Miracle-dz/UniFAR-FTUs huggingface dataset:
Advances in and the Applicability of Machine Learning-Based Screening and Early Detection Approaches for Cancer: A Primer

Simple Summary Non-communicable diseases in general, and cancer in particular, contribute greatly to the global burden of disease. Although significant advances have been made to address this burden, cancer is still among the top drivers of mortality, second only to cardiovascular diseases. Consensus has been established that a key factor to reduce the burden of disease from cancer is to improve screening for and the early detection of such conditions. To date, however, most approaches in this field relied on established screening methods, such as a clinical examination, radiographic imaging, tissue staining or biochemical markers. Yet, with the advances of information technology, new data-driven screening and diagnostic tools have been developed. This article provides a brief overview of the theoretical foundations of these data-driven approaches, highlights the promising use cases and underscores the challenges and limitations that come with the introduction of these approaches to the clinical field. Abstract Despite the efforts of the past decades, cancer is still among the key drivers of global mortality. To increase the detection rates, screening programs and other efforts to improve early detection were initiated to cover the populations at a particular risk for developing a specific malignant condition. These diagnostic approaches have, so far, mostly relied on conventional diagnostic methods and have made little use of the vast amounts of clinical and diagnostic data that are routinely being collected along the diagnostic pathway. Practitioners have lacked the tools to handle this ever-increasing flood of data. Only recently, the clinical field has opened up more for the opportunities that come with the systematic utilisation of high-dimensional computational data analysis. We aim to introduce the reader to the theoretical background of machine learning (ML) and elaborate on the established and potential use cases of ML algorithms in screening and early detection. Furthermore, we assess and comment on the relevant challenges and misconceptions of the applicability of ML-based diagnostic approaches. Lastly, we emphasise the need for a clear regulatory framework to responsibly introduce ML-based diagnostics in clinical practice and routine care.
Turbine Rotor Dynamics Models Considering Foundation and Stator Effects

The paper deals with the modelling of turbine rotors, including a foundation and stator parts, and with their dynamic analysis. The dynamics of turbines can be strongly influenced by the effects of a rotor foundation, and therefore suitable modelling approaches should be developed to obtain proper analysis tools. The standard methods for the modelling of rotating shafts are summarized in the paper, and two approaches to including foundation effects into dynamic models are introduced. The approach based on the dynamic compliance of the foundation with respect to the rotor angular velocity is compared with the approach based on the modal synthesis of rotor and foundation models. The calculation of modal properties is then demonstrated, and the characteristics of both presented methods are discussed. A comparison of the steady-state dynamic response calculated using both methods is shown. It can be concluded that both approaches can be advantageously used for several analyses, and our final recommendations are given in the conclusions.
Electronic Stopping Power Model based on Deep Learning and its Application in Ion Range Prediction

Deep learning algorithm emerges as a new method to take the raw features from large dataset and mine their deep implicit relations, which is promising for solving traditional physical challenges. A particularly intricate and difficult challenge is the energy loss mechanism of energetic ions in solid, where accurate prediction of stopping power is a long-time problem. In this work, we developed a deep-learning-based stopping power model with high overall accuracy, and overcame the long-standing deficiency of the existing classical models by improving the predictive accuracy of stopping power for ultra-heavy ion with low energy, and the corresponding projected range. This electronic stopping power model, which based on deep learning algorithm, could be hopefully applied for the study of ion-solid interaction mechanism and enormous relevant applications.
Identifying Incorrect Patches in Program Repair Based on Meaning of Source Code

Automatic Program Repair (APR) techniques have shown the potential of reducing debugging costs while improving software quality by generating patches for fixing bugs automatically. However, they often generate many overfitting patches which pass only a specific test-suite but do not fix the bugs correctly. This paper proposes MIPI, a novel approach to reducing the number of overfitting patches generated in the APR. We leverage recent advances in deep learning to exploit the similarity between the patched method’s name (which often encloses the developer’s intention about the code) and the semantic meaning of the method’s body (which represents the actual implemented behavior) for identifying and removing overfitting patches generated by APR tools. Experiments with a large dataset of patches for QuixBugs and Defects4J programs show the promise of our approach. Specifically, in a total of 1,191 patches generated by 23 existing APR tools, MIPI successfully filters out 254 of the total 797 overfitting patches with a precision of 90% while preserving 93% of the correct patches. MIPI is more precise and less damaging to the APR than existing heuristic patch assessment techniques, achieving a higher recall than automated testing-based techniques that do not have access to the test oracle. In addition, MIPI is highly complementary to existing automated patch assessment techniques.
Expanding access and method choice: Evidence of client self-administration of injectables and private sector provision of family planning services in three West African countries

Governments across West Africa have expressed their commitment to increasing access to voluntary family planning (FP) through global and regional initiatives such as FP2020 and the Ouagadougou Partnership, and through targeted national strategies. Ghana, Nigeria and Senegal, among other countries, have been exploring new strategies to expand access to voluntary FP service delivery. Context-specific evidence was needed for two promising strategies—1) task sharing of FP services to private sector drug shops and pharmacies, and 2) introducing self-injection of subcutaneous depot medroxyprogesterone acetate (DMPA-SC). The Evidence Project collaborated with stakeholders in Ghana, Nigeria, and Senegal to use implementation science (IS) to expand the evidence base on these promising approaches. In this brief, we describe key study results from the three countries and how results have been used to increase access to voluntary family planning and expand method choice.
THZ531 Induces a State of BRCAness in Multiple Myeloma Cells: Synthetic Lethality with Combination Treatment of THZ 531 with DNA Repair Inhibitors

Multiple myeloma (MM) is a hematological disease marked by abnormal growth of B cells in bone marrow. Inherent chromosomal instability and DNA damage are major hallmarks of MM, which implicates an aberrant DNA repair mechanism. Studies have implicated a role for CDK12 in the control of expression of DNA damage response genes. In this study, we examined the effect of a small molecule inhibitor of CDK12–THZ531 on MM cells. Treatment of MM cells with THZ531 led to heightened cell death accompanied by an extensive effect on gene expression changes. In particular, we observed downregulation of genes involved in DNA repair pathways. With this insight, we extended our study to identify synthetic lethal mechanisms that could be exploited for the treatment of MM cells. Combination of THZ531 with either DNA-PK inhibitor (KU-0060648) or PARP inhibitor (Olaparib) led to synergistic cell death. In addition, combination treatment of THZ531 with Olaparib significantly reduced tumor burden in animal models. Our findings suggest that using a CDK12 inhibitor in combination with other DNA repair inhibitors may establish an effective therapeutic regimen to benefit myeloma patients.
Enhanced exact solution methods for the Team Orienteering Problem

The Team Orienteering Problem (TOP) is one of the most investigated problems in the family of vehicle routing problems with profits. In this paper, we propose a Branch-and-Price approach to find proven optimal solutions to TOP. The pricing sub-problem is solved by a bounded bidirectional dynamic programming algorithm with decremental state space relaxation featuring a two-phase dominance rule relaxation. The new method is able to close 17 previously unsolved benchmark instances. In addition, we propose a Branch-and-Cut-and-Price approach using subset-row inequalities and show the effectiveness of these cuts in solving TOP.
Profiling Microglia in a Mouse Model of Machado–Joseph Disease

Microglia have been increasingly implicated in neurodegenerative diseases (NDs), and specific disease associated microglia (DAM) profiles have been defined for several of these NDs. Yet, the microglial profile in Machado–Joseph disease (MJD) remains unexplored. Here, we characterized the profile of microglia in the CMVMJD135 mouse model of MJD. This characterization was performed using primary microglial cultures and microglial cells obtained from disease-relevant brain regions of neonatal and adult CMVMJD135 mice, respectively. Machine learning models were implemented to identify potential clusters of microglia based on their morphological features, and an RNA-sequencing analysis was performed to identify molecular perturbations and potential therapeutic targets. Our findings reveal morphological alterations that point to an increased activation state of microglia in CMVMJD135 mice and a disease-specific transcriptional profile of MJD microglia, encompassing a total of 101 differentially expressed genes, with enrichment in molecular pathways related to oxidative stress, immune response, cell proliferation, cell death, and lipid metabolism. Overall, these results allowed us to define the cellular and molecular profile of MJD-associated microglia and to identify genes and pathways that might represent potential therapeutic targets for this disorder.
Adherence to Dietary Recommendations of 7-Year-Old Children from a Birth Cohort in Friuli Venezia Giulia, Italy

Few Italian and European studies have assessed adherence to dietary recommendations in primary school children using dietary records. No Italian studies have provided an index-based nutritional adequacy assessment. We provided a comprehensive overview of dietary intake in 381 7-year-old children from NAC-II cohort study, Friuli Venezia Giulia (Italy). Energy, macro-, and micronutrient intakes were derived from 3-day dietary records. Standard (median and percentage) and index-based (Nutrient Adequacy Ratio (NAR) and Mean Adequacy Ratio (MAR)) approaches were used to evaluate adequacy to Italian dietary reference values at nutrient- and overall-diet-level. Percentage contribution of macronutrients to energy intake (%En) was unbalanced towards total fats and protein. In 25% of children, total fats intake exceeded the reference intake upper limit. In ~63% of children, protein intake was at least doubled in their child-specific population reference intake. Median intakes of sodium (1.7 g/day), saturated fatty acids (12.2 %En), and soluble carbohydrates (19.4 %En) exceeded the suggested dietary target in most (65–84%) children. Inadequacy was also observed for micronutrients, with median NARs ranging from 0.11 (vitamin D) to 0.90 (zinc). The median MAR was 0.75 (0.69–0.79), with 1 indicating optimal overall dietary intake. In conclusion, the enrolled children showed suboptimal intakes of several macro- and micronutrients, in line with Italian and European studies on primary school children. Based on the current findings, public health interventions may be targeted to specific nutrients or subpopulations.
Smart distribution grid multistage expansion planning under load forecasting uncertainty

The optimal distribution system planning (ODSP) is a complicated problem with multi-objective function and multi-constraints. The complexity of the problem is increased in smart distribution grids with uncertainties in both load and generation. In this study, problem of optimal smart distribution grids multistage expansion planning is presented in which reinforcement or installation time, capacity and location of MV substation and DER are taken into consideration. The binary global search optimization algorithm is proposed to solve the ODSP problem. The proposed cost function considers the capital investment, operation and the levelized energy cost (LEC) of each energy source. Loss characteristic matrix has been used for locating of MV substation and DER. The aspect of modeling under load growth uncertainty and multistage planning and multiple objective functions which are related to ODSP problem are considered in an integrated model. The multistage planning procedure is proposed to consider the pseudo-dynamic behavior of planning and continuing growth of demand. Load uncertainty is represented by point estimated approach and the results are compared with the Monte Carlo simulation (MCS). Presented methodology has been tested from base to long-term period on distribution network. The obtained results confirm the ability and validity of the presented method.
Albumin Binds COVID-19 Spike 1 Subunit and Predicts In-Hospital Survival of Infected Patients—Possible Alteration by Glucose

(1) Background: This study aimed to analyze if the serum albumin levels of hospitalized SARS-CoV-2 (COVID-19) patients on admission could predict <30 days in-hospital all-cause mortality, and if glucose levels on admission affected this predictive ability. (2) Methods: A multicenter retrospective cohort of 1555 COVID-19-infected adult patients from public hospitals of the Madrid community were analyzed. (3) Results: Logistic regression analysis showed increased mortality for ages higher than 49 y. After adjusting for age, comorbidities and on-admission glucose levels, it was found that on-admission serum albumin ≥3.5 g/dL was significantly associated with reduced mortality (OR 0.48; 95%CI:0.36–0.62). There was an inverse concentration-dependent association between on-admission albumin levels and <30 days in-hospital all-cause mortality. However, when on-admission glucose levels were above 125 mg/dL, higher levels of serum albumin were needed to reach an association with survival. In vitro experiments showed that the spike protein S1 subunit of SARS-CoV-2 binds to native albumin. The binding ability of native albumin to the spike protein S1 subunit was decreased in the presence of an increasing concentration of glycated albumin. (4) Conclusions: On-admission serum albumin levels were inversely associated with <30 days in-hospital all-cause mortality. Native albumin binds the spike protein S1 subunit, suggesting that native albumin may act as a scavenger of the SARS-CoV-2 virus.
Extensional Framework for Pinched Hysteresis Systems

Abstract Recently, the pinched hysteresis loop phenomenon discovered in many nanoscale devices has been a hot research topic. This paper proposes a new mathematical framework for those hysteresis systems to accommodate those discontinuity or not “well-defined” situations in canonical model of memristive system. In particular, two special subtypes are pointed out to fit the hitherto memory device characterization data. By Atting the I-V hysteresis loop, the effects of the transfer function in output equation on hysteresis loop properties are analyzed.
Changes in HPV16/18 Prevalence among Unvaccinated Women with Cervical Intraepithelial Neoplasia in Japan: Assessment of Herd Effects following the HPV Vaccination Program

Since the human papillomavirus (HPV) vaccination program for Japanese girls aged 12–16 years began in 2010, vaccination uptake has been low in women born before 1993 but high (approximately 70%) in those born during 1994–1999. We previously compared the prevalence of vaccine types HPV16 and HPV18 in cervical intraepithelial neoplasia grade 1–3 (CIN1–3) or adenocarcinoma in situ (AIS) between vaccinated and unvaccinated cohorts and found direct protection effects among vaccinated women in Japan. In this study, we focused on changes in HPV16/18 prevalence among “unvaccinated” cohorts with CIN/AIS. We analyzed HPV16/18 prevalence among 5051 unvaccinated women aged <40 years, newly diagnosed with CIN/AIS during 2012–2021 for time trends. Declining trends in HPV16/18 prevalence over 9 years were observed in CIN1 (36.0–10.0%, Ptrend = 0.03) and CIN2–3/AIS (62.5–36.4%, Ptrend = 0.07) among women aged <25 years. HPV16/18 prevalence in CIN1 and CIN2–3/AIS diagnosed at age 20–24 years was lower in 1994–1999 birth cohorts compared with 1988–1993 birth cohorts (4.5% vs. 25.7% for CIN1 and 40.0% vs. 58.1% for CIN2–3/AIS, both p = 0.04). Significant reduction in HPV16/18 prevalence among young unvaccinated women with CIN1 and CIN2–3/AIS suggests herd effects of HPV vaccination in Japan.
They Told Me “This Isn’t a Hotel”: Young People’s Experiences and Perceptions of Care When Presenting to the Emergency Department with Suicide-Related Behaviour

In Australia, the number of young people presenting to the emergency department with mental health concerns, in particular, suicidal behaviour (defined here as suicidal ideation, thoughts, intent and attempts) is increasing. Little is known about the experiences of Australian young people who present to hospital emergency departments with suicidal behaviour. In this qualitative study, we conducted a series of focus groups with 55 young people aged 16–25 years, with a view to developing a framework for youth suicide prevention for Western Australia. The data were analysed using a general inductive analysis approach. We explored the experiences and perceptions of the care and management of 35 young people presenting to Western Australian hospital emergency departments. Participants described a range of negative experiences relating to the emergency department environment, staff attitudes and their treatment by staff. We argue that adapting ED practices and approaches to young people presenting with suicidal thoughts and behaviours based on these findings will result in lower rates of repeated presentations and admissions to hospital and lower rates of suicide attempts and deaths by suicide.
Learning with Dual Demonstration Domains: Random Domain-Adaptive Meta-Learning

Although robots have been widely applied in various fields, allowing a robot to perform a wide range of tasks like humans is a significant challenge. One promising method is meta-learning, which enables robots to learn from demonstrations with the concept of "learning to learn. " However, most meta-learning methods only focus on teaching robots to learn from a single demonstration domain, i.e., demonstrations recording videos of a human or robot performing tasks (human or robot demonstrations). Given that humans can acquire and merge knowledge from various related domains, this paper proposes a novel yet efficient Random Domain-Adaptive Meta-Learning (RDAML) framework to teach the robot to learn from multiple demonstration domains (e.g., human demonstrations + robot demonstrations) with different random sampling parameters. Once training is complete, the trained model can adapt to new environments given a corresponding visual demonstration. Extensive experimental results show that the model trained by our proposed RDAML algorithm obtains better generalization capability. We have demonstrated the effectiveness of our RDAML on real-world placing experiments using a UR5 robot arm, which significantly outperforms current state-of-the-art methods using either human demonstrations or robot demonstrations to teach the robot during testing.
Endoscopic Approach in the Diagnosis of Gastrointestinal Acute Graft Versus Host Disease in Children

Objectives: The objectives of this study were to describe clinical, histological, and endoscopic findings in children undergoing gastrointestinal (GI) endoscopy for symptoms of digestive acute graft-versus-host disease (a-GvHD), to evaluate the rate of steroid-resistant GvHD and transplant-related mortality, and to describe the feasibility and safety of the endoscopic procedure. Methods: This was a retrospective study conducted, at the IRCSS Istituto G. Gaslini in Genova, Italy, in 26 children undergoing upper or lower GI endoscopy after allogeneic hematopoietic stem cell transplantation between 2000 and 2017. Results: Histology confirmed a diagnosis of a-GvHD in 73% of patients; it was frequently associated with steroid-resistant a-GvHD (P = 0.001) and with an increased transplant-related mortality. Additionally, one patient developed duodenal hematoma after endoscopy for a high-grade GI a-GvHD. Conclusions: In our experience, the endoscopic approach in the diagnosis of GI a-GvHD in children was feasible and safe. Furthermore, the histological diagnosis of GI a-GvHD was associated with an increased risk of steroid-resistant GvHD and with high transplant-related mortality.
Impact of Substance Use Disorder on Between-Network Brain Connectivity in Early Psychosis

Abstract The Triple Network Model of psychopathology identifies the salience network (SN), central executive network (CEN), and default mode network (DMN) as key networks underlying the pathophysiology of psychiatric disorders. In particular, abnormal SN-initiated network switching impacts the engagement and disengagement of the CEN and DMN, and is proposed to lead to the generation of psychosis symptoms. Between-network connectivity has been shown to be abnormal in both substance use disorders (SUD) and psychosis. However, none have studied how SUD affects connectivity between sub-networks of the DMN, SN, and CEN in early stage psychosis (ESP) patients. In this study, we collected data from 113 ESP patients and 50 healthy controls to investigate the effect of SUD on between-network connectivity. In addition, we performed sub-group analysis by exploring whether past SUD vs current SUD co-morbidity, or diagnosis (affective vs non-affective psychosis) had a modulatory effect. Connectivity between four network-pairs, consisting of sub-networks of the SN, CEN, and DMN, was significantly different between ESP patients and controls. Two patterns of connectivity were observed when patients were divided into sub-groups with current vs past SUD. In particular, connectivity between right CEN and the cingulo-opercular salience sub-network (rCEN-CON) showed a gradient effect where the severity of abnormalities increased from no history of SUD to past+ to current+. We also observed diagnosis-specific effects, suggesting non-affective psychosis patients were particularly vulnerable to effects of substance use on rCEN-CON connectivity. Our findings reveal insights into how comorbid SUD affects between-network connectivity and symptom severity in ESP.