R0 surgical resection of giant dedifferentiated retroperitoneal liposarcomas in the COVID era with and without nephrectomy: A case report

: Retroperitoneal sarcomas (RPSs) are rare findings that can grow into large masses without eliciting severe symptoms. At present, surgical resection is the only radical therapy, whenever it can be performed with the aim to achieve a complete removal of the tumor. The present report describes two consecutive cases of RPSs that resulted in dedifferentiated liposarcomas (DDLPSs) and these patients underwent R0 surgical resection with and without a nephron-sparing procedure. The diagnostic workup, the surgical approach, the impact of late surgical management due to the COVID pandemic and the latest literature on the topic are discussed and analyzed. The patients, who refused to undergo any medical examination during the prior 2 years due to the COVID pandemic, were admitted to Federico II University Hospital (Naples, Italy) complaining about weight loss and general abdominal discomfort. In the first case, a primitive giant abdominal right neoplasm of retroperitoneal origin enveloping and medializing the right kidney was observed. The second patient had a similar primitive retroperitoneal giant left neoplasm, which did not affect the kidney. Given the characteristics of the masses and the absence of distant metastases, after a multidisciplinary discussion, radical surgical removal was carried out for both patients. The lesions appeared well-defined from the surrounding tissues, and markedly compressed all the adjacent organs, without signs of infiltration. In the first patient, the right kidney was surrounded and undetachable from the tumor and it was removed en bloc with the mass. The second patient benefited from a nephron-sparing resection, due to the existence of a clear cleavage plane. The postoperative courses were uneventful. Both the histological examinations were oriented towards a DDLPS and both patients benefited from adjuvant chemotherapy. In conclusion, the treatment of giant RPS is still challenging and requires multidisciplinary treatment as well as, when possible, radical surgical removal. The lack of tissue infiltration and the avoidance of excision or reconstruction of major organs (including the kidney) could lead to an easier postoperative course and an improved prognosis. When possible, surgical management of recurrences or incompletely resected masses must be pursued. Since the COVID pandemic caused limited medicalization of a number of population groups and delayed diagnosis of other oncologic diseases, an increased number of DDLPSs could be expected in the near future

Advances in Understanding High-Mass X-ray Binaries with INTEGRAL and Future Directions

High mass X-ray binaries are among the brightest X-ray sources in the Milky Way, as well as in nearby Galaxies. Thanks to their highly variable emissions and complex phenomenology, they have attracted the interest of the high energy astrophysical community since the dawn of X-ray Astronomy. In more recent years, they have challenged our comprehension of physical processes in many more energy bands, ranging from the infrared to very high energies. In this review, we provide a broad but concise summary of the physical processes dominating the emission from high mass X-ray binaries across virtually the whole electromagnetic spectrum. These comprise the interaction of stellar winds with the high gravitational and magnetic fields of compact objects, the behaviour of matter under extreme magnetic and gravity conditions, and the perturbation of the massive star evolutionary processes by presence in a binary system. We highlight the role of the INTEGRAL mission in the discovery of many of the most interesting objects in the high mass X-ray binary class and its contribution in reviving the interest for these sources over the past two decades. We show how the INTEGRAL discoveries have not only contributed to significantly increase the number of high mass X-ray binaries known, thus advancing our understanding of the population as a whole, but also have opened new windows of investigation that stimulated the multi-wavelength approach nowadays common in most astrophysical research fields. We conclude the review by providing an overview of future facilities being planned from the X-ray to the very high energy domain that will hopefully help us in finding an answer to the many questions left open after more than 18 years of INTEGRAL scientific observations.The INTEGRALteams in the participating countries acknowledge the continuous support from their space agencies and funding organizations: the Italian Space Agency ASI (via different agreements including the latest one, 2019-35HH, and the ASIINAF agreement 2017-14-H.0), the French Centre national d’études spatiales (CNES), the Russian Foundation for Basic Research (KP, 19-02-00790), the Russian Science Foundation (ST, VD, AL; 19-12-00423), the Spanish State Research Agency (via different grants including ESP2017-85691-P, ESP2017-87676-C5-1-R and Unidad de Excelencia María de Maeztu – CAB MDM-2017-0737). IN is partially supported by the Spanish Government under grant PGC2018-093741-B-C21/C22 (MICIU/AEI/FEDER, UE). LD acknowledges grant 50 OG 1902

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders

Genomic Relationships, Novel Loci, and Pleiotropic Mechanisms across Eight Psychiatric Disorders

Genetic influences on psychiatric disorders transcend diagnostic boundaries, suggesting substantial pleiotropy of contributing loci. However, the nature and mechanisms of these pleiotropic effects remain unclear. We performed analyses of 232,964 cases and 494,162 controls from genome-wide studies of anorexia nervosa, attention-deficit/hyper-activity disorder, autism spectrum disorder, bipolar disorder, major depression, obsessive-compulsive disorder, schizophrenia, and Tourette syndrome. Genetic correlation analyses revealed a meaningful structure within the eight disorders, identifying three groups of inter-related disorders. Meta-analysis across these eight disorders detected 109 loci associated with at least two psychiatric disorders, including 23 loci with pleiotropic effects on four or more disorders and 11 loci with antagonistic effects on multiple disorders. The pleiotropic loci are located within genes that show heightened expression in the brain throughout the lifespan, beginning prenatally in the second trimester, and play prominent roles in neurodevelopmental processes. These findings have important implications for psychiatric nosology, drug development, and risk prediction.Peer reviewe

Observation of the Λb0 → χc1 (3872) pK− decay

No abstract available

Advances in Understanding High-Mass X-ray Binaries with INTEGRALand Future Directions

High mass X-ray binaries are among the brightest X-ray sources in the Milky Way, as well as in nearby Galaxies. Thanks to their highly variable emissions and complex phenomenology, they have attracted the interest of the high energy astrophysical community since the dawn of X-ray Astronomy. In more recent years, they have challenged our comprehension of physical processes in many more energy bands, ranging from the infrared to very high energies.In this review, we provide a broad but concise summary of the physical processes dominating the emission from high mass X-ray binaries across virtually the whole electromagnetic spectrum. These comprise the interaction of stellar winds with the high gravitational and magnetic fields of compact objects, the behaviour of matter under extreme magnetic and gravity conditions, and the perturbation of the massive star evolutionary processes by presence in a binary system.We highlight the role of the INTEGRAL mission in the discovery of many of the most interesting objects in the high mass X-ray binary class and its contribution in reviving the interest for these sources over the past two decades. We show how the INTEGRAL discoveries have not only contributed to significantly increase the number of high mass X-ray binaries known, thus advancing our understanding of the population as a whole, but also have opened new windows of investigation that stimulated the multi-wavelength approach nowadays common in most astrophysical research fields.We conclude the review by providing an overview of future facilities being planned from the X-ray to the very high energy domain that will hopefully help us in finding an answer to the many questions left open after more than 18 years of INTEGRAL scientific observations.</p

Global urban environmental change drives adaptation in white clover

Urbanization transforms environments in ways that alter biological evolution. We examined whether urban environmental change drives parallel evolution by sampling 110,019 white clover plants from 6169 populations in 160 cities globally. Plants were assayed for a Mendelian antiherbivore defense that also affects tolerance to abiotic stressors. Urban-rural gradients were associated with the evolution of clines in defense in 47% of cities throughout the world. Variation in the strength of clines was explained by environmental changes in drought stress and vegetation cover that varied among cities. Sequencing 2074 genomes from 26 cities revealed that the evolution of urban-rural clines was best explained by adaptive evolution, but the degree of parallel adaptation varied among cities. Our results demonstrate that urbanization leads to adaptation at a global scale

Observation of the Lambda(0)(b) -> chi(c1) (3872)pK(-) decay

Using proton-proton collision data, collected with the LHCb detector and corresponding to 1.0, 2.0 and 1.9 fb^ 121 of integrated luminosity at the centre-of-mass energies of 7, 8, and 13 TeV, respectively, the decay \u39b0b \u2192 \u3c7c1(3872) p K 12 with \u3c7c1(3872) \u2192 J/\u3c8 \u3c0+ \u3c0 12 is observed for the first time. The significance of the observed signal is in excess of seven standard deviations. It is found that (58 \ub1 15)% of the decays proceed via the two-body intermediate state \u3c7c1(3872) \u39b(1520). The branching fraction with respect to that of the \u39b0b \u2192 \u3c8(2S) p K 12 decay mode, where the \u3c8(2S) meson is reconstructed in the J/\u3c8 \u3c0+ \u3c0 12 final state, is measured to be: B(\u39b0b \u2192 \u3c7c1(3872) p K 12) / B(\u39b0b \u2192 \u3c8(2S) p K 12) 7 B(\u3c7c1(3872) \u2192 J/\u3c8 \u3c0+ \u3c0 12) / B(\u3c8(2S) \u2192 J/\u3c8 \u3c0+ \u3c0 12) = (5.4 \ub1 1.1 \ub1 0.2) 7 10^ 122, where the first uncertainty is statistical and the second is systematic