Native Data Research Articles

Harnessing Deep Learning for Enhanced MPPT in Solar PV Systems: An LSTM Approach Using Real-World Data

Maximum Power Point Tracking (MPPT) is essential for maximizing the efficiency of solar photovoltaic (PV) systems. While numerous MPPT methods exist, practical implementations often lean towards conventional techniques due to their simplicity. However, these traditional methods can struggle with rapid fluctuations in solar irradiance and temperature. This paper introduces a novel deep learning-based MPPT algorithm that leverages a Long Short-Term Memory (LSTM) deep neural network (DNN) to effectively track maximum power from solar PV panels, utilizing real-world data. The simulations of three algorithms—Perturb and Observe (P&O), Artificial Neural Network (ANN), and the proposed LSTM-based MPPT—were conducted using MATLAB (2021b) and RT_LAB (24.3.3) with an OPAL-RT simulator for real-time analysis. The data used for this study were sourced from NASA/POWER’s Native Resolution Daily Data of solar irradiation and temperature specific to Imphal, Manipur, India. The obtained results demonstrate that the LSTM-based MPPT system achieves a superior power tracking accuracy under changing solar conditions, producing an average output of 74 W. In comparison, the ANN and P&O methods yield average outputs of 57 W and 62 W, respectively. This significant improvement, i.e., 20–30%, underscores the effectiveness of the LSTM technique in enhancing the power output of solar PV systems. By incorporating real-world data, valuable insights into solar power generation specific to the selected location are provided. Furthermore, the outputs of the model were verified through real-time simulations using the OPAL-RT simulator OP4510, showcasing the practical applicability of this approach in real-world scenarios.

Dynamics of small metazooplankton in relation with environmental factors: A 12-year study in the coastal zone of the Southwestern Crimea (the Black Sea)

Based on continuous 12-year (2010–2021) monitoring of the average summer abundance of small metazooplankton (SMZ) in the coastal zone of the Southwestern Crimea, the patterns of its interannual fluctuations were investigated. The total SMZ abundance during the study period varied from 37.8 to 176.0 thousand·m−3 in the open coastal area (on average, 104.0 ± 49.2 thousand·m−3) and from 90.3 to 556.8 thousand·m−3 at the bay mouth (on average, 264.5 ± 121.0 thousand·m−3). The crustacean fraction accounted for 85–91% of the total SMZ abundance. Principal component analysis (PCA) showed a noticeable contribution of 48 % of total temperatures and wind speed and different direction repeatability to the SMZ abundance variability. For native data, significant relationships were revealed between the abundance of individual SMZ fractions and hydrometeorological parameters: total temperatures of the warm period of the year (May–October), periodicity of the southern and southwestern, northern winds, calm conditions (events of the absence of wind), and the summer index of the North Atlantic Oscillation (NAO) with a 1-year lag. The application of fast Fourier transform (FFT) with a Hamming filter to the original series and periodograms analysis allowed revealing typical periods of variability in SMZ abundance and hydrometeorological parameters of the environment: 2–4-year and 5–6-year ones. At these scales, a higher number of significant relationships was established with the indicated hydrometeorological parameters, inter alia winter and summer NAO indices, than during the analysis of the original series. The total contribution of hydrometeorological and climatic factors to the variation in the abundance of SMZ increased to 78–79 %. This confirms the assumption that the relationship between SMZ and environmental parameters is characterized by its own features at different scales. Analysis of long-term SMZ different fractions tendencies for Summer 2010–2021, according to the Mann-Kendall criterion, did not show significant results.

A modelling approach to compare ADC deconjugation and systemic elimination rates of individual drug-load species using native ADC LC-MS data from human plasma

Native liquid chromatography mass spectrometry (LC-MS) is a commonly used approach for intact analysis of inter-chain cysteine conjugated antibody-drug conjugates (ADCs). Coupling native LC-MS with affinity capture provides a platform for intact ADC analysis from in vivo samples and characterisation of individual drug load species, specifically the impact of drug linker deconjugation, hydrolysis, and differential clearance in a biological system. This manuscript describes data generated from native LC-MS analysis of ADCs from human plasma, both in vitro incubations and clinical samples. It also details the pharmacokinetic (PK) model built to specifically characterise the disposition of individual drug load species from MMAE and MMAF interchain cysteine conjugated ADCs. In vitro deconjugation and hydrolysis rates were similar across both ADCs. Differential clearance of higher loaded species in vivo was pronounced for the MMAE conjugated ADC, while systemic elimination after accounting for deconjugation was similar across drug loads for the MMAF conjugated ADC. This is the first report of affinity capture native LC-MS analysis, and subsequent modelling of deconjugation, hydrolysis and clearance rates of individual drug load species using clinical data from cysteine conjugated ADCs.

Three classes of propofol binding sites on GABAA receptors

Propofol is a widely used anesthetic and sedative that acts as a positive allosteric modulator (PAM) of gamma-aminobutyric acid type A (GABAA) receptors. Several potential propofol binding sites that may mediate this effect have been identified using propofol-analogue photoaffinity labeling. o-PD labels β-H267, a pore-lining residue, whereas AziPm labels residues β-M286, β-M227 and α-I239 in the two membrane-facing interfaces (β(+)/α(-) and α(+)/β(-)) between α and β subunits. This study used photoaffinity labeling of α1β3 GABAA receptors to reconcile the apparently conflicting results obtained with AziPm and o-PD labeling, focusing on whether β3-H267 identifies specific propofol binding site(s). The results show that propofol, but not AziPm protects β3-H267 from labeling by o-PD, whereas both propofol and o-PD protect against AziPm labeling of β3-M286, β3-M227 and α1I239. These data indicate that there are three distinct classes of propofol binding sites, with AziPm binding to two of the classes and o-PD to all three. Analysis of binding stoichiometry using native mass spectrometry in β3 homomeric receptors, demonstrated a minimum of five AziPm labeled residues and three o-PD labeled residues per pentamer, suggesting that there are two distinct propofol binding sites per β-subunit. The native MS data, coupled with photolabeling performed in the presence of zinc, indicate that the binding site(s) identified by o-PD are adjacent to, but not within the channel pore, since the pore at the 17’ H267 residue can accommodate only one propofol molecule. These data validate the existence of three classes of specific propofol binding sites on α1β3 GABAA receptors.

Native MS-guided lipidomics to define endogenous lipid microenvironments of eukaryotic receptors and transporters.

The mammalian membrane is composed of various eukaryotic lipids interacting with extensively post-translationally modified proteins. Probing interactions between these mammalian membrane proteins and their diverse and heterogeneous lipid cohort remains challenging. Recently, native mass spectrometry (MS) combined with bottom-up 'omics' approaches has provided valuable information to relate structural and functional lipids to membrane protein assemblies in eukaryotic membranes. Here we provide a step-by-step protocol to identify and provide relative quantification for endogenous lipids bound to mammalian membrane proteins and their complexes. Using native MS to guide our lipidomics strategies, we describe the necessary sample preparation steps, followed by native MS data acquisition, tailored lipidomics and data interpretation. We also highlight considerations for the integration of different levels of information from native MS and lipidomics and how to deal with the various challenges that arise during the experiments. This protocol begins with the preparation of membrane proteins from mammalian cells and tissues for native MS. The results enable not only direct assessment of copurified endogenous lipids but also determination of the apparent affinities of specific lipids. Detailed sample preparation for lipidomics analysis is also covered, along with comprehensive settings for liquid chromatography-MS analysis. This protocol is suitable for the identification and quantification of endogenous lipids, including fatty acids, sterols, glycerolipids, phospholipids and glycolipids and can be used to interrogate proteins from recombinant sources to native membranes.

GenAI-Assisted Database Deployment for Heterogeneous Indigenous–Native Ethnographic Research Data

In ethnographic research, data collected through surveys, interviews, or questionnaires in the fields of sociology and anthropology often appear in diverse forms and languages. Building a powerful database system to store and process such data, as well as making good and efficient queries, is very challenging. This paper extensively investigates modern database technology to find out what the best technologies to store these varied and heterogeneous datasets are. The study examines several database categories: traditional relational databases, the NoSQL family of key-value databases, graph databases, document databases, object-oriented databases and vector databases, crucial for the latest artificial intelligence solutions. The research proves that when it comes to field data, the NoSQL lineup is the most appropriate, especially document and graph databases. Simplicity and flexibility found in document databases and advanced ability to deal with complex queries and rich data relationships attainable with graph databases make these two types of NoSQL databases the ideal choice if a large amount of data has to be processed. Advancements in vector databases that embed custom metadata offer new possibilities for detailed analysis and retrieval. However, converting contents into vector data remains challenging, especially in regions with unique oral traditions and languages. Constructing such databases is labor-intensive and requires domain experts to define metadata and relationships, posing a significant burden for research teams with extensive data collections. To this end, this paper proposes using Generative AI (GenAI) to help in the data-transformation process, a recommendation that is supported by testing where GenAI has proven itself a strong supplement to document and graph databases. It also discusses two methods of vector database support that are currently viable, although each has drawbacks and benefits.

Bayesian Multi-line Intensity Mapping

Line intensity mapping (LIM) has emerged as a promising tool for probing the 3D large-scale structure through the aggregate emission of spectral lines. The presence of interloper lines poses a crucial challenge in extracting the signal from the target line in LIM. In this work, we introduce a novel method for LIM analysis that simultaneously extracts line signals from multiple spectral lines, utilizing the covariance of native LIM data elements defined in the spectral–angular space. We leverage correlated information from different lines to perform joint inference on all lines simultaneously, employing a Bayesian analysis framework. We present the formalism, demonstrate our technique with a mock survey setup resembling the SPHEREx deep-field observation, and consider four spectral lines within the SPHEREx spectral coverage in the near-infrared: Hα, [O iii], Hβ, and [O ii]. We demonstrate that our method can extract the power spectrum of all four lines at the ≳10σ level at z < 2. For the brightest line, Hα, the 10σ sensitivity can be achieved out to z ∼ 3. Our technique offers a flexible framework for LIM analysis, enabling simultaneous inference of signals from multiple line emissions while accommodating diverse modeling constraints and parameterizations.

Using learner corpus data for grammatical accuracy development in written productions

Abstract This study investigates the differential effect of various noticing activities on grammatical accuracy development in EFL learners’ written productions. We focus on different types of noticing activities based on an error-tagged learner corpus and report on effective practical experiments involving learner corpus data. A pretest/posttest quasi-experimental design is used with three experimental groups (receiving different treatments) and one control group. Error frequencies, at both group and individual levels, and proportions of learners producing errors on three specific error types (articles, verb tense, verb agreement) are compared. Our results suggest that accuracy in the use of articles and verb agreement could be more easily fostered through the comparison of learner output with native data (the BNC, in our case). As for verb tenses, the impact of a more traditional form of corrective feedback seems greater while the use of online machine translation tools does not seem to foster much accuracy development.

Alternative proteoforms and proteoform-dependent assemblies in humans and plants

The variability of proteins at the sequence level creates an enormous potential for proteome complexity. Exploring the depths and limits of this complexity is an ongoing goal in biology. Here, we systematically survey human and plant high-throughput bottom-up native proteomics data for protein truncation variants, where substantial regions of the full-length protein are missing from an observed protein product. In humans, Arabidopsis, and the green alga Chlamydomonas, approximately one percent of observed proteins show a short form, which we can assign by comparison to RNA isoforms as either likely deriving from transcript-directed processes or limited proteolysis. While some detected protein fragments align with known splice forms and protein cleavage events, multiple examples are previously undescribed, such as our observation of fibrocystin proteolysis and nuclear translocation in a green alga. We find that truncations occur almost entirely between structured protein domains, even when short forms are derived from transcript variants. Intriguingly, multiple endogenous protein truncations of phase-separating translational proteins resemble cleaved proteoforms produced by enteroviruses during infection. Some truncated proteins are also observed in both humans and plants, suggesting that they date to the last eukaryotic common ancestor. Finally, we describe novel proteoform-specific protein complexes, where the loss of a domain may accompany complex formation.

Attribute mining multi-view contrastive learning network for recommendation

Knowledge graph, with its rich edge information, has demonstrated its superiority in improving interpretability and alleviating the cold start problem, and has been widely applied in recommendation systems. Currently, graph neural networks (GNNs)-based methods have become mainstream in knowledge-aware recommendation (KGR) due to their unique ability to capture cross-order structural information. However, because of the natural sparsity of user and item interaction data, GNNs-based knowledge-aware recommendation methods generally face the sparse supervised signals, which limits their performance in applications. In this paper, we propose a new model named Attribute Mining Multi-view Contrastive Learning Network (AMMCN) to address this challenge. Firstly, AMMCN enriches the originally sparse embedding representation by mining the potential information of native data and constructing four different views. Unlike previous works that generate new views through simple destruction and dropout or build contrastive views by guiding user–item graph enhancement with knowledge graph, we holistically integrate both the global collaborative knowledge graph and the local-level user–item graphs along with their associated knowledge graphs. We also adopt the intersection over union (IoU) method to mine the item attribute information on each original view, so as to generate a new contrastive view - item–item graph. On this basis, AMMCN conducts cross-view contrastive learning at the local and global-levels, integrating the collaborative information of each view and the global structural information in a self supervised manner, overcoming dependence on supervised signals. In addition, in the item–item graph, we designed a top-k similarity matching mechanism to capture collaboration information that was overlooked in previous work and minimize unnecessary noise. Adequate experiments on three public benchmark datasets show that AMMCN achieves considerable performance enhancements in comparison to current methods.

Demographic Disparities in Acute Myeloid Leukemia Mortality Trends in the United States.

Acute myeloid leukemia (AML) is a hematological malignancy with an overall poor prognosis; however, survival rates vary widely by clinical and demographic characteristics. This study sought to identify historical trends in AML mortality in the US and to identify any disparities by sex, race or ethnicity. For each demographic population, the age adjusted mortality rate (AAMR) per 1,000,000 for AML-related deaths from 1999 to 2020 in the United States was accessed from the CDC Wonder Database. These values were then used to calculate the average Annual Percent Change (AAPC) from 1999 to 2020 using the National Cancer Institute (NCI)'s Joinpoint Regression Program (Joinpoint V 4.9.0.0, NCI) with log-linear regression models. Statistical significance for all reported findings was determined using a 2-tailed t-test or parallel pairwise comparison with significance defined as p<0.05. The overall population had a significant downtrend in mortality rate between 2011 and 2020 with an APC of -0.61% [95% confidence interval (CI)=-1 to -0.2]. In 2020, the AAMR due to AML for males was 32 and for females was 20.2. Females did not have a significant overall AAPC from 1999 to 2020. Males had a significant AAPC of 0.5% (95%CI=0-0.9) from 1999 to 2020, signifying an overall uptrend. In 2020, the White population had the greatest mortality rate (29.6), followed by the Black or African American population (20.9), Asian or Pacific Islander (AAPI) population (18.6), and the American Indian/Alaska Native population (8.8). American Indian and Alaska Native population data could not be reliably compared. No race/ethnic group had a significant AAPC trend from 1999 to 2020. However, parallel pairwise comparison found a significant difference in the trend of mortality rates between the Black or African American and AAPI, Black or African American and White, and White and AAPI populations. Our findings highlight disparities in mortality due to AML and underscore the need for additional resources and support in affected populations and areas.

Native fish assemblages in natural lakes across Japan: Endemism deterioration lasting centuries

AbstractAimThis study aimed to illustrate the changing diversity patterns of native freshwater fish in the past two centuries and to identify priority locations for native fish conservation to counter future degradation.LocationJapanese archipelago.MethodsWe used the published native fish fauna data in 39 lakes across Japan, analysing historical and current diversity and projecting future distribution patterns based on the Japanese Red List. We assessed fish assemblages' taxonomic, phylogenetic, and functional alpha and beta diversity across different periods. Additionally, we proposed a cumulative diversity index that incorporated taxonomic, phylogenetic and functional facets, and then examined its relationship with the latitudinal positioning of the lakes across periods.ResultsWe observed a significant decrease in the richness of native freshwater fish since historical periods. Accompanying this decline were significant reductions in both phylogenetic relatedness and functional redundancy. Fish beta diversity increased from the past to the present and is predicted to decline drastically, indicating an ongoing homogenization process. Using the cumulative diversity index, we identified Hokkaido and Kyushu as crucial habitats for endemic fishes. These islands, with unique biogeographical backgrounds, contributed substantially to the national dissimilarity patterns of native fish assemblages in the historical period. However, the contribution is diminishing due to the ongoing decline in fish endemism.Main ConclusionsThe conservation priority of native freshwater fish in Japan should be assigned to Hokkaido and Kyushu due to the inhabited endemic species. The proposed framework for assessing the cumulative diversity of biotic communities presented the potential to aid macroecological explorations that underpin biodiversity conservation.

Video fragment processing by Ky Fan norm

In this study, we focused on the formalization of video frame descriptions in the context of solving video segmentation problem. Since native video data can have various sizes, dividing each frame into blocks allows present image frame as a square matrix for a formal description. The frame block is a matrix of arbitrary dimensions. The ability to skip the step of matrix transformation to a square dimension or vectorization using some descriptor allows to reduce computational costs, freeing up computational resources required for this transformation. In our study, we used Ky Fan norm value as image frame block descriptor. The Ky Fan norm is built on top of matrix singular values. A singular decomposition does not impose restrictions on either the dimension orthe character of the elements of the original matrix. We conducted a comparative analysis of the effectiveness of the obtained descriptor for different video data sizesand with different aspect ratios, showing that the change in the descriptor for each block is independent of the video sizeand aspect ratios. Changes in the descriptorsfor each block from frame to frame are identical for video data of varying sizes. This means that as a result of such fragment transform, a square matrix of a fixed size iscreated, regardless of the output video size.This makes it possible to unify further processing of the video, which can be useful for the task of information search in large video databases under the conditions of providing a query "ad exemplum". In thiscase, we can analyze the existing database in offline mode and match each video with a fixed square matrix of descriptors, which will significantly reduce the time and amount of resources when matching with the query. Also, this approach can be effectively used to analyze video data for the motion detection and scene change tracking.

Probabilistic Analysis of English Dative Constructions in Academic Writings of English EFL Learners

Grammatical patterns in learner writings are among the most investigated topics in second/foreign language acquisition, gaining particular momentum thanks to corpus studies. English dative constructions are among those for which comprehensive literature is also available, consisting of different perspectives to explain the linguistic phenomenon on various theoretical grounds. However, except for rare instances, the foci of interest have constantly been on comparing learner data to native speaker data, particularly in terms of frequency of use in the second/foreign language learning environment. Different from other studies, the current study investigated English dative alternation in learner data with a probabilistic point of view, scrutinizing 27 learner corpora of learners with different L1s. Results showed that differences in learners’ native languages had little added value to variations among learners. Moreover, a tendency similar to priming the verb ‘give’ in dative constructions was observed for the other variables in the construction.

MemFHE: End-to-end Computing with Fully Homomorphic Encryption in Memory

The increasing amount of data and the growing complexity of problems have resulted in an ever-growing reliance on cloud computing. However, many applications, most notably in healthcare, finance, or defense, demand security and privacy, which today’s solutions cannot fully address. Fully homomorphic encryption (FHE) elevates the bar of today’s solutions by adding confidentiality of data during processing. It allows computation on fully encrypted data without the need for decryption, thus fully preserving privacy. To enable processing encrypted data at usable levels of classic security, e.g., 128-bit, the encryption procedure introduces noticeable data size expansion—the ciphertext is much bigger than the native aggregate of native data types. In this article, we present MemFHE, which is the first accelerator of both client and server for the latest Ring-GSW (Gentry et al. [ 17 ])-based homomorphic encryption schemes using Processing in Memory (PIM). PIM alleviates the data movement issues with large FHE encrypted data while providing in situ execution and extensive parallelism needed for FHE’s polynomial operations. While the client-PIM can homomorphically encrypt and decrypt data, the server-PIM can process homomorphically encrypted data without decryption. MemFHE’s server-PIM is pipelined and is designed to provide flexible bootstrapping, allowing two encryption techniques and various FHE security levels based on the application requirements. We evaluate MemFHE for various security levels and compare it with state-of-the-art CPU implementations for Ring-GSW-based FHE. MemFHE is up to 20 k × (265×) faster than CPU (GPU) for FHE arithmetic operations and provides on average 2,007× higher throughput than [ 36 ] while implementing neural networks with FHE.

Abstract 2333: Methylation based phylogeny and timing in cancer

Abstract Knowing the chronological sequence of genetic alterations during cancer development and progression is crucial for understanding the process of transformation of normal tissue into malignant tumors. This can offer insights into the etiology and intrinsic cellular mechanisms of the process, presenting opportunities for early detection and treatment of initial disease stages, as well as highlight key events as potential therapeutic. Current methods of establishing progression histories of cancers rely either on direct sequencing of premalignant lesions, which are seldom available for tumors or on computational inference timing methods capable of reconstructing the history from primary tumor data. These methods require genome or exome sequencing data from a relatively large cohort of tumors and provide limited resolution due to overall scarcity of somatic mutations. We utilize a different source of genetic information, genome methylation, that also “records” historic information about acquisition and development of mutations. Over 28 million methylated CpG sites exist in the genome with several percent of them differentially methylated (DMS) between tissue types and tumor vs adjacent normal tissue. The several orders of magnitude higher density of distinctly methylated sites provides unprecedented resolution, needs fewer tumors, can be merged with driver mutation data and co-analyzed with “methylation clock” information that allows to establish early development, phylogeny and progression at unprecedented resolution. We developed methods for whole genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS) and Oxford Nanopore Technologies native methylation sequencing data. Somatic DMS sites have distinct properties from somatic point mutations, can appear and disappear in the process of tumor evolution and development allowing for unique configuration of methylated islands. Our tools perform single sample and cohort level “timing”, multi-sample phylogeny reconstruction and cancer population reconstruction by integrating methylation and mutation information from the same sample. These new methods will significantly contribute to our understanding of cancer initiation and progression at a resolution not available previously. Citation Format: Nataliya Guteneva, Eliezer Lichter, Ignaty Leshchiner. Methylation based phylogeny and timing in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2333.

The Effectiveness of Hybrid Learning in English Pronunciation Pedagogy in the Nigerian ESL Context

This paper is a study on the effectiveness of hybrid learning; a blend of traditional and technology-based training, vis-à-vis the second language (L2) learner of English pronunciation. Specifically, our motivation is centred on the functionality of combining the habitual classroom teaching style with the Telegram app (a mobile-based technological tool) for studies relating to speech production and perception. The methodical procedure and design for this study is both quantitative and descriptive; employing the use of a multiple-choice questionnaire in data collection from 401 Nigerian undergraduate and postgraduate students in the University of Benin, Nigeria, to ascertain the effectiveness of digital designs and stratagems. Pie charts are employed to display multiple divisions of the study's data comparison. This study emphasizes the Online-Driver Blended Learning Model; such a pedagogical approach exposes the L2 learner to a more involved and effective way of understanding how to avoid imposing an unfamiliar accent upon the target language. Consequently, we see a merger of online practical exercises and exposure to uploaded multimodal texts and native speakers' spoken data (audio-visual recordings/voice notes) with scheduled direct classroom interludes. Findings confirm that exploiting such a hybrid model enhances students' cognitive abilities and retentive capacity regarding English pronunciation. Such digital audio-visual tools are paramount phonetic strategies for learning how to avoid unacceptable phonetic alterations. Thus, we see the effectiveness of integrating the Telegram app into the traditional brick-and-mortar educational method in handling problems associated with speech sound production and second-language phonological interference.

Comparative study of the inhalation parameters of COPD patients through NEXThaler® and Ellipta® dry powder inhalers

The deposition of dry powder aerosol drugs depends on the inhalation parameters of the patients through the inhaler. These data are not directly measured in clinical practice. Their prediction based on the routinely measured spirometric data could help in choosing the appropriate device and optimizing the therapy. The aim of this study was to perform inhalation experiments to find correlations between inhalation parameters of COPD patients through two DPI devices and their native spirometric data, gender, age and disease severity. Another goal was to establish relationships between peak inspiratory flows through NEXThaler® and Ellipta® inhalers and their statistical determinants. Breathing parameters of 113 COPD patients were measured by normal spirometry and while inhaling through the two DPIs. Statistical analysis of the measured data was performed. The average values of peak inspiratory flow through the devices (PIFdev) were 68.4 L/min and 78.0 L/min for NEXThaler® and Ellipta®, respectively. PIFdev values were significantly higher for males than for females, but differences upon age, BMI and disease severity group were not significant. PIFdev values correlated best with their native spirometric counterparts (PIF) and linear relationships between them were revealed. Current results may be used in the future to predict the success of inhalation of COPD patients through DPI devices, which may help in the inhaler choice. By choosing the appropriate device-drug pair for each patient the lung dose can be increased and the efficiency of the therapy improved. Further results of the clinical study will be the subject of a next publication.

Characterization of Large Immune Complexes with Size Exclusion Chromatography and Native Mass Spectrometry Supplemented with Gas Phase Ion Chemistry.

Large immune complexes formed by the cross-linking of antibodies with polyvalent antigens play critical roles in modulating cell-mediated immunity. While both the size and the shape of immune complexes are important determinants in Fc receptor-mediated signaling responsible for phagocytosis, degranulation, and, in some instances, autoimmune pathologies, their characterization remains extremely challenging due to their large size and structural heterogeneity. We use native mass spectrometry (MS) supplemented with limited charge reduction in the gas phase to determine the stoichiometry of immune complexes formed by a bivalent (homodimeric) antigen, a 163 kDa aminopeptidase P2 (APP2), and a monoclonal antibody (mAb) to APP2. The observed (APP2·mAb)n complexes populate a wide range of stoichiometries (n = 1-4) with the largest detected species exceeding 1 MDa, although the gas-phase dissociation products are also evident in the mass spectra. While frequently considering a nuisance that complicates interpretation of native MS data, limited dissociation provides an additional dimension for characterization of the immune complex quaternary structure. APP2/mAb associations with identical composition but slightly different elution times in size exclusion chromatography exhibit notable differences in their spontaneous fragmentation profiles. The latter indicates the presence of both extended linear and cyclized (APP2·mAb)n configurations. The unique ability of MS to distinguish between such isomeric structures will be invaluable for a variety of applications where the biological effects of immune complexes are determined by their ability to assemble Fc receptor clusters of certain density on cell surfaces, such as platelet activation by clustering the low-affinity receptors FcγRIIa on their surface.

Muscling mussels: Understanding the invasive potential of the South American bivalve Mytella strigata (Hanley, 1843) in the Northern Indian Ocean

In past decades, non-native species invasion has emerged as one of the leading drivers of biodiversity loss in terrestrial and aquatic ecosystems globally. In aquatic ecosystems, invasion by bivalve species has increased substantially due to their evolutionary resilience and adaptability. This study aimed to determine the habitat suitability of the South American bivalve Mytella strigata in the northern Indian Ocean using Species distribution modelling. The species occurrence and environmental data for model building were extracted from GBIF, Bio-ORACLE, The World Bank Data Catalogue and GMED. Pearson's correlation (<0.7) and Variance inflation factor (<10) analyses were used to select the environmental covariates. Individual models were built by combining the native range occurrence data of Mytella strigata with the bioclimatic data under the current climatic setting. Ten individual models were built and ensembled to create the final model using the biomod2 package. The variable importance score and the response curve plot were used to identify the most crucial variable and its influence on the models. Distance to port had the highest influence on predicting the distribution of Mytella strigata. The results indicated that the western coast of India as more susceptible to invasion. Our predictions indicate that the species has the potential to become highly invasive in the region, given the vast habitat suitability and documented introduction and presence of the species in the region. This research generated baseline information on the habitat suitability of M. strigata that will aid in managing and restricting its spread in the region. Considering the substantial impact of the species in other introduced ranges worldwide, immediate action should be initiated for the swift management of M. strigata from the Indian coast.