Miracle-dz/UniFAR-FTUs huggingface dataset:
Normative database of response surface method for human trunk extension in isometric mode

Graphical abstract Recent studies show that asymmetric movements are important Low Back Disorders risk factors. Measuring the trunk strength and identifying the coupling effects in different postures can provide an objective tool to assess one’s task capacity. This paper estimates the maximum performance capacity for isometric trunk extension and accompanying torques. Thirty males performed maximum voluntary isometric extension in 33 trunk postures on Sharif Lumbar Isometric Strength Tester device. Corresponding moments and angular positions were collected. Second-order full response surface models (RSM) were exploited to formulate the relationship between strengths and three trunk angles. The results of correlation coefficient, percent of standard estimation error and lack of fit reflected the adequacy of models. In conclusion, the main torque was the extension, but at the same time lateral bending and rotation torques were observed. For predicting these three torques in a specific posture and injury prevention, the second order RSM is a useful tool. The presented models can be used in the fields of ergonomics, occupational biomechanics and sport.
Contrastive learning in protein language space predicts interactions between drugs and protein targets

Significance In time and money, one of the most expensive steps of the drug discovery pipeline is the experimental screening of small molecules to determine binding to a protein target of interest. Therefore, accurate high-throughput computational prediction of drug-target interactions would unlock significant value, guiding and prioritizing promising candidates for experimental screening. We introduce ConPLex, a machine learning method for predicting drug-target binding which achieves state-of-the-art accuracy on many types of targets by using a pretrained protein language model. The approach co-locates the proteins and potential drug molecules in a shared feature space while learning to contrast true drugs from similar nonbinding “decoy” molecules. ConPLex is extremely fast, which allows it to rapidly shortlist candidates for deeper investigation.
Malnutrition defined by GLIM criteria identifies a higher incidence of malnutrition and is associated with pulmonary complications after oesophagogastric cancer surgery, compared to ICD‐10‐defined malnutrition

Low muscle mass, measured using computed tomography (CT), is associated with poor surgical outcomes. We aimed to include CT‐muscle mass in malnutrition diagnosis using the Global Leadership Initiative on Malnutrition (GLIM) criteria, compare it to the International Classification of Diseases 10th Revision (ICD‐10) criteria, and assess the impact on postoperative outcomes after oesophagogastric (OG) cancer surgery.
Outcomes of a Career Development Award (Pre-K) Mock Review Program for Postdoctoral Fellows and Early-Career Faculty

Purpose Securing research funding for early-career investigators remains challenging. The authors present the results of a presubmission career development award (Pre-K) review program for postdoctoral fellows and early-career faculty. Method The Pre-K program is designed to help mentored postdoctoral fellows and early-career faculty write successful career development awards by assigning expert reviewers to score each application and provide written and oral critiques before a mock study section. Applicants and mentors attend the review and can ask questions directly to reviewers about their application. Quarterly, annual, and alumni surveys are sent to applicants who participated in the Pre-K program to assess satisfaction, confirm grant submission and status (i.e., funded and unfunded), and understand the long-term career impact of the program. Results A total of 212 applicants (136 [64%] female; 19 [9%] from underrepresented in medicine groups) participated in the program between 2014 and 2021. Outcome data from 194 grants were available. Among these grants, 71 were awarded (37% success rate). Among underrepresented in medicine applicants, 7 of 18 submitted grants were funded (39% success rate). Of 183 Pre-K participants sent the alumni survey, 123 (67%) responded. Academic degrees included 64 PhDs (52%), 46 MDs (37%), and 14 MDs/PhDs (11%). One hundred nine respondents (90%) were employed in an academic institution, and 106 (86%) devoted more than 50% of their time to research. One hundred twelve (91%) reported receipt of an award (87 [78%] federal and 59 [53%] intramural funding), the most common being National Institutes of Health K/Career Development Awards. Pre-K was rated as very useful to their careers by 102 respondents (83%). Conclusions A Pre-K mock review program can assist early-career investigators in securing funding and launching their research career. Continued investment in the next generation of clinical and translational researchers should remain an institutional priority.
Correlation of Quantitative Diffusion-Weighted MR Parameters and SUVmax from 18-FDG PET-CT in Lung Cancer: A Prospective Observational Study

Abstract Background Diffusion-weighted magnetic resonance imaging (DW-MRI) sequences report the cellularity in tissues and 18-fluorodeoxyglucose (18-FDG) positron emission tomography–computed tomography (PET-CT) provides information on glucose metabolism in cells, associated to tumor aggressiveness. The aim of this study was to assess the correlation between quantitative diffusion-weighted magnetic resonance parameters and maximum standardized uptake value (SUVmax) using 18-FDG PET-CT in lung cancer and metastatic lymph nodes. Methods Histologically proven 29 patients of lung cancers were subjected to 18-FDG PET-CT and DW-MRI (parameters: repetition time/time to echo [TR/TE] = 4,000/76 ms; b -values = 0, 400, and 800 s/mm 2 ) between June 2018 and June 2019. SUVmax was calculated on the PET-CT images representing region of interest (ROI) in the tumor. The apparent diffusion coefficient (ADC) values were quantified by placing an ROI over the tumor at a high b -value of 800 mm 2 /s. Statistical analyses for correlation between SUVmax and ADC were done using Pearson's correlation coefficient ( r ). Results Significant negative correlation was observed between analyses of ADC and SUVmax for primary lesions of all nonsmall-cell lung cancers (NSCLCs; p < 0.05) and its histological subtype adenocarcinoma ( p < 0.05) but not squamous cell carcinomas ( p = 0.35). Significant negative correlation was also observed for metastatic lymph nodes of adenocarcinoma ( p < 0.05) but not for metastatic lymph nodes of all NSCLCs ( p = 0.05) or squamous cell carcinomas ( p = 0.55). Conclusions Diffusion-weighted imaging (DWI) with ADC may represent a new prognostic marker due to a significant negative correlation between ADC determined by DWI and SUVmax by PET-CT in NSCLCs. Furthermore, DWI-MRI of the thorax can be added to routine 18-FDG PET-CT for staging and response assessment in lung cancer in prospects.
Predictors of Delayed Hyponatraemia After Surgery for Pituitary Tumour

Abstract Delayed hyponatraemia(DH) is a common complication following trans-sphenoidal surgery(TSS) for pituitary tumour. We evaluated the prevalence of DH following TSS, and assessed the factors associated with DH, including early post-operative diabetes insipidus(EPDI). This retrospective study included 100 TSS for pituitary tumours in 98 patients, over a period of 26 months. Subjects were divided into two groups: those who developed hyponatraemia and those who did not develop hyponatraemia, during post-operative days 4 to 14. The clinical characteristics and peri-operative parameters were compared between the two groups, to identify factors predicting DH. The mean age of the patients was 42.0±13.6 years, 58 (59%) were females and 61 (61%) had functional tumours. Thirty-six patients(36%) developed DH following TSS of whom majority(58%) were diagnosed on post-operative days 7 and 8; only 8/36 (22%) were symptomatic. Syndrome of inappropriate antidiuretic hormone secretion(SIADH) was found to be the most common aetiology of DH. On logistic regression analysis, intra-operative cerebrospinal fluid(CSF) leak (OR 5.0; 95% CI 1.9–13.8; p=0.002), EPDI (OR 3.4; 95% CI 1.3–9.2; p=0.015) and peri-operative steroid use (OR 3.6; 95% CI 1.3–9.8; p=0.014) were found to be significantly associated with DH. In conclusion, EPDI, intra-operative CSF leak and peri-operative steroid use were significant predictors of DH. EPDI predicts moderate to severe hyponatraemia with 80% specificity but has low sensitivity(47%). As most patients have asymptomatic hyponatraemia, serum sodium measurement on POD 7 to 10 would be helpful to identify DH in patients at increased risk.
General Hilbert's and Hardy's inequalities

In this paper we make some further generalizations of well known Hilbert’s inequality and its equivalent form in two dimensional case. We also derive some results on Hardy’s inequality. Then we apply our general results to homogeneous functions. A reverses of Hilbert’s inequality are also given in integral case. Many other results of this type in recent years, follows as a special case of our results. Mathematics subject classification (2000): 26D15.
An efficient approach for age-wise rice seeds classification using SURF-BOF with modified cascaded-ANFIS algorithm

It is a well-known fact that the quality of a seed highly impacts the germination of a rice seed. The age of the seed is one of the primary key points in assessing the seed quality. Therefore, this study aims to develop an AI-based machine-learning model to classify age-wise rice seeds. This study employs the SURF-BOF-based Cascaded-ANFIS algorithm for the implementation of the classifier. The proposed model performances were compared to the VGG16. Moreover, this research contributes a novel Japanese rice seed dataset to the scientific community. Furthermore, a 10-Fold cross-validation is performed to evaluate the robustness of the novel approach. The K-fold cross-validation’s mean accuracy confirmed the proposed algorithm’s higher robustness in the age-wise classification of rice seeds. Nevertheless, the results were evaluated using the confusion matrix and metrics such as precision, recall, and F1-Score. The Accuracy of Akitakomachi, Koshihikari, Yandao-8, and rice variety classification is 99%, 99%, 92%, and 97%. Analysis of the results determines the ability to classify rice by age and the general robustness of the algorithm.
In-cabin occupant monitoring system based on improved Yolo, deep reinforcement learning, and multi-task CNN for autonomous driving

Vehicle in-cabin occupant monitoring system is becoming a crucial feature of the automobile industry and challenging research topic to enhance both vehicle safety, security, and comfort of conventional and future intelligent vehicles. Precise information about the number, position, and characteristics of occupants as well as objects located inside the vehicle must be available. Current industrial systems for seat occupancy detection are based on multiple weight sensors, capacitive sensors, electric field, or ultrasonic sensors. They cannot necessarily make the right distinction in borderline cases. A simple pressure sensor cannot tell whether the weight on the seat comes from a person or an inanimate object. Recently, the Artificial Intelligence (AI) based advanced systems have attracted attention for various fields such as automobile industry. Especially, with the advancement of deep learning that has shown very high classification accuracies compared to hand-crafted features on various computer vision tasks. For the above reasons, we propose a new automatic AI occupant monitoring system based on two cameras installed inside the vehicle. Our goal is to develop an automatic detection and recognition system with high accuracy performance, low computational cost and small weight model. Our system fuses our modified deep convolutional network Yolo model and deep reinforcement learning to detect and classify passengers and objects inside the vehicle. It can predict the gender, the age and the emotion of occupants based on our proposed muti-task convolutional neural networks. In our end-to-end system, this approach is more efficient time and memory wise by solving all the tasks in the same process and storing a single CNN instead of storing a CNN for each task. Principal applications of our system are intelligent airbag management, seat belt reminder, life presence and in shared cabin preferences. We perform comparative evaluation based on the public datasets SVIRO, TiCaM, Aff-Wild and Adience dataset to demonstrate the superior performance of our proposed system.
Semantic food segmentation for health monitoring

This paper presents semantic food segmentation to detect individual food items in an image. The presented approach has been developed in the context of the FoodRec project, which aims to study and develop an automatic framework to track and monitor the dietary habits of people, during their smoke quitting protocol. The goal of food segmentation is to train a model that can look at the images of food items and infer semantic information to recognize individual food items present in an image. In this contribution, we propose a novel Convolutional Deconvolutional Pyramid Network for food segmentation to understand the semantic information of an image at a pixel level. This network employs convolution and deconvolution layers to build a feature pyramid and achieves high-level semantic feature map representation. As a consequence, the novel semantic segmentation network generates a dense and precise segmentation map of the input food image. Furthermore, the proposed method demonstrated significant improvements on a well-known public benchmark dataset.
Reappraisal of the role of radiation therapy in lymphoma treatment

Radiation therapy (RT) for lymphomas has improved dramatically with modern imaging and treatment techniques, encompassing only the necessary volume with minimal doses to normal structures. Prescribed radiation doses are reduced, and fractionation schedules are under revision. With effective systemic treatment only initial macroscopic disease is irradiated. With no or less effective systemic treatment, possible microscopic disease is also included. Risks of long‐term side effects of RT have diminished dramatically and should be weighed against risks from more systemic treatment or increased risk of relapse. Lymphoma patients are often elderly, they tolerate modern limited RT very well. Lymphomas refractory to systemic treatments often remain radioresponsive, and brief, mild RT may offer effective palliation. New roles for RT are emerging with immune therapies. RT for “bridging,” keeping the lymphoma under control while waiting for immune therapy, is well established. Enhancement of the immune response to lymphomas, so‐called “priming,” is being intensively researched.
Peripheral T‐cell lymphomas

The treatment of peripheral T‐cell lymphomas is challenging, as they often display a severe prognosis and lack effective treatment strategies. We will try to answer three burning questions: can we differentiate the initial treatment based on the histotype and the clinical presentation of peripheral T‐cell lymphoma patients? Do we require an autologous stem cell transplantation in all patients? Is there room for improvement in the setting of relapsed and refractory disease?
DIRECT AND INDIRECT EFFECTS: CLASSICAL AND BOOTSTRAP ESTIMATES OF VARIABILITY

The decomposition of effects in structural equation models has been of considerable interest to social scientists. Finite-sample or asymptotic results for the sampling distribution of estimators of direct effects are widely available. Statistical inferences about indirect effects have relied exclusively on asymptotic methods which assume that the limiting distribution of the estimator is normal, with a standard error derived from the delta method. We examine bootstrap procedures as another way to generate standard errors and confidence intervals and to estimate the sampling distributions of estimators of direct and indirect effects. We illustrate the classical and the bootstrap methods with three empirical examples. We find that in a moderately large sample, the bootstrap distribution of an estimator is close to that assumed with the
Global Digital Lords and Privatisation of Media Policy: The Australian Media Bargaining Code

After two decades of regulatory vacuum, powerful digital platforms such as Google and Meta/Facebook, also known as the “Digital Lords” (Brevini 2020), have faced growing criticism worldwide. They are accused of possessing excessive economic, political, and ideological influence while evading public accountability. Although there have been calls to address their monopolistic market dominance, such as antitrust measures to break their stranglehold on data, the most tangible interventions have focused on policy tools to make the Digital Lords contribute to journalism. With governments hesitant to allocate public funds for public interest journalism, policymakers in various countries are exploring avenues to make these wealthy and tax-avoiding Digital Lords pay for the news they distribute. The “Journalism Competition and Preservation Act” is presently being deliberated in Congress in the United States, with the objective of aiding small and mid-sized news organizations in their negotiations with digital platforms for fair compensation for the utilization of their content. The European Union has implemented a copyright reform granting press publishers the right to be remunerated by Digital Lords for the use of newspapers and magazines. One widely discussed policy tool in this regard is the Australian “News Media Bargaining Code. ” This article examines the successes and failures of the code, adopted a year and a half ago, in addressing Australia's journalism crisis and its pressing issues of media diversity and news scarcity. Ultimately, it argues that the Australian Media Bargaining Code exemplifies the progressive privatization of media policy.
Road Lane Segmentation Using Vehicle Trajectory Tracking and Lane Demarcation Lines

As levels of road traffic congestion increase relative to population density, it is becoming increasingly necessary for traffic managers to have awareness of road situations in real-time to keep up with traffic management. There are already existing techniques and applications in computer vision that traffic managers use to collect real-time telemetry, such as but not limited to vehicle counting algorithms. However, these algorithms and applications may not be lane-aware. Enabling lane awareness to these systems allows them to be more granular, which enables more in-depth telemetry such as lane usage, driver pattern recognition, and anomaly detection, among others. Lane awareness in these systems are enabled by performing lane segmentation. This study investigates two approaches to this. The first approach uses vehicle trajectories to generate aggregated trajectory maps, which are then clustered to determine trajectory lane membership and to generate representative trajectories that describes the lane. On the other hand, the second approach takes an end-to-end method and uses road lane features such as demarcation lines to segment lanes. The first approach proved to be more viable as a lane segmentation algorithm compared to the second approach as it was able to segment lanes more reliably, given enough vehicle trajectories are present.
On the use of synthetic images in deep learning for defect recognition in industrial infrastructures

The use of synthetic images in deep learning for object detection applications is recognized as a key technological lever in reducing time and cost constraints associated with data-driven processes. In this work, the applicability of training an instance recognition algorithm on a synthetic database in an industrial context is assessed based on the detection of dents in pipes. Photo-realistic artificial images are procedurally generated using a rendering software and used for the training of the YOLOv5 object recognition algorithm. Its prediction effectiveness is assessed on a small test set in different configurations to identify improvement steps towards the reliable use of artificial data in computer-vision.
SkeletonGAN: Fine-Grained Pose Synthesis of Human-Object Interactions

Synthesizing Human-Object Interactions (HOI) is a challenging problem since the human body has a complex and versatile representation. Existing solutions can generate individual objects or faces very well but still face difficulty in generating realistic human bodies and their interaction with multiple objects. In this work, we focus on synthesizing human poses based on HOI descriptive triplets and introduce a novel perspective that decomposes every action between humans and objects into sub-actions of human body parts to generate body poses in a fine-grained way. We propose SkeletonGAN, a conditional generative adversarial model to perform a body-parts-level control over the interaction between humans and objects. SkeletonGAN is trained and evaluated using the HICO-DET dataset, which is a knowledge base consisting of complex interaction poses of various human-object actions in realistic scenarios. We show through qualitative and quantitative evaluations that this model is capable of generating diverse and plausible poses consistent with the given semantic features, and especially our model can also predict the relative position of the object with the body pose. We also explore synthesizing composite poses that include co-occurring human actions, indicating that the model can learn multimodal relationships between human poses and the given conditional semantic features.
Application of ABC Classification, EOQ, Fuzzy AHP, Time Series Models to improve order fulfillment in a trading company of household appliances

Most small and medium-sized enterprises (SMEs) do not survive more than five years in the Peruvian market and one of the main reasons is the inefficient logistics administration and inventory control. This represents a serious problem because these businesses play a fundamental role in the growth of the country's economy and trade. Therefore, this research focuses on the analysis of 4 engineering tools applied integrally to improve order fulfillment (identified as the main problem) of a trading company of household appliances. This model makes use of tools such as: ABC classification for the categorization of the merchandise, time series models for demand forecasting, fuzzy AHP methodology for the selection of suppliers and EOQ principle for definition of inventory policies. After model validation, it was observed that order fill rate increased from 80.40% to 95.61%, demonstrating the positive impact that could be generated for the business.