Oral cancer survival rates have seen little improvement over the past few decades. This is mainly due to late detection and a lack of reliable markers to predict disease progression in oral potentially malignant disorders (OPMDs). There is a need for highly specific and sensitive screening tools to enable early detection of malignant transformation. Biochemical alterations to tissues occur as an early response to pathological processes; manifesting as modifications to molecular structure, concentration or conformation. Raman spectroscopy is a powerful analytical technique that can probe these biochemical changes and can be exploited for the generation of novel disease-specific biomarkers. Therefore, Raman spectroscopy has the potential as an adjunct tool that can assist in the early diagnosis of oral cancer and the detection of disease progression in OPMDs. This review describes the use of Raman spectroscopy for the diagnosis of oral cancer and OPMDs based on ex vivo and liquid biopsies as well as in vivo applications that show the potential of this powerful tool to progress from benchtop to chairside.

This paper discusses the challenges and opportunities in accessing data to improve workplace relations law enforcement, with reference to minimum employment standards such as wages and working hours regulation. Our paper highlights some innovative examples of government and trade union efforts to collect and use data to improve the detection of noncompliance. These examples reveal the potential of data science as a compliance tool but also suggest the importance of realizing a data ecosystem that is capable of being utilized by machine learning applications. The effectiveness of using data and data science tools to improve workplace law enforcement is impacted by the ability of regulatory actors to access useful data they do not collect or hold themselves. Under “open data” principles, government data is increasingly made available to the public so that it can be combined with nongovernment data to generate value. Through mapping and analysis of the Australian workplace relations data ecosystem, we show that data availability relevant to workplace law compliance falls well short of open data principles. However, we argue that with the right protocols in place, improved data collection and sharing will assist regulatory actors in the effective enforcement of workplace laws.

Although fluorescence detection is a sensitive method in the field of pollutant analysis, its application is restricted due to the fluorescence shown by organic material being quenched after aggregation and to low photo-thermal stability. To address these issues, a novel mineral/dye composite material was prepared by intercalating a fluorescence molecule, Rhodamine (R6G), into the interlayer space of montmorillonite (Mnt). This composite material greatly enhanced the light stability and efficiency of R6G. After enhancement, the fluorescence lifetime of R6G-Mnt was eight times longer than originally and the luminous intensity was 20 times greater. Chromium at the mmol/L (mM) level can be detected by the naked eye when its enhanced fluorescent property is fabricated into a solid test paper, even though a fluorescence spectrophotometer should be used for detection at the 0.01 μmol/L level in the sensing range 0.01 μmol/L to 100 mmol/L. These results can provide new avenues as well as a theoretical and experimental foundation for the development of novel supramolecular luminescent material.

For many deaths associated with influenza and Omicron infections, those viruses are not detected. We applied previously developed methodology to estimate the contribution of influenza and Omicron infections to all-cause mortality in France for the 2014–2015 through the 2018–2019 influenza seasons, and the period between week 33, 2022 and week 12, 2023. For the 2014–2015 through the 2018–2019 seasons, influenza was associated with annual average of 15,654 (95% CI (13,013, 18,340)) deaths, while between week 33, 2022 and week 12, 2023, we estimated 7,851 (5,213, 10,463) influenza-associated deaths and 32,607 (20,794, 44,496) SARS-CoV-2 associated deaths. For many Omicron-associated deaths for cardiac disease, mental&behavioural disorders, and other causes, Omicron infections are not characterised as a contributing cause of death – for example, between weeks 33–52 in 2022, we estimated 23,983 (15,307, 32,620) SARS-CoV-2-associated deaths in France, compared with 12,811 deaths with COVID-19 listed on death certificate. Our results suggest the need for boosting influenza vaccination coverage in different population groups in France, and for wider detection of influenza infections in respiratory illness episodes (including pneumonia) in combination with the use of antiviral medications. For Omicron epidemics, wider detection of Omicron infections in persons with underlying health conditions is needed.

Deciding whether or not eradication of an invasive species has been successful is one of the main dilemmas facing managers of eradication programmes. When the species is no longer being detected, a decision must be made about when to stop the eradication programme and declare success. In practice, this decision is usually based on ad hoc rules, which may be inefficient. Since surveillance undertaken to confirm species absence is imperfect, any declaration of eradication success must consider the risk and the consequences of being wrong. If surveillance is insufficient, then eradication may be falsely declared (a Type I error), whereas continuation of surveillance when eradication has already occurred wastes resources (a Type II error). We review the various methods that have been developed for quantifying these errors and incorporating them into the decision-making process. We conclude with an overview of future developments likely to improve the practice of determining invasive species eradication success.

The internet has expanded opportunities for wildlife traders to conduct business globally, but monitoring this activity is often challenging. As monitoring techniques frequently employ language-based searches for particular words, it is important to understand the language traders use, particularly the nomenclature used to refer to traded species. We recorded advert data from four UK websites, focusing on reptiles, which constitute a large proportion of the international pet trade. We analysed whether the language types used by advertisers to refer to individual reptiles were associated with certain trader characteristics. We found English common names were the most frequent name type used to refer to species, regardless of the geographical location of the seller and the CITES Appendix listing of the species. However, scientific names were recorded in twice as many adverts for threatened and Near Threatened species as in adverts for non-threatened species, and only scientific names were used for three families: Anguidae, Pseudaspididae and Sphaerodactylidae. These findings could inform the creation of tailored keyword detection tools, which must account for the numerous language types in use within online wildlife trade communities. Future studies should examine the nuances of language used by other online wildlife trader communities in other contexts, such as different online platforms or different languages. The ultimate aim of these language detection tools will be to track in detail trends in the online reptile trade, offering a better understanding of potentially unsustainable trade patterns and helping authorities to enforce laws against illegal online reptile trade.

Anxiety is common in long-term care (LTC), but it is unclear which anxiety detection tools are accurate when compared to a reference standard for residents of LTC. Four databases and grey literature sources were searched using the search concepts “anxiety” and “LTC”. Included studies evaluated the diagnostic accuracy of an anxiety detection tool compared to a reference standard in LTC residents. Diagnostic accuracy measures were extracted. Four articles out of 4,620 met the inclusion criteria. Despite limited evidence and poorly reported study procedures and characteristics, the Geriatric Anxiety Inventory (sensitivity: 90.0%, specificity: 86.2%) and the Hospital Anxiety and Depression Scale-Anxiety (sensitivity: 90.0%, specificity: 80.6%) had the best performance when detecting generalized anxiety disorder. We identified four anxiety detection tools appropriate for use in LTC; a critical first step to diagnosing and managing anxiety in residents of LTC. Non-generalized anxiety disorders and tool feasibility must be further evaluated.

Modern low-altitude unmanned aircraft (UA) detection and surveillance systems mostly adopt the multi-sensor fusion technology scheme of radar, visible light, infrared, acoustic and radio detection. Firstly, this paper summarises the latest research progress of UA and bird target detection and recognition technology based on radar, and provides an effective way of detection and recognition from the aspects of echo modeling and micro motion characteristic cognition, manoeuver feature enhancement and extraction, motion trajectory difference, deep learning intelligent classification, etc. Furthermore, this paper also analyses the target feature extraction and recognition algorithms represented by deep learning for other kinds of sensor data. Finally, after a comparison of the detection ability of various detection technologies, a technical scheme for low-altitude UA surveillance system based on four types of sensors is proposed, with a detailed description of its main performance indicators.

Prolonged sitting in a fixed or constrained position exposes aircraft passengers to long-term static loading of their bodies, which has deleterious effects on passengers’ comfort throughout the duration of the flight. The previous studies focused primarily on office and driving sitting postures and few studies, however, focused on the sitting postures of passengers in aircraft. Consequently, the aim of the present study is to detect and recognize the sitting postures of aircraft passengers in relation to sitting discomfort. A total of 24 subjects were recruited for the experiment, which lasted for 2 h. Furthermore, a total of 489 sitting postures were extracted and the pressure data between subjects and seat was collected from the experiment. After the detection of sitting postures, eight types of sitting postures were classified based on key parts (trunk, back, and legs) of the human bodies. Thereafter, the eight types of sitting postures were recognized with the aid of pressure data of seat pan and backrest employing several machine learning methods. The best classification rate of 89.26% was obtained from the support vector machine (SVM) with radial basis function (RBF) kernel. The detection and recognition of the eight types of sitting postures of aircraft passengers in this study provided an insight into aircraft passengers’ discomfort and seat design.

In 1889, The British Medical Journal published a piece titled, “Detective Medicine,” which describes feats of medical detection performed by physicians attending malingering prisoners. Though simulating illness had a long history, the medicalization of malingering at the fin de siècle led to a proliferation of such case histories and cheerful records of pathological feigners thwarted.