Browse Articles

Phase 1 Trial of CRISPR-Cas9 Gene Editing Targeting <i>ANGPTL3</i>

Hypertonic Saline or Carbocisteine in Bronchiectasis

Sacituzumab Govitecan in Advanced Triple-Negative Breast Cancer

Spontaneous Heparin-Induced Thrombocytopenia after Total Hip Arthroplasty

Using FDA Law to Threaten Medical Practice

A Placebo-Controlled Trial of the Oral PCSK9 Inhibitor Enlicitide

Influence of non-stationarity in friction angle on the performance of the braced excavation system

Proteomic atlas of human peritoneal tissue

Comparative assessment of machine learning models for daily streamflow prediction in a subtropical monsoon watershed

Facial expression recognition via variational inference

Abstract Facial expressions in the wild are rarely discrete; they often manifest as compound emotions or subtle variations that challenge the discriminative capabilities of conventional models. While psychological research suggests that expressions are often combinations of basic emotional units, most existing FER methods rely on deterministic point estimation, failing to model the intrinsic uncertainty and continuous nature of emotions. To address this, we propose POSTER-Var, a framework integrating a Variational Inference-based Classification Head (VICH). Unlike standard classifiers, VICH maps facial features into a probabilistic latent space via the reparameterization trick, enabling the model to learn the underlying distribution of expression intensities. Furthermore, we enhance feature representation by introducing layer embeddings and nonlinear transformations into the feature pyramid, facilitating the fusion of hierarchical semantic information. Extensive experiments on RAF-DB, AffectNet, and FER+ demonstrate that our method effectively handles fine-grained expression recognition, achieving state-of-the-art performance. The code has been open-sourced at: https://github.com/lg2578/poster-var .

Prognostic role of interim F-18 fluorodeoxyglucose positron emission tomography-computed tomography during chemoradiation therapy in patients with hypopharyngeal squamous cell carcinoma

This study aimed to investigate the usefulness of interim 18 F fluorodeoxyglucose positron emission tomography–computed tomography (FDG PET/CT) during definitive radiation therapy (RT) as a prognostic indicator of disease recurrence in patients with hypopharyngeal squamous cell carcinoma. This prospective analysis included 35 patients with biopsy-proven hypopharyngeal squamous cell carcinoma who received platinum-based chemoradiotherapy and underwent pretreatment FDG PET/CT and interim FDG PET/CT (iPET) at a cumulative RT doses of 36.0–45.0 Gy. The maximum standardized uptake value (SUVmax), metabolic tumor volume, and total lesion glycolysis of the primary tumor (PT) and combined total lymph nodes for both pre-PET and iPET were analyzed, and their percentage reductions in iPET were calculated. The optimal cutoff values of the metabolic parameters were derived from receiver operating characteristic curves. The outcomes were compared between patients with metabolic parameters above and below the respective cutoff values. Disease recurrence (locoregional or distant) was defined as a biopsy-proven tumor or unequivocal clinical and radiological evidence of progression. Twelve (34%) patients experienced disease recurrence during a median follow-up of 52 months. Univariate Cox regression analysis revealed that the reduction ratio of the SUVmax of the PT (ΔSUVp; hazard ratio, 7.685; p = 0.008) was a significant predictor of disease recurrence. Kaplan–Meier curve analysis revealed that a smaller ΔSUVp was associated with worse progression-free survival (log-rank, p = 0.002). Metabolic parameters measured using iPET may be useful predictors of disease recurrence in patients with hypopharyngeal squamous cell carcinoma treated with chemoradiotherapy. In this study, ΔSUVp was the best prognostic indicator.

LncRNA FTX promotes myocardial fibrosis by sponging miR-335-3p to regulate TFEC/ILK signaling

Precipitation nowcasting with radar data for evaluating multiple horizons using U-Net-based algorithm in Eastern Amazon

Severe meteorological events are increasingly frequent globally, with intense rainfall significantly impacting well-being, safety, and the economy, including agriculture and mining. Timely emergency alerts are crucial for mitigating losses and preventing fatalities from extreme weather. Precipitation forecasting tools, especially meteorological radars and satellites, are vital due to their high temporal resolution. This study utilizes a U-Net machine learning architecture for spatial-temporal precipitation nowcasting. We evaluate a multi-horizon nowcasting approach using meteorological radar data from the Eastern Amazon, investigating the input data (past horizons) needed for optimal forecast horizons. Our results show that increasing input data beyond 60 minutes degrades performance for short forecast horizon. For short-term forecasts, using 120 minutes of input data instead of 60 minutes resulted in a significant performance loss of 17.60% in RMSE and 7.18% in CSI. These findings identify the optimal input data for accurate nowcasting, enabling safer decision-making during severe weather.

Greenland is important for global research: what’s next for the island’s science?

Entropy guided multi level feature fusion network for high precision content based image retrieval

Prior beliefs &amp; automated fact checking: Limits on the effectiveness of AI-based corrections

Proliferation of misinformation poses significant challenges in contemporary society, necessitating efficient strategies for its identification and mitigation. Automated fact-checking systems might prove effective, but they face challenges, particularly in charged contexts where prior beliefs are likely to influence responses to fact-checks. Data from two studies where participants were given a piece of gun-control misinformation and an automated fact-checker correction ( N  = 1,372) illustrate the nuanced interplay between prior beliefs, trust in artificial intelligence (AI), and the perceived accuracy of fact-checking systems in shaping (a) post-correction misinformation endorsement, and (b) post-correction perceptions of system quality. Study 1 examined default perceptions of system accuracy and demonstrated a high degree of variability in those perceptions; when fact-checked by such a system, people’s prior beliefs predicted continued belief after the correction and post-correction perceptions of the fact-check system. Study 2 directly manipulated the purported accuracy of the system. When automated fact-checkers were said to have an accuracy level close to current expectations of existing AI systems (67%), people continued to believe misinformation more to the extent it was consistent with prior beliefs. This pattern was attenuated when participants were told that the fact-checker was highly (97%) accurate. Similarly, prior beliefs related more strongly to post-correction perceptions of system reliability when accuracy information was provided and especially when the system was described as not highly accurate. This research demonstrates biases in reactions to automated fact-checkers and highlights the importance of accounting for individual beliefs and perceived system characteristics in designing scalable interventions.

Spatio-temporal trends in COVID-19 morbidity and mortality due to elderly: a global perspective

Bridging the gap: Multi-sector perspectives on human, domestic animal, and wildlife leptospirosis in Ontario, Canada

Although leptospirosis is one of the most common zoonotic diseases worldwide, limited surveillance and poor coordination between human and animal health sectors have resulted in scarce and disparate data on its occurrence. Strengthening integrated surveillance requires cross-sector collaboration, beginning with the engagement of key organizations. The aims of this study were to 1) determine key health experts’ awareness and risk perceptions of leptospirosis and of zoonotic disease surveillance in Ontario, Canada, and 2) examine key components of engagement, such as perceived value and interest, during the initial stages of developing an integrated leptospirosis surveillance framework. A web-based survey was sent to 543 experts in human, animal, and environmental health in Ontario, and analyzed using a mixed-methods approach to identify key factors influencing perceptions of leptospirosis, including views on Leptospira distribution, the impact of human behavior, and the influence of environmental conditions. Leptospirosis was recognized as a health threat in Ontario by 90% (74/82) of respondents, and 91% (70/77) indicated that current surveillance efforts are inadequate. A higher proportion of animal health sector respondents identified leptospirosis as a threat to human (93%, 37/40) and animal health (90%, 44/49) compared to public health sector respondents (76%, 25/33 and 83%, 25/30, respectively). All participants (81/81) acknowledged the benefits of integrated surveillance over the current siloed approach. Our findings highlight that key public and animal health experts perceive leptospirosis as a health threat in Ontario and support more integrated disease surveillance to better respond to this emerging zoonotic pathogen.

Moisture impact on compressive strength and deformability of brick masonry

A methodological comparative analysis of monetary policy and trade openness on economic growth in West Africa: Does language play a role?

The study examines the effect of monetary policy and trade openness on economic growth in the ECOWAS countries. Using panel regression analysis and the Random Forest algorithm from the machine learning technique, the study reveals the existence of a major difference between the two language communities. The expansion of the broad money supply has a highly positive effect on economic growth in the two language communities; the impact is much greater in the Francophone countries. But trade openness has contradictory associations; it is positive in the Francophone countries and negative in the Anglophone countries. The Random Forest approach outperforms the standard econometric approach in terms of predictive accuracy (R² = 67.56%), thereby confirming the existence of non-linear associations. The evidence of major differences is linked to the existence of different post-colonial institutions; that is, the common CFA currency institutions in the Francophone countries and the independent national monetary policies in the Anglophone countries. The study implies that ECOWAS countries require distinct policies instead of standard policies.