A Characterization of Complex-Valued Random Variables With Rotationally-Invariant Moments

A complex-valued random variable Z is rotationally invariant if the moments of Z are the same as the moments of W=e^{i*theta}Z. In the first part of the article, we characterize such random variables, in terms of vanishing unbalanced moments, moment and cumulant generating functions, and polar decomposition. In the second part, we consider random variables whose moments are not necessarily finite, but which have a density. In this setting, we prove two characterizations that are equivalent to rotational invariance, one involving polar decomposition, and the other involving entropy. If a random variable has both a density and moments which determine it, all of these characterizations are equivalent

Anti-PD-L1 immunoconjugates for cancer therapy: Are available antibodies good carriers for toxic payload delivering?

Immune checkpoint mechanisms are important molecular cell systems that maintain tolerance toward autoantigens in order to prevent immunity-mediated accidental damage. It is well known that cancer cells may exploit these molecular and cellular mechanisms to escape recognition and elimination by immune cells. Programmed cell death protein-1 (PD-1) and its natural ligand programmed cell death ligand-1 (PD-L1) form the PD-L1/PD-1 axis, a well-known immune checkpoint mechanism, which is considered an interesting target in cancer immunotherapy. In fact, the expression of PD-L1 was found in various solid malignancies and the overactivation of PD-L1/PD-1 axis results in a poor patient survival rate. Breaking PD-L1/PD-1 axis, by blocking either the cancer side or the immune side of the axis, is currently used as anti-cancer strategy to re-establish a tumor-specific immune response. For this purpose, several blocking antibodies are now available. To date, three anti-PD-L1 antibodies have been approved by the FDA, namely atezolizumab, durvalumab and avelumab. The main advantages of anti-PD-L1 antibodies arise from the overexpression of PD-L1 antigen by a high number of tumor cells, also deriving from different tissues; this makes anti-PD-L1 antibodies potential pan-specific anti-cancer molecules. Despite the good results reported in clinical trials with anti-PD-L1 antibodies, there is a significant number of patients that do not respond to the therapy. In fact, it should be considered that, in some neoplastic patients, reduced or absent infiltration of cytotoxic T cells and natural killer cells in the tumor microenvironment or presence of other immunosuppressive molecules make immunotherapy with anti-PD-L1 blocking antibodies less effective. A strategy to improve the efficacy of antibodies is to use them as carriers for toxic payloads (toxins, drugs, enzymes, radionuclides, etc.) to form immunoconjugates. Several immunoconjugates have been already approved by FDA for treatment of malignancies. In this review, we focused on PD-L1 targeting antibodies utilized as carrier to construct immunoconjugates for the potential elimination of neoplastic cells, expressing PD-L1. A complete examination of the literature regarding anti-PD-L1 immunoconjugates is here reported, describing the results obtained in vitro and in vivo. The real potential of anti-PD-L1 antibodies as carriers for toxic payload delivery is considered and extensively discussed

A meteorological–hydrological regional ensemble forecast for an early-warning system over small Apennine catchments in Central Italy

Abstract. The weather forecasts for precipitation have considerably improved in recent years thanks to the increase of computational power. This allows for the use of both a higher spatial resolution and the parameterization schemes specifically developed for representing sub-grid scale physical processes at high resolution. However, precipitation estimation is still affected by errors that can impact the response of hydrological models. To the aim of improving the hydrological forecast and the characterization of related uncertainties, a regional-scale meteorological–hydrological ensemble is presented. The uncertainties in the precipitation forecast and how they propagate in the hydrological model are also investigated. A meteorological–hydrological offline coupled ensemble is built to forecast events in a complex-orography terrain where catchments of different sizes are present. The Best Discharge-based Drainage (BDD; both deterministic and probabilistic) index, is defined with the aim of forecasting hydrological-stress conditions and related uncertainty. In this context, the meteorological–hydrological ensemble forecast is implemented and tested for a severe hydrological event which occurred over Central Italy on 15 November 2017, when a flood hit the Abruzzo region with precipitation reaching 200 mm (24 h)−1 and producing damages with a high impact on social and economic activities. The newly developed meteorological–hydrological ensemble is compared with a high-resolution deterministic forecast and with the observations (rain gauges and radar data) over the same area. The receiver operating characteristic (ROC) statistical indicator shows how skilful the ensemble precipitation forecast is with respect to both rain-gauge- and radar-retrieved precipitation. Moreover, both the deterministic and probabilistic configurations of the BDD index are compared with the alert map issued by Civil Protection Department for the event showing a very good agreement. Finally, the meteorological–hydrological ensemble allows for an estimation of both the predictability of the event a few days in advance and the uncertainty of the flood. Although the modelling framework is implemented on the basins of the Abruzzo region, it is portable and applicable to other areas

Sequence, structure, and binding site analysis of kirkiin in comparison with ricin and other type 2 rips

Kirkiin is a new type 2 ribosome-inactivating protein (RIP) purified from the caudex of Adenia kirkii with a cytotoxicity compared to that of stenodactylin. The high toxicity of RIPs from Adenia genus plants makes them interesting tools for biotechnology and therapeutic applications, particularly in cancer therapy. The complete amino acid sequence and 3D structure prediction of kirkiin are here reported. Gene sequence analysis revealed that kirkiin is encoded by a 1572 bp open reading frame, corresponding to 524 amino acid residues, without introns. The amino acid sequence analysis showed a high degree of identity with other Adenia RIPs. The 3D structure of kirkiin preserves the overall folding of type 2 RIPs. The key amino acids of the active site, described for ricin and other RIPs, are also conserved in the kirkiin A chain. Sugar affinity studies and docking experiments revealed that both the 1α and 2γ sites of the kirkiin B chain exhibit binding activity toward lactose and D-galactose, being lower than ricin. The replacement of His246 in the kirkiin 2γ site instead of Tyr248 in ricin causes a different structure arrangement that could explain the lower sugar affinity of kirkiin with respect to ricin

High-dose chemotherapy as initial salvage treatment in relapsed testicular cancer patients

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Psychology and hereditary angioedema: A systematic review

Background: Hereditary angioedema (HAE) is caused by mutations in the C1 inhibitor (C1-INH) gene Serpin Family G Member 1(SERPING1), which results in either the decreased synthesis of normal C1-INH (C1-INH–HAE type I) or expression of unfunctional C1-INH (C1-INH–HAE type II). In recent studies, emotional stress was reported by patients as the most common trigger factor for C1-INH–HAE attacks. Moreover, patients reported considerable distress over the significant variability and uncertainty with which the disease manifests, in addition to the impact of physical symptoms on their overall quality of life. Objective: We did a systematic review of the literature to shed light on the advancements made in the study of how stress and psychological processes impact C1-INH–HAE. Methods: All of the articles on C1-INH–HAE were analyzed up to December 2019. Both medical data bases and psychological data bases were examined. The keywords (KWs) used for searching the medical and psychological data bases were the following: “hereditary angioedema,” “psychology,” “stress,” “anxiety,” and “depression.” Results: Of a total of 2549 articles on C1-INH–HAE, 113 articles were retrieved from the literature search by using the related KWs. Twenty-one of these articles were retrieved, examined, and classified. Conclusion: Although the literature confirmed that stress may induce various physical diseases, it also warned against making simplistic statements about its incidence that did not take into account the complexity and multicausality of factors that contribute to C1-INH–HAE expression

Net gain: Low-cost, trawl-associated eDNA samplers upscale ecological assessment of marine demersal communities

Marine biodiversity stewardship requires costly and time-consuming capture-based monitoring techniques, which limit our understanding of the distribution and status of marine populations. Here, we reconstruct catch and demersal community compo- sition in a set of 24 fishing sites in the central Tyrrhenian Sea by gathering environ- mental DNA (eDNA) aboard commercial bottom-trawl fishing vessels. We collected genetic material from two sources: the water draining from the net after the end of hauling operations (“slush”), and custom-made rolls of gauze tied to a hollow perfo- rated sphere placed inside the fishing net (“metaprobe”). Species inventories were generated using a combination of fish-specific (Tele02 12S) and universal metazoan (COI) molecular markers. DNA metabarcoding data recovered over 90% of the caught taxa and accurately reconstructed the overall structure of the assemblages of the examined sites, reflecting expected differences linked to major drivers of community structure in Mediterranean demersal ecosystems, such as depth, distance from the coast, and fishing effort. eDNA also returned a “biodiversity bonus” mostly consisting of pelagic species not catchable by bottom trawl but present in the surrounding en- vironment. Overall, the “metaprobe” gauzes showed a greater biodiversity detection power as compared to “slush” water, both qualitatively and quantitatively, strengthen- ing the idea that these low-cost sampling devices can play a major role in upscaling the gathering of data on both catch composition and the broader ecological charac- teristics of marine communities sustaining trawling activities. This approach has the potential to drastically expand the reach of ecological monitoring, whereby fishing vessels operating across the oceans may serve as opportunistic scientific platforms to increase the strength and granularity of marine biodiversity data

DNA metabarcoding of trawling bycatch reveals diversity and distribution patterns of sharks and rays in the central Tyrrhenian Sea

Conservation and management of chondrichthyans are becoming increasingly important, as many species are particularly vulnerable to fishing activities, primarily as bycatch, which leads to incomplete catch reporting, potentially hiding the impact on these organisms. Here, we aimed at implementing an eDNA metabarcoding approach to reconstruct shark and ray bycatch composition from 24 hauls of a bottom trawl fishing vessel in the central Mediterranean. eDNA samples were collected through the passive filtration of seawater by simple gauze rolls encapsulated in a probe (the "metaprobe"), which already showed great efficiency in detecting marine species from trace DNA in the environment. To improve molecular taxonomic detection, we enhanced the 12S target marker reference library by generating sequences for 14 Mediterranean chondrichthyans previously unrepresented in public repositories. DNA metabarcoding data correctly identifies almost all bycaught species and detected five additional species not present in the net, highlighting the potential of this method to detect rare species. Chondrichthyan diversity showed significant association with some key environmental variables (depth and distance from the coast) and the fishing effort, which are known to influence demersal communities. As DNA metabarcoding progressively positions itself as a staple tool for biodiversity monitoring, we expect that its melding with opportunistic, fishery-dependent surveys could reveal additional distribution features of threatened and elusive megafauna

Automatic lung segmentation and quantification of aeration in computed tomography of the chest using 3D transfer learning

Background: Identification of lung parenchyma on computer tomographic (CT) scans in the research setting is done semi-automatically and requires cumbersome manual correction. This is especially true in pathological conditions, hindering the clinical application of aeration compartment (AC) analysis. Deep learning based algorithms have lately been shown to be reliable and time-efficient in segmenting pathologic lungs. In this contribution, we thus propose a novel 3D transfer learning based approach to quantify lung volumes, aeration compartments and lung recruitability. Methods: Two convolutional neural networks developed for biomedical image segmentation (uNet), with different resolutions and fields of view, were implemented using Matlab. Training and evaluation was done on 180 scans of 18 pigs in experimental ARDS (u2NetPig) and on a clinical data set of 150 scans from 58 ICU patients with lung conditions varying from healthy, to COPD, to ARDS and COVID-19 (u2NetHuman). One manual segmentations (MS) was available for each scan, being a consensus by two experts. Transfer learning was then applied to train u2NetPig on the clinical data set generating u2NetTransfer. General segmentation quality was quantified using the Jaccard index (JI) and the Boundary Function score (BF). The slope between JI or BF and relative volume of non-aerated compartment (SJI and SBF, respectively) was calculated over data sets to assess robustness toward non-aerated lung regions. Additionally, the relative volume of ACs and lung volumes (LV) were compared between automatic and MS. Results: On the experimental data set, u2NetPig resulted in JI = 0.892 [0.88 : 091] (median [inter-quartile range]), BF = 0.995 [0.98 : 1.0] and slopes SJI = −0.2 {95% conf. int. −0.23 : −0.16} and SBF = −0.1 {−0.5 : −0.06}. u2NetHuman showed similar performance compared to u2NetPig in JI, BF but with reduced robustness SJI = −0.29 {−0.36 : −0.22} and SBF = −0.43 {−0.54 : −0.31}. Transfer learning improved overall JI = 0.92 [0.88 : 0.94], P < 0.001, but reduced robustness SJI = −0.46 {−0.52 : −0.40}, and affected neither BF = 0.96 [0.91 : 0.98] nor SBF = −0.48 {−0.59 : −0.36}. u2NetTransfer improved JI compared to u2NetHuman in segmenting healthy (P = 0.008), ARDS (P < 0.001) and COPD (P = 0.004) patients but not in COVID-19 patients (P = 0.298). ACs and LV determined using u2NetTransfer segmentations exhibited < 5% volume difference compared to MS. Conclusion: Compared to manual segmentations, automatic uNet based 3D lung segmentation provides acceptable quality for both clinical and scientific purposes in the quantification of lung volumes, aeration compartments, and recruitability

Automatic Lung Segmentation and Quantification of Aeration in Computed Tomography of the Chest Using 3D Transfer Learning

Background: Identification of lung parenchyma on computer tomographic (CT) scans in the research setting is done semi-automatically and requires cumbersome manual correction. This is especially true in pathological conditions, hindering the clinical application of aeration compartment (AC) analysis. Deep learning based algorithms have lately been shown to be reliable and time-efficient in segmenting pathologic lungs. In this contribution, we thus propose a novel 3D transfer learning based approach to quantify lung volumes, aeration compartments and lung recruitability. Methods: Two convolutional neural networks developed for biomedical image segmentation (uNet), with different resolutions and fields of view, were implemented using Matlab. Training and evaluation was done on 180 scans of 18 pigs in experimental ARDS (u2NetPig) and on a clinical data set of 150 scans from 58 ICU patients with lung conditions varying from healthy, to COPD, to ARDS and COVID-19 (u2NetHuman). One manual segmentations (MS) was available for each scan, being a consensus by two experts. Transfer learning was then applied to train u2NetPig on the clinical data set generating u2NetTransfer. General segmentation quality was quantified using the Jaccard index (JI) and the Boundary Function score (BF). The slope between JI or BF and relative volume of non-aerated compartment (SJI and SBF, respectively) was calculated over data sets to assess robustness toward non-aerated lung regions. Additionally, the relative volume of ACs and lung volumes (LV) were compared between automatic and MS. Results: On the experimental data set, u2NetPig resulted in JI = 0.892 [0.88 : 091] (median [inter-quartile range]), BF = 0.995 [0.98 : 1.0] and slopes SJI = 120.2 {95% conf. int. 120.23 : 120.16} and SBF = 120.1 { 120.5 : 120.06}. u2NetHuman showed similar performance compared to u2NetPig in JI, BF but with reduced robustness SJI = 120.29 { 120.36 : 120.22} and SBF = 120.43 { 120.54 : 120.31}. Transfer learning improved overall JI = 0.92 [0.88 : 0.94], P &lt; 0.001, but reduced robustness SJI = 120.46 { 120.52 : 120.40}, and affected neither BF = 0.96 [0.91 : 0.98] nor SBF = 120.48 { 120.59 : 120.36}. u2NetTransfer improved JI compared to u2NetHuman in segmenting healthy (P = 0.008), ARDS (P &lt; 0.001) and COPD (P = 0.004) patients but not in COVID-19 patients (P = 0.298). ACs and LV determined using u2NetTransfer segmentations exhibited &lt; 5% volume difference compared to MS. Conclusion: Compared to manual segmentations, automatic uNet based 3D lung segmentation provides acceptable quality for both clinical and scientific purposes in the quantification of lung volumes, aeration compartments, and recruitability