Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of nursing intervention on preoperative cataract

Abstract This retrospective study investigated the effect of nursing intervention (NIV) in Chinese patients under preoperative cataract (PC). A total of 70 eligible patients with PC were included. Thirty-five patients in an intervention group received regular clinical treatment and NIV before the surgery, while the other 35 patients received regular clinical treatment only. The NIV was applied 4 sessions, 1 week before the surgery. The primary outcome of satisfaction was measured by 11-points visual analog scale. The secondary outcomes were measured by the functional impairment caused by cataract (the VF-14), cooperativeness during the surgery period, and sleep quality. All the outcome measurements were assessed before and after the surgery. After NIV, patients in the intervention group exerted better outcomes in decreasing anxiety (P < .01), increasing current experience with satisfaction (P < .01), and enhancing the cooperativeness during the surgery period (P < .01), compared to those outcomes in the control group. The results of this study showed that NIV may help increase satisfaction in experience and cooperation, and decrease anxiety in Chinese patients with PC.
Constrained Expectation-Maximization Methods for Effective Reinforcement Learning

Recent advancement on reinforcement learning (RL) algorithms shows that effective learning of parametric action- selection policies can often be achieved through direct opti- mization of a performance lower bound subject to pre-defined policy behavioral constraints. Driven by this understanding, this paper seeks to develop new policy search techniques where RL is achieved through maximizing a performance lower bound ob- tained originally based on an Expectation-Maximization method. For reliable RL, our new learning techniques must also simul- taneously guarantee constrained policy behavioral changes mea- sured through KL divergence. Two separate approaches will be pursued to tackle our constrained policy optimization problems, resulting in two new RL algorithms. The first algorithm utilizes a conjugate gradient technique and a Bayesian learning method for approximate optimization. The second algorithm focuses on minimizing a loss function derived from solving the Lagrangian for constrained policy search. Both algorithms have been experi- mentally examined on several benchmark problems provided by OpenAI GYM. The experiment results clearly demonstrate that our algorithms can be highly effective in comparison to several well-known RL algorithms.
Affordances-in-practice: An ethnographic critique of social media logic and context collapse

Drawing on data gathered during ethnographic fieldwork in Mardin, a medium-sized town in southeast Turkey, this article shows that social media users actively appropriate online platforms and change privacy settings in order to keep different social spheres and social groups apart. Keeping different online social contexts distinct from each other is taken for granted as a way of using social media in Mardin. By contrast, social media scholars have extensively discussed the effects of social media in terms of context collapse. The article highlights how context collapse is the result of patterns of usage within Anglo-American contexts and not the consequence of a platform’s architecture or social media logic. It then suggests a theoretical refinement of affordances, and proposes the concept of affordances-in-practice.
Maternal right ventricular function, uteroplacental circulation in first trimester and pregnancy outcome in women with congenital heart disease

Pregnant women with congenital heart disease (CHD) have an increased risk of abnormal uteroplacental flow, measured from the second trimester onwards, which is associated with pregnancy complications affecting the mother and the fetus. Maternal right ventricular (RV) dysfunction has been suggested as a predisposing factor for impaired uteroplacental flow in these women. The aim of this study was to investigate the association of first‐trimester uteroplacental flow measurements with prepregnancy maternal cardiac function and pregnancy complications in women with CHD, with particular focus on the potential role of RV (dys)function.
Reducing the Computation Load of Convolutional Neural Networks through Gate Classification

Reducing the computational load of Convolutional Neural Networks (CNNs) is of utmost importance to allow their execution in computing systems characterized by constraints on computation and energy (e.g., embedded and cyber-physical systems and Internet-of-Things). To address this problem, which has been rarely addressed in the related literature, this paper introduces the Gate-Classification CNNs. The core of this novel family of CNNs is the presence of Gate-Classification layers that allow to incrementally process the input image through the CNN layers and take a decision as soon as “enough confidence” about the classification is gained, hence not requiring the processing of the whole CNN when not needed. The Gate-Classification CNNs rely on the ability of CNNs to process features characterized by increasing complexity and meaning and, in particular, the Gate-Classification layers allow to select the path within the CNN according to the information content provided by the input image and the processed features. A wide experimental campaign on public-available datasets supports the effectiveness of the proposed solution.
The Serum Creatinine Level Might Be Associated with the Onset of Impaired Fasting Glucose: A Community-based Longitudinal Cohort Health Checkup Study

Objective Skeletal muscle is the main target organ for glycemic control, and the serum creatinine level is a convenient indicator of the skeletal muscle mass. This study aimed to assess the potential relationship between the serum creatinine level and the onset of impaired fasting glucose (IFG). Methods In this large, community-based, retrospective longitudinal cohort study, we examined the records of 7,905 Japanese participants (3,863 men, 4,042 women) of 18-80 years of age who underwent annual health checkups at a single center between April 2003 and August 2013. After applying the exclusion criteria, 6,490 participants were reviewed to identify those with the onset of IFG, defined as a fasting plasma glucose ≥6.11 mM. Among the participants, 278 met the criterion for the onset of IFG during the observation period. Results Creatinine levels were higher in male subjects who exercised periodically and were exercise conscious in comparison to those who did not exercise, and were higher in female subjects who exercised periodically in comparison to female subjects who did not exercise and who were not exercise conscious. Additionally, the serum creatinine level was negatively associated with the onset of IFG in both men [adjusted hazard ratio, 0.98; 95% confidence interval (CI), 0.96-0.99; p=0.008] and women (adjusted hazard ratio, 0.94; 95% CI, 0.91-0.97; p<0.001) after adjustment for variables previously reported to be risk factors for the onset of glucose intolerance and factors associated with chronic kidney disease. Conclusion A low creatinine level might be associated with the onset of IFG. Moreover, the fact that serum creatinine levels increase with exercise might demonstrate the importance of exercise therapy.
PAR4 overexpression promotes colorectal cancer cell proliferation and migration

Protease-activated receptor 4 (PAR4), a member of the G-protein-coupled receptor family, was previously identified to be involved in the progression of cancer. Previous study revealed that the expression of PAR4 was increased in colorectal cancer tissues compared with the associated normal tissues, particularly in positive lymph node and poorly differentiated types of cancer. We hypothesized that PAR4 serves a function in the progression of colorectal cancer. In the present study, overexpression of PAR4 in colorectal cancer LoVo cells promoted proliferation, anchorage-independent growth and migration. In vivo, PAR4 increased LoVo cell tumorgenicity. In contrast, knockdown of PAR4 in HT-29 cells decreased proliferation, anchorage-independent growth and migration. Mechanistic studies revealed that PAR4 increased the phosphorylation of extracellular-signal-regulated kinase 1/2 in colorectal cancer cells, which is the potential molecular mechanism that promotes cellular proliferation and migration. Taken together, the results of the present study indicated that overexpression of PAR4 promoted colorectal cancer cell proliferation, survival and metastasis, indicating that PAR4 is a promising therapeutic target for preventing colon cancer progression.
Dynamic regulation of HIV-1 capsid interaction with the restriction factor TRIM5α identified by magic-angle spinning NMR and molecular dynamics simulations

Significance The mechanisms of how TRIM5α interferes with the integrity of the HIV-1 capsid to restrict HIV-1 infectivity remain poorly understood. We examined, at atomic resolution, the interactions with TRIM5α in the assembled capsid and the dynamics of capsid’s hexameric and pentameric building blocks. Remarkably, assemblies in the presence of the pentameric subunits are more rigid on microsecond to millisecond timescales at the sites of pentamer incorporation than tubes containing only hexamers. Furthermore, TRIM5α binding to capsid induces global rigidification and perturbs key intermolecular interfaces, essential for higher-order capsid assembly. TRIM5α thus uses several mechanisms to destabilize the capsid lattice, ultimately inducing its disassembly. Our results suggest that dynamic allostery plays a pivotal role in capsid assembly and HIV-1 infectivity. The host factor protein TRIM5α plays an important role in restricting the host range of HIV-1, interfering with the integrity of the HIV-1 capsid. TRIM5 triggers an antiviral innate immune response by functioning as a capsid pattern recognition receptor, although the precise mechanism by which the restriction is imposed is not completely understood. Here we used an integrated magic-angle spinning nuclear magnetic resonance and molecular dynamics simulations approach to characterize, at atomic resolution, the dynamics of the capsid’s hexameric and pentameric building blocks, and the interactions with TRIM5α in the assembled capsid. Our data indicate that assemblies in the presence of the pentameric subunits are more rigid on the microsecond to millisecond timescales than tubes containing only hexamers. This feature may be of key importance for controlling the capsid’s morphology and stability. In addition, we found that TRIM5α binding to capsid induces global rigidification and perturbs key intermolecular interfaces essential for higher-order capsid assembly, with structural and dynamic changes occurring throughout the entire CA polypeptide chain in the assembly, rather than being limited to a specific protein-protein interface. Taken together, our results suggest that TRIM5α uses several mechanisms to destabilize the capsid lattice, ultimately inducing its disassembly. Our findings add to a growing body of work indicating that dynamic allostery plays a pivotal role in capsid assembly and HIV-1 infectivity.
Effects of YuPingFeng granules on acute exacerbations of COPD: a randomized, placebo-controlled study

Purpose Recurrence of acute exacerbations has a major impact on patients with COPD. Therefore, effective prevention and treatment of exacerbation is crucial in the management of COPD, especially for patients with moderate to severe disease. This study evaluated the effectiveness of YuPingFeng granule administration in preventing exacerbation and improving symptom score, as well as its long-term (1 year) safety profile, in patients with COPD. Patients and methods This was a randomized, double-blind, parallel, placebo-controlled study of 240 patients from eight centers in China. Participants were eligible if they had mild to severe COPD as defined by Global Initiative for Chronic Obstructive Lung Disease, had a history of at least two COPD exacerbations or one hospitalization within the previous year, and had remained clinically stable for over 4 weeks before the study. They were randomly assigned to receive 5 g of YuPingFeng or placebo, three times per day, for 1 year. The primary end point was the exacerbation rate over 1 year, and the analysis was by intention to treat. Secondary end points included symptom score, which was assessed by COPD assessment test (CAT) score and safety profiles. This trial was registered in the Chinese Clinical Trial Registry (http://www.chictr.org.cn; registration number: ChiCTR-IPR-15007023). Results The YuPingFeng group had a significantly lower exacerbation rate than the placebo group (1.15 vs 1.55; risk ratio=0.677 [95% CI 0.531–0.863]; P=0.002) and a significantly reduced risk of second exacerbation (95% CI 0.326–0.772; P=0.002). After treatment, the mean change in the CAT score in the YuPingFeng group (−4.41±7.01) differed significantly from that in the placebo group (−2.49±5.31; P=0.001). YuPingFeng was well tolerated. Conclusion YuPingFeng granules can be considered as a treatment option for COPD; this treatment prevents acute exacerbations of COPD and has a good safety profile.
A Novel Structural-Entropy-based Classification Technique for Supporting Android Ransomware Detection and Analysis

Nowadays mobile devices are widespread. Considering the plethora of private and sensitive information stored in smartphone and tablets, it is easy to understand the reason why attackers develop everyday more and more aggressive malicious payloads with the aim to exfiltrate our data. One of the last trend in mobile malware landascape is represented by the so-called ransomware, a threat capable to lock the user interface and to cipher the data of the mobile device under attack. In this paper we present a method based on structural entropy and fuzzy logic classification algorithms able to identify whether an application exhibits a ransomware behaviour. We obtain encouraging results evaluating a dataset composed of 10,052 (malware and legitimate) real-world Android applications.
A Recommender System Approach for Predicting Drug Side Effects

The accurate identification of drug side effects represents a major concern for public health. We propose a collaborative filtering model for large-scale prediction of drug side effects. Our approach provides side effects recommendations for drugs to safety professionals. The proposed latent factor model relies solely on the public drug-side effect relationships from safety data. Applied to 1,525 marketed drugs and 2,050 side effect terms, we achieved an AUPRC (area under the precision- recall curve) of 0.342 in a test set, with a sensitivity of 0.73 given a specificity of 0.95, providing state-of-the-art performance in side effect prediction. We analyze the performance of the method on drug-specific Anatomical Therapeutic and Chemical (ATC) category and side effect- specific medical category of disorders. Our findings suggest that latent factor models can be useful for the early and accurate detection of unknown adverse drug events.
Genetic Dissection of Hybrid Male Sterility Across Stages of Spermatogenesis

Hybrids between new species are often sterile. To understand the causes of this reproductive barrier, Schwahn et al. examined testis histology in a large number of hybrids between two nascent species of house mice. Hybrid sterility is a common form of reproductive isolation between nascent species. Although hybrid sterility is routinely documented and genetically dissected in speciation studies, its developmental basis is rarely examined, especially in generations beyond the F1 generation. To identify phenotypic and genetic determinants of hybrid male sterility from a developmental perspective, we characterized testis histology in 312 F2 hybrids generated by intercrossing inbred strains of Mus musculus domesticus and M. m. musculus, two subspecies of house mice. Hybrids display a range of histologic abnormalities that indicate defective spermatogenesis. Among these abnormalities, we quantified decreased testis size, reductions in spermatocyte and spermatid number, increased apoptosis of meiosis I spermatocytes, and more multinucleated syncytia. Collectively, our phenotypic data point to defects in meiosis I as a primary barrier to reproduction. We identified seven quantitative trait loci (QTL) controlling five histologic traits. A region of chromosome 17 that contains Prdm9, a gene known to confer F1 hybrid male sterility, affects multinucleated syncytia and round spermatids, potentially extending the phenotypic outcomes of this incompatibility. The X chromosome also plays a key role, with loci affecting multinucleated syncytia, apoptosis of round spermatids, and round spermatid numbers. We detected an epistatic interaction between QTL on chromosomes 17 and X for multinucleated syncytia. Our results refine the developmental basis of a key reproductive barrier in a classic model system for speciation genetics.
FT-EST Framework: Reliability Estimation for the Purposes of Fault-Tolerant System Design Automation

The complexity of today's systems is growing along with the level of chip integration. This results in higher demand for reliability techniques; it also increases the difficulty of incorporating reliability in such systems. For this purpose, we are working on a method to automate reliability insertion; however, for this method, it is necessary to have feedback on the result. In this paper, one component of the automation flow enabling the estimation of the resulting reliability - Fault Tolerance ESTimation (FT-EST) framework - is presented along with an improvement for accelerating the time necessary to reach the estimation. For the purpose of evaluation, we are using our Redundant Data Types approach, which enables us to intentionally insert reliability in a particular operation. The estimation utilizes the concept of fault injection. The results indicate, that the concept of Redundant Data Types is functional, however, also suggest its future improvements (e.g. for the operation of subtraction).
Multisite Evaluation of a Custom Energy-Storing Carbon Fiber Orthosis for Patients with Residual Disability After Lower-Limb Trauma

Background: The Intrepid Dynamic Exoskeletal Orthosis (IDEO) is a custom energy-storing carbon fiber ankle-foot orthosis developed for lower-extremity trauma patients. Studies conducted at the military treatment facility where the IDEO was developed demonstrated benefits of the IDEO when used with the Return to Run Physical Therapy (RTR PT) program. The current study was designed to determine if results could be replicated at other military treatment facilities and to examine whether early performance gains in patient-reported functional outcomes remained at 12 months. Methods: Study participants included service members who had functional deficits that interfered with daily activities at least 1 year after a traumatic unilateral lower-extremity injury at or below the knee. Participants were evaluated before receiving the IDEO, immediately following completion of RTR PT, and at 6 and 12 months. Agility, strength/power, and speed were assessed using well-established performance tests. Self-reported function was measured using the Short Musculoskeletal Function Assessment (SMFA). The Orthotics and Prosthetics Users’ Survey was administered to assess satisfaction with the IDEO. Of 87 participants with complete baseline data, 6 did not complete any physical therapy and were excluded from the analysis. Follow-up rates immediately following completion of the RTR PT and at 6 and 12 months were 88%, 75%, and 79%, respectively. Results: Compared with baseline, improvement at completion of RTR PT was observed in all but 1 performance test. SMFA scores for all domains except hand and arm function were lower (improved function) at 6 and 12 months. Satisfaction with the IDEO was high following completion of RTR PT, with some attenuation at the time of follow-up. Conclusions: This study adds to the evidence supporting the efficacy of the IDEO coupled with RTR PT. However, despite improvement in both performance and self-reported functioning, deficits persist compared with population norms. Level of Evidence: Therapeutic Level IV. See Instructions for Authors for a complete description of levels of evidence.
Identification of Serum Biomarker Panels for the Early Detection of Pancreatic Cancer

Background: Pancreatic cancer is a deadly disease for which available biomarkers, such as CA19-9, lack the desired sensitivity and specificity for early detection. Additional biomarkers are needed to improve both its sensitivity and specificity. Methods: Multiplex immunoassays were developed for selected biomarkers using a Bio-Plex 200 system, and analytical performance was optimized. All proteins were analyzed in sera of patients diagnosed with pancreatic ductal adenocarcinoma (PDAC; n = 188) or benign pancreatic conditions (131) and healthy controls (89). The clinical performance of these markers was evaluated individually or in combination for their ability to complement CA19-9 for the early detection of pancreatic cancer. Results: A 6-plex immunoassay was developed with negligible cross-reactivity, wide dynamic range, recovery of 89% to 104%, and intra-assay and interassay precision of 10.2% to 19.6% and 13.7% to 29.3%, respectively. Individually, the best biomarkers to separate PDAC early stage from chronic pancreatitis or intraductal papillary mucinous neoplasm (IPMN) were CA19-9 and MIA or CA19-9 and MIC-1. Logistic regression modeling selected the two-marker panels that significantly improved the individual biomarker performance in discriminating PDAC early stage from chronic pancreatitis (AUCCA19-9+MIA = 0.86 vs. AUCCA19-9 = 0.81 or AUCMIA = 0.75 only, P < 0.05) or IPMN (AUCCA19-9+MIC-1 = 0.81 vs. AUCCA19-9 = 0.75 or AUCMIC-1 = 0.73 only, P < 0.05). It was observed that osteopontin (OPN) outperformed CA19-9 in separating IPMN from chronic pancreatitis (AUCOPN = 0.80 vs. AUCCA19-9 = 0.70, P < 0.01). Conclusions: The biomarker panels evaluated by assays with high analytical performance demonstrated potential complementary values to CA19-9, warranting additional clinical validation to determine their role in early detection of pancreatic cancer. Impact: The validated biomarker panels could lead to earlier intervention and better outcomes.
Fault Detection and Diagnosis in a Chemical Process using Long Short-Term Memory Recurrent Neural Network

Long short-term memory recurrent neural networks have been proved to be especially useful for learning sequences comprising longer-term patterns of unknown length as they are able to preserve long-term memory. The learning of higher level temporal features could be achieved by stacking recurrent hidden layers for faster learning with sparser representations. In this work, we propose a novel approach to data driven fault detection and diagnosis of a chemical process. The method employs a state-of-the-art deep-learning technique, viz. the long short-term memory recurrent neural network. An application of the proposed approach is performed with realistic simulated data from a chemical process literature benchmark. Results point out an excellent performance when compared to already published linear and nonlinear fault detection and diagnosis methods.