Subset Of Candidates Research Articles

UeLP: accurate user linkage across social platforms against location errors

Abstract User linkage across social platforms can connect the accounts of the same user across different social networks, which is crucial for the identification of users’ multiple social identities and cross-platform association analysis. Cross-platform user linkage based on location is a typical method in current research. These methods typically rely on check-in data to calculate user similarity. However, different from check-in location, the location data obtained from instant messaging social platforms may contain random errors, leading to low accuracy of user linkage of such methods. To solve this problem, this paper proposes an accurate user linkage method across social platforms against location errors. First, unlike existing methods that employ fixed-size grids, this paper uses a multi-grained spatio-temporal grid to organize data, in order to accurately extract user features from error locations. Then, by extracting coarse-grained movement pattern features from user trajectories, candidate users are filtered out, and a small subset of candidate uses is generated to effectively reduce the search space. Next, we establish a weight model based on grid contribution and motion sequence similarity to extract location and temporal features with stronger user orientation. Finally, according to the weight model, the weighted cluster center distance of trajectories is used to calculate the similarity between two different user trajectories. The user with the highest similarity is selected from the candidate subset to complete the user linkage. The extensive experiments are conducted on six public datasets containing 115 866 trajectories and a self-built dataset with 5358 trajectories. The results show the following: compared with the four existing typical location-based methods $k$-BCT, GS, TF-IDF, and TF-IWF, the accuracy Acc@1 is improved by an average of 33%, 44.94%, 15.2%, and 14.55%, respectively, and the accuracy Acc@3 is improved by 30.52%, 34.67%, 13.84%, and 13.19%, respectively.

Selecting the Optimal Classifier for Wrapper-Based Feature Selection Methods

Dimensionality reduction, the elimination of irrelevant features, and the selection of an optimal subset of features are critical components in the construction of an efficacious machine learning model. Among the various feature selection methodologies, wrapper-based methods yield superior results due to their evaluation of candidate subsets. Numerous meta-heuristic methods have been employed in this feature selection process. A significant and complex issue in feature selection utilizing these methods is the selection of the most suitable classifier. In this study, we propose a novel method for selecting the optimal classifier during the feature selection process. We employ ten distinct classifiers for two swarm intelligence methods, namely Bat and Gray Wolf, and compute their results on four cancer datasets: Leukemia, SRBCT, Prostate, and Colon. Our findings demonstrate that the proposed method identifies the optimal classifiers for all four datasets. Consequently, when employing wrapper-based methods to select features for each dataset, the optimal classifier is identified.

The SRG/eROSITA All-Sky Survey: X-ray beacons at late cosmic dawn

The Spektrum-Roentgen-Gamma (SRG)/extended Roentgen Survey with an Imaging Telescope Array (eROSITA) All-Sky Survey (eRASS) is expected to contain $ quasars that emitted their light when the universe was less than a billion years old, that is, at $z>5.6$. By selection, these quasars populate the bright end of the active galactic nuclei (AGN) X-ray luminosity function, and their space density offers a powerful demographic diagnostic of the parent super-massive black hole (SMBH) population. Of the $ 400$ quasars that have been discovered at $z>5.6$ to date, less than 15<!PCT!> have been X-ray detected. We present a pilot survey to uncover the elusive X-ray luminous end of the distant quasar population. We have designed a quasar selection pipeline based on optical, infrared and X-ray imaging data from DES DR2, VHS DR5, CatWISE2020 and the eRASS (up to its four-pass cumulative version, eRASS:4). The core selection method relies on SED template fitting. We performed optical follow-up spectroscopy with the Magellan /LDSS3 instrument for the redshift confirmation of a subset of candidates. We have further obtained a deeper X-ray image of one of our candidates with Chandra ACIS-S. We report the discovery of five new quasars in the redshift range $5.6 < z < 6.1$. Two of these quasars are detected in eRASS and are, therefore, X-ray ultra-luminous by selection. We also report the detection of these quasars at radio frequencies. The first one is a broad absorption line quasar, which shows significant, order-of-magnitude X-ray dimming over 3.5 years, corresponding to six months in the quasar rest frame. The second X-ray detected quasar is a jetted source with compact morphology. We show that a blazar configuration is likely for this source, making it one of the most distant blazars known to date. With our pilot study, we demonstrate the power of eROSITA as a discovery machine for luminous quasars in the epoch of reionization. The X-ray emission of the two eROSITA detected quasars are likely to be driven by different high-energetic emission mechanisms, a diversity which we will further explore in a future systematic full-hemisphere survey.

Development of chemical categories for per- and polyfluoroalkyl substances (PFAS) and the proof-of-concept approach to the identification of potential candidates for tiered toxicological testing and human health assessment

Per- and Polyfluoroalkyl substances (PFAS) are a class of manufactured chemicals that are in widespread use and many present concerns for persistence, bioaccumulation and toxicity. Whilst a handful of PFAS have been characterized for their hazard profiles, the vast majority have not been extensively studied. Herein, a chemical category approach was developed and applied to PFAS that could be readily characterized by a chemical structure. The PFAS definition as described in the Toxic Substances Control Act (TSCA) section 8(a)(7) rule was applied to the Distributed Structure-Searchable Toxicity (DSSTox) database to retrieve an initial list of 13,054 PFAS. Plausible degradation products from the 563 PFAS on the non-confidential TSCA Inventory were simulated using the Catalogic expert system, and the unique predicted PFAS degradants (2484) that conformed to the same PFAS definition were added to the list resulting in a set of 15,538 PFAS. Each PFAS was then assigned into a primary category using Organisation for Economic Co-operation and Development (OECD) structure-based classifications. The primary categories were subdivided into secondary categories based on a chain length threshold (>=7 vs < 7). Secondary categories were subcategorized using chemical fingerprints to achieve a balance between total number of structural categories vs.level of structural similarity within a category based on the Jaccard index. A set of 128 terminal structural categories were derived from which a subset of representative candidates could be proposed for potential data collection, considering the sparsity of relevant toxicity data within each category, presence on environmental monitoring lists, and the ability to identify plausible manufacturers/importers. Refinements to the approach taking into consideration ways in which the categories could be updated by mechanistic data and physicochemical property information are also described. This categorization approach may be used to form the basis of identifying candidates for data collection with related applications in QSAR development, read-across and hazard assessment.

Active learning approaches in molecule pKi prediction.

During the early stages of drug design, identifying compounds with suitable bioactivities is crucial. Given the vast array of potential drug databases, it's feasible to assay only a limited subset of candidates. The optimal method for selecting the candidates, aiming to minimize the overall number of assays, involves an active learning (AL) approach. In this work, we benchmarked a range of AL strategies with two main objectives: (1) to identify a strategy that ensures high model performance and (2) to select molecules with desired properties using minimal assays. To evaluate the different AL strategies, we employed the simulated AL workflow based on "virtual" experiments. These experiments leveraged ChEMBL datasets, which come with known biological activity values for the molecules. Furthermore, for classification tasks, we proposed the hybrid selection strategy that unified both exploration and exploitation AL strategies into a single acquisition function, defined by parameters n and c. We have also shown that popular minimal margin and maximal variance selection approaches for exploration selection correspond to minimization of the hybrid acquisition function with n=1 and 2 respectively. The balance between the exploration and exploitation strategies can be adjusted using a coefficient (c), making the optimal strategy selection straightforward. The primary strength of the hybrid selection method lies in its adaptability; it offers the flexibility to adjust the criteria for molecule selection based on the specific task by modifying the value of the contribution coefficient. Our analysis revealed that, in regression tasks, AL strategies didn't succeed at ensuring high model performance, however, they were successful in selecting molecules with desired properties using minimal number of tests. In analogous experiments in classification tasks, exploration strategy and the hybrid selection function with a constant c<1 (for n=1) and c≤0.2 (for n=2) were effective in achieving the goal of constructing a high-performance predictive model using minimal data. When searching for molecules with desired properties, exploitation, and the hybrid function with c≥1 (n=1) and c≥0.7 (n=2) demonstrated efficiency identifying molecules in fewer iterations compared to random selection method. Notably, when the hybrid function was set to an intermediate coefficient value (c=0.7), it successfully addressed both tasks simultaneously.

CAME: Competitively Learning a Mixture-of-Experts Model for First-stage Retrieval

The first-stage retrieval aims to retrieve a subset of candidate documents from a huge collection both effectively and efficiently. Since various matching patterns can exist between queries and relevant documents, previous work tries to combine multiple retrieval models to find as many relevant results as possible. The constructed ensembles, whether learned independently or jointly, do not care which component model is more suitable to an instance during training. Thus, they cannot fully exploit the capabilities of different types of retrieval models in identifying diverse relevance patterns. Motivated by this observation, in this paper, we propose a Mixture-of-Experts (MoE) model consisting of representative matching experts and a novel competitive learning mechanism to let the experts develop and enhance their expertise during training. Specifically, our MoE model shares the bottom layers to learn common semantic representations and uses differently structured upper layers to represent various types of retrieval experts. Our competitive learning mechanism has two stages: (1) a standardized learning stage to train the experts equally to develop their capabilities to conduct relevance matching; (2) a specialized learning stage where the experts compete with each other on every training instance and get rewards and updates according to their performance to enhance their expertise on certain types of samples. Experimental results on retrieval benchmark datasets show that our method significantly outperforms the state-of-the-art baselines in the in-domain and out-of-domain settings.

Clinical application of plasma P-tau217 to assess eligibility for amyloid-lowering immunotherapy in memory clinic patients with early Alzheimer’s disease

BackgroundWith the approval of disease-modifying treatments (DMTs) for early Alzheimer’s disease (AD), there is an increased need for efficient and non-invasive detection methods for cerebral amyloid-β (Aβ) pathology. Current methods, including positron emission tomography (PET) and cerebrospinal fluid (CSF) analysis, are costly and invasive methods that may limit access to new treatments. Plasma tau phosphorylated at threonine-217 (P-tau217) presents a promising alternative, yet optimal cutoffs for treatment eligibility with DMTs like aducanumab require further investigation. This study evaluates the efficacy of one- and two-cutoff strategies for determining DMT eligibility at the Butler Hospital Memory & Aging Program (MAP).MethodsIn this retrospective, cross-sectional diagnostic cohort study, we first developed P-tau217 cutoffs using site-specific and BioFINDER-2 training data, which were then tested in potential DMT candidates from Butler MAP (total n = 150). ROC analysis was used to calculate the area under the curve (AUC) and accuracy of P-tau217 interpretation strategies, using Aβ-PET/CSF testing as the standard of truth.ResultsPotential DMT candidates at Butler MAP (n = 50), primarily diagnosed with mild cognitive impairment (n = 29 [58%]) or mild dementia (21 [42%]), were predominantly Aβ-positive (38 [76%]), and half (25 [50%]) were subsequently treated with aducanumab. Elevated P-tau217 predicted cerebral Aβ positivity in potential DMT candidates (AUC = 0.97 [0.92–1]), with diagnostic accuracy ranging from 0.88 (0.76–0.95, p = 0.028) to 0.96 (0.86–1, p < .001). When using site-specific cutoffs, a subset of DMT candidates (10%) exhibited borderline P-tau217 (between 0.273 and 0.399 pg/mL) that would have potentially required confirmatory testing.ConclusionsThis study, which included participants treated with aducanumab, confirms the utility of one- and two-cutoff strategies for interpreting plasma P-tau217 in assessing DMT eligibility. Using P-tau217 could potentially replace more invasive diagnostic methods, and all aducanumab-treated participants would have been deemed eligible based on P-tau217. However, false positives remain a concern, particularly when applying externally derived cutoffs that exhibited lower specificity which could have led to inappropriate treatment of Aβ-negative participants. Future research should focus on prospective validation of P-tau217 cutoffs to enhance their generalizability and inform standardized treatment decision-making across diverse populations.

Automated Mapping of Vulnerability Advisories onto their Fix Commits in Open Source Repositories

The lack of comprehensive sources of accurate vulnerability data represents a critical obstacle to studying and understanding software vulnerabilities (and their corrections). In this article, we present an approach that combines heuristics stemming from practical experience and machine-learning (ML)—specifically, natural language processing (NLP)—to address this problem. Our method consists of three phases. First, we construct an advisory record object containing key information about a vulnerability that is extracted from an advisory, such as those found in the National Vulnerability Database (NVD). These advisories are expressed in natural language. Second, using heuristics, a subset of candidate fix commits is obtained from the source code repository of the affected project, by filtering out commits that can be identified as unrelated to the vulnerability at hand. Finally, for each of the remaining candidate commits, our method builds a numerical feature vector reflecting the characteristics of the commit that are relevant to predicting its match with the advisory at hand. Based on the values of these feature vectors, our method produces a ranked list of candidate fixing commits. The score attributed by the ML model to each feature is kept visible to the users, allowing them to easily interpret the predictions. We implemented our approach and we evaluated it on an open data set, built by manual curation, that comprises 2,391 known fix commits corresponding to 1,248 public vulnerability advisories. When considering the top-10 commits in the ranked results, our implementation could successfully identify at least one fix commit for up to 84.03% of the vulnerabilities (with a fix commit on the first position for 65.06% of the vulnerabilities). Our evaluation shows that our method can reduce considerably the manual effort needed to search open-source software (OSS) repositories for the commits that fix known vulnerabilities.

Hybrid wavelength selection strategy combined with ATR-FTIR spectroscopy for preliminary exploration of vintage labeling traceability of sauce-flavor baijiu

The allure of substantial profits has perpetuated the illicit trade of counterfeit vintage labels for baijiu. While various approaches have been employed to intelligently ascertain the vintage of baijiu, many of them are both cost-intensive and time-consuming. This work pioneered the use of Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, coupled with chemometric analysis, offering a non-destructive and economically viable method for discriminating sauce-flavor baijiu across different aging periods (1-, 2-, and 3-year). In this research, principal component analysis (PCA) was first conducted to explore clustering trends among distinct vintage groups. Subsequently, the effect of spectral pre-processing on modeling performance was explored. For wavelength selection, four wavelength selection methods (ReliefF, random forest variable importance (RFVI), variable importance in projection (VIP), and Venn) were first used to identify the subset of candidate features that potentially best mapped the vintage labels. Immediately following this, to explore the possibility of further improving the identification capabilities of the model as well as to reduce the redundant data that may still be present, sequential backward selection (SBS) was utilized for secondary feature reduction within the subset of candidates. The amalgamation of these two techniques is termed a “hybrid wavelength selection strategy.” Additionally, the dimensionality reduction effects of PCA and kernel principal component analysis (KPCA) were compared to demonstrate the robustness of the proposed method. Finally, classification models such as partial least squares discriminant analysis (PLS-DA), random forest (RF), and grasshopper optimization algorithm-based support vector machine (GOA-SVM) were developed. The results show that the spectral data need not be pre-processed, and the proposed hybrid wavelength selection strategy can further improve the identification ability of the model. Among the many models developed, ReliefF-SBS-GOA-SVM emerged as the most proficient classification model, yielding accuracy, sensitivity, and specificity rates of 94.44%, 95.23%, and 94.44%, respectively. This method not only holds promise for the discrimination of baijiu class attributes such as brand, origin, flavor, and vintage but also exhibits potential applicability in other non-targeted identification studies involving spectroscopy methodologies.

Clinical application of plasma P-tau217 to assess eligibility for amyloid-lowering immunotherapy in memory clinic patients with early Alzheimer's disease.

With the approval of disease-modifying treatments (DMTs) for early Alzheimer's disease (AD), there is an increased need for efficient and non-invasive detection methods for cerebral amyloid-β (Aβ) pathology. Current methods, including positron emission tomography (PET) and cerebrospinal fluid (CSF) analysis, are costly and invasive methods that may limit access to new treatments. Plasma tau phosphorylated at threonine-217 (P-tau217) presents a promising alternative, yet optimal cutoffs for treatment eligibility with DMTs like aducanumab require further investigation. This study evaluates the efficacy of one- and two-cutoff strategies for determining DMT eligibility at the Butler Hospital Memory & Aging Program (MAP). In this retrospective, cross-sectional diagnostic cohort study, we first developed P-tau217 cutoffs using site-specific training data and BioFINDER-2, which were then tested in potential DMT candidates from Butler MAP (total n = 150). ROC analysis was used to calculate the area under the curve (AUC) and accuracy of P-tau217 interpretation strategies, using Aβ-PET/CSF testing as the standard of truth. Potential DMT candidates at Butler MAP (n = 50), primarily diagnosed with mild cognitive impairment (n = 29 [58%]) or mild dementia (21 [42%]), were predominantly Aβ-positive (38 [76%]), and half (25 [50%]) were subsequently treated with aducanumab. Elevated P-tau217 predicted cerebral Aβ positivity in potential DMT candidates (AUC = 0.97 [0.92-1]), with diagnostic accuracy ranging from 0.88 (0.76-0.95, p = 0.028) to 0.96 (0.86-1, p < .001). When using site-specific cutoffs, a subset of DMT candidates (10%) exhibited borderline P-tau217 (between 0.273 and 0.399 pg/mL) that would have potentially required from confirmatory testing. This study, which included participants treated with aducanumab, confirms the utility of one- and two-cutoff strategies for interpreting plasma P-tau217 in assessing DMT eligibility. Using P-tau217 could potentially replace more invasive diagnostic methods, and all aducanumab-treated participants would have been deemed eligible based on P-tau217. However, false positives remain a concern, particularly when applying externally derived cutoffs that exhibited lower specificity which could have led to inappropriate treatment of Aβ-negative participants. Future research should focus on prospective validation of P-tau217 cutoffs to enhance their generalizability and inform standardized treatment decision-making across diverse populations.

Prospects of Identifying Hierarchical Triple Mergers for the Third-generation Ground-based Detectors

A hierarchical triple merger (HTM) constitutes a type of event in which two successive black hole (BH) mergers occur sequentially within the observational window of gravitational-wave (GW) detectors, which has an important role in testing general relativity and studying BH population. In this work, we conduct an analysis to determine the feasibility of identifying HTMs from a large GW event catalog using third-generation ground-based GW detectors. By comparing the Bhattacharyya coefficient that measures the overlap between the posterior distributions of the remnant and progenitor BH parameters, we find that the overlap between the event pair can serve as a preliminary filter, which balances between computational demand and the probability of false alarms. Following this initial, time-efficient, yet less accurate screening, a subset of potential HTM candidates will be retained. These candidates will subsequently be subjected to a more precise, albeit time-intensive, method of joint parameter estimation for verification. Ultimately, this process will enable us to robustly identify HTMs.

Abstract 6784: Cancer cell heterospheroids with stromal component used as drug testing and immune cell infiltration models

Abstract 3D cancer cell cultures have enabled new opportunities for replacing compound testing in experimental animals. However, driven by increased understanding of the need for more complex cancer cell models that consider drug resistance and penetration, hypoxia, and immuno-oncology applications, we have developed multicellular tumor heterospheroids composed of cancer cell lines, fibroblasts and immune cells. We have studied heterospheroids of cancer cell lines, HT29, MCF7, PANC1 and SW480 co-cultured with fibroblasts and 1) explored the architectural organization, 2) performed whole transcriptome analysis (WTA) of mono and co-cultures and 3) tested their capacity of immune cell infiltration. The architectural organization was studied in embedded heterospheroids. HT29 and MCF7 cells developed spheroids with the cancer cells surrounding the fibroblasts, whereas PANC1 cells mingled with the fibroblasts and SW480 cells were surrounded by fibroblasts. The fibroblasts expressed FAP-α mRNA as determined by ISH as well as collagen-1 as determined by immunohistochemistry. The hypoxia marker, Ca-IX, and the apoptosis marker, cleaved-Caspase-3, were dominating in the central core of the spheroids but the Ca-IX and Caspase-3 expression was not coordinated. In addition, WTA performed on day-7 heterospheroids, showed upregulated LOX, indicating that hypoxia-related markers known from genuine cancers are upregulated also in heterospheroids. WTA of day-7 mono-cell cultured and hetero-cultured spheroids, showed abundant ECM-, EMT-, and fibrosis-related expression in heterospheroids, thus reflecting a representative tumor-like microenvironment in heterospheroids compared to mono-culture spheroids. In particular, PANC1 spheroids showed a strong EMT profile with abundant FOXC2, TGF-β and TWIST expression. Genes related to Akt/mTOR and EGF pathways were consistently expressed in the tested heterospheroids. Evaluation of the cytotoxicity of anti-cancer compounds was performed using both cell viability and apoptosis assays. Exposure of the NCI drug library composed of 166 cytotoxic compounds to the heterospheroids, interestingly, revealed a complex output with limited correlation between cytotoxic and apoptotic effects. A subset of drug candidates is currently being further examined. PBMC-derived immune cells infiltrate heterospheroids and their infiltration is strongly enhanced after activation. The T cells' ability to infiltrate heterospheroids of different cancer types may serve as models of cold and hot tumors. We conclude that the cancer cell lines determine the architectural structure of heterospheroids, that ECM development is a prevalent part of the heterospheroids, and that the different heterospheroids possess varying ability of immune cell infiltration. Citation Format: Boye Schnack Nielsen, Natasha H. Madsen, Jesper Larsen, Monika Gad, Kim Holmstrøm. Cancer cell heterospheroids with stromal component used as drug testing and immune cell infiltration models [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 6784.

Selection and Ordering Policies for Hiring Pipelines via Linear Programming

Hiring the right person for the job is crucial for the success of any organization. In “Selection and Ordering Policies for Hiring Pipelines via Linear Programming,” Epstein and Ma study several problems motivated by a firm that is carrying out a recruitment process. Restricted to a finite time budget, the firm must decide who will be interviewed out of a pool of applicants and who will receive offers among the interviewed applicants. They develop approximation algorithms with constant factor guarantees that approach optimality when the number of vacant positions grows large. The algorithms they develop are nonadaptive: they fix a subset of candidates and an order to conduct the interviews and the order remains unchanged, independent of the outcomes of other interviews. Thus, they establish bounds on the adaptivity gap: a worst-case measure of how poorly non-adaptive algorithms can perform with respect to their adaptive counterparts.

Large-scale evaluation of the ability of RNA-binding proteins to activate exon inclusion

RNA-binding proteins (RBPs) modulate alternative splicing outcomes to determine isoform expression and cellular survival. To identify RBPs that directly drive alternative exon inclusion, we developed tethered function luciferase-based splicing reporters that provide rapid, scalable and robust readouts of exon inclusion changes and used these to evaluate 718 human RBPs. We performed enhanced cross-linking immunoprecipitation, RNA sequencing and affinity purification–mass spectrometry to investigate a subset of candidates with no prior association with splicing. Integrative analysis of these assays indicates surprising roles for TRNAU1AP, SCAF8 and RTCA in the modulation of hundreds of endogenous splicing events. We also leveraged our tethering assays and top candidates to identify potent and compact exon inclusion activation domains for splicing modulation applications. Using these identified domains, we engineered programmable fusion proteins that outperform current artificial splicing factors at manipulating inclusion of reporter and endogenous exons. This tethering approach characterizes the ability of RBPs to induce exon inclusion and yields new molecular parts for programmable splicing control.

Bioinformatics prediction and screening of viral mimicry candidates through integrating known and predicted DMI data.

Domain-motif interactions (DMIs) represent transient bonds formed when a Short Linear Motif (SLiM) engages a globular domain via a compact contact interface. Understanding the mechanics of DMIs is critical for maintaining diverse regulatory processes and deciphering how various viruses hijack host cellular machinery. However, identifying DMIs through traditional in vitro and in vivo experiments is challenging due to their degenerate nature and small contact areas. Predictions often carry a high rate of false positives, necessitating rigorous in-silico validation before embarking on experimental work. This study assessed the binding energy changes in predicted SLiM instances through in-silico peptide exchange experiment, elucidating how they interact with known 3D DMI complexes. We identified a subset of potential mimicry candidates that exhibited effective binding affinities with native DMI structures, suggesting their potential to be true mimicry candidates. The identified viral SLiMs can be potential targets in developing therapeutics, opening new opportunities for innovative treatments that can be finely tuned to address the complex molecular underpinnings of various diseases. To gain a comprehensive understanding of identified DMIs, it is imperative to conduct further validation through experimental approaches.

VIEW GRAPH CONSTRUCTION FOR LARGE-SCALE UAV IMAGES: AN EVALUATION OF STATE-OF-THE-ART METHODS

Abstract. Structure from Motion (SfM) is a 3D reconstruction framework that has achieved great success on large-scale Unmanned Aerial Vehicle (UAV) images. Due to the high time consumption of feature matching, a matching candidate subset is obtained by image retrieval to improve efficiency. Bag of Word (BoW) based image retrieval has been widely used in SfM systems, but the large number of local features and the high dimension of the BoW vector cause the retrieval method time-consuming. Vector of Locally Aggregated Descriptors (VLAD) and learning-based NetVLAD perform well in image retrieval, and these vector representation methods are evaluated in this study. After images are transformed into vectors, Nearest Neighbour (NN) searching methods like Brute-force and KD-Tree are used to find similar images. But as the number of images and the vector dimension increase, Approximate Nearest Neighbour (ANN) searching methods like Hierarchical Navigable Small World (HNSW) and Locality-Sensitive Hashing (LSH) are considered to replace NN searching to avoid efficiency degradation. These vector searching methods are also evaluated in this study. The test results demonstrate that the optimal method VLAD with HNSW can speed up about 100 times in finding matching candidate subset. A view graph that guides scene partition and sub-scene reconstruction in parallel SfM can be created by the optimal method. With this view graph construction method, the efficiency of SfM is significantly improved.

Knowledge-Informed Sparse Learning for Relevant Feature Selection and Optimal Quality Prediction

Industrial data are usually collinear, which can cause pure data-driven sparse learning to deselect physically relevant variables and select collinear surrogates. In this paper, a novel two-step learning approach to retaining knowledge-informed variables (KIV) is proposed to build inferential models. The first step is an improved knowledge-informed Lasso (KILasso) algorithm by removing penalty on the KIVs to produce a series of candidate subsets that guarantee the retention of the KIVs. The candidate subsets are then used to run the KILasso or ridge regression again to select the best sets of variables and estimate the final model. Two new algorithms are proposed and applied to datasets from an industrial boiler process and the Dow Chemical challenge problem. It is demonstrated that some important physically-relevant variables are deselected by pure data-driven sparse methods, but they are retained using the proposed knowledge-informed methods with superior prediction performance.

The A*orthogonal least square algorithm with the self-training dictionary for propeller signals reconstruction

The transmission of huge propeller signals via underwater wireless networks has high energy consumption, and conventional compression methods based on Nyquist sampling theorem cannot achieve efficient compression. Different from conventional compression methods, compressed sensing (CS) realizes the sub-Nyquist sampling rate, but the sparse signal is one of the preconditions of CS. Unfortunately, propeller signals are always non-sparse in both time and frequency domains. Therefore, the commonly used fixed dictionary is not suitable for sparse representation of propeller signals. To improve sparse representation, this paper proposes a self-training dictionary (STD) scheme, which updates the dictionary and sparse encoding simultaneously in iterations. Combining with the STD scheme, a CS reconstruction based on A*orthogonal least square (A*OLS) is proposed. In detail, a candidate subset selected by the orthogonal least square (OLS) algorithm is used as the node of the tree, and the search tree is established by A*search to find the optimal sparse solution. The experimental results confirm that the STD-A*OLS framework has significant advantages in performance including recovered signal-to-noise ratio (RSNR), percent norm difference (PND), and structure similarity index measure (SSIM).

Native lamin A/C proteomes and novel partners from heart and skeletal muscle in a mouse chronic inflammation model of human frailty.

Clinical frailty affects ∼10% of people over age 65 and is studied in a chronically inflamed (Interleukin-10 knockout; "IL10-KO") mouse model. Frailty phenotypes overlap the spectrum of diseases ("laminopathies") caused by mutations in LMNA. LMNA encodes nuclear intermediate filament proteins lamin A and lamin C ("lamin A/C"), important for tissue-specific signaling, metabolism and chromatin regulation. We hypothesized that wildtype lamin A/C associations with tissue-specific partners are perturbed by chronic inflammation, potentially contributing to dysfunction in frailty. To test this idea we immunoprecipitated native lamin A/C and associated proteins from skeletal muscle, hearts and brains of old (21-22months) IL10-KO versus control C57Bl/6 female mice, and labeled with Tandem Mass Tags for identification and quantitation by mass spectrometry. We identified 502 candidate lamin-binding proteins from skeletal muscle, and 340 from heart, including 62 proteins identified in both tissues. Candidates included frailty phenotype-relevant proteins Perm1 and Fam210a, and nuclear membrane protein Tmem38a, required for muscle-specific genome organization. These and most other candidates were unaffected by IL10-KO, but still important as potential lamin A/C-binding proteins in native heart or muscle. A subset of candidates (21 in skeletal muscle, 30 in heart) showed significantly different lamin A/C-association in an IL10-KO tissue (p < 0.05), including AldoA and Gins3 affected in heart, and Lmcd1 and Fabp4 affected in skeletal muscle. To screen for binding, eleven candidates plus prelamin A and emerin controls were arrayed as synthetic 20-mer peptides (7-residue stagger) and incubated with recombinant purified lamin A "tail" residues 385-646 under relatively stringent conditions. We detected strong lamin A binding to peptides solvent exposed in Lmcd1, AldoA, Perm1, and Tmem38a, and plausible binding to Csrp3 (muscle LIM protein). These results validated both proteomes as sources for native lamin A/C-binding proteins in heart and muscle, identified four candidate genes for Emery-Dreifuss muscular dystrophy (CSRP3, LMCD1, ALDOA, and PERM1), support a lamin A-interactive molecular role for Tmem38A, and supported the hypothesis that lamin A/C interactions with at least two partners (AldoA in heart, transcription factor Lmcd1 in muscle) are altered in the IL10-KO model of frailty.

Simultaneous heart-kidney transplant compared with heart transplant alone in patients with borderline renal function who are not dialysis dependent

ObjectiveThis study assessed characteristics and outcomes of patients who are not dependent on dialysis receiving simultaneous heart kidney transplantation versus heart transplantation alone (HTA) to identify optimal eGFR threshold where combined transplant strategy may be superior. MethodsThis study retrospectively analyzed 7896 adult patients with estimated glomerular filtration rate (eGFR) <60 mL/minute from the United Network for Organ Sharing database who received HTA or simultaneous heart kidney transplant between 2005 and 2021, excluding those who received pretransplant dialysis. Subjects were further stratified into 3 groups based on chronic kidney disease stage at time of transplant: Stage 3A (eGFR 45-59 mL/minute; n = 5044), Stage 3B (eGFR 30-44 mL/minute; n = 2193), and Stage 4 or 5 (eGFR <30 mL/minute; n = 659). Outcomes of interest were all-cause mortality, cardiac allograft failure, and freedom from chronic dialysis or renal transplant following heart transplant. ResultsSimultaneous heart kidney transplant and HTA recipients differed in various baseline characteristics. Simultaneous heart kidney transplant recipients with eGFR <45 mL/minute had greater short- and long-term overall survival and cardiac allograft survival compared with HTA, as well as greater long-term freedom from chronic dialysis or renal transplant. These results were consistent with both propensity matched analyses and multivariable Cox regression analysis of 10 year outcomes. Optimal cutoff value for pretransplant eGFR in predicting elevated risk of renal failure in recipients of heart transplant alone was found to be eGFR ∼45 mL/minute. ConclusionsSimilar to patients with eGFR <30 mL/minute, patients with eGFR 30 to 44 mL/minute who underwent simultaneous heart kidney transplant had superior outcomes compared with HTA, suggesting possible benefit of combined transplant strategy for this subset of heart transplant candidates.