Miracle-dz/UniFAR-FTUs huggingface dataset:
Multistate design of influenza antibodies improves affinity and breadth against seasonal viruses

Significance Influenza is an annual threat to global public health, in part because of constant antigenic drift that facilitates evasion of the antibody response. Rapid changes in the influenza HA protein make it difficult for an antibody to achieve broad activity against different virus subtypes. We developed a computational method that can optimize an antibody sequence to be robust against seasonal variation. As a proof of concept, we tested this method by redesigning a known antibody against a set of diverse HA antigens and showed that the variant redesigned antibodies have improved activity against the virus panel, as predicted. This work shows that computational design can improve naturally occurring antibodies for recognition of different virus strains. Influenza is a yearly threat to global public health. Rapid changes in influenza surface proteins resulting from antigenic drift and shift events make it difficult to readily identify antibodies with broadly neutralizing activity against different influenza subtypes with high frequency, specifically antibodies targeting the receptor binding domain (RBD) on influenza HA protein. We developed an optimized computational design method that is able to optimize an antibody for recognition of large panels of antigens. To demonstrate the utility of this multistate design method, we used it to redesign an antiinfluenza antibody against a large panel of more than 500 seasonal HA antigens of the H1 subtype. As a proof of concept, we tested this method on a variety of known antiinfluenza antibodies and identified those that could be improved computationally. We generated redesigned variants of antibody C05 to the HA RBD and experimentally characterized variants that exhibited improved breadth and affinity against our panel. C05 mutants exhibited improved affinity for three of the subtypes used in design by stabilizing the CDRH3 loop and creating favorable electrostatic interactions with the antigen. These mutants possess increased breadth and affinity of binding while maintaining high-affinity binding to existing targets, surpassing a major limitation up to this point.
Zinc oxide nanoparticles induced gene mutation at the HGPRT locus and cell cycle arrest associated with apoptosis in V‐79 cells

In recent years, the large‐scale production of ZnO nanoparticles (NPs) for various applications is increasing exponentially and may pose serious health issues when inhaled either during occupational exposure or in consumer settings. The mechanisms underlying the toxicity of NPs have recently been studied intensively. Despite the existing studies, the mutagenicity of ZnO NPs in the eukaryotic system is still unclear. Therefore, the aim of the present study was to investigate the mutagenic potential of ZnO NPs using Chinese hamster lung fibroblast cells (V‐79) as an in‐vitro model. The study has demonstrated a significant uptake of ZnO NPs by flow cytometry with the confirmation of transmission electron microscopy. A reduction in cell viability was observed with a concomitant increase in reactive oxygen species (**P < 0.01, ***P < 0.001) after ZnO NP (1‐20 μg/mL) exposure. Excessive reactive oxygen species can induce oxidative stress, which leads to genotoxic insult, and further gene mutation. Apart from measuring the genotoxicity by Comet assay, a change of 2.84‐fold in the HGPRT gene mutant frequency was observed by the mammalian gene forward mutation assay. All the genotoxicity endpoints such as chromosomal break, DNA damage and mutagenicity were observed at 6 hours of ZnO NP exposure. Our results also showed that ZnO NPs manifested the cell cycle arrest, ultrastructural modifications and further cell death. A significant (**P < 0.01, ***P < 0.001) increase in the apoptotic cells was detected using annexin V‐fluorescein isothiocyanate/propidium iodide double staining by flow cytometry. Our findings presented here clearly stimulate the need for careful regulations of ZnO NPs.
Episodic memory boosting in older adults: exploring the association of encoding strategies and physical activity

Abstract Background: Contextual memory is susceptible to the effects of aging and its impairment compromises episodic memories and quality of life in older adults. Objective: Compare the effects of cognitive support on incidental contextual memory free recall and recognition with a naturalistic experimental paradigm and explore the association of encoding strategies and physical activity on memory improvement. Methods: Subjects (≥60 years, n = 52) were assigned to one of two encoding conditions for the contextual memory task: with or without an incidental associative instruction to encourage association of an item to its spatial context. Immediate free recall and recognition tests were run to assess the encoding instruction efficiency. The association of memory performance and physical activity was analyzed using the scores on the International Physical Activity Questionnaire (IPAQ) to subdivide each experimental group into Low IPAQ (below median) and High IPAQ (above median) subgroups. Results: The associative encoding instruction increased contextual memory free recall and recognition, with greater effects on free recall. The most robust associations between physical activity and contextual memory were also seen on free recall, in which higher levels of physical activity corresponded to increased baseline performance (non-associative encoding condition) and greater improvement of memory by the encoding support (associative encoding condition). Conclusion: Cognitive support at encoding can improve contextual memory free recall and recognition, suggesting they are prone to rehabilitation. Moreover, higher physical activity levels were positively associated with encoding strategies on contextual memory improvement, increasing the availability of latent process-based components of the cognitive reserve.
A Quantitative Method to Measure Skin Thickness in Leg Edema in Pregnant Women Using B-Scan Portable Ultrasonography: A Comparison Between Obese and Non-Obese Women

Background This study aimed to use a portable ultrasound method to quantitatively measure skin thickness and to compare leg edema in obese and non-obese pregnant women. Material/Methods Thirty-six pregnant women (17 primiparas and 19 multiparas) at 27/28 and 37/38 weeks of pregnancy, with and without leg edema, had their lower leg skin thickness measured using a B-scan portable ultrasonography device (72 legs and maximum of 98 measurements). Measurements were compared between women who were obese prior to pregnancy, with a body mass index (BMI) ≥25 kg/m2 and non-obese with a BMI <25 kg/m2. Results Skin thickness of the legs in pregnant women with edema was significantly increased compared with that in pregnant women without edema (6.4±0.3 mm vs. 4.6±0.4 mm) (p=0.0001). There was a significant correlation between the degree of pitting edema and skin thickness in all edematous legs (ρ=0.56; n=98; p<0.0001). The cutoff level of edema measured by portable ultrasound in non-obese pregnant women was 4.7 mm (sensitivity 83.9%, specificity 66.7%) and was 7.5 mm in obese pregnant women. Obese pregnant women with edema had a significantly increased leg skin thickness compared with non-obese pregnant women with edema (11.3±1.3 mm vs. 5.7±0.2 mm) (p<0.0001). Conclusions Portable ultrasonography is a reliable method of quantitatively measuring skin thickness of the lower leg in edema associated with pregnancy. The thickness of the skin in obese pregnant women with edema can be expected to be significantly increased compared with non-obese pregnant women with edema.
Plasma kidney injury molecule-1 (p-KIM-1) levels and deterioration of kidney function over 16 years

Abstract Background The kidney injury molecule-1 (KIM-1) has previously been associated with kidney function in rodents and humans. Yet its role as a predictive marker for future decline in kidney function has remained less clear. Methods At baseline (1991–1994), fasting plasma KIM-1 (p-KIM-1) was measured in 4739 participants of the population-based Malmö Diet and Cancer Study. Creatinine and cystatin C were used to calculate estimated glomerular filtration rate (eGFR) according to Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) Collaboration 2012 creatinine–cystatin C equation at baseline and follow-up examination (2007–2012). Incident CKD was defined as an eGFR <60 mL/min/1.73 m2 at follow-up. Results During a mean follow-up time of 16.6 years, high p-KIM-1 levels were associated with a greater decline in eGFR (quartile 1 −1.36 versus quartile 4 −1.54 mL/min/1.73 m2; P < 0.001). In multivariate analyses, the risk for incident CKD at the follow-up examination was higher among participants with baseline p-KIM-1 levels in the highest quartile {odds ratio [OR] 1.45 [95% confidence interval (CI) 1.10–1.92]} compared with those within the lowest quartile. The relative impact of baseline p-KIM-1 on incidence of CKD [OR 1.20 (95% CI 1.08–1.33) per 1 standard deviation (SD) increase in p-KIM-1] was comparable to those of age and systolic blood pressure (SBP) [OR 1.55 (95% CI 1.38–1.74) and OR 1.21 (95% CI 1.09–1.35) per 1 SD increase, respectively]. Adding p-KIM-1 to a conventional risk model resulted in significantly improved C-statistics (P = 0.04) and reclassified 9% of the individuals into the correct risk direction (continuous net reclassification improvement P = 0.02). Furthermore, the risk for hospitalization due to impaired renal function increased with increasing baseline p-KIM-1 [hazard ratio per 1 SD 1.43; (95% CI 1.18–1.74)] during a mean follow-up time of 19.2 years. Conclusion Our results show that p-KIM-1 predicts the future decline of eGFR and risk of CKD in healthy middle-aged participants. Whether p-KIM-1 can be used to prioritize preventive action that needs to be further investigated.
Current trends in protein acetylation analysis

ABSTRACT Introduction: Acetylation is a widely occurring post-translational modification (PTM) of proteins that plays a crucial role in many cellular physiological and pathological processes. Over the last decade, acetylation analyses required the development of multiple methods to target individual acetylated proteins, as well as to cover a broader description of acetylated proteins that comprise the acetylome. Areas covered: This review discusses the different types of acetylation (N-ter/K-/O-acetylation) and then describes some major strategies that have been reported in the literature to detect, enrich, identify and quantify protein acetylation. The review highlights the advantages and limitations of these strategies, to guide researchers in designing their experimental investigations and analysis of protein acetylation. Finally, this review highlights the main applications of acetylomics (proteomics based on mass spectrometry) for understanding physiological and pathological conditions. Expert opinion: Recent advances in acetylomics have enhanced knowledge of the biological and pathological roles of protein acetylation and the acetylome. Besides, radiolabeling and western blotting remain also techniques-of-choice for targeted protein acetylation. Future challenges in acetylomics to analyze the N-ter and K-acetylome will most likely require enrichment/fractionation, MS instrumentation and bioinformatics. Challenges also remain to identify the potential biological roles of O-acetylation and cross-talk with other PTMs.
Model confidence bounds for variable selection

In this article, we introduce the concept of model confidence bounds (MCB) for variable selection in the context of nested models. Similarly to the endpoints in the familiar confidence interval for parameter estimation, the MCB identifies two nested models (upper and lower confidence bound models) containing the true model at a given level of confidence. Instead of trusting a single selected model obtained from a given model selection method, the MCB proposes a group of nested models as candidates and the MCB's width and composition enable the practitioner to assess the overall model selection uncertainty. A new graphical tool—the model uncertainty curve (MUC)—is introduced to visualize the variability of model selection and to compare different model selection procedures. The MCB methodology is implemented by a fast bootstrap algorithm that is shown to yield the correct asymptotic coverage under rather general conditions. Our Monte Carlo simulations and real data examples confirm the validity and illustrate the advantages of the proposed method.
Adolescent Substance Use and Sensation-Seeking on Sexual Behaviors Among Young Adults from Continuation High Schools

Abstract Background: Youth from continuation high schools report greater substance use and sensation-seeking than youth from regular high schools, yet their long-term consequences on age at sexual onset and the number of sexual partners are unknown. Objective: To examine substance use, sensation-seeking and sexual behaviors by gender and race/ethnicity and the effects of substance use and sensation-seeking in adolescence on age at sexual initiation and numbers of sexual partners by young adulthood. Methods: Baseline and 4-year follow-up data on youth from 14 continuation high schools in Southern California who participated in a drug abuse prevention intervention were analyzed. Structural equation modeling assessed whether or not substance use or sensation-seeking in adolescence predicted age at sexual onset and numbers of sexual partners by young adulthood. Results: Latinos had lower sensation-seeking and frequency of substance use and a later age at sexual onset than non-Latinos. Males were more likely than females to have multiple lifetime and recent sexual partners. The effects of adolescent substance use on the number of sexual partners by young adulthood were mediated fully by their age at sexual initiation. Sensation-seeking had no direct or indirect effects on sexual behaviors. Conclusions/Importance: Factors leading to and actual sexual risk behaviors among youth from continuation high schools vary by race/ethnicity and gender. Targeting these antecedent factors by race/ethnicity and gender may improve prevention efforts.
Biomarker Discovery for Immunotherapy of Pituitary Adenomas: Enhanced Robustness and Prediction Ability by Modern Computational Tools

Pituitary adenoma (PA) is prevalent in the general population. Due to its severe complications and aggressive infiltration into the surrounding brain structure, the effective management of PA is required. Till now, no drug has been approved for treating non-functional PA, and the removal of cancerous cells from the pituitary is still under experimental investigation. Due to its superior specificity and safety profile, immunotherapy stands as one of the most promising strategies for dealing with PA refractory to the standard treatment, and various studies have been carried out to discover immune-related gene markers as target candidates. However, the lists of gene markers identified among different studies are reported to be highly inconsistent because of the greatly limited number of samples analyzed in each study. It is thus essential to substantially enlarge the sample size and comprehensively assess the robustness of the identified immune-related gene markers. Herein, a novel strategy of direct data integration (DDI) was proposed to combine available PA microarray datasets, which significantly enlarged the sample size. First, the robustness of the gene markers identified by DDI strategy was found to be substantially enhanced compared with that of previous studies. Then, the DDI of all reported PA-related microarray datasets were conducted to achieve a comprehensive identification of PA gene markers, and 66 immune-related genes were discovered as target candidates for PA immunotherapy. Finally, based on the analysis of human protein–protein interaction network, some promising target candidates (GAL, LMO4, STAT3, PD-L1, TGFB and TGFBR3) were proposed for PA immunotherapy. The strategy proposed together with the immune-related markers identified in this study provided a useful guidance for the development of novel immunotherapy for PA.
Formal foundations for the specification of software architecture

This investigation establishes a formal foundation for software architecture that allows for the specification of large, non-trivial software systems using well founded, consistency preserving construction techniques. Two fundamental problems were addressed: how to define and express architectures formally using the concept of theories, and how architecture theories can be practically applied in specification construction. The initial stages of this investigation sought to establish a formal, mathematical relationship between functional specifications of behavior and specification defining system structure. Experimental results lead to the conclusion that architectures defining the structure of functional operations can be defined using functional logic, but more complex architectures require a separate process logic. A process logic based on Hoare's Communicating Sequential Processes (CSP) was selected for representing and reasoning about system structure and was used in the definition of a process-based specification development system. Specifically, CSP was used to define a category of process-based specifications and specification morphisms. This allowed well-founded specification construction techniques such as specification morphisms, colimits, and interpretations to be applied to the construction of consistent software architecture. Architecture theories expressed in terms of functional and process-based specification were defined, and translations between these architecture theories were investigated. A feasibility analysis on an image processing application demonstrated that architecture theories can be used to develop specifications for large, non-trivial applications.
Ovarian Cancer Prognostic Prediction Model Using RNA Sequencing Data

Ovarian cancer is one of the leading causes of cancer-related deaths in gynecological malignancies. Over 70% of ovarian cancer cases are high-grade serous ovarian cancers and have high death rates due to their resistance to chemotherapy. Despite advances in surgical and pharmaceutical therapies, overall survival rates are not good, and making an accurate prediction of the prognosis is not easy because of the highly heterogeneous nature of ovarian cancer. To improve the patient’s prognosis through proper treatment, we present a prognostic prediction model by integrating high-dimensional RNA sequencing data with their clinical data through the following steps: gene filtration, pre-screening, gene marker selection, integrated study of selected gene markers and prediction model building. These steps of the prognostic prediction model can be applied to other types of cancer besides ovarian cancer.
Effects of Task-Induced Fatigue in Prolonged Conditional Automated Driving

Objective: The aim of this study was to investigate the effects of task-induced fatigue in prolonged conditional automated driving on takeover performance. Background: In conditional automated driving, the driver can engage in non–driving related tasks (NDRTs) and does not have to monitor the system and the driving environment. In the event that the system hits its limits, the human driver must regain control of the car. To ensure safety, adequate driver fallback performance is necessary. Effects of the drivers’ state and the engagement in NDRTs need to be investigated. Method: Seventy-three participants experienced prolonged conditional automated rides and simultaneously had to engage in either an activating quiz or a fatiguing monitoring task (between subjects). After 50 minutes, a takeover situation occurred, and participants had to regain control of the car. Results: Prolonged conditional automated driving and simultaneously engaging in NDRTs affected the driver’s state and the takeover performance of the participants. Takeover performance was impaired when participants had to deal with monotonous NDRTs. Conclusion: An engagement in monotonous monitoring tasks in conditional automated driving affects the driver’s state and takeover performance when it comes to takeover situations. Especially in prolonged automated driving, an adequate driver state seems to be necessary for safety reasons. Application: The results of this study demonstrate that engagement in monotonous NDRTs while driving conditionally automated may negatively affect takeover performance. A monitoring of the driver state and adapted assistance in a takeover situation seems to be a good opportunity to ensure safety.
Adipose‐derived stem cells decreased microglia activation and protected dopaminergic loss in rat lipopolysaccharide model

Adult stem cell therapy is being used extensively to rejuvenate damaged tissue. One important tissue source to obtain these cells is adipose, which contains cells called adipose‐derived stem cells (ADSCs). These cells have a great therapeutic potential not only for their multipotent properties as well as for immunomodulatory effects on the immune system. Parkinson's disease is characterized as neurodegenerative disorder which etiology is undoubtedly related to neuroinflammation process. The properties of ADSCs can be used as a new tool in stem cells therapy to treat neurodegenerative disorders. However, their efficacies are still controversial. Some authors have reported neuroprotection effects, while others did not find differences or stem cells increased the damage. Our previous study showed that ADSCs can survive long time after transplantation, suggesting us some biological effects could need more time to be repaired. In this study, we assessed the neuroprotection 6 months after transplantation. Our results suggest ADSCs can protect the dopaminergic loss after lipopolysaccharide (LPS) injection both reducing the microglia activation and differentiating into dopaminergic cells.
Fast ScanNet: Fast and Dense Analysis of Multi-Gigapixel Whole-Slide Images for Cancer Metastasis Detection

Lymph node metastasis is one of the most important indicators in breast cancer diagnosis, that is traditionally observed under the microscope by pathologists. In recent years, with the dramatic advance of high-throughput scanning and deep learning technology, automatic analysis of histology from whole-slide images has received a wealth of interest in the field of medical image computing, which aims to alleviate pathologists’ workload and simultaneously reduce misdiagnosis rate. However, the automatic detection of lymph node metastases from whole-slide images remains a key challenge because such images are typically very large, where they can often be multiple gigabytes in size. Also, the presence of hard mimics may result in a large number of false positives. In this paper, we propose a novel method with anchor layers for model conversion, which not only leverages the efficiency of fully convolutional architectures to meet the speed requirement in clinical practice but also densely scans the whole-slide image to achieve accurate predictions on both micro- and macro-metastases. Incorporating the strategies of asynchronous sample prefetching and hard negative mining, the network can be effectively trained. The efficacy of our method is corroborated on the benchmark dataset of 2016 Camelyon Grand Challenge. Our method achieved significant improvements in comparison with the state-of-the-art methods on tumor localization accuracy with a much faster speed and even surpassed human performance on both challenge tasks.