Complex Signatures Research Articles

The critical role of glutamine and fatty acids in the metabolic reprogramming of anoikis-resistant melanoma cells.

Introduction: Circulating tumor cells (CTCs) represent the sub-population of cells shed into the vasculature and able to survive in the bloodstream, adhere to target vascular endothelial cells, and re-growth into the distant organ. CTCs have been found in the blood of most solid tumor-bearing patients and are used as a diagnostic marker. Although a complex genotypic and phenotypic signature characterizes CTCs, the ability to survive in suspension constitutes the most critical property, known as resistance to anoikis, e.g., the ability to resist apoptosis resulting from a loss of substrate adhesion. Here, we selected melanoma cells resistant to anoikis, and we studied their metabolic reprogramming, with the aim of identifying new metabolic targets of CTCs. Methods: Subpopulations of melanoma cells expressing a high anoikis-resistant phenotype were selected by three consecutive rocking exposures in suspension and studied for their phenotypic and metabolic characteristics. Moreover, we tested the efficacy of different metabolic inhibitors targeting glycolysis (2DG), LDHA (LDHA-in-3), the mitochondrial electron transport chain complex I (rotenone), glutaminase (BPTES), fatty acid transporter (SSO), fatty acid synthase (denifanstat), CPT1 (etomoxir), to inhibit cell survival and colony formation ability after 24h of rocking condition. Results: Anoikis-resistant cells displayed higher ability to grow in suspension on agarose-covered dishes respect to control cells, and higher cell viability and colony formation capability after a further step in rocking condition. They showed also an epithelial-to-mesenchymal transition associated with high invasiveness and a stemness-like phenotype. Anoikis-resistant melanoma cells in suspension showed a metabolic reprogramming from a characteristic glycolytic metabolism toward a more oxidative metabolism based on the use of glutamine and fatty acids, while re-adhesion on the dishes reversed the metabolism to glycolysis. The treatment with metabolic inhibitors highlighted the effectiveness of rotenone, BPTES, SSO, and etomoxir in reducing the viability and the colony formation ability of cells capable of surviving in suspension, confirming the dependence of their metabolism on oxidative phosphorylation, using glutamine and fatty acids as the most important fuels. Discussion: This finding opens up new therapeutic strategies based on metabolic inhibitors of glutaminase and fatty acid oxidation for the treatment of CTCs and melanoma metastases.

Integrative analysis of RNA expression data unveils distinct cancer types through machine learning techniques

Cancer is a highly complex and heterogeneous disease. Traditional methods of cancer classification based on histopathology have limitations in guiding personalized prognosis and therapy. Gene expression profiling provides a powerful approach to unraveling molecular intricacies and better-stratifying cancer subtypes. In this study, we performed an integrative analysis of RNA sequencing data from five cancer types - BRCA, KIRC, COAD, LUAD, and PRAD. A machine learning workflow consisting of dataset identification, normalization, feature selection, dimensionality reduction, clustering, and classification was implemented. The k-means algorithm was applied to categorize samples into distinct clusters based solely on gene expression patterns. Five unique clusters emerged from the unsupervised machine learning based analysis, significantly correlating with the known cancer types. BRCA aligned predominantly with one cluster, while COAD spanned three clusters. KIRC was represented within two main clusters. LUAD is associated strongly with a single cluster and PRAD with another cluster. This demonstrates the ability of machine learning approaches to unravel complex signatures within transcriptomic profiles that can delineate cancer subtypes. The proposed study highlights the potential of integrative analytics to derive meaningful biological insights from high-dimensional omics datasets. Molecular subtyping through machine learning clustering enhances our understanding of the intrinsic heterogeneities and pathways dysregulated in different cancers. Overall, this study exemplifies a powerful computational framework to classify gene expressions of patients having different types of cancers and guide personalized therapeutic decisions. Finally, Wide Neural Network demonstrates a significantly higher accuracy, achieving 99.834% on the validation set and an even more impressive 99.995% on the test set.

Single-cell analysis identifies phospholysine phosphohistidine inorganic pyrophosphate phosphatase as a target in ulcerative colitis.

Ulcerative colitis (UC) is a chronic gastrointestinal disorder characterized by inflammation and ulceration, representing a significant predisposition to colorectal cancer. Recent advances in single-cell RNA sequencing (scRNA-seq) technology offer a promising avenue for dissecting the complex cellular inter-actions and molecular signatures driving UC pathology. To utilize scRNA-seq technology to dissect the complex cellular interactions and molecular signatures that underlie UC pathology. In this research, we integrated and analyzed the scRNA-seq data from UC patients. Moreover, we conducted mRNA and protein level assays as well as pathology-related staining tests on clinical patient samples. In this study, we identified the sustained upregulation of inflammatory response pathways during UC progression, characterized the features of damaged endo-thelial cells in colitis. Furthermore, we uncovered the downregulation of phospholysine phosphohistidine inorganic pyrophosphate phosphatase (LHPP) has a negative correlation with signal transducer and activator of transcription 3. Significant downregulation of LHPP in UC patient tissues and plasma suggests that LHPP may serve as a potential therapeutic target for UC. This paper highlights the importance of LHPP as a potential key target in UC and unveils its potential role in inflammation regulation. The findings suggest that LHPP may serve as a potential therapeutic target for UC, emphasizing its importance as a potential key target in UC and unveiling its role in inflammation regulation.

(Invited) Pathogen Agnostic Biodetection at the Point of Need

Emerging pathogens are a constant threat to global health and human life, as exemplified by the recent COVID-19 pandemic. The availability of rapid testing - including in home tests - was instrumental in facilitating return to our 'normal' way of life. Diagnostics - especially those that can quickly and effectively inform on the condition at the point of need, enhance situational awareness and guide decision making and mitigation strategies can greatly impact our ability to counter emerging threats, epidemics and pandemics. Yet, current diagnostics are largely targeted- meaning they are tailored to identify a limited repertoire of anticipated threats. For true preparedness, it is important that the strategies we develop are pathogen agnostic and can be readily used against any organism quickly. Our team has sought inspiration from the human innate immune system in order to develop truly agnostic diagnostics. Innate immunity is medicated by pattern recognition receptors that have evolved to recognize evolutionarily conserved signatures on pathogens - allowing for their early identification. These germline receptors bind with conserved pathogen associated molecular patterns – PAMPs – produced by all pathogens, and result in the development of an associated cytokine and chemokine response. Innate immunity is thus a universal biosensor. Drawing inspiration from this natural system, our team has developed diagnostic assays for all pathogens. This involved understanding of common mechanisms of host-pathogen interactions, which then lead to the design of curated novel assay modalities - membrane insertion and lipoprotein capture - for the detection of pathogen. Beyond assay development and characterization of the response, we have evaluated the feasibility of these methods to detect signatures in blinded clinical samples for a variety of diseases (tuberculosis, invasive Salmonella, Shiga toxin carrying E. coli, Staphylococcal bacteremia, and others) with excellent sensitivity as compared to benchmark assays. These outcomes and strategies for pathogen agnostic detection will be presented.Transitioning this platform for field use, our team engineered a portable waveguide-based optical biosensor platform with integrated microfluidics for sample processes. The platform named Portable EnGineered Analytical Sensor with aUtomated Sampling (PEGASUS, R&D100 2021). This platform uses single mode planar optical waveguides as the sensing element, and builds on a bench top instrument that has been developed at the Los Alamos National Laboratory. PEGASUS miniaturizes this benchtop sensor, with integrated sample processing, moving us toward the goal of developing a truly fieldable biosensor. PEGASUS uses a different, smaller waveguide mounting apparatus, miniaturized robust components, sensing hardware and software.PEGASUS integrates Catch-all, the first ever sample processing system capable of lipid separation with ultra-sensitive optical detection of complex bioanalytes at the point of need. a microfluidic device capable of centrifugal separation of serum from blood at the point of need with a system that is compatible with biomarkers that are both hydrophilic and hydrophobic. The cross-flow filtration device separates serum from blood as efficiently as traditional methods and retains amphiphilic biomarkers in serum for detection.Whereas the scope of targets is extremely broad, the platform still relies on the use of reagents, which complicates field use in some instances. Therefore, we are beginning to explore multi-wavelength spectroscopy as a tool for the measurement of complex clinical signatures - within seconds, without reagents. Preliminary data from this development will also be presented.

Poly-omic risk scores predict inflammatory bowel disease diagnosis.

Inflammatory bowel disease (IBD) is characterized by complex etiology and a disrupted colonic ecosystem. We provide a framework for the analysis of multi-omic data, which we apply to study the gut ecosystem in IBD. Specifically, we train and validate models using data on the metagenome, metatranscriptome, virome, and metabolome from the Human Microbiome Project 2 IBD multi-omic database, with 1,785 repeated samples from 130 individuals (103 cases and 27 controls). After splitting the participants into training and testing groups, we used mixed-effects least absolute shrinkage and selection operator regression to select features for each omic. These features, with demographic covariates, were used to generate separate single-omic prediction scores. All four single-omic scores were then combined into a final regression to assess the relative importance of the individual omics and the predictive benefits when considered together. We identified several species, pathways, and metabolites known to be associated with IBD risk, and we explored the connections between data sets. Individually, metabolomic and viromic scores were more predictive than metagenomics or metatranscriptomics, and when all four scores were combined, we predicted disease diagnosis with a Nagelkerke's R2 of 0.46 and an area under the curve of 0.80 (95% confidence interval: 0.63, 0.98). Our work supports that some single-omic models for complex traits are more predictive than others, that incorporating multiple omic data sets may improve prediction, and that each omic data type provides a combination of unique and redundant information. This modeling framework can be extended to other complex traits and multi-omic data sets.IMPORTANCEComplex traits are characterized by many biological and environmental factors, such that multi-omic data sets are well-positioned to help us understand their underlying etiologies. We applied a prediction framework across multiple omics (metagenomics, metatranscriptomics, metabolomics, and viromics) from the gut ecosystem to predict inflammatory bowel disease (IBD) diagnosis. The predicted scores from our models highlighted key features and allowed us to compare the relative utility of each omic data set in single-omic versus multi-omic models. Our results emphasized the importance of metabolomics and viromics over metagenomics and metatranscriptomics for predicting IBD status. The greater predictive capability of metabolomics and viromics is likely because these omics serve as markers of lifestyle factors such as diet. This study provides a modeling framework for multi-omic data, and our results show the utility of combining multiple omic data types to disentangle complex disease etiologies and biological signatures.

Establishing mRNA and microRNA interactions driving disease heterogeneity in amyotrophic lateral sclerosis patient survival.

Amyotrophic lateral sclerosis is a fatal neurodegenerative disease, associated with the degeneration of both upper and lower motor neurons of the motor cortex, brainstem and spinal cord. Death in most patients results from respiratory failure within 3-4 years from symptom onset. However, due to disease heterogeneity some individuals survive only months from symptom onset while others live for several years. Identifying specific biomarkers that aid in establishing disease prognosis, particularly in terms of predicting disease progression, will help our understanding of amyotrophic lateral sclerosis pathophysiology and could be used to monitor a patient's response to drugs and therapeutic agents. Transcriptomic profiling technologies are continually evolving, enabling us to identify key gene changes in biological processes associated with disease. MicroRNAs are small non-coding RNAs typically associated with regulating gene expression, by degrading mRNA or reducing levels of gene expression. Being able to associate gene expression changes with corresponding microRNA changes would help to distinguish a more complex biomarker signature enabling us to address key challenges associated with complex diseases such as amyotrophic lateral sclerosis. The present study aimed to investigate the transcriptomic profile (mRNA and microRNA) of lymphoblastoid cell lines from amyotrophic lateral sclerosis patients to identify key signatures that are distinguishable in those patients who suffered a short disease duration (<12 months) (n = 22) compared with those that had a longer disease duration (>6 years) (n = 20). Transcriptional profiling of microRNA-mRNA interactions from lymphoblastoid cell lines in amyotrophic lateral sclerosis patients revealed differential expression of genes involved in cell cycle, DNA damage and RNA processing in patients with longer survival from disease onset compared with those with short survival. Understanding these particular microRNA-mRNA interactions and the pathways in which they are involved may help to distinguish potential therapeutic targets that could exert neuroprotective effects to prolong the life expectancy of amyotrophic lateral sclerosis patients.

Chemo-dynamical tagging in the outskirts: The origins of stellar substructures in the Magellanic Clouds

We present the first detailed chemical analysis from APOGEE-2S observations of stars in six regions of recently discovered substructures in the outskirts of the Magellanic Clouds extending to 20° from the Large Magellanic Cloud (LMC) center. We also present, for the first time, the metallicity and α-abundance radial gradients of the LMC and the Small Magellanic Cloud (SMC) out to 11° and 6°, respectively. Our chemical tagging includes 13 species including light, α-, and Fe-peak elements. We find that the abundances of all of these chemical elements in stars populating two regions in the northern periphery, along the northern “stream-like” feature, show good agreement with the chemical patterns of the LMC, and thus likely have an LMC origin. For substructures located in the southern periphery of the LMC we find more complex chemical and kinematical signatures, indicative of a mix of LMC-like and SMC-like populations. The southern region closest to the LMC shows better agreement with the LMC, whereas that closest to the SMC shows a much better agreement with the SMC chemical pattern. When combining this information with 3D kinematical information for these stars, we conclude that the southern region closest to the LMC likely has an LMC origin, whereas that closest to the SMC has an SMC origin and the other two southern regions have a mix of LMC and SMC origins. Our results add to the evidence that the southern substructures of the LMC periphery are the product of close interactions between the LMC and SMC, and thus likely hold important clues that can constrain models of their detailed dynamical histories.

Downregulation of Ubiquitin-Specific Protease 15 (USP15) Does Not Provide Therapeutic Benefit in Experimental Mesial Temporal Lobe Epilepsy

Structural epilepsies display complex immune activation signatures. However, it is unclear which neuroinflammatory pathways drive pathobiology. Transcriptome studies of brain resections from mesial temporal lobe epilepsy (mTLE) patients revealed a dysregulation of transforming growth factor β, interferon α/β, and nuclear factor erythroid 2-related factor 2 pathways. Since these pathways are regulated by ubiquitin-specific proteases (USP), in particular USP15, we hypothesized that USP15 blockade may provide therapeutic relief in treatment-resistant epilepsies. For validation, transgenic mice which either constitutively or inducibly lack Usp15 gene expression underwent intrahippocampal kainate injections to induce mTLE. We show that the severity of status epilepticus is unaltered in mice constitutively lacking Usp15 compared to wild types. Cell death, reactive gliosis, and changes in the inflammatory transcriptome were pronounced at 4 days after kainate injection. However, these brain inflammation signatures did not differ between genotypes. Likewise, induced deletion of Usp15 in chronic epilepsy did not affect seizure generation, cell death, gliosis, or the transcriptome. Concordantly, siRNA-mediated knockdown of Usp15 in a microglial cell line did not impact inflammatory responses in the form of cytokine release. Our data show that a lack of USP15 is insufficient to modulate the expression of relevant neuroinflammatory pathways in an mTLE mouse model and do not support targeting USP15 as a therapeutic approach for pharmacoresistant epilepsy.

Standardised images of novel objects created with generative adversarial networks

An enduring question in cognitive science is how perceptually novel objects are processed. Addressing this issue has been limited by the absence of a standardised set of object-like stimuli that appear realistic, but cannot possibly have been previously encountered. To this end, we created a dataset, at the core of which are images of 400 perceptually novel objects. These stimuli were created using Generative Adversarial Networks that integrated features of everyday stimuli to produce a set of synthetic objects that appear entirely plausible, yet do not in fact exist. We curated an accompanying dataset of 400 familiar stimuli, which were matched in terms of size, contrast, luminance, and colourfulness. For each object, we quantified their key visual properties (edge density, entropy, symmetry, complexity, and spectral signatures). We also confirmed that adult observers (N = 390) perceive the novel objects to be less familiar, yet similarly engaging, relative to the familiar objects. This dataset serves as an open resource to facilitate future studies on visual perception.

The geochronometry of Idaho and the bumpy barcode revisited: Snake River detrital zircons in Hells Canyon and beyond

Detrital zircons in modern stream sands and terraces of the Snake River drainage upstream of and within Hells Canyon, western Idaho, U.S.A, effectively reproduce the geochronometry of Idaho magmatic rocks, both from Laurentia in eastern Idaho, and from the accreted terranes west of the Salmon River Suture. We used large-n (300 grain) analysis of detrital zircon ages and Monte Carlo-based mixture modeling to estimate the percent contributions from the upper Snake River and several tributaries. The large number of grains analyzed allows more precise statistical modeling than previous studies, which used 60 or 100 grains.Immediately upstream of the Hells Canyon Complex of dams modeling suggests that the Snake River at Farewell Bend contains detrital zircon contributions of Payette River 50 % and Boise River 10 % (mainly from the Atlanta lobe, Idaho batholith). The upper Snake River has 30 % contribution and contains a complex detrital zircon signature including both primary zircons derived from magmatic sources and recycled zircons derived from sedimentary rocks with an ultimate magmatic source. Thus, 60 % of the detrital zircons come from the 98–73 Ma southern Idaho batholith, with less than a third coming from the upper Snake River drainage, despite it representing 60 % of the drainage area. The Owyhee, Malheur, and Weiser rivers contribute <5 % each.Within Hells Canyon, modeling suggests that the main Snake River contributes by far the largest component (85 %); side canyons contribute <5 % of the zircon load. Mixing models for Snake River stream sediment downstream of Lewiston, below Lower Granite Dam (sampled from sands deposited before installation of the dam), suggest 53 % contribution from the main Snake River above the Salmon River, 34 % from the Clearwater River, 7 % from the Salmon River, and 6 % from the Grande Ronde River.Comparing relative zircon inputs from the modern Snake and Salmon rivers to a pre-dam river terrace just downstream from their confluence suggests that the Snake River in Hells Canyon today contributes only 4 % of the detrital zircons whereas before installation of the Hells Canyon Dams it contributed 29 %. We interpret this reduction in detrital zircon supply to be caused by sediment trapping above the dams.One influence on the detrital zircon contribution of various streams is peak flow volume. Zircon fertility of specific source rocks is the main influence on detrital zircon percentages within individual drainages. Mesoproterozoic, Cretaceous and Eocene granitic rocks produce euhedral magmatic detrital zircon grains that dominate several drainages. Mesozoic metavolcanic and Eocene Challis Volcanic Group rocks yield fewer and often broken detrital zircon grains. Large outcrop areas of Neogene Columbia River and Snake River Plain basalts provide almost none. Recycling of older magmatic detrital zircon through Proterozoic and Paleozoic sandstones is a signature of the eastern and central Idaho thrust belt.

Immune cell identifier and classifier (ImmunIC) for single cell transcriptomic readouts

Single cell RNA sequencing has a central role in immune profiling, identifying specific immune cells as disease markers and suggesting therapeutic target genes of immune cells. Immune cell-type annotation from single cell transcriptomics is in high demand for dissecting complex immune signatures from multicellular blood and organ samples. However, accurate cell type assignment from single-cell RNA sequencing data alone is complicated by a high level of gene expression heterogeneity. Many computational methods have been developed to respond to this challenge, but immune cell annotation accuracy is not highly desirable. We present ImmunIC, a simple and robust tool for immune cell identification and classification by combining marker genes with a machine learning method. With over two million immune cells and half-million non-immune cells from 66 single cell RNA sequencing studies, ImmunIC shows 98% accuracy in the identification of immune cells. ImmunIC outperforms existing immune cell classifiers, categorizing into ten immune cell types with 92% accuracy. We determine peripheral blood mononuclear cell compositions of severe COVID-19 cases and healthy controls using previously published single cell transcriptomic data, permitting the identification of immune cell-type specific differential pathways. Our publicly available tool can maximize the utility of single cell RNA profiling by functioning as a stand-alone bioinformatic cell sorter, advancing cell-type specific immune profiling for the discovery of disease-specific immune signatures and therapeutic targets.

Is housing price distribution across cities, scale invariant? Fractal distribution of settlements' house prices as signature of self-organized complexity

Locations and sizes of settlements within regions often exhibit a self-organized central place pattern, where larger settlements are encircled by smaller ones, with the former more set apart. This hierarchical spatial pattern is displayed by the fractal signature of the power law typical of the rank-size distribution of settlements. Is a similar pattern also revealed in the housing prices across settlements? An empirical analysis on the almost 8000 Italian municipalities, whose residential population varies between 33 and ∼3 million, shows that, for 15 out of 20 regions, the upper tails rank-house price distribution of settlements is plausibly following a power law. This result discloses a universality in the function behaviour: a scale invariance. The average scaling parameters have been estimated via Maximum Likelihood Estimator and Kolmogorov–Smirnov statistics, and the goodness-of-fit assessed via bootstrapping. Ordinary Least Squares regressions have also been used for comparative purposes. In average, Maximum Likelihood Estimator found a scaling parameter of 4.4. Namely, doubling the rank of the settlement (in terms of housing price), its housing price decreases by 15 %: between the settlement with the highest average house prices (ranked 1), and the settlement with the second highest average house prices (ranked 2), there is a 15 % difference in the average house prices. Idem between the ranked 5 and 10, 10 and 20 … it does not matter; it is a universal behavioural pattern whose scale invariance suggests a self-organized complexity in the phenomena behind it.

Intraoperative cortical localization of music and language reveals signatures of structural complexity in posterior temporal cortex

Language and music involve the productive combination of basic units into structures. It remains unclear whether brain regions sensitive to linguistic and musical structure are co-localized. We report an intraoperative awake craniotomy in which a left-hemispheric language-dominant professional musician underwent cortical stimulation mapping (CSM) and electrocorticography of music and language perception and production during repetition tasks. Musical sequences were melodic or amelodic, and differed in algorithmic compressibility (Lempel-Ziv complexity). Auditory recordings of sentences differed in syntactic complexity (single vs. multiple phrasal embeddings). CSM of posterior superior temporal gyrus (pSTG) disrupted music perception and production, along with speech production. pSTG and posterior middle temporal gyrus (pMTG) activated for language and music (broadband gamma; 70-150Hz). pMTG activity was modulated by musical complexity, while pSTG activity was modulated by syntactic complexity. This points to shared resources for music and language comprehension, but distinct neural signatures for the processing of domain-specific structural features.

Abstract 5453: Individualized risk stratification in newly diagnosed multiple myeloma

Abstract Background: Clinical outcomes for newly diagnosed multiple myeloma (NDMM) patients are heterogenous with survival ranging from months to &amp;gt; 10 years. Though several clinical and genomic features predict outcomes, the “one-size-fits-all” treatment paradigm remains dominant for NDMM. Hypothesis: By integrating clinical, genomic and therapeutic data, using artificial intelligence, an individualized risk-prediction model for NDMM (IRM) can facilitate individually-tailored therapeutic decisions. Methods: We included 1933 patients with clinical and genomic data from 5 cohorts: MMRF CoMMpass (n=1062), MGP (n=492), Moffit AVATAR (n=177), UAMS (n=93), and MSKCC (n=109). The median follow-up was 43 months. Overall, we considered 160 clinical (e.g., age, ECOG, race), therapeutics, and genomic variables. To correct for time-dependent variables such as autologous stem cell transplant (ASCT) and continuous treatment, a multi-state model was designed across two phases: induction (phase 1), and post-induction (phase 2). Neural Cox Non-proportional-hazards (NCNPH) was used to integrate the data and build the model. Results: Overall, the 5-year overall survival (OS) c-index for IRM was 0.73, significantly higher than all existing prognostic models: R2-ISS (0.62), ISS (0.61) and R-ISS (0.56). The overall model accuracy was significantly improved by the inclusion of 12 genomic features, including 1q21 gain/amp, TP53 loss, t(4;14)(NSD2;IGH), complex copy number signatures, APOBEC mutational signature contribution, and del1p. Prescribed therapy emerged as a key determinant of risk, suggesting that effective combinations may have a different impact in the context of individual patient features, with the potential to significantly change clinical outcomes despite poor historical prognostication (i.e., treatment variance). Leveraging these concepts, we interrogated the clinical impact of ASCT and continuous treatment in the context of NDMM treated with bortezomib, lenalidomide and dexamethasone (VRd). Integrating predicted outcomes and treatment variance for all 4 possible treatment combinations (i.e., VRd +/- ASCT +/- continuous treatment) we identified 3 patient groups. In the first group (n=632), patients were characterized by complex genomic features, older age, high ISS, poor outcomes and limited treatment variance, reflecting aggressive and refractory myeloma. The second group (n=571) was characterized by high treatment variance, with favorable outcomes if ASCT and continuous treatment are provided. The last group (n=730) included patients with favorable clinical and genomic profiles, achieving good outcomes, with minimal advantage from ASCT. Conclusion: Integrating historical and emerging genomic features with clinical and therapeutic data, we developed the first individualized risk-prediction model for personally-tailored therapeutic decisions in NDMM. Citation Format: Arjun Raj Rajanna, Francesco Maura, Andriy Derkach, Bachisio Ziccheddu, Niels Weinhold, Kylee Maclachlan, Benjamin Diamond, Faith Davies, Eileen Boyle, Brian Walker, Alexandra Pos, Malin Hulcrantz, Ariosto Silva, Oliver Hampton, Jamie K. Teer, Niccolò Bolli, Graham Jackson, Martin Kaiser, Charlotte Pawlyn, Gordon Cook, Dennis Verducci, Dickran Kazandjian, Fritz Van Rhee, Saad Usmani, Kenneth H. Shain, Marc S. Raab, Gareth Morgan, Ola Landgren. Individualized risk stratification in newly diagnosed multiple myeloma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5453.

Metabolomics Analysis Reveals Molecular Signatures of Metabolic Complexity in Children with Hypercholesterolemia

Despite the importance of hypercholesterolemia in children, it is overlooked, and there are currently few metabolomics-based approaches available to understand its molecular mechanisms. Children from a birth cohort had their cholesterol levels measured with the aim of identifying the metabolites for the molecular biological pathways of childhood hypercholesterolemia. One hundred and twenty-five children were enrolled and stratified into three groups according to cholesterol levels (acceptable, <170 mg/dL, n = 42; borderline, 170-200 mg/dL, n = 52; and high, >200 mg/dL, n = 31). Plasma metabolomic profiles were obtained by using 1H-nuclear magnetic resonance (NMR) spectroscopy, and partial least squares-discriminant analysis (PLS-DA) was applied using the MetaboAnalyst 5.0 platform. Metabolites significantly associated with different cholesterol statuses were identified, and random forest classifier models were used to rank the importance of these metabolites. Their associations with serum lipid profile and functional metabolic pathways related to hypercholesterolemia were also assessed. Cholesterol level was significantly positively correlated with LDL-C and Apo-B level, as well as HDL-C and Apo-A1 level separately, whereas HDL-C was negatively correlated with triglyceride level (p < 0.01). Eight metabolites including tyrosine, glutamic acid, ornithine, lysine, alanine, creatinine, oxoglutaric acid, and creatine were significantly associated with the different statuses of cholesterol level. Among them, glutamic acid and tyrosine had the highest importance for different cholesterol statuses using random forest regression models. Carbohydrate and amino acid metabolisms were significantly associated with different cholesterol statuses, with glutamic acid being involved in all amino acid metabolic pathways (FDR-adjusted p < 0.01). Hypercholesterolemia is a significant health concern among children, with up to 25% having high cholesterol levels. Glutamic acid and tyrosine are crucial amino acids in lipid metabolism, with glutamic-acid-related amino acid metabolism playing a significant role in regulating cholesterol levels.

DiffNILM: A Novel Framework for Non-Intrusive Load Monitoring Based on the Conditional Diffusion Model.

Non-intrusive Load Monitoring (NILM) is a critical technology that enables detailed analysis of household energy consumption without requiring individual metering of every appliance, and has the capability to provide valuable insights into energy usage behavior, facilitate energy conservation, and optimize load management. Currently, deep learning models have been widely adopted as state-of-the-art approaches for NILM. In this study, we introduce DiffNILM, a novel energy disaggregation framework that utilizes diffusion probabilistic models to distinguish power consumption patterns of individual appliances from aggregated power. Starting from a random Gaussian noise, the target waveform is iteratively reconstructed via a sampler conditioned on the total active power and encoded temporal features. The proposed method is evaluated on two public datasets, REDD and UKDALE. The results demonstrated that DiffNILM outperforms baseline models on several key metrics on both datasets and shows a remarkable ability to effectively recreate complex load signatures. The study highlights the potential of diffusion models to advance the field of NILM and presents a promising approach for future energy disaggregation research.

Black Ephemera: The Crisis and Challenge of the Musical Archive

Black Ephemera: The Crisis and Challenge of the Musical Archive

Moving Targets Artifacts Removal in Multiaspect SAR Imagery Based on Logarithm Background Subtraction

Multiaspect SAR has the capability of providing a high-resolution image due to its long synthetic aperture feature. However, a moving target can generate long and complex signatures in a multiaspect SAR image, which may hamper the applications like image interpretation and target detection. In this article, two methods are proposed to remove the moving target signature in a single-channel multiaspect SAR image. The two methods are all based on logarithm background subtraction. The first one is a fast scheme with a cost of reduced resolution. While the second one focuses on preserving the high resolution, it takes more time than the previous one. The first method utilizes the fact of target signal position changes in subaperture image sequence to obtain the static background. The second method combines the detection results to exclude the moving target signal in each complex-valued subaperture image, then obtaining the high resolution of static background by coherent summation. The methods are validated by synthetic and real airborne SAR data.

Whole-genome sequencing reveals a complex African population demographic history and signatures of local adaptation.

We conduct high coverage (>30×) whole-genome sequencing of 180 individuals from 12 indigenous African populations. We identify millions of unreported variants, many predicted to be functionally important. We observe that the ancestors of southern African San and central African rainforest hunter-gatherers (RHG) diverged from other populations >200 kya and maintained a large effective population size. We observe evidence for ancient population structure in Africa and for multiple introgression events from "ghost" populations with highly diverged genetic lineages. Although currently geographically isolated, we observe evidence for gene flow between eastern and southern Khoesan-speaking hunter-gatherer populations lasting until ∼12kya. We identify signatures of local adaptation for traits related to skin color, immune response, height, and metabolic processes. We identify a positively selected variant in the lightly pigmented San that influences pigmentation invitro by regulating the enhancer activity and gene expression of PDPK1.

Homologous Recombination Deficiency in Ovarian Cancer: from the Biological Rationale to Current Diagnostic Approaches

The inability to efficiently repair DNA double-strand breaks using the homologous recombination repair pathway is defined as homologous recombination deficiency (HRD). This molecular phenotype represents a positive predictive biomarker for the clinical use of poly (adenosine diphosphate [ADP]-ribose) polymerase inhibitors and platinum-based chemotherapy in ovarian cancers. However, HRD is a complex genomic signature, and different methods of analysis have been developed to introduce HRD testing in the clinical setting. This review describes the technical aspects and challenges related to HRD testing in ovarian cancer and outlines the potential pitfalls and challenges that can be encountered in HRD diagnostics.