Miracle-dz/UniFAR-FTUs huggingface dataset:
Inhibitor Recurrence Following Immune Tolerance Induction: A Multicenter Retrospective Cohort Study

Background— Immune tolerance induction (ITI) in patients with congenital hemophilia A is successful in up to 70%. Although there is growing understanding of predictors of response to ITI, the probability and predictors of inhibitor recurrence following successful ITI are not well understood. Objectives— To determine the association of clinical characteristics, particularly adherence to FVIII prophylaxis following ITI, with inhibitor recurrence in patients with hemophilia A who were considered tolerant following ITI. Methods— In this multicenter retrospective cohort study, 64 subjects with FVIII level <2% who were considered successfully tolerant following ITI were analyzed to estimate the cumulative probability of inhibitor recurrence using the Kaplan-Meier method. The association of clinical characteristics with inhibitor recurrence was assessed using logistic regression. Results— A recurrent inhibitor titer ≥ 0.6 BU/ml occurred at least once in 19 (29.7%) and more than once in 12 (18.8%). The probability of any recurrent inhibitor at 1 and 5 years was 12.8% and 32.5% respectively. Having a recurrent inhibitor was associated with having received immune modulation during ITI (OR 3.8, 95% CI: 1.2-22.4) and FVIII recovery of <85% at the end of ITI (OR 2.6, 95% CI: 1.3-5.9), but was not associated with adherence to post-ITI prophylactic FVIII infusion (OR=0.5, 95% CI: 0.06-4.3). Conclusions— The use of immune modulation therapy during ITI and lower FVIII recovery at the end of ITI appear to be associated with an increased risk of inhibitor recurrence following successful ITI. Adherence to post-ITI prophylactic FVIII infusions is not a major determinant of recurrence.
TITLE: 177Lu-NM600 targeted radionuclide therapy extends survival in syngeneic murine models of triple-negative breast cancer. RUNNING TITLE: TRT of triple-negative breast cancer

Reinier Hernandez1,*, Joseph J. Grudzinski1, Eduardo Aluicio-Sarduy2, Christopher F. Massey1, Anatoly N. Pinchuk1, Ariana N. Bitton1, Ravi Patel3, Ray Zhang1, Aakarsha V. Kumar3, Gopal Iyer3, Jonathan W. Engle2, Jamey P. Weichert1,4 1 Department of Radiology, University of Wisconsin-Madison, Madison, WI, USA 2 Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA 3 Department of Human Oncology, University of Wisconsin-Madison, Madison, WI, USA 4 UW Carbone Cancer Center, University of Wisconsin-Madison, Madison, WI, USA
The Interactive Effect of Resource Seeking Foreign Direct Investments (RS-FDI) and Economic Growth in Nigeria: Evidence from Niger Delta

The oil and gas sector, which is governed by the source of income, receives the majority of foreign direct investment (FDI) inflows in Nigeria. This study established that natural resources are the driving force behind MNEs' internalisation efforts in the country. As a result, the impact of resource-seeking foreign direct investments (RSFDIs) in the Niger Delta on Nigeria's economic growth was critically analysed in this study. To achieve the research goal, the study looked into how RSFDIs affect economic growth. The data for the study include RSFDI and GPD growth which was obtained from important stakeholders in the oil and gas industry in the Niger Delta. The Central Bank of Nigeria, the National Bureau of Statistics, and the World Bank provided additional important statistics. The research variables were estimated using Ordinary Least Square (multiple) regression approaches. The findings reveal that RSFDI, and GDP have a positive correlation. The connection, though, is negligible. The conclusion drawn in the research is that RSFDI has not been an effective contributor to economic growth in the Niger Delta due to profit repatriations.
AN SQL EXTENSION FOR LATENT SEMANTIC ANALYSIS

-Latent Semantic Analysis (LSA) is a powerful statistical theory and method for indexing, retrieving and analyzing textual information. With the increased demand for more efficient databases and computing power, a lot of research and work has focused on the practical side of the LSA. In this paper we introduce a design and implementation of an SQL extension for LSA applications. The developed SQL extension for LSA is aimed to utilized as an easy and simple interface for basic LSA and cosine similarity functions provided for indexing, searching and retrieving large textual information stored in open source relational database management system like PostgresSQL.
Political Endorsements by Scienti c Organizations Reduce Trust in Scienti c Expertise During COVID- 19

High-profile political endorsements by scientific organizations and publications have been common in recent years, which raises concerns about political backlash against the endorsing organization in particular and scientific expertise in general. In a pre-registered large-sample online controlled experiment, I randomly assign subjects to receive information about Nature’s endorsement of Joe Biden for the 2020 U.S. presidential election in the context of the COVID-19 pandemic. Seeing the endorsement message leads to large reductions in self-reported trust in Nature among Trump supporters. This distrust translates into lower demand for COVID-related information provided by Nature, as evidenced by substantially reduced requests for Nature articles on vaccine efficacy and emerging COVID variants when offered. The endorsement also reduces Trump supporters’ stated trust in scientists in general, suggesting “reputational externalities” on the scientific community. Treatment effect estimates for Biden supporters are positive but small and often statistically insignificant across outcomes. Finally, I find little evidence that the endorsement is successful at changing subjects’ views about Biden and Trump. These results show political endorsement by scientific organizations can undermine public confidence in the endorsing organization and in the scientific community. (Full manuscript coming soon)
Predicting residue‐specific qualities of individual protein models using residual neural networks and graph neural networks

The estimation of protein model accuracy (EMA) or model quality assessment (QA) is important for protein structure prediction. An accurate EMA algorithm can guide the refinement of models or pick the best model or best parts of models from a pool of predicted tertiary structures. We developed two novel methods: MASS2 and LAW, for predicting residue‐specific or local qualities of individual models, which incorporate residual neural networks and graph neural networks, respectively. These two methods use similar features extracted from protein models but different architectures of neural networks to predict the local accuracies of single models. MASS2 and LAW participated in the QA category of CASP14, and according to our evaluations based on CASP14 official criteria, MASS2 and LAW are the best and second‐best methods based on the Z‐scores of ASE/100, AUC, and ULR‐1.F1. We also evaluated MASS2, LAW, and the residue‐specific predicted deviations (between model and native structure) generated by AlphaFold2 on CASP14 AlphaFold2 tertiary structure (TS) models. LAW achieved comparable or better performances compared to the predicted deviations generated by AlphaFold2 on AlphaFold2 TS models, even though LAW was not trained on any AlphaFold2 TS models. Specifically, LAW performed better on AUC and ULR scores, and AlphaFold2 performed better on ASE scores. This means that AlphaFold2 is better at predicting deviations, but LAW is better at classifying accurate and inaccurate residues and detecting unreliable local regions. MASS2 and LAW can be freely accessed from http://dna.cs. miami.edu/MASS2-CASP14/ and http://dna.cs. miami.edu/LAW-CASP14/, respectively.
Clinical and pathophysiological delineation of musculocontractural Ehlers—Danlos syndrome caused by dermatan sulfate epimerase deficiency (mcEDS‐DSE): A detailed and comprehensive glycobiological and pathological investigation in a novel patient

Musculocontractural Ehlers–Danlos syndrome caused by dermatan sulfate epimerase deficiency (mcEDS‐DSE) is a rare connective tissue disorder. This is the first report describing the detailed and comprehensive clinical and pathophysiological features of mcEDS‐DSE. The patient, with a novel homozygous nonsense variant (NM_013352.4:c.2601C>A:p.(Tyr867*)), exhibited mild skin hyperextensibility without fragility and small joint hypermobility, but developed recurrent large subcutaneous hematomas. Dermatan sulfate (DS) moieties on chondroitin sulfate/DS proteoglycans were significantly decreased, but remained present, in skin fibroblasts. Electron microscopy examination of skin specimens, including cupromeronic blue‐staining to visualize glycosaminoglycan (GAG) chains, revealed coexistence of normally assembled collagen fibrils with attached curved GAG chains and dispersed collagen fibrils with linear GAG chains from attached collagen fibrils across interfibrillar spaces to adjacent fibrils. Residual activity of DS‐epi1, encoded by DSE, and/or compensation by DS‐epi2, a minor homolog of DS‐epi1, may contribute to the mild skin involvement through this “mosaic” pattern of collagen fibril assembly.
Coherent change detection in repeat-pass synthetic aperture sonar

Change detection involves comparing two images to find the differences between them. With sonar data, this can be used to identify changes to both the seafloor and objects located on it. As well as mine-hunting applications, it has been used to spot changes made to the seafloor by bottom-feeding fish, detect the effects of sediment transport on the surface topography of the seafloor, and identify changes to the water properties. The ability to perform change detection is dependent on the images from the two passes of the sonar being precisely registered (aligned). The navigation data alone is not sufficiently accurate to perform this registration. Therefore, the registration must be corrected via a data-driven approach, estimating the errors from the data itself. Registering images of a bland seafloor has been described as the worst-case scenario due to the lack of distinguishing features to match between the images. This thesis presents a number of modifications to an existing rough facet synthetic aperture sonar (SAS) simulator to allow the generation of suitable repeat-pass imagery. The implementation of the scattering model is extended to allow the temporal decorrelation of a facet between passes to be simulated. These repeat-pass images are validated against published statistical models for the coherence of two speckle images. As with any coherent imaging system, SAS imagery is corrupted by multiplicative speckle noise. Despite its random appearance, speckle is a deterministic process. If the images are aligned, then the speckle patterns in corresponding areas are coherent (assuming the temporal decorrelation is sufficiently low). This implies that the speckle patterns in two images can be used to register them. A model of the position errors caused by incorrect track information is presented, along with a block-based correlation method to estimate these errors from two images. Using these detected errors, parameter estimation can be used to find the translation and heading errors in the assumed sonar track. Bland repeat-pass images generated by the simulator are used to test the performance of this registration algorithm. It is shown to be robust against both additive noise and temporal decorrelation, and capable of detecting the track errors. Guidelines are provided for both the design of the system and the implementation of the presented algorithm in order to maximise the registration accuracy. Deputy Vice-Chancellor’s Office Postgraduate Office
ANKHD1 contributes to the malignant phenotype of triple‐negative breast cancer cells

Ankyrin repeat and KH domain‐containing protein 1, ANKHD1, has been identified as a regulator of signaling pathways and cellular processes of relevance in carcinogenesis. However, the role of ANKHD1 in breast cancer remains unclear. The aim of the present study was to characterize the expression pattern and involvement of ANKHD1 in the malignant phenotype of breast cancer cell lines and to investigate the clinical relevance of ANKHD1 in a breast cancer context. Gene and protein expressions were assessed in the cell lines by quantitative reverse transcription PCR and Western blot analysis, respectively, and ANKHD1 silencing through siRNA transfection was conducted for further in vitro functional assays. The expression of ANKHD1 was identified in non‐tumorigenic breast epithelium and breast cancer cell lines, but differences in cellular localization were found among the neoplasia subtypes. ANKHD1 silencing reduced the viability, clonogenicity, and migration of triple‐negative breast cancer (TNBC) cells. Bioinformatics analyses demonstrated that patients with triple‐negative basal‐like 2 and mesenchymal breast cancer subtypes had high ANKHD1 expression associated with poor recurrence‐free survival. Therefore, these data indicate that ANKHD1 relevance in breast cancer varies among its subtypes, indicating the importance of ANKHD1 in TNBC.
Blood‐based biomarkers for the prediction of hypertrophic cardiomyopathy prognosis: a systematic review and meta‐analysis

Hypertrophic cardiomyopathy (HCM) is the most prevalent monogenic heart disease. HCM is an important cause of sudden cardiac death and may also lead to outflow tract obstruction and heart failure. Disease severity is highly variable and risk stratification remains limited. Therefore, we aimed to review current knowledge of prognostic blood‐based biomarkers in HCM.
Deep intronic NIPBL de novo mutations and differential diagnoses revealed by whole genome and RNA sequencing in Cornelia de Lange syndrome patients

Cornelia de Lange syndrome (CdLS; MIM# 122470) is a rare developmental disorder. Pathogenic variants in 5 genes explain approximately 50% cases, leaving the other 50% unsolved. We performed whole genome sequencing (WGS) ± RNA sequencing (RNA‐seq) in 5 unsolved trios fulfilling the following criteria: (i) clinical diagnosis of classic CdLS, (ii) negative gene panel sequencing from blood and saliva‐isolated DNA, (iii) unaffected parents' DNA samples available and (iv) proband's blood‐isolated RNA available. A pathogenic de novo mutation (DNM) was observed in a CdLS differential diagnosis gene in 3/5 patients, namely POU3F3, SPEN, and TAF1. In the other two, we identified two distinct deep intronic DNM in NIPBL predicted to create a novel splice site. RT‐PCRs and RNA‐Seq showed aberrant transcripts leading to the creation of a novel frameshift exon. Our findings suggest the relevance of WGS in unsolved suspected CdLS cases and that deep intronic variants may account for a proportion of them.
Does decentralized decision making increase company performance through its infrastructure Information Technology investment?

A company’s Information Technology (IT) infrastructure is a key factor in its sustainability and ongoing success and profitability. This paper explores the relationship between a company’s investment in IT and its performance. Performance is measured, with the help of a Balanced Scorecard (BSC), in four ways; financial, internal business processes, innovation & learning and customer perspective. The relationship between each BSC category serves as indicators of the effect of IT investment on a company’s performance. This will help establish the benefits of both financial and non-financial indicators. We focus on the Electrical and Electronic manufacturing performance of companies Malaysia. System Resource Theory (SRT) is used as the background theory to explain the concepts of organizational effectiveness, efficiency, productivity and multidimensional performance measurements and to link the variables used in this study. We conduct an empirical study in order to confirm the moderating effects of decentralized decision making . The results suggest that IT investment produces a significant relationship with all BSC perspectives, but the moderating effect is only significant only from a customer perspective.
Optimization in the parallelism extraction algorithm with spanning tree on a multi‐GPU environment

This article presents an optimization method of the parallelism extraction algorithm using spanning tree that automatically exploits the parallelism and determines an execution order of multiple kernel programs in a distributed environment. In stream‐based computing, efficient parallel execution requires careful scheduling of the invocation of the kernel programs. By mapping a kernel to a node and an I/O stream to an edge, the entire stream process can be treated as a spanning tree. The spanning tree, which allows feedback and feedforward edges, is effective for expressing dependencies that exist among kernels. In spanning tree, the nodes at the same depth do not have edges between them, and thus can be executed in parallel in the case parent nodes have been already executed. The series of the nodes can be executed in a pipelined manner. Thus, the proposed algorithm can extract both spatial and temporal parallelism. However, if the algorithm is applied for feedbacks as it is, because of waiting for the completion of the loop among the nodes, it causes the waste of time. To solve this problem, the parallel pattern can be optimized in the step of generating the communication pattern to increase the degree of parallelism. In addition, because of the difference in execution time among kernels, the load balancing can be considered for an optimization for the algorithm. To evaluate the effectiveness of the optimized algorithm, a k‐means application was developed and parallelized especially for the feedback processing. The results show that the parallel execution using two nodes of a graphics processing unit (GPU) cluster obtained 1.5 times speedup. With load balancing, the parallel execution using four nodes of the cluster obtained up to 3.5 times speedup in 2D‐FFT and 3.0 times speedup in LU decomposition, compared to the execution on a single GPU. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
DDDT_A_261321 3841..3852

1Discipline of Biomedical Sciences, Sydney Medical School, The University of Sydney, Cumberland Campus, Sydney, NSW, Australia; 2Department of Pharmacognosy, Riphah Institute of Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, Riphah International University, Islamabad, Pakistan; 3Department of Pharmaceutical Chemistry, Riphah Institute of Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, Riphah International University, Islamabad, Pakistan Context and Objective: Cisplatin is a platinum drug in current clinical use for the treatment of cervical cancer. However, drug toxicity and resistance are its two major limitations. The aim of this investigation was to test the cytotoxic activity of potential phytochemicals alone and in combination with cisplatin in cervical cancer cells. Methods: In this study, cytotoxicity of phytochemicals including wedelolactone (WDL), betulinic acid (BA) and epigallocatechin gallate (EGCG) was investigated in human cervical cancer cell line HeLa through 3-(4, 5-Dimethyl-2-thiazolyl)-2, 5-diphenyl-2H-tetrazolium bromide (MTT) reduction assay. Combined drug action resulting from the combination of cisplatin with WDL and BA was investigated in the same cell line through median effect principle. The combination index (CI) was taken as a measure of combined drug action. Results: BA resulted in synergistic outcome when co-administered with cisplatin at 0/0 time; (bolus administration) while administration of either drug (cisplatin or BA) four hours before the other (0/4 or 4/0) resulted in antagonistic action. WDL, on the other hand, was found out to be synergistic at any of the applied sequence of drug administration (0/0, 0/4 or 4/0). Discussion and Conclusion: This is the first study reporting cytotoxic activity of WDL in HeLa cells either as single agent or in combination with cisplatin. These results support the idea that sequential combination of cisplatin with WDL and BA may work effectively in cervical cancer cells.
Inverted Run-Time Behavior of Classic Data Structures on Modern Microprocessors: Technical Background and Performance Guidelines

—Classic data structures, such as vectors and lists are used for storage and organization of data. Certain basic operations have a speciﬁed run-time behavior, which is essentially inﬂuenced by the choice of the data structure. However, further advances in the development of modern microprocessors have achieved sophisticated optimizations in hardware. These optimizations affect the run-time behavior of certain operations, which further affects the choice of data structures. This paper presents the results of our research activities focused on the impacts of these changed conditions. We selected various algorithms and operations frequently used in todays software development. Remarkable differences and modiﬁed characteristics will be discussed. The performances of both selected data structures, namely vector and list, have been determined empirically using the programming language C++. The results are interpreted and discussed in terms of run-time complexity and modern processor development.
Optimization of a mRNA based in situ padlock probe approach

In situ padlock probes experiments are a common tool in single cell genomics. When applied to detect mRNA sequences, they allow quantification and localization of targeted transcript in each individual cell. These assays are mainly performed on cells attached to a glass slide or fixed tissue samples. However, if the padlock probes could be applied on cellular suspensions, the technique could be combined with a wide range of follow-up techniques, such as micromanipulation. In order to test this, in situ padlock probing assays were performed on cellular suspensions and on cell seeded slides. The probes targeted mRNAs for β-actin and androgen receptor full length and were employed in three different cell lines: LNCaP, VCaP and HT-29. The results were observed by fluorescence microscopy and quantified by CellProfiler. In order to test the feasibility of a low coverage genetic sequencing, single cells were selected from the probed cellular suspension via micromanipulation, its DNA was amplified using whole genome amplification and the quality of the amplification product was verified by multiplex PCR. In situ padlock probes assays could be applied to cellular suspensions and expression of the transcripts could be quantified and located in a similar manner to the methods for cells attached onto slides. The number of signals between both supports showed comparable results. Hence any further modifications of the protocol should be focused on improving its efficiency. DNA from probed cells might be of good quality to undergo sequencing.
Image analysis of natural products

Natural products are exposed to various environmental inﬂuences resulting in a high phenotypical variability. This makes it very difﬁcult to develop automatic recognition algorithms in contrast to the recognition of manufactured products. Recent developments in the ﬁeld of computer science, especially the development of powerful classiﬁcation algorithms like the support vector machine make it possible to face also such complicated tasks. In this paper we present an approach to classify the impurities of a wheat sample to analyse the quality.