Miracle-dz/UniFAR-FTUs huggingface dataset:
“Do you just have to know that?” Novice and experts’ procedure when solving science problem tasks

Only teachers who possess problem-solving skills can develop them in their students. These skills therefore need to be accentuated during teachers’ pre-service training. In this study, attention was given to pre-service chemistry teachers’ (students) problem-solving skills measured with the use of two sets of problem tasks–chemistry and general science tasks. Based on a pre-test consisting of both types of tasks, one successful, one partially successful and one unsuccessful solver was selected from a group of first-year bachelor chemistry teacher students. To compare, the tasks were also given to three experts (post-docs in the field of chemistry education). All the participants solved two tasks on a computer with their eye movements recorded. After the procedure, retrospective think-aloud and interviews were conducted to provide data about the problem-solving process. The results showed several trends. (1) Students–novices considered the chemistry task more difficult than the science task, which correlated with their task results. (2) Experts considered the science task more complex, therefore more difficult, however scored better than the students. (3) Even the successful student only solved the chemistry task using memorized facts without the support provided. (4) Experts’ direct focus on relevant parts was confirmed, whereas unsuccessful (novice) students distributed their focus toward other task parts too. (5) When students faced a problem during task solving, they used limiting strategies. This behavior was not identified in the expert group. The results thus showed a need to support students’ problem-solving strategies in several areas, especially careful reading, and identifying the main problem and supporting information. Moreover, the results showed a need to present chemistry tasks to students with more variability and explain their reasoning rather than testing field-specific, separated, memorized information.
A cognitive modeling approach to learning and using reference biases in language

During real-time language processing, people rely on linguistic and non-linguistic biases to anticipate upcoming linguistic input. One of these linguistic biases is known as the implicit causality bias, wherein language users anticipate that certain entities will be rementioned in the discourse based on the entity's particular role in an expressed causal event. For example, when language users encounter a sentence like “Elizabeth congratulated Tina…” during real-time language processing, they seemingly anticipate that the discourse will continue about Tina, the object referent, rather than Elizabeth, the subject referent. However, it is often unclear how these reference biases are acquired and how exactly they get used during real-time language processing. In order to investigate these questions, we developed a reference learning model within the PRIMs cognitive architecture that simulated the process of predicting upcoming discourse referents and their linguistic forms. Crucially, across the linguistic input the model was presented with, there were asymmetries with respect to how the discourse continued. By utilizing the learning mechanisms of the PRIMs architecture, the model was able to optimize its predictions, ultimately leading to biased model behavior. More specifically, following subject-biased implicit causality verbs the model was more likely to predict that the discourse would continue about the subject referent, whereas following object-biased implicit causality verbs the model was more likely to predict that the discourse would continue about the object referent. In a similar fashion, the model was more likely to predict that subject referent continuations would be in the form of a pronoun, whereas object referent continuations would be in the form of a proper name. These learned biases were also shown to generalize to novel contexts in which either the verb or the subject and object referents were new. The results of the present study demonstrate that seemingly complex linguistic behavior can be explained by cognitively plausible domain-general learning mechanisms. This study has implications for psycholinguistic accounts of predictive language processing and language learning, as well as for theories of implicit causality and reference processing.
CP-25 exerts a protective effect against ConA-induced hepatitis via regulating inflammation and immune response

Hepatitis is a complex multifactorial pathological disorder, which can eventually lead to liver failure and even potentially be life threatening. Paeoniflorin-6′-O-benzene sulfonate (CP-25) has proven to have critical anti-inflammatory effects in arthritis. However, the effects of CP-25 in the pathogenesis of hepatitis remains unclear. In this experiment, mice were intragastrically administered with CP-25 (25, 50 and 100 mg/kg), and then ConA (25 mg/kg) was intravenous injected to establish hepatitis model in vivo. CP-25 administration attenuated liver damage and decreased ALT and AST activities in mice with hepatitis. Besides, CP-25 modulated immune responses including down-regulated the proportions of activated CD4+, activated CD8+ T cells, and ratio of Th1/Th2 in ConA-injected mice. Furthermore, ConA-mediated production of reactive oxygen species (ROS), release of inflammatory cytokines including IFN-γ, TNF-α, activation of MAPK pathways and nuclear translocation of nuclear factor-kappaB (NF-κB) were significantly decreased in CP-25 administrated mice. In ConA-stimulated RAW264.7 cells, CP-25 suppressed inflammatory cytokines secretion and reduced ROS level, which were consistent with animal experiments. Otherwise, the data showed that CP-25 restrained phosphorylation of ERK, JNK and p38 MAPK pathways influenced by ROS, accompanied with inhibiting NF-κB nuclear translocation. In conclusion, our findings indicated that CP-25 protected against ConA-induced hepatitis may through modulating immune responses and attenuating ROS-mediated inflammation via the MAPK/NF-κB signaling pathway.
Effect of levothyroxine replacement therapy in patients with subclinical hypothyroidism and chronic heart failure: A systematic review

Background Chronic heart failure (CHF) affects the health care system with high social and economic costs due to recurrent hospital admissions or frequent ambulatory reassessments. Subclinical hypothyroidism (SCH) is commonly observed in patients with CHF and negatively affects myocardial function and remodeling and, ultimately, increases the risk of hospitalizations and all-cause and cardiovascular (CV) mortality. The role of levothyroxine replacement on relevant CV outcomes in patients with SCH and CHF is unclear. Objective To assess the effect of levothyroxine (compared to placebo or no treatment) on the incidence of all-cause and CV mortality, major adverse CV events, and heart failure in patients with SCH and CHF. Methods PubMed/MEDLINE, Cochrane Library, and ClinicalTrial.gov were searched for randomized clinical trials, non-randomized observational, multicentric, and comparative studies. No language restrictions were included. After duplicate removal, articles were screened and extracted for the synthesis according to a hierarchical strategy that included title, abstract, and full-text appraisal. The risk of bias was assessed by RoB2 and ROBIN-I tools. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was applied to rate the quality of evidence and grade the strength of recommendations. Results Two trials were included in the systematic review with considerable indirectness and inaccuracy that down-graded the level of evidence. Discussion No evidence supports the use of levothyroxine for treating SCH in CHF due to the lack of reliable and well-designed clinical trials. Conclusion CV outcome and dose-response trials are needed to understand better the role of levothyroxine replacement treatment for a safer prescription in this clinical setting.
Data-driven support to decision-making in molecular tumour boards for lymphoma: A design science approach

Background The increasing amount of molecular data and knowledge about genomic alterations from next-generation sequencing processes together allow for a greater understanding of individual patients, thereby advancing precision medicine. Molecular tumour boards feature multidisciplinary teams of clinical experts who meet to discuss complex individual cancer cases. Preparing the meetings is a manual and time-consuming process. Purpose To design a clinical decision support system to improve the multimodal data interpretation in molecular tumour board meetings for lymphoma patients at Karolinska University Hospital, Stockholm, Sweden. We investigated user needs and system requirements, explored the employment of artificial intelligence, and evaluated the proposed design with primary stakeholders. Methods Design science methodology was used to form and evaluate the proposed artefact. Requirements elicitation was done through a scoping review followed by five semi-structured interviews. We used UML Use Case diagrams to model user interaction and UML Activity diagrams to inform the proposed flow of control in the system. Additionally, we modelled the current and future workflow for MTB meetings and its proposed machine learning pipeline. Interactive sessions with end-users validated the initial requirements based on a fictive patient scenario which helped further refine the system. Results The analysis showed that an interactive secure Web-based information system supporting the preparation of the meeting, multidisciplinary discussions, and clinical decision-making could address the identified requirements. Integrating artificial intelligence via continual learning and multimodal data fusion were identified as crucial elements that could provide accurate diagnosis and treatment recommendations. Impact Our work is of methodological importance in that using artificial intelligence for molecular tumour boards is novel. We provide a consolidated proof-of-concept system that could support the end-to-end clinical decision-making process and positively and immediately impact patients. Conclusion Augmenting a digital decision support system for molecular tumour boards with retrospective patient material is promising. This generates realistic and constructive material for human learning, and also digital data for continual learning by data-driven artificial intelligence approaches. The latter makes the future system adaptable to human bias, improving adequacy and decision quality over time and over tasks, while building and maintaining a digital log.
On certain Iwahori representations of unramified $U(2, 1)$ in characteristic $p$

Let $F$ be a non-archimedean local field of odd residue characteristic $p$, and $G$ be the unramified unitary group $U(2, 1)(E/F)$ in three variable. Let $K$ be a maximal compact open subgroup of $G$, and $\sigma $ be an irreducible smooth representation of $K$ over $\overline{\mathbf{F}}_p$. For an irreducible smooth representation $\pi$ of $G$ containing $\sigma$, we follow the work of Hu (\cite{Hu-2012}) to study certain $I_K$-subrepresentation of $\pi$, where $I_K$ is the Iwahori subgroup in $K$. When $\pi$ is an irreducible principal series, we prove such $I_K$-subrepresentation is equal to $\pi^{I_{1, K}}$, where $I_{1, K}\subset I_K$ is the pro-$p$ Sylow subgroup.
Cannabis use and opioid relapse: An exploratory survival analysis of prospectively collected data

Importance It is known that only minority of patients with opioid use disorder (OUD) receive treatment, of which only a fraction successfully complete treatment as intended. Factors associated with poor treatment outcomes remain unclear, and there is emerging but conflicting evidence that cannabis use may mitigate opioid use. Objective To analyze predictors of relapse amongst patients receiving buprenorphine-naloxone for OUD and identify the association between cannabis use and time to relapse. Design Data were prospectively collected between May 2018 and October 2020, and patients were followed for 12 months. Setting Thirty-one outpatient opioid agonist treatment clinics across Ontario, Canada. Participants All patients 16 years of age or older receiving buprenorphine-naloxone for OUD who had a urine toxicology screen negative for opioids at baseline were eligible for inclusion. Of the 488 patients consecutively sampled, 466 were included. Exposure Cannabis use. Main outcome and measure Relapse to opioid use assessed using urine toxicology screens. We employed a multivariable Cox-proportional hazard model for our analyses. Results We found that cannabis use was not protective against relapse [hazard ratio (HR) = 1.03, 95% confidence interval (CI): 0.78, 1.36, p = 0.84]. We found that participants who have been in treatment for at least two years had a 44% decrease in the hazard of relapse compared to those in treatment for less than a year (HR = 0.56, 95% CI: 0.34, 0.92, p = 0.021). We also found that the hazard of relapse was 2.6 times higher for participants who were intravenous drug users (HR = 2.61, 95% CI: 1.74, 3.91, p < 0.001), and that for every 1mg increase in the participants’ buprenorphine-naloxone dose, the hazard of relapse is 2% greater (HR = 1.02, 95% CI: 1.01, 1.03, p < 0.001). Conclusion Our analysis failed to show cannabis to be protective against relapse to opioid use in patients receiving buprenorphine-naloxone for OUD. We identified that individuals who inject drugs, are on higher doses of buprenorphine-naloxone, or have been in treatment for less than two years have a higher hazard for relapse. The presence of such factors may thus warrant closer patient follow-up and more stringent treatment protocols to mitigate risk of relapse and potential overdose.
Clinical characteristics of the host DNA-removed metagenomic next-generation sequencing technology for detecting SARS-CoV-2, revealing host local immune signaling and assisting genomic epidemiology

Background Metagenomic next-generation sequencing (mNGS) technology has been central in detecting infectious diseases and helping to simultaneously reveal the complex interplay between invaders and their hosts immune response characteristics. However, it needs to be rigorously assessed for clinical utility. The present study is the first to evaluate the clinical characteristics of the host DNA-removed mNGS technology for detecting SARS-CoV-2, revealing host local immune signaling and assisting genomic epidemiology. Methods 46 swab specimens collected from COVID-19 patients were assayed by two approved commercial RT-qPCR kits and mNGS. The evolutionary tree of SARS-CoV-2 was plotted using FigTree directly from one sample. The workflow of removing the host and retaining the host was compared to investigate the influence of host DNA removal on the performances of mNGS. Functional enrichment analysis of DEGs and xCell score were used to explore the characteristics of host local immune signaling. Results The detection rate of mNGS achieved 92.9% (26/28) for 28 samples with a Ct value ≤ 35 and 81.1% (30/37) for all 46 samples. The genome coverage of SARS-CoV-2 could reach up to 98.9% when the Ct value is about 20 in swab samples. Removing the host could enhance the sensitivity of mNGS for detecting SARS-CoV-2 from the swab sample but does not affect the species abundance of microbes RNA. Improving the sequencing depth did not show a positive effect on improving the detection sensitivity of SARS-CoV-2. Cell type enrichment scores found multiple immune cell types were differentially expressed between patients with high and low viral load. Conclusions The host DNA-removed mNGS has great potential utility and superior performance on comprehensive identification of SARS-CoV-2 and rapid traceability, revealing the microbiome’s transcriptional profiles and host immune responses.
Recent HIV infection among newly diagnosed cases and associated factors in the Amhara regional state, Northern Ethiopia: HIV case surveillance data analysis (2019-2021)

Background Distinguishing a recent from long-standing HIV infection is a critical step to reduce new infections in 2030. Therefore, this analysis determines the proportion of recent HIV infections among newly diagnosed cases and associated factors in the Amhara regional state between 2019 and 2021. Methods We got the HIV case-based surveillance dataset (July 2019 up to August 12/2021) from the Amhara Public Health Institute. Recent infection is an infection gained within the last 12 months as identified by Asante recency test kits. Logistic regression was carried out to identify factors associated with recent infection. Adjusted odd with 95% CI and a p-value of < 0.05 was considered to declare significant associations. Results Out of 5,689 eligible cases, 3,129 (55%) recency tests had been performed. The proportion of recent HIV infection is 443 (14.2%, 95% CI: 13, 15.4%). High proportion of recent infections is reported from Bahir Dar city (23.3%), Central Gondar (17.7%), West Gojjam (16.5%), North Shewa (16.5%) and South Gondar zones (15.7%). Besides, the proportion of recent infection is high among clients aged ≥ 51 years (32.4%), illicit drug users (30.6 %), homelessness (28.5%), current commercial sex workers (27.9%), prisoners (21.1%), and among clients with invasive medical procedures (22.2%). Recent infection is significantly associated with females (AOR: 1.9, 95% CI: 1.2–3.1), secondary and above education (AOR: 2.1, 95% CI: 1.3–3.4), commercial sex workers (AOR: 1.8, 95% CI: 1.2–2.7), having contact with index case (AOR: 0.5, 95% CI: 0.3–0.8) and illicit drug utilization (AOR: 3.6, 95% CI: 1.1–12.4). Conclusion In the Amhara region, the proportion of recent HIV infection is high with marked variation across sociodemographic characteristics. We identified the risk or preventive factors associated with a recent infection. Therefore, all HIV responders should target their prevention efforts toward hot spot areas and sub-populations to stop further transmission.
The impact of early aging on visual perception of space and time

Visual perception of space and time has been shown to rely on context dependency, an inferential process by which the average magnitude of a series of stimuli previously experienced acts as a prior during perception. This article aims to investigate the presence and evolution of this phenomenon in early aging. Two groups of participants belonging to two different age ranges (Young Adults: average age 28.8 years old; Older Adults: average age 62.8 years old) participated in the study performing a discrimination and a reproduction task, both in a spatial and temporal conditions. In particular, they were asked to evaluate lengths in the spatial domain and interval durations in the temporal one. Early aging resulted to be associated to a general decline of the perceptual acuity, which is particularly evident in the temporal condition. The context dependency phenomenon was preserved also during aging, maintaining similar levels as those exhibited by the younger group in both space and time perception. However, the older group showed a greater variability in context dependency among participants, perhaps due to different strategies used to face a higher uncertainty in the perceptual process.
The moderating effect of cognitive reserve on cognitive function in patients with Acute Ischemic Stroke

Background Recovery of cognitive function after stroke has inter-individual variability. The theory of cognitive reserve offers a potential explanation of the variability in cognitive function after stroke. Objective This study aimed to investigate the moderating effect of cognitive reserve on the relationship between the stroke severity and cognitive function after stroke. Materials and methods A total of 220 patients with Acute Ischemic Stroke (AIS) were recruited in 2021 from two stroke centers in Nanjing, China. The National Institutes of Health Stroke Scale (NIHSS) was used to assess stroke severity upon admission. Cognitive Reserve Index questionnaire (CRIq) and validated Montreal Cognitive Assessment, Changsha Version (MoCA-CS) were used to assess cognitive reserve and cognitive function within 7 days after stroke onset, respectively. A series of multivariate linear regression models were applied to test the moderating effect of cognitive reserve. Results Patients with a higher level of cognitive reserve had better cognitive function after stroke compared with those with a lower level of cognitive reserve (β = 0.074, p = 0.003). The interaction of NIHSS and cognitive reserve was statistically significant (β = −0.010, p = 0.045) after adjusting for some key covariates [e.g., age, marital status, Oxfordshire Community Stroke Project (OCSP) classification, Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification, cerebral vascular stenosis, diabetes and atrial fibrillation]. Conclusion Cognitive reserve may help to buffer the effect of stroke-related pathology on cognitive decline in Chinese acute stroke patients. Enhancing cognitive reserve in stroke patients may be one of the potential strategies for preventing vascular dementia.
Fluctuations, under time reversal, of the natural time and the entropy distinguish similar looking electric signals of different dynamics

We show that the scale dependence of the fluctuations of the natural time itself under time reversal provides a useful tool for the discrimination of seismic electric signals (critical dynamics) from noises emitted from man-made sources, as well as for the determination of the scaling exponent. We present recent data of electric signals detected at the Earth’s surface, which confirm that the value of the entropy in natural time as well as its value under time reversal are smaller than that of the entropy of a “uniform” distribution.
Symmetric and asymmetric nexus between economic policy uncertainty, oil price, and renewable energy consumption in the United States, China, India, Japan, and South Korea: Does technological innovation influence?

This study aimed to gauge the impact of economic policy uncertainty, oil price, and technological innovation on renewable energy consumption in the top five oil-importing nations for the period 1990–2021. The study employed a linear and nonlinear framework in exploring the association and variable elasticities on renewable energy consumption. According to linear assessment, the study documented positive effects from technological innovation and oil price volatility, whereas economic policy uncertainty adversely caused renewable energy integration, especially in the long run. The study disclosed long-run and short-run asymmetric connections between TI, EPU, and REC for asymmetric assessment. For directional causality, the study documented feedback hypothesis that explain the nexus between oil price and renewable energy consumption in China [OIL←→REC]; economic policy uncertainty and renewable energy consumption [EPU←→REC] in China, India, Japan, and South Korea; and technological innovation and renewable energy consumption [TI←→REC] in South Korea. On a policy note, the study established that efficient energy transition from fossil fuel to renewable energy demands economic stability and, therefore, stability must be ensured. Furthermore, oil prices should be considered while formulating energy policies.