Synergistic toxic effects of high-strength ammonia and ZnO nanoparticles on biological nitrogen removal systems and role of exogenous C10-HSL regulation.

The inhibitory effects of zinc oxide nanoparticles (ZnO NPs) and impacts of N-acyl-homoserine lactone (AHL)-based quorum sensing (QS) on biological nitrogen removal (BNR) performance have been well-investigated. However, the effects of ammonia nitrogen (NH4+-N) concentrations on NP toxicity and AHL regulation have seldom been addressed yet. This study consulted on the impacts of ZnO NPs on BNR systems when high NH4+-N concentration was available. The synergistic toxic effects of high-strength NH4+-N (200 mg/L) and ZnO NPs resulted in decreased ammonia oxidation rates and dropped the nitrogen removal efficiencies by 17.5% ± 0.2%. The increased extracellular polymeric substances (EPS) production was observed in response to the high NH4+-N and ZnO NP stress, which indicated the defense mechanism against the toxic effects in the BNR systems was stimulated. Furthermore, the regulatory effects of exogenous N-decanoyl-homoserine lactone (C10-HSL)-mediated QS system on NP-stressed BNR systems were revealed to improve the BNR performance under different NH4+-N concentrations. The C10-HSL regulated the intracellular reactive oxygen species levels, denitrification functional enzyme activities, and antioxidant enzyme activities, respectively. This probably synergistically enhanced the defense mechanism against NP toxicity. However, compared to the low NH4+-N concentration of 60 mg/L, the efficacy of C10-HSL was inhibited at high NH4+-N levels of 200 mg/L. The findings provided the significant application potential of QS system for BNR when facing toxic compound shock threats.

Anion-binding-induced selective aggregation and self-degradation: Influence of positional isomerism on the anion selectivity and mode of binding of an imine-based receptor.

Imine based positional isomers (8E)-N-(4-((E)-(perfluorophenylimino)methyl)benzylidene)-2,3,4,5,6-pentafluorobenzenamine, L and (10E)-N-(3-(E-Perfluorophenylimino)methyl)benzylidene)-2,3,4,5,6-pentafluorobenzenamine, L1 have been designed, and synthesized by functionalizing two electron deficient aromatic moieties at the para-para'/ortho-ortho' positions in the phenyl core of the L and L1 respectively. The responses of L and L1 towards various anionic species are examined. The positional isomers L and L1 differs not only by showing distinguishable color change upon addition of anions but also differentiates themselves by the way of self-assembling together upon binding with cyanide anion. The naked-eye colorimetric experiments, UV-Vis, Nuclear Magnetic Resonance, and Infra-Red spectroscopic analyses reveal that the isomer L binds fluoride anion through 2:1 stoichiometry ratio. Unlike fluoride complex, the isomer L form aggregates while binding with cyanide ion. On the other hand, isomer L1 does not show any instant color change upon additions of any anion. Interestingly, after thirty minutes, only the color of the cyanide complex is turned into dark brown. While analyzing the spectroscopic results of cyanide complex of L1, it is found that the cyanide complex begins to decompose and finally it is completely decomposed within 30 min. This unprecedented phenomenon about the colorimetric sensing of cyanide and destruction of cyanide complex with respect to time has not been reported in the literature yet. To the best of our knowledge this is the first example of study of sensing controlling the selectivity, mode of binding, self-aggregating and degradation properties of anionic complexes under the influence of positional isomeric effects. This present investigation provides simple and effective strategy to construct the sensor molecules with tunable binding properties in terms of easy to prepare as well as easy to use as a colorimetric sensor. _____________________________________________________________________________________________________.

Effect of structure on sensing performance of nitro explosives with high sensitivity and mechanism of two Tb(III) coordination polymers.

The use of a conjugate N-containing ligand resulted in the decreasing of structural dimensions from 2D network of [Tb(2-pyia)(Ac)(H2O)] (CP1) to 1D chain [Tb(2-pyia)(Ac)(IDP)] (CP2) (2-H2pyia = 5-(pyridin-2-ylmethoxy) isophthalic acid and IDP=imidazo[4,5-f]-[1,10] phenanthroline). Both of them exhibit the characteristic luminescence of Tb ions and could have high fluorescence sensing properties for cefixime and fluridine. The different sensing properties for nitro explosives are manifested as CP1 for nitrobenzene and CP2 for 4-nitrophenol due to the difference in structure. Furthermore, CP2 exhibits the ratiometric fluorescence sensing for Fe3+ ion with a low detection limit of 0.405 µM. The fluorescence sensing mechanism of the two Tb complexes for different analytes was investigated using experimental methods and theoretical calculations. CP1 was used for the detection of Flu residues in the actual system and better results were obtained. The work shows the introduction of the chelated ligand might affect the structural and sensing performance changes of coordination polymers.

Establishment and verification of anthropogenic speciated VOCs emission inventory of Central China.

Improving the accuracy of anthropogenic volatile organic compounds (VOCs) emission inventory is crucial for reducing atmospheric pollution and formulating control policy of air pollution. In this study, an anthropogenic speciated VOCs emission inventory was established for Central China represented by Henan Province at a 3 km × 3 km spatial resolution based on the emission factor method. The 2019 VOCs emission in Henan Province was 1003.5 Gg, while industrial process source (33.7%) was the highest emission source, Zhengzhou (17.9%) was the city with highest emission and April and August were the months with the more emissions. High VOCs emission regions were concentrated in downtown areas and industrial parks. Alkanes and aromatic hydrocarbons were the main VOCs contribution groups. The species composition, source contribution and spatial distribution were verified and evaluated through tracer ratio method (TR), Positive Matrix Factorization Model (PMF) and remote sensing inversion (RSI). Results show that both the emission results by emission inventory (EI) (15.7 Gg) and by TR method (13.6 Gg) and source contribution by EI and PMF are familiar. The spatial distribution of HCHO primary emission based on RSI is basically consistent with that of HCHO emission based on EI with a R-value of 0.73. The verification results show that the VOCs emission inventory and speciated emission inventory established in this study are relatively reliable.

Fluorescent hydrogen-bonded organic framework act as a multifunctional platform for Fe3+ and F- sensing, and for information encryption.

Due to their exceptional optical properties and adjustable functional characteristics, hydrogen-bonded organic frameworks (HOFs) demonstrate significant potential in applications such as sensing, information encryption. However, studies on the synthesis of HOFs designed to construct multifunctional platforms are scant. In this work, we report the synthesis of a new fluorescent HOF by assembling melem and isophthalic acid (IPA), designated as HOF-IPA. HOF-IPA exhibited good selectivity and sensitivity towards Fe3+, making it suitable as a fluorescent sensor for Fe3+ detection. The sensor achieved satisfactory recoveries ranging from 97.79 % to106.42 % for Fe3+ sensing, with a low relative standard deviation (RSD) of less than 3.33 %, indicating significant application potential for HOF-IPA. Due to the ability of F- to mask the electrostatic action on the surface of Fe3+ and inhibit the photoelectron transfer (PET) of HOF-IPA, the HOF-IPA - Fe3+ system can be utilized as a fluorescent "off-on" sensor for F- detection. Additionally, owing to the colorless, transparent property of HOF-IPA in aqueous solution under sunlight and its blue fluorescence property under UV light (color) or microplate reader (fluorescence intensity), HOF-IPA based ink can be used for various types of information encryption, and all yielding favorable outcomes.

Landsat-based spatiotemporal estimation of subtropical forest aboveground carbon storage using machine learning algorithms with hyperparameter tuning.

Introduction: The aboveground carbon storage (AGC) in forests serves as a crucial metric for evaluating both the composition of the forest ecosystem and the quality of the forest. It also plays a significant role in assessing the quality of regional ecosystems. However, current technical limitations introduce a degree of uncertainty in estimating forest AGC at a regional scale. Despite these challenges, remote sensing technology provides an accurate means of monitoring forest AGC. Furthermore, the implementation of machine learning algorithms can enhance the precision of AGC estimates. Lishui City, with its rich forest resources and an approximate forest coverage rate of 80%, serves as a representative example of the typical subtropical forest distribution in Zhejiang Province. Methods: Therefore, this study uses Landsat remote sensing images, employing backpropagation neural network (BPNN), random forest (RF), and categorical boosting (CatBoost) to model the forest AGC of Lishui City, selecting the best model to estimate and analyze its forest AGC spatiotemporal dynamics over the past 30 years (1989-2019). Results: The study shows that: (1) The texture information calculated based on 9×9 and 11×11 windows is an important variable in constructing the remote sensing estimation model of the forest AGC in Lishui City; (2) All three machine learning techniques are capable of estimating forest AGC in Lishui City with high precision. Notably, the CatBoost algorithm outperforms the others in terms of accuracy, achieving a model training accuracy and testing accuracy R2 of 0.95 and 0.83, and RMSE of 2.98 Mg C ha-1 and 4.93 Mg C ha-1, respectively. (3) Spatially, the central and southwestern regions of Lishui City exhibit high levels of forest AGC, whereas the eastern and northeastern regions display comparatively lower levels. Over time, there has been a consistent increase in the total forest AGC in Lishui City over the past three decades, escalating from 1.36×107 Mg C in 1989 to 6.16×107 Mg C in 2019. Discussion: This study provided a set of effective hyperparameters and model of machine learning suitable for subtropical forests and a reference data for improving carbon sequestration capacity of subtropical forests in Lishui City.

Accuracy and role of consumer facing wearable technology for continuous monitoring during endoscopic procedures.

Background: Consumer facing wearable devices capture significant amounts of biometric data. The primary aim of this study is to determine the accuracy of consumer-facing wearable technology for continuous monitoring compared to standard anesthesia monitoring during endoscopic procedures. Secondary aims were to assess patient and provider perceptions of these devices in clinical settings. Methods: Patients undergoing endoscopy with anesthesia support from June 2021 to June 2022 were provided a smartwatch (Apple Watch Series 7, Apple Inc., Cupertino, CA) and accessories including continuous ECG monitor and pulse oximeter (Qardio Inc., San Francisco, CA) for the duration of their procedure. Vital sign data from the wearable devices was compared to in-room anesthesia monitors. Concordance with anesthesia monitoring was assessed with interclass correlation coefficients (ICC). Surveys were then distributed to patients and clinicians to assess patient and provider preferences regarding the use of the wearable devices during procedures. Results: 292 unique procedures were enrolled with a median anesthesia duration of 34 min (IQR 25-47). High fidelity readings were successfully recorded with wearable devices for heart rate in 279 (95.5%) cases, oxygen in 203 (69.5%), and respiratory rate in 154 (52.7%). ICCs for watch and accessories were 0.54 (95% CI 0.46-0.62) for tachycardia, 0.03 (95% CI 0-0.14) for bradycardia, and 0.33 (0.22-0.43) for oxygen desaturation. Patients generally felt the devices were more accurate (56.3% vs. 20.0% agree, p < 0.001) and more permissible (53.9% vs. 33.3% agree, p < 0.001) to wear during a procedure than providers. Conclusion: Smartwatches perform poorly for continuous data collection compared to gold standard anesthesia monitoring. Refinement in software development is required if these devices are to be used for continuous, intensive vital sign monitoring.

Integrated water resource management in the Segura Hydrographic Basin: An artificial intelligence approach.

Managing resources effectively in uncertain demand, variable availability, and complex governance policies is a significant challenge. This paper presents a paradigmatic framework for addressing these issues in water management scenarios by integrating advanced physical modelling, remote sensing techniques, and Artificial Intelligence algorithms. The proposed approach accurately predicts water availability, estimates demand, and optimizes resource allocation on both short- and long-term basis, combining a comprehensive hydrological model, agronomic crop models for precise demand estimation, and Mixed-Integer Linear Programming for efficient resource distribution. In the study case of the Segura Hydrographic Basin, the approach successfully allocated approximately 642 million cubic meters (hm3) of water over six months, minimizing the deficit to 9.7% of the total estimated demand. The methodology demonstrated significant environmental benefits, reducing CO2 emissions while optimizing resource distribution. This robust solution supports informed decision-making processes, ensuring sustainable water management across diverse contexts. The generalizability of this approach allows its adaptation to other basins, contributing to improved governance and policy implementation on a broader scale. Ultimately, the methodology has been validated and integrated into the operational water management practices in the Segura Hydrographic Basin in Spain.

A portable micro-nanochannel bio-3D printed liver microtissue biosensor for DON detection.

We investigated a portable micro-nanochannel biosensor 3D-printed liver microtissues for rapid and sensitive deoxynivalenol (DON) detection. The screen-printed carbon electrode (SPCE) was modified with nanoporous anodic aluminum oxide (AAO), gold nanoparticles (AuNPs), and cytochrome C oxidase (COx) to enhance sensor performance. Gelatin methacrylate hydrogel, combined with hepatocellular carcinoma cells, formed the bioink for 3D printing. Liver microtissues were prepared through standardized and high-throughput techniques via bio-3D printing technology. These microtissues were immobilized onto modified electrodes to fabricate liver microtissue sensors. The peak current of this biosensor was positively correlated with DON concentration, as determined by cyclic voltammetry (CV), within a linear detection range of 2∼40 µg/mL. The standard curve equation is denoted by ICV(µA) = = 18.76956 + 0.03107CDON(µg/mL), with a correlation coefficient R2 was 0.99471(n＝3). A minimum detection limit of 1.229 µg/mL was calculated from the formula, indicating the successful construction of a portable micro-nanochannel bio-3D printed liver microtissue biosensor. It provides innovative ideas for developing rapid and convenient instrumentation to detect mycotoxin hazards after grain production. It also holds significant potential for application in the prediction and assessment of post-production quality changes in grain.

Modulation of fungal phosphate homeostasis by the plant hormone strigolactone.

Inter-kingdom communication through small molecules is essential to the coexistence of organisms in an ecosystem. In soil communities, the plant root is a nexus of interactions for a remarkable number of fungi and is a source of small-molecule plant hormones that shape fungal compositions. Although hormone signaling pathways are established in plants, how fungi perceive and respond to molecules is unclear because many plant-associated fungi are recalcitrant to experimentation. Here, we develop an approach using the model fungus, Saccharomyces cerevisiae, to elucidate mechanisms of fungal response to plant hormones. Two plant hormones, strigolactone and methyl jasmonate, produce unique transcript profiles in yeast, affecting phosphate and sugar metabolism, respectively. Genetic analysis in combination with structural studies suggests that SLs require the high-affinity transporter Pho84 to modulate phosphate homeostasis. The ability to study small-molecule plant hormones in a tractable genetic system should have utility in understanding fungal-plant interactions.

Unlocking Bright and Switchable Dimeric Singlet Oxygen Electrochemiluminescence by Surface Engineering.

Visible electrochemiluminescence (ECL) of singlet oxygen (1O2) from the dimeric 1Δg state is a versatile and cost-efficient tool for sensing and imaging in various application fields such as biochemistry, pharmaceuticals, and material science. However, its implementation is hindered by weak emission and complex generation mechanisms. In this work, we enable a bright and switchable dimeric 1O2 ECL through facile yet effective surface engineering strategies on a screen-printed carbon electrode in aqueous media. Specifically, we complement a stepwise potential procedure with a pre-cathodic process to switch on the anodic 1O2 ECL and unravel how the in situ electrochemical pretreatments remarkably amplify the ECL intensity by modifying the surface oxygenates and promoting the 1O2-generating reactions. Additionally, ex situ oxygen plasma treatment on the electrode surface, which switches off the 1O2 ECL, further demonstrates the surface specificity of the 1O2 ECL from another perspective. Leveraging these surface strategies, we establish a sensing capability of the 1O2 ECL system with high sensitivity and selectivity toward tertiary amines. This work paves the way for translating a laboratory-scale 1O2-ECL system to portable and patternable sensing, imaging, and display applications.

Comprehensive Insights on MXene-Based TENGs: from Structures, Functions to Applications.

The rapid advancement of triboelectric nanogenerators (TENGs) has introduced a transformative approach to energy harvesting and self-powered sensing in recent years. Nonetheless, the untapped potential of TENGs in practical scenarios necessitates multiple strategies like material selections and structure designs to enhance their output performance. Given the various superior properties, MXenes, a kind of novel 2D materials, have demonstrated great promise in enhancing TENG functionality. Here, this review comprehensively delineates the advantages of incorporating MXenes into TENGs, majoring in six pivotal aspects. First, an overview of TENGs is provided, stating their theoretical foundations, working modes, material considerations, and prevailing challenges. Additionally, the structural characteristics, fabrication methodologies, and family of MXenes, charting their developmental trajectory are highlighted. The selection of MXenes as various functional layers (negative and positive triboelectric layer, electrode layer) while designing TENGs is briefed. Furthermore, the distinctive advantages of MXene-based TENGs and their applications are emphasized. Last, the existing challenges are highlighted, and the future developing directions of MXene-based TENGs are forecasted.

PEDOT:PSS-Based Wearable Flexible Temperature Sensor and Integrated Sensing Matrix for Human Body Monitoring.

Flexible temperature sensors have been widely used in electronic skins and health monitoring. Body temperature as one of the key physiological signals is crucial for detecting human body's abnormalities, which necessitates high sensitivity, quick responsiveness, and stable monitoring. In this paper, we reported a resistive temperature sensor designed as an ultrathin laminated structure with a serpentine pattern and a bioinspired adhesive layer, which was fabricated with a composite of poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)/single-wall carbon nanotubes/reduced graphene oxide (PEDOT:PSS/SWCNTs/rGO) and polydimethylsiloxane (PDMS). The temperature sensor exhibited a high temperature sensitivity of 0.63% °C-1, coupled with outstanding linearity of 0.98 within 25-45 °C. Furthermore, it showed fast response and recovery speeds of 4.8 and 5.8 s, respectively, between 25 and 36 °C. It also demonstrated exceptional stability when subjected to stress and bending disturbances with the maximum bending interference deviation of 0.03%. Additionally, it displayed good cyclic stability over a broad temperature range from 25 to 85 °C, and the standard deviation at 25 °C is 0.14%. A series of experiments including blowing detection, respiratory monitoring with or without a mask, and during rest or sleep were conducted to show the potential of the flexible temperature sensors in human body monitoring. Furthermore, a 4 × 4 flexible temperature sensor matrix was integrated to detect and map objects such as wrenches and blood vessels through human hand skin. The results were consistent with those of infrared measurements. The flexible temperature sensor is capable of real-time temperature monitoring and has the potential in tracking human respiration, assessing sleep quality, and mapping the temperature of various objects.

Effect of spider's weight on signal transmittance in vertical orb webs.

Spider orb web is a sophisticated structure that needs to fulfil multiple roles, such as trapping prey and transmitting web-borne signals. When building their web, heavier spiders tend to increase the pretension on the web, which seems counterintuitive since a tighter web would decrease the chances of stopping and retaining prey. In this article, we claim that heavier orb-weaving spiders increase tension on the web in order to reduce the attenuation of the vibratory signal coming from the bottom part of the web. We support our claim by first building a detailed spider web model, which is tuned by a tension-adjusting algorithm to fit the experimentally observed profiles. Then, the effects of the spider weight and the web tension on the signal transmittance properties are investigated using state-of-the-art finite element analysis tools.

The phytopathogen Xanthomonas campestris senses and effluxes salicylic acid via a sensor HepR and an RND family efflux pump to promote virulence in host plants.

Salicylic acid (SA) plays an essential role in plant defense against biotrophic and semi-biotrophic pathogens. Following pathogen recognition, SA biosynthesis dramatically increases at the infection site of the host plant. The manner in which pathogens sense and tolerate the onslaught of SA stress to survive in the plant following infection remains to be understood. The objective of this work was to determine how the model phytopathogen Xanthomonas campestris pv. campestris (Xcc) senses and effluxes SA during infection inside host plants. First, RNA-Seq analysis identified an SA-responsive operon Xcc4167-Xcc4171, encoding a MarR family transcription factor HepR and an RND (resistance-nodulation-cell division) family efflux pump HepABCD in Xcc. Electrophoretic mobility shift assays and DNase I footprint analysis revealed that HepR negatively regulated hepABCD expression by specifically binding to an AT-rich region of the promoter of the hepRABCD operon, Phep. Second, isothermal titration calorimetry and further genetic analysis suggest that HepR is a novel SA sensor. SA binding released HepR from its cognate promoter Phep and then induced the expression of hepABCD. Third, the RND family efflux pump HepABCD was responsible for SA efflux. The hepRABCD cluster was also involved in the regulation of culture pH and quorum sensing signal diffusible signaling factor turnover. Finally, the hepRABCD cluster was transcribed during the XC1 infection of Chinese radish and was required for the full virulence of Xcc in Chinese radish and cabbage. These findings suggest that the ability of Xcc to co-opt the plant defense signal SA to activate the multidrug efflux pump may have evolved to ensure Xcc survival and virulence in susceptible host plants.

Metabolic activities of marine ammonia-oxidizing archaea orchestrated by quorum sensing.

Ammonia-oxidizing archaea (AOA) play crucial roles in marine carbon and nitrogen cycles by fixing inorganic carbon and performing the initial step of nitrification. Evaluation of carbon and nitrogen metabolism popularly relies on functional genes such as amoA and accA. Increasing studies suggest that quorum sensing (QS) mainly studied in biofilms for bacteria may serve as a universal communication and regulatory mechanism among prokaryotes; however, this has yet to be demonstrated in marine planktonic archaea. To bridge this knowledge gap, we employed a combination of metabolic activity markers (amoA, accA, and grs) to elucidate the regulation of AOA-mediated nitrogen, carbon processes, and their interactions with the surrounding heterotrophic population. Through co-transcription investigations linking metabolic markers to potential key QS genes, we discovered that QS molecules could regulate AOA's carbon, nitrogen, and lipid metabolisms under different conditions. Interestingly, specific AOA ecotypes showed a preference for employing distinct QS systems and a distinct QS circuit involving a typical population. Overall, our data demonstrate that QS orchestrates nitrogen and carbon metabolism, including the exchange of organic metabolites between AOA and surrounding heterotrophic bacteria, which has been previously overlooked in marine AOA research.

Discovery of ß-nitrostyrene derivatives as potential quorum sensing inhibitors for biofilm inhibition and antivirulence factor therapeutics against Serratia marcescens.

Quorum sensing (QS) inhibition has emerged as a promising target for directed drug design, providing an appealing strategy for developing antimicrobials, particularly against infections caused by drug-resistant pathogens. In this study, we designed and synthesized a total of 33 ß-nitrostyrene derivatives using 1-nitro-2-phenylethane (NPe) as the lead compound, to target the facultative anaerobic bacterial pathogen Serratia marcescens. The QS-inhibitory effects of these compounds were evaluated using S. marcescens NJ01 and the reporter strain Chromobacterium violaceum CV026. Among the 33 new ß-nitrostyrene derivatives, (E)-1-methyl-4-(2-nitrovinyl)benzene (m-NPe, compound 28) was proven to be a potent inhibitor that reduced biofilm formation of S. marcescens NJ01 by 79%. Scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM) results revealed that treatment with m-NPe (50 µg/ml) not only enhanced the susceptibility of the formed biofilms but also disrupted the architecture of biofilms by 84%. m-NPe (50 µg/ml) decreased virulence factors in S. marcescens NJ01, reducing the activity of protease, prodigiosin, and extracellular polysaccharide (EPS) by 36%, 72%, and 52%, respectively. In S. marcescens 4547, the activities of hemolysin and EPS were reduced by 28% and 40%, respectively, outperforming the positive control, vanillic acid (VAN). The study also found that the expression levels of QS- and biofilm-related genes (flhD, fimA, fimC, sodB, bsmB, pigA, pigC, and shlA) were downregulated by 1.21- to 2.32-fold. Molecular dynamics analysis showed that m-NPe could bind stably to SmaR, RhlI, RhlR, LasR, and CviR proteins in a 0.1 M sodium chloride solution. Importantly, a microscale thermophoresis (MST) test revealed that SmaR could be a target protein for the screening of a quorum sensing inhibitor (QSI) against S. marcescens. Overall, this study highlights the efficacy of m-NPe in suppressing the virulence factors of S. marcescens, identifying it as a new potential QSI and antibiofilm agent capable of restoring or improving antimicrobial drug sensitivity.

YOLO-Faster: An efficient remote sensing object detection method based on AMFFN.

As a pivotal task within computer vision, object detection finds application across a diverse spectrum of industrial scenarios. The advent of deep learning technologies has significantly elevated the accuracy of object detectors designed for general-purpose applications. Nevertheless, in contrast to conventional terrestrial environments, remote sensing object detection scenarios pose formidable challenges, including intricate and diverse backgrounds, fluctuating object scales, and pronounced interference from background noise, rendering remote sensing object detection an enduringly demanding task. In addition, despite the superior detection performance of deep learning-based object detection networks compared to traditional counterparts, their substantial parameter and computational demands curtail their feasibility for deployment on mobile devices equipped with low-power processors. In response to the aforementioned challenges, this paper introduces an enhanced lightweight remote sensing object detection network, denoted as YOLO-Faster, built upon the foundation of YOLOv5. Firstly, the lightweight design and inference speed of the object detection network is augmented by incorporating the lightweight network as the foundational network within YOLOv5, satisfying the demand for real-time detection on mobile devices. Moreover, to tackle the issue of detecting objects of different scales in large and complex backgrounds, an adaptive multiscale feature fusion network is introduced, which dynamically adjusts the large receptive field to capture dependencies among objects of different scales, enabling better modeling of object detection scenarios in remote sensing scenes. At last, the robustness of the object detection network under background noise is enhanced through incorporating a decoupled detection head that separates the classification and regression processes of the detection network. The results obtained from the public remote sensing object detection dataset DOTA show that the proposed method has a mean average precision of 71.4% and a detection speed of 38 frames per second.

Enhancing Patient Safety Through an Integrated Internet of Things Patient Care System: Large Quasi-Experimental Study on Fall Prevention.

BACKGROUND: The challenge of preventing in-patient falls remains one of the most critical concerns in health care. OBJECTIVE: This study aims to investigate the effect of an integrated Internet of Things (IoT) smart patient care system on fall prevention. METHODS: A quasi-experimental study design is used. The smart patient care system is an integrated IoT system combining a motion-sensing mattress for bed-exit detection, specifying different types of patient calls, integrating a health care staff scheduling system, and allowing health care staff to receive and respond to alarms via mobile devices. Unadjusted and adjusted logistic regression models were used to investigate the relationship between the use of the IoT system and bedside falls compared with a traditional patient care system. RESULTS: In total, 1300 patients were recruited from a medical center in Taiwan. The IoT patient care system detected an average of 13.5 potential falls per day without any false alarms, whereas the traditional system issued about 11 bed-exit alarms daily, with approximately 4 being false, effectively identifying 7 potential falls. The bedside fall incidence during hospitalization was 1.2% (n=8) in the traditional patient care system ward and 0.1% (n=1) in the smart ward. We found that the likelihood of bedside falls in wards with the IoT system was reduced by 88% (odds ratio 0.12, 95% CI 0.01-0.97; P=.047). CONCLUSIONS: The integrated IoT smart patient care system might prevent falls by assisting health care staff with efficient and resilient responses to bed-exit detection. Future product development and research are recommended to introduce IoT into patient care systems combining bed-exit alerts to prevent inpatient falls and address challenges in patient safety.

Extracytoplasmic polysaccharides control cellulosomal and non-cellulosomal systems in Herbivorax saccincola A7.

Herbivorax saccincola A7 is an anaerobic alkali-thermophilic lignocellulolytic bacterium that possesses a cellulosome and high xylan degradation ability. To understand the expression profile of extracellular enzymes by carbon sources, quantitative real-time PCR was performed on all cellulosomal and non-cellulosomal enzyme genes of H. saccincola A7 using cellulose and xylan as carbon sources. The results confirmed that the scaffolding proteins of H. saccincola A7 were expressed. In general, the cellulosomal genes belonging to the glycoside hydrolase families 9, 10, 11, and 48 were repressed when xylan was the sole carbon source, but these genes were significantly induced in the presence of cellulose. These results indicate that cellulose, not xylan, is a key inducer of cellulosomal genes in H. saccincola A7. The RsgI-like proteins, which regulate a carbohydrate-sensing mechanism in Clostridium thermocellum, were also found to be encoded in the H. saccincola A7 genome. To confirm the regulation by RsgI-like proteins, the relative expression of σI1-σI4 factors was analyzed on both carbon sources. The expression of alternative σI1 and σI2 factors was enhanced by the presence of cellulose. By contrast, the expression of σI3 and σI4 factors was activated by both cellulose and xylan. Taken together, the results reveal that the cellulosomal and non-cellulosomal genes of H. saccincola A7 are regulated through a carbohydrate-sensing mechanism involving anti-σ regulator RsgI-like proteins. KEY POINTS: • qRT-PCR performed on cellulosomal and non-cellulosomal genes of H. saccincola A7 • Cellulose is a key inducer of the cellulosome of H. saccincola A7 • H. saccincola A7 possesses a similar system of anti-σ regulator RsgI-like proteins.