Real-like synthetic sperm video generation from learned behaviors

Computer-assisted sperm analysis is an open research problem, and a main challenge is how to test its performance. Deep learning techniques have boosted computer vision tasks to human-level accuracy, when sufficiently large labeled datasets were provided. However, when it comes to sperm (either human or not) there is lack of sufficient large datasets for training and testing deep learning systems. In this paper we propose a solution that provides access to countless fully annotated and realistic synthetic video sequences of sperm. Specifically, we introduce a parametric model of a spermatozoon, which is animated along a video sequence using a denoising diffusion probabilistic model. The resulting videos are then rendered with a photo-realistic appearance via a style transfer procedure using a CycleGAN. We validate our synthetic dataset by training a deep object detection model on it, achieving state-of-the-art performance once validated on real data. Additionally, an evaluation of the generated sequences revealed that the behavior of the synthetically generated spermatozoa closely resembles that of real ones

How AI is making affordable air pollution sensors more accurate

Clean air is a fundamental right. However, every day, 100 children under the age of five tragically lose their lives in east Asia and the Pacific due to a silent killer – air pollution. In response to this crisis, huge investments have been made in outdoor air pollution monitoring systems. These fridge-sized monitoring stations are expensive costing at least £10,000 each, so scaling this up everywhere isn’t financially viable. Now, a new generation of small, roaming air sensors could better inform people about pollution levels in their local area. Currently, these sensors just aren’t precise enough. Our recent research shows that AI could enhance their accuracy by up to 46%

Preliminary investigation into the use of amino-acid-derived ionic liquids for extracting cellulose from waste biomass to prepare cellulose aerogel adsorbents

To investigate the feasibility of cellulose extraction from lignocellulosic waste biomass using ionic liquids—a sustainable and efficient approach—for preparing cellulose aerogel adsorbents, we employed a fully green amino acid-derived ionic liquid, cysteine nitrate ([Cys][NO3]), for cellulose separation from diverse biomass sources. The extracted cellulose, with a purity range of 83.8–93.9%, was processed into cellulose aerogels (CAs) via a conventional aerogel preparation protocol. The resulting CA exhibited promising adsorption capacities, including 0.2–11.6 mg/g for Na+, 4.4–19.9 mg/g for Ca2+, 4.15–35.6 mg/g for Mg2+, and 1.85–13.3 mg/g for Cd2+, as well as 9.7–17.7 g/g for engine oil. These results demonstrate the presence of effective mass transfer channels in the CA, proving that the cellulose’s fibrillation capacity was preserved in the pre-treatment. This study illuminates the potential of this green, straightforward method for preparing aerogels from cellulose derived from waste biomass, with promising applications in wastewater treatment and material recovery

Bioinspired double-layer thermogalvanic cells with engineered ionic gradients for high-efficiency waste heat recovery

Thermogalvanic cells (TGCs) have emerged as a promising technology for harvesting low-grade thermal energy, but their widespread application has been hindered by limited conversion efficiencies. A critical factor in enhancing TGC performance lies in establishing substantial ion concentration gradients, which remains challenging due to the inherent tendency of ion pairing. Here, we present a breakthrough double-layer thermogalvanic cell (DTGC) architecture that spatially segregates redox pairs into two distinct gel layers, enabling unprecedented control over ion concentration gradients. This innovative design yields a single p-type gelatin-K4[Fe(CN)6]/K3[Fe(CN)6] DTGC unit with remarkable performance metrics of an open-circuit voltage of 220 mV, a power density of 1.73 mW m-2 K-2, and a relative Carnot efficiency (ηr) of 1.34% at ΔT = 10 K, representing a tenfold improvement over conventional TGCs. Scaling up this technology, we demonstrate a modular thermoelectric generator comprising a 4×12 array of alternating p-type and n-type DTGCs, capable of delivering an output voltage exceeding 11.3 V at ΔT = 20 K, sufficient to directly power commercial LED lights and electronic displays. This work establishes a new paradigm for efficient low-grade thermal energy conversion, offering a scalable and practical solution for waste heat recovery applications

Absence of fetal heart rate cycling on the intrapartum cardiotocograph (CTG) is associated with intrapartum pyrexia and lower Apgar scores

Cycling consists of alternating periods of reduced and normal fetal heart variability, reflecting changes in fetal behavioral states. Occurrence of active and quiet sleep cycles is considered to be a hallmark of fetal autonomic nervous system integrity, demonstrating healthy interaction between the parasympathetic and sympathetic nervous systems. Cycling is an overlooked feature in most international cardiotocography (CTG) guidelines. The authors tested the hypothesis that fetuses showing no cycling in the intrapartum period have poorer outcomes. To investigate whether the absence of cycling at the commencement of intrapartum fetal monitoring is associated with poorer neonatal outcomes (umbilical arterial cord pH, Apgar scores and neonatal unit admission). Analysis of a database of sequentially acquired intrapartum CTG traces from a single center. Only cases of singleton pregnancies over 36 weeks gestation in cephalic presentation with recorded umbilical artery cord pH were considered. Neonatal outcomes were assessed based on umbilical cord artery pH, Apgar ≤7 at 5 min and unexpected admission to the neonatal unit. Intrapartum pyrexia, presence of meconium-stained amniotic fluid and mode of delivery were also recorded. A total of 684 cases were analyzed. Absence of cycling from the beginning of the intrapartum CTG recording was noted in 5% of cases. Cases with no cycling were more likely to have maternal pyrexia (≥37.8 °C) (  = .006) and Apgars ≤7 at 5 min (  = .04). There was an association between increasing baseline fetal heart rate and the proportion of cases with no cycling. There was no significant difference between the two groups with regard to the mode of delivery or umbilical cord arterial pH <7.05 (  = .53). Absence of cycling is associated with intrapartum maternal pyrexia and fetuses with the absence of cycling are more likely to have poorer perinatal outcomes measured by Apgar ≤ 7 at 5 min, despite no association with fetal acidosis. Results from this research were presented at the XXVI European Congress of Perinatal Medicine in September 2018