Acquired cystic disease-associated renal cell carcinoma with PTCH1 mutation: a case report

Acquired cystic disease-associated renal cell carcinoma (ACD-RCC) is an extremely rare kidney tumor seen mainly in patients with end-stage renal disease. Currently, there are few reports on this type of tumor. We describe the case of a 58-year-old man who had been receiving peritoneal dialysis for more than nine years due to chronic renal insufficiency and uremia. One year after undergoing left renal clear cell renal cell carcinoma resection, a space-occupying lesion was found in the right kidney for which he underwent right nephrectomy. The histopathology of this tumor showed solid or tubular cell arrangements, with some areas of cyst formation. Vacuoles of varying sizes were present in the cytoplasm, and varying amounts of calcium oxalate crystals were found in the tumor cells or interstitium. The pathological diagnosis was ACD-RCC. Next-generation sequencing detected mutations in the PTCH1, MTOR, FAT1, SOS1, RECQL4, and CDC73 genes in the right renal tumor. This is a rare case of a patient with ACD-RCC in the right kidney and clear cell renal cell carcinoma in the left kidney. The findings suggest that mutations in PTCH1 associated with ACD-RCC may have acted as oncogenic drivers for the development of ACKD-RCC, together with providing insight into mechanisms underlying ACD-RCC development, as well as diagnostic and treatment options

Comparison of variations detection between whole-genome amplification methods used in single-cell resequencing

Background: Single-cell resequencing (SCRS) provides many biomedical advances in variations detection at the single-cell level, but it currently relies on whole genome amplification (WGA). Three methods are commonly used for WGA: multiple displacement amplification (MDA), degenerate-oligonucleotide-primed PCR (DOP-PCR) and multiple annealing and looping-based amplification cycles (MALBAC). However, a comprehensive comparison of variations detection performance between these WGA methods has not yet been performed. Results: We systematically compared the advantages and disadvantages of different WGA methods, focusing particularly on variations detection. Low-coverage whole-genome sequencing revealed that DOP-PCR had the highest duplication ratio, but an even read distribution and the best reproducibility and accuracy for detection of copy-number variations (CNVs). However, MDA had significantly higher genome recovery sensitivity (~84 %) than DOP-PCR (~6 %) and MALBAC (~52 %) at high sequencing depth. MALBAC and MDA had comparable single-nucleotide variations detection efficiency, false-positive ratio, and allele drop-out ratio. We further demonstrated that SCRS data amplified by either MDA or MALBAC from a gastric cancer cell line could accurately detect gastric cancer CNVs with comparable sensitivity and specificity, including amplifications of 12p11.22 (KRAS) and 9p24.1 (JAK2, CD274, and PDCD1LG2). Conclusions: Our findings provide a comprehensive comparison of variations detection performance using SCRS amplified by different WGA methods. It will guide researchers to determine which WGA method is best suited to individual experimental needs at single-cell level

The effect of urbanization and exposure to multiple environmental factors on life-history traits and breeding success of Barn Swallows (Hirundo rustica) across China

In addition to landscape changes, urbanization also brings about changes in environmental factors that can affect wildlife. Despite the common referral in the published literature to multiple environmental factors such as light and noise pollution, there is a gap in knowledge about their combined impact. We developed a multidimensional environmental framework to assess the effect of urbanization and multiple environmental factors (light, noise, and temperature) on life-history traits and breeding success of Barn Swallows (Hirundo rustica) across rural to urban gradients in four locations spanning over 2500 ​km from North to South China. Over a single breeding season, we measured these environmental factors nearby nests and quantified landscape urbanization over a 1 ​km2 radius. We then analysed the relationships between these multiple environmental factors through a principal component analysis and conducted spatially explicit linear-mixed effects models to assess their effect on life-history traits and breeding success. We were particularly interested in understanding whether and how Barn Swallows were able to adapt to such environmental conditions associated with urbanization. The results show that there is significant variation in the exposure to environmental conditions experienced by Barn Swallows breeding across urbanization gradients in China. These changes and their effects are complex due to the behavioural responses ameliorating potential negative effects by selecting nesting sites that minimize exposure to environmental factors. However, significant relationships between landscape urbanization, exposure to environmental factors, and life-history traits such as laying date and clutch size were pervasive. Still, the impact on breeding success was, at least in our sample, negligible, suggesting that Barn Swallows are extremely adaptable to a wide range of environmental features

Contextual Bandits in Imperfect Environments: Analysis and Applications

The data explosion and development of artificial intelligence (AI) has fueled the demand for recommendation systems, information retrieval, personalization, among others. Consequently, the need of a solution to optimize these systems “on-the-fly” has also grown rapidly. Contextual bandit is a machine learning framework designed to tackle complex situations in an online manner, where the agent can select actions (i.e., arms) based on available context information. Based the feedback, the agent can learn the relations between context information and rewards for each arm, which further improves arm selection in the future. In practice, however, the learning environment may be far from being perfect. For example, the available context information may not be accurate, the reward feedback may be delayed or even missing, and data may not be centrally available due to user privacy concerns.In this dissertation, we consider the practical scenario of contextual bandits in an imperfect environment. First, we focus on imperfect context and study learning with probabilistic contexts, where a bundle of contexts are revealed to the agent along with their corresponding probabilities instead of true context. Second, we study reward imperfectness by considering delayed or missing reward feedback. Third, we turn to an adversarial environment and study a novel combinatorial setting with arm removal and submodular utility where some selected arms can be removed adversarially. Finally, we consider a privacy-preserving federated bandit where a group of agents cooperate to solve the bandit problem, while ensuring that their communication remains private. For each of the settings, we propose new learning algorithms, analyze the cumulative regret, and conduct empirical evaluations based on real-world applications

Contextual Bandits in Imperfect Environments: Analysis and Applications

The data explosion and development of artificial intelligence (AI) has fueled the demand for recommendation systems, information retrieval, personalization, among others. Consequently, the need of a solution to optimize these systems “on-the-fly” has also grown rapidly. Contextual bandit is a machine learning framework designed to tackle complex situations in an online manner, where the agent can select actions (i.e., arms) based on available context information. Based the feedback, the agent can learn the relations between context information and rewards for each arm, which further improves arm selection in the future. In practice, however, the learning environment may be far from being perfect. For example, the available context information may not be accurate, the reward feedback may be delayed or even missing, and data may not be centrally available due to user privacy concerns.In this dissertation, we consider the practical scenario of contextual bandits in an imperfect environment. First, we focus on imperfect context and study learning with probabilistic contexts, where a bundle of contexts are revealed to the agent along with their corresponding probabilities instead of true context. Second, we study reward imperfectness by considering delayed or missing reward feedback. Third, we turn to an adversarial environment and study a novel combinatorial setting with arm removal and submodular utility where some selected arms can be removed adversarially. Finally, we consider a privacy-preserving federated bandit where a group of agents cooperate to solve the bandit problem, while ensuring that their communication remains private. For each of the settings, we propose new learning algorithms, analyze the cumulative regret, and conduct empirical evaluations based on real-world applications

Regulation of dopamine D1 receptor dynamics within the postsynaptic density of hippocampal glutamate synapses.

Dopamine receptor potently modulates glutamate signalling, synaptic plasticity and neuronal network adaptations in various pathophysiological processes. Although key intracellular signalling cascades have been identified, the cellular mechanism by which dopamine and glutamate receptor-mediated signalling interplay at glutamate synapse remain poorly understood. Among the cellular mechanisms proposed to aggregate D1R in glutamate synapses, the direct interaction between D1R and the scaffold protein PSD95 or the direct interaction with the glutamate NMDA receptor (NMDAR) have been proposed. To tackle this question we here used high-resolution single nanoparticle imaging since it provides a powerful way to investigate at the sub-micron resolution the dynamic interaction between these partners in live synapses. We demonstrate in hippocampal neuronal networks that dopamine D1 receptors (D1R) laterally diffuse within glutamate synapses, in which their diffusion is reduced. Disrupting the interaction between D1R and PSD95, through genetical manipulation and competing peptide, did not affect D1R dynamics in glutamatergic synapses. However, preventing the physical interaction between D1R and the GluN1 subunit of NMDAR abolished the synaptic stabilization of diffusing D1R. Together, these data provide direct evidence that the interaction between D1R and NMDAR in synapses participate in the building of the dopamine-receptor-mediated signalling, and most likely to the glutamate-dopamine cross-talk

Novel color-tunable Gd₂O₂CN₂:Tb³⁺, Eu³⁺ phosphors: Characterization and photoluminescence properties

International audienceIn this paper, color-tunable Gd2O2CN2:Tb3+, Eu3+ phosphors were obtained by co-doping Eu3+ and Tb3+ ions into Gd2O2CN2 host and singly varying the Eu3+ doping concentration. The characteristics of the crystal structure, photoluminescence lifetime and photoluminescence of Tb3+, Eu3+ single-doped and Tb3+and Eu3+ co-doped Gd2O2CN2, were carefully investigated by XRD, FTIR, PL decay curves and photoluminescence (PL). The results indicated that Tb3+ single-doped Gd2O2CN2 phosphor show a green emission, and by increasing Eu3+ content, Gd2O2CN2:Tb3+, Eu3+ phosphors emit green to orange and then to red light under the excitation of 379 n

Table2_Identifying key m6A-methylated lncRNAs and genes associated with neural tube defects via integrative MeRIP and RNA sequencing analyses.docx

Objective: N6-methyladenosine (m6A) is a common post-transcriptional modification of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs). However, m6A-modified lncRNAs are still largely unexplored. This study aimed to investigate differentially m6A-modified lncRNAs and genes involved in neural tube defect (NTD) development.Methods: Pregnant Kunming mice (9–10 weeks of age) were treated with retinoic acid to construct NTD models. m6A levels and methyltransferase-like 3 (METTL3) expression were evaluated in brain tissues of the NTD models. Methylated RNA immunoprecipitation sequencing (MeRIP-seq) and RNA sequencing (RNA-seq) were performed on the NovaSeq platform and Illumina HiSeq 2,500 platform, respectively. Differentially m6A-methylated differentially expressed lncRNAs (DElncRNAs) and differentially expressed genes (DEGs) were identified, followed by GO biological process and KEGG pathway functional enrichment analyses. Expression levels of several DElncRNAs and DEGs were evaluated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) for validation.Results: m6A levels and METTL3 expression levels were significantly lower in the brain tissues of the NTD mouse model than in controls. By integrating MeRIP-seq and RNA-seq data, 13 differentially m6A-methylated DElncRNAs and 170 differentially m6A-methylated DEGs were identified. They were significantly enriched in the Hippo signaling pathway and mannose-type O-glycan biosynthesis. The qRT-PCR results confirmed the decreased expression levels of lncRNAs, such as Mir100hg, Gm19265, Gm10544, and Malat1, and genes, such as Zfp236, Erc2, and Hmg20a, in the NTD group.Conclusion:METTL3-mediated m6A modifications may be involved in NTD development. In particular, decreased expression levels of Mir100hg, Gm19265, Gm10544, Malat1, Zfp236, Erc2, and Hmg20a may contribute to the development of NTD.</p

Table4_Identifying key m6A-methylated lncRNAs and genes associated with neural tube defects via integrative MeRIP and RNA sequencing analyses.docx

Objective: N6-methyladenosine (m6A) is a common post-transcriptional modification of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs). However, m6A-modified lncRNAs are still largely unexplored. This study aimed to investigate differentially m6A-modified lncRNAs and genes involved in neural tube defect (NTD) development.Methods: Pregnant Kunming mice (9–10 weeks of age) were treated with retinoic acid to construct NTD models. m6A levels and methyltransferase-like 3 (METTL3) expression were evaluated in brain tissues of the NTD models. Methylated RNA immunoprecipitation sequencing (MeRIP-seq) and RNA sequencing (RNA-seq) were performed on the NovaSeq platform and Illumina HiSeq 2,500 platform, respectively. Differentially m6A-methylated differentially expressed lncRNAs (DElncRNAs) and differentially expressed genes (DEGs) were identified, followed by GO biological process and KEGG pathway functional enrichment analyses. Expression levels of several DElncRNAs and DEGs were evaluated by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) for validation.Results: m6A levels and METTL3 expression levels were significantly lower in the brain tissues of the NTD mouse model than in controls. By integrating MeRIP-seq and RNA-seq data, 13 differentially m6A-methylated DElncRNAs and 170 differentially m6A-methylated DEGs were identified. They were significantly enriched in the Hippo signaling pathway and mannose-type O-glycan biosynthesis. The qRT-PCR results confirmed the decreased expression levels of lncRNAs, such as Mir100hg, Gm19265, Gm10544, and Malat1, and genes, such as Zfp236, Erc2, and Hmg20a, in the NTD group.Conclusion:METTL3-mediated m6A modifications may be involved in NTD development. In particular, decreased expression levels of Mir100hg, Gm19265, Gm10544, Malat1, Zfp236, Erc2, and Hmg20a may contribute to the development of NTD.</p