General

ArXiv Preprint

Boolean Matrix Factorization (BMF) aims to find an approximation of a given binary matrix as the Boolean product of two low-rank binary matrices. Binary data is ubiquitous in many fields, and representing data by binary matrices is common in medicine, natural language processing, bioinformatics, computer graphics, among many others. Unfortunately, BMF is computationally hard and heuristic algorithms are used to compute Boolean factorizations. Very recently, the theoretical breakthrough was obtained independently by two research groups. Ban et al. (SODA 2019) and Fomin et al. (Trans. Algorithms 2020) show that BMF admits an efficient polynomial-time approximation scheme (EPTAS). However, despite the theoretical importance, the high double-exponential dependence of the running times from the rank makes these algorithms unimplementable in practice. The primary research question motivating our work is whether the theoretical advances on BMF could lead to practical algorithms. The main conceptional contribution of our work is the following. While EPTAS for BMF is a purely theoretical advance, the general approach behind these algorithms could serve as the basis in designing better heuristics. We also use this strategy to develop new algorithms for related $\mathbb{F}_p$-Matrix Factorization. Here, given a matrix $A$ over a finite field GF($p$) where $p$ is a prime, and an integer $r$, our objective is to find a matrix $B$ over the same field with GF($p$)-rank at most $r$ minimizing some norm of $A-B$. Our empirical research on synthetic and real-world data demonstrates the advantage of the new algorithms over previous works on BMF and $\mathbb{F}_p$-Matrix Factorization.

Fedor Fomin, Fahad Panolan, Anurag Patil, Adil Tanveer

2022-07-25

Surgery

Machine learning improves the accuracy of graft weight prediction in living donor liver transplantation.

In Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society

BACKGROUND : Precise graft weight estimation is essential for planning living donor liver transplantation to select grafts of adequate size for the recipient. This study aimed to investigate whether a machine learning model can improve the accuracy of graft weight estimation.

METHODS : Data from 872 consecutive living donors of a left lateral sector, left lobe, right lobe to adults or children for living-related liver transplantation were collected from January 2011 to December 2019. Supervised machine learning models were trained (80% of observations) to predict graft weight using the following information: donor's age, sex, height, weight, and BMI, graft type (left, right, or left lateral lobe), CT estimated graft and total liver volumes. Models' performance was measured in a random independent set (20% of observations) and in an external validation cohort using the mean absolute error and the mean absolute percentage error, and compared with methods currently available for graft weight estimation.

RESULTS : The best-performing machine learning model showed a mean absolute error value of 50 ± 62 grams in predicting graft weight, with a mean error of 10.3%. These errors were significantly lower than those observed with alternative methods. Additionally, 62% of predictions had errors <10%, while errors>15% were observed in only 18.4% of the cases, compared to the 34.6% of the predictions obtained with the best alternative method (p<0.001). The machine learning model is made available as a web application (http://graftweight.shinyapps.io/prediction).

CONCLUSIONS : Machine learning can improve the precision of graft weight estimation compared to currently available methods by reducing the frequency of significant errors. The coupling of anthropometric variables to the preoperatively estimated graft volume seems necessary to improve the accuracy of graft weight estimation.

Giglio Mariano Cesare, Zanfardino Mario, Franzese Monica, Zakaria Hazem, Alobthani Salah, Zidan Ahmed, Ayoub Islam Ismail, Shoreem Hany Abdelmeguid, Lee Boram, Han Ho-Seong, Della Penna Andrea, Nadalin Silvio, Troisi Roberto Ivan, Broering Dieter Clement

2022-Sep-27

General

Erratum to "Application of Machine Learning Approaches to Predict Postnatal Growth Failure in Very Low Birth Weight Infants" by Han JH, et al. (Yonsei Med J 2022;63(7):640-647).

In Yonsei medical journal

This corrects the article on p. 640 in vol. 63, PMID: 35748075.

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2022-Oct

Radiology

Ageing and degeneration analysis using ageing-related dynamic attention on lateral cephalometric radiographs.

In NPJ digital medicine

With the increase of the ageing in the world's population, the ageing and degeneration studies of physiological characteristics in human skin, bones, and muscles become important topics. Research on the ageing of bones, especially the skull, are paid much attention in recent years. In this study, a novel deep learning method representing the ageing-related dynamic attention (ARDA) is proposed. The proposed method can quantitatively display the ageing salience of the bones and their change patterns with age on lateral cephalometric radiographs images (LCR) images containing the craniofacial and cervical spine. An age estimation-based deep learning model based on 14142 LCR images from 4 to 40 years old individuals is trained to extract ageing-related features, and based on these features the ageing salience maps are generated by the Grad-CAM method. All ageing salience maps with the same age are merged as an ARDA map corresponding to that age. Ageing salience maps show that ARDA is mainly concentrated in three regions in LCR images: the teeth, craniofacial, and cervical spine regions. Furthermore, the dynamic distribution of ARDA at different ages and instances in LCR images is quantitatively analyzed. The experimental results on 3014 cases show that ARDA can accurately reflect the development and degeneration patterns in LCR images.

Zhang Zhiyong, Liu Ningtao, Guo Zhang, Jiao Licheng, Fenster Aaron, Jin Wenfan, Zhang Yuxiang, Chen Jie, Yan Chunxia, Gou Shuiping

2022-Sep-27

General

Quantifying wood decomposition by insects and fungi using computed tomography scanning and machine learning.

In Scientific reports ; h5-index 158.0

Wood decomposition is a central process contributing to global carbon and nutrient cycling. Quantifying the role of the major biotic agents of wood decomposition, i.e. insects and fungi, is thus important for a better understanding of this process. Methods to quantify wood decomposition, such as dry mass loss, suffer from several shortcomings, such as destructive sampling or subsampling. We developed and tested a new approach based on computed tomography (CT) scanning and semi-automatic image analysis of logs from a field experiment with manipulated beetle communities. We quantified the volume of beetle tunnels in wood and bark and the relative wood volume showing signs of fungal decay and compared both measures to classic approaches. The volume of beetle tunnels was correlated with dry mass loss and clearly reflected the differences between beetle functional groups. Fungal decay was identified with high accuracy and strongly correlated with ergosterol content. Our data show that this is a powerful approach to quantify wood decomposition by insects and fungi. In contrast to other methods, it is non-destructive, covers entire deadwood objects and provides spatially explicit information opening a wide range of research options. For the development of general models, we urge researchers to publish training data.

Seibold Sebastian, Müller Jörg, Allner Sebastian, Willner Marian, Baldrian Petr, Ulyshen Michael D, Brandl Roland, Bässler Claus, Hagge Jonas, Mitesser Oliver

2022-Sep-27

General

Predicting the effects of winter water warming in artificial lakes on zooplankton and its environment using combined machine learning models.

In Scientific reports ; h5-index 158.0

This work deals with the consequences of climate warming on aquatic ecosystems. The study determined the effects of increased water temperatures in artificial lakes during winter on predicting changes in the biomass of zooplankton taxa and their environment. We applied an innovative approach to investigate the effects of winter warming on zooplankton and physico-chemical factors. We used a modelling scheme combining hierarchical clustering, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) algorithms. Under the influence of increased water temperatures in winter, weight- and frequency-dominant Crustacea taxa such as Daphnia cucullata, Cyclops vicinus, Cryptocyclops bicolor, copepodites and nauplii, and the Rotifera: Polyarthra longiremis, Trichocerca pusilla, Keratella quadrata, Asplanchna priodonta and Synchaeta spp. tend to decrease their biomass. Under the same conditions, Rotifera: Lecane spp., Monommata maculata, Testudinella patina, Notholca squamula, Colurella colurus, Trichocerca intermedia and the protozoan species Centropyxis acuelata and Arcella discoides with lower size and abundance responded with an increase in biomass. Decreases in chlorophyll a, suspended solids and total nitrogen were predicted due to winter warming. Machine learning ensemble models used in innovative ways can contribute to the research utility of studies on the response of ecological units to environmental change.

Kruk Marek, Goździejewska Anna Maria, Artiemjew Piotr

2022-Sep-27