A Visual Analysis of the Research Dynamics in Resistance to EGFR Inhibitors for NSCLC.

Purpose: Activating mutations in epidermal growth factor receptor (EGFR) have been identified as key predictive biomarkers for the customized treatment with EGFR tyrosine kinase inhibitors (TKIs) in non-small cell lung cancer (NSCLC), aiding in improving patient response rates and survival. However, resistance challenges the efficacy of these treatments, with limited understanding of post-resistance therapeutic strategies. A deep understanding of the biology and resistance mechanisms of EGFR-mutant NSCLC is crucial for developing new treatment approaches. This study, through bibliometric analysis, summarizes the trends in research on resistance to EGFR-TKIs. Methods: Research papers on NSCLC with EGFR inhibitor resistance were collected from the Web of Science Core Collection (WoSCC). The analysis utilized bibliometric tools like CiteSpace, VOSviewer, and other platforms for comprehensive analysis and visualization of the outcomes. Results: The WoSCC database contains a total of 5866 documents on resistance to EGFR-TKIs treatment, including 4727 articles (93.48%) and 1139 reviews (6.52%), spanning 81 countries and regions, 4792 institutions, with the involvement of 23,594 authors. Since 2016, there has been a significant increase in publications in this field. China has the highest publication output, while the United States has the highest citation count for papers. Harvard University leads in terms of the number of publications. Among the top ten journals with the highest output, Clinical Cancer Research has the highest impact factor at 11.5, with 90% of the journals classified in Q1 or Q2. Rafael Rosell is one of the most influential authors in this field, ranking second in publication volume and fourth in citation count. Research on EGFR-TKIs resistance mainly focuses on genetic testing, resistance mechanisms, and post-resistance treatment strategies. Conclusion: This study provides researchers with a reliable basis and guidance for finding authoritative references, understanding research trends, and exploring potential directions.

Extralong hot-spots sensor for SERS sensitive detection of phthalate plasticizers in biological tear and serum fluids.

Phthalate plasticizers (PAEs) illegally used in food pose a great threat to human health. A new and efficient sensing platform for the sensitive detection of the PAE residues in biological fluids needs to be designed and developed. Here, we report a simple and reliable surface-enhanced Raman spectroscopy (SERS) active platform with extralong hot spots of Au nanobipyramids@Ag nanorods (Au NBPs@Ag NRs) for the rapid and sensitive detection of PAEs in biological fluids. To achieve high activity, Au NBPs@Ag NRs with different shell lengths were fabricated by controlling the synthesis conditions, and the corresponding SERS properties were investigated by using crystal violet (CryV) and butyl benzyl phthalate (BBP). The experimental results showed that a longer shell length correlated to greater Raman activity, which was confirmed by finite-difference time-domain (FDTD) electromagnetic simulation. More importantly, the extralong hot spots of the Au NBPs@Ag NR SERS-active substrate showed excellent homogeneity and reproducibility for the CryV probe molecules (6.21%), and the detection limit was 10-9 M for both BBP and diethylhexyl phthalate (DEHP). Furthermore, through the standard addition method, an extralong hot spots SERS substrate could achieve highly sensitive detection of BBP and DEHP in serum and tears fluids, and the detection limit was as low as 3.52 × 10-8 M and 2.82 × 10-8 M. Therefore, the Au NBPs@Ag NR substrate with an extraordinarily long surface is efficient and versatile, and can potentially be used for high-efficiency sensing analysis in complex biological fluids.

Prediction of Protein-DNA Interface Hot Spots Based on Empirical Mode Decomposition and Machine Learning.

Protein-DNA complex interactivity plays a crucial role in biological activities such as gene expression, modification, replication and transcription. Understanding the physiological significance of protein-DNA binding interfacial hot spots, as well as the development of computational biology, depends on the precise identification of these regions. In this paper, a hot spot prediction method called EC-PDH is proposed. First, we extracted features of these hot spots' solid solvent-accessible surface area (ASA) and secondary structure, and then the mean, variance, energy and autocorrelation function values of the first three intrinsic modal components (IMFs) of these conventional features were extracted as new features via the empirical modal decomposition algorithm (EMD). A total of 218 dimensional features were obtained. For feature selection, we used the maximum correlation minimum redundancy sequence forward selection method (mRMR-SFS) to obtain an optimal 11-dimensional-feature subset. To address the issue of data imbalance, we used the SMOTE-Tomek algorithm to balance positive and negative samples and finally used cat gradient boosting (CatBoost) to construct our hot spot prediction model for protein-DNA binding interfaces. Our method performs well on the test set, with AUC, MCC and F1 score values of 0.847, 0.543 and 0.772, respectively. After a comparative evaluation, EC-PDH outperforms the existing state-of-the-art methods in identifying hot spots.

Fiber Optic LSPR Sensing AFM1 in Milk with Enhanced Sensitivity by the Hot Spot Effect Based on Nanogap Construction.

The detection of the amount of aflatoxin M1 (AFM1) in milk is crucial for food safety. Here, we utilize a fiber optic (FO) localized surface plasmon resonance (LSPR) biosensor by constructing gold nanoparticle (AuNP) multimers, in which the nanogaps amplified the LSPR signal by the hot spot effect, and achieved a highly sensitive detection of f AFM1. Through the optimization of parameter conditions for the fabrication of the sensor and detection system, a high performance result from the FO LSPR biosensor was obtained, and the method for AFM1 detection was established, with a wide detection range of 0.05-100 ng/mL and a low limit of detection (LOD) of 0.04 ng/mL, and it has been successfully validated with the actual sample milk. Therefore, it is a good strategy to fabricate highly sensitive FO LSPR sensors for detecting AFM1 by constructing AuNP multimers, and this approach is suitable for developing other biosensors.

Characteristics of the top 100 cited electroencephalography articles on aging: a bibliometric analysis.

Electroencephalography (EEG) is a widely used tool in neuroscience. To explore the features of the top 100 cited articles related to EEG and aging over the past decade, we conducted a bibliometric analysis using Web of Science Core Collection (WoSCC) data as of January 21, 2024. The selected top 100 cited papers were analyzed using VOSviewer and Excel. We examined the distribution of publication years, authors, institutions, countries/regions, and journals. Hotspots were identified through keyword analysis. The analyzed articles were published between 2014 and 2021, with the majority being published before 2020 (n=91). Citation counts in WoSCC ranged from 24 to 250, with a median of 40 and a mean of 53. A total of 818 authors from 283 institutions in 35 countries/territories contributed to these top papers. The United States of America (USA) (n=37), Germany (n=14), and Canada (n=11) ranked in the top three in terms of total publications or citations. The predominant journals were in the fields of Neuroscience (n=58), Geriatrics & Gerontology (n=22), Clinical Neurology (n=13), and Anesthesiology (n=9), which published most of the high-quality articles. Key themes included EEG, aging, Alzheimer's disease, mild cognitive impairment, functional connectivity, and alpha oscillations. Emerging topics included sleep, machine learning, delirium, postoperative cognitive function, virtual reality, monitoring, resting state, coherence, and transcranial direct current stimulation. In conclusion, this study provides a comprehensive overview of the trends in scientific literature on EEG in aging over the past decade. Authors and institutions from North America, Europe, and East Asia led in contributions. Journals focusing on neuroscience, geriatrics, and anesthesiology published the majority of articles. Degenerative neurological diseases and cognitive impairment were prominent topics, suggesting future studies should explore EEG's diagnostic utility for these disorders.

Molecular mechanisms behind the generation of pro-oncogenic HIV-1 matrix protein p17 variants.

HIV-1 matrix protein p17 variants (vp17s), characterized by amino acid insertions at the COOH-terminal region of the viral protein, have been recently identified and studied for their biological activity. Different from their wild-type counterpart (refp17), vp17s display a potent B cell growth and clonogenic activity. Recent data have highlighted the higher prevalence of vp17s in people living with HIV-1 (PLWH) with lymphoma compared with those without lymphoma, suggesting that vp17s may play a key role in lymphomagenesis. Molecular mechanisms involved in vp17 development are still unknown. Here we assessed the efficiency of HIV-1 Reverse Transcriptase (RT) in processing this genomic region and highlighted the existence of hot spots of mutation in Gag, at the end of the matrix protein and close to the matrix-capsid junction. This is possibly due to the presence of inverted repeats and palindromic sequences together with a high content of Adenine in the 322-342 nucleotide portion, which constrain HIV-1 RT to pause on the template. To define the recombinogenic properties of hot spots of mutation in the matrix gene, we developed plasmid vectors expressing Gag and a minimally modified Gag variant, and measured homologous recombination following cell co-nucleofection by next-generation sequencing. Data obtained allowed us to show that a wide range of recombination events occur in concomitance with the identified hot spots of mutation and that imperfect events may account for vp17s generation.

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.

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.

An Assessment of Trends in HIV-1 Prevalence and Incidence and Spatio-Temporal Analyses of HIV-1 Recent Infection Among MSM During the Surveillance Period Between 2018 and 2022 in Sichuan, China.

Background: Men who have sex with men (MSM) is one main type of high-risk activities facilitating HIV-1 transmission in Sichuan province. Previous works on HIV-1 incidence and prevalence among MSM only concentrated before 2018, the situation after that is unknown. In addition, the distribution of hot-spots related to current HIV-1 epidemic is also rarely known among MSM in Sichuan. Objective: To update trends of HIV-1 prevalence and incidence and to visualize hot-spots of ongoing transmission in Sichuan province during surveillance period among MSM between 2018 and 2022. Methods: Limiting Antigen Avidity assay was performed to detect recent infection within new HIV-1 diagnoses founded during surveillance period among MSM. The HIV-1 prevalence and incidence were calculated according to an extrapolation method proposed by publications and guidelines. Trend tests were performed using χ2 tests with linear-by-linear association. The spatial analysis was conducted with ArcGIS 10.7 to figure hot-spots of HIV-1 recent infections among MSM. Results: Between 2018 and 2022, 16,697 individuals participated in HIV-1 MSM sentinel surveillance program, of which 449 samples (98.25%) were tested with LAg-Avidity EIA, and 230 samples were classified as recent infection. Respectively, the overall prevalence and incidence were 2.74% and 3.69% (95% CI: 3.21, 4.16) and both had significant declining trends (p < 0.001). Luzhou city had a highest HIV-1 incidence (10.74%, 95% CI: 8.39, 13.10) over the study period and was recognized as a hot-spot for recent HIV-1 infection among MSM. Conclusion: During the surveillance period, both HIV-1 prevalence and incidence were declining. However, Luzhou city had an unusually high HIV-1 incidence and became an emerging hot-spot of recent HIV-1 infection among MSM. This finding suggested focused attention, cross-regional intervention strategies, and prevention programs are urgently required to curb the spread of ongoing transmission.

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.

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.

A district-level emission inventory of anthropogenic PM2.5 from the primary sources over the Indian Indo Gangetic Plain: Identification of the emission hotspots.

A district-wise emission inventory was made for the states and union territories (UTs) of the Indian Indo-Gangetic Plain for the base year of 2018 to estimate the emissions of PM2.5 from various sectors. In addition to conventional sectors, emissions from road dust, fossil-fuelled irrigation pumps, and construction dust were also taken into account. Total primary anthropogenic PM2.5 emission was estimated to be 3157.3 Gg (or kilo-tones) for the year 2018 of which 32 % originated from the industrial sector, 27 % from domestic fuel consumption, 23 % from open burning, 14 % from road dust, 2 % from vehicular and 2 % from various unorganized sectors. The highest emissions were observed during the premonsoon (1013 Gg/year) followed by postmonsoon (802Gg/year), winter (788 Gg/year), and lowest during the monsoon (554Gg/year). Among the states and UTs, Uttar Pradesh contributes the most in total emissions (39 %), followed by Punjab (19 %), Bihar (17 %), West Bengal (13 %), Haryana (11 %), Delhi (0.9 %) and Chandigarh (0.1 %). Emission for per capita and for billion-rupee of state gross domestic product (GDP) were the highest for Punjab and Haryana. Results have identified the districts of Punjab (Firozpur, Ludhiana, Jalandhar), scattered pockets of Uttar Pradesh (Sonbhadra, Agra, Varanasi, Kanpur, Lucknow, Prayagraj) and lower Gangetic delta (Gaya, Muzaffarpur, Burdwan, both 24-parganas and Murshidabad) as potent hotspots of cumulative PM2.5 emissions. On the other hand, the districts of Punjab (Faridkot, Mansa, Muktsar, Fatehgarh) were found to be the hotspots for per capita emissions. High emissions were observed from the domestic sector, brick kilns, and micro and small-scale industries, and regulating norms should be more stringent for these sectors. Such a study will be a value add for the policymakers and health experts to assess emission hot spots, pollution simulation, and associated mortality analysis of the region.

Factors influencing geographic gender disparity in neurosurgery: a nationwide geospatial clustering analysis.

OBJECTIVE: Women neurosurgeons (WNs) continue to remain a minority in the specialty despite significant initiatives to increase their representation. One domain less explored is the regional distribution of WNs, facilitated by the hiring practices of neurosurgical departments across the US. In this analysis, the authors coupled the stated practice location of WNs with regional geospatial data to identify hot spots and cold spots of prevalence and examined regional predictors of increases and decreases in WNs over time. METHODS: The authors examined the National Provider Identifier (NPI) numbers of all neurosurgeons obtained via the National Plan and Provider Enumeration System (NPPES), identifying the percentage of WNs in each county for which data were appended with data from the US Census Bureau. Change in WN rates was identified by calculating a regression slope for all years included (2015-2022). Hot spots and cold spots of WNs were identified through Moran's clustering analysis. Population and surgeon features were compared for hot spots and cold spots. RESULTS: WNs constituted 10.73% of all currently active neurosurgical NPIs, which has increased from 2015 (8.81%). Three hot spots were found-including the Middle Atlantic and Pacific divisions-that contrasted with scattered cold spots throughout the East Central regions that included Memphis as a major city. Although relatively rapidly growing, hot spots had significant gender inequality, with a median WN percentage of 11.38% and a median of 0.61 WNs added to each respective county per year. CONCLUSIONS: The authors analyzed the prevalence of WNs by using aggregated data from the NPPES and US Census Bureau. The authors also show regional hot spots of WNs and that the establishment of WNs in a region is a predictor of additional WNs entering the region. These data suggest that female neurosurgical mentorship and representation may be a major driver of acceptance and further gender diversity in a given region.

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.

Plasmonic Dodecahedral-Walled Elongated Nanoframes for Surface-Enhanced Raman Spectroscopy.

Here, elongated pseudohollow nanoframes composed of four rectangular plates enclosing the sides and two open-frame ends with four ridges pointing at the tips for near-field focusing are reported. The side facets act as light-collecting domains and transfer the collected light to the sharp tips for near-field focusing. The nanoframes are hollow inside, allowing the gaseous analyte to penetrate through the entire architecture and enabling efficient detection of gaseous analytes when combined with Raman spectroscopy. The resulting nanostructures are named Au dodecahedral-walled nanoframes. Synthesis of the nanoframes involves shape transformation of Au nanorods with round tips to produce Au-elongated dodecahedra, followed by facet-selective Pt growth, etching of the inner Au, and regrowth steps. The close-packed assembly of Au dodecahedral-walled nanoframes exhibits an attomolar limit of detection toward benzenethiol. This significant enhancement in SERS is attributed to the presence of a flat solid terrace for a large surface area, sharp edges and vertices for strong electromagnetic near-field collection, and open frames for effective analyte transport and capture. Moreover, nanoframes are applied to detect chemical warfare agents, specifically mustard gas simulants, and 20 times higher sensitivity is achieved compared to their solid counterparts.

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.

Evolution of spatial disease clusters via a Bayesian space-time variability modelling.

This study proposes to use exceedance posterior probabilities of a space-time random-effects model to study the temporal dynamics of clusters. The local time trends specified for each area is further smoothed over space. We modelled the common spatial and the space-varying temporal trend using a multivariate Markov Random field to incorporate within-area correlations. We estimate the model parameters within a fully Bayesian framework. The exceedance posterior probabilities are further used to classify the common spatial trend into hot-spots, cold-spots, and neutral-spots. The local time trends are classified into increasing, decreasing, and stable trends. The results is a 3×3 table depicting the time trends within clusters. As a demonstration, we apply the proposed methodology to study the evolution of spatial clustering of intestinal parasite infections in Ghana. We find the methodology presented in this paper applicable and extendable to other or multiple tropical diseases which may have different space-time conceptualizations.

A bibliometric analysis of research trends and hotspots in alpine grassland degradation on the Qinghai-Tibet Plateau.

A bibliometric analysis of current research, hotspots, and development trends was used to develop an overall framework of mechanisms of alpine grassland degradation on the Qinghai-Tibet Plateau. This investigation includes data from 1,330 articles on alpine grassland degradation on the Qinghai-Tibet Plateau, acquired from the Chinese Science Citation Database (CSCD) and Web of Science Core Collection (WOS). Research was divided into three themes: spatial scope and management of typical grassland degradation problems, dynamic mechanisms of grassland degradation and effects of ecological engineering, and grassland degradation risk based on remote sensing technology. The results of the analysis showed that the research can be summarized into three aspects: typical grassland degradation identification, dynamic mechanism analysis of grassland degradation, and grassland ecosystem stability strategy. The main findings can summarized, as follows: (1) Ecological analyses using the river source as a typical region defined the formation of "black soil beach" type degraded grasslands in the region, and promoted the ecological environment management and protection of the alpine grassland by discussing the causes of regional ecological environment changes; (2) Dynamic mechanism analyses of climate change and characteristics analyses of grassland vegetation-soil degradation revealed that alpine grassland degradation is the result of multiple main factors; and (3) Risk prediction methods for grassland degradation, methods of grassland management and sustainable countermeasures for agriculture and animal husbandry development, and the development of a comprehensive index of influencing factors on grassland degradation all indicate that selecting the right grassland restoration measures is the key to grassland restoration. Remote sensing monitoring and high-throughput sequencing technology should be used in future research on grassland ecosystems. In addition, multiscale, multidimensional, and multidisciplinary systematic research methods and long-term series data mining could help identify the characteristics and causes of alpine grassland system degradation. These findings can help identify a more effective coordination of landscape, water, lake, field, forest, grass, and sand management for the prevention of alpine grassland degradation.

The Update of HIV-1 Prevalence and Incidence and Spatio-Temporal Analyses of HIV Recent Infection Among Four Sub-Groups in Sichuan, China During Surveillance Period Between 2016 and 2022.

Background: Sichuan Province has gradually become a hot-spot for HIV/AIDS. Little is known about the HIV-1 incidence and prevalence among four sub-groups since 2015. Meanwhile, the distributions of hot-spot areas related to recent infection cases that indicate current transmission among the four subgroups are also rare. Objective: The main purpose of this study was to assess the HIV-1 prevalence and incidence and to visualize the distributions of hot-spot areas of current transmission among four subgroups (people who inject drugs, male STD clinic attendees, female sex workers and men who had sex with men) during the surveillance period in Sichuan province between 2016 and 2022. Results: Of the 267,617 individuals, 2158 HIV-positive samples were tested with Lag-Avidity EIA, among which 493 samples were identified as recent infections. Among people who inject drugs (PWID), both HIV-1 prevalence (from 1.41% to 0.34%) and incidence (from 0.03% to 0.140%) showed a significant decreasing trend. Among men who had sex with men (MSM), female sex workers (FSWs), and male STD clinic attendees, HIV-1 prevalence indicated significant decreasing trends, whereas HIV-1 incidence showed no significant changes. Spatial analysis demonstrated the formation of hot-spots and clusters of current transmissions sharing regional differences, mainly concentrated in the southeast, and most of these were consecutive hot-spots. Conclusion: The prevention and control were efficacious and persistent. However, among the other three subgroups, there is a need for a regional cooperative in prevention and control approaches and collaborative research in many aspects.

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.