Apparent Clustering Research Articles

First whole genome sequencing and analysis of human parechovirus type 3 causing a healthcare-associated outbreak among neonates in Hungary.

In November 2023, the National Reference Laboratory for Enteroviruses (Budapest, Hungary) received stool, pharyngeal swab and cerebrospinal fluid samples from five newborns suspected of having human parechovirus (PEV-A) infection. The neonates were born in the same hospital and presented with fever and sepsis-like symptoms at 8-9 days of age, and three of them showed symptoms consistent with central nervous system involvement. PEV-A positivity was confirmed by quantitative reverse transcription polymerase chain reaction. To determine the PEV-A genotype responsible for the infections, fecal samples of four neonates were subjected to metagenomic sequencing. For further analyses, amplicon-based whole genome sequencing was performed directly from the clinical samples. On the basis of whole genome analysis, sequences were allocated to PEV-A genotype 3 (PEV-A3) and consensus sequences were identical. Two ambiguities were identified in the viral protein 1 (VP1) region of all sequences at a frequency of 17.7-53.7%, indicating the simultaneous presence of at least two quasispecies in the clinical samples. The phylogenetic analysis and similarity plotting showed that all sequences clustered without any topological inconsistencies between the P1 capsid and P2, P3 non-capsid regions, suggesting that recombination events during evolution were unlikely. Our findings suggest that the apparent cluster of cases were microbiologically related, and the results may also inform future investigations on the evolution and pathogenicity of PEV-A3 infections.

Spatial and temporal analysis and forecasting of TB reported incidence in western China

ObjectiveTuberculosis (TB) remains an important public health concern in western China. This study aimed to explore and analyze the spatial and temporal distribution characteristics of TB reported incidence in 12 provinces and municipalities in western China and to construct the optimal models for prediction, which would provide a reference for the prevention and control of TB and the optimization of related health policies.MethodsWe collected monthly data on TB reported incidence in 12 provinces and municipalities in western China and used ArcGIS software to analyze the spatial and temporal distribution characteristics of TB reported incidence. We applied the seasonal index method for the seasonal analysis of TB reported incidence and then established the SARIMA and Holt-Winters models for TB reported incidence in 12 provinces and municipalities in western China.ResultsThe reported incidence of TB in 12 provinces and municipalities in western China showed apparent spatial clustering characteristics, and Moran’s I was greater than 0 (p < 0.05) over 8 years during the reporting period. Among them, Tibet was the hotspot for TB incidence in 12 provinces and municipalities in western China. The reported incidence of TB in 12 provinces and municipalities in western China from 2004 to 2018 showed clear seasonal characteristics, with seasonal indices greater than 100% in both the first and second quarters. The optimal models constructed for TB reported incidence in 12 provinces and municipalities in western China all passed white noise test (p > 0.05).ConclusionsAs a hotspot of reported TB incidence, Tibet should continue to strengthen government leadership and policy support, explore TB intervention strategies and causes. The optimal prediction models we developed for reported TB incidence in 12 provinces and municipalities in western China were different.

Collective gradient sensing by dilute swimming bacteria without clustering

We characterize the taxis enhancement of swimming bacteria by collective migration without apparent clustering. We confine a dilute suspension in a shallow channel and evaluate the thermotaxis response to local heating and diffusion. By combining cell tracking analysis and numerical simulation based on simple modeling, we show that the alignment interaction suppresses orientation fluctuation, strengthens migration bias, and also prevents the dispersion of accumulated population. The results show a prominent example of how a collective motion of active matter implements a biological function. Published by the American Physical Society 2024

Suspected serial killers and unsuspected statistical blunders.

A whole branch of theoretical statistics devotes itself to the analysis of clusters, the aim being to distinguish an apparent cluster arising randomly from one that is more likely to have been produced as a result of some systematic influence. There are many examples in medicine and some that involve both medicine and the legal field; criminal law in particular. Observed clusters or a series of cases in a given setting can set off alarm bells, the recent conviction of Lucy Letby in England being an example. It was an observed cluster, a series of deaths among neonates, that prompted the investigation of Letby. There have been other similar cases in the past and there will be similar cases in the future. Our purpose is not to reconsider any particular trial but, rather, to work with similar, indeed more extreme numbers of cases as a way to underline the statistical mistakes that can be made when attempting to make sense of the data. These notions are illustrated via a made-up case of 10 incidents where the anticipated count was only 2. The most common statistical analysis would associate a probability of less than 0.00005 with this outcome: A very rare event. However, a more careful analysis that avoids common pitfalls results in a probability close to 0.5, indicating that, given the circumstances, we were as likely to see 10 or more as we were to see less than 10.

Experimental study of the effect of particle–wall interactions on inertial particle dynamics in wall turbulence

Based on Voronoi analysis, the properties related to the near-wall motion of particles in a turbulent boundary layer were experimentally investigated via different release modes, with a friction Reynolds number $Re_\tau =3530$ . For high-inertia sand particles with Stokes number $St^+ \sim O(10^2\unicode{x2013}10^3)$ and a volume fraction $\varPhi _v \sim O(10^{-4})$ , particle image tracking velocimetry was used to determine the particle position and near-wall distribution properties. We established three particle release modes, including top-released, bottom overall-released and bottom partially released sand particles, under the same flow field conditions and calculated the differences in particle near-wall clustering and void properties. It was confirmed that wall effects (including collision and strike-splash) have a great influence on particle clustering and void behaviour near the wall. In the top-released sand particle and locally laid sand particle cases, particles bounced off the smooth walls and re-entered the carrier flow, causing significant clustering and sparsing of particles near the walls. In contrast, in the overall sand-laying case where the bottom wall was completely covered with sand particles, there is no apparent cluster or void phenomenon near the wall $(z/\delta &lt;0.12)$ and the particles are randomly distributed, due to the combined effect of particle impact and splashing. In addition, the clustering and voids of particles become more pronounced with increasing wall-normal distance in the three release modes, and the particle distribution shows some self-similarity at each flow layer. The probability density function of the concentration of cluster particles decreases following a ‘ $-5/3$ ’ power law. However, due to the particle–wall interaction, the probability density function gradually deviates from the ‘ $-5/3$ ’ power law.

Linking electromagnetic induction data to soil properties at field scale aided by neural network clustering

IntroductionThe mapping of soil properties, such as soil texture, at the field scale is important Q6 in the context of national agricultural planning/policy and precision agriculture. Electromagnetic Induction (EMI) surveys are commonly used to measure soil apparent electrical conductivity and can provide valuable insights into such subsurface properties. MethodsMulti-receiver or multi-frequency instruments provide a vertical distribution of apparent conductivity beneath the instrument, while the mobility of such instruments allows for spatial coverage. Clustering is the grouping together of similar multi-dimensional data, such as the processed EMI data over a field. A neural network clustering process, where the number of clusters can be objectively determined, results in a set of one-dimensional apparent electrical conductivity cluster centers, which are representative of the entire three-dimensional dataset. These cluster centers are used to guide inversions of apparent conductivity data to give an estimate of the true electrical conductivity distribution at a site.Results and discussionThe method is applied to two sites and the results demonstrate a correlation between (true) electrical conductivity with soil texture (sampled prior to the EMI surveys) which is superior to correlations where no clustering is included. The method has the potential to be developed further, with the aim of improving the prediction of soil properties at cluster scale, such as texture, from EMI data. A particularly important conclusion from this initial study is that EMI data should be acquired prior to a focused soil sampling campaign to calibrate the electrical conductivity – soil property correlations.

An Econometric Study on Volatility Clusters, Dynamic Risk Return Relationship, and Asymmetry in Bitcoin Returns

The most sought-after cryptocurrency sector is facing dicey environment in India due to stringent ideology of its government that put Indian investors in dilemma in envisaging this sector as a virtuous investment avenue. The investors are very curious for this sector and their curiosity aroused the need for its evaluation in terms of risk-return dynamics in contemporary scenario. The present study is an endeavor to econometrically explore volatility clusters, dynamic risk return relationship, and asymmetry in Bitcoin return series covering the period from August 2010 to February 2022. The results of Augmented Dickey–Fuller test, Ng–Perron tests, Ljung Box Q test, Engle’s ARCH, and White test asserted that the Bitcoin return series is stationary and has apparent volatility clusters in it. The estimates from GARCH-M model confirmed the absence of risk return relationship and the estimates from ARMA-EGARCH model confirmed the presence of asymmetry (leverage effect) in Bitcoin return series. However, the results of ARMA-TARCH model confirmed the absence of asymmetry in the series and further diagnostic checking asserted that ARMA- TARCH model is the best fitted model. These estimations may help the investors in comprehending risk-return dynamics of investment in Bitcoins for framing better hedging strategy in contemporary scenario.

Allometry: revealing evolution's engineering principles.

An animal's body size impacts many aspects of its structure and function (Y); insights that only become apparent when viewed over several orders of magnitude of body mass (M) and expressed allometrically in the form of power law equations (Y=aMb). The resulting relationships are apparent clusters of similar exponents (b) revealing emergent 'patterns of design' that shed light on the universal principles of structure and function. Basic physical principles of surface area, volume and heat exchange apply to all objects, including animals, and many consequences must be attributed to these fundamental properties. Starting with Galileo's description of the shapes of bones in the 16th century and extending to 19th century explanations of heat production and loss by Sarrus and Rameaux, allometric patterns have provided numerous biological insights. Here, we examine several of these insights and explore how the selective pressures and scaling may differ when comparing animals in a vegetative (basal) state and those utilizing their maximum metabolic capacities. It seems that the selective pressures under those two conditions differ. We caution that allometric patterns invite explanations that lack supporting data or may be dismissed because there is hesitation among biologists to make comparisons lacking phylogenetic support. We argue that emergent allometric patterns have inherent value and continue to be the fodder for many fruitful hypotheses.

Quality evaluation of Pterocephali Herba through simultaneously quantifying 18 bioactive components by UPLC-TQ-MS/MS analysis

Pterocephali Herba (PH), the dried whole plant of Pterocephalus hookeri, is a Tibetan medicine commonly used to treat rheumatoid arthritis (RA). Iridoids, triterpenoids, flavonoids and phenylpropanoids are the major groups of bioactive constituents from PH. However, only ursolic acid and oleanolic acid, two unspecific triterpenoid components, are used as markers for the quality control of PH in Chinese Pharmacopoeia. Herein, an UPLC-TQ-MS/MS integrating SIR and MRM mode method for simultaneously quantifying 18 components, i.e., 9 iridoids, 3 triterpenoids, 3 phenylpropanoids, 2 flavonoids and quinic acid, in PH was developed and validated, and was used to evaluate 10 batches of PH samples from different origins. Hierarchical cluster analysis (HCA) was used to show the clustering of PH samples, while spearman correlation analysis was adopted to evaluate the correlation between ursolic acid/oleanolic acid and other quantified components. It was found that the established method was sensitive, precise, and accurate enough for the simultaneous quantification of 18 analytes in PH samples. Significant differences were found among the contents of 18 components in PH samples, no apparent clustering of the quality of PH samples was found to be related to its origins, and the contents of ursolic acid/oleanolic acid were only significantly correlated to the content of sylvestroside I, dipsanoside B, dipsanoside A in PH. Our results suggested that the newly established multi-components quantitative method is an improved approach for quality evaluation of PH samples. Furthermore, the holistic quality was inconsistent among PH samples, and ursolic acid/oleanolic acid alone could not indicate the holistic quality variation trend of PH.

PSV-4 Proteomics and Metabolomics Profiling of Meat Exudate to Understand the Impact of Postmortem Aging on Oxidative Stability of Beef Muscles

Abstract The objectives of this study were to characterize the meat exudate proteome and metabolome composition that may be related to the color and oxidative stability of beef muscles during aging. Beef longissimus lumborum (LD) and psoas major (PM) muscles from seven carcasses were obtained at 2 d postmortem. Each muscle was cut into three sections, vacuum packaged and assigned into three aging periods (9, 16 and 23 d). At the end of each aging period, the exudate was collected from each sample and immediately frozen for both proteomics and metabolomics analyses. Steak cuts were made for display color and oxidative stability analyses. Exudate samples were prepared and analyzed using the UPLC-ESI-MS for metabolomics profiling and HPLC-MS/MS for proteomics analyses. The metabolites were annotated using the HMDB database, and proteins were compared with the UniProt database. The relative abundance of the metabolites and proteins was quantified and normalized for statistical analysis. Both proteomes and metabolites were analyzed using ANOVA (P &amp;lt; 0.05). Principal component analysis (PCA), hierarchical cluster analysis (HCA) and correlation test between color and lipid oxidative stability and metabolites/proteins were performed. Untargeted protein profiling identified a total of 737 proteins from the meat exudate, of which 222 proteins were affected by the treatments (P &amp;lt; 0.05). PCA analysis indicated an apparent clustering of the proteins, mainly separating the cluster by the muscle types. A greater number of oxidative metabolism enzymes was identified in PM exudate, while more glycolytic metabolism enzymes were identified in LD exudate. Additionally, a greater abundance of cytochrome c was observed in PM muscle exudate along with laminin proteins, indicating a greater extent of degradation in the muscle, potentially explaining the lower oxidative stability of PM muscle. A total of 518 metabolite features was detected from the meat exudate using untargeted metabolomics analysis, in which 167 features were affected by the treatments (P &amp;lt; 0.05). The PCA analysis exhibited a distinct grouping of the treatments, separating not only muscle types but also the aging periods. Greater antioxidant compounds, such as carnitines and glucosides, were identified in LD muscle exudate. A greater number of lipid and nucleotide metabolites were also observed in LD exudate and 23d exudate. HSP70 and laminin proteins, along with glucosides metabolites identified in the exudates, which were significantly correlated (r &amp;gt; 0.5) to muscle color and oxidative stability. The current results indicated that meat exudate could be a viable analytical matrix to understand oxidative stability and protein degradation of muscles during aging. The proteomics and metabolomics profiling identified specific compounds in meat exudate that could be related to different oxidative stability of beef muscles. Further research should be warranted to validate the identified compounds as potential biomarkers to predict oxidative stability and quality attributes of specific muscles during aging.

Change in observed long-term greening across Switzerland – evidence from a three decades NDVI time-series and its relationship with climate and land cover factors

ABSTRACT Environmental changes are significantly modifying terrestrial vegetation dynamics, with serious consequences on Earth system functioning and provision of ecosystem services. Land conditions are an essential element underpinning global sustainability frameworks, such as the Sustainable Development Goals (SDGs), requiring effective solutions to assess the impacts of changing land conditions induced by various driving forces. At the global scale, long-term increase of vegetation greening has been widely reported notably in seasonally snow-covered ecosystems as a response to warming climate. However, greening trends at the national scale have received less attention, although countries like Switzerland are prone to important changing climate conditions. Hereby, we used a 35-year satellite-derived annual and seasonal time-series of Normalized Difference Vegetation Index (NDVI) to assess vegetation greenness evolution at different spatial and temporal scales across Switzerland and related them to temperature, precipitation, and land cover to investigate possible responses of changing climatic conditions. Results indicate that there is a statistically significant greening trend at the national scale with an NDVI mean increasing slope of 0.6%/year for the 61% significant pixels across Switzerland. In particular, the seasonal mean NDVI shows an important break for winter, autumn and spring seasons starting from 2010, potentially indicating a critical point of changing land conditions. At biogeographical scale, we observed an apparent clustering (Jura-Plateau; Northern-Southern Alps; Eastern-Western Alps) related to landscape characteristics, while forested land cover classes are more responsive to NDVI changes. Finally, the NDVI values are mostly a function of temperature at elevations below the tree line rather than precipitation. The findings suggest that multi-annual and seasonal NDVI can be a valuable indicator to monitor vegetation conditions at different scales and can provide complementary observations for national statistics on the ecological state of vegetation to monitor land affected by changing environmental conditions. This work is aiming at strengthening the insights into the driving factors of vegetation change and supporting monitoring changing land conditions to provide guidance for effective and efficient environmental management and sustainable development policy advice at the national scale.

Time–frequency analysis of ultrasonic signals for quality assessment of bonded concrete

In accelerated bridge construction (ABC), 3D-printed concrete and structural retrofitting using concrete jacketing are used to reduce the onsite construction time. Both techniques involve bonded concrete, where a concrete layer is cast on the old substrate. As a result, the interfacial transition zone between these concrete layers is the weakest part of the structure. In this paper, a machine learning-based model is proposed to predict the bond strength between concrete layers from extracted ultrasonic features. Ultrasound tests provide the input parameters, and bi-surface shear test results are the targets for the machine learning model. To validate the method, laboratory testing is performed on 54 concrete specimens with different design mixtures and bond zone conditions. Variational Mode Decomposition (VMD) is applied to the ultrasonic signals, and three intrinsic mode functions (IMFs) are extracted from each signal leading to 21 independent features. Four machine learning algorithms are deployed, namely K-nearest-neighbors, support vector machine, random forest, and XGB regressors. XGB is found to be the most accurate model, and its hyperparameters are tuned to optimize the model performance. To improve the method, the features’ importance values are derived, and the most compelling features for training are identified, which are the third quartile and center frequency of the first IMF’s instantaneous frequency, and the center frequency and kurtosis of the second IMF’s instantaneous frequency. The resulting final model was a tuned XGBoost regression model that obtained an R-squared value of 95.5 percent taking the four features as input. For the four selected features, apparent clustering was found at a bond strength of 3 MPa, ensuring a reliable prediction of quality bond conditions. These results demonstrate the effectiveness of the proposed model in predicting the bi-surface shear strength of concrete layers based on ultrasonic testing.

Using ecotourism boats for estimating the abundance of a bottlenose dolphin population in south-eastern Australia.

It is challenging to collect robust, long-term datasets to properly monitor the viability and social structure of large, long-lived animals, especially marine mammals. The present study used a unique long-term dataset to investigate the population parameters and social structure of a poorly studied population of bottlenose dolphins (Tursiops sp.) in southern Port Phillip Bay, south-eastern Australia. Photo-identification images have been collected between 2012-2022 both opportunistically and following a protocol by patrons, staff, and volunteers of ecotourism companies using their vessels as platforms. The resulting large dataset was available to be processed through the online platform Flukebook and used in capture recapture models to estimate abundance and demographic parameters. In addition, the social structure of the population and the reproductive parameters were investigated. The marked adult population abundance (45.2 ± 2.7 individuals) was found to be stable over the last decade and the calving rate ranged between 0.06-0.19 new calves per identified individuals per year, while the inter-birth interval was 3.7 ± 0.8 years. Social analysis suggested the population has a fission-fusion structure with no apparent clusters. The stability of the population over the study period suggests no deleterious effect of anthropogenic or environmental factors during the last decade. This study is the outcome of the effort of the ecotourism organisations and the results obtained, along with their similarity to those of other dolphin populations worldwide, highlight the importance of such data sources for long-term information that would otherwise be too expensive or logistically difficult to obtain.

804-P: Similar Hypoglycemia Duration with Once-Weekly Icodec vs. Degludec or Glargine U100 in Insulin-Treated T2D—A Post Hoc CGM Analysis from ONWARDS 2 and 4

Once weekly (OW) icodec is a basal insulin in development. Hypoglycemia duration during the switch (weeks 0-4) and steady state (weeks 22-26) periods from two phase 3, randomized, treat-to-target trials in T2D was investigated, using double-blinded Dexcom G6 CGM. Insulin-treated individuals were randomized to OW icodec or once-daily (OD) degludec (ONWARDS 2), or OW icodec or OD glargine U100 with mealtime insulin aspart (ONWARDS 4). When switching, a one-time only additional 50% icodec dose was administered. Basal insulins were titrated weekly. Median hypoglycemia &amp;lt;70 mg/dL duration for episodes lasting ≥ 15 minutes and the proportion of such episodes spent &amp;lt;54 mg/dL were assessed. Median duration of hypoglycemia &amp;lt;70 mg/dL was similar between icodec and comparators during switch and at steady state (Figure), with no apparent clustering in either time-period. At steady state (icodec/degludec and icodec/glargine U100), of the episodes &amp;lt;70 mg/dL, 66.6/63.9% and 60.4/60.0% spent no time &amp;lt;54 mg/dL; 23.2/26.6% and 29.2/28.8% included episodes &amp;lt;54 mg/dL for ≥15 minutes; and 10.2/9.5% and 10.4/11.2% included episodes &amp;lt;54 mg/dL for &amp;lt;15 minutes, respectively. In insulin-treated T2D, the duration of hypoglycemic episodes &amp;lt;70 mg/dL was similar, with no apparent clustering, during switch and at steady state for OW icodec versus OD degludec or glargine U100. Disclosure H.S.Bajaj: Research Support; Amgen Inc., AstraZeneca, Boehringer Ingelheim International GmbH, Anji Pharmaceuticals, Eli Lilly and Company, Kowa Company, Ltd., Novo Nordisk, Pfizer Inc., Sanofi, Tricida, Inc. B.Ásbjörnsdóttir: Employee; Novo Nordisk. L.Carstensen: Employee; Novo Nordisk A/S, Stock/Shareholder; Novo Nordisk A/S. L.Lang lehrskov: Employee; Novo Nordisk A/S, Stock/Shareholder; Novo Nordisk A/S. C.Mathieu: Advisory Panel; Novo Nordisk A/S, Boehringer Ingelheim Inc., Eli Lilly and Company, Medtronic, Vertex Pharmaceuticals Incorporated, Roche Diabetes Care, Imcyse, Speaker's Bureau; Novo Nordisk A/S, AstraZeneca, Boehringer Ingelheim Inc., Eli Lilly and Company, Medtronic, Vertex Pharmaceuticals Incorporated. A.Philis-tsimikas: Advisory Panel; Dexcom, Inc., Novo Nordisk A/S, Sanofi, Other Relationship; Medtronic, Research Support; Novo Nordisk A/S, Lilly, Viking Therapeutics, NIH - National Institutes of Health. T.Battelino: Advisory Panel; Eli Lilly and Company, Novo Nordisk, Sanofi, Medtronic, Abbott Diabetes, Consultant; Indigo Diabetes, Research Support; Novo Nordisk, Sanofi, Medtronic, Novartis, Speaker's Bureau; Eli Lilly and Company, Novo Nordisk, Sanofi, Medtronic, Abbott Diabetes, Dexcom, Inc. Funding Novo Nordisk A/S

Prospects for localizing Planet 9 with a future Uranus mission

ABSTRACT Past years have seen various publications attempting to explain the apparent clustering features of trans-Neptunian objects, the most popular explanation being an unconfirmed ‘Planet 9’. The recently proposed Uranus Orbiter and Probe mission by NASA’s Planetary Science and Astrobiology Decadal Survey could offer the opportunity to precisely determine Planet 9’s sky location and mass by carefully monitoring ranging data during the interplanetary cruise. We use Monte Carlo Markov chain methods to reconstruct simulated spacecraft trajectories in a simplified Solar system model containing Planet 9, providing an estimate of the mission’s localization capacity depending on sky location, Earth-spacecraft Doppler link noise level and data collection rate. We characterize the noise via the Allan deviation σA, scaled to the Cassini-era value $\sigma _{\rm A}^{\rm \scriptscriptstyle Cass} = 3 \times 10^{-15}$, finding that daily measurements of the spacecraft position can lead to ∼0.2 deg2 localization of Planet 9 (assuming M9 = 6.3 M⊕, d9 = 460 au). As little as a three-fold improvement in σA drastically decreases the sky localization area size to ∼0.01 deg2. Thus, we showcase that a future Uranus mission carries a significant potential also for non-Uranian science.

A bibliometric review on marine economy: Current status, development and future directions

The growth and management of the marine economy have received significant attention since the United Nations launched the "Our Ocean, Our Future: Call for Action" initiative. However, a lack of systematic review in the field of the marine economy has hindered progress. This study aims to address this issue by offering a unified knowledge analysis framework with bibliometric technology. We comprehensively analyze 6002 records from the Web of Science using software such as CiteSpace, VOSviewer, and ArcGIS for visualization analysis. This paper has drawn some interesting findings. Firstly, the development of the marine economy has experienced three stages: enlightenment, foundation and rapid growth, with the development process showing more and more apparent geographical cluster characteristics. Secondly, "Sustainable development", "Marine climate change", "Marine ecosystems government" and "Comprehensive performance evaluation" are identified as the mature clusters; meanwhile, "Sea level change", "Marine pollution treatment" and "Sustainable energy exploitation" are potential forefront clusters in marine economy. Based on this, the evolution of knowledge structure about marine economy is found, which changed from discussing the utilization of elements to studying the relationship between them to perform collaborative governance. Finally, this paper concluded by identifying research frontiers and future directions for further study, which includes the development of emerging industries, integration of marine and regional economy, protection of marine ecological and so on. This study can help researchers, policymakers and other participants better understand the knowledge structure and research frontiers of marine economy, and discover the future directions.

Systemic risk prevention policies targeting systemically important banks: Does clustering pattern matter?

It is well known that strengthening the soundness of individual banks that are too large or too interconnected is essential for maintaining financial stability. The clustering among homogeneous banks may also cause financial fragility but has received less attention. This paper discusses the policy improvement for preventing systemic risk from the perspective of the clustering pattern of systemically important banks (SIBs) based on a network optimization model. The results show that the clustering pattern of SIBs is closely related to systemic risk contagion. Remarkably, networks with fewer connections among SIBs exhibit lower systemic risk than those featuring apparent clustering of SIBs. The potential mechanism is that the systemic vulnerability of small and medium-sized banks is greatly reduced in the disassortative networks. The proposed tools based on this-inter-SIBs exposure limits and pairwise capital requirements-can guide network optimization and significantly reduce systemic risk. Furthermore, combining existing capital surcharges for SIBs (focusing on the stability of individual SIBs) and proposed network-based tools (focusing on the cluster structure of the network) will be an effective way to enhance financial stability over existing policies.

Symptoms, symptom clusters and associated factors among cancer patients receiving immune checkpoint inhibitor therapy: A cross-sectional survey.

The purpose of this study was to explore the symptoms and symptom clusters and analyse associated factors among cancer patients receiving ICI therapy. We analysed the data of 216 cancer patients who received immune checkpoint inhibitor therapy from the internal medicine unit of a university cancer centre in China. Participants were surveyed using the Eastern Cooperative Oncology Group Performance Score (ECOG PS) assessment, the ICI therapy symptom assessment scale, and demographic and disease characteristic questionnaires designed for this study. Exploratory factor analysis and multiple linear regression analysis were performed to analyse the data. The most common symptoms in patients with grade 1-2 symptom severity were fatigue (57.4%), itching (34.3%) and cough (33.3%), and those with grade 3-4 symptom severity were rash (7.9%), joint pain (6.9%), muscle soreness (6.5%) and fatigue (6.5%). Four symptom clusters were identified: nonspecific, musculoskeletal, respiratory and cutaneous (the cumulative contribution to the variance was 64.070%). ECOG PS, disease course and gender were significantly associated with the nonspecific symptom cluster (Adj R2=14.3). ECOG PS and disease course were significantly associated with the respiratory symptom cluster (Adj R2=8.9). ECOG PS, disease course and education level were significantly associated with the musculoskeletal symptom cluster (Adj R2=20.2). Cancer patients receiving ICI therapy experience various symptoms with apparent clustering. The factors associated with symptom clusters included gender, education level, ECOG PS and disease course. These findings would be useful for medical personnel to provide relevant interventions to promote symptom management of ICI therapy.

Exceptional Scarp Preservation in SW Namibia Reveals Geological Controls on Large Magnitude Intraplate Seismicity in Southern Africa

AbstractFour previously unrecognized neotectonic fault scarps in southwest Namibia are described. These relatively straight, simple but segmented structures are 16–80 km long and have measured vertical separations of 0.7–10.2 m. We estimate that each is capable of producing earthquakes ofMw6.4 or greater, indicating that large earthquakes may occur despite limited cumulative displacement. There is strong evidence that some of these scarps were formed by repeated earthquakes. Comparison with aeromagnetic and geological maps reveal that the normal faults reactivate major crustal weaknesses that are orientated north‐south and northwest‐southeast and perpendicular to the local gravitational potential energy gradient. The presence of these structures in an area with a limited record of instrumental seismicity suggests that theMmaxof this region may be much larger than generally assumed. They highlight the necessity of incorporating information from fault studies into probabilistic seismic hazard assessments in this region, in a similar way to other stable continental regions such as Australia. The fact that such major structures have gone hitherto unrecorded suggests significant further research is needed to characterize these sources of hazard. The identification of an apparent cluster of large magnitude neotectonic earthquakes in the area may be related to the exceptional preservation potential of scarps rather than indicating an area of comparatively rapid deformation. If this interpretation is correct, then these scarps represent an important indication of the potential seismic hazard across the region, and the occurrence of infrequent large‐magnitude seismicity on similar structures should be considered throughout southwestern Africa.

Assessing the structural connectivity of international trade networks along the "Belt and Road".

Assessing the trade network connectivity is essential for understanding the trade network structure, optimizing trade development patterns, and improving uneven trade development along the "Belt and Road" (BRI). From the perspective of connectivity, this paper integrates the frontier algorithms in network science and constructs an analytical framework to identify the mesoscale structures, including the community structure, core-periphery structure, and backbone structure embedded in the network, and further explore the structural connectivity of the BRI trade network. The results show that: (1) The BRI trade network represents a trade pattern of "one superpower, many great powers", with three major trade groups in Southeast Asia, the Middle East, and Northern Central and Eastern Europe in terms of geographical space. China is the super core of the BRI trade network, and the most considerable trade links are all centred in China. (2) Five distinctive trade blocs have formed in the BRI trade network. Nevertheless, the structure of the trade blocs shows significant geographical proximity, indicating that geographical distance still plays a vital role in the international trade system at the regional scale. (3) The BRI trade network demonstrates a significant core-periphery structure, with apparent trade clustering among the core countries within the trade network. Among them, nine countries led by China constitute the core structure, and the peripheral structure is large, reaching forty-four. (4) The trade links with China constitute the backbone structure of the whole trade network in the BRI region. In addition, the trade links related to energy trade and re-export trade are also crucial components of the BRI backbone structure. Methodologically, the analytical framework proposed for assessing the network structural connectivity has great potential to be widely applied to other disciplines and fields.