Notes

Unambiguous discovery of nitrated intense vapours through fluorescence quenching involving dendrimer videos.
The Southwest Oncology Group (SWOG)1931 trial, also known as PROBE (ClinicalTrials.gov Identifier NCT04510597) is a phase III study evaluating the role of cytoreductive nephrectomy (CN) in metastatic renal cell cancer (RCC). Kidney cancer presenting with synchronous metastases has demonstrated shorter survival outcome compared to the patients relapsing with metastases after nephrectomy. Previously, CN has been associated with survival improvement when interferon-based systemic therapy was used. In the setting of antivascular therapy sunitinib, a prospective randomized clinical trial demonstrated no benefit of CN. Immune checkpoint-based combination therapy has now become the standard-of-care in the frontline setting for RCC. The role of nephrectomy or primary resection has not been evaluated in the setting of immune checkpoint-based systemic therapy. The sequence and optimal timing of nephrectomy is also not established. The PROBE study design attempts to answer the question whether CN has an impact on overaltential mechanism leading to improved survival is the broader antigen spread and higher neoantigen load enabled by the primary tumor enhancing the efficacy of the immune therapy. CN after initial systemic therapy would help select the patient subset most likely to benefit and will potentially enable eradication of immune resistant clones within the primary tumor.
Multiple myeloma (MM) patients with triple- and penta-refractory disease have a poor survival and limited treatment options. Selinexor, in combination with bortezomib and dexamethasone, demonstrated clinical activity in the STOMP study as well as in the BOSTON study in previously treated patients with disease refractory to a proteasome inhibitor (PI).

Here, we report a real-world case series of 7 heavily pretreated MM patients who had been extensively pretreated with bortezomib and had disease refractory to PIs, including carfilzomib; whowere administered a starting dose of 100 mg of selinexor, 20-40 mg dexamethasone and 1.3 mg/m
of bortezomib, each once weekly. The majority of these patients (6 patients, 86.0%) had penta-refractory disease, with 5 patients (71.4%) having disease refractory to bortezomib and carfilzomib, and all 7 patients having pomalidomide refractory disease. The median number of prior lines of therapy was 8 (range 4-12).

The seven patients in this case series received selinexor for a median of 5 cycles (range 1-10). Four patients (57.1%) had a dose reduction of selinexor. Five patients (71.4%) had a response, of which 2 (29.0%) had a very good partial response (VGPR) and 3 (43.0%) had a partial response (PR). One patient (14.3%) had stable disease (SD) and 1 (14.3%) had progressive disease (PD). There were no new safety signals.

The selinexor, bortezomib, and dexamethasone triplet combination demonstrates activity in PI-resistant MM and patients with heavily pretreated MM with refractory disease and after multiple lines of therapy.
The selinexor, bortezomib, and dexamethasone triplet combination demonstrates activity in PI-resistant MM and patients with heavily pretreated MM with refractory disease and after multiple lines of therapy.The development of digital cancer twins relies on the capture of high-resolution representations of individual cancer patients throughout the course of their treatment. Our research aims to improve the detection of metastatic disease over time from structured radiology reports by exposing prediction models to historical information. We demonstrate that Natural language processing (NLP) can generate better weak labels for semi-supervised classification of computed tomography (CT) reports when it is exposed to consecutive reports through a patient's treatment history. Around 714,454 structured radiology reports from Memorial Sloan Kettering Cancer Center adhering to a standardized departmental structured template were used for model development with a subset of the reports included for validation. selleck chemical To develop the models, a subset of the reports was curated for ground-truth 7,732 total reports in the lung metastases dataset from 867 individual patients; 2,777 reports in the liver metastases dataset from 315 patieedict the presence of metastatic disease in multiple organs with higher performance when compared with a single-report-based prediction. It demonstrates a promising automated approach to label large numbers of radiology reports without involving human experts in a time- and cost-effective manner and enables tracking of cancer progression over time.How do speakers produce novel words? This programmatic paper synthesizes research in linguistics and neuroscience to argue for a parallel distributed architecture of the language system, in which distributed semantic representations activate competing form chunks in parallel. This process accounts for both the synchronic phenomenon of paradigm uniformity and the diachronic process of paradigm leveling; i.e., the shaping or reshaping of relatively infrequent forms by semantically-related forms of higher frequency. However, it also raises the question of how leveling is avoided. A negative feedback cycle is argued to be responsible. The negative feedback cycle suppresses activated form chunks with unintended semantics or connotations and allows the speaker to decide when to begin speaking. The negative feedback cycle explains away much of the evidence for paradigmatic mappings, allowing more of the grammar to be described with only direct form-meaning mappings/constructions. However, there remains an important residue of cases for which paradigmatic mappings are necessary. I show that these cases can be accounted for by spreading activation down paradigmatic associations as the source of the activation is being inhibited by negative feedback. The negative feedback cycle provides a mechanistic explanation for several phenomena in language change that have so far eluded usage-based accounts. In particular, it provides a mechanism for degrammaticalization and affix liberation (e.g., the detachment of -holic from the context(s) in which it occurs), explaining how chunks can gain productivity despite occurring in a single fixed context. It also provides a novel perspective on paradigm gaps. Directions for future work are outlined.Proper identification of collocations (and more generally of multiword expressions (MWEs), is an important qualitative step for several NLP applications and particularly so for translation. Since many MWEs cannot be translated literally, failure to identify them yields at best inaccurate translation. This paper is mostly be concerned with collocations. We will show how they differ from other types of MWEs and how they can be successfully parsed and translated by means of a grammar-based parser and translator.Expectation-based theories of sentence processing posit that processing difficulty is determined by predictability in context. While predictability quantified via surprisal has gained empirical support, this representation-agnostic measure leaves open the question of how to best approximate the human comprehender's latent probability model. This article first describes an incremental left-corner parser that incorporates information about common linguistic abstractions such as syntactic categories, predicate-argument structure, and morphological rules as a computational-level model of sentence processing. The article then evaluates a variety of structural parsers and deep neural language models as cognitive models of sentence processing by comparing the predictive power of their surprisal estimates on self-paced reading, eye-tracking, and fMRI data collected during real-time language processing. The results show that surprisal estimates from the proposed left-corner processing model deliver comparable and often superior fits to self-paced reading and eye-tracking data when compared to those from neural language models trained on much more data. This may suggest that the strong linguistic generalizations made by the proposed processing model may help predict humanlike processing costs that manifest in latency-based measures, even when the amount of training data is limited. Additionally, experiments using Transformer-based language models sharing the same primary architecture and training data show a surprising negative correlation between parameter count and fit to self-paced reading and eye-tracking data. These findings suggest that large-scale neural language models are making weaker generalizations based on patterns of lexical items rather than stronger, more humanlike generalizations based on linguistic structure.A digital twin is a promising evolving tool for prognostic health monitoring. However, in rotating machinery, the transfer function between the rotating components and the sensor distorts the vibration signal, hence, complicating the ability to apply a digital twin to new systems. This paper demonstrates the importance of estimating the transfer function for a successful transfer across different machines (TDM). Furthermore, there are few algorithms in the literature for transfer function estimation. The current algorithms can estimate the magnitude of the transfer function without its original phase. In this study, a new approach is presented that enables the estimation of the transfer function with its phase for a gear signal. The performance of the new algorithm is demonstrated by measured signals and by a simulated transfer function.As the Internet of Things (IoT) applications have been introduced into daily life, privacy issues have become significant concerns to users, network service providers, device producers, and related roles. This study provides a high-level introduction of current privacy-preserving solutions in IoT systems within the three phases of data collection, transmission, and storage. In these three phases, the following aspects were examined (1). security protocols at the physical and data link layers; (2). network solutions; and (3). data storage and sharing approaches. Real-world implementations often involve more than one phase, and numerous technologies are combined to ensure privacy. Thus, an understanding of all phases and their technologies can be helpful for IoT research, design, development, and operation.We develop a spatially dependent generalization to the Wells-Riley model, which determines the infection risk due to airborne transmission of viruses. We assume that the infectious aerosol concentration is governed by an advection-diffusion-reaction equation with the aerosols advected by airflow, diffused due to turbulence, emitted by infected people, and removed due to ventilation, inactivation of the virus and gravitational settling. We consider one asymptomatic or presymptomatic infectious person breathing or talking, with or without a mask, and model a quasi-three-dimensional set-up that incorporates a recirculating air-conditioning flow. We derive a semi-analytic solution that enables fast simulations and compare our predictions to three real-life case studies-a courtroom, a restaurant, and a hospital ward-demonstrating good agreement. We then generate predictions for the concentration and the infection risk in a classroom, for four different ventilation settings. We quantify the significant reduction in the concentration and the infection risk as ventilation improves, and derive appropriate power laws.
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