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Purging frequency and number of purging methods as indicators of severity in bulimia nervosa: Interactive effects with binge eating episodes
Get PDFObjective:
In the Diagnostic and Statistical Manual of Mental Disorders-5, severity of bulimia nervosa (BN) is defined by the frequency of purging behaviour. Previous research suggests the number of purging methods as an alternative rating. The current analysis investigated characteristics (sociodemographic and treatment-related variables, body mass index [BMI], eating disorder-specific and general psychopathology) of persons with BN as a function of purging frequency and number of purging methods in order to examine which approach might be better suited for indicating severity of BN.
Method:
Two-hundred and sixty-one persons (98.5% female; mean age 25.2 years, SD = 9.41; mean BMI 22.0 kg/m2, SD = 3.79) with BN completed self-report questionnaires on eating disorder-specific and general psychopathology at admission to inpatient (n = 214) or daypatient (n = 47) treatment.
Results:
Higher severity based on either purging frequency or number of purging methods tended to relate to lower BMI and higher eating disorder-specific and general psychopathology. In addition, binge-eating frequency differentially related to eating disorder-specific and general psychopathology as a function of severity groups.
Conclusions:
This study partially supports the utility of both purging frequency and the number of purging methods as indicators of severity in persons with BN. However, focussing only on purging behaviours may be short-sighted as it appears necessary to consider the number of binge-eating episodes as well when evaluating severity of BN
Adamantane‐Type Nitridophosphate Phosphors AExLi10−2xP4N10 : Eu2+ via Medium‐Pressure Ion‐Exchange Reactions
Get PDFNitridophosphates have emerged as promising host compounds in the field of solid-state lighting. Their industrial relevance has increased significantly, mainly due to recent advances in synthetic approaches under medium-pressure (MP) conditions, including ammonothermal synthesis and hot isostatic pressing (HIP). In this study, we report on the synthesis and characterization of the quaternary representatives CaxLi10−2xP4N10 (x=2, 2.7, 4) and Sr3Li4P4N10, prepared via a simplified ion exchange reaction under MP conditions, starting from the nitridophosphate-based lithium ion conductor Li10P4N10. The synthesis route allowed for the preservation of the anionic [P4N10]10− structural motif of the starting material, while simultaneously introducing potential doping sites for Eu2+ by incorporating divalent alkaline earth cations (Ca2+/Sr2+). Upon excitation of Eu2+ doped samples with blue light, strong luminescence due to parity-allowed 4f6(7F)5d1→4f7(8S7/2) transition can be observed in the red (Ca2Li6P4N10 : Eu2+: λmax=626 nm), yellow/orange (Ca2.7Li4.6P4N10 : Eu2+: λmax1=506 nm, λmax2=592 nm and Sr3Li4P4N10 : Eu2+: λmax=596 nm) and green (Ca4Li2P4N10 : Eu2+: λmax=546 nm) spectral regions of the visible light. The compounds presented, together with the simplified synthetic approach, demonstrate the significant potential of ion exchange on Li ion conductors for the development of novel nitridophosphates in the future
Modular Design and Scaffold‐Synthesis of Multi‐Functional Fluorophores for Targeted Cellular Imaging and Pyroptosis
Get PDFFluorophores are essential tools for optical imaging and biomedical research. Their synthetic modification to incorporate new functions, however, remains a challenging task. Conventional strategies rely on linear synthesis in which a parent framework is gradually extended. We here designed and synthesized a versatile library of multi-functional fluorophores via a scaffold-based Ugi four-component reaction (U-4CR). The adaptability of the scaffold is achieved through modification of starting materials. This allows to use a small range of starting materials for the creation of fluorogenic probes that can detect reactive-oxygen species and where the localization into subcellular organelles or membranes can be controlled. We present reaction yields ranging from 60 % to 90 % and discovered that some compounds can even function as imaging and therapeutic agents via Fenton chemistry inducing pyroptosis in living cancer cells. Our study underlines the potential of scaffold-based synthesis for versatile creation of functional fluorophores and their applications
Truthmaker Semantics and Natural Language Semantics
Get PDFTruthmaker semantics is a non-classical logical framework that has recently garnered significant interest in philosophy, logic, and natural language semantics. It redefines the propositional connectives and gives rise to more fine-grained entailment relations than classical logic. In its model theory, truth is not determined with respect to possible worlds, but with respect to truthmakers, such as states or events. Unlike possible worlds, these truthmakers may be partial; they may be either coherent or incoherent; and they are understood to be exactly or wholly relevant to the truth of the sentences they verify. Truthmaker semantics generalises collective, fusion-based theories of conjunction; alternative-based theories of disjunction; and nonstandard negation semantics. This article provides a gentle introduction to truthmaker semantics aimed at linguists; describes applications to various natural language phenomena such as imperatives, ignorance implicatures, and negative events; and discusses its similarities and differences to related frameworks such as event semantics, situation semantics, alternative semantics, and inquisitive semantics
Step‐by‐Step Towards Biological Homochirality – from Prebiotic Randomness To Perfect Asymmetry
Get PDFThe history of life's formation and the origin of its stereochemistry are nearly as multifaceted as the life itself. In this review, we focus on analyzing the step-by-step path leading to what we can define as “life” in parallel to what we know about the emergence of enantiomeric imbalance and subsequent transition to full homochirality. We start at the level of assembly of the building blocks of life from inorganic molecules and build up to the polymerization and formation of nucleic acids and peptides. We report and analyze different theories at various stages of this development and try to elucidate the most plausible theory
Vestibular dysfunction: a hidden risk factor for Alzheimer’s disease
Get PDFBackground: London taxi drivers’ navigationally challenged hippocampi are known to be enlarged, and reduced Alzheimer’s disease (AD)-related mortality has recently been shown in similarly well-versed drivers, implying a neuroprotective effect through hippocampal engagement. Vestibular function has been linked to hippocampal size, suggesting that vestibular input may influence AD risk.
Methods: Including 16 known modifiable lifestyle factors as covariates, we analyzed UK Biobank (UKB) volunteers aged over 55 years and without dementia at baseline to assess how peripheral vestibular dysfunction (PVD) influences the likelihood of an AD diagnosis.
Results: 4684 AD and 2133 PVD cases were identified based on their ICD diagnoses; even accounting for other risk factors, PVD increased the risk of AD 1.7 times in UKB volunteers.
Discussion: Vestibular loss, linked to hippocampal atrophy and default mode network disruption, appears to increase AD risk. Consequently, active vestibular stimulation by balance training or neuromodulation could offer potential for modifying AD progression
Deep learning based super‐resolution for CBCT dose reduction in radiotherapy
Get PDFBackground:
Cone-beam computed tomography (CBCT) is a crucial daily imaging modality in image-guided and adaptive radiotherapy. However, the use of ionizing radiation in CBCT imaging increases the risk of secondary cancers, which is particularly concerning for pediatric patients. Deep learning super-resolution has shown promising results in enhancing the resolution of photographic and medical images but has not yet been explored in the context of CBCT dose reduction.
Purpose:
To facilitate CBCT imaging dose reduction, we propose using an enhanced super-resolution generative adversarial network (ESRGAN) in both the projection and image domains to restore the image quality of low-dose CBCT.
Methods:
An extensive projection database, containing 2997 CBCT scans from head and neck cancer patients, was used to train two different ESRGAN models to generate super-resolution CBCTs. One model operated in the projection domain, using pairs of simulated low-resolution (low-dose) and original high-resolution (high-dose) projections and yielded CBCTSRpro. The other model operated in the image domain, using pairs of axial slices from reconstructed low-resolution and high-resolution CBCTs (CBCTLR and CBCTHR) and resulted in CBCTSRimg. Super-resolution CBCTs were evaluated in terms of image similarity (MAE, ME, PSNR, and SSIM), noise characteristics, spatial resolution, and registration accuracy, using the original CBCT as a reference. To test the perceptual difference between the original and super-resolution CBCT, we performed a visual Turing test.
Results:
Visually, both super-resolution approaches in the projection and image domains improved the image quality of low-dose CBCTs. This was confirmed by the visual Turing test, that showed low accuracy, sensitivity, and specificity, indicating almost no perceptual difference between CBCTHR and the super-resolution CBCTs. CBCTSRimg (accuracy: 0.55, sensitivity: 0.59, specificity: 0.50) performed slightly better than CBCTSRpro (accuracy: 0.59, sensitivity: 0.61, specificity: 0.57). Image similarity metrics were affected by varying noise levels and did not reflect the visual improvements, with MAE/ME/PSNR/SSIM values of 110.4 HU/2.9 HU/40.4 dB/0.82 for CBCTLR, 136.6 HU/−0.4 HU/38.6 dB/0.77 for CBCTSRpro, and 128.2 HU/1.9 HU/39.0 dB/0.80 for CBCTSRimg. In terms of spatial resolution, quantified by calculating 10% levels of the task transfer function, both CBCTSRpro and CBCTSRimg outperformed CBCTLR and nearly matched the reference CBCTHR (CBCTLR: 0.66 lp/mm, CBCTSRpro: 0.88 lp/mm, CBCTSRimg: 0.95 lp/mm, CBCTHR: 1.01 lp/mm). Noise characteristics of CBCTSRimg and CBCTSRpro were comparable to the reference CBCTHR. Registration parameters showed negligible differences for all CBCTs (CBCTLR, CBCTSRpro, CBCTSRimg), with average absolute differences in registration parameters being below 0.4° for rotations and below 0.06 mm for translations (CBCTHR as reference).
Conclusions:
This study demonstrates that deep learning can be a valuable tool for CBCT dose reduction in CBCT-guided radiotherapy by acquiring low-dose CBCTs and restoring the image quality using deep learning super-resolution. The results suggest that higher quality images can be generated when super-resolution is performed in the image domain compared to the projection domain
Myeloid Mir34a suppresses colitis-associated colon cancer: characterization of mediators by single-cell RNA sequencing
Get PDFWe have previously shown that general deletion of the gene encoding the p53-inducible Mir34a microRNA enhances the number and invasion of colitis-associated colorectal cancers (CACs) in mice. Since the p53-pathway has been implicated in tumor-suppression mediated by cells in the tumor microenvironment (TME) we deleted Mir34a in myeloid cells and characterized CACs in these with scRNA-Seq (single cell RNA sequencing). This revealed an increase in specific macrophage subtypes, such as Cdk8+ macrophages and Mrc1+, M2-like macrophages. The latter displayed elevated expression of 21 known Mir34a target mRNAs, including Csf1r, Axl, Foxp1, Ccr1, Nampt, and Tgfbr2, and 32 predicted Mir34a target mRNAs. Furthermore, Mir34a-deficient BMDMs showed enhanced migration, elevated expression of Csf1r and a shift towards M2-like polarization when compared to Mir34a-proficient BMDMs. Concomitant deletion of Csf1r or treatment with a Csf1r inhibitor reduced the CAC burden and invasion in these mice. Notably, loss of myeloid Mir34a function resulted in a prominent, inflammatory CAC cell subtype, which displayed epithelial and macrophage markers. These cells displayed high levels of the EMT transcription factor Zeb2 and may therefore enhance the invasiveness of CACs. Taken together, our results provide in vivo evidence for a tumor suppressive role of myeloid Mir34a in CACs which is, at least in part, mediated by maintaining macrophages in an M1-like state via repression of Mir34a targets, such as Csf1r. Collectively, these findings may serve to identify new therapeutic targets and approaches for treatment of CAC
Inverse problems are solvable on real number signal processing hardware
Get PDFDespite the success of Deep Learning (DL) serious reliability issues such as non-robustness persist. An interesting aspect is, whether these problems arise due to insufficient tools or fundamental limitations of DL. We study this question from the computability perspective by characterizing the limits the applied hardware imposes. For this, we focus on the class of inverse problems, which, in particular, encompasses any task to reconstruct data from measurements. On digital hardware, a conceptual barrier on the capabilities of DL for solving finite-dimensional inverse problems has in fact already been derived. This paper investigates the general computation framework of Blum-Shub-Smale (BSS) machines, describing the processing and storage of arbitrary real values. Although a corresponding real-world computing device does not exist, research and development towards real number computing hardware, usually referred to by “neuromorphic computing”, has increased in recent years. In this work, we show that the framework of BSS machines does enable the algorithmic solvability of finite dimensional inverse problems. Our results emphasize the influence of the considered computing model in questions of accuracy and reliability
Optimal Operation and Valuation of Electricity Storages in Intraday Markets
No full textThis paper applies computational techniques of convex stochastic optimization to optimal operation and valuation of electricity storages in the face of uncertain electricity prices. Our approach is applicable to various specifications of storages, and it allows for e.g.\ hard constraints on storage capacity and charging speed. Our valuations are based on the indifference pricing principle, which builds on optimal trading strategies and calibrates to the user's initial position, market views and risk preferences. We illustrate the effects of storage capacity and charging speed by numerically computing the valuations using stochastic dual dynamic programming