Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-Driven Assisted Decision Making for Surgical Procedure of Hepatocellular Carcinoma Resection and Prognostic Prediction: Development and Validation of Machine Learning Models

Simple Summary Clinically, surgical decisions for HCC resection are difficult and not sufficiently personalized. The aim of this study was to develop a surgical procedure decision-making and effectiveness prediction system for hepatectomy through extensive expert surgical decision-making experience and long-term follow-up, which can assist surgeons. The proposed machine learning models demonstrated their superior performance in surgical decision-making, OS, and RFS prediction tasks. Additionally, a web server that aids physician decision-making was deployed, which is expected to be an effective tool for auxiliary surgeons. Abstract Background: Currently, surgical decisions for hepatocellular carcinoma (HCC) resection are difficult and not sufficiently personalized. We aimed to develop and validate data driven prediction models to assist surgeons in selecting the optimal surgical procedure for patients. Methods: Retrospective data from 361 HCC patients who underwent radical resection in two institutions were included. End-to-end deep learning models were built to automatically segment lesions from the arterial phase (AP) of preoperative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Clinical baseline characteristics and radiomic features were rigorously screened. The effectiveness of radiomic features and radiomic-clinical features was also compared. Three ensemble learning models were proposed to perform the surgical procedure decision and the overall survival (OS) and recurrence-free survival (RFS) predictions after taking different solutions, respectively. Results: SegFormer performed best in terms of automatic segmentation, achieving a Mean Intersection over Union (mIoU) of 0.8860. The five-fold cross-validation results showed that inputting radiomic-clinical features outperformed using only radiomic features. The proposed models all outperformed the other mainstream ensemble models. On the external test set, the area under the receiver operating characteristic curve (AUC) of the proposed decision model was 0.7731, and the performance of the prognostic prediction models was also relatively excellent. The application web server based on automatic lesion segmentation was deployed and is available online. Conclusions: In this study, we developed and externally validated the surgical decision-making procedures and prognostic prediction models for HCC for the first time, and the results demonstrated relatively accurate predictions and strong generalizations, which are expected to help clinicians optimize surgical procedures.
Real-Life Problems to Increase the Critical Analysis Thinking Among Novice Counselors in Humanistic Counseling Courses: Is It Effective?

Abstract: This study aims to determine the effectiveness of using real-life problems to improve novice counsellors' critical analytical thinking in humanistic counselling courses. The research method uses an experiment with a pretest-posttest control group design. The instrument used is a critical-analytical thinking scale. The research sample used a purposive sampling technique, which consisted of 84 students of the 2018 Guidance and Counseling UNY Study Program who took the Humanistic Counseling course. The samples were separated into experimental and control groups of 42 students. The pre-test and post-test data were analyzed using an independent sample t-test using SPSS Amos 25 software. The results show that the use of real-life problems in the Humanistic Counseling course was proven effective in improving students' critical-analytical thinking as novice counsellors. As a result, students are more stimulated to practice analyzing cases in various ways and can activate their cognitive complexity processes as novice counsellors. Abstrak: Penelitian ini bertujuan untuk mengetahui keefektifan penggunaan masalah hidup riil untuk meningkatkan pemikiran kritis-analitis calon konselor pada mata kuliah konseling humanistik. Metode penelitian yang digunakna ialah eksperimen dengan rancangan pretest-posttest control group design. Instrumen yang digunakan ialah skala berpikir kritis-analitis. Sampel penelitian menggunakan teknik purposive sampling, yakni terdiri dari 84 mahasiswa angkatan 2018 porgram studi Bimbingan dan Konseling Universitas Negeri Yogyakarta yang menempuh mata kuliah Konseling Humanistik. Sampel dipisah pada kelompok eksperimen dan kelompok kontrol yang masing-masing berjumlah 42 mahasiswa. Data pre-test dan post-test dianalisis menggunakan independent sample t-test dengan menggunakan software SPSS Amos 25. Hasil penelitian menunjukkan bahwa, penggunaan masalah hidup riil pada mata kuliah konseling humanistik terbukti efektif meningkatkan pemikiran kritis-analitis mahasiswa calon konselor. Mahasiswa lebih terstimulasi untuk berlatih menganalisis kasus secara beragam serta mampu mengaktivasi proses kompleksitas kognitifnya sebagai calon konselor.
The theory of super-resolution electron microscopy via Wigner-distribution deconvolution

The theory of deconvolving the microdiffraction data-set available in a scanning transmission electron microscope or, equivalently, the set of all bright- and dark-field images available in a conventional transmission electron microscope to obtain super- resolution micrographs (which are not limited by the transfer function of the objective lens) is developed and described with reference to holography and other phase-retrieval schemes. By the use of a Wigner distribution, influences of the instrument function can be entirely separated from the information pertaining to the specimen. The final solution yields an unambiguous estimate of the complex value of the specimen function at a resolution which in theory is only limited by the electron wavelength. The faithfulness of the image processing is shown to be not seriously affected by specimen thickness or partial coherence in the illuminating beam. The inversion procedure is remarkably noise insensitive, implying that it should result in a robust and practicable experimental technique, though one that will require very large computing facilities.
Assessing public perception and willingness to pay for renewable energy in Pakistan through the theory of planned behavior

With growing urbanization and increasing world population, energy demand also increases. A significant portion of the world’s energy comes from fossil fuels, and these sources of energy are declining rapidly at the current consumption rate. There are also growing environmental concerns on the use of fossil fuels increasing greenhouse gas emissions. In this regard, renewable energy (RE) shows promising solutions which are both sustainable and environmentally friendly. Developed countries and leading organizations are investing heavily in the RE sector. However, the developing world has anxieties over social acceptability and people’s willingness to pay for renewable energy. This study is conducted in Pakistan to understand the public perception and willingness to pay. The Theory of Planned Behavior (TPB) was utilized with background factors such as awareness, perceived advantages, perceived challenges, and moral obligations to examine its influence on people’s willingness to pay. In addition to this, the study also assessed the indirect effects of background factors (awareness, perceived advantages, and perceived challenges) on willingness to pay through public attitude. Furthermore, the indirect relationship between background factors (awareness and moral obligation) and willingness to pay through subjective norms was also examined. A total of 512 samples were gathered from participants and were analyzed through partial least square–structural equation modeling (PLS-SEM) and SPSS. The study findings are very interesting and back up our hypotheses that the background factors (awareness, perceived advantages, and perceived challenges) are positively associated with public attitude and have an indirect effect on willingness to pay through public attitude. Similarly, variables such as awareness and moral obligation are negatively and positively associated with subjective norms, respectively. However, the variables, awareness and moral obligation, have no indirect relationship with willingness to pay through subjective norms. Additionally, the study reveals that the components (attitude and perceived behavior control) of TPB have a significantly positive effect on willingness to pay. The study also concludes that the participants having formal education and knowledge about climate change and renewable energy are inclined toward green energy and are willing to pay, and they are hardly influenced by others' opinions. Furthermore, the study also provides insights for policymakers, suggestions, and recommendations for the future.
Peritoneal Mesothelioma in a High Volume Peritoneal Surface Malignancies Unit

Diffuse malignant peritoneal mesothelioma (PM) is a rare neoplasm, traditionally associated with a poor prognosis. There are other varieties of PM that are even less frequent and of uncertain malignancy. Cytoreductive surgery (CRS) with hyperthermic intraperitoneal chemotherapy (HIPEC) has achieved prolonged survival in selected patients. The aim of this study is to analyze the patients with PM assessed in our center. Clinicopathological characteristics, diagnostic procedures and survival results from patients with PM appraised at our unit, according to the applied treatment, were analyzed. Seventeen patients were assessed between 2007 and 2019. Three cases had multicystic PM that were treated with complete CRS + HIPEC; all patients are alive and free of disease after a long follow-up. Three other cases had biphasic PM; a curative treatment could be performed in none of them, and their survival was minimal (<6 moths). Lastly, 11 cases with epithelioid PM were treated. Two cases were considered unresectable at laparoscopy (PCI 39); one of them had a long survival (67 months) with three iterative laparoscopic palliatives HIPECs for refractory ascites. The other nine cases were treated with curative CRS + HIPEC, with a median PCI of 14 (range 4–25), and a median overall survival (OS) of 58 months, with a 5-year OS of 47.4%. In conclusion, CRS + HIPEC, when possible, appears to be the optimal treatment for patients with PM. Knowledge of this therapeutic option is crucial, both to offer it to patients and to avoid delays in their referral to appropriate centers for treatment.
Integrating the potential of ion mobility spectrometry-mass spectrometry in the separation and structural characterisation of lipid isomers

It is increasingly evident that a more detailed molecular structure analysis of isomeric lipids is critical to better understand their roles in biological processes. The occurrence of isomeric interference complicates conventional tandem mass spectrometry (MS/MS)-based determination, necessitating the development of more specialised methodologies to separate lipid isomers. The present review examines and discusses recent lipidomic studies based on ion mobility spectrometry combined with mass spectrometry (IMS-MS). Selected examples of the separation and elucidation of structural and stereoisomers of lipids are described based on their ion mobility behaviour. These include fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, and sterol lipids. Recent approaches for specific applications to improve isomeric lipid structural information using direct infusion, coupling imaging, or liquid chromatographic separation workflows prior to IMS-MS are also discussed, including: 1) strategies to improve ion mobility shifts; 2) advanced tandem MS methods based on activation of lipid ions with electrons or photons, or gas-phase ion-molecule reactions; and 3) the use of chemical derivatisation techniques for lipid characterisation.
Dividing Protein Interaction Networks for Modular Network

Abstract 7 The increasing growth of data on protein-protein interaction (PPI) networks has boosted research 8 on their comparative analysis. In particular, recent studies proposed models and algorithms for 9 performing network alignment, that is, the comparison of networks across species for discovering 10 conserved functional complexes. In this paper, we present an algorithm for dividing PPI networks, 11 prior to their alignment, into small sub-graphs that are likely to cover conserved complexes. This 12 allows one to perform network alignment in a modular fashion, by acting on pairs of resulting small 13 sub-graphs from diﬀerent species. The proposed dividing algorithm combines a graph theoretical 14 property (articulation) with a biological one (orthology). Extensive experiments on various PPI 15 networks are conducted in order to assess how well the sub-graphs generated by this dividing 16 algorithm cover protein functional complexes and whether the proposed pre-processing step can
Evaluation of the Machinability of Ti-6Al-4V Titanium Alloy by AWJM Using a Multipass Strategy

Non-conventional machining processes offer various advantages, including the capability of processing hard-to-cut materials with a reasonable cost and sufficient productivity. However, depending on the application, different machining strategies need to be employed, in order to increase the flexibility of the process and produce parts with a better quality. In this study, experimental work was conducted and the use of a multipass strategy during slot milling of titanium alloy with abrasive water jet milling (AWJM) was explored, by comparing the effect of different numbers of passes under different process conditions, such as the jet pressure and traverse feed rate. The performance was evaluated by means of the kerf characteristics, and the productivity through material removal rate (MRR) values. The results indicated that the use of a multipass strategy had a considerable impact on the kerf taper angle, apart from the depth of penetration; and although it leads to reduced MRR and cutting efficiency, choosing appropriate values of process parameters, such as a higher jet pressure and moderate traverse feed, in combination with a moderate amount of passes, can be beneficial for AWJM from different points of view.
Fostering Organizational Performance at Central and Eastern European Universities: The Impact of Knowledge Creation and Affective Commitment

The purpose of this study is to examine the impact of knowledge creation and affective commitment on organizational performance. The study also explores the mediating role of affective commitment in this relationship. The results based on the sample of 825 academics and members of the administrative staff at universities from Central and Eastern European countries support the proposed hypotheses of this research. Exploratory and Confirmatory factor analyses were performed to assess the reliability of scales, while structural equation modeling was used to evaluate the hypotheses. This study demonstrates that knowledge creation and affective commitment are predictors of organizational performance. It also shows that affective commitment, which is influenced by knowledge creation, is an important mediating factor that affects organizational performance at public and private universities in Central and Eastern European countries.
Home Health Care Clinicians’ Use of Judgment Language for Black and Hispanic Patients: Natural Language Processing Study

Background A clinician’s biased behavior toward patients can affect the quality of care. Recent literature reviews report on widespread implicit biases among clinicians. Although emerging studies in hospital settings show racial biases in the language used in clinical documentation within electronic health records, no studies have yet investigated the extent of judgment language in home health care. Objective We aimed to examine racial differences in judgment language use and the relationship between judgment language use and the amount of time clinicians spent on home visits as a reflection of care quality in home health care. Methods This study is a retrospective observational cohort study. Study data were extracted from a large urban home health care organization in the Northeastern United States. Study data set included patients (N=45,384) who received home health care services between January 1 and December 31, 2019. The study applied a natural language processing algorithm to automatically detect the language of judgment in clinical notes. Results The use of judgment language was observed in 38% (n=17,141) of the patients. The highest use of judgment language was found in Hispanic (7,167/66,282, 10.8% of all clinical notes), followed by Black (7,010/65,628, 10.7%), White (10,206/107,626, 9.5%), and Asian (1,756/22,548, 7.8%) patients. Black and Hispanic patients were 14% more likely to have notes with judgment language than White patients. The length of a home health care visit was reduced by 21 minutes when judgment language was used. Conclusions Racial differences were identified in judgment language use. When judgment language is used, clinicians spend less time at patients’ homes. Because the language clinicians use in documentation is associated with the time spent providing care, further research is needed to study the impact of using judgment language on quality of home health care. Policy, education, and clinical practice improvements are needed to address the biases behind judgment language.
Proximity Protein Labeling In Dictyostelium With Engineered Ascorbic Acid Peroxidase 2

To fully understand any cellular process, we not only need to identify the proteins implicated, but also how the protein network is structurally and spatially organized and changes over time. However, the dynamic nature of many protein interactions involved in cellular signaling pathways continues to be the bottleneck in mapping and studying protein networks. Fortunately, a recently developed proximity labeling method using engineered ascorbic acid peroxidase 2 (APEX2) in mammalian cells allows the identification of weak and/or transient protein interactions with spatial and temporal resolution. Here, we describe a protocol for successfully using the APEX2-proximity labeling method in Dictyostelium, using the cAMP receptor cAR1 as example. Coupled to the identification of the labeled proteins by mass spectrometry, this method expands Dictyostelium’s proteomics toolbox and should be widely useful for identifying interacting partners involved in a variety of biological processes in Dictyostelium.
Radioactive Ion Beams and Radiopharmaceuticals

Experiments performed at radioactive ion beam facilities shed new light on nuclear physics and nuclear structure, as well as nuclear astrophysics, materials science and medical science. The many existing facilities, as well as the new generation of facilities being built and those proposed for the future, are a testament to the high interest in this rapidly expanding field. The opportunities inherent in radioactive beam facilities have enabled the search for radioisotopes suitable for medical diagnosis or therapy. In this article, an overview of the production techniques and the current status of RIB facilities and proposals will be presented. In addition, accelerator-generated radiopharmaceuticals will be reviewed.
Frequency-multiplexed Hong-Ou-Mandel interference

The implementation of quantum repeaters needed for long-distance quantum communication requires the generation of quantum entanglement distributed among the elementary links. These entanglements must be swapped among the quantum repeaters through Bell-state measurements. This study aims to improve the entanglement generation rate by frequency multiplexing the Bell-state measurements. As a preliminary step of the frequency-multiplexed Bell-state measurements, three frequency modes are mapped to a temporal mode by an atomic frequency comb prepared in $\mathrm{Pr^{3+}}$ ion-doped $\mathrm{Y_2SiO_5}$ crystals using a weak coherent state, and Hong-Ou-Mandel interference, which is a measure of the indistinguishability of two inputs, is observed in each frequency mode by coincidence detection. The visibility for all the modes was 40%-42% (theoretically up to 50%). Furthermore, we show that a mixture of different modes is avoided. The present results are connected to frequency-selective Bell-state measurements and therefore frequency-multiplexed quantum repeaters.
Iterative transfer learning for automatic collective motion tuning on multiple robot platforms

This paper proposes an iterative transfer learning approach to achieve swarming collective motion in groups of mobile robots. By applying transfer learning, a deep learner capable of recognizing swarming collective motion can use its knowledge to tune stable collective motion behaviors across multiple robot platforms. The transfer learner requires only a small set of initial training data from each robot platform, and this data can be collected from random movements. The transfer learner then progressively updates its own knowledge base with an iterative approach. This transfer learning eliminates the cost of extensive training data collection and the risk of trial-and-error learning on robot hardware. We test this approach on two robot platforms: simulated Pioneer 3DX robots and real Sphero BOLT robots. The transfer learning approach enables both platforms to automatically tune stable collective behaviors. Using the knowledge-base library the tuning procedure is fast and accurate. We demonstrate that these tuned behaviors can be used for typical multi-robot tasks such as coverage, even though they are not specifically designed for coverage tasks.
Blind Detection and Recognition of RS Code Based on Spectral Preprocessing

In order to solve the problem of blind detection and recognition of the RS(Reed-Solomon) code,a method based on spectral preprocessing is proposed.In this method,the spectral character is used in the presence of error codes,nonlinear transformation and median filter are used to combine the spectra of large number of codes to improve the performance of the new method in the case of high error rate.Theoretical upper bound of adaptability of error code rate is thus proved.Numerical examples are carried out to show that the proposed method almost reaches the theoretical upper bound.
Relationship of insight to neurocognitive function and risk of recurrence in depression: A naturalistic follow-up study

Introduction Major depressive disorder (MDD) is a highly recurrent mental illness accompanied by impairment of neurocognitive function. Lack of insight may affect patients’ motivation to seek treatment, resulting in poor clinical outcomes. This study explores the relationship of insight to neurocognitive function and the risk of recurrence of depressive episodes in patients with MDD. Methods Demographic, clinical variables, and neurocognitive function measured with Intra-Extra Dimensional Set Shift (IED) from the Cambridge Neuropsychological Test Automated Battery (CANTAB) were collected from 277 patients with MDD. Among them, 141 participants completed a follow-up visit within 1–5 years. Insight was measured using the 17-item Hamilton Depression Rating Scale (HAM-D). To explore the factors associated with recurrence, binary logistic regression models were used. Results Patients with MDD, without insight, had significantly higher total and factor scores (anxiety/somatization, weight, retardation, and sleep) on the HAM-D and worse performance in the neurocognition task, compared to those with insight. Furthermore, binary logistic regression revealed that insight and retardation can predict recurrence. Conclusion Lack of insight is associated with recurrence and impaired cognitive flexibility in patients with MDD.