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A Multiagent-Driven Robotic AI Chemist Enabling Autonomous Chemical Research On Demand

Establishment of a simple prediction method for DNA melting temperature: high-resolution melting curve analysis of PCR products

High-resolution melting analysis is a technique that leverages the principle that the thermal stability of dsDNA is influenced by its length and base composition. This method generates a melting curve by real-time monitoring of the changes in fluorescence signal as dsDNA melts during the heating process. The melting temperature serves as a fundamental indicator of sample characteristics in HRM analysis. During the initial stages of designing a new HRM experimental system, accurately predicting the Tm position of the established system can significantly enhance research efficiency. Currently, there is a limited number of studies focused on the prediction of Tm values in HRM analysis, with varying levels of predictive accuracy. The nearest-neighbor method model can well reflect the interaction of adjacent base pairs. Therefore, we combined the nearest neighbor method model and applied parameters such as enthalpy change, entropy change, GC content and number of base pairs of the DNA sequence to derive a new empirical formula for predicting Tm values. In this study, five species of seawater diatoms were selected as the research subjects. Four specific primers were employed to amplify the extracted DNA through PCR, and the resulting amplified products underwent HRM analysis and Sanger sequencing. Based on the obtained DNA sequences, we calculated the corresponding GC content, number of base pairs, enthalpy change and entropy change, combined with the Tm value obtained from the experiment. Finally, the following formula for predicting Tm value is obtained: (1) When the GC content is 40%≤GC content≤60%: Tm=ΔH/ΔS–0.27GC%–(150+2n)/n–273.15; (2) When the GC content is <40%: Tm=ΔH/ΔS–GC%/3–(150+2n)/n–273.15. After that, the DNA sequences amplified using two other specific primers were verified, and the predicted Tm values were compared with the measured values. The average error was within 1 °C.The results show that the formula obtained in the study can accurately predict the Tm value, which can be effectively used to identify the species of unknown samples.Therefore, this Tm value prediction method provides new methods and ideas for solving practical problems in multiple related fields.

Case-only analysis in small studies of predictive biomarkers

Abstract Characteristics of tumors and patients can be used as predictive biomarkers to guide treatment choice. Although many potential biomarkers are evaluated each year, only few will eventually be used since evidence is usually based on small studies leading to inconclusive results. Such data are often analyzed with Cox proportional hazards regression using a multiplicative interaction term between biomarker and treatment, with insufficient power and possibly biased results. Instead of analyzing patients who do (cases) and do not experience (non-cases) the survival event of interest, case-only analysis with logistic regression has been proposed, however with unknown small sample properties. We evaluated the performance of case-only analysis with bias-eliminating Firth correction and confidence intervals obtained with a profile likelihood method in a simulation study tailored to breast cancer. Our results show that this approach is generally inferior to the full cohort analysis but has acceptable properties when the marker is protective or null among patients treated with the standard treatment, the event rate is low (e.g., a rare event and a protective marker) and treatment assignment is independent of the marker level (e.g., in randomized studies). In such situations, the case-only design offers substantial cost savings. However, the model is sensitive to these assumptions.

Visible light-mediated dearomative spirocyclization/imination of nonactivated arenes through energy transfer catalysis

Abstract Aromatic compounds serve as key feedstocks in the chemical industry, typically undergoing functionalization or full reduction. However, partial reduction via dearomative sequences remains underexplored despite its potential to rapidly generate complex three-dimensional scaffolds and the existing dearomative strategies often require metal-mediated multistep processes or suffer from limited applicability. Herein, a photocatalytic radical cascade approach enabling dearomative difunctionalization through selective spirocyclization/imination of nonactivated arenes is reported. The method employs bifunctional oxime esters and carbonates to introduce multiple functional groups in a single step, forming spirocyclic motifs and iminyl functionalities via N–O bond cleavage, hydrogen-atom transfer, radical addition, spirocyclization, and radical-radical cross-coupling. The reaction constructs up to four bonds (C−O, C−C, C−N) from simple starting materials. Its broad applicability is demonstrated on various substrates, including pharmaceuticals, and it is compatible with scale-up under flow conditions, offering a streamlined approach to synthesizing highly decorated three-dimensional frameworks.

Association between medication adherence and cardiovascular outcomes in patients with both diabetes and hypertension in primary care settings in Canada: A retrospective cohort study

Objectives The impact of concurrent adherence to antihypertensives, antidiabetics, and statins on cardiovascular disease (CVD) outcomes and intermediate clinical outcomes in people with hypertension and diabetes remains unclear. This study aimed to evaluate the association between medication adherence and CVD outcomes in such patients. Methods This retrospective cohort study analyzed the electronic medical records of 36,211 adults aged 18 or older diagnosed with hypertension and diabetes between January 2008 and June 2016 in Canada. Patients were prescribed antihypertensives, antidiabetics, and statins, with a minimum 1-year follow-up post-diagnosis. Medication adherence was determined by the proportion of days covered (PDC). For monotherapy, a PDC≥80% and <80% was reflected high and low adherence respectively. In multiple medication scenarios, adherence was considered high when each medication was at PDC≥80%; low when any medication fell below 80%. The primary outcome encompassed cardiac events, including coronary heart disease, stroke, and heart failure. Intermediate clinical outcomes included changes in diastolic blood pressure (DBP), Systolic Blood Pressure (SBP), glycated hemoglobin (HbA1c), low-density lipoprotein cholesterol (LDL-C), and total cholesterol (TC). Cox regression models assessed the association between medication adherence and CVD morbidity, all-cause mortality, and intermediate clinical outcomes. Results High adherence to antidiabetic and statin monotherapy was associated with a lower all-cause mortality risk (aHR=0.67, P=0.001; aHR=0.68, P<0.001, respectively). For patients simultaneously prescribed three medications, higher adherence was linked to significant reductions in DBP (6 months: coefficient −0.52, P=0.01; 12 months: coefficient −0.44, P=0.02; 18 months: coefficient −0.55, P=0.004) and LDL-C (6 months: coefficient −0.04, P=0.02; 12 months: coefficient −0.05, P=0.01; 18 months: coefficient −0.04, P=0.02). Conclusions High adherence to antidiabetic and statin monotherapy correlated with lower all-cause mortality risk and improved intermediate clinical outcomes. However, simultaneous adherence to three medications did not significantly affect CVD outcomes, but influenced intermediate outcomes. Therefore, improving adherence to antihypertensives, antidiabetics, and statins among patients with hypertension and diabetes is important in primary care settings.

Assessing motorcyclist injury severity on curved road segments with temporal dynamics and unobserved heterogeneity

Nanoscale heterophase regulation enables sunlight-like full-spectrum white electroluminescence

Novel empirical models & comparative probabilistic analysis of interconnectedness of volcano eruption & nearby earthquakes

This paper takes a probabilistic approach to validate novel empirical models and the directional distributional similarities of nearby earthquake counts related to a typical volcano with its eruption duration. Considering the datasets on volcanic eruptions and earthquakes, focusing mainly on earthquakes within a 100-kilometer radius and within a three-year time frame of the volcano eruption. An empirical probabilistic models for the same; statistical model validation tests favor the proposed models is proposed. In addition, a novel directional statistical approach to characterize the interconnection and distributional similarities of volcanic eruptions and earthquakes near volcanoes, utilizing the directional nature of the datasets. The project and partition the volcanic eruption and earthquake data to assess its directional distribution is shown. The analysis demonstrated that the data adhered to a Von Mises distribution and unsupervised equal partition revealed for both datasets, highlighting the interconnected nature of volcanic eruptions and earthquakes. Also, Von Mises-Fisher distribution fit test is applied to this work; the analysis produced partition results that closely aligned with the partitions obtained through the 2D projection. This congruence emphasizes the robustness of the findings in a spherical context. the proposed empirical models and conclusions on distributional similarities may provide insights into the underlying mechanisms connecting these geological phenomena.

A target detection model HR-YOLO for advanced driver assistance systems in foggy conditions

Medial Orbitofrontal, Prefrontal, and Amygdalar Circuits Support Dissociable Component Processes of Risk/Reward Decision-Making

The medial orbitofrontal cortex (mOFC) has been implicated in shaping decisions involving reward uncertainty, in part by using memories to infer future outcomes. This region is interconnected with other key systems that mediated these decisions, including the basolateral amygdala (BLA) and prelimbic (PL) region of the medial prefrontal cortex, yet the functional importance of these circuits remains unclear. The present study used chemogenetic silencing to examine the contribution of different input and output pathways of the mOFC to risk/reward decision-making. Male rats were well-trained on a probabilistic discounting task where they chose between a small/certain (one pellet) and a large/uncertain (four pellets) option, the odds for which changed systematically across a session. Suppressing activity of descending mOFC terminals in the BLA impaired adjustment in choice biases as reward probabilities change, suggesting this circuit tracks changes in relative value to support flexible reward-seeking. Inhibiting bottom-up BLA → mOFC circuits had no effect on choice. With respect to corticocortical circuits, inhibiting mOFC inputs to PL led to more random choice patterns, indicating this circuit promotes advantageous choice by processing context-dependent information regarding wins and losses. In comparison, PL inputs to mOFC attenuate the allure of larger yet uncertain rewards and reduce loss sensitivity, particularly early in the choice sequence. The present findings provide novel insight into the functional contribution that mOFC–BLA and PL interactions make to distinct processes that shape decision-making in situations of reward uncertainty.

GRA12 is a common virulence factor across Toxoplasma gondii strains and mouse subspecies

Abstract Toxoplasma gondii parasites exhibit extraordinary host promiscuity owing to over 250 putative secreted proteins that disrupt host cell functions, enabling parasite persistence. However, most of the known effector proteins are specific to Toxoplasma genotypes or hosts. To identify virulence factors that function across different parasite isolates and mouse strains that differ in susceptibility to infection, we performed systematic pooled in vivo CRISPR-Cas9 screens targeting the Toxoplasma secretome. We identified several proteins required for infection across parasite strains and mouse species, of which the dense granule protein 12 (GRA12) emerged as the most important effector protein during acute infection. GRA12 deletion in IFNγ-activated macrophages results in collapsed parasitophorous vacuoles and increased host cell necrosis, which is partially rescued by inhibiting early parasite egress. GRA12 orthologues from related coccidian parasites, including Neospora caninum and Hammondia hammondi, complement TgΔGRA12 in vitro, suggesting a common mechanism of protection from immune clearance by their hosts.

Awareness of periodontitis and its relationship with systemic health among undergraduate medical students at a teaching hospital in Nepal: A cross-sectional study

Background Periodontitis is a multifactorial chronic inflammatory disease primarily caused by bacterial plaque, but may be affected by the host immune response, diabetes, inadequate nutrition, smoking and stress. The systemic implications of periodontitis, particularly its associations with cardiovascular diseases, diabetes mellitus, and respiratory infections, highlight the importance of medical students acquiring in-depth insight into this condition. This knowledge is essential for promoting both oral and general health in the public. Objective To assess the awareness of periodontitis and its relationship with systemic health among undergraduate medical students at a teaching hospital in Nepal. Materials and methods A structured self-administered questionnaire was used to assess awareness of periodontitis and its relationship with systemic health. A cross-sectional study was conducted among 3rd-, 4th-, and final-year medical students and interns at the BP Koirala Institute of Health Sciences, Dharan, from January 2024 to March 2024. The data were analyzed via descriptive statistics in SPSS version 27. Results A total of 218 participants were included in the study with a mean age of 24.03 ± 1.85 years. The majority (76.1%) of the students were male. Approximately 95.9% (209) of the participants had heard of term periodontitis. The majority of participants identified bad breath (83.5%), gum recession (78.4%), bleeding gums (85.3%), and loose teeth (71.1%) as symptoms of periodontitis, with more than 80% correctly recognizing gum recession in all groups except for 3rd-year students (52.8%). The majority of the students were aware of the risk factors associated with periodontitis. Conclusion There was a notable gap in the recognition of symptoms, risk factors, and systemic links related to periodontitis among 3rd year students compared with 4th year, final year students and interns. This highlights the need for improved education on the topic among 3rd and 4th year medical students.

A porous metal–organic framework (Pd-MOF) as an efficient and recyclable catalyst for the C–O cross-coupling reactions

Abstract This report outlines the development of a novel and efficient metal–organic framework (MOF) synthesized through a hydrothermal reaction using palladium acetate salt and trimesic acid as the organic ligand. A series of detailed analyses, including FT-IR, XRD, EDS, TEM, XPS, BET, ICP, and SEM, were performed to characterize the resulting MOF. These analyses confirmed the successful integration of Pd within the metal–organic framework structure. Nitrogen adsorption–desorption analysis assessed the porosity of the Pd-T-MOF metal–organic framework. The specific surface area was measured at 206.3 m2/g based on isotherms. Using the BJH method, the total pore volume was calculated as 0.4 cm3/g, with an average pore diameter of 2.8 nm. The catalyst demonstrated exceptional catalytic performance and stability in facilitating the C–O cross-coupling reaction. The proposed protocol offers several advantages, such as catalyst reusability, mild reaction conditions, high product yields ranging from 58 to 98%, and short reaction times between 30 and 120 min. Furthermore, the adaptable nanocatalyst (Pd-T-MOF) can be easily separated from the reaction mixture via centrifugation and reused across four successive cycles with only a slight decrease in efficiency.

Reflexive Chirality Transfer (RCT): Asymmetric 1,3-Dipolar Cycloaddition of α-Amino Acid Schiff Base with Nonchiral Copper Catalyst

Extreme compound events in the equatorial and South Atlantic

Live streaming mode selection strategy under the background of virtual anchor supplementation

In the context of virtual anchors as a supplement to human anchors in live streaming e-commerce. In this paper, for the first time, virtual anchors are included in the live streaming mode selection strategy, combined with the characteristics of the live streaming anchor and the cost of live streaming, constructed a model of the four live streaming modes, and used the Stackelberg game method to study. The results show that: (1) live streaming prices are positively correlated with cross price elasticity coefficient, market share of live streaming channels and consumer sensitivity to live streaming e-commerce. (2) When the cross price elasticity coefficient or market share of live streaming channels is small, the influence of influencer anchors can attract more consumers to purchase products, and the influencer anchor live streaming mode is the optimal choice; When the cross price elasticity coefficient or market share of live streaming channels is large, the merchant combined with virtual anchors live streaming mode is the optimal choice due to the low cost of live streaming for merchant staff anchors, as well as the advantage of continuous live streaming for virtual anchors. (3) When the consumer sensitivity to live streaming e-commerce is small, due to the low cost of live streaming for merchant staff anchors, the merchant live streaming mode is the optimal choice; When the consumer sensitivity to live streaming e-commerce is large, based on the influence of influencer anchors and the advantage of continuous live streaming for virtual anchors, the influencer anchor combined with virtual anchors live streaming mode is the optimal choice.

Validation of the Swedish Eating Assessment Tool, S-EAT-10, for patients with head and neck cancer

Abstract The aim of this study was to validate the Swedish version of Eating Assessment Tool (S-EAT-10) for head and neck cancer patients. The participants (n = 60) had persistent swallowing difficulties 6–36 months after completion of curative radiotherapy. The videofluoroscopic swallowing study was assessed using the Penetration Aspiration Scale and the Yale Pharyngeal Residue Severity Rating Scale modified for videofluoroscopy. Participants completed questionnaires S-EAT-10, M.D. Anderson Dysphagia Inventory (MDADI) and study-specific questions. Internal consistency was excellent and the test–retest reliability was good. Regarding convergent validity, S-EAT-10 showed moderate to strong correlation with the MDADI and no to weak correlation with study-specific questions regarding meal duration and weight change. Regarding criterion validity, there was a weak correlation between S-EAT-10 and instrumental measures. S-EAT-10 showed 85% sensitivity in identifying patients with dysphagia. S-EAT-10 could not discriminate between different degrees of dysphagia. Thus, S-EAT-10 showed sufficient psychometric properties regarding head and neck cancer patients.

The effects of disorder in superconducting materials on qubit coherence

Target sample mining with modified activation residual network for speaker verification

In the domain of speaker verification, Softmax can be used as a backend for multi-classification, but traditional Softmax methods have some limitations that limit performance. During the training phase, Softmax is used for multi-class training, while the speaker verification stage is a binary classification validation, leading to a discrepancy between the multi-class training in the training phase and the binary classification validation in the verification stage. It is also important to notice the issue of the disparity in the number of positive and negative samples in the sampling process of a binary classification problem. The difference in positive and negative samples can lead to the dominance of negative sample gradients during machine learning training, which can affect the performance of the speaker verification system. During the process of calculating similarity between positive and negative samples, there may be encountered an issue of overlapping similarity scores. If the overlapping portion is too large, it can reduce the discriminability between positive and negative samples, affecting the speaker system’s ability to distinguish between positive and negative samples. Considering the relatively compact distribution of positive and negative sample spaces, it is beneficial for enhancing the performance of the speaker system, and focusing more on the learning of difficult samples is conducive to improving the network’s convergence and generalization. Thus, this paper introduces an adaptive target function capable of solving these issues (SphereSpeaker). SphereSpeaker introduces different types of hyperparameters on the basis of Softmax, making it more suitable for handling speaker verification problems. SphereSpeaker also introduces three different angular margins to update the network, further enhancing the stability and generalization ability of the network model. Meanwhile, considering the issues of gradient vanishing, gradient explosion, and model degradation that can occur in deep neural networks, this paper introduces a deep neural network, which is named as Residual Network PReLu(ResNet-P). The experimental results indicate that compared to other deep neural network methods, this method has the lowest equal error rate, significantly improving the performance of the speaker verification system.

Multi-omics analysis reveals key immunogenic signatures induced by oncolytic Zika virus infection of paediatric brain tumour cells

Abstract Brain tumours disproportionately affect children and are the largest cause of paediatric cancer-related death. Novel therapies that engage the immune system, such as oncolytic viruses (OVs), hold great promise and are desperately needed. Zika virus (ZIKV) infects and destroys aggressive cells from multiple paediatric central nervous system (CNS) tumours. Despite this, the molecular mechanisms underpinning this response are largely unknown. We comprehensively investigate the transcriptomic response of paediatric medulloblastoma and atypical teratoid rhabdoid tumour (ATRT) cells to ZIKV infection. We observe conserved TNF signalling and cytokine signalling-related signatures and show that the TNF-alpha signalling pathway is implicated in oncolysis by reducing the viability of ZIKV-infected brain tumour cells. Our findings highlight TNF-alpha as a potential prognostic marker for oncolytic ZIKV (oZIKV) therapy. Complementing our analysis with a 49-plex ELISA, we demonstrate that ZIKV infection induces a clinically relevant and diverse pro-inflammatory brain tumour cell secretome, including TNF-alpha. We assess publicly available scRNA-Seq data to model how ZIKV-induced secretome paracrine and endocrine signalling may orchestrate the anti-tumoural immune response during oZIKV infection of brain tumours. Our findings significantly contribute to understanding the molecular mechanisms governing oZIKV infection and will help pave the way towards oZIKV therapy.