Immune checkpoint therapy responders display early clonal expansion of tumor infiltrating lymphocytes.

Immune checkpoint therapy (ICT) causes durable tumour responses in a subgroup of patients, but it is not well known how T cell receptor beta (TCRß) repertoire dynamics contribute to the therapeutic response. Using murine models that exclude variation in host genetics, environmental factors and tumour mutation burden, limiting variation between animals to naturally diverse TCRß repertoires, we applied TCRseq, single cell RNAseq and flow cytometry to study TCRß repertoire dynamics in ICT responders and non-responders. Increased oligoclonal expansion of TCRß clonotypes was observed in responding tumours. Machine learning identified TCRß CDR3 signatures unique to each tumour model, and signatures associated with ICT response at various timepoints before or during ICT. Clonally expanded CD8+ T cells in responding tumours post ICT displayed effector T cell gene signatures and phenotype. An early burst of clonal expansion during ICT is associated with response, and we report unique dynamics in TCRß signatures associated with ICT response.

Selecting embedding delays: An overview of embedding techniques and a new method using persistent homology.

Delay embedding methods are a staple tool in the field of time series analysis and prediction. However, the selection of embedding parameters can have a big impact on the resulting analysis. This has led to the creation of a large number of methods to optimize the selection of parameters such as embedding lag. This paper aims to provide a comprehensive overview of the fundamentals of embedding theory for readers who are new to the subject. We outline a collection of existing methods for selecting embedding lag in both uniform and non-uniform delay embedding cases. Highlighting the poor dynamical explainability of existing methods of selecting non-uniform lags, we provide an alternative method of selecting embedding lags that includes a mixture of both dynamical and topological arguments. The proposed method, Significant Times on Persistent Strands (SToPS), uses persistent homology to construct a characteristic time spectrum that quantifies the relative dynamical significance of each time lag. We test our method on periodic, chaotic, and fast-slow time series and find that our method performs similar to existing automated non-uniform embedding methods. Additionally, n-step predictors trained on embeddings constructed with SToPS were found to outperform other embedding methods when predicting fast-slow time series.

Reservoir time series analysis: Using the response of complex dynamical systems as a universal indicator of change.

We present the idea of reservoir time series analysis (RTSA), a method by which the state space representation generated by a reservoir computing (RC) model can be used for time series analysis. We discuss the motivation for this with reference to the characteristics of RC and present three ad hoc methods for generating representative features from the reservoir state space. We then develop and implement a hypothesis test to assess the capacity of these features to distinguish signals from systems with varying parameters. In comparison to a number of benchmark approaches (statistical, Fourier, phase space, and recurrence analysis), we are able to show significant, generalized accuracy across the proposed RTSA features that surpasses the benchmark methods. Finally, we briefly present an application for bearing fault distinction to motivate the use of RTSA in application.

Characterisation of neonatal cardiac dynamics using ordinal partition network.

The maturation of the autonomic nervous system (ANS) starts in the gestation period and it is completed after birth in a variable time, reaching its peak in adulthood. However, the development of ANS maturation is not entirely understood in newborns. Clinically, the ANS condition is evaluated with monitoring of gestational age, Apgar score, heart rate, and by quantification of heart rate variability using linear methods. Few researchers have addressed this problem from the perspective nonlinear data analysis. This paper proposes a new data-driven methodology using nonlinear time series analysis, based on complex networks, to classify ANS conditions in newborns. We map 74 time series given by RR intervals from premature and full-term newborns to ordinal partition networks and use complexity quantifiers to discriminate the dynamical process present in both conditions. We obtain three complexity quantifiers (permutation, conditional, and global node entropies) using network mappings from forward and reverse directions, and considering different time lags and embedding dimensions. The results indicate that time asymmetry is present in the data of both groups and the complexity quantifiers can differentiate the groups analysed. We show that the conditional and global node entropies are sensitive for detecting subtle differences between the neonates, particularly for small embedding dimensions (m < 7). This study reinforces the assessment of nonlinear techniques for RR interval time series analysis. Graphical Abstract.

Gliomas in children and adolescents: investigation of molecular alterations with a potential prognostic and therapeutic impact.

PURPOSE: Gliomas represent the most frequent central nervous system (CNS) tumors in children and adolescents. However, therapeutic strategies for these patients, based on tumor molecular profile, are still limited compared to the wide range of treatment options for the adult population. We investigated molecular alterations, with a potential prognostic marker and therapeutic target in gliomas of childhood and adolescence using the next-generation sequencing (NGS) strategy. METHODS: We selected 95 samples with initial diagnosis of glioma from patients treated at Pediatric Oncology Institute-GRAACC/UNIFESP. All samples were categorized according to the 2021 World Health Organization Classification of Tumors of the CNS, which included 39 low-grade gliomas (LGGs) and 56 high-grade gliomas (HGGs). Four HGG samples were classified as congenital glioblastoma (cGBM). NGS was performed to identify somatic genetic variants in tumor samples using the Oncomine Childhood Cancer Research Assay® (OCCRA®) panel, from Thermo Fisher Scientific®. RESULTS: Genetic variants were identified in 76 of 95 (80%) tumors. In HGGs, the most common molecular alteration detected was H3F3A c.83A > T variant (H3.3 K27M) and co-occurring mutations in ATRX, TP53, PDGFRA, MET, and MYC genes were also frequently observed. One HGG sample was reclassified as supratentorial ependymoma ZFTA-fusion positive after NGS was performed. In LGGs, four KIAA1549-BRAF fusion transcripts were detected and this alteration was the most recurrent genetic event and favorable prognostic factor identified. Additionally, genetic variants in ALK and NTRK genes, which provide potential targets for therapy with Food and Drug Administration-approved drugs, were identified in two different cases of cGBM that were classified as infant-type hemispheric glioma, a newly recognized subgroup of pediatric HGG. CONCLUSION: Molecular profiling by the OCCRA® panel comprehensively addressed the most relevant genetic variants in gliomas of childhood and adolescence, as these tumors have specific patterns of molecular alterations, outcomes, and effectiveness to therapies.

Molecular profiling of pediatric and adolescent ependymomas: identification of genetic variants using a next-generation sequencing panel.

PURPOSE: Ependymoma (EPN) accounts for approximately 10% of all primary central nervous system (CNS) tumors in children and in most cases, chemotherapy is ineffective and treatment remains challenging. We investigated molecular alterations, with a potential prognostic marker and therapeutic target in EPNs of childhood and adolescence, using a next-generation sequencing (NGS) panel specific for pediatric neoplasms. METHODS: We selected 61 samples with initial diagnosis of EPN from patients treated at Pediatric Oncology Institute-GRAACC/UNIFESP. All samples were divided according to the anatomical compartment of the CNS - 42 posterior fossa (PF), 14 supratentorial (ST), and five spinal (SP). NGS was performed to identify somatic genetic variants in tumor samples using the Oncomine Childhood Cancer Research Assay® (OCCRA®) panel, from Thermo Fisher Scientific®. RESULTS: Genetic variants were identified in 24 of 61 (39.3%) tumors and over 90% of all variants were pathogenic or likely pathogenic. The most commonly variants detected were in CIC, ASXL1, and JAK2 genes and have not been reported in EPN yet. MN1-BEND2 fusion, alteration recently described in a new CNS tumor type, was identified in one ST sample that was reclassified as astroblastoma. Additionally, YAP1-MAMLD1 fusion, a rare event associated with good outcome in ST-EPN, was observed in two patients diagnosed under 2 years old. CONCLUSIONS: Molecular profiling by the OCCRA® panel showed novel alterations in pediatric and adolescent EPNs, which highlights the clinical importance in identifying genetic variants for patients' prognosis and therapeutic orientation.

Grading your models: Assessing dynamics learning of models using persistent homology.

Assessing model accuracy for complex and chaotic systems is a non-trivial task that often relies on the calculation of dynamical invariants, such as Lyapunov exponents and correlation dimensions. Well-performing models are able to replicate the long-term dynamics and ergodic properties of the desired system. We term this phenomenon "dynamics learning." However, existing estimates based on dynamical invariants, such as Lyapunov exponents and correlation dimensions, are not unique to each system, not necessarily robust to noise, and struggle with detecting pathological errors, such as errors in the manifold density distribution. This can make meaningful and accurate model assessment difficult. We explore the use of a topological data analysis technique, persistent homology, applied to uniformly sampled trajectories from constructed reservoir models of the Lorenz system to assess the learning quality of a model. A proposed persistent homology point summary, conformance, was able to identify models with successful dynamics learning and detect discrepancies in the manifold density distribution.

On detecting dynamical regime change using a transformation cost metric between persistent homology diagrams.

This work outlines a pipeline for time series analysis that incorporates a measure of similarity not previously applied between homological summaries. Specifically, the well-established, but disparate, methods of persistent homology and TrAnsformation Cost Time Series (TACTS) are combined to provide a metric for tracking dynamics via changing homological features. TACTS allows subtle changes in dynamics to be accounted for, gives a quantitative output that can be directly interpreted, and is tunable to provide several complementary perspectives simultaneously. Our method is demonstrated first with known dynamical systems and then with a real-world electrocardiogram dataset. This paper highlights inadequacies in existing persistent homology metrics and describes circumstances where TACTS can be more sensitive and better suited to detecting a variety of regime changes.

Abnormal spindle-like microcephaly-associated (ASPM) gene expression in posterior fossa brain tumors of childhood and adolescence.

PURPOSE: In neurogenesis, ASPM (abnormal spindle-like microcephaly-associated) gene is expressed mainly in the ventricular zone of posterior fossa and is the major determinant in the cerebral cortex. Besides its role in embryonic development, ASPM overexpression promotes tumor growth, including central nervous system (CNS) tumors. This study aims to investigate ASPM expression levels in most frequent posterior fossa brain tumors of childhood and adolescence: medulloblastoma (MB), ependymoma (EPN), and astrocytoma (AS), correlating them with clinicopathological characteristics and tumor solid portion size. METHODS: Quantitative reverse transcription (qRT-PCR) is used to quantify ASPM mRNA levels in 80 pre-treatment tumor samples: 28 MB, 22 EPN, and 30 AS. The tumor solid portion size was determined by IOP-GRAACC Diagnostic Imaging Center. We correlated these findings with clinicopathological characteristics and tumor solid portion size. RESULTS: Our results demonstrated that ASPM gene was overexpressed in MB (p = 0.007) and EPN (p = 0.0260) samples. ASPM high expression was significantly associated to MB samples from patients with worse overall survival (p = 0.0123) and death due to disease progression (p = 0.0039). Interestingly, two patients with AS progressed toward higher grade showed ASPM overexpression (p = 0.0046). No correlation was found between the tumor solid portion size and ASPM expression levels in MB (p = 0.1154 and r = - 0.4825) and EPN (p = 0.1108 and r = - 0.3495) samples. CONCLUSION: Taking in account that ASPM gene has several functions to support cell proliferation, as mitotic defects and premature differentiation, we suggest that its overexpression, presumably, plays a critical role in disease progression of posterior fossa brain tumors of childhood and adolescence.

The reliability of recurrence network analysis is influenced by the observability properties of the recorded time series.

Recurrence network analysis (RNA) is a remarkable technique for the detection of dynamical transitions in experimental applications. However, in practical experiments, often only a scalar time series is recorded. This requires the state-space reconstruction from this single time series which, as established by embedding and observability theory, is shown to be hampered if the recorded variable conveys poor observability. In this work, we investigate how RNA metrics are impacted by the observability properties of the recorded time series. Following the framework of Zou et al. [Chaos 20, 043130 (2010)], we use the Rössler and Duffing-Ueda systems as benchmark models for our study. It is shown that usually RNA metrics perform badly with variables of poor observability as for recurrence quantification analysis. An exception is the clustering coefficient, which is rather robust to observability issues. Along with its efficacy to detect dynamical transitions, it is shown to be an efficient tool for RNA-especially when no prior information of the variable observability is available.

Search for appropriate reference genes for quantitative reverse transcription PCR studies in somite, prosencephalon and heart of early mouse embryo.

qRT-PCR requires reliable internal control genes stably expressed in different samples and experimental conditions. The stability of reference genes is rarely tested experimentally, especially in developing tissues given the singularity of these samples. Here we evaluated the suitability of a set of reference genes (Actb, Gapdh, Tbp, Pgk1 and Sdha) using samples from early mouse embryo tissues that are widely used in research (somites, prosencephalon and heart) at different developmental stages. The comparative ΔCq method and five software packages (NormFinder, geNorm, BestKeeper, DataAssist and RefFinder) were used to rank the most stable genes while GenEx and GeNorm programs determined the optimal total number of reference genes for a reliable normalization. The ranking of most reliable reference genes was different for each tissue evaluated: (1) in somite from embryos with 16-18 somite pairs stage, the combination of Pgk1 and Actb provided the best normalization and Actb also presented high stability levels at an earlier developmental stage; (2) Gapdh is the most stable gene in prosencephalon in the two developmental stages tested; and (3) in heart samples, Sdha, Gapdh and Actb were the best combination for qPCR normalization. The analysis of these three tissues simultaneously indicated the combination of Gapdh, Actb and Tbp as the most reliable internal control. This study highlights the importance of appropriate reference genes according to the cell type and/or tissue of interest. The data here described can be applied in future research using mouse embryos as a model for mammalian development.

Consistency in echo-state networks.

Consistency is an extension to generalized synchronization which quantifies the degree of functional dependency of a driven nonlinear system to its input. We apply this concept to echo-state networks, which are an artificial-neural network version of reservoir computing. Through a replica test, we measure the consistency levels of the high-dimensional response, yielding a comprehensive portrait of the echo-state property.

Is Bach's brain a Markov chain? Recurrence quantification to assess Markov order for short, symbolic, musical compositions.

It is rarely possible to precisely characterise the system underlying a series of observations. Hypothesis testing, which involves assessing simple assumptions about driving mechanisms, provides hope that we can at least rule out certain possibilities regarding the nature of the system. Unfortunately, the brevity, nonstationarity, and symbolic nature of certain time series of interest undermines traditional hypothesis tests. Fortunately, recurrence quantification analysis (RQA) has an established record of success in understanding short and nonstationary time series. We evaluate the suitability of measures of RQA as test statistics in surrogate data tests of the hypothesis that ten compositions by the Baroque composer J. S. Bach (1685-1750) arose from a Markov chain. More specifically, we estimate the size (the rate at which true hypotheses are incorrectly rejected) and power (the rate at which false hypotheses are correctly rejected) from empirical rejection rates across 1000 realisations, for each of the ten compositions, of the surrogate algorithm. We compare hypothesis tests based on RQA measures to tests based on the conditional entropy, an established test statistic for surrogate data tests of Markov order, and find that the RQA measure Lmax provides more consistent rejection of the fairly implausible hypothesis that Bach's brain was a Markov chain.

On system behaviour using complex networks of a compression algorithm.

We construct complex networks of scalar time series using a data compression algorithm. The structure and statistics of the resulting networks can be used to help characterize complex systems, and one property, in particular, appears to be a useful discriminating statistic in surrogate data hypothesis tests. We demonstrate these ideas on systems with known dynamical behaviour and also show that our approach is capable of identifying behavioural transitions within electroencephalogram recordings as well as changes due to a bifurcation parameter of a chaotic system. The technique we propose is dependent on a coarse grained quantization of the original time series and therefore provides potential for a spatial scale-dependent characterization of the data. Finally the method is as computationally efficient as the underlying compression algorithm and provides a compression of the salient features of long time series.

Inspiratory muscle function in asthmatic and healthy subjects: influence of age, nutrition and physical activity.

OBJECTIVE: To compare inspiratory muscle function (strength and endurance) between asthmatics and healthy controls, and the influence of age, nutritional status and physical activity on them. METHODS: This is a cross-sectional study. Asthmatic and healthy subjects, aged 6 to 18 years old, recruited from two public schools in Southern Brazil were included in the study. Asthmatic subjects were selected using the criteria presented by the International Study on Asthma and Allergies in Children and control subjects based on the absence of respiratory symptoms. Anthropometric data was measured, body mass index calculated and subjects classified as normal weight, overweight or obese. Physical activity levels, maximum inspiratory pressure (MIP) and inspiratory muscle endurance (IME) were also evaluated. RESULTS: A total of 314 participants were included, separated into control group (181) and asthmatics (133), with a total mean age of 11 years. When both groups were compared, there were no significant differences in both MIP and IME. However, when groups were analyzed subdivided in children and adolescents, IME was significantly reduced (p = 0.003) in asthmatic adolescents. Indeed, when groups were also stratified considering the nutritional status, IME showed a reduction in asthmatic adolescents with overweight (p = 0.042) and obesity (p = 0.041) when compared to healthy controls. No effects of physical activity levels between groups were found. CONCLUSIONS: Results demonstrate a reduction in the IME in asthmatic adolescents with overweight and obesity, indicating an association between asthma, nutritional status and respiratory muscle function.

Peri-implantite: etiologia e tratamento / Periimplantitis: etiology and treatment

A peri-implantite tem sido descrita como uma alteração patológica dos tecidos ao redor dos implantes osseointegrados, sendo a microbiota e o trauma oclusal considerados seus principais fatores etiológicos. A profundidade de sondagem, o sangramento, a mucosa queratinizada, o fluxo do fluido sulcular e o infiltrado inflamatório servem como parâmetros para avaliar a saúde peri-implantar. O tratamento da peri-implantite inclui debridamento mecânico, instrução de higiene oral, destoxificação da superfície implantar, uso de antimicrobianos e terapias ressectivas e regenerativas. Uma correta intervenção parece ser necessária para a resolução da lesão peri-implantar.

The peri-implantitis has been described as a pathological alteration of tissues around dental implants, and the microbiota and occlusal trauma considered its main etiological factors. The probing depth, bleeding, keratinized tissue, the flow of sulcular fluid and inflammatory infiltrate serve as parameters to evaluate the peri-implant health. Treatment of peri-implant includes mechanical debridement, oral hygiene instruction, surface detoxification deploy, use of antibiotics and resective and regenerative therapies. A correct intervention seems necessary for the resolution of the peri-implant injury.

Characterization of freshwater benthic biofilm-forming Hydrocoryne (Cyanobacteria) isolates from Antarctica.

The aims of this work were to study cyanobacterial isolates resembling the genus Hydrocoryne using a combination of morphology and phylogeny of 16S rRNA and nifH sequences and to investigate genes involved in cyanotoxin and protease inhibitor production. Four new cyanobacterial strains, isolated from biofilm samples collected from King George Island, Antarctica, were studied. In terms of morphology, these new strains share traits similar to true Anabaena morphotypes (benthic ones), whereas phylogenetic analysis of their 16S rRNA gene sequences grouped them with the sequence of the type species Hydrocoryne spongiosa (H. Schwabe ex Bornet and Flahault 1886-1888), but not with sequences of the type species from the genus Anabaena. This cluster is the sister group of Anabaena morphotypes isolated only from the Gulf of Finland. In addition, this cluster is related to two other clusters formed by sequences of Anabaena isolated from different sites. Partial nifH genes were sequenced from two strains and the phylogenetic tree revealed that the Antarctic nifH sequences clustered with sequences from Anabaena. Furthermore, two strains were tested, using PCR with specific primers, for the presence of genes involved in cyanotoxins (microcystin and saxitoxin) and protease inhibitor (aeruginosin, and cyanopeptolin). Only cyanopeptolin was amplified using PCR. These four Hydrocoryne strains are the first to be isolated and sequenced from Antarctica, which improves our knowledge on this poorly defined cyanobacterial genus.

A new method for fixing Biomphalaria glabrata for histologic studies, using shell perforation.

A new technique for fixation of Biomphalaria glabrata for histologic studies is described. It consists in performing several external holes in the shell, before placing the entire snail into the fixative. It is a very practical and quick procedure that showed excellent results when compared to the usual techniques.

A new method for fixing Biomphalaria glabrata for histologic studies, using shell perforation

A new technique for fixation of Biomphalaria glabrata for histologic studies is described. It consists in performing several external holes in the shell, before placing the entire snail into the fixative. It is a very practical and quick procedure that showed excellent results when compared to the usual techniques