Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictive value of microRNA let-7a expression for efficacy and prognosis of radiotherapy in patients with lung cancer brain metastasis

Background: As a well-known cancer with high mortality, lung cancer has been implied to be closely associated with brain metastasis. Despite notable advances, effective treatment methods are still in urgent need. This study aims to investigate the value of serum microRNA-let-7a (miR-let-7a) expression in predicting efficacy and prognosis of radiotherapy in patients with lung cancer brain metastasis. Methods: To begin with, reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed for better understand of the correlation between miR-let-7a and lung cancer. Afterwards, the relationship between serum miR-let-7a expression and radiotherapy efficacy was analyzed by receiver operating characteristic curve analysis. Following successful transfection, RT-qPCR and Western blot assay were utilized for evaluating the involvement of miR-let-7a in regulation of DICER1 expression in lung cancer cell line. Then, whether miR-let-7a was implicated in proliferation and cell cycle distribution of lung cancer cells were confirmed by cell counting kit-8 assay and flow cytometry respectively. Results: Initially, it was revealed that serum miR-let-7a expression was decreased in lung cancer. Later, we found that decreased miR-let-7a displayed an unfavorable role in radiotherapy efficacy and overall survival rate of patients with lung cancer brain metastasis. After the successful transfection, the inverse relationship between miR-let-7a and DICER1 expression was uncovered. Meanwhile, biological behaviors of lung cancer cells were presented to be limited after transfection of overexpressed miR-let-7a. Conclusion: Our findings demonstrated that the lower expression of miR-let-7a in patients with lung cancer brain metastasis was closely related to unfavorable efficacy and prognosis of radiotherapy, and it may be an important predictive biomarker by regulation of DICER1.
MODS-Wayne, a Colorimetric Adaptation of the Microscopic-Observation Drug Susceptibility (MODS) Assay for Detection of Mycobacterium tuberculosis Pyrazinamide Resistance from Sputum Samples

Although pyrazinamide (PZA) is a key component of first- and second-line tuberculosis treatment regimens, there is no gold standard to determine PZA resistance. Approximately 50% of multidrug-resistant tuberculosis (MDR-TB) and over 90% of extensively drug-resistant tuberculosis (XDR-TB) strains are also PZA resistant. ABSTRACT Although pyrazinamide (PZA) is a key component of first- and second-line tuberculosis treatment regimens, there is no gold standard to determine PZA resistance. Approximately 50% of multidrug-resistant tuberculosis (MDR-TB) and over 90% of extensively drug-resistant tuberculosis (XDR-TB) strains are also PZA resistant. pncA sequencing is the endorsed test to evaluate PZA susceptibility. However, molecular methods have limitations for their wide application. In this study, we standardized and evaluated a new method, MODS-Wayne, to determine PZA resistance. MODS-Wayne is based on the detection of pyrazinoic acid, the hydrolysis product of PZA, directly in the supernatant of sputum cultures by detecting a color change following the addition of 10% ferrous ammonium sulfate. Using a PZA concentration of 800 µg/ml, sensitivity and specificity were evaluated at three different periods of incubation (reading 1, reading 2, and reading 3) using a composite reference standard (MGIT-PZA, pncA sequencing, and the classic Wayne test). MODS-Wayne was able to detect PZA resistance, with a sensitivity and specificity of 92.7% and 99.3%, respectively, at reading 3. MODS-Wayne had an agreement of 93.8% and a kappa index of 0.79 compared to the classic Wayne test, an agreement of 95.3% and kappa index of 0.86 compared to MGIT-PZA, and an agreement of 96.9% and kappa index of 0.90 compared to pncA sequencing. In conclusion, MODS-Wayne is a simple, fast, accurate, and inexpensive approach to detect PZA resistance, making this an attractive assay especially for low-resource countries, where TB is a major public health problem.
Design-insights for Devising Persuasive IoT Devices for Sustainability in the Workplace

Sustainable HCI scholars have shown a growing interest in augmenting with technology everyday devices for promoting energy conservation behaviors in different contexts of use. The workspace has not been sufficiently explored as space where everyday appliances and work equipment might be converted into persuasive devices which raise eco-awareness. In this paper, we present the design of persuasive IoT devices that help to promote energy-efficient behaviour change in the workplace. This approach issued by applying Grounded Theory analysis over qualitativedata collected at ten focus groups sessions after running a one year study where participants were treated to different persuasive strategies. With these data, five design categories emerged to be taken into account in the conception phase when devising tangible objects that aim toform pro-environmental behaviour change at workplaces. Our design-insights are also derived from an evaluation of the literature on ambient devices that encourage energy-efficient practices.
Eye-Width and Eye-Height Estimation Method Based on Artificial Neural Network (ANN) for USB 3.0

As technology develops, the amount of data used increases exponentially and the operating speed of electronic devices increases accordingly. As the operating speed increases, the transmission speed of data exchanged through the channel also increases. To get an eye-diagram, a time-domain simulation is required, which consumes a lot of time and computer power. However, a channel simulation in frequency-domain is fast and it consumes less computer power. Therefore, using the frequency-domain simulation data is more efficient than the time-domain simulation. This paper proposes an eye-width and eye-height estimation method using an artificial neural network (ANN). The input of the ANN is insertion loss and the outputs of the ANN are eye-width and eye-height. Finally, the performance of the proposed method is verified by transient eye-diagram simulations with arbitrarily-selected channel parameters.
Networks of E-cadherin, β1 integrin, and focal adhesion kinase in the pathogenesis of tubal pregnancy

Abstract E-cadherin, β1 integrin, and focal adhesion kinase (FAK) are reported to involved in eutopic implantation by mediating cell adhesion. However, less is documented about their roles in ectopic implantation. This study was undertaken to evaluate the roles and networks of E-cadherin, β1 integrin, and FAK in tubal pregnancy. A total of 31 Fallopian tube specimens were obtained from tubal pregnant women. Immunohistochemistry and western blot were used to analyze the distributions and levels of E-cadherin, β1 integrin and phosphorylated-FAK (Pho-FAK) in the Fallopian tube epithelium. Normal Fallopian tube samples derived from non-pregnant women with benign genital diseases were used for comparison. E-cadherin presented in the cytomembrane of tubal epithelial cells and β1 integrin mainly expressed in the cytoplasm. A lowest-level of E-cadherin was detected in the implantation site (0.63 ± 0.29) when compared with the non-implantation site (0.95 ± 0.37) and the controls (0.89 ± 0.33) (P < 0.05). β1 integrin, as well as Pho-FAK in the implantation site (0.81 ± 0.35; 0.72 ± 0.24), showed a higher-level than that in the non-implantation site (0.59 ± 0.26; 0.48 ± 0.27) or the control group (0.38 ± 0.19; 0.36 ± 0.25) (p < .05). The decreased E-cadherin and increased β1 integrin are implicated in tubal pregnancy. The involvement of β1 integrin maybe depends on β1 integrin/FAK signaling.
A large impact crater beneath Hiawatha Glacier in northwest Greenland

Researchers present the first unambiguous discovery of a 31-km-wide impact crater buried beneath the Greenland Ice Sheet. We report the discovery of a large impact crater beneath Hiawatha Glacier in northwest Greenland. From airborne radar surveys, we identify a 31-kilometer-wide, circular bedrock depression beneath up to a kilometer of ice. This depression has an elevated rim that cross-cuts tributary subglacial channels and a subdued central uplift that appears to be actively eroding. From ground investigations of the deglaciated foreland, we identify overprinted structures within Precambrian bedrock along the ice margin that strike tangent to the subglacial rim. Glaciofluvial sediment from the largest river draining the crater contains shocked quartz and other impact-related grains. Geochemical analysis of this sediment indicates that the impactor was a fractionated iron asteroid, which must have been more than a kilometer wide to produce the identified crater. Radiostratigraphy of the ice in the crater shows that the Holocene ice is continuous and conformable, but all deeper and older ice appears to be debris rich or heavily disturbed. The age of this impact crater is presently unknown, but from our geological and geophysical evidence, we conclude that it is unlikely to predate the Pleistocene inception of the Greenland Ice Sheet.
Blackbox Learning of Parametric Dependencies for Performance Models

Modeling parametric dependencies in architectural performance models increases performance prediction accuracy. However, manually modeling parametric dependencies is time-intensive and requires expert knowledge. Existing automated extraction approaches require dedicated performance tests, which is often infeasible. In this paper, we propose to characterize parametric dependencies based on monitoring data. We create a representative dataset and show that different machine learning approaches perform best, depending on the characteristics of the dependency. Based on these results, we introduce a meta-selector that chooses the most suitable machine learning approach based on the dependency characteristics. In our evaluation, the meta-selector reduces the prediction error compared to the best individual machine learning approach, SVR, by 30%. As a proof of concept, we show that our approach is capable of automatically characterizing a manually modeled dependency from a previous case-study, resulting in a response time prediction accuracy of 92.8%.
Fast Robust Image Feature Matching Algorithm Improvement and Optimization

This paper quantitatively analyzes different types of image changes according to the characteristics of each algorithm, and put forward different optimal algorithms for different types of pictures. Firstly, four classical matching algorithms are selected and compared for scale, photometric and rotational robustness. In order to solve the limitation of the robustness of single algorithm, three improved algorithms are proposed. Based on the combination of SURF and ORB algorithms and one or more feature point screening, the improved algorithm is used to improve accuracy. Secondly, the improved algorithm is tested by using images with multiple types of changes at the same time. It is concluded that the improved algorithm has strong robustness and can effectively improve image matching accuracy. Finally, the simulation result shows that the selection of the optimal algorithm according to the features of the picture maximizes the advantages of different algorithms to meet the quantity of matching points and the matching accuracy.
Social Conformity Effects on Trust in Simulation-Based Human-Robot Interaction

Objective: We investigated the co-acting influences of communication and social conformity on trust in human-robot interaction. Background: Previous work has investigated aspects of the robot, the human, and the environment as influential factors in the human-robot relationship. Little work has examined the conjoint effects of social conformity and communication on this relationship. As social conformity and communication have been shown to affect human-human trust, there are a priori reasons to believe that they will play an influential role in human-robot trust also. Method: The experiment examined the influences of social conformity and robot communication on trust. A 2 × 2 (communication × social group) design was implemented with each variable having two levels (communication, no communication; positive social group, negative social group). Results: We created a communication manipulation which we then demonstrated to mediate the trust level between human and robot. However, this influence on trust was overcome by social information in which the subsequent trust level, attributed to the robot, was dominated by expressed social group attitudes to that robot. Conclusion: The results confirm the importance of human social assessments over direct robot communication in setting human-robot trust levels. When social opinions are expressed, observers appear to conform to the trust displayed by the group than relying on their own judgment. Application: In human-robot teams, the perceptions of the group may exert a greater impact than even robot communication. This may be especially important when new human members are introduced into such teams.
Determination of chemical ordering in the complex perovskite Pb(Cd1/3Nb2/3)O3

Using various experimental methods including X-ray diffraction and electron microscopy, this work determines the range and nature of the chemical ordering in Pb(Cd1/3Nb2/3)O3 (PCN). The influence of the chemical ordering on the dielectric property is also discussed, based on the similarities and differences between PCN and other classical relaxors.
Rescuing the negative impact of human endogenous retrovirus envelope protein on oligodendroglial differentiation and myelination

Remyelination in the adult CNS depends on activation, differentiation, and functional integration of resident oligodendroglial precursor cells (OPCs) and constitutes the only spontaneous neuroregenerative process able to compensate for functional deficits upon loss of oligodendrocytes and myelin sheaths as it is observed in multiple sclerosis. The proteins encoded by p57kip2‐ and by human endogenous retrovirus type W (pHERV‐W) envelope genes were previously identified as negative regulators of OPC maturation. We here focused on the activity of the ENV protein and investigated how it can be neutralized for an improved myelin repair. We could demonstrate that myelination in vitro is severely affected by this protein but that application of an anti‐ENV neutralizing antibody, currently investigated in clinical trials, can rescue the generation of internodes. We then compared p57kip2 and ENV dependent inhibitory mechanisms and found that a dominant negative version of the p57kip2 protein can equally save OPCs from myelination failure in response to ENV‐mediated TLR4 activation. Additional experiments addressing p57kip2's underlying mode of action revealed a direct interaction with ATP6v1d, a central component of a vascular ATPase. Its pharmacological blocking was then shown to exert an analogous myelination rescue effect in presence of the ENV protein. Therefore, our study provides mechanistic insights into oligodendroglial inhibition processes and presents three different means to counteract the anti‐myelination effect of the ENV protein. These observations are therefore of interest in light of understanding the complexity of the numerous oligodendroglial inhibitors and might promote the establishment of novel regenerative therapies.
Machine Learning Based Approaches for SWEDD diagnosis in DaTSCAN SPECT imaging

DaTSCAN (123I iophlipane injection) is a Singlephoton emission computed tomography molecular imaging agent of Dopamine Transporter (DaT). DaTSCAN is widely used as a hallmark of nigrostriatal nerve cells degeneration such as in Parkinson’s Disease (PD). DaT density is commonly assessed by calculating the Striatum Binding Ratio (SBR). For some subjects showing abnormal motor signs, SBR falls in the normal range which is known as Scan Without Evidence of Dopaminergic Deficit (SWEED). The fate of these subjects to develop PD is not obvious. In this paper, we developed Machine Learning based models for PD classification and SWEDD condition prediction using data from the Parkinson’s Progression Markers Initiative (PPMI). We availed of the Multivariate Logistic Regression (MLR) and Principal Component Analysis-Linear Support Vector Machine (PCA-LSVM) to establish competent models. The SBR values were calculated from the least affected- and most affected-side of Putamen and Caudate regions and the Unified Parkinson’s Disease Rating Scale (UPDRS) were used as significant contributors to our models. The MLR provided a correct classification between PD and controls at an area under the curve (AUC) of 0.98. The R2 from Cox and Snell test was significantly high (0.65) which indicate a high significance of the model. At an optimal threshold of 98%, 8 subjects from SWEED were detected as participants who would develop PD. The PCA-LSVM model provided a correct classification between PD and controls at an average accuracy of 0.92. The LSVM predicted 5 SWEED subjects as participants who would develop PD. Four subjects were cross identified from the MLR and PCA-LSVM. Developed models provided high capacities to distinguish PD participants from controls. The MLR at its optimum threshold coequal the PCALSVM model in the prognostic of SWEED group.
Verification of Recurrent Neural Networks Through Rule Extraction

The verification problem for neural networks is verifying whether a neural network will suffer from adversarial samples, or approximating the maximal allowed scale of adversarial perturbation that can be endured. While most prior work contributes to verifying feed-forward networks, little has been explored for verifying recurrent networks. This is due to the existence of a more rigorous constraint on the perturbation space for sequential data, and the lack of a proper metric for measuring the perturbation. In this work, we address these challenges by proposing a metric which measures the distance between strings, and use deterministic finite automata (DFA) to represent a rigorous oracle which examines if the generated adversarial samples violate certain constraints on a perturbation. More specifically, we empirically show that certain recurrent networks allow relatively stable DFA extraction. As such, DFAs extracted from these recurrent networks can serve as a surrogate oracle for when the ground truth DFA is unknown. We apply our verification mechanism to several widely used recurrent networks on a set of the Tomita grammars. The results demonstrate that only a few models remain robust against adversarial samples. In addition, we show that for grammars with different levels of complexity, there is also a difference in the difficulty of robust learning of these grammars.
Gradual acquisition of visuospatial associative memory representations via the dorsal precuneus

Activation of parietal cortex structures like the precuneus is commonly observed during explicit memory retrieval, but the role of parietal cortices in encoding has only recently been appreciated and is still poorly understood. Considering the importance of the precuneus in human visual attention and imagery, we aimed to assess a potential role for the precuneus in the encoding of visuospatial representations into long‐term memory. We therefore investigated the acquisition of constant versus repeatedly shuffled configurations of icons on background images over five subsequent days in 32 young, healthy volunteers. Functional magnetic resonance imaging was conducted on Days 1, 2, and 5, and persistent memory traces were assessed by a delayed memory test after another 5 days. Constant compared to shuffled configurations were associated with significant improvement of position recognition from Day 1 to 5 and better delayed memory performance. Bilateral dorsal precuneus activations separated constant from shuffled configurations from Day 2 onward, and coactivation of the precuneus and hippocampus dissociated recognized and forgotten configurations, irrespective of condition. Furthermore, learning of constant configurations elicited increased functional coupling of the precuneus with dorsal and ventral visual stream structures. Our results identify the precuneus as a key brain structure in the acquisition of detailed visuospatial information by orchestrating a parieto–occipito–temporal network.
"What Is 'Enough,' and How Do I Make It?": A Qualitative Examination of Questions Mothers Ask on Social Media About Pumping and Providing an Adequate Amount of Milk for Their Infants.

BACKGROUND Mothers commonly cite an inadequate milk "supply" as a reason for stopping human milk feeding. Further, pumping may affect mothers' understanding of their milk production. We aimed to characterize the questions mothers ask each other online related to the adequacy of the milk they pumped and provided to their infants. MATERIALS AND METHODS We conducted a secondary analysis of 543 posts containing questions related to pumping on an online discussion forum. These posts were provided by an open cohort of ∼25,000 women between 1 month before due date and 4.5 months postpartum. We used thematic analysis with Atlas.ti to analyze the posts. RESULTS Mothers commonly asked how many ounces they should be pumping and inquired about strategies to increase their pump output. They also wondered how many ounces or bottles of pumped milk they should provide to their infants or store for future use. Finally, mothers reported the inadequacy of the milk they were pumping or providing to their infants as potential reasons for stopping human milk feeding. CONCLUSION Our findings suggest that mothers may benefit from additional guidance from health care providers on the limitations of using pumps to draw conclusions about their milk production, the current evidence related to the use of herbal galactagogues, and the importance of responsive infant feeding. These findings also highlight the need for future research into how pumping or using herbal galactagogues may affect mothers' actual or perceived milk production and how styles for providing pumped milk compare to styles for feeding directly at the breast.
Predictive value of echocardiography in Type 2 diabetes.

AIMS Echocardiography is suggested in the diagnostic work-up of patients with Type 2 diabetes (T2D). We investigated which echocardiographic parameters that best predicted cardiovascular disease (CVD) and whether this was persistent in both genders in a large cohort of outpatients with T2D. METHODS AND RESULTS We performed comprehensive echocardiography in 933 patients with T2D followed at specialized out-patients clinics in Copenhagen, Denmark. Follow-up was performed using national registries and included admission with future CVD events and non-CVD death as competing risk. Median follow-up was 4.8 years and 138 CVD events occurred. In univariable and multivariable analyses, a wide range of structural, diastolic, and systolic measurements predicted CVD including mean E/e' [hazard ratio (HR) 1.06, 95% confidence interval: (1.03-1.10), P < 0.001, C-statistics 0.74 (0.70-0.78)] and global longitudinal strain (GLS) [1.10 (1.01-1.20), P = 0.03, C-statistics 0.73 (0.69-0.77)]. However, this was modified by gender. In men, mean E/e' remained the strongest predictor in multivariable analyses and performed best measured by highest C-statistics [HR 1.15, 95% confidence interval: (1.08-1.21), P < 0.001, C-statistics 0.75 (0.71-0.80)] whereas in women this was GLS [1.39 (1.14-1.70), P = 0.001, C-statistics 0.79 (0.70-0.87)]. These findings persisted when excluding patients with known heart disease and when regarding all-cause mortality as a competing risk. CONCLUSION A range of echocardiographic parameters predicted CVD in patients with Type 2 diabetes, however, in multivariable analyses, mean E/e' was the strongest predictor and had the highest model performance. Importantly, this study identifies a hitherto undescribed gender interaction as mean E/e' performed best in men, whereas in women this was GLS.