance of spatial autocorrelation, and Local Moran's I to identify significant cluster locations with High-High (HH) and Low-Low (LL) crash severity values. The ANND analysis reveals a significant clustering of crashes by severity in both cities, except in Berlin's fatal crashes. However, different Global Moran's I results were obtained for the two cities, with a strong and statistically significant value for Addis Ababa compared to Berlin. The Local Moran's I result indicates that the central business district and residential areas have LL values, while the city's outskirts exhibit HH values in Addis Ababa. With some persistent HH value locations, Berlin's HH and LL grid clusters are intermingled on the city's periphery. Socio-economic factors, road user behavior and roadway factors contribute to the difference in the result. Nevertheless, it is interesting to note the similarity of significant HH value locations on the outskirts of both cities. Finally, the results are consistent with previous studies and indicate the need for further investigation in other locations.

Insights into biogeochemistry and hot spots distribution characteristics of redox-sensitive elements in the hyporheic zone: Transformation mechanisms and contributing factors

Biogeochemical hot spots play a crucial role in the cycling and transport of redox-sensitive elements (RSEs) in the hyporheic zone (HZ). However, the transformation mechanisms of RSEs and patterns of RSEs hot spots in the HZ remain poorly understood. In this study, hydrochemistry and multi-isotope (N/C/S/O) datasets were collected to investigate the transformation mechanisms of RSEs, and explore the distribution characteristics of RSEs transformation hot spots. The results showed that spatial variability in key drivers was evident, while temporal change in RSEs concentration was not significant, except for dissolved organic carbon. Bacterial sulfate reduction (BSR) was the primary biogeochemical process for sulfate and occurred throughout the area. Ammonium enrichment was mainly caused by the mineralization of nitrogenous organic matter and anthropogenic inputs, with adsorption serving as the primary attenuation mechanism. Carbon dynamics were influenced by various biogeochemical processes, with dissolved organic carbon mainly derived from C3 plants and dissolved inorganic carbon from weathering of carbonate rocks and decomposition of organic matter. The peak contribution of dissolved organic carbon decomposition to the DIC pool was 46.44 %. The concentration thresholds for the ammonium enrichment and BSR hot spots were identified as 1.5 mg/L and 8.84 mg/L, respectively. The distribution pattern of RSEs hot spots was closely related to the hydrogeological conditions. Our findings reveal the complex evolution mechanisms and hot spots distribution characteristics of RSEs in the HZ, providing a basis for the safe utilization and protection of groundwater resources.

Heat and mass transfer simulation of the microwave-assisted toluene desorption for activated carbons regeneration

The low cost and high efficiency of microwave-assisted regeneration render it a viable alternative to conventional regeneration methods. To enhance the regeneration performance, we developed a coupled electromagnetic, heat, and mass transfer model to investigate the heat and mass transfer mechanisms of activated carbon during microwave-assisted regeneration. Simulation results demonstrated that the toluene desorption process is governed by temperature distribution. Changing the input power and flow rate can promote the intensity of hot spots and adjust their distribution, respectively, thereby accelerating toluene desorption, inhibiting readsorption, and promoting regeneration efficiency. Ultimately, controlling the input power and flow rate can flexibly adjust toluene emissions to satisfy the processing demands of desorbed toluene. Taken together, this study provides a comprehensive understanding of the heat and mass transfer mechanisms of microwave-assisted regeneration and insights into adsorbent regeneration.

Highly sensitive SERS detection of pesticide residues based on multi-hotspot buckypaper modified with gold nanoparticles

To effectively extract target analytes from complex sample surfaces is of great significance for the practical application of surface-enhanced Raman scattering (SERS) spectroscopy. A plasmonic substrate with multiple "hotspots" for highly sensitive detection of pesticide residues were prepared successfully by assembling gold nanoparticles on buckypaper (AuNPs-BP). The substrate exhibited high SERS enhancement and excellent detection sensitivity, with a detection limit (LOD) of 2.03 × 10-11 M and 6.88 × 10-12 M for the probe molecule R6G and MB, respectively. Combined with 3D finite-difference time-domain (3D-FDTD) simulation, the excellent SERS performance of the substrate was attributed to the enhancement of the electromagnetic field around the "hotspots". Additionally, the substrates exhibited excellent flexibility, allowing easy contact with irregular surfaces and facilitating the collection of target molecules on the sample surface. Using a portable Raman spectrometer, the substrate achieved in situ analysis of chlorpyrifos residues on peach, with a LOD as low as 6.8 × 10-11 M. The method showed high accuracy, with a recovery value ranging from 94.2 % to 115.5 %. The results indicate that the substrate has great potential for rapid and highly sensitive detection of pollutants, especially on non-planar surfaces.

Meta-analysis on Plasmodium falciparum sulfadoxine-pyrimethamine resistance-conferring mutations in India identifies hot spots for genetic surveillance

BACKGROUND

India is on track to eliminate malaria by 2030 but emerging resistance to first-line antimalarials is a recognised threat. Two instances of rapid development, spread, and natural selection of drug-resistant mutant parasites in India (chloroquine across the country and artesunate + sulfadoxine-pyrimethamine [AS+SP] in the northeastern states) translated into drug policy changes for Plasmodium falciparum malaria in 2010 and 2013, respectively. Considering these rapid changes in the SP drug resistance-conferring mutation profile of P. falciparum, there is a need to systematically monitor the validated mutations in Pfdhfr and Pfdhps genes across India alongside AS+SP therapeutic efficacy studies. There has been no robust, systematic countrywide surveillance reported for these parameters in India, hence the current study was undertaken.

METHODS

Studies that reported data on WHO-validated SP resistance markers in P. falciparum across India from 2008 to January 2023 were included. Five major databases, PubMedⓇ, Web of ScienceTM, ScopusⓇ, EmbaseⓇ, and Google Scholar, were exhaustively searched. Individual and pooled prevalence estimates of mutations were obtained through random- and fixed-effect models. Data were depicted using forest plots created with a 95% confidence interval. The study is registered with PROSPERO (CRD42021236012).

RESULTS

A total of 37 publications, and 533 Pfdhfr and 134 Pfdhps National Centre of Biotechnology Information (NCBI) DNA sequences were included from >4000 samples. The study included information from 80 districts, 21 states and 3 union territories (UTs) from India. The two PfDHFR mutations, C59R (62%) and S108N (74%), were the most prevalent mutations (pooled estimates 61% and 71%, respectively) and appeared to be stabilised/fixed. Although rarest overall, the prevalence of I164L was observed to be as high as 32%. The PfDHFR double mutants were the most prevalent overall (51%; pooled 42%). The prevalence of triple and quadruple mutations was 6% and 5%, respectively, and is an immediate concern for some states. The most prevalent PfDHPS mutation was A437G (39%), followed by K540E (25%) and A581G (12%). There was a low overall prevalence of PfDHFR/PfDHPS quintuple and sextuple mutations but surveillance for these mutations is critical for some areas.

CONCLUSION

The analyses span the two critical policy changes, highlight the areas of concern, and guide policymakers in strategising and refining the anti-malaria drug policy for malaria elimination. The results of the analyses also highlight the SP-resistance hot spots, critical gaps and challenges, and indicate that focal and local malaria genetic surveillance (including drug-resistance markers) is needed until malaria is successfully eliminated.

Mapping COVID-19's potential infection risk based on land use characteristics: A case study of commercial activities in two Egyptian cities

The contagious COVID-19 has recently emerged and evolved into a world-threatening pandemic outbreak. After pursuing rigorous prophylactic measures two years ago, most activities globally reopened despite the emergence of lethal genetic strains. In this context, assessing and mapping activity characteristics-based hot spot regions facilitating infectious transmission is essential. Hence, our research question is: How can the potential hotspots of COVID-19 risk be defined intra-cities based on the spatial planning of commercial activity in particular? In our research, Zayed and October cities, Egypt, characterized by various commercial activities, were selected as testbeds. First, we analyzed each activity's spatial and morphological characteristics and potential infection risk based on the Centre for Disease Control and Prevention (CDCP) criteria and the Kriging Interpolation method. Then, using Google Mobility, previous reports, and semi-structured interviews, points of interest and population flow were defined and combined with the last step as interrelated horizontal layers for determining hotspots. A validation study compared the generated activity risk map, spatial COVID-19 cases, and land use distribution using logistic regression (LR) and Pearson coefficients (rxy). Through visual analytics, our findings indicate the central areas of both cities, including incompatible and concentrated commercial activities, have high-risk peaks (LR = 0.903, rxy = 0.78) despite the medium urban density of districts, indicating that urban density alone is insufficient for public health risk reduction. Health perspective-based spatial configuration of activities is advised as a risk assessment tool along with urban density for appropriate decision-making in shaping pandemic-resilient cities.

Hotspots of microplastic accumulation at the land-sea transition and their spatial heterogeneity: The Po River prodelta (Adriatic Sea)

Deltas are the locus of river-borne sediment accumulation, however, their role in sequestering plastic pollutants is still overlooked. By combining geomorphological, sedimentological, and geochemical analyses, which include time-lapse multibeam bathymetry, sediment provenance, and μFT-IR analyses, we investigate the fate of plastic particles after a river flood event providing an unprecedented documentation of the spatial distribution of sediment as well as of microplastics (MPs), including particles fibers, and phthalates (PAEs) abundances in the subaqueous delta. Overall sediments are characterized by an average of 139.7 ± 80 MPs/kg d.w., but display spatial heterogeneity of sediment and MPs accumulation: MPs are absent within the active sandy delta lobe, reflecting dilution by clastic sediment (ca. 1.3 Mm3) and sediment bypass. The highest MP concentration (625 MPs/kg d.w.) occurs in the distal reaches of the active lobe where flow energy dissipates. In addition to MPs, cellulosic fibers are relevant (of up to 3800 fibers/kg d.w.) in all the analyzed sediment samples, and dominate (94 %) with respect to synthetic polymers. Statistically significant differences in the relative concentration of fiber fragments ≤0.5 mm in size were highlighted between the active delta lobe and the migrating bedforms in the prodelta. Fibers were found to slightly follow a power law size distribution coherent with a one-dimensional fragmentation model and thus indicating the absence of a size dependent selection mechanism during burial. Multivariate statistical analysis suggests traveling distance and bottom-transport regime as the most relevant factors controlling particle distribution. Our findings suggest that subaqueous prodelta should be considered hot spots for the accumulation of MPs and associated pollutants, albeit the strong lateral heterogeneity in their abundances reflects changes in the relative influence of fluvial and marine processes.

Autoimmune diseases and gut microbiota: a bibliometric and visual analysis from 2004 to 2022

Many studies have shown that gut microbiota is closely related to autoimmune diseases (ADs). Studies on gut microbiota and ADs have also increased significantly, but no bibliometric analysis has summarized the association between gut microbiota and ADs. This study aimed to conduct a bibliometric and visual analysis of published studies on gut microbiota and ADs. Based on the Web of Science Core Collection SCI-expanded database, we utilize Excel 2019 and visualization analysis tools VOSviewer and co-occurrence13.2 (COOC13.2) for analysis. A total of 2516 related kinds of literature were included, and the number of papers presented an overall increasing trend. The country/region with the most publications is the USA, the institution is the Harvard Medical School, and the author is Mikael Knip from the USA. Hot research areas include intestinal regulation (such as dysbiosis, short chain fatty acids, and probiotics), multisystem ADs (such as multiple sclerosis, rheumatoid arthritis, and inflammatory bowel disease), and immune-related cells (such as T cells, and dendritic cells). Psoriasis, dysbiosis, autoimmune liver disease, and fecal microbiota transplantation may be the future research direction. Our research results can help researchers grasp the current status of ADs and gut microbiota research and find new research directions in the future.

TWN-FS method: A novel fragment screening method for drug discovery

Fragment-based drug discovery (FBDD) is a well-established and effective method for generating diverse and novel hits in drug design. Kinases are suitable targets for FBDD due to their well-defined structure. Water molecules contribute to structure and function of proteins and also influence the environment within the binding pocket. Water molecules form a variety of hydrogen-bonded cyclic water-ring networks, collectively known as topological water networks (TWNs). Analyzing the TWNs in protein binding sites can provide valuable insights into potential locations and shapes for fragments within the binding site. Here, we introduce TWN-based fragment screening (TWN-FS) method, a novel screening method that suggests fragments through grouped TWN analysis within the protein binding site. We used this method to screen known CDK2, CHK1, IGF1R and ERBB4 inhibitors. Our findings suggest that TWN-FS method has the potential to effectively screen fragments. The TWN-FS method package is available on GitHub at https://github.com/pkj0421/TWN-FS.

Trace metal and TBT pollution in the Gulf and Oman: spatial variation and hot spots

This review assesses trace metal concentrations in nearshore and offshore bottom sediments in the Arabian Gulf and Oman focusing particularly on targeted monitoring studies of point sources of contamination near industrial zones (sometimes with ports) and desalination and power plants (often co-located). Concerns have been raised about harmful impacts from accumulation of metals in the semi-enclosed Gulf. The sediment trace metal contaminants of the greatest concern are highlighted with maximum levels of toxic trace metals such as As, Cd, Cu, Pb, Hg, Ni, Zn, and TBT recorded near port and ship repair yards/dry docks as well as industrial and power/desalination plant discharge outfalls. The levels of metals such as Cd, Pb, and Hg exceeding sediment quality guidelines at identified hot spots are of concern. Efforts to mitigate future impacts are recommended.

Analysis of hot spots and frontiers of nursing scenario simulation teaching research at home and abroad

OBJECTIVE

To analyze the research hotspots and trends of nursing scenario simulation teaching at home and abroad, and to provide reference for future nursing talent education.

METHODS

CNKI and Web of Science databases were searched. From the establishment of the database to April 2022, relevant literature on nursing scenario simulation teaching research at home and abroad was retrieved, and Cite Space software was used for visual analysis.

RESULTS

The research focus on China was the application and application effect of nursing scenario simulation teaching. The research hotspots abroad are the quality evaluation, reliability and influence of nursing scenario simulation teaching.

CONCLUSION

The research and development of nursing scenario simulation teaching gradually tend to be systematic.

Undernutrition among the children below five years of age in Uganda: a spatial analysis approach

BACKGROUND

Undernutrition is a health condition caused by a lack of enough food intake, not having enough of the right combination of food nutrients, or the body's failure to utilize the food eaten resulting in either, stunting, being underweight, or wasting. Globally, undernutrition affects more than 149 million under-five children, while in Uganda about 3 in every 10 children suffer from undernutrition. Undernutrition and its risk factors among under-five children in Uganda were unevenly distributed across the country and a study that focused on spatial distribution was prudent to examine the nature of the problem and salient factors associated with it. The current study addressed the issues of spatial heterogeneity of undernutrition and its determinants with the goal to identify hot spots and advise policymakers on the best actions to be taken to address the problem.

METHODS

Data were obtained from the 2016 Uganda Demographic and Health Survey. Prevalence rates and percentages of risk factors were combined with the Uganda district shape file to allow spatial analysis. Moran's I, Getis-Ord (GI*), and Geographically Weighted Regressions were respectively used to establish the local, global, and geographically weighted regressions across the country. Stata 15 and ArcGIS 10. 7 soft wares were used.

RESULTS

The results indicate that undernutrition in Uganda shows varies spatially across regions. Evidence of hot spots exists in the Karamoja and Arua regions, cold spot areas exist around the central part of the country while the greatest part of Western Uganda, Northern, and Eastern were not significant.

CONCLUSION

The study reveals that a variation in the distribution of undernutrition throughout the country. Significant spatial patterns associated with undernutrition as identified through the hotspot and cold spot analysis do exist in Uganda. Programs targeting to reduce the undernutrition of under-five children in Uganda should consider the spatial distribution of undernutrition and its determinants whereby priority should be given to hotspot areas. The spatial intensity of undernutrition and its determinants indicate that focus should be tailored to meet the local needs as opposed to a holistic national approach.

Past and present: a bibliometric study on polycystic ovary syndrome

BACKGROUND

Polycystic ovary syndrome (PCOS) is a common gynecological endocrine disease that has a great impact on women's physical and mental health. It is a burden to social and patients' economy. In recent years, researchers' understanding of PCOS has reached a new level. However, many PCOS reports have different directions, and overlapping phenomena exist. Therefore, clarifying the research status of PCOS is important. This study aims to summarise the research status of PCOS and predict the hot spots of PCOS in the future by Bibliometricx.

RESULTS

The keywords of PCOS research focused on PCOS, insulin resistance (IR), obesity and metformin. Keywords plus co-occurrence network showed that PCOS, IR and prevalence were hot spots in the recent 10 years. Moreover, we found that gut microbiota may be a carrier that can be used to study hormone levels, IR-related mechanisms, prevention and treatment in the future.

CONCLUSIONS

This study is helpful for researchers to quickly grasp the current situation of PCOS research and enlighten researchers to explore new problems in PCOS.

Determinants of exposure to polychlorinated biphenyls in the Italian population in the last decades

Polychlorinated biphenyls (PCBs) are industrial products extensively used in the past. Because of their widespread presence and toxic effects, the international community adopted control measures to reduce their release into the environment. Currently, PCB concentrations are decreasing, but humans are still exposed. In this paper, we reported the results of a study concerning PCB concentrations in human serum samples collected in Italy over two decades. The aim of the study was to investigate the trend of major determinants of PCB human exposure, several decades after the end of their production. PCB concentrations ranged over three orders of magnitude (from 0.4 to 958 ng/g lipid), with a median value of 85 ng/g lipid. We identified age, sampling year, body mass index, sex, and living near hot spots or being occupationally exposed as relevant factors in determining body burden. Our results can give indications to refine regulatory policies on PCBs in Italy, with particular attention to the disposal of residue PCB-containing products. To improve control measures can further decrease the exposure of citizens to PCBs, limit health implications, and improve citizens' perception about chemical risk management.

The spatial dynamics of commercial burglary during the COVID-19 lockdown in San Francisco

Purpose

This paper investigated the impact of COVID-19 stay-at-home regulations on the spatial distribution of commercial burglary in San Francisco.

Method

Geocoded commercial burglary data for 2003-2021 from San Francisco were analyzed using the Andresen Spatial Point Pattern Test to examine potential spatial movement in commercial burglary.

Results

Concentrations of commercial burglary spatially shifted following the emergence of COVID-19 and the subsequent stay-at-home orders. Original hot spots experienced a relative decrease in commercial burglary, and crime spread out throughout the city. These spatial changes in commercial burglary appear to be related to variations in guardianship and criminal opportunity.

Conclusion

These findings suggest that the dramatic shifts in routine activities resulting from the COVID-19 policies could disrupt spatial burglary patterns, especially burglary clustering. Law enforcement should consider the spatial dynamics of commercial burglary, as well as land use heterogeneity, to effectively respond to crime during periods of social distancing and business shutdowns.

Low water level drives high nitrous oxide emissions from treatment wetland

Wetlands that are restored for carbon sequestration or created for water treatment are an important sources of greenhouse gases, especially methane. The emission of nitrous oxide (N₂O) from these systems is often considered negligible due to the inundation and anerobic conditions that support complete denitrification. We used closed chamber method to analyze N₂O fluxes over a long-term period across heterogeneous wetland ecosystem constructed for treating nitrate-rich agricultural runoff. Our results showed that the water depth and temperature were most important factors affecting high N₂O emissions. The shallow areas where water depth was less than 9 cm created N₂O hot spots that emitted 48.8% of the total wetlands annual emission while only covering 6% of the total area. The annual emission from shallow-water hot spots with dense helophytic vegetation was 4.85 ± 0.5 g N₂O-N m^-2 y^-1 while it was only 0.37 ± 0.01 g N₂O-N m^-2 y^-1 in deeper zones. While the water depth was the main factor for high N₂O emissions, the temperatures increased the magnitude of the flux and therefore summer droughts and water drawdown created even larger hot spots. These results also suggest that IPCC benchmarks could underestimate N₂O emission from shallow waterbodies. Thus, it is important that the shallow zones and water level drawdown in the created or restored wetlands is avoided to minimize the N₂O flux.

Research hotspot and trend of microneedles in biomedical field: A bibliometric analysis from 2011 to 2020

BACKGROUND

Microneedles composed of arrays of micro-sized needles assembled on a patch, has attracted increasing interest in transdermal drug delivery due to its ease of use and lack of painful responses. Here, a bibliometric analysis was conducted to determine a hotspot and trend of microneedles in the biomedical field.

METHODS

All relevant articles about microneedles between 2011 and 2020 were obtained from the databases of Web of Science (WOS) and PubMed of the National Center for Biotechnology Information (NCBI). A series of software such as VOSviwewer, the online bibliometric analysis website, CiteSpace, BICOMB and gCLUTO were used to process the data and get visual images. Processed data and visualized images were conducted to predict the trend of this research field.

RESULTS

The number of articles published over the last decade had increased rapidly (37 in 2011, 165 in 2020), the Journal of Controlled Release was the most productive journal in microneedle studies. The United States was the most productive country, while the Queens Univ Belfast topped the other institutions. Ryan F Donnelly was the most productive author in the field, while the two most cited articles were published by Gu Zhen group. More importantly, the research trend of microneedles had ranged from physicochemical properties and pharmacokinetics to insulin transdermal injection and vaccine development over the past decade. The four hot spots in microneedle studies, including skin rejuvenation, vaccines, fabrication technology and insulin delivery, were identified. Microneedle vaccination shows promising application prospects, and polymers are considered as the most promising materials for microneedles manufacturing.

CONCLUSIONS

This study will help researchers understand the hot spots and trends of microneedles in the biomedical field accurately and quickly. Moreover, the exploitation of novel polymeric microneedles will be a solid direction for subsequent research and development of transdermal drug delivery.

Geospatial approach to investigate spatial clustering and hotspots of blood lead levels in children within Kabwe, Zambia

BACKGROUND

Communities around Kabwe, Zambia are exposed to lead due to deposits from an old lead (Pb) and zinc (Zn) mining site. Children are particularly more vulnerable than adults, presenting with greatest risk of health complications. They have increased oral uptake due to their hand to mouth activities. Spatial analysis of childhood lead exposure is useful in identifying specific areas with highest risk of pollution. The objective of the current study was to use a geospatial approach to investigate spatial clustering and hotspots of blood lead levels in children within Kabwe.

METHODS

We analysed existing data on blood lead levels (BLL) for 362 children below the age of 15 from Kabwe town. We used spatial autocorrelation methods involving the global Moran's I and local Getis-Ord Gi*statistic in ArcMap 10.5.1, to test for spatial dependency among the blood lead levels in children using the household geolocations.

RESULTS

BLL in children from Kabwe are spatially autocorrelated with a Moran's Index of 0.62 (p < 0.001). We found distinct hotspots (mean 51.9 μg/dL) in communities close to the old lead and zinc-mining site, lying on its western side. Whereas coldspots (mean 7 μg/dL) where observed in areas distant to the mine and traced on the eastern side. This pattern suggests a possible association between observed BLL and distance from the abandoned lead and zinc mine, and prevailing winds.

CONCLUSION

Using geocoded data for households, we found clustering of childhood blood lead and identified distinct hotspot areas with high lead levels for Kabwe town. The geospatial approach used is especially valuable in resource-constrained settings like Zambia, where the precise identification of high risk locations allows for the initiation of targeted remedial and treatment programs.

MOF-Derived hierarchical porous 3D ZnO/Ag nanostructure as a reproducible SERS substrate for ultrasensitive detection of multiple environmental pollutants

The three-dimensional (3D) surface-enhanced Raman scattering (SERS) substrate for trace molecule detection has recently attracted considerable interest; however, these substrates generally either show poor sensitivity or require a complex preparation process. In this work, we have fabricated a 3D ZnO/Ag substrate using porous zeolite imidazole frameworks (ZIF-8) derived ZnO nanoparticles (NPs) followed by evaporation-induced self-assembly of Ag NPs over it, which can detect multiple environmental pollutants by a facile and cost-effective method. This 3D porous substrate showed an ultra-sensitivity for detecting various types of molecules, e.g., rhodamine 6G (R6G), crystal violet (CV), tetracycline, and thiram, simultaneously suggesting its generality. Notably, the lowest detectable concentration (LDC) attained for R6G is 10^-13 M, and the enhancement factor (EF) reaches up to 1.8 × 10⁸. The most important reason for ultra-sensitivity is that ZnO derived from ZIF-8 has a hierarchical porous structure and large surface area to provide more "hot spots" and absorb more probe molecules. Consequently, the ZnO/Ag nanostructures show excellent photocatalytic performance. The detected probe molecules could be completely degraded in situ within a short UV exposure time (<30 min), thereby enabling outstanding reusability of this substrate. Finite-different time-domain (FDTD) simulations were used to understand the underlying mechanism of the substrate by calculating electric fields and hot spot distributions. The simulations suggested that the widespread hot spots structures on the substrate are the main reason for its SERS ultra-sensitivity.

Highly sensitive SERS substrates with multi-hot spots for on-site detection of pesticide residues

Pesticide residues will be a huge threat to food security and ecological environment; therefore, there is an urgent need to achieve rapid and on-site detection of pesticide residues. Herein, a plasmonic substrate with multiple "hot spots" was fabricated by transferring three-dimensional (3D) Au nanoparticles (NPs) onto the polydimethylsiloxane (PDMS) membrane for highly sensitive surface-enhanced Raman scattering (SERS) detection of pesticide residues. In combination with 3D-FDTD simulations, high SERS enhancement (EF = 1.2 × 10⁸) and high detection sensitivity (LOD = 6.3 × 10^-10 M) were achieved, mainly due to the enhanced electromagnetic fields around the "hot spots". Additionally, the PDMS-based SERS substrate held good transparency and flexibility, enabling conformal contact with non-planar surfaces and allowing the laser to penetrate the back of the analytes. Combined with a portable Raman spectrometer, the substrates holds great potential for rapid, high-sensitive, and on-site detection of contaminants in food, especially for the analyte on the nonplanar surfaces.

The impact of COVID-19 on the spatial distribution of shooting violence in Buffalo, NY

OBJECTIVES

This paper examines the extent to which hotspots of shooting violence changed following the emergence of COVID-19.

METHODS

This analysis uses Andresen's Spatial Point Pattern test on 1500 by 1500 foot grid cells, correcting for multiple comparisons, on a 10-year sample of geocoded shooting data from Buffalo New York.

RESULTS

This work finds zero micro-grid cells are not statistically different from pre to post COVID stay at home orders and instead that the observed rise in shootings in the sample appears to be a consistent proportional increase across the city.

CONCLUSIONS

These findings provide law enforcement with useful information about how to respond to the recent rise in shooting violence, but additional work is needed to better understand what, among a number of competing theories, is driving the increase.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11292-021-09497-4.

Proximity to coal-fired power plants and neurobehavioral symptoms in children

BACKGROUND

Coal-fired power plants are a major source of air pollution that can impact children's health. Limited research has explored if proximity to coal-fired power plants contributes to children's neurobehavioral disorders.

OBJECTIVE

This community-based study collected primary data to investigate the relationships of residential proximity to power plants and neurobehavioral problems in children.

METHODS

235 participants aged 6-14 years who lived within 10 miles of two power plants were recruited. Exposure to particulate matter ≤10 μm (PM₁₀) was measured in children's homes using personal modular impactors. Neurobehavioral symptoms were assessed using the Child Behavior Checklist (CBCL). Multiple regression models were performed to test the hypothesized associations between proximity/exposure and neurobehavioral symptoms. Geospatial statistical methods were used to map the spatial patterns of exposure and neurobehavioral symptoms.

RESULTS

A small proportion of the variations of neurobehavioral problems (social problems, affective problems, and anxiety problems) were explained by the regression models in which distance to power plants, traffic proximity, and neighborhood poverty was statistically associated with the neurobehavioral health outcomes. Statistically significant hot spots of participants who had elevated levels of attention deficit hyperactivity disorder, anxiety, and social problems were observed in the vicinity of the two power plants.

SIGNIFICANCE

Results of this study suggest an adverse impact of proximity to power plants on children's neurobehavioral health. Although coal-fired power plants are being phased out in the US, health concern about exposure from coal ash storage facilities remains. Furthermore, other countries in the world are increasing coal use and generating millions of tons of pollutants and coal ash. Findings from this study can inform public health policies to reduce children's risk of neurobehavioral symptoms in relation to proximity to power plants.

Introduction to the Special Issue: Urban Mobility and Crime Patterns

This Special Issue is a collection of seven papers that seek to better our understanding of how urban mobility relates to crime patterns, and how day to day movement of people in urban spaces (urban mobility) is related to spatio-temporal patterns of crime. It focusses on urban mobility, or the dynamic movement of people in relation to crime risk. Moreover, it questions how to best measure this risk using an appropriate crime denominator. Building on the work of Sarah Boggs, this special issue contends that we need more than an appropriate denominator related to the type of crime we are measuring, for example violence based on the number of potential victims present (the exposed or ambient population), or the number of burglaries per households in an area, or the number of shoplifting offences per number of shops present. It argues that this denominator needs to be both 'crime type' appropriate, and to be spatially and temporally appropriate. When considering urban mobility as flows of people, the challenge is that the denominator can not be considered as a fixed or static concept, and that we need to consider the 'dynamic denominator' challenge. Indeed, crime hot spots which do not account for dynamic denominators may be misleading for resource prioritisation. This special issue explores a range of potential solutions to this including mobile/cell phone data, transportation data, land use data, and other possible measures to address this.

FDTD Study on Evolution of Trimer Silver@Silica Nanospheres to Dimer for SERS Characteristics

Light enhancement occurs strongly within the plasmonic clusters by interaction with surface plasmons. Surface-enhanced Raman spectroscopic (SERS) characteristics of a series of silver@silica trimer core-shell (CS) nanosphere (NS) clusters are investigated in this paper. It is significant to understand the electric field (EF) enhancement mechanism behind the SERS technique. The effect of symmetry breaking is studied for the series starting from the highly symmetric trimer cluster and transformed to linear dimer geometry which progresses through the gradual reduction in the vertex NS. The optical activity such as the evolution of LSPR peak is discussed, the formation of hot spots is demonstrated and the strength of the local EF enhancement is calculated and correlated with the plasmon dipolar modes by using plasmon hybridization theory to understand the underlying physical concepts.

Malignant mixes: The overlap of motor vehicle crashes and crime in Stockholm, Sweden

Places that concentrate both motor vehicle crashes and crime in Stockholm, Sweden were examined for common socio-economic, land use, and neighborhood characteristics. Using vehicle crash (N = 3,700) and non-traffic crime (N = 605,052) data from 2016 to 2018, hot spots of these two sets of events and their overlap were identified. Crash hot spots captured 14% of the crashes in only 0.5% of Stockholm's area while crime hot spots captured 27% of the recorded offences in less than 1% of the area. There was overlap in these hot spots for 7% of the crashes and 10% of the crimes. To model predictors, the events were allocated to roadway segments (N = 5511) and tested using a Poisson-Gamma-CAR spatial regression model. Both crashes and crimes exhibit a clear center-periphery pattern that varies over time and by type of crashes and crimes. Crashes tended to occur on roadways with higher average daily traffic (ADT) while crimes tend to occur on roadways with lower ADT with around half occurring on residential streets. Both types of incidents tended to be higher in lower income neighborhoods. Land uses common to both types of harm were the location of underground stations, ATM machines, and alcohol-serving businesses. These are places where people and cars converge at particular times. The effect of these events on police, emergency, and medical services is discussed.

A reflection on four impactful Ambio papers: The biotic perspective : This article belongs to Ambio's 50th Anniversary Collection. Theme: Climate change impacts

Climate change represents one of the most pressing societal and scientific challenges of our time. While much of the current research on climate change focuses on future prediction, some of the strongest signals of warming can already be seen in Arctic and alpine areas, where temperatures are rising faster than the global average, and in the oceans, where the combination of rising temperatures and acidification due to increased CO₂ concentrations has had catastrophic consequences for sensitive marine organisms inhabiting coral reefs. The scientific papers highlighted as part of this anniversary issue represent some of the most impactful advances in our understanding of the consequences of anthropogenic climate change. Here, we reflect on the legacy of these papers from the biotic perspective.

Binding mechanism of inhibitors to p38α MAP kinase deciphered by using multiple replica Gaussian accelerated molecular dynamics and calculations of binding free energies

The p38α MAP Kinase has been an important target of drug design for treatment of inflammatory diseases and cancers. This work applies multiple replica Gaussian accelerated molecular dynamics (MR-GaMD) simulations and the molecular mechanics generalized Born surface area (MM-GBSA) method to probe the binding mechanism of inhibitors L51, R24 and 1AU to p38α. Dynamics analyses show that inhibitor bindings exert significant effect on conformational changes of the active helix α2 and the conserved DFG loop. The rank of binding free energies calculated with MM-GBSA not only agrees well with that determined by the experimental IC50 values but also suggests that mutual compensation between the enthalpy and entropy changes can improve binding of inhibitors to p38α. The analyses of free energy landscapes indicate that the L51, R24 and 1AU bound p38α display a DFG-out conformation. The residue-based free energy decomposition method is used to evaluate contributions of separate residues to the inhibitor-p38α binding and the results imply that residues V30, V38, L74, L75, I84, T106, H107, L108, M109, L167, F169 and D168 can be utilized as efficient targets of potent inhibitors toward p38α.

Prospects in interfaces of biomolecule DNA and nanomaterials as an effective way for improvising surface enhanced Raman scattering: A review

Surface Enhanced Raman Scattering (SERS) is a field of research that has shown promising application in the analysis of various substrate molecules by means of rough metallic surfaces. In directing the enhancement of substrate molecules in micro and nano-molar concentrations, plasmonic coupling of metal nanoparticles (NPs), morphology of metal NPs and the closely arrangement of rough metal surfaces that produces 'hot spots' can effectively increase the so-called enhancement factor (EF) that will be applicable in various fields. As the mechanistic aspects are still not clear, research has been triggered all over the world for the past two decades to have a clear understanding in chemical and electromagnetic effects. As the reproducibility of intensity of signals at low concentrations of probe molecules is of a big concern, metal NPs with various scaffolds were prepared and recently bio-molecule, DNA has been studied and showed promising advantages. This review first time highlights metal NPs with DNA interface as an effective rough metallic surface for SERS with high intensity and also with better reproducibility. Based on this review, similar kinds of scaffolds like DNA can be used to further analyze SERS activities of various metal NPs with different morphologies to have high intense signals at low concentrations of probe molecules.

The Incidence of Amyotrophic Lateral Sclerosis in Ohio 2016-2018: The Ohio Population-Based ALS Registry

INTRODUCTION

Amyotrophic lateral sclerosis (ALS) is a fatal, neuromuscular disease with no cure. ALS incidence rates have not been assessed specifically in Ohio, yet the state contains both metropolitan and rural areas with a variety of environmental factors that could contribute to disease etiology. We report the incidence of ALS in Ohio residents diagnosed from October 2016 through September 2018.

METHODS

We engaged practitioners from 9 Ohio sites to identify newly diagnosed ALS patients and to complete case report forms with demographic and clinical information. ALS was diagnosed according to the Awaji criteria and classified as either definite, probable, or possible. We developed a method to estimate missing cases using a Poisson regression model to impute cases in counties with evidence of undercounting.

RESULTS

We identified 333 newly diagnosed ALS patients residing in Ohio during the 2-year index period and found incidence rates varied in the 88 state counties. After incorporating the estimated 27% of missing cases, the corrected crude annual incidence was 1.96/100,000 person-years, and the age- and gender-standardized incidence was 1.71/100,000 person-years (standardized to the 2010 US census).

DISCUSSION/CONCLUSION

The estimated Ohio incidence of ALS is overall similar to that reported in other states in the USA. This study reveals a geospatial variation in incidence within the state, and areas with higher rates warrant future investigation.

Hot spots and hot moments of nitrogen removal from hyporheic and riparian zones: A review

The Earth is experiencing excessive nitrogen (N) input to its various ecosystems due to human activities. How to effectively and efficiently remove N from ecosystems has been, is and will be at the center of attention in N research. Hyporheic and riparian zones are widely acknowledged for their buffering capacity to reduce contaminants (especially N) transport downstream. However, these zones are usually misunderstood that they can remove N at all spots and at any moments. Here pathways of N removal from hyporheic and riparian zones are reviewed and summarized with an emphasize on their hot spots and hot moments. N is biogeochemically removed by denitrification, anammox, nitrifier denitrification, denitrifying anaerobic methane oxidation, Feammox and Sulfammox. Hot moments of N removal are mainly triggered by precipitation, fire and snowmelt. Finally, some research needs are outlined and discussed, such as developing approaches for multiscale sampling and monitoring, quantifying the effects of hot spots and hot moments at hyporheic and riparian zones and evaluating the impacts of human activities on hot spots and hot moments, to inspire more research on hot spots and hot moments of N removal. By this review, we hope to bring awareness of the heterogeneity of hyporheic and riparian zones to catchment managers and policy makers when tackling N pollution problems.

Epidemiological and spatiotemporal analyses of HIV/AIDS prevalence among older adults in Sichuan, China between 2008 and 2019: A population-based study

OBJECTIVES

To describe epidemiological trends and spatial distributions of HIV/AIDS among older adults (aged ≥50) in Sichuan Province, China during 2008-19, and provide scientific reference for HIV/AIDS prevention, intervention and treatment.

METHODS

Data on HIV/AIDS cases reported in 2008-19 was extracted from the Case Report System. The Cochran-Armitage trend test was used to determine epidemic trends. Spatial autocorrelation and space-time analysis were conducted with ArcGIS10.6 and ArcGIS Pro2.4, respectively.

RESULTS

A total of 77854 HIV/AIDS cases among older adults were included in the study. Newly reported cases increased from 320 in 2008 to 22189 in 2019, and the reported incidence rate (number of new reported cases/older adult population) rose from 0.001% to 0.077%. Infections through heterosexual transmission increased from 65.3% to 98.2% of total cases in older adults in this period. Spatial analysis at the county-level showed significant clustering throughout Sichuan, with the main hot spots concentrated in the southeast. Spatiotemporal analysis indicated that most of the southeastern counties/districts were Consecutive Hot Spots.

CONCLUSIONS

Older adults have become a key population in the HIV/AIDS epidemic in Sichuan; comprehensive prevention and intervention measures targeted to older adults are urgently needed to control the spread of HIV/AIDS.

LCR based quick detection of hotspot G1896A mutation in patients with different spectrum of hepatitis B

G1896A switch is one of the hotspots in subjects affected with hepatitis B. This hotspot mutation is observed in all the different spectrum of hepatitis B, and it has a very dangerous and a long lasting effect. The major purpose of the study was to screen G1986A mutations at a large scale and also to establish ligase chain reaction as a mutation testing tool. Polymerase chain reaction (PCR) and Nucleotide Sequencing was done to identify the G1896A mutation in the precore region of the genome. All the 331 HBV positive patients were screened. Almost 29% (24/82) of the cases remarkably had the presence of G1896A mutation confirmed by LCR and direct sequencing. The precore G1896A mutation is responsible for one third of the patients suffering from precore stop codon mutation. It clearly exhibits that LCR is 100% in sync with direct sequencing and is extremely reliable and the results are highly reproducible.

Analysis of incidence of air quality on human health: a case study on the relationship between pollutant concentrations and respiratory diseases in Kennedy, Bogotá

Thousands of deaths associated with air pollution each year could be prevented by forecasting the behavior of factors that pose risks to people's health and their geographical distribution. Proximity to pollution sources, degree of urbanization, and population density are some of the factors whose spatial distribution enables the identification of possible influence on the presence of respiratory diseases (RD). Currently, Bogotá is among the cities with the poorest air quality in Latin America. Specifically, the locality of Kennedy is one of the zones in the city with the highest recorded concentration levels of local pollutants over the last 10 years. From 2009 to 2016, there were 8619 deaths associated with respiratory and cardiovascular diseases in the locality. Given these characteristics, this study set out to identify and analyze the areas in which the primary socioeconomic and environmental conditions contribute to the presence of symptoms associated with RD. To this end, information collected in field by performing georeferenced surveys was analyzed through geostatistical and machine learning tools which carried out cluster and pattern analyses. Random forests and AdaBoost were applied to establish hot spots where RD could occur, given the conjugation of predictor variables in the micro-territory. It was found that random forests outperformed AdaBoost with 0.63 AUC. In particular, this study's approach applies to densely populated municipalities with high levels of air pollution. In using these tools, municipalities can anticipate environmental health situations and reduce the cost of respiratory disease treatments.

Predicting Hot Spots Using a Deep Neural Network Approach

Targeting protein-protein interactions is a challenge and crucial task of the drug discovery process. A good starting point for rational drug design is the identification of hot spots (HS) at protein-protein interfaces, typically conserved residues that contribute most significantly to the binding. In this chapter, we depict point-by-point an in-house pipeline used for HS prediction using only sequence-based features from the well-known SpotOn dataset of soluble proteins (Moreira et al., Sci Rep 7:8007, 2017), through the implementation of a deep neural network. The presented pipeline is divided into three steps: (1) feature extraction, (2) deep learning classification, and (3) model evaluation. We present all the available resources, including code snippets, the main dataset, and the free and open-source modules/packages necessary for full replication of the protocol. The users should be able to develop an HS prediction model with accuracy, precision, recall, and AUROC of 0.96, 0.93, 0.91, and 0.86, respectively.

A disposable paper-based hydrophobic substrate for highly sensitive surface-enhanced Raman scattering detection

Detection of target analytes with high sensitivity and reproducibility remains a challenge for surface-enhanced Raman scattering (SERS) due to the lack of cost-effective and highly sensitive substrates. In this study, a hydrophobic SERS substrate capable of concentrating nanoparticles and analytes was prepared by spin-coating lubricating liquid onto commercial paper. The condensation effect of the paper-based hydrophobic substrate induced aggregation of gold nanoparticles (Au NPs) to generate ''hot spots'' for SERS and to drive analytes to the hot-spot areas for more sensitive detection. The obtained SERS signal intensity was 5-fold higher than that obtained using common paper, and a detection limit (LOD) of 4.3 × 10^-10 M for rhodamine 6G (R6G) was achieved. Randomly selected points on the substrate and different batches of substrates all exhibited high reproducibility, and the relative standard deviation (RSD) at 1362 cm^-1 is approximately 11%. A further application of the hydrophobic substrate was demonstrated by the detection of cytochrome C within a linear detection range of 3.90 × 10^-8 M-1.25 × 10^-6 M. In addition, the prepared substrate can obtain identifiable SERS spectra of cancer cells and non-cancer cells because a large number of AuNP or Au NPs clusters can adhere to cells, resulting in the construction of a 3D hotspot matrix. The disposable hydrophobic paper substrate eliminates the problem of solution diffusion, and also provides an effective platform for biomolecular screening detection.

Current status, spatial features, health risks, and potential driving factors of soil heavy metal pollution in China at province level

In this study we systematically reviewed 1203 research papers published between 2008 and 2018 in China and recorded related data on eight kinds of soil heavy metals (Cr, Pb, Cd, Hg, As, Cu, Zn, and Ni). Based on that, the pollution levels, ecological risk and health risk caused by soil heavy metals were evaluated and the pollution hot spots and potential driving factors of different heavy metals in different provinces were also identified. Results indicated accumulation of heavy metals in soils of most provinces in China compared with background values. Consistent with previous findings, the most prevalent polluted heavy metals were Cd and Hg. Polluted regions are mainly located in central, southern and southwestern China. Hunan, Guangxi, Yunnan, and Guangdong provinces were the most polluted provinces. For the potential health risk caused by heavy metals pollution, children are more likely confront with non-carcinogenic risk than adults and seniors. And children in Hunan and Guangxi province were experiencing relatively larger non-carcinogenic risk. In addition, children in part of provinces were undergoing potentially carcinogenic risks due to soil heavy metals exposure. Furthermore, in our study the 31 provinces in mainland China were divided into six subsets according to corresponding potential driving factors for heavy metal accumulation. Our study provide more comprehensive and updated information for contributing to better soil management, soil remediation, and soil contamination control in China.

Computationally identifying hot spots in protein-DNA binding interfaces using an ensemble approach

BACKGROUND

Protein-DNA interaction governs a large number of cellular processes, and it can be altered by a small fraction of interface residues, i.e., the so-called hot spots, which account for most of the interface binding free energy. Accurate prediction of hot spots is critical to understand the principle of protein-DNA interactions. There are already some computational methods that can accurately and efficiently predict a large number of hot residues. However, the insufficiency of experimentally validated hot-spot residues in protein-DNA complexes and the low diversity of the employed features limit the performance of existing methods.

RESULTS

Here, we report a new computational method for effectively predicting hot spots in protein-DNA binding interfaces. This method, called PreHots (the abbreviation of Predicting Hotspots), adopts an ensemble stacking classifier that integrates different machine learning classifiers to generate a robust model with 19 features selected by a sequential backward feature selection algorithm. To this end, we constructed two new and reliable datasets (one benchmark for model training and one independent dataset for validation), which totally consist of 123 hot spots and 137 non-hot spots from 89 protein-DNA complexes. The data were manually collected from the literature and existing databases with a strict process of redundancy removal. Our method achieves a sensitivity of 0.813 and an AUC score of 0.868 in 10-fold cross-validation on the benchmark dataset, and a sensitivity of 0.818 and an AUC score of 0.820 on the independent test dataset. The results show that our approach outperforms the existing ones.

CONCLUSIONS

PreHots, which is based on stack ensemble of boosting algorithms, can reliably predict hot spots at the protein-DNA binding interface on a large scale. Compared with the existing methods, PreHots can achieve better prediction performance. Both the webserver of PreHots and the datasets are freely available at: http://dmb.tongji.edu.cn/tools/PreHots/ .

Computational methods-guided design of modulators targeting protein-protein interactions (PPIs)

Protein-protein interactions (PPIs) play a pivotal role in extensive biological processes and are thus crucial to human health and the development of disease states. Due to their critical implications, PPIs have been spotlighted as promising drug targets of broad-spectrum therapeutic interests. However, owing to the general properties of PPIs, such as flat surfaces, featureless conformations, difficult topologies, and shallow pockets, previous attempts were faced with serious obstacles when targeting PPIs and almost portrayed them as "intractable" for decades. To date, rapid progress in computational chemistry and structural biology methods has promoted the exploitation of PPIs in drug discovery. These techniques boost their cost-effective and high-throughput traits, and enable the study of dynamic PPI interfaces. Thus, computational methods represent an alternative strategy to target "undruggable" PPI interfaces and have attracted intense pharmaceutical interest in recent years, as exemplified by the accumulating number of successful cases. In this review, we first introduce a diverse set of computational methods used to design PPI modulators. Herein, we focus on the recent progress in computational strategies and provide a comprehensive overview covering various methodologies. Importantly, a list of recently-reported successful examples is highlighted to verify the feasibility of these computational approaches. Finally, we conclude the general role of computational methods in targeting PPIs, and also discuss future perspectives on the development of such aids.

iPNHOT: a knowledge-based approach for identifying protein-nucleic acid interaction hot spots

Background

The interaction between proteins and nucleic acids plays pivotal roles in various biological processes such as transcription, translation, and gene regulation. Hot spots are a small set of residues that contribute most to the binding affinity of a protein-nucleic acid interaction. Compared to the extensive studies of the hot spots on protein-protein interfaces, the hot spot residues within protein-nucleic acids interfaces remain less well-studied, in part because mutagenesis data for protein-nucleic acids interaction are not as abundant as that for protein-protein interactions.

Results

In this study, we built a new computational model, iPNHOT, to effectively predict hot spot residues on protein-nucleic acids interfaces. One training data set and an independent test set were collected from dbAMEPNI and some recent literature, respectively. To build our model, we generated 97 different sequential and structural features and used a two-step strategy to select the relevant features. The final model was built based only on 7 features using a support vector machine (SVM). The features include two unique features such as ∆SASsa^1/2 and esp3, which are newly proposed in this study. Based on the cross validation results, our model gave F1 score and AUROC as 0.725 and 0.807 on the subset collected from ProNIT, respectively, compared to 0.407 and 0.670 of mCSM-NA, a state-of-the art model to predict the thermodynamic effects of protein-nucleic acid interaction. The iPNHOT model was further tested on the independent test set, which showed that our model outperformed other methods.

Conclusion

In this study, by collecting data from a recently published database dbAMEPNI, we proposed a new model, iPNHOT, to predict hotspots on both protein-DNA and protein-RNA interfaces. The results show that our model outperforms the existing state-of-art models. Our model is available for users through a webserver: http://zhulab.ahu.edu.cn/iPNHOT/.

Label-free discrimination of glioma brain tumors in different stages by surface enhanced Raman scattering

According to the WHO classification criteria, the most common intracranial tumor gliomas can be divided into four grades based on their symptoms. Among them, Grade Ⅰ and Grade II are low-grade gliomas, and Grade III and Grade IV are high-grade gliomas. Because gliomas have a high lethal rate, they have received widespread attention in the medical field. Based on these circumstances, a rapid and facile surface enhanced Raman scattering (SERS) method using silver nano particle-decorated silver nanorod (AgNPs@AgNR) as substrates were developed for the discrimination of gliomas. Compared with SERS-active silver nanoparticles and silver nanorod substrates, the prepared AgNPs@AgNR substrates showed an outstanding SERS performance with an enhancement factor up to 1.37 × 10⁹. Combined AgNPs@AgNR substrate with principal component analysis (PCA), we achieved rapid discrimination of healthy brain tissue and gliomas at different grades. The spectra obtained from the tissue illustrate prominently spectral differences which can be applied to identify whether it came from a healthy region or from a glioma. The most prominently difference between the SERS spectrum of healthy brain tissue and that of gliomas at different grades is the reduction in quotient of two characteristic peaks at 653 and 724 cm^-1. Furthermore, healthy brain tissue and Grade II gliomas as low grade gliomas as well as Grade III and Grade IV as high-grade gliomas can be clearly distinguished by three-dimensional PCA. Preliminary results indicate that the SERS spectra based on AgNPs@AgNR substrates can be applied for a rapid identification owing to its simple preparation of specimen and high-speed spectral acquirement.

Energy efficient sludge solubilization by microwave irradiation under carbon nanotube (CNT)-coated condition

Microwaves (MW) have great potential for sludge solubilization, and carbon materials can act as good microwave absorbers and heat transfer media because of their high dielectric loss tangent and thermal conductivity. In this study, carbon nanotube-coated MW vessels were developed by preparing a silane-CNT mixture and spray coating. In addition, sludge solubilization by microwave irradiation was performed to evaluate the effects of the CNT-coating at different initial total suspended solid (TSS) concentrations, target temperatures, and MW irradiation times in the uncoated and CNT-coated MW vessels. The sludge solubilization efficiency increased with increasing MW irradiation time and temperature and followed a first-order reaction in both vessels. However, the energy requirement to maintain the temperature was reduced in the CNT-coated MW vessel compared to the uncoated vessel. In addition, the Arrhenius equation revealed the catalytic site in the CNT-coated MW vessel to have a temperature of around 130 °C at an average sludge temperature of 100 °C. The maximum chemical oxygen demand (COD) solubilization and soluble COD (sCOD) increase per MW energy used were 1.64 and 1.67 times higher in the CNT-coated MW vessel than in the uncoated vessel, respectively. The increase in soluble total nitrogen and phosphorus in the CNT-coated MW vessel was attributed to cell wall destruction and intracellular protoplast dissolution, because of the acceleration of the MW thermal effect and high conductivity of CNTs, as well as the MW-induced cell wall and membrane disruption by hot spots on the CNT surface. This suggests that CNTs can be applied to increase the energy efficiency in MW-based pretreatment methods.

Prediction and targeting of GPCR oligomer interfaces

GPCR oligomerization has emerged as a hot topic in the GPCR field in the last years. Receptors that are part of these oligomers can influence each other's function, although it is not yet entirely understood how these interactions work. The existence of such a highly complex network of interactions between GPCRs generates the possibility of alternative targets for new therapeutic approaches. However, challenges still exist in the characterization of these complexes, especially at the interface level. Different experimental approaches, such as FRET or BRET, are usually combined to study GPCR oligomer interactions. Computational methods have been applied as a useful tool for retrieving information from GPCR sequences and the few X-ray-resolved oligomeric structures that are accessible, as well as for predicting new and trustworthy GPCR oligomeric interfaces. Machine-learning (ML) approaches have recently helped with some hindrances of other methods. By joining and evaluating multiple structure-, sequence- and co-evolution-based features on the same algorithm, it is possible to dilute the issues of particular structures and residues that arise from the experimental methodology into all-encompassing algorithms capable of accurately predict GPCR-GPCR interfaces. All these methods used as a single or a combined approach provide useful information about GPCR oligomerization and its role in GPCR function and dynamics. Altogether, we present experimental, computational and machine-learning methods used to study oligomers interfaces, as well as strategies that have been used to target these dynamic complexes.

Benthic diversity patterns and predictors: A study case with inferences for conservation

Understanding which drivers cause diversity patterns is a key issue in conservation. Here we applied a spatially explicit model to predict marine benthic diversity patterns according to environmental factors in the NW Mediterranean Sea. While most conservation-oriented diversity studies consider species richness only and neglect equitability, we measured separately species richness, equitability, and 'overall' diversity (i.e., the Shannon-Wiener H' function) on a dataset of 890 benthic species × 209 samples. Diversity values were predicted by means of Random Forest regression, on the basis of 10 factors: depth, distance from the coast, distance from the shelf break, latitude, sea-floor slope, sediment grain size, sediment sorting, distance from harbours and marinas, distance from rivers, and sampling gear. Predictions by Random Forests were accurate, the main predictors being latitude, sediment grain size, depth and distance from the coast. Based on predicted values, diversity hotspots were identified as those localities where indices were in the 15% top segment of ranked values. Only a minority of the diversity hotspots was included within the boundaries of the protection institutes established in the region. Marine protected areas are often created in sites harbouring important coastal habitats, which risks neglecting the diversity hidden in the sedimentary seafloor. We suggest that marine protected areas should accommodate portions of sedimentary habitat within their boundaries to improve diversity conservation.

Mapping theme trends and recognizing hot spots in postmenopausal osteoporosis research: a bibliometric analysis

Background

This study aimed to draw a series of scientific maps to quantitatively and qualitatively evaluate hot spots and trends in postmenopausal osteoporosis research using bibliometric analysis.

Methods

Scientific papers published on postmenopausal osteoporosis were extracted from the Web of Science Core Collection and PubMed database. Extracted information was analyzed quantitatively with bibliometric analysis by CiteSpace, the Online Analysis Platform of Literature Metrology and Bibliographic Item Co-Occurrence Matrix Builder (BICOMB). To explore the hot spots in this field, co-word biclustering analysis was conducted by gCLUTO based on the major MeSH terms/MeSH subheading terms-source literatures matrix.

Results

We identified that a total of 5,247 publications related to postmenopausal osteoporosis were published between 2013 and 2017. The overall trend decreased from 1,071 literatures in 2013 to 1,048 literatures in 2017. Osteoporosis International is the leading journal in the field of postmenopausal osteoporosis research, both in terms of impact factor score (3.819) and H-index value (157). The United States has retained a top position and has exerted a pivotal influence in this field. The University of California, San Francisco was identified as a leading institution for research collaboration, and Professors Reginster and Kanis have made great achievements in this area. Eight research hot spots were identified.

Conclusions

Our study found that in the past few years, the etiology and drug treatment of postmenopausal osteoporosis have been research hot spots. They provide a basis for the study of the pathogenesis of osteoporosis and guidelines for the drug treatment of osteoporosis.

Analyzing spatial and space-time clustering of facility-based deliveries in Bangladesh

Background

A spatial and temporal study of the distribution of facility-based deliveries can identify areas of low and high facility usage and help devise more targeted interventions to improve delivery outcomes. Developing countries like Bangladesh face considerable challenges in reducing the maternal mortality ratio to the targets set by the Sustainable Development Goals. Recent studies have already identified that the progress of reducing maternal mortality has stalled. Giving birth in a health facility is one way to reduce maternal mortality.

Methods

Facility delivery data from a demographic surveillance site was analyzed at both village and Bari (comprising several households with same paternal origins) level to understand spatial and temporal heterogeneity. Global spatial autocorrelation was detected using Moran’s I index while local spatial clusters were detected using the local Getis G_i* statistics. In addition, space-time scanning using a discrete Poisson approach facilitated the identification of space-time clusters. The likelihood of delivering at a facility when located inside a cluster was calculated using log-likelihood ratios.

Results

The three cluster detection approaches detected significant spatial and temporal heterogeneity in the distribution of facility deliveries in the study area. The hot and cold spots indicated contiguous and relocation type diffusion and increased in number over the years. Space-time scanning revealed that when a parturient woman is located in a Bari inside the cluster, the likelihood of delivering at a health facility increases by twenty-seven times.

Conclusions

Spatiotemporal studies to understand delivery patterns are quite rare. However, in resource constraint countries like Bangladesh, detecting hot and cold spot areas can aid in the detection of diffusion centers, which can be targeted to expand regions with high facility deliveries. Places and periods with reduced health facility usages can be identified using various cluster detection techniques, to assess the barriers and facilitators in promoting health facility deliveries.

Electronic supplementary material

The online version of this article (10.1186/s41182-019-0170-9) contains supplementary material, which is available to authorized users.

Detoxification of municipal solid waste incinerator (MSWI) fly ash by single-mode microwave (MW) irradiation: Addition of urea on the degradation of Dioxin and mechanism

The detoxification of municipal solid waste incinerator (MSWI) fly ash dioxins urgently requires an effective treatment technology. In this study, we adopted a single-mode microwave (MW)-based pyrolysis to treat MSWI fly ash under N₂ atmosphere and further elucidated the main influencing factors, including the chemical inhibitor, for dioxin control. The results show that (1) the detoxification process was optimized with a mass ratio of fly ash to SiC of 1:9, 23.1% (wt%) urea addition and pyrolysis temperature of ˜ 480 °C; (2) the total polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) destruction efficiency and the bioassay-derived 2,3,7,8-TCDD toxic equivalent (Bio-TEQ) removal efficiency reached 98.5% and 97.9%, respectively, accompanied with ˜ 1.3% of the total amount of dioxin being submitted to exhaust gas; (3) the MW-based pyrolysis of urea (133˜300 °C) was favourable for the generation of hot spots as well as the PCDD/F rapid destruction in fly ash. In addition, the leaching toxicity of heavy metals was also partially reduced after MW pyrolysis reactions. To the best of our knowledge, this is the first report adopting a MW-based pyrolysis to eliminate dioxin in MSWI fly ash with the addition of urea, which is a promising alternative to current methods.

Identifying mutation positions in all segments of influenza genome enables better differentiation between pandemic and seasonal strains

Influenza has a negative sense, single-stranded, and segmented RNA. In the context of pandemic influenza research, most studies have focused on variations in the surface proteins (Hemagglutinin and Neuraminidase). However, new findings suggest that all internal and external proteins of influenza viruses can contribute in pandemic emergence, pathogenicity and increasing host range. The occurrence of the 2009 influenza pandemic and the availability of many external and internal segments of pandemic and non-pandemic sequences offer a unique opportunity to evaluate the performance of machine learning models in discrimination of pandemic from seasonal sequences using mutation positions in all segments. In this study, we hypothesized that identifying mutation positions in all segments (proteins) encoded by the influenza genome would enable pandemic and seasonal strains to be more reliably distinguished. In a large scale study, we applied a range of data mining techniques to all segments of influenza for rule discovery and discrimination of pandemic from seasonal strains. CBA (classification based on association rule mining), Ripper and Decision tree algorithms were utilized to extract association rules among mutations. CBA outperformed the other models. Our approach could discriminate pandemic sequences from seasonal ones with more than 95% accuracy for PA and NP, 99.33% accuracy for NA and 100% accuracy, precision, specificity and sensitivity (recall) for M1, M2, PB1, NS1, and NS2. The values of precision, specificity, and sensitivity were more than 90% for other segments except PB2. If sequences of all segments of one strain were available, the accuracy of discrimination of pandemic strains was 100%. General rules extracted by rule base classification approaches, such as M1-V147I, NP-N334H, NS1-V112I, and PB1-L364I, were able to detect pandemic sequences with high accuracy. We observed that mutations on internal proteins of influenza can contribute in distinguishing the pandemic viruses, similar to the external ones.

Methods for Discovering and Targeting Druggable Protein-Protein Interfaces and Their Application to Repurposing

Drug repurposing is a creative and resourceful approach to increase the number of therapies by exploiting available and approved drugs. However, identifying new protein targets for previously approved drugs is challenging. Although new strategies have been developed for drug repurposing, there is broad agreement that there is room for further improvements. In this chapter, we review protein-protein interaction (PPI) interface-targeting strategies for drug repurposing applications. We discuss certain features, such as hot spot residue and hot region prediction and their importance in drug repurposing, and illustrate common methods used in PPI networks to identify drug off-targets. We also collect available online resources for hot spot prediction, binding pocket identification, and interface clustering which are effective resources in polypharmacology. Finally, we provide case studies showing the significance of protein interfaces and hot spots in drug repurposing.

In Silico Target Druggability Assessment: From Structural to Systemic Approaches

This chapter will focus on today's in silico direct and indirect approaches to assess therapeutic target druggability. The direct approach tries to infer from the 3D structure the capacity of the target protein to bind small molecule in order to modulate its biological function. Algorithms to recognize and characterize the quality of the ligand interaction sites whether within buried protein cavities or within large protein-protein interface will be reviewed in the first part of the paper. In the case a ligand-binding site is already identified, indirect aspects of target druggability can be assessed. These indirect approaches focus first on target promiscuity and the potential difficulties in developing specific drugs. It is based on large-scale comparison of protein-binding sites. The second aspect concerns the capacity of the target to induce resistant pathway once it is inhibited or activated by a drug. The emergence of drug-resistant pathways can be assessed through systemic analysis of biological networks implementing metabolism and/or cell regulation signaling.