The impact of sleep deprivation and nighttime light exposure on clock gene expression in humans

Aim To examine the effect of acute sleep deprivation under light conditions on the expression of two key clock genes, hPer2 and hBmal1, in peripheral blood mononuclear cells (PBMC) and on plasma melatonin and cortisol levels. Methods Blood samples were drawn from 6 healthy individuals at 4-hour intervals for three consecutive nights, including a night of total sleep deprivation (second night). The study was conducted in April-June 2006 at the University Medical Centre Ljubljana. Results We found a significant diurnal variation in hPer2 and hBmal1 expression levels under baseline (P < 0.001, F = 19.7, df = 30 for hPer2 and P < 0.001, F = 17.6, df = 30 for hBmal1) and sleep-deprived conditions (P < 0.001, F = 9.2, df = 30 for hPer2 and P < 0.001, F = 13.2, df = 30 for hBmal1). Statistical analysis with the single cosinor method revealed circadian variation of hPer2 under baseline and of hBmal1 under baseline and sleep-deprived conditions. The peak expression of hPer2 was at 13:55 ± 1:15 hours under baseline conditions and of hBmal1 at 16:08 ± 1:18 hours under baseline and at 17:13 ± 1:35 hours under sleep-deprived conditions. Individual cosinor analysis of hPer2 revealed a loss of circadian rhythm in 3 participants and a phase shift in 2 participants under sleep-deprived conditions. The plasma melatonin and cortisol rhythms confirmed a conventional alignment of the central circadian pacemaker to the habitual sleep/wake schedule. Conclusion Our results suggest that 40-hour acute sleep deprivation under light conditions may affect the expression of hPer2 in PBMC

IoT electrochemical sensor with integrated ▫Ni(OH)2–NiNi(OH)_2–Ni▫ nanowires for detecting formaldehyde in tap water

Simple, low-cost methods for sensing volatile organic compounds that leave no trace and do not have a detrimental effect on the environment are able to protect communities from the impacts of contaminants in water supplies. This paper reports the development of a portable, autonomous, Internet of Things (IoT) electrochemical sensor for detecting formaldehyde in tap water. The sensor is assembled from electronics, i.e., a custom-designed sensor platform and developed HCHO detection system based on Ni(OH)2–Ni nanowires (NWs) and synthetic-paper-based, screen-printed electrodes (pSPEs). The sensor platform, consisting of the IoT technology, a Wi-Fi communication system, and a miniaturized potentiostat can be easily connected to the Ni(OH)2–Ni NWs and pSPEs via a three-terminal electrode. The custom-made sensor, which has a detection capability of 0.8 µM/24 ppb, was tested for an amperometric determination of the HCHO in deionized (DI) and tap-water-based alkaline electrolytes. This promising concept of an electrochemical IoT sensor that is easy to operate, rapid, and affordable (it is considerably cheaper than any lab-grade potentiostat) could lead to the straightforward detection of HCHO in tap water

A parallel-beam wavelength-dispersive X-ray emission spectrometer for high energy resolution in-air micro-PIXE analysis

A new parallel-beam wavelength dispersive (PB-WDS) X-ray emission spectrometer was constructed at the external proton beamline at the Microanalytical Centre of the Jožef Stefan Institute in Ljubljana. The spectrometer combines polycapillary X-ray optics for efficient X-ray collection with diffraction on a flat crystal analyzer and achieves energy resolution in the eV range. The whole set-up is enclosed within a He bag to be able to operate in the tender X-ray energy range. The basic design is described together with the results of characterization measurements yielding the main operation characteristics. Finally, an application for the micro-PIXE analysis of biological tissue is demonstrated exploiting both spatial and energy resolution of the new set-up

Annotated corpora and tools of the PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions (edition 1.1)

This multilingual resource contains corpora in which verbal MWEs have been manually annotated. VMWEs include idioms (let the cat out of the bag), light-verb constructions (make a decision), verb-particle constructions (give up), inherently reflexive verbs (help oneself), and multi-verb constructions (make do). VMWEs were annotated according to the universal guidelines in 19 languages. The corpora are provided in the cupt format, inspired by the CONLL-U format. The corpora were used in the 1.1 edition of the PARSEME Shared Task (2018). For most languages, morphological and syntactic information ­­­­– not necessarily using UD tagsets – including parts of speech, lemmas, morphological features and/or syntactic dependencies are also provided. Depending on the language, the information comes from treebanks (e.g., Universal Dependencies) or from automatic parsers trained on treebanks (e.g., UDPipe). This item contains training, development and test data, as well as the evaluation tools used in the PARSEME Shared Task 1.1 (2018). The annotation guidelines are available online: http://parsemefr.lif.univ-mrs.fr/parseme-st-guidelines/1.

Annotated corpora and tools of the PARSEME Shared Task on Automatic Identification of Verbal Multiword Expressions (edition 1.1)

This multilingual resource contains corpora in which verbal MWEs have been manually annotated. VMWEs include idioms (let the cat out of the bag), light-verb constructions (make a decision), verb-particle constructions (give up), inherently reflexive verbs (help oneself), and multi-verb constructions (make do). VMWEs were annotated according to the universal guidelines in 19 languages. The corpora are provided in the cupt format, inspired by the CONLL-U format. The corpora were used in the 1.1 edition of the PARSEME Shared Task (2018). For most languages, morphological and syntactic information ­­­­– not necessarily using UD tagsets – including parts of speech, lemmas, morphological features and/or syntactic dependencies are also provided. Depending on the language, the information comes from treebanks (e.g., Universal Dependencies) or from automatic parsers trained on treebanks (e.g., UDPipe). This item contains training, development and test data, as well as the evaluation tools used in the PARSEME Shared Task 1.1 (2018). The annotation guidelines are available online: http://parsemefr.lif.univ-mrs.fr/parseme-st-guidelines/1.