Miracle-dz/UniFAR-FTUs huggingface dataset:
Disc Thickness and Spacing Distance Impacts on Flow Characteristics of Multichannel Tesla Turbines

Tesla turbines are a kind of unconventional bladeless turbines, which utilize the viscosity of working fluid to rotate the rotor and realize energy conversion. They offer an attractive substitution for small and micro conventional bladed turbines due to two major advantages. In this study, the effects of two influential geometrical parameters, disc thickness and disc spacing distance, on the aerodynamic performance and flow characteristics for two kinds of multichannel Tesla turbines (one-to-one turbine and one-to-many turbine) were investigated and analyzed numerically. The results show that, with increasing disc thickness, the isentropic efficiency of the one-to-one turbine decreases a little and that of the one-to-many turbine reduces significantly. For example, for turbine cases with 0.5 mm disc spacing distance, the former drops less than 7% and the latter decreases by about 45% of their original values as disc thickness increases from 1 mm to 2 mm. With increasing disc spacing distance, the isentropic efficiency of both kinds of turbines increases first and then decreases, and an optimal value and a high efficiency range exist to make the isentropic efficiency reach its maximum and maintain at a high level, respectively. The optimal disc spacing distance for the one-to-one turbine is less than that for the one-to-many turbine (0.5 mm and 1 mm, respectively, for turbine cases with disc thickness of 1 mm). To sum up, for designing a multichannel Tesla turbine, the disc spacing distance should be among its high efficiency range, and the determination of disc thickness should be balanced between its impacts on the aerodynamic performance and mechanical stress.
OCT Angiography: A Technique for the Assessment of Retinal and Optic Nerve Diseases in the Pediatric Population

Optical coherence tomography angiography (OCT-A) is a novel, rapidly evolving, non-invasive imaging technique that allows images of the retinal vasculature to be obtained in a few seconds. Blood vessels of different retinal vascular plexuses and the foveal avascular zone (FAZ) can be examined without the administration of any contrast or dye. Due to these characteristics, OCT-A could be an excellent complementary test to study retinal vascularization in children. Until now, most of the studies with OCT-A have been conducted in adults and only a few have been carried out in children. In this review, we describe the principles and advantages of OCT-A over traditional imaging methods and provide a summary of the OCT-A findings in retinopathy of prematurity and other retinal and optic disc pathologies in children. In view of the promising results from studies, the advantages of a relatively rapid and non-invasive method to assess the retinal vasculature makes OCT-A a tool of which applications in the field of pediatric ophthalmology will be expanded in the near future for patient diagnosis and follow-up in every day clinical practice.
Patient Preference Studies for Advanced Prostate Cancer Treatment Along the Medical Product Life Cycle: Systematic Literature Review

Background Patient preference studies can inform decision-making across all stages of the medical product life cycle (MPLC). The treatment landscape for advanced prostate cancer (APC) treatment has substantially changed in recent years. However, the most patient-relevant aspects of APC treatment remain unclear. This systematic review of patient preference studies in APC aimed to summarize the evidence on patient preferences and patient-relevant aspects of APC treatments, and to evaluate the potential contribution of existing studies to decision-making within the respective stages of the MPLC. Methods We searched MEDLINE and EMBASE for studies evaluating patient preferences related to APC treatment up to October 2020. Two reviewers independently performed screening, data extraction and quality assessment in duplicate. We descriptively summarized the findings and analyzed the studies regarding their contribution within the MPLC using an analytical framework. Results Seven quantitative preference studies were included. One study each was conducted in the marketing approval and the health technology assessment (HTA) and reimbursement stage, and five were conducted in the post-marketing stage of the MPLC. While almost all stated to inform clinical practice, the specific contributions to clinical decision-making remained unclear for almost all studies. Evaluated attributes related to benefits, harms, and other treatment-related aspects and their relative importance varied relevantly between studies. All studies were judged of high quality overall, but some methodological issues regarding sample selection and the definition of patient-relevant treatment attributes were identified. Conclusion The most patient-relevant aspects regarding the benefits and harms of APC treatment are not yet established, and it remains unclear which APC treatments are preferred by patients. Findings from this study highlight the importance of transparent reporting and discussion of study findings according to their aims and with respect to their stage within the MPLC. Future research may benefit from using the MPLC framework for analyzing or determining the aims and design of patient preference studies.
Transporter-Enzyme Interplay in the Pharmacokinetics of PF-06835919, a First-In-Class Ketohexokinase Inhibitor for Metabolic Disorders and Nonalcoholic Fatty Liver Disease

Excess dietary fructose consumption promotes metabolic dysfunction, thereby increasing the risk of obesity, type 2 diabetes, nonalcoholic steatohepatitis (NASH), and related comorbidities. PF-06835919, a first-in-class ketohexokinase inhibitor, showed reversal of such metabolic disorders in preclinical models and clinical studies and is under clinical development for the potential treatment of NASH. In this study, we evaluated the transport and metabolic pathways of PF-06835919 disposition and assessed pharmacokinetics in preclinical models. PF-06835919 showed active uptake in cultured primary human hepatocytes and substrate activity to organic anion transporter (OAT)2 and organic anion transporting polypeptide (OATP)1B1 in transfected cells. “solute carrier-phenotyping” studies in human hepatocytes suggested contribution of passive uptake and OAT2- and OATP1B-mediated transport to the overall uptake to be about 15%, 60%, and 25%, respectively. PF-06835919 showed low intrinsic metabolic clearance in vitro and was found to be metabolized via both oxidative pathways (58%) and acyl glucuronidation (42%) by CYP3A, CYP2C8, CYP2C9, and UGT2B7. After intravenous dosing, PF-06835919 showed low clearance (0.4–1.3 ml/min/kg) and volume of distribution (0.17–0.38 l/kg) in rats, dogs, and monkeys. Human oral pharmacokinetics are predicted within 20% error when considering transporter-enzyme interplay in a physiologically-based pharmacokinetic model. Finally, unbound liver-to-plasma ratio (Kpuu) measured in vitro using rat, monkey, and human hepatocytes was found to be approximately 4, 25, and 10, respectively. Similarly, liver Kpuu in rat and monkey after intravenous dosing of PF-06835919 was found to be 2.5 and 15, respectively, and notably higher than the muscle and brain Kpuu, consistent with the active uptake mechanisms observed in vitro. SIGNIFICANCE STATEMENT This work characterizes the transport/metabolic pathways in the hepatic disposition of PF-06835919, a first-in-class ketohexokinase (KHK) inhibitor for the treatment of metabolic disorders and NASH. Phenotyping studies using transfected systems, human hepatocytes, and liver microsomes signify the role of OAT2 and OATP1B1 in the hepatic uptake and multiple enzymes in the metabolism of PF-06835919. Data presented suggest hepatic transporter-enzyme interplay in determining its systemic concentrations and potential enrichment in liver, a target site for KHK inhibition.
Physiologically Based Pharmacokinetic Models Can Be Used to Predict the Unique Nonlinear Absorption Profiles of Vismodegib

Predicting human pharmacokinetics (PK) during the drug discovery phase is valuable to assess doses required to reach therapeutic exposures. For orally administered compounds, however, this can be especially difficult, since the absorption process is complex. Vismodegib is a compound with unique nonlinear oral PK characteristics in humans. Oral physiologically based pharmacokinetic (PBPK) models were built using preclinical in vitro and in vivo data and successfully predicted the oral PK profiles in rats, dogs, and monkeys. Simulated drug exposures (area under the concentration-time curve from time 0 to infinity and Cmax) following oral administration were within twofold of observed values for dogs and monkeys, and close to twofold for rats, providing validation to the model structure. Adaptation of this oral PBPK model to humans, using human physiologic parameters coupled with predicted human PK, resulted in underpredictions of vismodegib exposure following both single and multiple doses. When observed human PK was used to drive the oral PBPK model, oral PK profiles in humans were well predicted, with fold errors in predicted versus observed drug exposures being close to 1. Importantly, the oral PBPK model captured the unique nonlinear, nondose-dependent PK of vismodegib at a steady state. The mechanism responsible for nonlinearity was consistent with oral absorption being influenced by nonsink permeation conditions. We introduce a new parameter, the permeation gradient factor, to characterize the effect of nonsink conditions on permeation. Using vismodegib as an example, we demonstrate the value of using oral PBPK models in drug discovery to predict the oral PK of compounds with nonlinear absorption characteristics in human. SIGNIFICANCE STATEMENT A physiologically based pharmacokinetic (PBPK) model was built to demonstrate the value of these models early in the drug discovery stage for the prediction of human pharmacokinetics for compounds with unusual oral pharmacokinetics. In this study, our PBPK model could successfully capture the unique steady-state oral pharmacokinetics of our model compound, vismodegib. The mechanism for nonlinearity can be attributed to nonsink permeation conditions in vivo. We introduce the permeation gradient factor as a parameter to assess this effect.
Superconducting niobium nitride: a perspective from processing, microstructure, and superconducting property for single photon detectors

Superconducting niobium nitride (NbN) continues to be investigated decades on, largely in part to its advantageous superconducting properties and wide use in superconducting electronics. Particularly, NbN-based superconducting nanowire single-photon detectors (SNSPDs) have shown exceptional performance and NbN remains as the material of choice in developing future generation quantum devices. In this perspective, we describe the processing–structure–property relationships governing the superconducting properties of NbN films. We further discuss the complex interplay between the material properties, processing parameters, substrate materials, device architectures, and performance of SNSPDs. We also highlight the latest progress in optimizing SNSPD performance parameters.
Preliminary Findings from a Telephone-Based Cognitive Screening of an Adult HIV Research Cohort during the COVID-19 Pandemic

Abstract Objectives Few publications have documented the utility of in-home telephone-based cognitive screeners during COVID-19. This manuscript describes the adaptation of select face-to-face (FTF) neuropsychological tests to telephonic administration in a longitudinal cohort of people with HIV (PWH). Using the cohort’s pre-pandemic neuropsychological data, we explore the utility of telephonic administration in this population. Methods Of a longitudinal cohort of 170 adult PWH, 59 completed telephonic medical and cognitive screenings with comparable pre-pandemic FTF data. Telephone screeners and FTF evaluations were compared using repeated measures ANCOVAs to examine whether test performance differed between administration types and levels of pre-pandemic cognitive performance. Individuals with pre-pandemic test scores more than a standard deviation below the demographically-corrected mean were categorized as “below average” cognitive performance (n = 23), and the remainder as “average” (n = 36). Results Over 90% of participants gave positive feedback about the telephone encounter. The average cognitive performance group scored higher than the below average group on all measures across both administration types. Telephone and FTF test scores did not differ significantly for measures of category fluency, letter fluency, and verbal learning. However, the below average group scored higher on a verbal memory measure administered via telephone compared with FTF. Conclusions Support for telephonic adaptation of select FTF measures in longitudinal research is mixed, with verbal fluency tasks showing the strongest equivalency. When employed carefully with a clear understanding of their limitations, telephone adaptations can provide an opportunity to continue study objectives, promote equity, and monitor participant well-being during times of duress.
The ubiquitin–26S proteasome system and autophagy relay proteome homeostasis regulation during silique development

SUMMARY Functional studies of the ubiquitin–26S proteasome system (UPS) have demonstrated that virtually all aspects of the plant's life involve UPS‐mediated turnover of abnormal or short‐lived proteins. However, the role of the UPS during development, including in seeds and fruits, remains to be determined in detail, although mutants of several of its core elements are known to be embryonically lethal. Unfortunately, early termination of embryogenesis limits the possibility to characterize the activities of the UPS in reproductive organs. Given both the economic and the societal impact of reproductive production, such studies are indispensable. Here, we systematically compared expression of multiple 26S proteasome subunits along with the dynamics of proteasome activity and total protein ubiquitylation in seedlings, developing siliques, and embryos of Arabidopsis thaliana. Since autophagy plays the second largest role in maintaining proteome stability, we parallelly studied three rate‐limiting enzymes that are involved in autophagy flux. Our experiments unexpectedly discovered that, in contrast to the activities in seedlings, both protein and transcript levels of six selected 26S proteasome subunits gradually decline in immature siliques or embryos toward maturation while the autophagy flux rises despite the nutrient‐rich condition. We also discovered a reciprocal turnover pathway between the proteasome and autophagy. While the autophagy flux is suppressed in seedlings by UPS‐mediated degradation of its three key enzymes, transcriptional reprogramming dampens this process in siliques, which in turn stimulates a bulk autophagic degradation of proteasomes. Collectively, our study of the developmental changes of the UPS and autophagy activities suggests that they relay the proteome homeostasis regulation in early silique and/or seed development, highlighting their interactions during development.
Biomechanics evaluation of sacroiliac joint pain after lumbosacral fusion: A finite element analysis

The sacroiliac joint (SIJ) constitutes the predominant pain source following lumbar or lumbosacral fusion. Although studies have investigated the biomechanical patterns of SIJ behaviors after lumbosacral fusion, the relationship between ligament strain and SIJ pain following lumbosacral fusion remains unclear. The present study developed a three‐dimensional finite element model including L4, L5, sacrum, ilium, SIJ, and seven mainly ligaments. After successful validation, the model was used to investigate the biomechanics of SIJ and ligaments in simulating lumbosacral fusion process. Our results showed that small motion in a stable SIJ may significantly increases the contact pressure and stress of the SIJ, which increase the maximum contact pressure by 171%, 676%, 199%, and 203% and stress by 130%, 424%, 168%, and 241% for flexion, extension, bending, and axial rotation, respectively. An increase in contact pressure and stress in SIJ possibly causes pain at the SIJ, especially in extension and axial rotation. A comparison between the lumbosacral and intact models exhibited the maximum strain increase in the iliosacral ligament (ISL) and the ileal ligament (IL) under all loading conditions. The present study suggests that after lumbosacral fusion process, the ligament sudden increase or decrease is likely to lead sprain or strain ligament, especially ISL and IL thereby causing SIJ pain. This study may contribute to understand the relationship between SIJ ligaments and SIJ pain.
A pan‐genome and chromosome‐length reference genome of narrow‐leafed lupin (Lupinus angustifolius) reveals genomic diversity and insights into key industry and biological traits

SUMMARY Narrow‐leafed lupin (NLL; Lupinus angustifolius) is a key rotational crop for sustainable farming systems, whose grain is high in protein content. It is a gluten‐free, non‐genetically modified, alternative protein source to soybean (Glycine max) and as such has gained interest as a human food ingredient. Here, we present a chromosome‐length reference genome for the species and a pan‐genome assembly comprising 55 NLL lines, including Australian and European cultivars, breeding lines and wild accessions. We present the core and variable genes for the species and report on the absence of essential mycorrhizal associated genes. The genome and pan‐genomes of NLL and its close relative white lupin (Lupinus albus) are compared. Furthermore, we provide additional evidence supporting LaRAP2‐7 as the key alkaloid regulatory gene for NLL and demonstrate the NLL genome is underrepresented in classical NLR disease resistance genes compared to other sequenced legume species. The NLL genomic resources generated here coupled with previously generated RNA sequencing datasets provide new opportunities to fast‐track lupin crop improvement.
ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning for Action Recognition

Capitalizing on large pre-trained models for various downstream tasks of interest have recently emerged with promising performance. Due to the ever-growing model size, the standard full fine-tuning based task adaptation strategy becomes prohibitively costly in terms of model training and storage. This has led to a new research direction in parameter-efficient transfer learning. However, existing attempts typically focus on downstream tasks from the same modality (e.g., image understanding) of the pre-trained model. This creates a limit because in some specific modalities, (e.g., video understanding) such a strong pre-trained model with sufficient knowledge is less or not available. In this work, we investigate such a novel cross-modality transfer learning setting, namely parameter-efficient image-to-video transfer learning. To solve this problem, we propose a new Spatio-Temporal Adapter (ST-Adapter) for parameter-efficient fine-tuning per video task. With a built-in spatio-temporal reasoning capability in a compact design, ST-Adapter enables a pre-trained image model without temporal knowledge to reason about dynamic video content at a small (~8%) per-task parameter cost, requiring approximately 20 times fewer updated parameters compared to previous work. Extensive experiments on video action recognition tasks show that our ST-Adapter can match or even outperform the strong full fine-tuning strategy and state-of-the-art video models, whilst enjoying the advantage of parameter efficiency. The code and model are available at https://github.com/linziyi96/st-adapter
Multi-features based Semantic Augmentation Networks for Named Entity Recognition in Threat Intelligence

Extracting cybersecurity entities such as attackers and vulnerabilities from unstructured network texts is an important part of security analysis. However, the sparsity of intelligence data resulted from the higher frequency variations and the randomness of cybersecurity entity names makes it difficult for current methods to perform well in extracting security-related concepts and entities. To this end, we propose a semantic augmentation method which incorporates different linguistic features to enrich the representation of input tokens to detect and classify the cybersecurity names over unstructured text. In particular, we encode and aggregate the constituent feature, morphological feature and part of speech feature for each input token to improve the robustness of the method. More than that, a token gets augmented semantic information from its most similar K words in cybersecurity domain corpus where an attentive module is leveraged to weigh differences of the words, and from contextual clues based on a large-scale general field corpus. We have conducted experiments on the cybersecurity datasets DNRTI and MalwareTextDB, and the results demonstrate the effectiveness of the proposed method.
Enhancing cluster analysis via topological manifold learning

We discuss topological aspects of cluster analysis and show that inferring the topological structure of a dataset before clustering it can considerably enhance cluster detection: we show that clustering embedding vectors representing the inherent structure of a dataset instead of the observed feature vectors themselves is highly beneficial. To demonstrate, we combine manifold learning method UMAP for inferring the topological structure with density-based clustering method DBSCAN. Synthetic and real data results show that this both simplifies and improves clustering in a diverse set of low- and high-dimensional problems including clusters of varying density and/or entangled shapes. Our approach simplifies clustering because topological pre-processing consistently reduces parameter sensitivity of DBSCAN. Clustering the resulting embeddings with DBSCAN can then even outperform complex methods such as SPECTACL and ClusterGAN. Finally, our investigation suggests that the crucial issue in clustering does not appear to be the nominal dimension of the data or how many irrelevant features it contains, but rather how separable the clusters are in the ambient observation space they are embedded in, which is usually the (high-dimensional) Euclidean space defined by the features of the data. The approach is successful because it performs the cluster analysis after projecting the data into a more suitable space that is optimized for separability, in some sense.