Unknown Samples Research Articles

VOC data-driven evaluation of vehicle cabin odor: from ANN to CNN-BiLSTM.

Emissions of volatile organic compounds (VOCs) in vehicles represent a significant problem, causing unpleasant odors. To mitigate VOCs and odors in vehicles, it is critical to choose interior parts with low odor and VOC emissions. However, prevailing odor evaluation methods are subjective, costly, and potentially harmful to the health of evaluators. In this study, we analyzed 139 automotive interior parts and 92 vehicles, establishing a cost-effective, data-driven method for odor evaluation. The contents of benzene, toluene, ethylbenzene, xylene, styrene, formaldehyde, acetaldehyde, acrolein, and total volatile organic compounds (TVOC) were detected by thermal desorption gas chromatography-mass spectrometry (TD-GC/MS) and high-performance liquid chromatography with an ultraviolet detector (HPLC-UV). Professional odor evaluators assessed the odors, identifying intensity levels from 2.0 to 4.5 in interior parts and 2.5 to 3.5 in whole vehicles. Leveraging this data, we applied four supervised learning algorithms to develop predictive models for the odor intensity of both interior parts and entire vehicles. During model training, we implemented early stopping techniques for the artificial neural network (ANN) and convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) models, while optimizing the support vector machine (SVM) and extreme gradient boosting (XGBoost) models using the GridSearch algorithm. The evaluation results reveal that the CNN-BiLSTM model performs the best, achieving an average accuracy of 89% for unknown samples within an odor intensity level of 0.5. The root mean square error (RMSE) is 0.24, and the mean absolute error (MAE) is 0.08. The model also underwent a sevenfold cross-validation, achieving an accuracy of 83.43%. Additionally, we employed SHapley Additive exPlanations (SHAP) for the interpretative analysis of the model, which confirmed the consistency of each VOC's odor contribution with human olfactory rules. By predicting odors based on VOCs through supervised learning, this study reduces the costs and enhances the efficiency and applicability of odor assessment across various vehicle interiors.

Species discrimination from hair using ATR-FTIR spectroscopy: Application in wildlife forensics

Hair is a commonly encountered trace evidence in wildlife crimes involving mammals and can be used for species identification which is essential for subsequent judicial proceedings. This proof of concept study aims, to distinguish the black guard hair of three wild cat species belonging to the genus Panthera i.e. Royal Bengal Tiger (Panthera tigris tigris), Indian Leopard (Panthera pardus fusca), and Snow Leopard (Panthera uncia) using a rapid and non-destructive ATR-FTIR spectroscopic technique in combination with chemometrics. A training dataset including 72 black guard hair samples of three species (24 samples from each species) was used to construct chemometric models. A PLS2-DA model successfully classified these three species into distinct classes with R-Square values of 0.9985 (calibration) and 0.8989 (validation). VIP score was also computed, and a new PLS2DA-V model was constructed using variables with a VIP score ≥ 1. External validation was performed using a validation dataset including 18 black guard hair samples (6 samples per species) to validate the constructed PLS2-DA model. It was observed that PLS2-DA model provides greater accuracy and precision compared to the PLS2DA-V model during cross-validation and external validation. The developed PLS2-DA model was also successful in differentiating human and non-human hair with R-Square values of 0.99 and 0.91 for calibration and validation, respectively. Apart from this, a blind test was also carried out using 10 unknown hair samples which were correctly classified into their respective classes providing 100 % accuracy. This study highlights the advantages of ATR-FTIR spectroscopy associated with PLS-DA for differentiation and identification of the Royal Bengal Tiger, Indian Leopard, and Snow Leopard hairs in a rapid, accurate, eco-friendly, and non-destructive way.

Quantification of total human DNA using qPCR in mixture samples of Crimes Against Sexual Freedom to estimate the success of analysis with autosomal STRs and Y-STRs

ABSTRACT In this study, 241 mixed autosomal STR genetic profiles (swabs − 172, slides − 69) from processed casework samples were analysed. From these samples, DNA was extracted and quantified by qPCR; the results were analysed with the PowerQuant Analysis Software. The extracted DNA samples were amplified with the Investigator 24Plex QS and VeriFiler kits and separated and detected in an ABI 3500 Genetic Analyzer. A quantification correlation was observed between the total autosomal DNA targets [Auto] and the Y chromosome target [Y] in mixed samples in which the success of amplification of the male autosomal STRs markers was associated with high values of the quantification of the target of [Y] and low DNA ratio values [Auto]/[Y] (p = 0.00 < 0.05). Samples with [Auto]/[Y] ratio values much higher than the estimated value required analysis with Y-STR markers. The study herein, which assessed a relatively large number of sexual assault samples, demonstrates that quantitation ratio values of DNA mixtures effectively contribute to the decision-making process on whether and what genetic marker system should be selected for analysis of a male component in unknown casework samples.

Machine learning assists the sensor array constructed by the tri-emission carbon dots to detect multiple metal ions

Metal ions are related to the health of daily life, which urges to be detected and identified by efficient sensors. We first synthesize tri-emission carbon dots for the functional determination of six metal ions and obtain the multi-dimension data to construct the fluorescent sensor array in the work. Inspired by the data-driven method, machine learning is employed in a sensor array to detect and analyze the six metal ions. The ion classification and concentration models are constructed to identify metal ion types and corresponding concentrations. The classification model could correctly discriminate six metal ions, reaching 99.5 % accuracy and the concentration model exhibits an excellent performance range from 30 to 330 μM, with a mean concentration difference value of 0.12 μM and R2 value of 0.9845 for the unknown sample. Furthermore, six metal ions in tap water also can be distinguished with 96 % accuracy and the recovery rate is between 101.89 % and 106.37 %. This work shows that the machine learning combined sensor array has significant advantages in detecting multiple metal ions and provides a novel thought for analyzing sensing data, accelerating the development of data-driven strategies in the sensing field. © 2001 Elsevier Science. All rights reserved.

Comparison of laser-induced breakdown spectroscopy (LIBS) and ICP analysis results for measuring Pb and Zn in soil

Context Laser-induced breakdown spectroscopy (LIBS) is a rapid, multielement analytical technique. It is particularly suitable for the qualitative and quantitative analyses of heavy metals in solid samples. Aims To validate the technique, the LIBS data were compared with the data obtained via conventional inductively coupled plasma (ICP) spectroscopy for the same soil samples. Methods In this study, standard and unknown soil samples from contaminated areas were prepared and fixed to an adhesive tape for LIBS analysis. The soils were also digested with acids for ICP analysis. The emission intensity of one selected line for each of the two analytes, i.e. lead (Pb) and zinc (Zn), was normalised to the background signal and plotted as a function of the concentration values previously determined via ICP analysis. Key results The data demonstrated good linearity for the calibration lines drawn, and the correlation between the ICP and LIBS data was confirmed by the satisfactory agreement between the corresponding values. Conclusions The concentration coefficient of determination (R2) between LIBS and ICP-aqua regia digestion analysis or ICP-total digestion analysis were &gt;0.86 and &gt;0.89 for Pb and Zn, respectively. The total analysis time for the LIBS method was 310 min, which was 54.40% shorter than that for the ICP method (680 min). Implications Consequently, LIBS can be used to measure Pb and Zn in soils without any chemical preparation.

Seabream Quality Monitoring Throughout the Supply Chain Using a Portable Multispectral Imaging Device

Monitoring food quality throughout the supply chain in a rapid and cost-effective way allows on-time decision−making, reducing food waste, and increasing sustainability. A portable multispectral imaging sensor was used for the rapid prediction of microbiological quality of fish fillets. Seabream fillets, packaged either in aerobic or vacuum conditions, were collected from both aquaculture and retail stores, while images were also acquired both from the skin and the flesh side of the fish fillets. In parallel to image acquisition, the microbial quality was also estimated for each fish fillet. The data were used for the training of predictive artificial neural network (ANN) models for the estimation of total aerobic counts (TACs). Models were built separately for fish parts (i.e., skin, flesh) and packaging conditions and were validated using two approaches (i.e., validation with data partitioning and external validation using samples from retail stores). The performance of the ANN models for the validation set with data partitioning was similar for the data collected from the flesh (RMSE = 0.402–0.547) and the skin side (RMSE = 0.500–0.533) of the fish fillets. Similar performance also was obtained from validation of the models of the different packaging conditions (i.e., aerobic, vacuum). The prediction capability of the models combining both air and vacuum packaged samples (RMSE = 0.531) was slightly lower compared to the models trained and validated per packaging condition, individually (RMSE = 0.510, 0.516 in air and vacuum, respectively). The models tested with unknown samples (i.e., fish fillets from retail stores-external validation) showed poorer performance (RMSE = 1.061–1.414) compared to the models validated with data partitioning (RMSE = 0.402–0.547). Multispectral imaging sensor appeared to be efficient for the rapid assessment of the microbiological quality of fish fillets for all the different cases evaluated. Hence, these outcomes could be beneficial not only for the industry and food operators but also for the authorities and consumers.

An intelligent indicating label using mulberry pigment, polylactic acid and rutile TiO2 nanoparticle for monitoring the freshness of cold fresh pork

The purpose of this work is to develop a new type of intelligent indicating label with high sensitivity to volatile bioamine, anti-ultraviolet light and anti-pigment leakage characteristics by electrospinning for real-time visual monitoring the freshness of cold fresh pork. By investigating pH discoloration sensitivity and light stability of plant pigments with different structures, mulberry pigment was selected as the indicator of indicating label. The content of label components (mulberry pigment, polylactic acid and TiO2 nanoparticle) significantly affected label performance (light stability, responsiveness to trimethylamine, and anti-leakage of mulberry pigment), and the optimal component content was determined to be 6.01% (w/w) of polylactic acid, 0.62% (w/w) of TiO2 nanoparticles and 0.29% (w/w) of mulberry pigment by response surface test based on label performance as response value. A close correlation (the correlation coefficient R values of 0.86–0.97) between the total color difference (ΔE) of the label and the freshness indexes (total plate count, total volatile basic nitrogen content, pH, thiobarbituric acid value and sensory score) of cold fresh pork during cold storage was revealed, and the freshness level of unknown pork samples could be well quantitatively predicted by partial least squares regression models with the determination coefficient (R2) values of 0.89–0.92, facilitating producers, dealers and consumers to know pork quality promptly.

Adulterants present in the San Diego county fentanyl supply: a laboratory analysis of seized law enforcement samples

BackgroundThe opioid overdose crisis is one of the worst public health crises ever to face the US and emerging evidence suggests its effects are compounded by the presence of drug adulterants. Here we report our efforts to characterize the adulterants present within the local fentanyl supply of San Diego County, obtained from undifferentiated drug samples seized by local law enforcement over the calendar year 2021.MethodsThirty-two participating local law enforcement agencies across San Diego submitted 4838 unknown individual illicit drug samples (total of 312 kg) to the San Diego County Sheriff’s Department Regional Crime Laboratory for identification.ResultsQualitative analysis of these samples via FTIR and GC-MS identified methamphetamine (38.7%), fentanyl (20.8%), diacetylmorphine (heroin) (10.2%), codeine (5.8%) and alprazolam (4.3%) as the most common illicit substances and the presence of 52 unique adulterants. The most common adulterants included 4-methylaminoantipyrine (4-MAAP) (10.9%), mannitol (9%), acetaminophen (8.5%), methamphetamine (4.2%), diacetylmorphine (heroin) (3.6%), tramadol (1.9%), and xylazine (1.7%). Several additional pharmacologically active adulterants and contaminants of interest were also identified.ConclusionThis analysis is vital for public health use and harm reduction efforts at the level of the individual consumer. Continued direct surveillance of the drug supply is necessary for the detection of potentially harmful adulterants that may pose serious threats to the public.

Nanopore sequencing in distinguishing between wild-type and vaccine strains of Varicella-Zoster virus

BackgroundThe introduction of varicella vaccines into routine pediatric immunization programs has led to a considerable reduction in varicella incidence. However, there have been reports of varicella, herpes zoster, and meningitis caused by the vaccine strain of varicella-zoster virus (VZV), raising concerns. Establishing the relationship between the wild-type and vaccine strains in VZV infections among previously vaccinated individuals is crucial. Differences in the single nucleotide polymorphisms (SNPs) among vaccine strains can be utilized to identify the strain. In this study, we employed nanopore sequencing to identify VZV strains and analyzed clinical samples. MethodsWe retrospectively examined vesicle and cerebrospinal fluid samples from patients with VZV infections. One sample each of the wild-type and vaccine strains, previously identified using allelic discrimination real-time PCR and direct sequencing, served as controls. Ten samples with undetermined VZV strains were included. After DNA extraction, a long PCR targeting the VZV ORF62 region was executed. Nanopore sequencing identified SNPs, allowing discrimination between the vaccine and wild-type strains. ResultsNanopore sequencing confirmed SNPs at previously reported sites (105,705, 106,262, 107,136, and 107,252), aiding in distinguishing between wild-type and vaccine strains. Among the ten unknown samples, nine were characterized as wild strains and one as a vaccine strain. Even in samples with low VZV DNA levels, nanopore sequencing was effective in strain identification. ConclusionThis study validates that nanopore sequencing is a reliable method for differentiating between the wild-type and vaccine strains of VZV. Its ability to produce long-read sequences is remarkable, allowing simultaneous confirmation of known SNPs and the detection of new mutations. Nanopore sequencing can serve as a valuable tool for the swift and precise identification of wild-type and vaccine strains and has potential applications in future VZV surveillance.

Open Set Bearing Fault Diagnosis with Domain Adaptive Adversarial Network under Varying Conditions

Bearing fault diagnosis is a pivotal aspect of monitoring rotating machinery. Recently, numerous deep learning models have been developed for intelligent bearing fault diagnosis. However, these models have typically been established based on two key assumptions: (1) that identical fault categories exist in both the training and testing datasets, and (2) the datasets used for testing and training are assumed to follow the same distribution. Nevertheless, these assumptions prove impractical and fail to accurately depict real-world scenarios, particularly those involving open-world assumption fault diagnosis in multi-condition scenarios. For that purpose, an open set domain adaptive adversarial network framework is proposed. Specifically, in order to improve the learning of distribution characteristics in different fields, comprehensive training is implemented using a deep convolutional autoencoder model. Additionally, to mitigate the negative transfer resulting from unknown fault samples in the target domain, the similarity of each target domain sample and the shared classes in the source domain are estimated using known class classifiers and extended classifiers. Similarity weight values are assigned to each target domain sample, and an unknown boundary is established in a weighted manner. This approach is employed to establish the alignment between the classes shared between the two domains, enabling the classification of known fault classes, while allowing the recognition of unknown fault classes in the target domain. The efficacy of our suggested approach is empirically validated using different datasets.

Research on automotive scrap metal classification method using laser-induced breakdown spectroscopy and two-step clustering algorithm

In the recycling of scrap metal, the establishment of the classification database of recyclables has the advantages of fast classification speed and high analysis accuracy. However, the classification and recycling of unknown samples become highly significant due to the extensive variety of standard metal samples and the challenges in obtaining them. In this study, a method for multi-element classification of automotive scrap metals in general environmental conditions was achieved by utilizing laser-induced breakdown spectroscopy (LIBS) and two-step clustering algorithm (K-means, hierarchical clustering). The two unsupervised learning algorithms were employed to cluster the LIBS spectral data of 60 automotive scrap metal samples rapidly and hierarchically. Three rare metal elements and three elements for distinguishing metal categories were selected to meet the recycling requirements. After applying the multiplicative scatter correction to the spectral data for calibration, the initial clustering clusters were determined using the Davies–Bouldin index, Calinski–Harabasz index, and silhouette coefficient. Then, the Kruskal–Wallis test was conducted on each cluster to check the significance. The clusters that failed the test were split and reclustered until all clusters met the significance criterion (α=0.05). The accuracy of the proposed method for classifying the collected automotive scrap metals reached 97.6%. This indicates the great potential of this method in the field of automotive scrap metal classification.

An auto-regulated universal domain adaptation network for uncertain diagnostic scenarios of rotating machinery

In recent years, domain adaptation techniques have garnered significant attention in the field of intelligent fault diagnosis for mechanical equipment. Domain adaptation techniques can effectively enable the reuse of diagnostic knowledge, thereby improving the generalization performance of the trained model. However, many intelligent diagnostic models are tailored to specific diagnostic scenarios, including closed-set domain adaptation, partial domain adaptation, and open-set domain adaptation. When these models built for specific scenarios are applied to other scenarios, their diagnostic performance will deteriorate catastrophically. This study proposes an auto-regulated universal domain adaptation network (AUDAN) for uncertain diagnostic scenarios of rotating machinery, which can handle multiple diagnostic scenarios and adaptively set threshold boundaries for each fault category based on data distribution structure to distinguish known and unknown samples. Specifically, a novel integration loss function is designed to train the proposed AUDAN. The self-adaptive reweight module is introduced into the proposed method to distribute the relative weight for each loss function automatically. Experiments on two rotating machinery datasets are carried out for validations. The experimental results demonstrate that the proposed AUDAN performs effectively in uncertain diagnostic scenarios and is promising in addressing the fault classification tasks in real industries.

Entropic Open-Set Active Learning

Active Learning (AL) aims to enhance the performance of deep models by selecting the most informative samples for annotation from a pool of unlabeled data. Despite impressive performance in closed-set settings, most AL methods fail in real-world scenarios where the unlabeled data contains unknown categories. Recently, a few studies have attempted to tackle the AL problem for the open-set setting. However, these methods focus more on selecting known samples and do not efficiently utilize unknown samples obtained during AL rounds. In this work, we propose an Entropic Open-set AL (EOAL) framework which leverages both known and unknown distributions effectively to select informative samples during AL rounds. Specifically, our approach employs two different entropy scores. One measures the uncertainty of a sample with respect to the known-class distributions. The other measures the uncertainty of the sample with respect to the unknown-class distributions. By utilizing these two entropy scores we effectively separate the known and unknown samples from the unlabeled data resulting in better sampling. Through extensive experiments, we show that the proposed method outperforms existing state-of-the-art methods on CIFAR-10, CIFAR-100, and TinyImageNet datasets. Code is available at https://github.com/bardisafa/EOAL.

ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning

Open-set graph learning is a practical task that aims to classify the known class nodes and to identify unknown class samples as unknowns. Conventional node classification methods usually perform unsatisfactorily in open-set scenarios due to the complex data they encounter, such as out-of-distribution (OOD) data and in-distribution (IND) noise. OOD data are samples that do not belong to any known classes. They are outliers if they occur in training (OOD noise), and open-set samples if they occur in testing. IND noise are training samples which are assigned incorrect labels. The existence of IND noise and OOD noise is prevalent, which usually cause the ambiguity problem, including the intra-class variety problem and the inter-class confusion problem. Thus, to explore robust open-set learning methods is necessary and difficult, and it becomes even more difficult for non-IID graph data. To this end, we propose a unified framework named ROG_PL to achieve robust open-set learning on complex noisy graph data, by introducing prototype learning. In specific, ROG_PL consists of two modules, i.e., denoising via label propagation and open-set prototype learning via regions. The first module corrects noisy labels through similarity-based label propagation and removes low-confidence samples, to solve the intra-class variety problem caused by noise. The second module learns open-set prototypes for each known class via non-overlapped regions and remains both interior and border prototypes to remedy the inter-class confusion problem. The two modules are iteratively updated under the constraints of classification loss and prototype diversity loss. To the best of our knowledge, the proposed ROG_PL is the first robust open-set node classification method for graph data with complex noise. Experimental evaluations of ROG_PL on several benchmark graph datasets demonstrate that it has good performance.

Exploring Diverse Representations for Open Set Recognition

Open set recognition (OSR) requires the model to classify samples that belong to closed sets while rejecting unknown samples during test. Currently, generative models often perform better than discriminative models in OSR, but recent studies show that generative models may be computationally infeasible or unstable on complex tasks. In this paper, we provide insights into OSR and find that learning supplementary representations can theoretically reduce the open space risk. Based on the analysis, we propose a new model, namely Multi-Expert Diverse Attention Fusion (MEDAF), that learns diverse representations in a discriminative way. MEDAF consists of multiple experts that are learned with an attention diversity regularization term to ensure the attention maps are mutually different. The logits learned by each expert are adaptively fused and used to identify the unknowns through the score function. We show that the differences in attention maps can lead to diverse representations so that the fused representations can well handle the open space. Extensive experiments are conducted on standard and OSR large-scale benchmarks. Results show that the proposed discriminative method can outperform existing generative models by up to 9.5% on AUROC and achieve new state-of-the-art performance with little computational cost. Our method can also seamlessly integrate existing classification models. Code is available at https://github.com/Vanixxz/MEDAF.

“Reagent-free determination of γ-butyrolactone in beverages”

A green partial least square (PLS) attenuated total reflectance infrared spectroscopy (ATR-FTIR) method has been developed for the prediction of the γ-butyrolactone (GBL) in all type of soft and alcoholic beverages. The method involves the use of a drop (less than 5 µL) of sample and a single calibration model based on aqueous and alcoholic solutions of GBL and beverages free from GBL. A selected wavenumber range from 1225 to 1171 cm−1 and from 999 to 972 cm−1 of the second derivative (SD) spectra of samples and standards were employed. The validation set provided root mean square error of prediction (RMSEP) of the order of 300 µg g−1 GBL with a ratio of standard error of performance to standard deviation value (RPD) of 13.4. The prediction of unknown samples not considered in the calibration nor in the validation sets offered accuracy relative errors between −1.0 and 2.9 % GBL. The proposed PLS-FTIR based method allows a fast and direct determination of GBL in all type of beverages, in less than 2 min and avoiding the use of neither reagents nor solvents, without any sample treatment required, being in agreement with the principles of Green Analytical Chemistry.

Optimization and Validation of an FTIR-based, All-in-one System for Viable MDR Bacteria Detection in Combat-related Wound Infection.

The U.S. Military members experiencing combat-related injuries have a higher chance of developing infections by multidrug-resistant (MDR) bacteria at admission to military hospitals. MDR wound infections result in higher amputation rates and greater risks for subsequent or chronic infections that require readmission or extended stay in the hospital. Currently, there is no FDA-clear, deployable early diagnostic system for suitable field use.We are reporting our efforts to improve a previously developed Rapid Label-free Pathogen Identification (RAPID) system to detect viable MDR bacteria in wound infections and perform antibiotic susceptibility testing (AST). Specifically, we added multiplex and automation capability and significantly simplified the sample preparation process. A functional prototype of the improved system was built, and its performance was validated using a variety of lab-prepared spiked samples and real-world samples. To access the baseline performance of the improved RAPID system in detecting bacteria presence, we selected 17 isolates, most of them from blood or wound infections, and prepared mono-strain spiked samples at 104 to 106 cfu/mL concentration. These samples were processed and analyzed by the RAPID system. To demonstrate the AST capability of the system, we selected 6 strains against 6 different antibiotics and compared the results from the system with the ones from the gold standard method.To validate the system's performance with real-world samples, we first investigated its performance on 3 swab samples from epicutaneous methicillin-resistant Staphylococcus aureus-exposed mouse model. The AST results from our system were compared with the ones from the gold standard method. All animal experiments were approved by the Johns Hopkins University Animal Care and Use Committee (Protocol No. MO21M378). Then, we obtained swab samples from 7 atopic dermatitis (AD) patients and compared our AST results with the ones from the gold standard method. The human subject protocol was approved by the Johns Hopkins Medicines Institutional Review Boards (Study No. CR00043438/IRB00307926) and by USAMRDC (Proposal Log Number/Study Number 20000251). High-quality data were obtained from the spiked samples of all 17 strains. A quantitative analysis model built using these data achieved 94% accuracy in predicting the species ID in 8 unknown samples. The AST results on the spiked samples had shown 100% matching with the gold standard method. Our system successfully detects the presence/absence of viable bacteria in all 3 mouse and 7 AD patient swab samples. Our system shows 100% and 85.7% (6 out of 7) accuracy when compared to the oxacillin susceptibility testing results for the mouse and the AD patient swabs, respectively. Our system has achieved excellent performance in detecting viable bacteria presence and in performing AST in a multiplex, automated, and easy-to-operate manner, on both lab-prepared and real samples. Our results have shown a path forward to a rapid (sample-to-answer time ≤3 hours), accurate, sensitive, species-specific, and portable system to detect the presence of MDR combat-related wound infections in the field environment. Our future efforts involve ruggedizing the RAPID system and evaluating performance under relevant environmental conditions.

Chinese herbal medicine for post-viral fatigue: A systematic review of randomized controlled trials.

Fatigue is a common symptom after viral infection. Chinese herbal medicine (CHM) is thought to be a potential effective intervention in relieving fatigue. To assess the effectiveness and safety of CHM for the treatment of post-viral fatigue. Systematic review and meta-analysis of randomized controlled trials (RCTs). The protocol of this systematic review was registered on PROSPERO (CRD42022380356). Trials reported changes of fatigue symptom, which compared CHM to no treatment, placebo or drugs, were included. Six electronic databases and three clinical trial registration platforms were searched from inception to November 2023. Literature screening, data extraction, and risk bias assessment were independently carried out by two reviewers. Quality of the included trials was evaluated using Cochrane risk of bias tool, and the certainty of the evidence was evaluated using GRADE. The meta-analysis was performed using Review Manager 5.4, mean difference (MD) and its 95% confidence interval (CI) was used for estimate effect of continuous data. Heterogeneity among trials was assessed through I2 value. Overall, nineteen studies with 1921 patients were included. Results of individual trial or meta-analysis showed that CHM was better than no treatment (MD = -0.80 scores, 95%CI -1.43 to -0.17 scores, P = 0.01, 60 participants, 1 trial), placebo (MD = -1.90 scores, 95%CI -2.38 to -1.42 scores, P<0.00001, 184 participants, 1 trial), placebo on basis of rehabilitation therapy (MD = -14.90 scores, 95%CI -24.53 to -5.27 scores, P = 0.02, 118 participants, 1 trial) or drugs (MD = -0.38 scores, 95%CI -0.48 to -0.27 scores, I2 = 0%, P<0.00001, 498 participants, 4 trials) on relieving fatigue symptoms assessing by Traditional Chinese Medicine fatigue scores. Trials compared CHM plus drugs to drugs alone also showed better effect of combination therapy (average MD = -0.56 scores). In addition, CHM may improve the percentage of CD4 T lymphocytes and reduce the level of serum IL-6 (MD = -14.64 scores, 95%CI 18.36 to -10.91 scores, I2 = 0%, P<0.00001, 146 participants, 2 trials). Current systematic review found that the participation of CHM can improve the symptoms of post-viral fatigue and some immune indicators. However, the safety of CHM remains unknown and large sample, high quality multicenter RCTs are still needed in the future.

Enhancing open-set domain adaptation through unknown-filtering multi-classifier adversarial network

Domain adaptation is a fundamental research problem that aims to address the domain shift issue during the transfer of a model from a labeled source domain to an unlabeled target domain. In traditional domain adaptation scenarios, it is assumed that the class spaces of both the source and target domains are identical. However, real-world applications often entail situations where the target domain contains private classes that are absent in the source domain. Forcing the alignment of these two domains may result in negative transfer. This specific concern is addressed by the emerging field of Open-set Domain Adaptation (OSDA). Previous OSDA methods attempted to align the known classes between the source and target domains while separating the unknown samples. However, these methods are inadequate in effectively discerning instances from the unknown classes. Therefore, we propose Enhancing Open-Set Domain Adaptation through Unknown-Filtering Multi-Classifier Adversarial Network (UFMCAN), which leverages multiple classifiers including a weighted auxiliary classifier, an open-set recognizer, a primary classifier and a three-way domain discriminator through adversarial learning to effectively filter instances from the unknown classes present in the target domain while concurrently aligning the source and target-known distribution. Experiments on extensive benchmarks (Office-31, Office-Home, VisDA-2017 and Office-Mix) demonstrate the superior performance of UFMCAN compared to existing state-of-the-art methods.

MoS2-Based Sensor Array for Accurate Identification of Cancer Cells with Ensemble-Modified Aptamers.

In this work, we present an array-based chemical nose sensor that utilizes a set of ensemble-modified aptamer (EMAmer) probes to sense subtle physicochemical changes on the cell surface for cancer cell identification. The EMAmer probes are engineered by domain-selective incorporation of different types and/or copies of positively charged functional groups into DNA scaffolds, and their differential interactions with cancer cells can be transduced through competitive adsorption of fluorophore-labeled EMAmer probes loaded on MoS2 nanosheets. We demonstrate that this MoS2-EMAmer-based sensor array enables rapid and effective discrimination among six types of cancer cells and their mixtures with a concentration of 104 cells within 60 min, achieving a 94.4% accuracy in identifying blinded unknown cell samples. The established MoS2-EMAmer sensing platform is anticipated to show significant promise in the advancement of cancer diagnostics.