Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting MLC modeling errors using radiomics-based machine learning in patient-specific QA with an EPID for intensity-modulated radiation therapy.

PURPOSE We sought to develop machine learning models to detect multileaf collimator (MLC) modeling errors with the use of radiomic features of fluence maps measured in patient-specific quality assurance (QA) for intensity-modulated radiation therapy (IMRT) with an electric portal imaging device (EPID). METHODS Fluence maps measured with EPID for 38 beams from 19 clinical IMRT plans were assessed. Plans with various degrees of error in MLC modeling parameters (i.e., MLC transmission factor [TF] and dosimetric leaf gap [DLG]) and plans with an MLC positional error for comparison were created. For a total of 152 error plans for each type of error, we calculated fluence difference maps for each beam by subtracting the calculated maps from the measured maps. A total of 837 radiomic features were extracted from each fluence difference map, and we determined the number of features used for the training dataset in the machine learning models by using random forest regression. Machine learning models using the five typical algorithms (decision tree, kNN, SVM, logistic regression, and random forest) for binary classification between the error-free plan and the plan with the corresponding error for each type of error were developed. We used part of the total dataset to perform four-fold cross-validation to tune the models, and we used the remaining test dataset to evaluate the performance of the developed models. A gamma analysis was also performed between the measured and calculated fluence maps with the criteria of 3%/2 mm and 2%/2 mm for all of the types of error. RESULTS The radiomic features and its optimal number were similar for the models for the TF and the DLG error detection, which was different from the MLC positional error. The highest sensitivity was obtained as 0.913 for the TF error with SVM and logistic regression, 0.978 for the DLG error with kNN and SVM, and 1.000 for the MLC positional error with kNN, SVM and random forest. The highest specificity was obtained as 1.000 for the TF error with a decision tree, SVM and logistic regression, 1.000 for the DLG error with a decision tree, logistic regression and random forest, and 0.909 for the MLC positional error with a decision tree and logistic regression. The gamma analysis showed the poorest performance in which sensitivities were 0.737 for the TF error and the DLG error and 0.882 for the MLC positional error for 3%/2 mm. The addition of another type of error to fluence maps significantly reduced the sensitivity for the TF and the DLG error, whereas no effect was observed for the MLC positional error detection. CONCLUSIONS Compared to the conventional gamma analysis, the radiomics-based machine learning models showed higher sensitivity and specificity in detecting a single type of the MLC modeling error and the MLC positional error. Although the developed models need further improvement for detecting multiple types of error, radiomics-based IMRT QA was shown to be a promising approach for detecting the MLC modeling error.
Comprehensive Biology and Genetics Compendium of Wilms Tumor Cell Lines with Different WT1 Mutations

Simple Summary Wilms tumor is a childhood kidney tumor arising from embryonal cells. Wilms tumors are heterogeneous with several distinct subgroups that differ in their response to treatment. The genetic basis for these diverse forms of Wilms tumor is not fully understood. One subgroup of Wilms tumors is associated with mutations in the WT1 gene, encoding a transcription factor with a role in early kidney differentiation. Patients with WT1 mutant Wilms tumor may harbor germline mutations in this gene. Cell lines from Wilms tumors are notoriously difficult to establish and only few exist. We developed a method to cultivate cells from the WT1 mutant subtype of Wilms tumors and have established 11 cell lines with different mutations in WT1 to date. These cells will be instrumental to study the biology and genetics ultimately to develop precision treatments Abstract Purpose: WT1 mutant Wilms tumors represent a distinct subgroup, frequently associated with CTNNB1 mutations. The genetic basis for the development of this subtype is currently not fully understood. Methods: Live WT1 mutant Wilms tumors were collected during surgery of patients and cell cultures established in mesenchymal stem cell medium. They were studied for mutations in WT1 and CTNNB1, their differentiation capacity and protein activation status. Four cell lines were immortalized with a triple mutant ts SV40 largeT antigen and Telomerase. Results: 11 cell lines were established from Wilms tumors of nine patients, including a left and right tumor from the same patient and a primary and second tumor from another patient. Six patients had germ line and three were tumor specific mutations. All cell lines harbored only mutant or deleted WT1 genes. CTNNB1 was wild type in three, all others carried mutations affecting amino acid S45. They had variable and limited capacities for mesenchymal differentiation, a high migratory capacity and a low invasive potential. All cells showed an activation of multiple receptor tyrosine kinases and downstream signaling pathways. Conclusions: These cell lines represent an important new tool to study WT1 mutant Wilms tumors, potentially leading to new treatment approaches.
Modified Fasting Compared to True Fasting Improves Blood Glucose Levels and Subjective Experiences of Hunger, Food Cravings and Mental Fatigue, But Not Cognitive Function: Results of an Acute Randomised Cross-Over Trial

Recent dietary trends have prompted growing support for a variety of fasting paradigms involving extreme restriction or nil-caloric intake on fasting days. Some studies indicate that fasting may negatively influence factors including cognitive function through inducing fatigue, which may prove problematic in the context of completing a range of cognitively demanding activities required by daily obligations such as work. This randomised within-subjects cross-over trial explored the effects of true fasting (i.e., nil-caloric intake) versus modified fasting, the latter of which involved two sub-conditions: (1) extended distribution (three small meals distributed across the day; 522 kcal total); and (2) bulking (two meals eaten early in the day; 512 kcal total) over a period of 7.5 h on a single day with a 7-day washout period between conditions. Participants were n = 17 females (Body Mass Index (BMI) Mean (M) = 25.80, Standard Deviation (SD) = 2.30) aged 21–49 years. Outcomes included cognitive function, subjective mental fatigue, satiety, food cravings and blood glucose. Results showed that there were no differences in cognitive test performance between conditions;however, both modified fasting sub-conditions had improved blood glucose levels, cravings, hunger and fullness compared to true fasting. Moreover, subjective mental fatigue was significantly reduced in the modified fasting conditions relative to true fasting. Overall, results indicated that the subjective experience of true fasting and modified fasting is different, but that cognition does not appear to be impaired.
Graph Convolutional Networks for Epigenetic State Prediction Using Both Sequence and 3D Genome Data

Predictive models of DNA epigenetic state such as transcription factor binding are essential for understanding regulatory processes and developing gene therapies. It is known that the 3D genome, or spatial structure of DNA, is highly influential in the epigenetic state. Deep neural networks have achieved state of the art performance on epigenetic state prediction by using short windows of DNA sequences independently. These methods, however, ignore the long-range dependencies when predicting the epigenetic states because modeling the 3D genome is challenging. In this work, we introduce ChromeGCN, a graph convolutional network for epigenetic state prediction by fusing both local sequence and long-range 3D genome information. By incorporating the 3D genome, we relax the i.i. d. assumption of local windows for a better representation of DNA. ChromeGCN explicitly incorporates known long-range interactions into the modeling, allowing us to identify and interpret those important long-range dependencies in influencing epigenetic states. We show experimentally that by fusing sequential and 3D genome data using ChromeGCN, we get a significant improvement over the state-of-the-art deep learning methods as indicated by three metrics. Importantly, we show that ChromeGCN is particularly useful for identifying epigenetic effects in those DNA windows that have a high degree of interactions with other DNA windows.
How widowhood status relates to engagement in advance care planning among older adults: does race/ethnicity matter?

Abstract Objectives This study investigated whether and to what extent widowhood status is related to engagement in advance care planning (ACP), and further whether race/ethnicity moderated the relation. Methods We analyzed a total of 11,257 older Americans from the Health and Retirement Study using random-effect regression models after controlling for covariates and year-fixed effects. Results We found that both being a widow/widower ever and having been widowed for a longer period of time were associated with a higher probability of engagement in ACP. Specifically, we found that a one-year increase in the number of years since spousal death was associated with 1.02 (p < 0.05, 95% CI = 1.00, 1.03) changes in the odds ratios of informal ACP; however, inclusion of a quadratic term indicated that this association reversed after the peak. Moreover, our findings suggested a moderating effect of race/ethnicity on the relations of the length of time since spousal loss with engagement in ACP. Specifically, the odds of widowed non-Hispanic Blacks discussing with someone the care or medical treatment (informal ACP) and having a living will (formal ACP) were 0.96 (p < 0.05, 95% CI = 0.93, 1.00) and 0.88 (p < 0.05, 95% CI = 0.79, 0.97) times that of non-widowed non-Hispanic Whites. Compared with their non-Hispanic White counterparts, widowed non-Hispanic Blacks were less likely to engage in ACP, and the negative relations were exacerbated when they became widows/widowers. Conclusion We elaborated on these findings and discussed their implications for understanding the moderating effect of race/ethnicity on the relation between late‐life widowhood and engagement in ACP. In order to develop programs that enhance engagement in ACP and reduce racial/ethnic disparities, research must incorporate intersectionality theory with attention to motivations and decision-making style among diverse widows/widowers. The findings from this study could help inform policy makers when developing public health programs and health care reimbursement programs that enhance engagement in ACP among widows/widowers.
Revisiting the changes in the Banff classification for antibody‐mediated rejection after kidney transplantation

The Banff classification for antibody‐mediated rejection (ABMR) has undergone important changes, mainly by inclusion of C4d‐negative ABMR in Banff’13 and elimination of suspicious ABMR (sABMR) with the use of C4d as surrogate for HLA‐DSA in Banff’17. We aimed to evaluate the numerical and prognostic repercussions of these changes in a single‐center cohort study of 949 single kidney transplantations, comprising 3662 biopsies that were classified according to the different versions of the Banff classification. Overall, the number of ABMR and sABMR cases increased from Banff’01 to Banff’13. In Banff’17, 248 of 292 sABMR biopsies were reclassified to No ABMR, and 44 of 292 to ABMR. However, reclassified sABMR biopsies had worse and better outcome than No ABMR and ABMR, which was mainly driven by the presence of microvascular inflammation and absence of HLA‐DSA, respectively. Consequently, the discriminative performance for allograft failure was lowest in Banff’17, and highest in Banff’13. Our data suggest that the clinical and histological heterogeneity of ABMR is inadequately represented in a binary classification system. This study provides a framework to evaluate the updates of the Banff classification and assess the impact of proposed changes on the number of cases and risk stratification. Two alternative classifications introducing an intermediate category are explored.
Role of affective instability on suicidal risk in complex regional pain syndrome: a diary approach (preliminary report)

Background Many patients with complex regional pain syndrome (CRPS) have been known to be at risk of suicide, due to severe pain and its comorbid conditions. The risk of suicide may be associated with affective instability, which is an indicator of emotional dysregulation. Particularly, unstable shifts in negative emotions are difficult to cope with, which may result in individuals feeling uncontrollable, hopeless, and entrapped. This study aimed to examine the role of affective instability in the relationship between pain intensity and suicide risk (suicidal ideation and impulsivity) in patients with CRPS, by employing a daily diary. Methods Twenty-three patients registered at the CRPS Association in Korea were asked to complete a day-to-day routine for 15 days, followed by a diary composed of pain intensity, suicidal ideation, impulsivity, and positive and negative affects. Results Results showed that the interactions between negative affective instability and daily pain intensity were statistically significant on daily suicidal ideation (coefficient = 0.41, t (21) = 2.56, P < 0.050) and daily impulsiveness (coefficient = 1.20, t (19) = 3.35, P < 0.010). However, those between positive affective instability and daily pain intensity were not. Conclusions This study is the first attempt to investigate the role of affective instability on the relationship between daily pain intensity and daily suicide risk in patients with CRPS. Our findings suggest that health professionals pay considerable attention to the instability of negative affects when assessing and managing patients with CRPS at risk of suicide.
MIMO Self-Heterodyne OFDM Using Band Selection Technique

The 5G technology is a promising technology to cope with the increasing demand for higher data rate and quality of service. In this paper, two proposed techniques are implemented for multiple input multiple output (MIMO) self-heterodyne OFDM system to enhance data rate and minimize the bit error rate (BER). In both of the two proposed techniques, Band Selection (BS) approach is used, once with Space Time Block Coded (STBC) for the first proposed technique (BS- STBC), and once again with Frequency Space Time Block Coded (FSTBC) for the second proposed technique (BS-FSTBC). The use of the BS in the proposed techniques helps to choose the sub-band with better subchannels gains for sending the information and consequently, minimize the BER. Moreover, the use of the FSTBC instead of STBC helps to use the spectral efficiently and hence increase data rate. The simulation results show that the proposed techniques BS-STBC and BS-FSTBC, for the MIMO self-heterodyne OFDM system, provide a great enhancement in the BER performance when compared to the conventional techniques. Moreover, the simulation results show that the first proposed technique BS-FSTBC outperform the second propose technique BS-STBC in term of the BER performance.
Interpretable Machine Learning Model for Locoregional Relapse Prediction in Oropharyngeal Cancers

Simple Summary Machine learning may be used to personalize cancer care. However, physicians need interpretability to understand and use a predictive model powered by machine learning. We present a radiomics based model, interpretable for each patient, trained on an American multicentric cohort that yielded a 92% predictive value for relapse at 18 months in oropharyngeal cancers when tested on an external multicentric prospective French cohort. Abstract Background: There is no evidence to support surgery or radiotherapy as the best treatment for resectable oropharyngeal cancers with a negative HPV status. Predictive algorithms may help to decide which strategy to choose, but they will only be accepted by caregivers and European authorities if they are interpretable. As a proof of concept, we developed a predictive and interpretable algorithm to predict locoregional relapse at 18 months for oropharyngeal cancers as a first step towards that goal. Methods: The model was based on clinical and Pyradiomics features extracted from the dosimetric CT scan. Intraclass correlation was used to filter out features dependant on delineation. Correlated redundant features were also removed. An XGBoost model was cross-validated and optimised on the HN1 cohort (79 patients), and performances were assessed on the ART ORL cohort (45 patients). The Shapley Values were used to provide an overall and local explanation of the model. Results: On the ART ORL cohort, the model trained on HN1 yielded a precision—or predictive positive value—of 0.92, a recall of 0.42, an area under the curve of the receiver operating characteristic of 0.68 and an accuracy of 0.64. The most contributory features were shape Voxel Volume, grey level size zone matrix Small Area Emphasis (glszmSAE), gldm Dependence Non Uniformity Normalized (gldmDNUN), Sex and Age. Conclusions: We developed an interpretable and generalizable model that could yield a good precision—positive predictive value—for relapse at 18 months on a different test cohort.
A genetic screen in Drosophila reveals an unexpected role for the KIP1 ubiquitination-promoting complex in male fertility

A unifying feature of polycystin-2 channels is their localization to both primary and motile cilia/flagella. In Drosophila melanogaster, the fly polycystin-2 homologue, Amo, is an ER protein early in sperm development but the protein must ultimately cluster at the flagellar tip in mature sperm to be fully functional. Male flies lacking appropriate Amo localization are sterile due to abnormal sperm motility and failure of sperm storage. We performed a forward genetic screen to identify additional proteins that mediate ciliary trafficking of Amo. Here we report that Drosophila homologues of KPC1 and KPC2, which comprise the mammalian KIP1 ubiquitination-promoting complex (KPC), form a conserved unit that is required for the sperm tail tip localization of Amo. Male flies lacking either KPC1 or KPC2 phenocopy amo mutants and are sterile due to a failure of sperm storage. KPC is a heterodimer composed of KPC1, an E3 ligase, and KPC2 (or UBAC1), an adaptor protein. Like their mammalian counterparts Drosophila KPC1 and KPC2 physically interact and they stabilize one another at the protein level. In flies, KPC2 is monoubiquitinated and phosphorylated and this modified form of the protein is located in mature sperm. Neither KPC1 nor KPC2 directly interact with Amo but they are detected in proximity to Amo at the tip of the sperm flagellum. In summary we have identified a new complex that is involved in male fertility in Drosophila melanogaster.
The biological characteristics of the canine adenovirus type 1 from fox and the transcriptome analysis of the infected MDCK cell

Canine adenovirus type 1 (CAdV‐1) is the etiologic agent of fox encephalitis, and a virus strain from fox encephalitis is isolated and related research are conducted. In this experiment, the results showed that the F1301 strain was confirmed to be the CAdV‐1. The whole genome of the CAdV‐1 F1301 strain isolated from fox was 30,535 bp and had higher homology to the other reported CAdV‐1 strains. After 0, 12, and 36 h of CAdV‐1 infection, the difference gene of the 592 long noncoding RNA and 11,215 microRNA were involved in cell responses to CAdV‐1 infection through the PI3K‐AKT, Wnt, Herpes simplex, hepatitis C, and Epstein–Barr virus infection pathway in Madin–Darby canine kidney cell line (MDCK). The results indicate that the biological characterization of the CAdV‐1 and the MDCK cell‐CAdV‐1 interaction are clarified.
Plasmodium vivax Cysteine-Rich Protective Antigen Polymorphism at Exon-1 Shows Recombination and Signatures of Balancing Selection

Plasmodium vivax Cysteine-Rich Protective Antigen (CyRPA) is a merozoite protein participating in the parasite invasion of human reticulocytes. During natural P. vivax infection, antibody responses against PvCyRPA have been detected. In children, low anti-CyRPA antibody titers correlated with clinical protection, which suggests this protein as a potential vaccine candidate. This work analyzed the genetic and amino acid diversity of pvcyrpa in Mexican and global parasites. Consensus coding sequences of pvcyrpa were obtained from seven isolates. Other sequences were extracted from a repository. Maximum likelihood phylogenetic trees, genetic diversity parameters, linkage disequilibrium (LD), and neutrality tests were analyzed, and the potential amino acid polymorphism participation in B-cell epitopes was investigated. In 22 sequences from Southern Mexico, two synonymous and 21 nonsynonymous mutations defined nine private haplotypes. These parasites had the highest LD-R2 index and the lowest nucleotide diversity compared to isolates from South America or Asia. The nucleotide diversity and Tajima’s D values varied across the coding gene. The exon-1 sequence had greater diversity and Rm values than those of exon-2. Exon-1 had significant positive values for Tajima’s D, β-α values, and for the Z (HA: dN > dS) and MK tests. These patterns were similar for parasites of different origin. The polymorphic amino acid residues at PvCyRPA resembled the conformational B-cell peptides reported in PfCyRPA. Diversity at pvcyrpa exon-1 is caused by mutation and recombination. This seems to be maintained by balancing selection, likely due to selective immune pressure, all of which merit further study.
Edge of the Present: A Virtual Reality Tool to Cultivate Future Thinking, Positive Mood and Wellbeing

Depression and suicidality are characterized by negative imagery as well as impoverished positive imagery. Although some evidence exists supporting the link between positive imagery and enhanced mood, much work needs to be done. This study explored the impact of an immersive virtual reality experience (Edge of the Present—EOTP) on an individual’s mood, state of well-being, and future thinking. Using a 10-min mixed reality experience, 79 individuals explored virtual landscapes within a purposefully built, physical room. A pre and post survey containing mental health measures were administered to each participant. An optional interview following the virtual work was also conducted. The results indicated that positive mood and well-being increased significantly post-intervention. Hopelessness scores and negative mood decreased, whilst sense of presence was very high. This pilot study is among the first to assess the feasibility of a mixed reality experience as a potential platform for depression and suicide prevention by increasing well-being and mood as well as decreasing hopelessness symptoms.