Miracle-dz/UniFAR-FTUs huggingface dataset:
Preoperative prediction model for macrotrabecular-massive hepatocellular carcinoma based on contrast-enhanced CT and clinical characteristics: a retrospective study

Objective To investigate the predictive value of contrast-enhanced computed tomography (CECT) imaging features and clinical factors in identifying the macrotrabecular-massive (MTM) subtype of hepatocellular carcinoma (HCC) preoperatively. Methods This retrospective study included 101 consecutive patients with pathology-proven HCC (35 MTM subtype vs. 66 non-MTM subtype) who underwent liver surgery and preoperative CECT scans from January 2017 to November 2021. The imaging features were evaluated by two board-certified abdominal radiologists independently. The clinical characteristics and imaging findings were compared between the MTM and non-MTM subtypes. Univariate and multivariate logistic regression analyses were performed to investigate the association of clinical-radiological variables and MTM-HCCs and develop a predictive model. Subgroup analysis was also performed in BCLC 0-A stage patients. Receiver operating characteristic (ROC) curves analysis was used to determine the optimal cutoff values and the area under the curve (AUC) was employed to evaluate predictive performance. Results Intratumor hypoenhancement (odds ratio [OR] = 2.724; 95% confidence interval [CI]: 1.033, 7.467; p = .045), tumors without enhancing capsules (OR = 3.274; 95% CI: 1.209, 9.755; p = .03), high serum alpha-fetoprotein (AFP) (≥ 228 ng/mL, OR = 4.101; 95% CI: 1.523, 11.722; p = .006) and high hemoglobin (≥ 130.5 g/L; OR = 3.943; 95% CI: 1.466, 11.710; p = .009) were independent predictors for MTM-HCCs. The clinical-radiologic (CR) model showed the best predictive performance, achieving an AUC of 0.793, sensitivity of 62.9% and specificity of 81.8%. The CR model also effectively identify MTM-HCCs in early-stage (BCLC 0-A stage) patients. Conclusion Combining CECT imaging features and clinical characteristics is an effective method for preoperatively identifying MTM-HCCs, even in early-stage patients. The CR model has high predictive performance and could potentially help guide decision-making regarding aggressive therapies in MTM-HCC patients.
Detection of mental fatigue with wearable ECG devices based on HRV analysis

: Mental fatigue is a key cause of many chronic diseases such as CVD, diabetes and cancer. It is illusive and hard to measure or detect. This research proposes a feasible accurate and cost saving method to detect fatigue level of mental workers via smart wearable devices. In order to detect mental fatigue level, this paper firstly extracted HRV features, then used Man-Whitney U Test to evaluate the statistical significances of HRV features between Normal and Fatigue state. This paper used Random Forest for feature selection to determine the importance of HRV features. The most important HRV features are NN. mean, PNN50, VLF, LF and TP respectively. This paper then took four machine learning algorithms on selected features to predict the state of mental fatigue. The experiments demonstrated the effectiveness of KNN classifier with 75.5% accuracy rate on cross validation and 0.74 AUC.
Plant Knowledge Mining and Organization Construction in Pre-Qin Classics from the Perspective of Digital Humanities

: [Purpose/significance] The knowledge mining of plants in pre-Qin classics and the construction of pre-Qin plant knowledge map are of great significance for understanding the society and living conditions of ancient Chinese people. [Method/process] This paper makes a detailed labeling and quantitative analysis of plant words in pre-Qin classics. Based on CRF and a variety of deep learning models, a plant named entity recognition model for pre-Qin classics was constructed, and the performance of each model was compared and analyzed to determine the optimal model. A knowledge map-oriented knowledge organization model of classics and plants was designed. [Result/conclusion] The plant entity recognition model based on the domain pre-trained language model SikuRoBERTa has the best performance, and the harmonic average reaches 85.44%, which provides an effective method for entity-based plant knowledge mining. Aggregation and visualization of plant knowledge in pre-Qin classics.
Formation energies of group I and II metal oxides using random phase approximation

The enthalpy of formation for 23 metal oxides, which include group I and II as well as two transition metals (Ti and Ru), are calculated using random phase approximation (RPA). Compared to PBE xc-functional, RPA reduces the mean absolute error (MAE) per oxygen from 0.44 eV to 0.15 eV. The calculated deviations with experiments are seperated into two parts: a systematic and uniform error related to the reference energy of O and the ones speciﬁc to diﬀferent oxidation states O 2 − , O 2 − 2 , O − 2 . Our results show that RPA improves both the reference energy, although the error is not completely eliminated, and simultaneously over the three oxidation states.
Experiences of whānau Māori caring for a young child on the autism spectrum

This mixed-methods study aimed to explore the experiences and goals of Māori parents and whānau (families) caring for young autistic children. Data were collected via a rōpū kōrero (focus group) and an online questionnaire, with a total of 33 parents and whānau participating in at least one phase of the study. Reflexive thematic analysis was used to analyse rōpū kōrero data with findings used to inform the design of an online questionnaire. Questionnaire responses were summarised using descriptive statistics. Results indicate that experience and perceived helpfulness of supports varied across participants. Communication and values-based goals were a high priority for most participants, and most rated cultural goals as important. Findings highlight the value of considering parent and whānau voices, especially those from Indigenous communities.
THE ORIGIN OF THE OBLIQUE-SUBJECT CONSTRUCTION: AN INDO-EUROPEAN COMPARISON *

Recent research on subject-like obliques in Germanic suggests that noncanonically-case-marked subject-like arguments behave syntactically as subjects not only in Modern Icelandic and Modern Faroese, but also in Modern German, Old Norse-Icelandic, Old Swedish and Early Middle English. This research calls into question the hypothesis that oblique or nonnominative subjects have developed from objects, as it suggests that they have been subjects from at least the beginning of the Germanic period. In this paper we examine six different hypotheses on the possible origin of the oblique-subject construction in the Indo-European languages. We conclude that five of these hypotheses are inadequate while arguing for the superiority of the sixth hypothesis, namely that Proto-Indo-European was a stative‐active language, either a Split-S or a Fluid-S language, in which a subset of syntactic subjects is case marked in the same way as objects. On such an account, oblique subjects are a natural part of the alignment system and need not be postulated as having developed from objects.
Dimensional emotion recognition based on ConvLSTM network with double stream convolution feature

Academic emotions are closely related to the achievement of online learning goals, for the current problem that discrete emotion recognition cannot describe the continuous dimensional academic emotion changes, this paper proposes a dimensional emotion recognition method based on a dual-streams convolutional feature ConvLSTM network. Firstly, static convolution features of single frame images in video sequences are extracted, and dynamic convolution features are extracted from video sequences, secondly, the concatenated double stream convolution features are analyzed by ConvLSTM network to extract the sequence features containing spatio-temporal information; finally, the generated feature passes through two full connection layers and outputs the predicted value of Valence-Arousal. The average of the Concordance Correlation Coefficient (CCC) on the public data set AVEC2015 reached 0.198. The experiment proved that the CCC correlation coefficient index of the proposed method on the AVEC2015 data set increased by 2.64%~6.28% compared with the baseline method, which can effectively identify dimensional emotions, providing a method support for dimensional emotion recognition.
An Improved Spectral Subtraction Method Based on Multitaper Estimation

In all speech enhancement methods,spectral subtraction,which is simply and efficient,is widely used.Usually,the periodogram method is used in spectral subtraction to estimate the speech signal′s power spectra.However,it has large and inaccurate variance which causes musical noise.The multitaper estimation has a lower variance.So the combination of multitaper estimation and spectral subtraction can reduce musical noise obviously.The experiment results also show that the multitaper method is better than the periodogram method in restraining musical noise,and improves the SNR by 1～2 dB.
Research and implementation of deep-learning-based stock opening price forecasting system

As economic globalization advances, financial market is increasingly favored by investors. With the development and strong demand of financial market, the forecast of stock price trend has aroused widespread attentions from both the academic and industry. As is well known, stock investment has both high returns and high risks. However, it is difficult to quantify the internal and external factors that affect stock market fluctuations, and it is also difficult to process massive and complex stock data. Therefore, traditional non-artificial intelligence approaches are not always satisfactory in forecasting stock price. Therefore, it has great significance to use big data technologies to excavate massive useful information hidden in stocks as well as to use neural network technology such as LSTM to further solve the problem of stock price trend forecast. In the paper, we report a development and implementation of deep learning-based stock opening price forecasting system based.
Oxidative Stress Underpins Clinical, Social, and Genetic Risk Factors for Atherosclerotic Cardiovascular Disease

Background: Atherosclerotic cardiovascular disease (ASCVD) remains the leading cause of death worldwide and is poorly predicted with current risk estimation tools. The biological mechanisms relating ASCVD risk factors to oxidative stress (OS) and how this accumulates ASCVD risk are misunderstood. Purpose: To develop a comprehensive conceptual model explaining how expanded clinical, social, and genetic ASCVD risk factors accumulate ASCVD risk through OS. Conclusions: OS (primarily from excess reactive oxygen species) and inflammation are present along the entire ASCVD pathophysiologic continuum. An expanded list of clinical and social ASCVD risk factors (including hypertension, obesity, diabetes, kidney disease, inflammatory diseases, substance use, poor nutrition, psychosocial stress, air pollution, race, and genetic ancestry) influence ASCVD largely through increased OS. Many risk factors exert a positive feedback mechanism to increase OS. One genetic risk factor, haptoglobin (Hp) genotype, is associated with higher ASCVD risk in diabetes and hypothesized to do the same in those with insulin resistance due to the Hp 2-2 genotype increasing OS. Implications: Understanding the biological mechanisms of OS informs how these ASCVD risk factors relate to each other and compound ASCVD risk. Individualized ASCVD risk estimation should include a comprehensive, holistic perspective of risk factors to better address the clinical, social, and genetic influences of OS. Preventing and reducing OS is key to preventing ASCVD development or progression.
Genetic Predisposition to Hematologic Malignancies in Childhood and Adolescence

Advances in molecular biology and genetic testing have greatly improved our understanding of the genetic basis of hematologic malignancies and have enabled the identification of new cancer predisposition syndromes. Recognizing a germline mutation in a patient affected by a hematologic malignancy allows for a tailored treatment approach to minimize toxicities. It informs the donor selection, the timing, and the conditioning strategy for hematopoietic stem cell transplantation, as well as the comorbidities evaluation and surveillance strategies. This review provides an overview of germline mutations that predispose to hematologic malignancies, focusing on those most common during childhood and adolescence, based on the new International Consensus Classification of Myeloid and Lymphoid Neoplasms.
A Rare Presentation of Rhodococcus Equi Bacteremia as a Result of Right Upper Arm Cellulitis: A Case Report and Literature Review

Rhodococcus equi is an emerging opportunistic pathogen in immunocompromised patients. Owing to its resemblance to Mycobacterium, Nocardia, and Corynebacterium, R. equi is frequently misdiagnosed as a contaminant, which can result in treatment delays. A 65-year-old man with a history of end-stage renal disease (ESRD) presented to the emergency room with pain and increased swelling in his right upper extremity. Shortly after he arrived in the emergency room, his condition deteriorated. Intravenous vancomycin was administered after collecting blood cultures. The blood cultures grew Rhodococcus equi, and oral azithromycin and oral rifampin were added for a 14-day course of treatment. The patient recovered without any further complications and was subsequently discharged home. R. equi is a partially acid-fast actinomycete that spreads through contact with grazing animals and contaminated soil. R. equi invades macrophages to survive and causes infection within a host. In this particular case, the patient worked on a farm taking care of goats. He was exposed to the bacteria after falling and sustaining multiple lacerations to the right arm. This case is unique due to the development of bacteremia with R. equi, an uncommon cause of bacteremia that led to cardiopulmonary arrest. The treatment with oral azithromycin combined with oral rifampin and intravenous vancomycin was effective for the complete resolution of the infection.
Arrest Outcome Consortium Registry Analysis [AOCRA 2022]: Outcome Statistics of Cardiac Arrest in Tertiary Care Hospitals in India, Analysis of Five Year Data of Indian Online Cardiac Arrest Registry, www.aocregistry.com

Abstract Aim and background To publish data with outcome statistics from our online cardiac arrest (CA) outcome consortium (AOC) online registry. Materials and methods Data on cardiac arrest (CA) from tertiary care hospitals were collected on the AOC registry online portal from January 2017 to May 2022. Survival endpoints from cardiac arrest events like ROSC, and survival at hospital discharge with neurological status at discharge were analyzed and presented. Studies of demographics, the association of outcome with age, gender, bystander CPR, low and no flow times, and admission lactate were also done along with suitable statistical analysis. Results Out of 2,235 CA, 2,121 received CPR (1,998 IHCA, 123 Out of hospital Cardiac Arrest (OHCA)) as 114 were DNR. The males-female ratio was 70:30. Average age at arrest was 58.7 years. 26% OHCA received bystander CPR but survival advantage was not significant. (with 16%, without 14% p = 0.78). Asystole (67.7%), Pulseless Electrical Activity (PEA) (25.6%), and VF/pVT (6.7%) as first rhythm significantly influence survival (4.9, 8.6 and 39.4%: p < 0.001) ROSC was achieved in 355 (16.7%), with 173 (8.2%) alive and 141 (6.6%) having good (CPC ≤ 2) neurological state at discharge. At discharge, survival as well as CPC ≤ 2 outcomes were significantly better in females. On multivariate regression analysis, first rhythm and low flow time influence survival at discharge. Admission lactate (available only in 102 OHCA) was lower in survivors than non-survivors 10.3 vs 11.5 mmol/L but the difference was not statistically significant (p = 0.397]. Conclusion Data from our AOC registry shows poor overall survival from CA. The Female gender had a higher survival rate. Ventricular Fibrillation/Pulseless Ventricular Tachycardia (VF/pVT) as first rhythm and low flow time influence the survival to discharge (CTRI/2022/11/047140). How to cite this article Clerk AM, Patel K, Shah BA, Prajapati D, Shah RJ, Rachhadia J, et al. Arrest Outcome Consortium Registry Analysis [AOCRA 2022]: Outcome Statistics of Cardiac Arrest in Tertiary Care Hospitals in India, Analysis of Five Year Data of Indian Online Cardiac Arrest Registry, www.aocregistry. com. Indian J Crit Care Med 2023;27(5):322–329.
Imaging dark matter using sparsity

We present an application of sparse regularization of ill-posed linear inverse problems to the reconstruction of the 3D distribution of dark matter in the Universe. By its very nature dark matter cannot be directly observed. Nevertheless, it can be studied through its gravitational effects can it be studied. In particular, the presence of dark matter induces small deformations to the shapes of background galaxies which is known as weak gravitational lensing. However, reconstructing the 3D distribution of dark matter from tomographic lensing measurements amounts to solving an ill-posed linear inverse problem. Considering that the 3D dark matter density is sparse in an appropriate wavelet based 3D dictionary, we propose an iterative thresholding algorithm to solve a penalized least-squares problem. We present our results on simulated dark matter halos and compare them to state of the art linear reconstruction techniques. We show that thanks to our 3D sparsity constraint the quality of the reconstructed maps can be greatly improved.
Approximation stochastique en analyse factorielle multiple

We study the multiple factorial analysis of a random vector $X$ in $\Bbb R^p$ as a principal component analysis of $X$ with a particular choice of the inner product in $\Bbb R^p$. We use a stochastic approximation process to estimate recursively the principal components of this analysis by means of a sequence of i.i. d. observation of $X$. Stochastic approximations of the symmetrical positive definite matrix $M$ defining the inner product in $\Bbb R^p$ and of the principal components are made simultaneously
Infrared and Visible Image Fusion Based on NSCT

Aimed at the problems that the edge is fuzzy and the visual effect is poor during the fusion of the infrared and visible images,a fusion algorithm based on NSCT is proposed.NSCT has shift-invariance and agood direction selection,only thus can the algorithm fully capture the contour and more details of the image.Firstly,the NSCT is used to take multi-scale,multi-direction decomposition for the infrared and visible image,and then the edges of the low frequency image is extracted by using the isotropic Soble gradient operator,and fused the low frequency coefficients by edge information to attain the low frequency fusion image.Meanwhile,the high frequency coefficients are fused by region energy to attain the high frequency fusion image.Finally,the fusion image is reconstructed by performing the inverse NSCT on the low frequency fusion image and the high frequency fusion image.The simulation experiments according to both the subjective and objective criteria,including the information entropy,space frequency and average gradient,show that the algorithm can get a good fusion image with more clearly details such as edges.Besides,the fusion image is more conform to the requirements of human vision.
Analisis Representasi Perlawanan Perempuan Pada Film Marlina Si Pembunuh Dalam Empat Babak

AbstrakFilm merupakan salah satu media massa yang memiliki pengaruh yang kuat dalam menyampaikan pesan kepada khalayak. Pesan yang disampaikan oleh film bersifat multitafsir. Ada yang berpandangan bahwa film hanya sebagai hiburan, tetapi ada juga yang berpandangan bahwa film menggambarkan realitas ataupun fenomena yang terjadi di tengah masyarakat. Isu yang sering diangkat adalah terkait dengan gender. Ketidakadilan dapat terjadi pada laki-laki atau perempuan, tetapi sering kali perempuanlah yang menjadi korban. Kesadaran akan hal itu pada akhirnya membuat perempuan melakukan perlawanan untuk dapat membebaskan dirinya. Tujuan penelitian ini adalah untuk mendeskripsikan bentuk-bentuk kekerasan yang terjadi pada perempuan dan bagaimana perlawanan yang dilakukan tokoh perempuan dalam film”Marlina si Pembunuh dalam Empat Babak”. Metode yang digunakan dalam penelitian ini adalah metode kualitatif deskriptif dengan menggunakan analisis semiotika Charles Sanders Peirce. Penelitian ini menemukan bahwa bentuk kekerasan yang terjadi yaitu kekerasan verbal, kekerasan non verbal, kekerasan fisik dan kekerasan seksual. Adapun perlawanan yang dilakukan oleh tokoh perempuan dalam film ini adalah dengan cara memberikan peringatan, meracuni, memenggal kepala, mengancam, mengabaikan dan memaki. AbstractFilm is one of the mass media that has a strong influence in conveying messages to the audience. The message conveyed by the film is multi-interpretive. Some of the audience describe that movie only as entertainment, but other describe that films depict reality or phenomena that occur in society. An issue that is often raised is related to gender. Injustice can happen to men or women, but often it is women who are victims. Awareness of it eventually makes women fight to be able to free themselves. The aims of this study are to describe the forms of violence that occur in women and how the woman in film "Marlina the Murderer in Four Acts" carried out their problem. This researh used descriptive qualitative method using the semiotic analysis of Charles Sanders Peirce. It is used to describe and understand the portrait of violence. This study found several forms of violence that occur namely verbal violence, non-verbal violence, physical violence and sexual violence. The resistance that is carried out by the female character in the film by giving warnings, poisoning, beheading, threatening, ignoring and cursing.
Improved affinity propagation optimal clustering number algorithm based on merging similar clusters

When it is used to cluster datasets with complex structure, the Affinity Propagation (AP) algorithm faces a number of problems such as excessive local clustering, low accuracy, and invalid clustering evaluation results of some internal evaluation indexes due to excessive clustering. In view of this, this paper proposes an algorithm designed to determine the optimal clustering number. In this paper, the methods of coarse clustering and merging similar clusters are adopted to reduce the clustering number and optimize the maximum clustering number (Kmax), and new calculation methods for intra-cluster compact density, inter-cluster relative density and cluster separation are provided, based on which a new internal evaluation index is designed. The experimental results regarding UCI and NSL-KDD datasets show that the proposed model can provide correct clustering partitioning and accurate clustering range and can well outperform the other three improved algorithms in relevant detection indexes such as detection rate and false alarm rate.