Miracle-dz/UniFAR-FTUs huggingface dataset:
The Generalizability of Clinical Prediction Models for Patients with Acute Coronary Syndromes: Results from Independent External Validations

Purpose: It is increasingly recognized that clinical prediction models (CPMs) often do not perform as expected when they are tested on new databases. Independent external validations of CPMs are recommended but often not performed. Here we conduct independent external validations of acute coronary syndrome (ACS) CPMs. Methods: A systematic review identified CPMs predicting outcomes for patients with ACS. Independent external validations were performed by evaluating model performance using individual patient data from 5 large clinical trials. CPM performance with and without various recalibration techniques was evaluated with a focus on CPM discrimination (c-statistic, % relative change in c-statistic) as well as calibration (Harrells Eavg, E90, Net Benefit). Results: Of 269 ACS CPMs screened, 23 (8.5%) were compatible with at least one of the trials and 28 clinically appropriate external validations were performed. The median c statistic of the CPMs in the derivation cohorts was 0.76 (IQR, 0.74 to 0.78). The median c-statistic in these external validations was 0.70 (IQR, 0.66 to 0.71) reflecting a 24% decrement in discrimination. However, this decrement in discrimination was due mostly to narrower case-mix in the validation cohorts compared to derivation cohorts, as reflected in the median model based c-statistic [0.71 (IQR 0.66 to 0.75). The median calibration slope in external validations was 0.84 (IQR, 0.72 to 0.98) and the median Eavg (standardized by the outcome rate) was 0.4 (IQR, 0.3 to 0.8). Net benefit indicates that most CPMs had a high risk of causing net harm when not recalibrated, particularly for decision thresholds not near the overall outcome rate. Conclusion: Independent external validations of published ACS CPMs demonstrate that models tested in our sample had relatively well-preserved discrimination but poor calibration when externally validated. Applying off-the-shelf CPMs often risks net harm unless models are recalibrated to the populations on which they are used.
Agent-Based Simulation of Covid-19 Vaccination Policies in CovidSIMVL

An agent-based infectious disease modeling tool (CovidSIMVL) is employed in this paper to explore outcomes associated with MRNA two-dose vaccination regimens set out in Emergency Use Authorization (EUA) documents submitted by Pfizer and Moderna to the US Department of Health & Human Services. As well, the paper explores outcomes associated with a third "Hybrid" policy that reflects ranges of expected levels of protection according to Pfizer and Moderna EUA's, but entails a 35 day separation between first and second dose, which exceeds the 21 days set out in Pfizer documentation or the 28 days in Moderna documentation. Four CovidSIMVL parameters are varied in the course of 75 simulated clinical trials. Two relate directly to the vaccines and their impacts (duration between doses; degree of expected protection conferred by different vaccines following first or second dose). Two relate to the simulation contexts to which the vaccines are applied (degree of infectivity; duration of infectivity). The simulated trials demonstrate expected effects for timing of second dose, and for degree of protection associated with first and second dose of Pfizer and Moderna vaccines, and the effects are consistent with an assumed value of 75% for degree of protection after first and second doses for the Hybrid vaccine. However, the simulated trials suggest a more complex interaction between expected level of protection following first dose, timing of second dose and degree of infectivity. These results suggest that policy options should not be considered independent of the transmission dynamics that are manifested in the contexts in which the policies could be applied. CovidSIMVL embodies stochasticity in the mechanisms that govern viral transmission, and it treats the basic reproduction number (R0)as an emergent characteristic of transmission dynamics, not as a pre-set value that determines those dynamics. As such, results reported in this paper reflect outcomes that could happen, but do not necessarily reflect what is more or less likely to happen, given different configurations of parameters. The discussion section goes into some measure of detail regarding next steps that could be pursued to enhance the potential for agent-based models such as CovidSIMVL to inform exploration of possible vaccination policies, and to project outcomes that are possible or likely in local contexts, where stochasticity and heterogeneity of transmission must be featured in models that are intended to reflect local realism.
Medical Device Assembly Yield Improvement: Application of Quality Foundations

Quality foundations application is not a new concept for medical device, manufacturing, and service industries. The key quality concepts have origins from the quality pioneers named as “gurus” Joseph Juran, Deming and Crosby. There are some key principles which many companies apply as part of project development and implementation. This article, as a final design project, presents the application of quality foundations on a yield improvement project for a catheter assembly line, where the customer survey scores were 48% out of 100%. The combination of voice of customer to achieve customer satisfaction, quality foundation tools application, and lean manufacturing concepts integration resulted on a yield improvement of 4%, and customer survey score of 60% due to the implementation of a new re-design UV fixture in order to reduce significantly the defect know such as coil damage.
MODEL ORDER REDUCTION FOR SINE-GORDON EQUATION USING POD AND DEIM BY MR

This work applies model order reduction techniques on a nonlinear dynamical system arising from finite difference (FD) discretization of the sine-Gordon equation. Specifically, the proper orthogonal decomposition (POD) is employed to construct a low dimensional basis that can accurately capture the dynamics of the solution space. This POD basis is then used with the Galerkin projection to obtain a reduced-order model that is much smaller than the original discretized system. In addition, this work uses the POD technique in conjunction with the discrete empirical interpolation method (DEIM) to further reduce the computational complexity of the nonlinear term. The numerical results from both POD and DEIM are compared and shown to be efficient in decreasing both computational memory storage and simulation time while maintaining accuracy of the approximate solution. This work also investigates the effect of using different amount of snapshots in constructing the POD basis, as well as demonstrates the applicability of the POD-DEIM approach on the parametrized sine-Gordon equation.
Vermittlung psychodynamischer Interventionskompetenzen in der Ausbildung psychologischer Psychotherapeuten

Background. In Germany, the launch of the new university “psychotherapy degree program” in autumn 2020 and the wishes of current and future psychotherapists at psychotherapy training institutes have made it necessary to integrate experience-based and competence-oriented teaching into future university and postgraduate curricula. Material and methods. Following Kern’s sixstep approach to curriculum development, the conceptualization, development, and implementation of a longitudinal curriculum for teaching psychodynamic intervention skills (DYNAMIK curriculum) to future psychological psychotherapists is showcased. Results. A longitudinal curriculum with eight modules using peer role play and standardized patientswas developed to teach general and specific psychodynamic intervention skills. The DYNAMIK curriculumwas incorporated into the current postgraduate, depth psychology-focused, psychotherapist training program. Conclusion. The DYNAMIK curriculum is an important first step towards longitudinal experience-based and competence-oriented teaching in current postgraduate and future university degree program curricula followed by postgraduate psychotherapist training. Hence, the effectiveness of such curricula and intervention competencies need to be reviewed.
PTSD and the Link to Obesity

Seventy percent of American adults are overweight or obese. [1] Within the military about 7.8% of personnel are now considered overweight up from 1.6% in 2001. [2] Being overweight is unhealthy for anyone, but it causes specific problems for service members, including the possibility of administrative separation for weight control failure. There may be a number of reasons why military personnel are becoming overweight, just like reasons among the civilian population vary. However, service members and veterans may be more likely to face an additional cause of weight gain than civilians: PTSD.
GENDER DIFFERENCES IN EXPERIENCING AND COPINGWITH A SENSEOF GUILT IN INTERPERSONAL AND INTERGROUP RELATION

The article deals with the results of the study aimed at evaluation of both genders representatives’ sense of guilt that arises at different levels of communication. Gender peculiarities of experiencing and coping with the sense of guilt in interpersonal and intergroup relationships were detected on the basis of three surveys of Ukrainian students (582, 63, and 34 persons). It is shown that majority of differences between women and men are consistent with existing stereotypes in the mass consciousness. Women are more clearly oriented on guilt that occur in the narrow circle of communication and more often emphasize its emotional and communicative content. Instead, men are more interested in superpersonal contexts and prefer instrumental positions. At the same time, the differences were recorded that contradict gender stereotypes. In assessments of students’ guilt women more frequently pay attention to effectiveness/inefficiency, while men emphasize the importance of such negative moral trait as dishonesty. In the sphere of broad social contacts women relatively more actively accuse the government authorities and insist on the need to increase effectiveness of all Ukrainians.
Motivation and Attitude and their Relationship with Reading Comprehension Ability among Iranian Undergraduate Students

Studies in reading strategies bring together the assumption that individual characteristics may influence reading performance; different readers may process the same text in different ways, depending on their purposes, motivation, attitudes, interests and background knowledge. The research aims to study the possible relationship between Iranian undergraduate learners' motivation and attitude towards reading comprehension. Therefore, a total number of 285 participants from six different fields of study, social sciences, math, primary education, chemistry, biology and Persian literature took part in this study. The researchers gave the instruments over a 2-day period; the Language Proficiency Test was given on day one, the Motivation for Reading Questionnaire (MRQ), Reading Attitudes Questionnaire (RAQ) and the Reading Comprehension Test on day two with oneweek‎ interval. ‎ Participants’‎ responses‎ to‎ the‎ reading‎ motivation‎ and‎ attitude‎ statements, reading comprehension questions, and English language proficiency questions were analyzed through a multiple regression, One-way ANOVA, T-test and correlation. The findings indicated reading motivations and attitudes contribute to better reading comprehension among the subjects. It was also demonstrated that the participants' discipline was a significant contributing factor to the relationship between reading motivation, attitude and reading comprehension ability.
A method for determining optical properties of human tissues by measuring diffuse reflectance with CCD

It is important to measure optical properties noninvasively, quickly and accurately in vivo for disease diagnostics and medical therapeutics. In this study, we measured the absorption coefficient and the reduced scattering coefficient of human tissues by measuring diffuse reflectance with CCD, examined the techniques involved, such as quantization of diffusion approximation theory, effective reverse fitting algorithm, and the data selection and processing method, and finally concluded about the accuracy of this method. The experimental results indicate that the error is less than or equal to 8% using the diffusion theory, under the condition that the reduced scattering coefficient is one order of magnitude greater than the absorption coefficient. The stability and precision of optical property measurements are significantly improved by using the multi-step iterative fitting method and using the ring areas to determine the diffuse reflectance center. The efficiency of reverse algorithm is greatly enhanced by selecting a one-dimensional array on the straight line crossing both the entry point and the diffusion center for fitting. The error of measured absorption coefficient and scattering coefficient of the Intralipid solution and human forearm tissues are less than 5% with our method. These results can provide important technical information for application of the diffusion theory.
Weakened Reporting ? The Effect of Clawback Regime and Bonus Compensation on Fraud Reporting

The Dodd-Frank Act of 2010 (DF) clawback provision expands the range of executives susceptible to forfeiture of incentive-based compensation in the event of a restatement. Under Section 304 of the Sarbanes-Oxley Act of 2002 (SOX), incentive compensation of only Chief Executive Officers (CEO) and Chief Financial Officers (CFO) is subject to clawback. DF Section 954 requires companies to recover excess incentive compensation from any executive officer, regardless of fault or cause of the restatement (e.g., due to error or intentional misconduct). The DF clawback provision is likely to be consequential because executive compensation packages are tilted toward incentive-based compensation in the form of bonus and equity compensation, with incentive-based pay increasing as a percentage of total pay in recent years (Gao and Li, 2015; Conyon, 2006; Leider, 2016; Shue and Townsend, 2017).
Effect of Volume Exclusion on the Dimensions of Polymer Chains

The effect of volume exclusion on the mean‐square radius of gyration s2 is treated by use of the empirical expression W0(s)=const s6exp[−(7/2)〈s2〉0−1s2] for the statistical distribution of s for a system of linear chain molecules unperturbed by volume exclusion. Segment interactions within the perturbed molecule are treated in the smoothed‐density approximation. The result is α5—α3=Azh(z/α3), where α2=〈s2〉/〈s2〉0, A is a numerical constant, z is the usual parameter proportional to the excluded volume for a segment pair and to the square root of the chain length, and h(ζ) is a function which decreases by almost one‐half as ζ increases from 0 to ∞. Asymptotic dependence of α2 on the ⅕th power of the chain length is thus confirmed. Extremely long chains, exceeding 106 bonds, would be required however for realization of this limiting behavior.
Autonomous Navigation for Mobile Robots: Machine Learning-based Techniques for Obstacle Avoidance

Autonomous navigation of unmanned aerial vehicles (UAVs) has posed several challenges due to the limitations regarding the number and size of sensors that can be attached to the mobile robots. Although sensors such as LIDARs that directly obtain distance information of the surrounding environment have proven to be effective for obstacle avoidance, the weight and cost of the sensor contribute to the restrictions on usage for UAVs as recent trends require smaller sizes of UAVs. One practical option is the utilization of monocular vision sensors which tend to be lightweight and have a relatively low cost, yet still the main drawback is that it is difficult to draw a certain rule from the sensor data. Conventional methods regarding visual navigation makes use of features within the image data or estimate the depth of the image using various techniques such as optical flow. These features and methodologies however still rely on human-based rules and features, meaning that robustness can become an issue. A more recent approach to vision-based obstacle avoidance exploits heuristic methods based on artificial intelligence such as deep learning technologies, which have shown state-of-the-art performance in fields such as image processing or voice recognition. These technologies are capable of automatically selecting important features for classification or prediction tasks, hence allowing superior performance. Such heuristic methods have proven to be more efficient as the rules and features that are drawn from the image are automatically determined, unlike conventional methods where the rules and features are explicitly determined by humans. In this thesis, we propose an imitation learning framework based on deep learning technologies that can be applied to the obstacle avoidance of UAVs, where the neural networks in this framework are trained upon the flight data obtained from human experts, extracting the necessary features and rules to carry out designated tasks. The system introduced in this thesis mainly consists of three parts: the data acquisition and preprocessing phase, the model training phase, and the model application phase. A CNN (Convolutional Neural Network), 3D-CNN, and a DNN (Deep Neural Network) will each be applied to the framework and tested with respect to the collision ratios to validate the obstacle avoidance performance.
Hypothesis: daptomycin permeabilizes membranes by forming self-assembled nanotubes

Daptomycin is the only lipopeptide antibiotic that is widely used in clinical practice. It was discovered by Eli Lilly and then studied and commercialized by Cubist Pharmaceuticals in 2003. Although this antibiotic has been used for 17 years, the debate over its mechanism of action is ongoing. In this paper, we discuss the different hypotheses on the mode of action of this antibiotic with a primary focus on the bacterial membrane permeabilization as the main mechanism of action. By comparing the experimental data on the oligomerization of daptomycin in membranes with properties of self-assembling cyclic peptides, we conclude that the structure of daptomycin oligomer should resemble the structures of peptide nanotubes that serve as ion channels in membranes.
Predicting Substance Use at a Youth Mass Gathering Event: The Role of Norms and the Importance of Their Source.

OBJECTIVE Mass gathering events often involve high levels of substance use, yet the psychological predictors of substance use in these contexts have received minimal attention. This study examined the relationship between social norms and mass gathering attendees' anticipated substance use. We (a) tested this relationship while controlling for established predictors of substance use, (b) assessed longitudinally the impact of intraindividual changes in perceived substance use norms on intraindividual changes in participants' anticipated substance use, and (c) compared the relative impact of two normative referents (friends and typical mass gathering attendees). METHOD Data were collected in situ at Schoolies (Australia's largest youth mass gathering). On Days 1, 3, and 5 of the 7-day festival, participants (N = 427; ages 16-19, 66% female) reported normative perceptions of drinking and drug taking among friends and other attendees, and indicated their own anticipated drinking and drug taking. RESULTS Friend norms were associated with anticipated drinking and drug taking at each corresponding time point, with particularly strong relationships observed for anticipated drinking. Changes in friend norms also predicted changes in anticipated drinking and drug taking. Conversely, the effects of norms for typical attendees were weak. CONCLUSIONS People's substance use at mass gatherings is particularly influenced by perceptions of friends' substance use. Friend norm perceptions change over time and dynamically influence young people's anticipated substance use in these high-risk settings. Interventions to reduce substance use at mass gatherings may be enhanced by correcting misperceptions of the normative behaviors of friends.
Registry Study of Clinical Data and in Hospital Outcome in Patients Admitted with Acute Coronary Syndrome at Tanta University Hospital in 2019

Background: A quick but thorough assessment of the patient’s history and findings on physical examination, electrocardiography, and cardiac biomarker tests permit accurate diagnosis and aid in early risk stratification. This work aimed to analyze the diagnostic and prognostic tools, the modalities of management, and the hospital outcome of patients with acute coronary syndrome (ACS) at Tanta University Hospital in one year. Methods: This ACS registry at Tanta university hospital is a prospective observational registry for 200 consecutive admitted patients with proven ACS from January 2019 to January 2020. Results: A higher percent of hypertension, family history of ischemic heart disease and SCD, previous history of chronic kidney disease (CKD), and lower percent of a previous history of IHD in STEMI compared to NSTEMI/UA. In-hospital death, in-hospital reinfarction, and reduced ejection fraction are higher in STEMI than in NSTEMI/UA patients. (P value = 0.015, 0.018 and 0.001 respectively) without significant differences regarding in-hospital congestive heart failure (CHF) and ischemic stroke. History of CKD, higher Killip class, and in-hospital stroke were independently affecting in-hospital mortality. Also, the history of higher Killip class was independently affecting in-hospital reinfarction and in-hospital CHF. Old age and occurrence of in-hospital reinfarction were independently affecting in-hospital stroke. Conclusion: Hypertension, diabetes, dyslipidemia, and smoking are the major risk factors for ACS so, controlling these risk factors will improve in-hospital outcomes. In STEMI, most patients underwent PPCI, which was reflected in the outcome. In NSTEMI/UA patients, both conservative and invasive management was done, taking into consideration the risk stratification of each patient, making management easier and with a good outcome.
Decoding locomotion from population neural activity in moving C. elegans

We investigated the neural representation of locomotion in the nematode C. elegans by recording population calcium activity during movement. We report that population activity more accurately decodes locomotion than any single neuron. Relevant signals are distributed across neurons with diverse tunings to locomotion. Two largely distinct subpopulations are informative for decoding velocity and curvature, and different neurons’ activities contribute features relevant for different aspects of a behavior or different instances of a behavioral motif. To validate our measurements, we labeled neurons AVAL and AVAR and found that their activity exhibited expected transients during backward locomotion. Finally, we compared population activity during movement and immobilization. Immobilization alters the correlation structure of neural activity and its dynamics. Some neurons positively correlated with AVA during movement become negatively correlated during immobilization and vice versa. This work provides needed experimental measurements that inform and constrain ongoing efforts to understand population dynamics underlying locomotion in C. elegans.