Hasil untuk "South Asia. Southeast Asia. East Asia"

Amir Asiaee, Kaveh Aryan

Boosting algorithms enjoy strong theoretical guarantees: when weak learners maintain positive edge, AdaBoost achieves geometric decrease of exponential loss. We study how to incorporate group fairness constraints into boosting while preserving analyzable training dynamics. Our approach, FairBoost, projects the ensemble-induced exponential-weights distribution onto a convex set of distributions satisfying fairness constraints (as a reweighting surrogate), then trains weak learners on this fair distribution. The key theoretical insight is that projecting the training distribution reduces the effective edge of weak learners by a quantity controlled by the KL-divergence of the projection. We prove an exponential-loss bound where the convergence rate depends on weak learner edge minus a "fairness cost" term $δ_t = \sqrt{\mathrm{KL}(w^t \| q^t)/2}$. This directly quantifies the accuracy-fairness tradeoff in boosting dynamics. Experiments on standard benchmarks validate the theoretical predictions and demonstrate competitive fairness-accuracy tradeoffs with stable training curves.

Amir Asiaee, Kaveh Aryan

Machine learning practitioners frequently observe tension between predictive accuracy and group fairness constraints -- yet sometimes fairness interventions appear to improve accuracy. We show that both phenomena can be artifacts of training data that misrepresents subgroup proportions. Under subpopulation shift (stable within-group distributions, shifted group proportions), we establish: (i) full importance-weighted correction is asymptotically unbiased but finite-sample suboptimal; (ii) the optimal finite-sample correction is a shrinkage reweighting that interpolates between target and training mixtures; (iii) apparent "fairness helps accuracy" can arise from comparing fairness methods to an improperly-weighted baseline. We provide an actionable evaluation protocol: fix representation (optimally) before fairness -- compare fairness interventions against a shrinkage-corrected baseline to isolate the true, irreducible price of fairness. Experiments on synthetic and real-world benchmarks (Adult, COMPAS) validate our theoretical predictions and demonstrate that this protocol eliminates spurious tradeoffs, revealing the genuine fairness-utility frontier.

Amir Asiaee, Samhita Pal

Many differentially private (DP) data release systems either output DP synthetic data and leave analysts to perform inference as usual, which can lead to severe miscalibration, or output a DP point estimate without a principled way to do uncertainty quantification. This paper develops a clean and tractable middle ground for exponential families: release only DP sufficient statistics, then perform noise-calibrated likelihood-based inference and optional parametric synthetic data generation as post-processing. Our contributions are: (1) a general recipe for approximate-DP release of clipped sufficient statistics under the Gaussian mechanism; (2) asymptotic normality, explicit variance inflation, and valid Wald-style confidence intervals for the plug-in DP MLE; (3) a noise-aware likelihood correction that is first-order equivalent to the plug-in but supports bootstrap-based intervals; and (4) a matching minimax lower bound showing the privacy distortion rate is unavoidable. The resulting theory yields concrete design rules and a practical pipeline for releasing DP synthetic data with principled uncertainty quantification, validated on three exponential families and real census data.

Amir Asiaee, Samhita Pal

Randomized controlled trials (RCTs) are the gold standard for estimating heterogeneous treatment effects, yet they are often underpowered for detecting effect heterogeneity. Large observational studies (OS) can supplement RCTs for conditional average treatment effect (CATE) estimation, but a key barrier is covariate mismatch: the two sources measure different, only partially overlapping, covariates. We propose CALM (Calibrated ALignment under covariate Mismatch), which bypasses imputation by learning embeddings that map each source's features into a common representation space. OS outcome models are transferred to the RCT embedding space and calibrated using trial data, preserving causal identification from randomization. Finite-sample risk bounds decompose into alignment error, outcome-model complexity, and calibration complexity terms, identifying when embedding alignment outperforms imputation. Under the calibration-based linear variant, the framework provides protection against negative transfer; the neural variant can be vulnerable under severe distributional shift. Under sparse linear models, the embedding approach strictly generalizes imputation. Simulations across 51 settings confirm that (i) calibration-based methods are equivalent for linear CATEs, and (ii) the neural embedding variant wins all 22 nonlinear-regime settings with large margins.

Amir Asiaee

Neural networks are hypothesized to implement interpretable causal mechanisms, yet verifying this requires finding a causal abstraction -- a simpler, high-level Structural Causal Model (SCM) faithful to the network under interventions. Discovering such abstractions is hard: it typically demands brute-force interchange interventions or retraining. We reframe the problem by viewing structured pruning as a search over approximate abstractions. Treating a trained network as a deterministic SCM, we derive an Interventional Risk objective whose second-order expansion yields closed-form criteria for replacing units with constants or folding them into neighbors. Under uniform curvature, our score reduces to activation variance, recovering variance-based pruning as a special case while clarifying when it fails. The resulting procedure efficiently extracts sparse, intervention-faithful abstractions from pretrained networks, which we validate via interchange interventions.

Amir Asiaee, Kaveh Aryan

Machine learning systems are often trained and evaluated for fairness on historical data, yet deployed in environments where conditions have shifted. A particularly common form of shift occurs when the prevalence of positive outcomes changes differently across demographic groups--for example, when disease rates rise faster in one population than another, or when economic conditions affect loan default rates unequally. We study group-conditional prior probability shift (GPPS), where the label prevalence $P(Y=1\mid A=a)$ may change between training and deployment while the feature-generation process $P(X\mid Y,A)$ remains stable. Our analysis yields three main contributions. First, we prove a fundamental dichotomy: fairness criteria based on error rates (equalized odds) are structurally invariant under GPPS, while acceptance-rate criteria (demographic parity) can drift--and we prove this drift is unavoidable for non-trivial classifiers (shift-robust impossibility). Second, we show that target-domain risk and fairness metrics are identifiable without target labels: the invariance of ROC quantities under GPPS enables consistent estimation from source labels and unlabeled target data alone, with finite-sample guarantees. Third, we propose TAP-GPPS, a label-free post-processing algorithm that estimates prevalences from unlabeled data, corrects posteriors, and selects thresholds to satisfy demographic parity in the target domain. Experiments validate our theoretical predictions and demonstrate that TAP-GPPS achieves target fairness with minimal utility loss.

Amir Asiaee, Samhita Pal, Jared D. Huling

Randomized controlled trials (RCTs) yield internally valid causal effect estimates, but generalizing these results to target populations with different characteristics requires an untestable selection ignorability assumption: conditional on observed covariates, trial participation must be independent of potential outcomes. This assumption fails when unobserved effect modifiers are distributed differently between trial and target populations. We develop a sensitivity analysis framework for trial generalization grounded in omitted variable bias (OVB). Our key theoretical contribution is an exact decomposition showing that external-validity bias equals moderation strength $\times$ moderator imbalance: (i) how strongly an unobserved variable shifts the treatment effect, times (ii) how differently that variable is distributed across populations after covariate adjustment. We introduce scale-free sensitivity parameters based on partial $R^2$ values, enabling closed-form bounds and benchmarking against observed covariates -- practitioners can assess whether conclusions would change if an unobserved moderator were "as strong as" a particular observed variable. Simulations demonstrate that our bounds achieve nominal coverage and remain conservative under model misspecification, while comparisons with alternative sensitivity frameworks highlight the interpretive advantages of the OVB decomposition.

Amir Asiaee, Kavey Aryan, James P. Long

Selective conformal prediction can yield substantially tighter uncertainty sets when we can identify calibration examples that are exchangeable with the test example. In interventional settings, such as perturbation experiments in genomics, exchangeability often holds only within subsets of interventions that leave a target variable "unaffected" (e.g., non-descendants of an intervened node in a causal graph). We study the practical regime where this invariance structure is unknown and must be learned from data. Our contributions are: (i) a contamination-robust conformal coverage theorem that quantifies how misclassification of "unaffected" calibration examples degrades coverage via an explicit function $g(δ,n)$ of the contamination fraction and calibration set size, providing a finite-sample lower bound that holds for arbitrary contaminating distributions; (ii) a task-driven partial causal learning formulation that estimates only the binary descendant indicators $Z_{a,i}=\mathbf{1}\{i\in\mathrm{desc}(a)\}$ needed for selective calibration, rather than the full causal graph; and (iii) algorithms for descendant discovery via perturbation intersection patterns (differentially affected variable set intersections across interventions), and for approximate distance-to-intervention estimation via local invariant causal prediction. We provide recovery conditions under which contamination is controlled. Experiments on synthetic linear structural equation models (SEMs) validate the bound: under controlled contamination up to $δ=0.30$, the corrected procedure maintains $\ge 0.95$ coverage while uncorrected selective CP degrades to $0.867$. A proof-of-concept on Replogle K562 CRISPR interference (CRISPRi) perturbation data demonstrates applicability to real genomic screens.

Amir Asiaee, Samhita Pal, Cole Beck et al.

Generalizing treatment effects from a randomized trial to a target population requires the assumption that potential outcome distributions are invariant across populations after conditioning on observed covariates. This assumption fails when unmeasured effect modifiers are distributed differently between trial participants and the target population. We develop a sensitivity analysis framework that bounds how much conclusions can change when this transportability assumption is violated. Our approach constrains the likelihood ratio between target and trial outcome densities by a scalar parameter $Λ\geq 1$, with $Λ= 1$ recovering standard transportability. For each $Λ$, we derive sharp bounds on the target average treatment effect -- the tightest interval guaranteed to contain the true effect under all data-generating processes compatible with the observed data and the sensitivity model. We show that the optimal likelihood ratios have a simple threshold structure, leading to a closed-form greedy algorithm that requires only sorting trial outcomes and redistributing probability mass. The resulting estimator runs in $O(n \log n)$ time and is consistent under standard regularity conditions. Simulations demonstrate that our bounds achieve nominal coverage when the true outcome shift falls within the specified $Λ$, provide substantially tighter intervals than worst-case bounds, and remain informative across a range of realistic violations of transportability.

Amir Asiaee, Zhuohui J. Liang, Chao Yan

Tabular synthetic data generators are typically trained to match observational distributions, which can yield high conventional utility (e.g., column correlations, predictive accuracy) yet poor preservation of structural relations relevant to causal analysis and out-of-distribution (OOD) reasoning. When the downstream use of synthetic data involves causal reasoning -- estimating treatment effects, evaluating policies, or testing mediation pathways -- merely matching the observational distribution is insufficient: structural fidelity and treatment-mechanism preservation become essential. We propose CausalWrap (CW), a model-agnostic wrapper that injects partial causal knowledge (PCK) -- trusted edges, forbidden edges, and qualitative/monotonic constraints -- into any pretrained base generator (GAN, VAE, or diffusion model), without requiring access to its internals. CW learns a lightweight, differentiable post-hoc correction map applied to samples from the base generator, optimized with causal penalty terms under an augmented-Lagrangian schedule. We provide theoretical results connecting penalty-based optimization to constraint satisfaction and relating approximate factorization to joint distributional control. We validate CW on simulated structural causal models (SCMs) with known ground-truth interventions, semi-synthetic causal benchmarks (IHDP and an ACIC-style suite), and a real-world ICU cohort (MIMIC-IV) with expert-elicited partial graphs. CW improves causal fidelity across diverse base generators -- e.g., reducing average treatment effect (ATE) error by up to 63% on ACIC and lifting ATE agreement from 0.00 to 0.38 on the intensive care unit (ICU) cohort -- while largely retaining conventional utility.

Amir Asiaee, Chao Yan, Zachary B. Abrams et al.

When synthetic data is released, some individuals are harder to protect than others. A patient with a rare disease combination or a transaction with unusual characteristics stands out from the crowd. Differential privacy provides worst-case guarantees, but empirical attacks -- particularly membership inference -- succeed far more often against such outliers, especially under moderate privacy budgets and with auxiliary information. This paper introduces risk-equalized DP synthesis, a framework that prioritizes protection for high-risk records by reducing their influence on the learned generator. The mechanism operates in two stages: first, a small privacy budget estimates each record's "outlierness"; second, a DP learning procedure weights each record inversely to its risk score. Under Gaussian mechanisms, a record's privacy loss is proportional to its influence on the output -- so deliberately shrinking outliers' contributions yields tighter per-instance privacy bounds for precisely those records that need them most. We prove end-to-end DP guarantees via composition and derive closed-form per-record bounds for the synthesis stage (the scoring stage adds a uniform per-record term). Experiments on simulated data with controlled outlier injection show that risk-weighting substantially reduces membership inference success against high-outlierness records; ablations confirm that targeting -- not random downweighting -- drives the improvement. On real-world benchmarks (Breast Cancer, Adult, German Credit), gains are dataset-dependent, highlighting the interplay between scorer quality and synthesis pipeline.

Meng Li, Junichi Kurokawa, Qiang Zhang et al.

Abstract. The MIXv2 Asian emission inventory is developed under the framework of the Model Inter-Comparison Study for Asia (MICS-Asia) Phase IV and produced from a mosaic of up-to-date regional emission inventories. We estimated the emissions for anthropogenic and biomass burning sources covering 23 countries and regions in East, Southeast and South Asia and aggregated emissions to a uniform spatial and temporal resolution for seven sectors: power, industry, residential, transportation, agriculture, open biomass burning and shipping. Compared to MIXv1, we extended the dataset to 2010–2017, included emissions of open biomass burning and shipping, and provided model-ready emissions of SAPRC99, SAPRC07, and CB05. A series of unit-based point source information was incorporated covering power plants in China and India. A consistent speciation framework for non-methane volatile organic compounds (NMVOCs) was applied to develop emissions by three chemical mechanisms. The total Asian emissions for anthropogenic/open biomass sectors in 2017 are estimated as follows: 41.6/1.1 Tg NOx, 33.2/0.1 Tg SO2, 258.2/20.6 Tg CO, 61.8/8.2 Tg NMVOC, 28.3/0.3 Tg NH3, 24.0/2.6 Tg PM10, 16.7/2.0 Tg PM2.5, 2.7/0.1 Tg BC (black carbon), 5.3/0.9 Tg OC (organic carbon), and 18.0/0.4 Pg CO2. The contributions of India and Southeast Asia were emerging in Asia during 2010–2017, especially for SO2, NH3 and particulate matter. Gridded emissions at a spatial resolution of 0.1° with monthly variations are now publicly available. This updated long-term emission mosaic inventory is ready to facilitate air quality and climate model simulations, as well as policymaking and associated analyses.

Shynbolat Rsaliyev, Amangeldy Sarbaev, Aidarkhan Eserkenov et al.

The Sunn pest (<i>Eurygaster integriceps</i> Puton) ranks among the most harmful pests affecting wheat yield and grain quality in Kazakhstan. In particular, it poses a serious threat to regions in which winter wheat cultivation is dominant. Climate change, parasites, predators, and recent transformations in agriculture and human activities in Kazakhstan and throughout Central Asia have significantly influenced the population dynamics of the Sunn pest. This study reports the findings on Sunn pest population dynamics in Kazakhstan’s winter wheat growing regions from 2022 to 2024, based on surveys of 233 hectares across four regions. In total, 1753 specimens of the Sunn pest were studied. The obtained results were analyzed in comparison with historical data (1991–2020) and recent findings in this field. We found that a combination of ecological factors are the main determinants of the Sunn pest population dynamics in different regions of the country. The pest population increased in seasons with optimal temperature (sum of effective temperatures—SET) and humidity conditions (hydrothermal coefficient—HTC), as well as when wheat cultivation areas and forest belts expanded. Moreover, the results highlighted that the pest population is controlled by the activity of egg parasites (<i>Telenomus</i>) in the south, unfavorable weather conditions during overwintering in the east and west, and the growing of resistant varieties in the southeast of the country. Compared to wild grasses, wheat crops increased the reproductive potential of the pest.

Zhiyuan Chen, Joseph L.-H. Tsui, Jun Cai et al.

Abstract East, South, and Southeast Asia (together referred to as Southeastern Asia hereafter) have been recognized as critical areas fuelling the global circulation of seasonal influenza. However, the seasonal influenza migration network within Southeastern Asia remains unclear, including how pandemic-related disruptions altered this network. We leveraged genetic, epidemiological, and airline travel data between 2007-2023 to characterise the dispersal patterns of influenza A/H3N2 and B/Victoria viruses both out of and within Southeastern Asia, including during perturbations by the 2009 A/H1N1 and COVID-19 pandemics. During the COVID-19 pandemic, consistent autumn-winter movement waves from Southeastern Asia to temperate regions were interrupted for both subtype/lineages, however the A/H1N1 pandemic only disrupted A/H3N2 spread. We find a higher persistence of A/H3N2 than B/Victoria circulation in Southeastern Asia and identify distinct pandemic-related disruptions in A/H3N2 antigenic evolution between two pandemics, compared to interpandemic levels; similar patterns are observed in B/Victoria using genetic distance. The internal movement structure within Southeastern Asia markedly diverged during the COVID-19 pandemic season, and to a lesser extent, during the 2009 A/H1N1 pandemic season. Our findings provide insights into the heterogeneous impact of two distinct pandemic-related disruptions on influenza circulation, which can help anticipate the effects of future pandemics and potential mitigation strategies on influenza dynamics.

Burhanuddin Muhtadi, Eve Alicia Warburton, Liam Gammon

Indonesia’s population skews young, so political analysts are increasingly concerned with what the “youth vote” looks like, and what generational change will bring to Indonesia’s democracy. On the one hand, analysts have historically focused on the liberal political activism of more educated cohorts of young people, and especially those in urban areas. On the other, and most recently, young Indonesians overwhelmingly voted for Prabowo Subianto in the 2024 presidential elections, suggesting this cohort to be either unaware of, or unperturbed by, his authoritarian history. This paper examines how young Indonesians perceive their country’s democratic trajectory. We analyze two decades of nationally representative survey data, and examine the democratic preferences of Indonesian voters whose political socialization took place entirely in the post-authoritarian era (1998–). The results suggest both life-cycle and intriguing cohort effects: on average, Indonesians become more positive towards their democracy as they age; but we also find that Indonesia’s Gen Zs are more satisfied with democracy than other generational cohorts—despite a precipitous decline in the quality of Indonesian democracy over the past decade. We argue, therefore, that while all Indonesians show high levels of satisfaction with their weakening democracy, young Indonesians, more than other generations, can be understood as ‘complacent democrats.’

FAN Xiucai, ZHANG Ying, LI Min et al.

Vitis L. belongs to Vitaceae family. Vitis can be divided into Subgenus Euvitis Planch and Subgenus Muscadinia Planch. Subgenus Euvitis Planch has more than 70 species whose chromosome number is 2n=38. Within the subgenus, the interspecies cross is easy, and they are mainly distributing in the temperate regions of Northern Hemisphere. They are intensively originated from three centers of West Asia, North America and East Asia: Europe-West Asia distribution center, North America distribution center and East Asia distribution center and form 3 species group according to geographic origin. Eurasian population originated from the center of Europe-West Asia only has a V. vinifear L. and 3 wild subspecies ssp. sativa D.C., ssp. silvestris Gm. and ssp. caucasica Vav.. V. vinifear L. is the only cultispecies in Vitis species. The region between the Black Sea and the Caspian Sea is the origin of European grape. The European grape spread to Europe from this region and further spread around the world by European. By long-term introduction, domestication, breeding and clonal selection, the culti species has different characteristics and the diverse resources. At present, in the world, more than 90% production of grape are made from this species and more than 80% grape varieties are evolved from this species. It is widely cultivated in Mediterranean climate conditions around the world. There are about 30 species originated from North America Dstribution Center, which includes America, Canada and Mexico, to form South America species group. They are important disease-resistant grape germplasm resources, and play an important role in grape resistance breeding against phylloxera and downy mildew. East Asia Distribution Center includes China, Japan, Korea, Russia Far East and the northern area of Southeast Asia. It has about 40 species to form East Asia Species Group, which has extremely abundant diversity and resistant types. Each species of East Asia Species Group has a distribution in China, and all species of South America population mainly distribute in America. Therefore, China is one of the centers of origin with the most abundant wild grape resources in the world, including all the wild grape species from the East Asian origin center. The resistance types are extremely rich, providing important material support for grape breeding and industrial development. There are many cultivated varieties of grape. Long time ago, the description and recordation of grape varieties arose. With the extension of cultivation area and the escalation of varieties, more variety populations formed with different regional characteristics. Because Vitis plants have the wide origins, the activities of artificial introduction to exchange, breeding, clonal selection and so on, have made wide grape varieties and rich genotypes. Grape is one of the cultivated plants that have the most varieties. After a long time of natural selection and industrious breeding by human, it forms extremely abundant grape variety resources. Grape has been cultivated in China for over 2000 years, it was developed from west to east, and from north to south step-by-step. Nowadays, grape is cultivated all over China. China is a major producer of grapes and also a major consumer of grapes in the world, with grapes occupying an important position in our country. With the development of grape industry in China, the work of grape genetic resource and breeding is highly regarded than before. In 1978, the Chinese Agricultural Ministry planned to establish National Fruit Tree Germplasm Repositories. This plan was started in 1981 and finished in 1989. Good results are achieved in identification, evaluation and utilization of grape genetic resources to a certain extent. The National Grape and Peach Germplasm Repository (Zhengzhou) is the primary unit for preserving grape germplasm resources in China, and is currently one of the most abundant nurseries for preserving grape germplasm resources in the world. By December 2024, the National Grape and Peach Germplasm Repository (Zhengzhou) had collected and preserved 2140 grape germplasm resources. More than 30 000 hybrid off-springs were established using the selected excellent germplasm, and multiple new grape varieties such as Zhengyan Seedless, Hongyan Seedless, and Zhong Pu Jin Xiang were bred. Since 2000, the Zhengzhou Grape Germplasm Repository has cumulatively provided resources for utilization to 356 entities, including Nanjing Agricultural University, Northwest A&F University, China Agricultural University, Institute of Botany, Chinese Academy of Sciences, Shanghai Jiao Tong University, and other universities and research institutes, as well as agricultural technology extension departments, cooperatives, and planting enterprises at the provincial, municipal, and county levels in Henan, Hebei, Xinjiang, Hubei, Yunnan, and other provinces. A total of 16 349 resource utilizations have been provided, primarily for basic research, new variety breeding, production, and science popularization education, effectively supporting the development of grape scientific research, breeding, and the industry in China. This article introduces the development history of the National Grape Germplasm Resources Repository (Zhengzhou), summarizes and reviews the current status of grape germplasm resource collection and preservation in the past 20 years, as well as their innovative utilization. It also looks forward to future research directions, in order to provide a reference for the effective utilization and industrial development of grape germplasm resources in China.

Min Hee Go

Throughout the twentieth century, Taiwan and South Korea underwent rapid economic development and successfully democratized without reversal to authoritarianism. Despite their similar trajectories, the two countries diverge significantly in political and public support for gender equality. Taiwan is widely seen as the most gender-equal country in Asia, while South Korea remains deeply polarized, with uneven progress in women’s representation. What accounts for this divergence between two democracies? This article advances a political institutions thesis, arguing that differences in democratic institution-building—particularly the actors and modes of democratization—have shaped the contour of gender politics of each country. Contrasting the histories of party-driven democratization in Taiwan and mass-driven democratization in South Korea, this article shows that the process of building democracy has had lasting effects on the institutionalization and sustainability of gender equality.

D. Muthuvel, X. Qin

<p>This study examines future drought propagation (the temporal transition from meteorological to agricultural droughts), persistence (inter-seasonal agricultural droughts), and spatial concurrence (simultaneous occurrence of monsoonal agricultural droughts across regions) under climate change using a multivariate copula approach in monsoon-dominant Asia. The standardised precipitation index (SPI) and standardised soil moisture index (SSI) are used to analyse meteorological and agricultural droughts, respectively. Under the worst-case emission scenario (Shared Socioeconomic Pathway, SSP5-8.5), South Asia (excluding western and peninsular India) and eastern China are projected to experience intensified drought propagation compared to in the historical period (1975–2014). In addition to increased propagation in these regions, the propagated agricultural droughts are expected to persist across seasons in the future. On the hydrologically significant Tibetan Plateau, all-season droughts that were historically rare, with return periods exceeding 50 years, could occur as frequently as once every 5 years in the far-future period (2061–2100). Random forest models indicate that the temperature is a key driver of future agricultural droughts in nearly half of the study area. The increasing non-rainfall-related agricultural droughts in the far future could be attributed to the rise in temperature. Based on bivariate return periods of spatial concurrence, frequent future spatial drought concurrence is anticipated between populous South Asia and East Asia compared to the historical time frame, posing risks to water and food security. Conversely, Southeast Asia is projected to experience reduced spatial drought concurrence with other regions, which could encourage greater regional cooperation. Overall, this comprehensive approach, which integrates three aspects of drought dynamics, offers valuable insights for climate change mitigation, planning, and adaptation.</p>

Siqi Zhang, Chunzhong Zhao, Guoping Liu et al.

This study tested the efficacy of xenomonitoring using contaminated flies and cockroaches at ports in Shenzhen by analysing sample data from imported flies and cockroaches from October 2023 to April 2024 to identify the pathogens they carried. Among all the samples of flies and cockroaches collected, <i>Musca domestica vicina</i> and <i>Blattella germanica</i> accounted for the highest proportion, 27.59% and 66.47%, respectively. Their positive rates for carrying <i>Staphylococcus aureus</i> were also the most significant, reaching 4.35% and 6.47%, respectively. The imported flies and cockroaches mainly came from Asia, with the highest proportion coming from Hong Kong, at 97.71% and 92.11%, respectively. Metagenomic sequencing indicated that the pathogens carried by the flies and cockroaches from different regions of Asia were generally similar but showed some differences. Flies from Southeast Asia, East Asia, South Asia, and West Asia and cockroaches from Southeast Asia, East Asia, and West Asia harboured unique opportunistic pathogens capable of causing gastrointestinal and respiratory infections in humans. Specifically, flies carried pathogens such as <i>Campylobacter jejuni</i>, <i>Bacillus anthracis</i>, <i>Bacteroides fragilis</i>, and <i>Bordetella bronchiseptica</i>, while cockroaches carried <i>B. fragilis</i>, <i>Clostridium tetani</i>, and <i>Bacillus cereus</i>. Our findings provide data support for future risk assessments of pathogens carried by imported vectors.

Romanchev Danila D.

The article is devoted to a phenomenon underexplored in Russian Japanese studies – Ouetsu-reppan-dōmei (奥羽越列藩同盟), the “Alliance of domains of the Provinces of Mutsu, Dewa, and Echigo”, or simply the Northern Alliance. This confederation of domains in Northern Japan emerged in 1868 to oppose the policies of the new Meiji government in Kyoto. The Alliance initially aimed to mediate peacefully between the new government and the Aizu domain, which was being subjected to a punitive campaign. Later, the Alliance itself became the target of attacks and chose the path of military resistance, becoming a political competitor to Kyoto. The study leaves aside the direct military component of the Northern Alliance’s history, referring to descriptions of military operations only when necessary. The emphasis is placed on examining the political processes which made the armed conflict inevitable.