Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing for Human Papillomaviruses in Urine, Blood, and Oral Specimens: an Update for the Laboratory

Twelve high-risk alpha human papillomavirus (HPV) genotypes cause approximately 690,000 cancer cases annually, with cervical and oropharyngeal cancer being the two most prominent types. HPV testing is performed in laboratory settings for various applications of a clinical, epidemiological, and research nature using a range of clinical specimens collected by clinicians or by individuals (self-collected specimens). ABSTRACT Twelve high-risk alpha human papillomavirus (HPV) genotypes cause approximately 690,000 cancer cases annually, with cervical and oropharyngeal cancer being the two most prominent types. HPV testing is performed in laboratory settings for various applications of a clinical, epidemiological, and research nature using a range of clinical specimens collected by clinicians or by individuals (self-collected specimens). Here, we reflect on the importance and justification of using the right test for the right application and provide practical updates for laboratories either participating in or anticipating involvement in HPV testing in three specimen types, namely, urine, blood, and oral specimens, which are considered “alternative” specimens by many. In addition to clinician-collected cervical samples and self-collected cervicovaginal samples, first-void urine is emerging as a credible specimen for HPV-based cervical cancer screening, triage of HPV screen-positive women, monitoring HPV vaccine impact, and HPV testing in groups for which a less invasive sample is preferred. Detection of cell-free DNA (including HPV DNA) in blood has great promise for the early detection of HPV-attributable oropharyngeal cancer (HPV-AOC) and potentially other HPV-driven cancers and as an adjunct prognostic marker in long-term tumor surveillance, including treatment response. The moderate sensitivity of HPV testing in oral rinses or swabs at HPV-AOC diagnosis prevents its use in HPV-AOC secondary prevention but represents a promising prognostic tool in HPV-AOC tertiary prevention, where the HPV persistence in oral rinses throughout treatment may predict early HPV-AOC recurrences and/or the development of secondary HPV-AOC. The increasing sophistication of specific collection devices designed for alternative samples and the enhanced precision of novel molecular technologies are likely to support the evolution of this field and catalyze potential translation into routine practice.
First-order splitting algorithms for nonconvex matrix optimization problems

In this thesis, we consider two classes of matrix optimization problems. One is the matrix decomposition problem (MDP), which aims to decompose a given data matrix into the sum of two matrices with different desirable structures. The other one is the matrix factorization problem (MFP), which aims to factorize a given data matrix into the product of two small factor matrices with different desirable structures. These two classes of problems cover many existing widely-studied models with many applications in areas such as machine learning and imaging sciences. To solve MDP and MFP, which are possibly nonconvex, nonsmooth and even non-Lipschitz, we develop two efficient first-order splitting algorithms for them, respectively. We first consider MDP. Specifically, we adapt the alternating direction method of multipliers (ADMM) with a general dual step-size to solve a reformulation of the original problem that contains three blocks of variables, and analyze its convergence. We show that for any dual step-size less than the golden ratio, there exists a computable threshold such that if the penalty parameter is chosen above such a threshold and the sequence thus generated by our ADMM is bounded, then the cluster point of the sequence gives a stationary point of the nonconvex optimization problem. We achieve this via a potential function specifically constructed for our ADMM. Moreover, we establish the global convergence of the whole sequence if, in addition, this special potential function is a KurdykaLojasiewicz function. Furthermore, we present a simple strategy for initializing the ADMM to guarantee boundedness of
Some applications of the repeated integrals of the error function

The functions (1) arise naturally in the theory of conduction of heat in the semiinfinite solid (or the sphere or slab) with prescribed surface temperature or flow of heat, since the Laplace transforms of the solutions of many such problems involve the functions on the right hand side of (4). The objects of this note are, firstly, to indicate an extension of (4) which applies in the same way to problems with heat transfer at the surface, and, secondly, to give solutions in terms of the functions (1) of a number of problems of practical interest which involve heat generation in the solid. 2. Problems involving heat transfer at a surface at a rate proportional to its temperature difference from its surroundings. The required extension of (4) is that, if n is a positive integer, and a, h, and x are positive,
The Effectiveness and Characteristics of Communication Partner Training Programs for Families of People With Dementia: A Systematic Review

Abstract Background and Objectives Communication partner training (CPT) is essential in dementia care. Despite families being the largest group of community carers, previous reviews primarily focused on formal carers. This study aimed to understand the characteristics and effectiveness of CPT for families of people with dementia. Research Design and Methods The systematic review included intervention/protocol studies on dementia CPT for families, excluding formal carers and programs not focused on communication. CINAHL, PsycINFO, SpeechBITE, Medline, SCOPUS, and Embase were searched between November 30 and December 6, 2021. After deduplication, 3,172 records were screened. Quality assessment used JBI Critical Appraisal tools and the Mixed Methods Appraisal Tool. Data synthesis utilized three reporting tools, the International Classification of Functioning, Disability, and Health, and content analysis. Results Of 30 studies (27 programs), there were 10 quasi-experimental, 5 RCTs, 4 mixed methods, 4 case studies, 4 qualitative, and 2 protocols. Studies were published between 1998 and 2021 and included 671 family members. Characteristics varied with 7/27 programs including consumers during creation and one program including telehealth. One study included all reporting tool criteria. Programs typically used 4 intervention functions, with 12/27 programs addressing 3 behavior change areas. 33/74 outcome measures targeted the “Environment” of the person with dementia. Studies showed positive improvements in communication skills and knowledge, with mixed results on behavior/psychosocial outcomes. Qualitative results identified improvements in conversation and attitudes. Discussion and Implications CPT for families improves communication outcomes, however, quality of studies varied significantly. Future research should address gaps in telehealth, consumer involvement, and intervention descriptions.
Trend over time of HIV‐1 drug resistance to nonnucleoside reverse transcriptase inhibitors (NNRTIs) and their drivers: A cohort study from Antiviral Response Cohort Analysis (ARCA)

The rise of HIV‐1 drug resistance to nonnucleoside reverse transcriptase inhibitors (NNRTIs) threatens the long‐term success of NNRTI‐based therapies. Our study aims to describe the circulation of major resistance‐associated mutations (RAMs) for NNRTIs in people living with HIV (PLWH) in Italy from 2000 to 2020. We included 5982 naïves and 28 505 genotypes from 9387 treatment‐experienced PLWH from the Antiviral Response Cohort Analysis (ARCA) cohort. Transmitted drug resistance (TDR) was found in 12.5% and declined from 17.3% in 2000–2003 to 10.9% in 2016–2020 (p = 0.003). Predictors of TDR were viral subtype B [vs. non‐B, adjusted odds ratio (aOR) = 1.94, p < 0.001], zenith viral load (VL) (per 1 log10 higher, aOR = 0.86, p = 0.013), nadir CD4 cell count (per 100 cells/μL increase aOR = 0.95, p = 0.013). At least one RAM for NNRTIs among treatment experienced PLWH was detected in 33.2% and pre‐treatment drug resistance (PDR) declined from 43.4% in 2000–2003 to 20.9% in 2016–2020 (p < 0.001). Predictors of PDR were sexual transmission route (vs. others, aOR = 0.78, p < 0.001), time since HIV diagnosis (per 1 month longer, aOR = 1.002, p < 0.001), viral subtype B (vs. non B, aOR = 1.37, p < 0.001), VL (per 1 log10 higher, aOR = 1.12, p < 0.001), nadir CD4 count (per 100 cells/μL increase, aOR = 0.91, p < 0.001), previous exposure to any NNRTI (aOR = 2.31, p < 0.001) and a more recent calendar year sequence (any time span > 2008 vs. 2000–2003, any aOR <1, p < 0.001). Circulation of RAMs to NNRTIs declined during the last 20 years in Italy. NNRTIs remain pivotal drugs for the management of HIV‐1 due to safety concerns and long‐acting options.
PMSR model for low illumination image enhancement

Enhancement of low illumination images is of great importance in poor imaging conditions. A new image enhancement model was proposed in this paper. The model divided an image into blocks and used the local standard deviation to design the center/surround filter and utilized amplitude compensation factor to compensate the shortage of logarithmic function in compressing the near-zero data’s amplitude. In addition, the amplitude compensation factor can suppress noise. At the same time, the normalized brightness can maintain the normal brightness region of the image while the brightness of the image is increased. In order to verify the performance of the proposed model, the proposed model and existing models are applied to image enhancement. To evaluate its performance in image enhancement, results are compared from the subjective and objective aspects. The experimental results show that the proposed model preserved the image details better and avoided the excessive enhancement of the normal brightness region.
A loop cutter is an ideal gripper for endoscopic removal of press-through-package sheets

A press-through package (PTP) sheet is sometimes accidentally swallowed [1]. PTP sheets have sharp edges (▶Fig. 1) and may cause mucosal damage and/or perforation to the gastrointestinal tract [2, 3]. Thus, ingested PTP sheets should be removed endoscopically as soon as possible. Several methods have been reported using hoods and covers to prevent mucosal damage during removal [4, 5]. However, a firm grip on the PTP sheet is necessary for successful removal. Grasping forceps or a snare are commonly used as grasping devices but can often fail because the PTP is slippery with saliva and the working space is insufficient for snaring. To address this, we used a Loop Cutter (FS-5L-1; Olympus, Tokyo, Japan), whose teeth can penetrate the PTP plastic sheet to secure a firm grip. The Loop Cutter’s strong grip allows the PTP to be easily removed. To evaluate the gripping force of various devices, we attached a PTP sheet to the E-Videos
An Improved Harmonic Current Detection Method Based on Parallel Active Power Filter

Harmonic detection technology plays an important role in the applications of active power filter. The accuracy and real-time performance of harmonic detection are the precondition to ensure the compensation performance of Active Power Filter (APF). This paper proposed an improved instantaneous reactive power harmonic current detection algorithm. The algorithm uses an improved ip-iq algorithm which is combined with the moving average value filter. The proposed ip-iq algorithm can remove the αβ and dq coordinate transformation, decreasing the cost of calculation, simplifying the extraction process of fundamental components of load currents, and improving the detection speed. The traditional low-pass filter is replaced by the moving average filter, detecting the harmonic currents more precisely and quickly. Compared with the traditional algorithm, the THD (Total Harmonic Distortion) of the grid currents is reduced from 4.41% to 3.89% for the simulations and from 8.50% to 4.37% for the experiments after the improvement. The results show the proposed algorithm is more accurate and efficient.
Need for a nutrition-specific scientific paradigm for research quality improvement

Nutrition science has been criticised for its methodology, apparently contradictory findings and generating controversy rather than consensus. However, while certain critiques of the field are valid and informative for developing a more cogent science, there are also unique considerations for the study of diet and nutrition that are either overlooked or omitted in these discourses. The ongoing critical discourse on the utility of nutrition sciences occurs at a time when the burden of non-communicable cardiometabolic disease continues to rise in the population. Nutrition science, along with other disciplinary fields, is tasked with producing a translational evidence-base fit for the purpose of improving population and individual health and reducing disease risk. Thus, an exploration of the unique methodological and epistemic considerations for nutrition research is important for nutrition researchers, students and practitioners, to further develop an improved scientific discipline for nutrition. This paper will expand on some of the challenges facing nutrition research, discussing methodological facets of nutritional epidemiology, randomised controlled trials and meta-analysis, and how these considerations may be applied to improve research methodology. A pragmatic research paradigm for nutrition science is also proposed, which places methodology at its centre, allowing for questions over both how we obtain knowledge and research design as the method to produce that knowledge to be connected, providing the field of nutrition research with a framework within which to capture the full complexity of nutrition and diet.
Reliability study of stock index forecasting in volatile and trending cities using public sentiment ——based on word2Vec and LSTM models

ABSTRACT Accurate forecasting of the stock market has always been a key concern of academics and investors, and few researchers have discussed the reliability of stock index forecasting in volatile and trending markets based on public sentiment. This article will first use Word2Vec and CNN to classify the sentiment of 754,000 text data excavated from the Oriental Fortune Stock Forum and construct public sentiment indicators; then, select characteristic parameters such as the closing value of the Shanghai Composite Index, the inflow of northbound funds, and the exchange rate of RMB against the US dollar. Introducing sentiment indicators and building an LSTM model to explore the effect of public sentiment factors on the prediction of the Shanghai Composite Index in the unilateral rise, unilateral fall, and volatile markets. The research shows that the reliability of using public sentiment to predict unilateral falling and volatile markets is high, especially the prediction error of predicting volatile markets is the smallest. In addition, it is also found that due to the existence of the ‘disposition effect’, the error is significantly larger when using public sentiment factors to predict the unilateral rising market.
Runtime Verification of Hash Code in Mutable Classes

Most mainstream object-oriented languages provide a notion of equality between objects which can be customized to be weaker than reference equality, and which is coupled with the customizable notion of object hash code. This feature is so pervasive in object-oriented code that incorrect redefinition or use of equality and hash code may have a serious impact on software reliability and safety. Despite redefinition of equality and hash code in mutable classes is unsafe, many widely used API libraries do that in Java and other similar languages. When objects of such classes are used as keys in hash tables, programs may exhibit unexpected and unpredictable behavior. In this paper we propose a runtime verification solution to avoid or at least mitigate this issue. Our proposal uses RML, a rewriting-based domain specific language for runtime verification which is independent from code instrumentation and the programming language used to develop the software to be verified.
EvoSpex: A Search-Based Tool for Postcondition Inference

Postconditions are predicates that specify the intended behavior of a program by capturing properties about the program state when the program finishes its execution. Although postconditions can help to improve many software reliability analyses, they are seldom found accompanying source code. Thus, tools that assist developers in specifying postconditions are useful. This tool demo paper presents EvoSpex, a tool based on evolutionary computation that automatically infers postconditions of Java methods. Given a target Java method and a test suite for it, our tool executes the test suite to obtain valid pre/post state pairs for the method under analysis. Then, these pairs are mutated to obtain (allegedly) invalid ones, and finally a postcondition assertion characterizing the current method behavior is produced, by using an evolutionary algorithm that searches for an assertion that is satisfied by the valid pre/post state pairs and leaves out the invalid ones. EvoSpex implements a classic genetic algorithm that explores the space of candidate postconditions over a JML-like specification language. The algorithm is guided by a fitness function that aims at precisely capturing the valid state pairs, rejecting the invalid ones, and that also favors more succinct assertions.
Arc Length Restricted Knot Vector Construction for NURBS Subdivision

This paper presents a method to construct an arc-length restricted knot vector for subdivision of a non-uniform rational B-spline (NURBS) curve. Based on Boehm's knot insertion algorithm of restriction free, the new method calculates the inserted knot value by considering the arc length of the subdivided sub-curves as a restriction. The paper shows in detail the mathematical reasoning and computing procedure for the method as well as the numerical experiments that demonstrate the new method is particularly helpful to meet the needs of arc length restricted subdivision of NURBS, such as the CNC interpolation, computer vision and so on.
CGuard: Scalable and Precise Object Bounds Protection for C

Spatial safety violations are the root cause of many security attacks and unexpected behavior of applications. Existing techniques to enforce spatial safety work broadly at either object or pointer granularity. Object-based approaches tend to incur high CPU overheads, whereas pointer-based approaches incur both high CPU and memory overheads. SGXBounds, an object-based approach, provides precise out-of-bounds protection for objects at a lower overhead compared to other tools with similar precision. However, a major drawback of this approach is that it cannot support address space larger than 32-bit. In this paper, we present CGuard, a tool that provides precise object-bounds protection for C applications with comparable overheads to SGXBounds without restricting the application address space. CGuard stores the bounds information just before the base address of an object and encodes the relative offset of the base address in the spare bits of the virtual address available in x86_64 architecture. For an object that cannot fit in the spare bits, CGuard uses a custom memory layout that enables it to find the base address of the object in just one memory access. Our study revealed spatial safety violations in the gcc and x264 benchmarks from the SPEC CPU2017 benchmark suite and the string_match benchmark from the Phoenix benchmark suite. The execution time overheads for the SPEC CPU2017 and Phoenix benchmark suites were 42% and 26% respectively, whereas the reduction in the throughput for the Apache webserver when the CPUs were fully saturated was 30%. These results indicate that CGuard can be highly effective while maintaining a reasonable degree of efficiency.
Towards Verified Scalable Parallel Computing with Coq and Spark

SyDPaCC (Systematic Development of programs for Parallel and Cloud Computing) is a framework for the Coq interactive theorem prover. It allows to systematically develop correct parallel programs from specifications via verified and automated program transformations. The obtained programs are scalable, i.e. able to run on numerous processors. SyDPaCC produces programs written in the multi-paradigm and functional programming language OCaml with calls to the BSML (Bulk Synchronous parallel ML) parallel programming library. In this paper we present ongoing work towards an extension of SyDPaCC to be able to produce Scala programs using Apache Spark for parallel processing.
DDLDroid: A Static Analyzer for Automatically Detecting Data Loss Issues in Android Applications

DDLDroid is a static analyzer for detecting data loss issues in Android apps during activity restart or app relaunch. It is bootstrapped by a saving-restoring bipartite graph which correlates variables that need saving to those that need restoring according to their carrier widgets, and is based on the analysis of saving and restoring data flows. It reports data loss issues once missed or broken data flows are identified. DDLDroid detects 302 true data loss issues out of 66 Android apps in 73 minutes, including 180 previously-unknown issues, demonstrating its effectiveness and efficiency.
Document-level Relation Extraction via Separate Relation Representation and Logical Reasoning

Document-level relation extraction (RE) extends the identification of entity/mentions’ relation from the single sentence to the long document. It is more realistic and poses new challenges to relation representation and reasoning skills. In this article, we propose a novel model, SRLR, using Separate Relation Representation and Logical Reasoning considering the indirect relation representation and complex reasoning of evidence sentence problems. Specifically, we first expand the judgment of relational facts from the entity-level to the mention-level, highlighting fine-grained information to capture the relation representation for the entity pair. Second, we propose a logical reasoning module to identify evidence sentences and conduct relational reasoning. Extensive experiments on two publicly available benchmark datasets demonstrate the effectiveness of our proposed SRLR as compared to 19 baseline models. Further ablation study also verifies the effects of the key components.
Data Constraint Mining for Automatic Reconciliation Scripts Generation

Fund loss is an increasingly critical problem caused by the misbehavior of software, especially in fintech and e-commerce platforms. Data reconciliation is one of the most commonly used approaches in detecting and preventing fund loss by executing reconciliation scripts on data storage systems (e.g., database and cache systems). The core of reconciliation scripts is the data constraints, which can be expressed as implications with two parts: preconditions and assertions. However, due to the complexity and diversity of business, the construction of data constraints and reconciliation scripts usually heavily relies on business experts. To this end, we propose AutoReconciler to mine data constraints from business data and generate reconciliation scripts automatically. It can mine assertions via enhanced symbolic regression, discover preconditions via association rule mining, and generate reconciliation scripts in SQL form. We have performed extensive experiments on the synthesized data. The result shows that our approach outperforms the baseline by a large margin (an average improvement in precision and recall of 22.1% and 51.6%, respectively), especially for complex data constraints. Our solution has been implemented, deployed, and adopted in production, and we conducted several case studies further to confirm the benefits of our solution in industrial scenarios.