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Many infectious diseases are transmitted via the air and are, therefore, particularly difficult to combat. These infections include various invasive mycoses caused by molds. The usual route of infection is the inhalation of conidia. In hospitals, infection can also occur through the deposition of conidia in otherwise sterile anatomical sites during surgical and other invasive procedures. Therefore, knowledge of airborne mold concentrations can lead to measures to protect patients from fungal infections. The literature on this topic contains insufficient and sometimes ambiguous information. This is evidenced by the fact that there are no international recommendations or guidelines defining the methodology of air sampling and the interpretation of the results obtained. Surgical departments, intensive care units and medical mycology laboratories are, therefore, left to their own devices, leading to significant differences in the implementation of mycological surveillance in hospitals. The aim of this mini-review is to provide an overview of the current methods of air sampling and interpretation of results used in medical mycology laboratories.

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The active quickest detection problem with unknown post-change parameters is studied under the sampling control constraint, where there are p local streams in a system but one is only able to take observations from one and only one of these p local streams at each time instant. The objective is to raise a correct alarm as quickly as possible once the change occurs subject to both false alarm and sampling control constraints. Here we assume that exactly one of the p local streams is affected, and the post-change distribution involves unknown parameters. In this context, we propose an efficient greedy-cyclic-sampling-based quickest detection algorithm, and show that our proposed algorithm is asymptotically optimal in the sense of minimizing the detection delay under both false alarm and sampling control constraints. Numerical studies are conducted to show the effectiveness and applicability of the proposed algorithm.

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In recent years, healthcare data from various sources such as clinical institutions, patients, and pharmaceutical industries have become increasingly abundant. However, due to the complex healthcare system and data privacy concerns, aggregating and utilizing these data in a centralized manner can be challenging. Federated learning (FL) has emerged as a promising solution for distributed training in edge computing scenarios, utilizing on-device user data while reducing server costs. In traditional FL, a central server trains a global model sampled client data randomly, and the server combines the collected model from different clients into one global model. However, for not independent and identically distributed (non-i.i.d.) datasets, randomly selecting users to train server is not an optimal choice and can lead to poor model training performance. To address this limitation, we propose the Federated Multi-Center Clustering algorithm (FedMCC) to enhance the robustness and accuracy for all clients. FedMCC leverages the Model-Agnostic Meta-Learning (MAML) algorithm, focusing on training a robust base model during the initial training phase and better capturing features from different users. Subsequently, clustering methods are used to ensure that features among users within each cluster are similar, approximating an i.i.d. training process in each round, resulting in more effective training of the global model. We validate the effectiveness and generalizability of FedMCC through extensive experiments on public healthcare datasets. The results demonstrate that FedMCC achieves improved performance and accuracy for all clients while maintaining data privacy and security, showcasing its potential for various healthcare applications.

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Plastic has been demonstrated to release nanoplastics (NPs) into the atmosphere under sunlight irradiation, posing a continuous health risk to the respiratory system. However, due to lack of reliable quantification methods, the occurrence and distribution of NPs in the atmosphere remain unclear. Polystyrene (PS) micro- and nanoplastics (MNPs) represent a crucial component of atmospheric MNPs. In this study, we proposed a simple and robust method for determining the concentration of atmospheric PS NPs using pyrolysis-gas chromatography-mass spectrometry (Py-GC/MS). Following active sampling, the filter membrane is directly ground and introduced into the Py-GC/MS system to quantify PS NPs. The proposed method demonstrates excellent reproducibility and high sensitivity, with a detection limit as low as down to 15 pg/m3 for PS NPs. By using this method, the occurrence of PS NPs in both indoor and outdoor atmospheres has been confirmed. Furthermore, the results showed that the abundance of outdoor PS NPs was significantly higher than that of indoor samples, and there was no significant difference in NP vertical distribution within a height of 28.6 m. This method can be applied for the routine monitoring of atmospheric PS NPs and for evaluating their risk to human health.

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Contrastive learning has shown great promise over annotation scarcity problems in the context of medical image segmentation. Existing approaches typically assume a balanced class distribution for both labeled and unlabeled medical images. However, medical image data in reality is commonly imbalanced (i.e., multi-class label imbalance), which naturally yields blurry contours and usually incorrectly labels rare objects. Moreover, it remains unclear whether all negative samples are equally negative. In this work, we present ACTION, an Anatomical-aware ConTrastive dIstillatiON framework, for semi-supervised medical image segmentation. Specifically, we first develop an iterative contrastive distillation algorithm by softly labeling the negatives rather than binary supervision between positive and negative pairs. We also capture more semantically similar features from the randomly chosen negative set compared to the positives to enforce the diversity of the sampled data. Second, we raise a more important question: Can we really handle imbalanced samples to yield better performance? Hence, the key innovation in ACTION is to learn global semantic relationship across the entire dataset and local anatomical features among the neighbouring pixels with minimal additional memory footprint. During the training, we introduce anatomical contrast by actively sampling a sparse set of hard negative pixels, which can generate smoother segmentation boundaries and more accurate predictions. Extensive experiments across two benchmark datasets and different unlabeled settings show that ACTION significantly outperforms the current state-of-the-art semi-supervised methods.

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Studies on airborne microplastics (AMPs) have reported higher abundance of AMPs in indoor air compared to outdoors. Most people spend more time indoors compared to outdoors, and it is therefore important to identify and quantify AMPs in indoor air to understand human exposure to AMPs. This exposure can vary among different individuals as they spend their time in different locations and different activity levels, and thus experience different breathing rates. In this study, AMPs ranging from 20-5000 µm were sampled across different indoor sites of Southeast Queensland using an active sampling technique. The highest indoor MP concentration was observed at a childcare site (2.25 ± 0.38 particles/m3), followed by an office (1.20 ± 0.14 particles/m3) and a school (1.03 ± 0.40 particles/m3). The lowest indoor MP concentration was observed inside a vehicle (0.20 ± 0.14 particles/m3), comparable to outdoor concentrations. The only shapes observed were fibers (98%) and fragments. MP fibers ranged from 71 to 4950 µm in length. Polyethylene terephthalate was the prominent polymer type at most sites. Using our measured airborne concentrations as inhaled air concentrations, we calculated the annual human exposure levels to AMPs using scenario-specific activity levels. Males between the ages of 18 to 64 were calculated to have the highest AMP exposure at 3187 ± 594 particles/year, followed by males ≥65 years at 2978 ± 628 particles/year. The lowest exposure of 1928 ± 549 particles/year was calculated for females between the ages of 5 to 17. This study provides the first report on AMPs for various types of indoor locations where individuals spend most of their time. Considering acute, chronic, industrial, and individual susceptibility, more detailed human inhalation exposure levels to AMPs should be estimated for a realistic appraisal of the human health risk, including how much of the inhaled particles are exhaled. SYNOPSIS: Limited research exists on the occurrence and the associated human exposure levels to AMPs in indoor locations where people spend most of their time. This study reports on the occurrence of AMPs at indoor locations and associated exposure levels using scenario-specific activity levels.

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The Earth's atmosphere plays a critical role in transporting and dispersing biological aerosols. Nevertheless, the amount of microbial biomass in suspension in the air is so low that it is extremely difficult to monitor the changes over time in these communities. Real-time genomic studies can provide a sensitive and rapid method for monitoring changes in the composition of bioaerosols. However, the low abundance of deoxyribose nucleic acid (DNA) and proteins in the atmosphere, which is of the order of the contamination produced by operators and instruments, poses a challenge for the sampling process and the analyte extraction. In this study, we designed an optimized, portable, closed bioaerosol sampler based on membrane filters using commercial off-the-shelf components, demonstrating its end-to-end operation. This sampler can operate autonomously outdoors for a prolonged time, capturing ambient bioaerosols and avoiding user contamination. We first performed a comparative analysis in a controlled environment to select the optimal active membrane filter based on its ability to capture and extract DNA. We have designed a bioaerosol chamber for this purpose and tested three commercial DNA extraction kits. The bioaerosol sampler was tested outdoors in a representative environment and run for 24 h at 150 L/min. Our methodology suggests that a 0.22-µm polyether sulfone (PES) membrane filter can recover up to 4 ng of DNA in this period, sufficient for genomic applications. This system, along with the robust extraction protocol, can be automated for continuous environmental monitoring to gain insights into the time evolution of microbial communities within the air.

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BACKGROUND: Sensory perception is profoundly shaped by attention. Attending to an odor strongly regulates if and how it is perceived - yet the brain systems involved in this process are unknown. Here we report integration of the medial prefrontal cortex (mPFC), a collection of brain regions integral to attention, with the olfactory system in the context of selective attention to odors. METHODS: First, we used tracing methods to establish the tubular striatum (TuS, also known as the olfactory tubercle) as the primary olfactory region to receive direct mPFC input in rats. Next, we recorded (i) local field potentials from the olfactory bulb (OB), mPFC, and TuS, or (ii) sniffing, while rats completed an olfactory selective attention task. RESULTS: Gamma power and coupling of gamma oscillations with theta phase were consistently high as rats flexibly switched their attention to odors. Beta and theta synchrony between mPFC and olfactory regions were elevated as rats switched their attention to odors. Finally, we found that sniffing was consistent despite shifting attentional demands, suggesting that the mPFC-OB theta coherence is independent of changes in active sampling. CONCLUSIONS: Together, these findings begin to define an olfactory attention network wherein mPFC activity, as well as that within olfactory regions, are coordinated based upon attentional states.

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BACKGROUND: Lung cancer is the leading cause of cancer death worldwide. Prognostic prediction plays a vital role in the decision-making process for postoperative non-small cell lung cancer (NSCLC) patients. However, the high imbalance ratio of prognostic data limits the development of effective prognostic prediction models. METHODS: In this study, we present a novel approach, namely ensemble learning with active sampling (ELAS), to tackle the imbalanced data problem in NSCLC prognostic prediction. ELAS first applies an active sampling mechanism to query the most informative samples to update the base classifier to give it a new perspective. This training process is repeated until no enough samples are queried. Next, an internal validation set is employed to evaluate the base classifiers, and the ones with the best performances are integrated as the ensemble model. Besides, we set up multiple initial training data seeds and internal validation sets to ensure the stability and generalization of the model. RESULTS: We verified the effectiveness of the ELAS on a real clinical dataset containing 1848 postoperative NSCLC patients. Experimental results showed that the ELAS achieved the best averaged 0.736 AUROC value and 0.453 AUPRC value for 6 prognostic tasks and obtained significant improvements in comparison with the SVM, AdaBoost, Bagging, SMOTE and TomekLinks. CONCLUSIONS: We conclude that the ELAS can effectively alleviate the imbalanced data problem in NSCLC prognostic prediction and demonstrates good potential for future postoperative NSCLC prognostic prediction.

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Working memory (WM) refers to the ability to maintain and actively process information-either derived from perception or long-term memory (LTM)-for intelligent thought and action. This chapter focuses on the contributions of the temporal lobe, particularly medial temporal lobe (MTL) to WM. First, neuropsychological evidence for the involvement of MTL in WM maintenance is reviewed, arguing for a crucial role in the case of retaining complex relational bindings between memorized features. Next, MTL contributions at the level of neural mechanisms are covered-with a focus on WM encoding and maintenance, including interactions with ventral temporal cortex. Among WM use processes, we focus on active sampling of environmental information, a key input source to capacity-limited WM. MTL contributions to the bidirectional relationship between active sampling and memory are highlighted-WM control of active sampling and sampling as a way of selecting input to WM. Memory-based sampling studies relying on scene and object inspection, visual-based exploration behavior (e.g., vicarious behavior), and memory-guided visual search are reviewed. The conclusion is that MTL serves an important function in the selection of information from perception and transfer from LTM to capacity-limited WM.

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Active samplers are widely used in the quantification of gaseous semi-volatile organic compounds (SVOCs). A sampling tube is often assembled upstream of the sampler, especially in the active samplers used for separating the particle-phase and gas-phase SVOCs and in the newly-designed active sampler based on solid-phase microextraction (SPME). However, gaseous SVOCs can be easily adsorbed by the sampling tube, which may induce significant errors to the quantitative results. Taking the SPME-based active sampler as an example, a mass-transfer model was developed to characterize the sampling-tube loss of gaseous SVOCs. Experiments involving six SVOCs were conducted. The model predictions (with a best-fit surface/air partition coefficient of SVOCs) were found to be consistent with the measurements. Both model predictions and experimental data indicated that the measured concentrations were significantly lower than the actual concentration (around 60% lower) due to the sampling-tube loss. The duration of sampling-tube loss (τe, minutes to days) varied with the volatility of SVOCs (vapor pressure, Vp), i.e., log τe linearly increased as increasing log Vp. The relationship could be helpful for determining the sampling strategies to eliminate (reduce) the effects of sampling-tube loss according to the volatility of SVOCs. The above conclusions may be also applicable for other active samplers of gaseous SVOCs. However, further studies are required to quantify the effects of sampling-tube loss for other active samplers due to the difference in the size and shape of the sampling tube between them and the SPME-based active sampler. The corresponding mass-transfer model and experimental procedure may require adjustment as appropriate.

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When acquiring information about choice alternatives, decision makers may have varying levels of control over which and how much information they sample before making a choice. How does control over information acquisition affect the quality of sample-based decisions? Here, combining variants of a numerical sampling task with neural recordings, we show that control over when to stop sampling can enhance (i) behavioral choice accuracy, (ii) the build-up of parietal decision signals, and (iii) the encoding of numerical sample information in multivariate electroencephalogram patterns. None of these effects were observed when participants could only control which alternatives to sample, but not when to stop sampling. Furthermore, levels of control had no effect on early sensory signals or on the extent to which sample information leaked from memory. The results indicate that freedom to stop sampling can amplify decisional evidence processing from the outset of information acquisition and lead to more accurate choices.

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SignificanceTo move efficiently, animals must continuously work out their x,y,z positions with respect to real-world objects, and many animals have a pair of eyes to achieve this. How photoreceptors actively sample the eyes' optical image disparity is not understood because this fundamental information-limiting step has not been investigated in vivo over the eyes' whole sampling matrix. This integrative multiscale study will advance our current understanding of stereopsis from static image disparity comparison to a morphodynamic active sampling theory. It shows how photomechanical photoreceptor microsaccades enable Drosophila superresolution three-dimensional vision and proposes neural computations for accurately predicting these flies' depth-perception dynamics, limits, and visual behaviors.

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The omnipresence of microplastic (MP) in various environmental samples, including aerosols, has raised public health concerns; however, there is presently very limited information on MPs in indoor aerosol. This paper presents a unique dataset where smaller MPs have been sampled using a six-stage cascade impactor from indoor environments in Kuwait. The MP concentration in the indoor air varied between 3.2 and 27.1 particles m-3, and the relative MP concentration decreased linearly from the lowest to the highest size fraction. A significant effect of location was observed for the total number of MPs (F2,14 = 5.80, p = 0.02) and the inhalable fraction (F2,14 = 8.38, p = 0.005), while location had no effect on the respirable fraction (F2,14 = 0.54, p = 0.60). A significant effect of the type of air conditioning used was also observed for the total number of MPs (F2,19 = 5.58, p = 0.01) and the inhalable fraction (F2,19 = 6.45, p = 0.008), while location had no effect on the respirable fraction (F2,19 = 1.30, p = 0.30). For the total number of MPs and the inhalable fraction, the concentration was significantly higher for the split unit air-conditioning as compared to the central air-conditioning plants. The presence/absence of carpets had no significant effect on the MP concentrations (total: F1,19 = 4.08, p = 0.06; inhalable: F1,19 = 3.03, p = 0.10; respirable: F1,19 = 4.27, p = 0.05). The shape was dominantly fibers, with few fragments in lower size fractions. These datasets represent the first baseline information for Kuwait, and the smaller MPs in all the samples further underscore the need to develop standardized protocols of MP collection in the ≤2.5 µm fraction that can have more conspicuous health implications.

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Artificial sediment used in studies according to OECD 218/219 (Sediment Water Chironomid Toxicity Test Using Spiked Sediment/Water) does not necessarily mirror the characteristics of natural sediments. To investigate the influence of sediment characteristics on the spatial-temporal behaviors of bixafen (KfOM = 2244 mL/g), fluopyram (KfOM = 162 mL/g) and N,N-dimethylsulfamide (KfOM ≈ 0 mL/g), experiments according to OECD 218/219 with two contrasting natural sediments were conducted. The silt loam sediment provided a high content of organic matter (OM) (13.1%), while the OM (0.45%) of the sandy sediment was low. Diffusion into (OECD 219) or out (OECD 218) of the sediment was dependent on the extent of adsorption, which is linked to the model compounds ́ adsorption affinities and the sediments ́ OM. Consequently, N,N-dimethylsulfamide showed unhindered mobility in each experimental set up, while the high adsorption affinities of fluopyram and bixafen limited the diffusion in the respective sediments. Therefore, in experiments with the silt loam sediment, both compounds revealed a limited mobility and either accumulated in the top 5 mm of the sediment (OECD 219) or remained homogenously distributed over the sediment depth (OECD 218). A greater mobility was observed within the sandy sediment.The influence of OM as found in a study using artificial sediment could be confirmed. Moreover, the applicability of a TOXSWA model was reassured to predict the measured concentrations at different sediment depths. TOXSWA is used in the regulatory exposure assessment to simulate the behavior of pesticides in surface waters. Calibration of three driving input parameters by inverse modelling (diffusion-, adsorption coefficient and OM) revealed no potential for improvement. The core sampling technique used and the model may contribute to a more realistic determination of concentration to which the Chironomid larvae are exposed to. This applies to water sediment test systems where the test organisms do not evenly inhabit the sediment.

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Melamine has received increasing public attention as a persistent, mobile and toxic (PMT) substance. To better assess environmental exposure and risks of melamine and related triazines (cyromazine, ammeline, and atrazine), a new passive sampling method based on the diffusive gradients in thin films (DGT) technique has been developed and validated in this study. The studied triazines were adsorbed quickly and strongly by the selected mixed cation exchange (MCX) binding gels. This MCX-DGT can linearly accumulate these chemicals over at least 5 days, with neither significant individual influence from pH (6-8), ionic strength (0.01-0.5 M) or dissolved organic matter (0-10 M), or interaction effects. Field applications in Southern China showed that DGT performed well in both sewage treatment plant (STP) and river samples. Melamine was found to be the dominant triazine with the concentrations at µg·L-1 in the STP and receiving river. Surprisingly, much higher concentration of melanine was found in the STP effluent than influent, and appeared to be some of the highest concentrations reported in STPs worldwide to date. Comparable melamine and atrazine concentraions in the STP effluent and receiving river suggested other sources to the river. The MCX-DGT sampler developed here was demonstrated to be reliable and robust for measuring the triazines in waters, and is promising as an in situ tool in understanding the occurrence, sources, and fate of the emerging PMT substances in aquatic environment.

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A first test of the field capabilities of a novel in situ sampling technique combining active and passive sampling (APS) was conducted in the sea. The proof-of-concept device uses a pump to draw water into a diffusion cell where dissolved target substances are accumulated onto sorbents which are selective for different classes of contaminants (i.e., metal cations, polar and non-polar organic compounds), simultaneously. A controlled laminar flow established in the diffusion cell enables measurements of contaminant concentrations that are fully independent from the hydrodynamic conditions in the bulk solution. APS measurements were consistent with those obtained using conventional passive sampling techniques such as organic diffusive gradients in thin films (o-DGT) and silicone rubber (SR) samplers (generally < 40% difference), taking into account the prevailing hydrodynamic conditions. The use of performance reference compounds (PRC) for hydrophobic contaminants provided additional information. Field measurements of metal ions in seawater showed large variability due to issues related to the device configuration. An improved field set-up deployed in supplementary freshwater mesocosm experiments provided metal speciation data that was consistent with passive sampling measurements (DGT), taking into account the hydrodynamic conditions. Overall, the results indicate that the APS technique provides a promising approach for the determination of a wide range of contaminants simultaneously, and independently from the hydrodynamic conditions in the bulk solution.

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A novel active sampling method enabled determination of sediment depth profiles revealing the spatial distribution of model compounds N,N-dimethylsulfamide, fluopyram and bixafen (low, medium, high adsorption affinity) in sediment microcosms according to OECD Test 218/219 (Sediment-Water Chironomid Toxicity Test Using Spiked Sediment/Spiked Water). After the overlying water was removed, plastic tubes were inserted into the sediment and the microcosms were frozen. For depth-related analysis, each "sediment core" was mounted in a cutting device and sawed into three 5-mm-slices, respectively (top, middle, bottom). Each slice was centrifuged for sediment and pore water separation. By various sampling dates within 28 days, we could follow the behavior of model compounds depending on sorption affinities and display specific distribution patterns within the sediment. N,N-dimethylsulfamide showing no sediment adsorption, migrated unhindered in (OECD 219) and out (OECD 218) of the sediment via pore water, resulting in homogenous distributions in both test designs. Fluopyram with moderate adsorption affinity revealed a concentration gradient with declining amounts from top to bottom layer (OECD 219) and higher amounts in the middle and bottom layer as compared to the top layer (OECD 218). Bixafen providing a strong adsorption affinity accumulated in the top layer in OECD 219, while no concentration gradients became visible in OECD 218. For establishing a Toxic Substances in Surface Waters (TOXSWA) model, we compared our measurements with simulated results revealing good agreements. The presented methodology is a useful tool to determine more realistic sediment and pore water concentrations, which the Chironomid larvae are exposed to.

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As a result of its unique location, Turkey receives air masses from Europe, Russia, Middle East, and Africa, making it an important place in terms of long-range atmospheric transport (LRT) of contaminants. Atmospheric levels of 22 organochlorine pesticides (OCPs), 45 polychlorinated biphenyls (PCBs), and 14 polybrominated diphenyl ethers (PBDEs) were measured in two metropolitan cities, Istanbul and Izmir, on a weekly basis from May 2014 to May 2015. Dichlorodiphenyltrichloroethane (DDT) and its derivatives were dominant OCP species, followed by isomers of hexachlorocyclohexane (HCH) at both sites. The annual mean concentration of ∑DDX (sum of o,p'-DDT, p,p'-DDT, o,p'-DDD, p,p'-DDD, o,p'-DDE, and p,p'-DDE) was 82 pg/m3 for Istanbul and 89 pg/m3 for Izmir, while these levels were about 46 pg/m3 for ∑HCHs (sum of α-, ß-, γ-, and δ-HCH) at both of the sites. At both stations, tri- and tetra-PCBs and tetra- and penta-PBDEs were dominant congeners. The temperature dependence indicates that both LRT and local contaminated areas contribute to the elevated levels. A Lagrangian particle dispersion model (FLEXPART) showed a few potential source regions in northern Africa and Middle East, southern-southwestern and eastern Europe including Russia, as well as from local domestic metropolitan areas.

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For sensory systems of the brain, the dynamics of an animal's own sampling behavior has a direct consequence on ensuing computations. This is particularly the case for mammalian olfaction, where a rhythmic flow of air over the nasal epithelium entrains activity in olfactory system neurons in a phenomenon known as sniff-locking. Parameters of sniffing can, however, change drastically with brain states. Coupled to the fact that different observation methods have different kinetics, consensus on the sniff-locking properties of neurons is lacking. To address this, we investigated the sniff-related activity of olfactory sensory neurons (OSNs), as well as the principal neurons of the olfactory bulb (OB), using 2-photon calcium imaging and intracellular whole-cell patch-clamp recordings in vivo, both in anesthetized and awake mice. Our results indicate that OSNs and OB output neurons lock robustly to the sniff rhythm, but with a slight temporal shift between behavioral states. We also observed a slight delay between methods. Further, the divergent sniff-locking by tufted cells (TCs) and mitral cells (MCs) in the absence of odor can be used to determine the cell type reliably using a simple linear classifier. Using this classification on datasets where morphological identification is unavailable, we find that MCs use a wider range of temporal shifts to encode odors than previously thought, while TCs have a constrained timing of activation due to an early-onset hyperpolarization. We conclude that the sniff rhythm serves as a fundamental rhythm but its impact on odor encoding depends on cell type, and this difference is accentuated in awake mice.