Diverse Information Research Articles

Time-Domain-Filtered Terahertz Nanoscopy of Intrinsic Light-Matter Interactions.

Terahertz (THz) technology holds great potential across diverse applications, including biosensing and information communications, but conventional far-field techniques are limited by diffraction. Near-field optical microscopy overcomes this barrier through a sharp tip that concentrates incident THz waves into nanometric volumes, detecting scattered near-field to reveal nanoscale optical properties. However, owing to the large THz wavelengths, resonant surface waves arising on the tip and cantilever obscure the intrinsic response. Here we combine near-field microscopy with THz time-domain spectroscopy and implement time-domain filtering with an elongated cantilever to eliminate this artifact, achieving intrinsic nanospectroscopy and nanoimaging. By applying this technique, we distinguish and characterize historical pigments of an ancient sculpture, such as vermilion and red lead, on the nanoscale. We also unravel deep-subwavelength localized resonance modes in THz optical antennas, demonstrating capabilities for THz nanophotonics. Our work advances THz nanoimaging and nanospectroscopy techniques to probe intrinsic nanoscale THz light-matter interactions.

DeepRSMA: a cross-fusion based deep learning method for RNA-small molecule binding affinity prediction.

RNA is implicated in numerous aberrant cellular functions and disease progressions, highlighting the crucial importance of RNA-targeted drugs. To accelerate the discovery of such drugs, it is essential to develop an effective computational method for predicting RNA-small molecule affinity (RSMA). Recently, deep learning based computational methods have been promising due to their powerful nonlinear modeling ability. However, the leveraging of advanced deep learning methods to mine the diverse information of RNAs, small molecules and their interaction still remains a great challenge. In this study, we present DeepRSMA, an innovative cross-attention-based deep learning method for RSMA prediction. To effectively capture fine-grained features from RNA and small molecules, we developed nucleotide-level and atomic-level feature extraction modules for RNA and small molecules, respectively. Additionally, we incorporated both sequence and graph views into these modules to capture features from multiple perspectives. Moreover, a Transformer-based cross-fusion module is introduced to learn the general patterns of interactions between RNAs and small molecules. To achieve effective RSMA prediction, we integrated the RNA and small molecule representations from the feature extraction and cross-fusion modules. Our results show that DeepRSMA outperforms baseline methods in multiple test settings. The interpretability analysis and the case study on spinal muscular atrophy (SMA) demonstrate that DeepRSMA has the potential to guide RNA-targeted drug design. The codes and data are publicly available at https://github.com/Hhhzj-7/DeepRSMA. Supplementary data are available at Bioinformatics online.

Enhancing Reading Ability Through the Cooperative Integrated Reading and Composition (CIRC) Model: A Study with Fifth-Grade Elementary School Students

Reading comprehension is a vital aspect of language proficiency that elementary school students must master. Proficient reading skills enable students to actively acquire diverse information efficiently. The cooperative integrated reading and composition (CIRC), is a teaching model designed to enhance students’ reading comprehension skills through collaborative learning activities. This research aims to investigate the impact of the CIRC model on the reading comprehension abilities of fifth-grade students at SD Negeri 1 Kairatu. Employing an experimental design with a one-group pretest and post-test setup, the study involved 41 students. The CIRC model served as an independent variable, while reading comprehension ability constituted the dependent variable. The research instrument utilized was a reading comprehension test. Data analysis was conducted via a paired sample t-test, revealing a significant difference in students’ reading comprehension abilities before and after learning with the CIRC model (p = 0.00, < 0.05). These findings underscore the effectiveness of the CIRC model in improving students’ reading proficiency. The study’s results underscore the importance of prioritizing reading skills development to enhance elementary students’ literacy levels. It recommends the widespread adoption and continuous practice of the CIRC model in elementary schools to cultivate students’ interest and competence in reading. This research contributes to the ongoing efforts to enhance reading education methodologies and promote literacy among elementary school students. Keywords: CIRC model, reading comprehension, reading literacy, elementary school students

Канібалізм у Дніпропетровській області під час Голодомору 1932–1933 рр. (за матеріалами кримінальних справ ГДА МВС України)

The topic of cannibalism has not gained popularity in Ukrainian historiography today. Such a situation has developed due to high triggering, psychologically and morally painful components of the problem and the specifics of working with it. At the same time, its study is highly important for highlighting the consequences of the Holodomor for the Ukrainian people. Isolated expressions of cannibalism were known in steppe regions of Ukraine during the mass artificial famine of 1921–1923. During the Holodomor, this phenomenon gained mass and spread to the entire territory of the USSR. As before, it also arose based on artificially created socio-economic conditions of life. Long-lasting starvation led to a mental breakdown with subsequent destruction of the established prohibitions of the starving’s behavior and became one of the uncharacteristic ways of adaptation. People were driven to despair and lost their dignity. The study covers the territory of the Dnipropetrovsk region during the Holodomor. The reproduced dynamics of cannibalism (significant increase) coincides with the escalation and worsening of famine in the spring-summer of 1933 in the region. The localization of places of concentration of cannibalism has a high probability of the most catastrophic food situation in the western part of the region. The highest number of cases of cannibalism are in the Dolynskyi, Novoprazskyi, Oleksandriyskyi, Pavlogradskyi, Petrikivskyi, and Pyatikhatskyi regions. There are 72 known cases of cannibalism in the Dnepropetrovsk region. By gender, 64 women and 29 men were involved in criminal cases. Individual crimes significantly dominate group crimes (in a ratio of 56 to 16, respectively). Most cases of cannibalism occurred within the family or were related to relatives. It was determined that the main group of victims were children (61 cases). The biggest percentage was for own children. Victims are also those children who were complicit in murder and cannibalism but, after the conviction of their parents, remained with “such an experience” in society. Society reacted aggressively to cases of cannibalism - the local population often executed cannibals. Calls and actions against cannibals forced the authorities to intervene and intercept the initiative to solve the problem legally. During the first open cases, the culprits were sentenced to death through fusillade. We see this as an attempt to stop cannibalism as a phenomenon. In total, 17 people were sentenced to death for cannibalism in the Dnipropetrovsk region. Secrecy of criminal cases against cannibals and the transfer of documents for consideration to the State Political Administration authorities shows that the communist regime did not expect the scale of the problem and an attempt to hide such cases from society. The authorities skillfully transferred their blame for driving people to despair and cannibalism onto the “socially hostile element”. Manifestations of cannibalism on the ground were used to continue the polarization of the village, further maraud rhetoric, further entrenchment of postulates of communism, and promotion of agricultural tasks. Criminal cases against cannibals were opened in the local area, in the regional area, they were reviewed and brought forward, and the remainder were confirmed in Kharkiv. In addition to execution, cannibals were sentenced to 10 years in a concentration camp - 70 people. There were also sentences of 5 years of exile, sending to the correctional facility, dismissal for health reasons, or acquittal. At the same time, witnesses of the Holodomor in the Dnipropetrovsk region mentioned a much larger number of cannibalism cases. The absence of criminal cases indicates the likely loss or deliberate destruction of such documents by the communists. In general, criminal cases with accusations of cannibalism and corpse-eating are one of the understudied specific sources of information for the formation of a martyrology of genocide victims - both the killed and the killers. At the same time, the cases contain diverse information of the auxiliary or initial level of research on the regional specifics of the course and consequences of the Holodomor, everyday realities, and factors that led to cases of cannibalism. Keywords: Holodomor, cannibalism, carrion-eating, SPA (State Political Administration), criminal cases, Dnipropetrovsk region.

A study of feature importance for king salmon health classification with feature selection

King salmon is important for aquaculture in New Zealand, contributing significant economic value. Fish health is a priority for the industry, and the change in the health status of king salmon needs to be accurately detected at the earliest possible stage. Many factors affect the health of king salmon, such as temperature. Identifying the key features that influence health prediction is a crucial step toward achieving this goal. This study utilizes trial data collected by the Cawthron Institute, which includes diverse information on king salmon, such as blood biochemistry and hematology. We explore the data by employing statistical methods and feature selection techniques in machine learning to identify the most relevant features for king salmon health prediction, aiming to classify individuals as healthy or unhealthy with a small number of features. The results show that although the most efficient feature selection techniques on different datasets vary, overall, feature selection approaches can successfully identify relevant and informative features for king salmon health classification. Through the incorporation of a few selected features, the learned classifiers could still achieve statistically equal or better classification performance. This study not only contributes to the understanding of the health indicators of king salmon but also provides crucial insights into health prediction, which will be beneficial to the improvement of the health of king salmon, leading to the development of more effective management strategies for aquaculture.

Transformer-Based Quantification of the Echo Chamber Effect in Online Communities

An Echo Chamber on social media refers to the environment where like-minded people hear the echo of each others' voices, opinions, or beliefs, which reinforce their own. Echo Chambers can turn social media platforms into collaborative venues that polarize and radicalize users rather than broadening their exposure to diverse information. Having a quantified metric for measuring the Echo Chamber effect can aid moderators and policymakers in tracking and mitigating online polarization and radicalization. Existing methods for Echo Chamber detection are either one-dimensional, only considering the network behavior of users while ignoring their semantic behavior, or require demanding supervised labeling, which is both expensive and less generalizable. This paper proposes a new metric to quantify the Echo Chamber effect using Transformer models for context-sensitive processing of natural language (NLP). Our metric quantifies (1) the effect of an Echo Chamber through the inverse effect of user diversity , and (2) polarization by means of user separability between two Echo Chambers in a topic. Leveraging this metric, we further propose an NLP-based embedding that represents the users' activity. Our model is simultaneously effective, computationally cheap, and unsupervised. As our method is unsupervised, it makes existing collaborative moderation efforts to thwart Echo Chamber effects more efficient by addressing the problem of identifying narrow information bases for algorithmic biases and misinformation detection. We run our analysis on three recent highly controversial political topics and a non-controversial topic: Russo-Ukrainian War, Abortion, Gun-Control, and SXSW music festival. Our results offer data-driven findings such as a higher Echo Chamber effect among Republicans over Democrats and diverse explicit support for Ukraine, especially among Democrats. We also observe a direct relationship between the Echo Chamber effect and polarization while observing that the low Echo Chamber effect for the Russo-Ukraine war is accompanied by a low polarization; and vice versa for Gun-Control.

Information seeking behaviour of informal caregivers at the premier tertiary teaching hospital in Nigeria

This study was conducted to examine the information behaviour of informal caregivers in a university college hospital in Ibadan, Nigeria. Informal caregivers in the teaching hospitals constitute critical pubic health care players whose information behaviours have not been studied. Data was collected from 208 caregivers using a questionnaire that consisted of structured and open-ended questions. The study found that healthcare providers were the primary source of health for caregivers, with 34.1% actively seeking information from them. The caregivers seldom utilised sources like the internet, community organizations, books, family, and friends. The preference for healthcare providers as sources is due to their accessibility and ability to provide prompt and accurate answers. Caregivers have diverse information needs, including medication-related information, first aid techniques, nutrition, exercise, mental health, and lifestyle choices. They seek this knowledge to make informed decisions that improve their loved ones’ well-being. The study also revealed that caregivers heavily rely on information technology for health information due to its accessibility and reliability. The study highlights the need for ongoing training and proactive information sharing by healthcare providers to better support informal caregivers in Nigeria, emphasizing the importance of integrated support systems within healthcare facilities. To meet diverse information needs, healthcare institutions should diversify dissemination strategies, including culturally sensitive materials and digital literacy programs, while developing tailored mobile applications and online support communities for caregivers. Policymakers must focus on creating supportive healthcare policies, funding caregiver programs, and expanding insurance coverage to alleviate financial burdens, ensuring that caregivers have access to necessary resources and information.

Electrostatic Self‐Assembly of Ag‐NPs Mediated by Eu3+ Complexes for Physically Unclonable Function Labels

ABSTRACTPhysically unclonable functions (PUFs) are essential for anticounterfeiting. Creating high‐stability, multimode, and secure labels remains challenging. Herein, we present a novel self‐assembly method for modulating the optical signals of rare‐earth (RE) complexes via interactions with Ag nanoparticles (Ag‐NPs). Initially, we engineered a positively charged Eu3+ complex ([EuL3]3+), which promotes the self‐assembly of negatively charged Ag‐NPs to form Eu/Ag‐NPs composites. The assembly of Ag‐NPs induces a surface plasmon effect that boosts the luminescent quantum yield and Raman signal intensities, and modifies the luminescence lifetime of the [EuL3]3+. Crucially, these micron‐scale Eu/Ag‐NPs can be applied to substrates, facilitating high‐resolution signal acquisition and diverse information encoding within limited space. Validation experiments reveal that PUF labels crafted using Eu/Ag‐NPs exhibit inherent randomness and uniqueness, along with stable and repeatable signal output. The strategic self‐assembly of Ag‐NPs, mediated by [EuL3]3+, along with the effective modulation of material properties, paves the way for advancing high‐resolution, high‐information‐density solutions in anticounterfeiting technologies.

A Comprehensive Review of Sensor-Based Smart Building Monitoring and Data Gathering Techniques

In an era where buildings are increasingly becoming multifaceted entities, the paradigm of smart buildings has witnessed significant evolution. This advancement integrates sophisticated communication technologies, the Internet of Things (IoT), artificial intelligence (AI), and data analytics. Intending to design an effective smart building monitoring system, this research paper explores and compares various solutions for measuring building parameters by identifying a broad spectrum of review articles considering building occupant behavior, sensor deployment, and implementation complexity. The objective of our paper is to compile diverse information on various sensors used for monitoring building conditions and provide a comprehensive overview of data structuring and processing, all within a single article. Additionally, this paper addresses the challenges of combining data from decentralized systems and the need for managerial tools to optimize user experiences. The findings contribute to the advancement of smart building management, offering valuable insights for improving building performance and user experience as well as evaluating future research directions in this field. This review is designed to serve as an introduction for anyone venturing into the field of building monitoring.

Analysing the evolution of argumentation quality among pre-service biology teachers amidst the COVID-19 pandemic

ABSTRACT The COVID-19 pandemic has become one of the most controversial issues in recent years. The pandemic’s impact on argumentation skills, crucial for science teachers, needs exploration to understand its effects on how they evaluate evidence and engage in discussions. This study investigated how biology pre-service teachers’ (PSTs) argumentation quality changed over the pandemic’s first two months of social distancing and quarantine. Using qualitative and quantitative methodologies, data were collected through semi-structured interviews, and Toulmin’s Argument Pattern (TAP) was employed to evaluate the arguments. Initially, nearly half of the participants demonstrated low-level arguments. However, during the second round of interviews, a decrease in Level 1 and 2 arguments and an increase in Level 3 arguments were observed. The observed improvements in argumentation quality, particularly in the use of counterclaims, warrants, and rebuttals, underscore the role of increased familiarity with the issue and exposure to diverse information sources. These findings suggest that real-life contexts, such as the pandemic, offer opportunities for PSTs to develop more sophisticated argumentation skills when exposed to multiple perspectives and ongoing engagement with the topic. Understanding these changes can inform teaching strategies and teacher training programmes, especially during global crises. Future research could examine argumentation quality related to issues such as vaccination and COVID-19 variants.

Navigating diverse information environments on environmental sustainability: information practices of decision-makers in Finnish high-tech companies

Navigating diverse information environments on environmental sustainability: information practices of decision-makers in Finnish high-tech companies

Development of artificial intelligence-based teaching factory in vocational high schools in Central Java Province

The objective of this study is to examine and assess the progress of utilizing artificial intelligence (AI) in teaching factory learning to enhance the digital skills of vocational high school (SMK) students in the province of Central Java. This study employed a qualitative approach utilizing metaethnography, as well as a quantitative approach employing Aiken’s V formula and strength, weakness, opportunity, threat (SWOT) analysis to identify and analyze the issues that arise. The present study examines the locations of SMKN 2 Purbalingga and SMKS Telkom in Central Java. Furthermore, relevant documentation was also utilized as a source of data. The research findings indicate that the implementation of AI in teaching manufacturing learning has effectively facilitated the development of an online learning management system. This system is characterized by its organized and integrated approach inside the online learning management system. The administration of teaching materials may be conducted autonomously and facilitated by the utilization of diverse information and communication technology or e-learning functionalities, including chat, email, blogs, and social media platforms. The creation of new enterprises within the domain of AI. It is imperative for educational institutions to align their policies with the demands and requirements of the business.

Address wind farm layout problems by an adaptive moth-flame optimization algorithm

As a clean renewable energy source, wind energy has been widely used to generate electricity in wind farms. Plenty of turbines work together to form a wind farm to increase electricity output. However, this produces an inevitable wake effect, which affects the efficiency of turbines in capturing wind energy, further leading to a decrease in the power generated by wind farms. To minimize the wake effect, optimizing the turbines layout in wind farms is necessary. Therefore, an adaptive Moth-Flame Optimization algorithm with enhanced Exploration and Exploitation capabilities (MFOEE) is proposed. The refinements of the algorithm include: i) defining a selection probability to utilize diverse information of moths; ii) an enhanced exploitation strategy by pushing moths towards the best flame; iii) developing an enhanced exploration strategy, in which three moths exchange information and two inferior moths fly to the superior moth. To validate the performance of MFOEE, four scenarios are set up using grid-based simulations of actual wind farm conditions. The experimental findings demonstrate that MFOEE can offer the most optimal scheme among the four scenarios, which indicates that MFOEE proves highly effective in addressing wind farm layout optimization challenges.

Self-Powered Machine-Learning-Assisted Material Identification Enabled by a Thermogalvanic Dual-Network Hydrogel with a High Thermopower.

Wearable devices equipped with high-performance flexible sensors that can identify diverse physical information free from batteries are playing an indispensable role in various fields. However, previous studies on flexible sensors have primarily focused on their elasticity and temperature-sensing capability, with few reports on material identification. In this paper, a thermogalvanic dual-network hydrogel is fabricated with [Fe(CN)6]3-/4- as a redox couple and lithium magnesium silicate, Gdm+ and lithium bromideas key electrolytes to optimize the interconnected porous structure of the gel, which shows excellent mechanical and thermoelectric properties with a thermopower as high as 4.01mV K-1. A self-powered material identification ring is developed based on the temperature-triggered thermoelectric response of the gel in conjunction with machine learning, which can actively infer materials without an external power connection by analyzing the voltage signals correlated with interfacial heat transfer produced upon contact with different materials. The proposed gel ring has important applications for future areas such as human-computer interaction and haptic-associated artificial intelligence.

Education for people with serious chronic respiratory diseases and their informal caregivers: how to address challenges that impact learning.

Shared decision making is crucial in palliative care for people with serious chronic respiratory diseases and their informal caregivers. Patient education is a critical component in this process, as it provides patients and their informal caregivers the necessary knowledge for informed decisions regarding symptom management, coping with breathlessness, and advance care planning. However, education does not automatically lead to acquiring knowledge. This review describes challenges for education for people with serious chronic respiratory diseases and their informal caregivers and describes how learner-centered education can address these. People with serious chronic respiratory diseases and their informal caregivers face diverse information needs and learning challenges, with low health literacy and cognitive problems being common. Healthcare professionals can facilitate learner-centered education by enhancing motivation for knowledge acquisition by meeting information needs and learning preferences of patients and their informal caregivers, and by ensuring that information is understandable and readable for those with low health literacy and cognitive problems. E-health applications may serve as valuable tools in this process. Learner-centered education may enhance knowledge acquisition in people with serious chronic respiratory diseases and their informal caregivers aiming to gain control over symptoms and optimize shared decision making and advance care planning.

RSPS-SAM: A Remote Sensing Image Panoptic Segmentation Method Based on SAM

Satellite remote sensing images contain complex and diverse ground object information and the images exhibit spatial multi-scale characteristics, making the panoptic segmentation of satellite remote sensing images a highly challenging task. Due to the lack of large-scale annotated datasets for panoramic segmentation, existing methods still suffer from weak model generalization capabilities. To mitigate this issue, this paper leverages the advantages of the Segment Anything Model (SAM), which can segment any object in remote sensing images without requiring any annotations and proposes a high-resolution remote sensing image panoptic segmentation method called Remote Sensing Panoptic Segmentation SAM (RSPS-SAM). Firstly, to address the problem of global information loss caused by cropping large remote sensing images for training, a Batch Attention Pyramid was designed to extract multi-scale features from remote sensing images and capture long-range contextual information between cropped patches, thereby enhancing the semantic understanding of remote sensing images. Secondly, we constructed a Mask Decoder to address the limitation of SAM requiring manual input prompts and its inability to output category information. This decoder utilized mask-based attention for mask segmentation, enabling automatic prompt generation and category prediction of segmented objects. Finally, the effectiveness of the proposed method was validated on the high-resolution remote sensing image airport scene dataset RSAPS-ASD. The results demonstrate that the proposed method achieves segmentation and recognition of foreground instances and background regions in high-resolution remote sensing images without the need for prompt input, while providing smooth segmentation boundaries with a panoptic segmentation quality (PQ) of 57.2, outperforming current mainstream methods.

Navigating the infodemic through digital health literacy education in schools

Abstract Background High digital health literacy (DHL) can help to navigate the internet’s vast information landscape. This research explores the impact of learning DHL in schools on students’ DHL, and how it might mitigates confusion surrounding health-related information. Moreover, this research sheds light on how learning DHL in schools influences students’ inclinations towards specific sources of health information. Methods 1448 secondary school students aged 9-18 years participated in face-to-face interviews. Associations between learning digital health literacy (DHL) and students’ DHL, comprehension of health information, sense of being informed on health topics, and utilization of diverse health information sources were evaluated using the chi-squared test. Results The learning of DHL within school settings demonstrated a significant correlation with students’ overall DHL (X2 (6) = 120.99, p < .001). Those reporting high levels of DHL learning exhibited reduced confusion regarding health information (X2 (4) = 16.65, p = .002) and felt notably more informed (X2 (4) = 231.06, p < .001). Contrasted with peers who reported insufficient learning of DHL, students with high learning reported learning more about health from parents (X2 (2) = 55.28, p < .001), peer interactions (X2 (2) = 214.51, p < .001), online sources (X2 (2) = 78.54, p < .001), influencer content (X2 (2) = 68.46, p < .001), offline literature (X2 (2) = 24.32, p < .001), television (X2 (2) = 140.36, p < .001), and in school (X2 (2) = 205.53, p < .001). However, they were less reliant on physicians for health information (X2 (2) = 53.62, p < .001). Conclusions Integrating DHL into school curricula holds promise for enhancing students’ DHL and alleviating apprehensions regarding health-related information. Moreover, such educational initiatives have the potential to expand students’ repertoire of resources for accessing health information, encompassing both digital and non-digital sources.

A netnographic study of anabolic-androgenic steroid initiation videos on YouTube.

YouTube personalities (YP) have amassed significant followings by distributing health information. There is concern that YP involved in anabolic androgenic steroid (AAS) communities are sharing information on how people should approach initiating AAS use. This research sought to explore how YP are using their platform to distribute information on AAS initiation, what information is being presented and how people are responding. This mixed methods research utilised a netnographic approach, a method adapted from traditional ethnographic techniques for the study of online sociocultural phenomena. The research included a selection of YouTube videos (n = 37) and user comments replying to the videos (n = 584). YP addressed the positives and negatives of AAS initiation (76%; n = 28 and 76%; n = 28 respectively), provided harm reduction strategies (76%; n = 28), and advice on which AAS people should use (97%; n = 36). A qualitative thematic analysis of the comments found two overarching themes: 'Authenticity, capital and the influence of the YP on their audiences' and 'The evolving landscape of enhancement and health'. Viewers engaged with the videos due to the YPs lived experience using AAS, and YP influenced the health behaviours of viewers. Many believed that the videos were useful for health and lifestyle enhancement purposes. YP are disseminating diverse information on AAS initiation which is derived from their lived experience using AAS. Harm reduction is a key component of the YPs' message and viewers responded positively to a balanced and honest assessment of AAS initiation which is perceived to respect their personal autonomy.

Promoting Upcycled Food: An analysis of social media communication strategies of Upcycled Food Association

Global food loss and waste are significant challenges in the food system, critical to addressing food insecurity and national security in the U.S. Upcycled food, which reduces waste at the source, is a promising solution. Social media offers both opportunities and challenges for communicating food benefits. This study examines publicly available data from Instagram, Facebook, and Twitter of a non-profit organization Upcycled Food Association (UFA) using content analysis and machine learning techniques. The analysis revealed that social media posts focused on promoting food waste awareness and benefits related to sustainability, health, and the economy, with environmental information being prioritized. UFA employs diverse impact information frames and uses positive emotion framing to increase food waste awareness and empower consumers by highlighting the impact of their purchasing power. Communication strategies are tailored to the unique characteristics of each social media platform and its users. We recommend social media initiatives continue emphasizing food waste awareness and upcycling, alongside increasing health information to encourage upcycled food consumption. Diverse strategies with diverse benefits and tailored to each platform's characteristics and sociodemographic groups are recommended. Future research can explore efficient topic modeling methods in larger datasets in social media analysis.

Sympatric diversity pattern driven by the secondary contact of two deeply divergent lineages of the soybean pod borer Leguminivora glycinivorella.

The soybean pod borer, Leguminivora glycinivorella (Matsumura), is an important tortricid pest species widely distributed in most parts of China and its adjacent regions. Here, we analyzed the genetic diversity and population differentiation of L. glycinivorella using diverse genetic information including the standard cox1 barcode sequences, mitochondrial genomes (mitogenomes), and single-nucleotide polymorphisms (SNPs) from genotyping-by-sequencing. Based on a comprehensive sampling (including adults or larvae of L. glycinivorella newly collected at 22 of the total 30 localities examined) that covers most of the known distribution range of this pest, analyses of 543 cox1 barcode sequences and 60 mitogenomes revealed that the traditionally recognized and widely distributed L. glycinivorella contains two sympatric and widely distributed genetic lineages (A and B) that were estimated to have diverged ∼1.14 million years ago during the middle Pleistocene. Moreover, low but statistically significant correlations were recognized between genetic differentiation and geographic or environmental distances, indicating the existence of local adaptation to some extent. Based on SNPs, phylogenetic inference, principal component analysis, fixation index, and admixture analysis all confirm the two divergent sympatric lineages. Compared with the stable demographic history of Lineage B, the expansion of Lineage A had possibly made the secondary contact of the two lineages probable, and this process may be driven by the climate fluctuation during the late Pleistocene as revealed by ecological niche modeling.