Non-overlapping Sets Research Articles

Crosstalk with keratinocytes causes GNAQ oncogene specificity in melanoma.

Different melanoma subtypes exhibit specific and non-overlapping sets of oncogene and tumor suppressor mutations, despite a common cell of origin in melanocytes. For example, activation of the Gαq/11 signaling pathway is a characteristic initiating event in primary melanomas that arise in the dermis, uveal tract, or central nervous system. It is rare in melanomas arising in the epidermis. The mechanism for this specificity is unknown. Here, we present evidence that in the mouse, crosstalk with the epidermal microenvironment actively impairs the survival of melanocytes expressing the GNAQQ209L oncogene. We found that GNAQQ209L, in combination with signaling from the interfollicular epidermis (IFE), stimulates dendrite extension, leads to actin cytoskeleton disorganization, inhibits proliferation, and promotes apoptosis in melanocytes. The effect was reversible and paracrine. In contrast, the epidermal environment increased the survival of wildtype and BrafV600E expressing melanocytes. Hence, our studies reveal the flip side of Gαq/11 signaling, which was hitherto unsuspected. In the future, the identification of the epidermal signals that restrain the GNAQQ209L oncogene could suggest novel therapies for GNAQ and GNA11 mutant melanomas.

Structured iterative hard thresholding with on- and off-grid applications

We consider linear sparse recovery problems where additional structure regarding the support of the solution is known. The form of the structure considered is non-overlapping sets of indices that each contain part of the support. An algorithm based on iterative hard thresholding is proposed to solve this problem. The convergence and error of the method are analyzed with respect to mutual coherence. Numerical simulations are examined in the context of an inverse source problem, including modifications for off-grid recovery.

The genomic basis of high-elevation adaptation in wild house mice (Mus musculus domesticus) from South America.

Understanding the genetic basis of environmental adaptation in natural populations is a central goal in evolutionary biology. The conditions at high elevation, particularly the low oxygen available in the ambient air, impose a significant and chronic environmental challenge to metabolically active animals with lowland ancestry. To understand the process of adaptation to these novel conditions and to assess the repeatability of evolution over short timescales, we examined the signature of selection from complete exome sequences of house mice (Mus musculus domesticus) sampled across two elevational transects in the Andes of South America. Using phylogenetic analysis, we show that house mice colonized high elevations independently in Ecuador and Bolivia. Overall, we found distinct responses to selection in each transect and largely nonoverlapping sets of candidate genes, consistent with the complex nature of traits that underlie adaptation to low oxygen availability (hypoxia) in other species. Nonetheless, we also identified a small subset of the genome that appears to be under parallel selection at the gene and SNP levels. In particular, three genes (Col22a1, Fgf14, and srGAP1) bore strong signatures of selection in both transects. Finally, we observed several patterns that were common to both transects, including an excess of derived alleles at high elevation, and a number of hypoxia-associated genes exhibiting a threshold effect, with a large allele frequency change only at the highest elevations. This threshold effect suggests that selection pressures may increase disproportionately at high elevations in mammals, consistent with observations of some high-elevation diseases in humans.

Abstract PR-007: Lung-tropic, liver-averse, primary PDAC tumors are associated with greater peripheral T cell diversity and have a unique, subtype-independent, gene-expression signature that significantly correlates with longer survival

Abstract Pancreatic Ductal Adenocarcinoma (PDAC) is predicted to become the second leading cause of cancer-related death in the United States, and most patients who present with metastatic PDAC die within a year. However, we and others have found that patients with lung metastases in the absence of liver metastases survive significantly longer than patients who present with liver metastases. We analyzed an unpublished RNASeq dataset from ~300 tumor-enriched samples from primary and metastatic PDAC specimens. Consistent with many previous publications, we found that patients with basal/squamoid-subtype tumors had significantly worse outcomes than patients with classical/ductal-subtype tumors. Additionally, we identified that most primary tumors from patients who develop lung – but not liver – metastases are classical subtype. However, this association did not wholly account for the pro-survival effect of lung-tropic, liver-averse metastatic disease because patients with lung-tropic, liver-averse, classical-subtype, primary tumors had significantly better outcomes than patients with liver-tropic, classical-subtype tumors. To identify and parse metastatic organotropism from subtype, we used organotropism-independent and subtype-independent, primary-tumor training cohorts to generate two non-overlapping gene sets that were significantly enriched in test cohorts of either primary, basal-subtype or liver-tropic tumors over primary tumors that were classical-subtype or lung-tropic and liver-averse, respectively. When applied to all primary tumors in our dataset, both the subtype-specific and organotropism-specific gene sets significantly correlated with patient outcome. From an unpublished analysis of TCRbeta CDR3 sequences from ~250 paired blood and primary tumor samples, we identified significantly greater TCRbeta diversity in blood and primary tumors from patients with lung-tropic, liver-averse disease. Additionally, we found evidence that TCRbeta rearrangements from liver-tropic primary tumors were more likely to be found in autologous peripheral blood samples than TCRbeta rearrangements from lung-tropic, liver-averse primary tumors. We also found that TCRbeta sequences were often shared between samples from patients with liver-tropic disease but never shared between samples from patients with lung-tropic, liver-averse disease. Overall, our results point to a lung-tropic, liver-averse form of PDAC that – independent of tumor subtype – leads to positive outcomes, and that T cell diversity may have a causal relationship and/or may serve as a biomarker of long-term survival with lung-tropic, liver-averse disease. Citation Format: Jason M. Link, Patrick J. Worth, Dove Keith, Sydney Owen, Alison Grossblatt-Wait, Carl Pelz, Hannah Holly, Motoyuki Tsuda, Kevin MacPherson, Jonathan Brody, Charles Lopez, Brett C. Sheppard, Rosalie C. Sears. Lung-tropic, liver-averse, primary PDAC tumors are associated with greater peripheral T cell diversity and have a unique, subtype-independent, gene-expression signature that significantly correlates with longer survival [abstract]. In: Proceedings of the AACR Virtual Special Conference on Pancreatic Cancer; 2021 Sep 29-30. Philadelphia (PA): AACR; Cancer Res 2021;81(22 Suppl):Abstract nr PR-007.

The dominance of item learning in the location-specific proportion congruence paradigm.

Prior research has shown that various cues are exploited to reactively adjust attention, and such adjustments depend on learning associations between cues and proportion congruence. This raises the intriguing question of what will be learned when more than one cue is available, a question that has implications for understanding which cue(s) will dominate in guiding reactive adjustments. Evidence from a picture-word Stroop task demonstrated that item learning dominated over location learning in a location-specific proportion congruence (LSPC) paradigm, a pattern that may explain the difficulty researchers have faced in replicating and reproducing the LSPC effect. One goal was to reproduce this pattern using a non-overlapping two-item set design that more closely matched prior studies, and another goal was to examine generalisability of the pattern to two other tasks. Using a prime-probe, colour-word Stroop task (Experiment 1), and a flanker task (Experiment 2), we again found clear dominance of item learning. In Experiment 3, we attempted to disrupt item learning and promote location learning by using a counting procedure that directed participants' attention to location. Once again, we found the same pattern of item dominance. In addition, in none of the experiments did we find evidence for conjunctive (location-item) learning. Collectively, the findings suggest item learning is neither design- or task-specific; rather, it is robust, reliable, and not easily disrupted. Discussion centres on factors dictating dominance of item- over location-based adjustments and implications for the broader literature on LSPC effects.

Automatic Assessment Of Hip Effusion From MRI.

Joint effusion is a hallmark of osteoarthritis (OA) associated with stiffness, and may relate to pain, disability, and long-term outcomes. However, it is difficult to quantify accurately. We propose a new Deep Learning (DL) approach for automatic effusion assessment from Magnetic Resonance Imaging (MRI) using volumetric quantification measures (VQM). We developed a new multiplane ensemble convolutional neural network (CNN) approach for 1) localizing bony anatomy and 2) detecting effusion regions. CNNs were trained on femoral head and effusion regions manually segmented from 3856 images (63 patients). Upon validation on a non-overlapping set of 2040 images (34 patients) DL showed high agreement with ground-truth in terms of Dice score (0.85), sensitivity (0.86) and precision (0.83). Agreement of VQM per-patient was high for DL vs experts in term of Intraclass correlation coefficient (ICC)= 0.88[0.80,0.93]. We expect this technique to reduce inter-observer variability in effusion assessment, reducing expert time and potentially improving the quality of OA care.Clinical Relevance- Our technique for automatic assessment of hip MRI can be used for volumetric measurement of effusion. We expect this to reduce variability in OA biomarker assessment and provide more reliable indicators for disease progression.

Surface residues and nonadditive interactions stabilize a consensus homeodomain protein

Despite the widely reported success of consensus design in producing highly stabilized proteins, little is known about the physical mechanisms underlying this stabilization. Here, we explore the potential sources of stabilization by performing a systematic analysis of the 29 substitutions that we previously found to collectively stabilize a consensus homeodomain compared with an extant homeodomain. By separately introducing groups of consensus substitutions that alter or preserve charge state, occur at varying degrees of residue burial, and occur at positions of varying degrees of conservation, we determine the extent to which these three features contribute to the consensus stability enhancement. Surprisingly, we find that the largest total contribution to stability comes from consensus substitutions on the protein surface and that the largest per substitution contributions come from substitutions that maintain charge state. This finding suggests that, although consensus proteins are often enriched in charged residues, consensus stabilization does not result primarily from interactions involving charged residues. Although consensus substitutions at strongly conserved positions also contribute disproportionately to stabilization, significant stabilization is also contributed from substitutions at weakly conserved positions. Furthermore, we find that identical consensus substitutions show larger stabilizing effects when introduced into the consensus background than when introduced into an extant homeodomain, indicating that synergistic, stabilizing interactions among the consensus residues contribute to consensus stability enhancement of the homeodomain. By measuring DNA binding affinity for the same set of variants, we find that, although consensus design of the homeodomain increases both affinity and folding stability, it does so using a largely nonoverlapping set of substitutions.

Application of a Machine Learning Algorithms in a Wrist-Wearable Sensor for Patient Health Monitoring during Autonomous Hospital Bed Transport

Smart sensors, coupled with artificial intelligence (AI)-enabled remote automated monitoring (RAMs), can free a nurse from the task of in-person patient monitoring during the transportation process of patients between different wards in hospital settings. Automation of hospital beds using advanced robotics and sensors has been a growing trend exacerbated by the COVID crisis. In this exploratory study, a polynomial regression (PR) machine learning (ML) RAM algorithm based on a Dreyfusian descriptor for immediate wellbeing monitoring was proposed for the autonomous hospital bed transport (AHBT) application. This method was preferred over several other AI algorithm for its simplicity and quick computation. The algorithm quantified historical data using supervised photoplethysmography (PPG) data for 5 min just before the start of the autonomous journey, referred as pre-journey (PJ) dataset. During the transport process, the algorithm continued to quantify immediate measurements using non-overlapping sets of 30 PPG waveforms, referred as in-journey (IJ) dataset. In combination, this algorithm provided a binary decision condition that determined if AHBT should continue its journey to destination by checking the degree of polynomial (DoP) between PJ and IJ. Wrist PPG was used as algorithm’s monitoring parameter. PPG data was collected simultaneously from both wrists of 35 subjects, aged 21 and above in postures mimicking that in AHBT and were given full freedom of upper limb and wrist movement. It was observed that the top goodness-of-fit which indicated potentials for high data accountability had 0.2 to 0.6 cross validation score mean (CVSM) occurring at 8th to 10th DoP for PJ datasets and 0.967 to 0.994 CVSM at 9th to 10th DoP for IJ datasets. CVSM was a reliable metric to pick out the best PJ and IJ DoPs. Central tendency analysis showed that coinciding DoP distributions between PJ and IJ datasets, peaking at 8th DoP, was the precursor to high algorithm stability. Mean algorithm efficacy was 0.20 as our proposed algorithm was able to pick out all signals from a conscious subject having full freedom of movement. This efficacy was acceptable as a first ML proof of concept for AHBT. There was no observable difference between subjects’ left and right wrists.

Granular Description With Multigranularity for Multidimensional Data: A Cone-Shaped Fuzzy Set-Based Method

Information granules are fundamental, abstract, and easy-to-operate constructs supporting the human-centered handling way in granular computing (GrC). One of the basic properties of information granules comes with its hierarchy. Information granularity is the quantification expression of hierarchy of an information granule. Forming information granules with multigranularity to hierarchically describe the nature of data is an important task in GrC. In this article, a cone-shaped fuzzy set-based granular description method with multigranularity is proposed for multidimensional data. Its fundamental idea is to realize the synergy of quantification of information granularity and a hierarchical description of data by means of α-cuts of cone-shaped fuzzy sets. The proposed method first partition the entire data set into a series of data chunks. Then, some mutually nonoverlapping cone-shaped fuzzy sets are constructed by optimally determining their cores and support radii in terms of the coverage of α-cuts of these fuzzy sets to the data located in individual chunks and the corresponding specificity. Finally, by implementing α-cuts processing for these constructed cone-shaped fuzzy sets, a collection of families of hyper-spherical information granules used to hierarchically describe the structural characteristics of the data set are completely emerged. Besides, the quality of the resulting families of hyper-spherical information granules is evaluated along the granular perspective and the application perspective, respectively. A series of experimental studies concerning several synthetic and publicly available data sets are covered. The experimental results demonstrate the superiority of the proposed granular description method of data with multigranularity.

Abstract 2137: Differential gene expression identifies a transcriptional regulatory network involving ESR1and PITX1 in invasive epithelial ovarian cancer

Abstract The heterogeneous subtypes and stages of epithelial ovarian cancer (EOC) differ in invasive processes, but the molecular factors that drive aggressive behavior remain nebulous. In this study, we analyzed two types of EOC at opposite ends of the invasiveness spectrum, high-grade serous ovarian cancers (HGSOCs) and serous borderline tumors (SBTs). We identified differentially expressed genes to guide follow on studies of regulatory networks. Initially, in a discovery sample set, we generated transcriptome sequences to identify 11 differentially expressed genes in SBTs and HGSOCs, and a nonoverlapping set of 17 differentially expressed genes stage II and stage III HGSOCs. We validated the classification performance of these gene expression signatures on a validation set of SBT and HGSOC with classification rates of 94 and 98% using the 11-gene and 17-gene sets, respectively. Transcription factor binding sites enriched in the differentially expressed gene promoter regions were examined by Chip-seq analysis in ovarian cancer and other cell lines. We identified a suite of transcription factors implicated in the differential regulation associated with invasiveness, including ERα, RARA, PITX1, FOXA1/FOXF1, and BHLHE41/E40. These data implicate estrogen responsive gene regulatory networks in ovarian cancers and suggest that dynamic regulatory interactions determine malignant EOC outcomes. Citation Format: Yichao Li, Sushil Kumar Jaiswal, Rupleen Kaur, Dana Alsaadi, Xiaoyu Liang, Frank Drews, Julie A. DeLoia, Thomas Krivak, Hanna M. Petrykowska, Valer Gotea, Lonnie Welch, Elnitski Laura. Differential gene expression identifies a transcriptional regulatory network involving ESR1and PITX1 in invasive epithelial ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2137.

Legal Aspects of Adaptation Finance under the UNFCCC Regime with Special Reference to the Adaptation Fund

Abstract This article addresses the legal aspects of the financing of adaptation to climate change, with special consideration given to one of the climate funds – the Adaptation Fund (AF). In the complex structure of climate finance, the AF attracts attention as it differs from other funds in several aspects. As an exception from other United Nations (UNs) climate funds, AF has not been recognised as an operating entity of the Financial Mechanism of the Convention. AF is also an unprecedented example under the Convention of a fund serving in parallel two decision-making bodies (CMP and CMA) under two agreements (Kyoto Protocol and Paris Agreement) ratified by non-overlapping sets of parties; with a Fund Board elected by both bodies collectively. AFs funding source is specific, it was designed to be financed from shares of the proceeds of sales of certified emission reduction units (CERs) generated by Clean Development Mechanism (CDM) projects but since 2012 there is a limited possibility of offsetting the emissions with CERs under the EU emissions trading system (ETS). For several years Parties were unable to agree the operating principles and procedures of a new mechanism established in article 6(4) of the Paris Agreement, which will resemble CDM, and would constitute a source of funding for the AF. Once this source of funding is available, the AF would cease to serve the Kyoto Protocol. Despite the above problems, AF was seen as a good example of how future climate funding can be designed. We examine the evolution of the above legal problems, including the most recent decisions and conclusions adopted during Conference of Parties (COP)25 in Madrid.

The dystrophia myotonica WD repeat-containing protein DMWD and WDR20 differentially regulate USP12 deubiquitinase.

Despite its potential clinical relevance, the product of the DMWD (dystrophia myotonica, WD repeat containing) gene is a largely uncharacterized protein. The DMWD amino acid sequence is similar to that of WDR20, a known regulator of the USP12 and USP46 deubiquitinases (DUBs). Here, we apply a combination of insilico and experimental methods to investigate several aspects of DMWD biology. Molecular evolution and phylogenetic analyses reveal that WDR20 and DMWD, similar to USP12 and USP46, arose by duplication of a common ancestor during the whole genome duplication event in the vertebrate ancestor lineage. The analysis of public human gene expression datasets suggests that DMWD expression is positively correlated with USP12 expression in normal tissues and negatively correlated with WDR20 expression in tumors. Strikingly, a survey of the annotated interactome for DMWD and WDR20 reveals a largely nonoverlapping set of interactors for these proteins. Experimentally, we first confirmed that DMWD binds both USP12 and USP46 through direct coimmunoprecipitation of epitope-tagged proteins. We found that DMWD and WDR20 share the same binding interface in USP12, suggesting that their interaction with the DUB may be mutually exclusive. Finally, we show that both DMWD and WDR20 promote USP12 enzymatic activity, but they differentially modulate the subcellular localization of the DUB. Altogether, our findings suggest a model whereby mutually exclusive binding of DMWD and WDR20 to USP12 may lead to formation of deubiquitinase complexes with distinct subcellular localization, potentially targeting different substrate repertoires.

Identification of pediatric respiratory diseases using a fine-grained diagnosis system

Respiratory diseases, including asthma, bronchitis, pneumonia, and upper respiratory tract infection (RTI), are among the most common diseases in clinics. The similarities among the symptoms of these diseases precludes prompt diagnosis upon the patients’ arrival. In pediatrics, the patients’ limited ability in expressing their situation makes precise diagnosis even harder. This becomes worse in primary hospitals, where the lack of medical imaging devices and the doctors’ limited experience further increase the difficulty of distinguishing among similar diseases. In this paper, a pediatric fine-grained diagnosis-assistant system is proposed to provide prompt and precise diagnosis using solely clinical notes upon admission, which would assist clinicians without changing the diagnostic process. The proposed system consists of two stages: a test result structuralization stage and a disease identification stage. The first stage structuralizes test results by extracting relevant numerical values from clinical notes, and the disease identification stage provides a diagnosis based on text-form clinical notes and the structured data obtained from the first stage. A novel deep learning algorithm was developed for the disease identification stage, where techniques including adaptive feature infusion and multi-modal attentive fusion were introduced to fuse structured and text data together. Clinical notes from over 12000 patients with respiratory diseases were used to train a deep learning model, and clinical notes from a non-overlapping set of about 1800 patients were used to evaluate the performance of the trained model. The average precisions (AP) for pneumonia, RTI, bronchitis and asthma are 0.878, 0.857, 0.714, and 0.825, respectively, achieving a mean AP (mAP) of 0.819. These results demonstrate that our proposed fine-grained diagnosis-assistant system provides precise identification of the diseases.

Niche overlap among anglers, fishers and cormorants and their removals of fish biomass: A case from brackish lagoon ecosystems in the southern Baltic Sea

We used time series, diet studies and angler surveys to examine the potential for conflict in brackish lagoon fisheries of the southern Baltic Sea in Germany, specifically focusing on interactions among commercial and recreational fisheries as well as fisheries and cormorants (Phalacrocorax carbo sinensis). For the time period between 2011 and 2015, commercial fisheries were responsible for the largest total fish biomass extraction (5,300 t per year), followed by cormorants (2,394 t per year) and recreational fishers (966 t per year). Commercial fishing dominated the removals of most marine and diadromous fish, specifically herring (Clupea harengus), while cormorants dominated the biomass extraction of smaller-bodied coastal freshwater fish, specifically perch (Perca fluviatilis) and roach (Rutilus rutilus). Pike (Esox lucius) as large-bodied freshwater fish was the only species where recreational fisheries were responsible for the major fraction of the annual biomass extraction. A strong trophic overlap and hence a similar foraging niche was documented among commercial fishers and recreational anglers and among non-resident and resident anglers, indicating that the aversion expressed by anglers against commercial fisheries in a survey had an objective underpinning related to resource competition. By contrast, the foraging niches of cormorants and of both fishers and anglers differed strongly as evidenced by largely non-overlapping sets of species that were caught and removed by cormorants and by commercial as well as recreational fishers. However, for individual species of commercial and recreational interest, specifically perch, cormorants were responsible for a major fraction of total biomass extraction, suggesting that at the individual fish species level competition with fishers and anglers may still occur. In an angler survey, respondents expressed a preference for cormorant control, indicating the existence of conflict between fisheries and cormorants. We recommend that conflicts in the lagoon fisheries be proactively managed, e.g., through improved communication, zoning, predator control and outreach. Further research should clarify the population-level impacts of cormorants on target species of commercial and recreational fisheries as well as the relative impact of commercial and recreational fisheries on selected species of joint interest.

Internally cross-linked poly (2,6-dimethyl-1,4-phenylene ether) based anion exchange membrane for recovery of different acids by diffusion dialysis

Acid recovery from acidic waste is a critical issue to be focused on nowadays. Chemical approaches for recovery are not commercially viable and energy intensive to save the environment. Here, we report internally cross-linked poly (2,6-dimethyl-1,4-phenylene ether) (PPE) based anion exchange membranes (AEMs) for acid recovery by diffusion dialysis, prepared via quick grafting of N-methyl-4-piperidone using ultrasonication. The prepared AEMs are assessed for their physicochemical parameters and depicted water uptake (WU) and ion-exchange capacity (IEC) in the range 22.00%–31.00% and 0.64 meq/g–2.18 meq/g respectively for Cl−1, NO3− and SO42− ions. AEMs were investigated for recovery of hydrochloric acid (HCl), nitric acid (HNO3) and sulphuric acid (H2SO4) from simulated effluent based on their proton diffusion coefficient (UH+), separation factor (S) and recovery efficiency (Ƞ). These membranes illustrated the proton diffusion coefficient as high as 0.065 m h−1 and enhanced separation factor values up to 132 at 27 °C. Recovery performance for different acids is corroborated based on unique self-organized membrane structure, studied using AFM phase imaging and cumulative influence of bulk diffusion coefficient, ion-exchange capacity, and hydration radii of Cl−1, NO3− and SO42− ions. The acid recovery efficiency illustrated the trend HCl > HNO3 > H2SO4. The hydrate (–OH) functionality with a non-overlapping set of lone pairs on oxygen enhances the proton mobility via Grotthuss mechanism inside the membrane matrix and provides a cradle-like pathway for high proton mobility. The results are pronounced for fabricated membranes compared to commercially available standards for acid recovery via diffusion dialysis.

Abstract PO092: Multi-region sequencing analysis of metastatic solid tumors to inform targeting of personalized cancer immunotherapies

Abstract Personalized cancer immunotherapies (pCIT) rely on targeting of somatic cancer mutations, which in their translated peptide form are known as neoantigens. Each tumor has a unique set of mutations, and thus, the vast majority of cancer neoantigens targeted by these personalized therapies are private to an individual's tumor. We sought to understand whether a single strategy for targeting private neoantigens—prioritizing clonal neoantigens over subclonal ones—could apply equally well across primary and metastatic lesions in patients with advanced metastatic solid tumors, and across different cancer indications. Previous studies of largely primary tumors in non-metastatic settings have suggested that indications such as melanoma and NSCLC consistently have low mutational heterogeneity and a preponderance of clonal neoantigens, whereas other indications such as CRC, RCC, and breast cancer often have a higher degree of mutational heterogeneity and fewer clonal neoantigens. It remains unclear whether standard clonality metrics (e.g., cancer cell fraction or "CCF") can accurately predict global mutation clonality across tumor lesions and whether CCF could offer any predictive benefit over simple variant allele frequencies. By multi-region sequencing analysis of five metastatic solid tumors across four indications (CRC, NSCLC, RCC, UBC), with all lesions sampled at the same time point, we characterized neoantigen heterogeneity and whether the clonality and expression level of mutations would influence the likely immunogenicity of neoantigen candidates selected for pCIT. We show that the ability to target cancer neoantigens effectively depends on the type of lesion sampled and on indication. In NSCLC and UBC, where most mutations were shared across tumor lesions, effective targeting could be accomplished by sampling either the primary or certain metastatic lesions. However, CRC and RCC tumors had more complex, branching mutational phylogenies, leading to non-overlapping sets of mutations across different tumor lesions. On average, across the five metastatic cases, a given tumor sample’s predictive value for global mutation clonality varied from 20% (RCC) to 70% (NSCLC). Enrichment of clonal neoantigens could be accomplished by prioritizing mutations with higher variant allele frequency (VAF), as expected, but using CCF for mutation prioritization led to poorer enrichment of clonal neoantigens. In one case (UBC), we observed truncal neoantigen reduction via down-regulation of mutant allele expression, suggesting that early immune recognition of tumors is an important factor in pCIT targeting. Our data suggest that indication-specific neoantigen targeting strategies, which consider mutation presence and expression across multiple tumor lesions, may be necessary for pCIT to be broadly effective. Citation Format: Amy Lo, Andrew Wallace, Daniel Oreper, Nicolas Lounsbury, Charles Havnar, Carmina Espiritu, Ximo Pechuan-Jorge, Richard Bourgon, Ryan Jones, Katrina Krogh, Guang-Yu Yang, Oliver Zill. Multi-region sequencing analysis of metastatic solid tumors to inform targeting of personalized cancer immunotherapies [abstract]. In: Abstracts: AACR Virtual Special Conference: Tumor Immunology and Immunotherapy; 2020 Oct 19-20. Philadelphia (PA): AACR; Cancer Immunol Res 2021;9(2 Suppl):Abstract nr PO092.

Informing Seasonal Proxy‐Based Flow Reconstructions Using Baseflow Separation: An Example From the Potomac River, United States

AbstractPaleoclimatic perspectives on hydrological variability can offer valuable information on the frequency and magnitude of extreme events. Tree‐ring records are a common proxy used to reconstruct past streamflow due to their interannual resolution and often strong correlation with hydroclimate variability. The separation of streamflow into theoretical baseflow and stormflow constituents is regularly utilized to differentiate between flow‐generating processes; however, it has yet to see use in paleoclimate reconstructions. We compare three approaches which use log‐linear, gamma‐distributed, and baseflow‐separated regression, respectively, to reconstructing flow at a well‐studied gage—the Potomac River at Point of Rock, Maryland. Preinstrumental baseflow and streamflow for summer were estimated for the past 350 years using a regional network of tree‐ring chronologies. Additionally, estimates of winter flow were produced for the same period using a nonoverlapping set of tree‐ring data. Tree growth appears to have a stronger relationship with baseflow than with streamflow for both seasons, supporting the use of baseflow as predictand. The number of chronologies subsequently chosen as predictors for streamflow was also lower than for baseflow and represents an additional source of reconstruction bias/uncertainty when total streamflow is the predictand. Historical records support the validity of both summer and winter reconstructions. The winter reconstruction indicates that several years of consecutive below‐mean flows, on par with the 1960s drought, occurred with higher frequency prior to the instrumental era. Our results suggest that baseflow separation can improve reconstruction skill and provide additional information to water resource management on the long‐term variability of hydrological extremes.

A phylogeny for the Drosophila montium species group: A model clade for comparative analyses

The Drosophila montium species group is a clade of 94 named species, closely related to the model species D. melanogaster. The montium species group is distributed over a broad geographic range throughout Asia, Africa, and Australasia. Species of this group possess a wide range of morphologies, mating behaviors, and endosymbiont associations, making this clade useful for comparative analyses. We use genomic data from 42 available species to estimate the phylogeny and relative divergence times within the montium species group, and its relative divergence time from D. melanogaster. To assess the robustness of our phylogenetic inferences, we use 3 non-overlapping sets of 20 single-copy coding sequences and analyze all 60 genes with both Bayesian and maximum likelihood methods. Our analyses support monophyly of the group. Apart from the uncertain placement of a single species, D. baimaii, our analyses also support the monophyly of all seven subgroups proposed within the montium group. Our phylograms and relative chronograms provide a highly resolved species tree, with discordance restricted to estimates of relatively short branches deep in the tree. In contrast, age estimates for the montium crown group, relative to its divergence from D. melanogaster, depend critically on prior assumptions concerning variation in rates of molecular evolution across branches, and hence have not been reliably determined. We discuss methodological issues that limit phylogenetic resolution – even when complete genome sequences are available – as well as the utility of the current phylogeny for understanding the evolutionary and biogeographic history of this clade.

Rhythmic semantic structures of the “particular-universal” in the lyrics of the author song and their search algorithms

Cultural phenomena and their correlation with social processes spark specific scientific interest. One of such cultural trends is the author song of the 1950s – 1980s, which became the subject to this research. Adequate interpretation of the content of compositions and their connection with public life requires a particular toolset, especially due to the fact that some texts contain interesting semantic structures. In reading the lyrics, the alternation of fragments with opposite meanings was revealed. Generalization of these meanings to abstract concepts demonstrates that they comprise a semantic rhythm. One of such rhythmic structures consists in rotation of the particular and the universal. This article describes and formalizes the algorithm that resembles the principle of LR (k)-analyzer of context-free grammars, which was used for the analysis of two author songs:  “Ten Stars” by A. Krupp and “Romance of the Old Age” by A. Sukhanov based on the poetry of Omar Khayyám. In the indicated compositions, the author revealed common structures in form of the particular-universal rhythm and the motive of coping, as well as certain structural peculiarities. The songs under review differ in the degree of expression of dialectical contradiction of the “particular” and the “universal”. They also vary in the forms of translation of this semantic rhythm: through non-overlapping sets, additional orthographic dimensions, or storyline expansion on space-time scale.

Managing the Interference for Down-Link in LTE Using Fractional Frequency Reuse

Long Term Evolution has developed a new radio technology called femtocell or Femto Base station, which is well-suited to improve cellular network capacity and mobile coverage to indoor user's areas. Providing additional capacity and coverage expansion through FBSs could lead to large interference in a cellular radio communication network. In this paper, we proposed an efficient resource allocation scheme based on Fraction Frequency Reuse (FFR) for interference mitigation, where the entire spectrum is shared among network entities. FFR mechanism aims to reduce co-tier and cross-tier downlink interferences by allocating non-overlapping sets of bands to the user equipment at different geographic locations. The main purpose of this work is to compare two main types of FFR schemes, respectively, Strict FFR and Soft Frequency Reuse with the proposed scheme. The three types of FFR schemes were explained and evaluated with Monte-Carlo simulation based on some performance metrics, namely, sum-rate, spectral efficiency, and outage probability. Simulation results showed that the impacts of the proposed scheme are significantly high in comparison with two other methods. The proposed scheme proved to enhance spectral efficiency, reduced the outage probability, and increased the sum rate for all the users.