A Turing test of whether AI chatbots are behaviorally similar to humans.

We administer a Turing test to AI chatbots. We examine how chatbots behave in a suite of classic behavioral games that are designed to elicit characteristics such as trust, fairness, risk-aversion, cooperation, etc., as well as how they respond to a traditional Big-5 psychological survey that measures personality traits. ChatGPT-4 exhibits behavioral and personality traits that are statistically indistinguishable from a random human from tens of thousands of human subjects from more than 50 countries. Chatbots also modify their behavior based on previous experience and contexts "as if" they were learning from the interactions and change their behavior in response to different framings of the same strategic situation. Their behaviors are often distinct from average and modal human behaviors, in which case they tend to behave on the more altruistic and cooperative end of the distribution. We estimate that they act as if they are maximizing an average of their own and partner's payoffs.

Chronic trans fatty acid consumption shortens lifespan in male Drosophila melanogaster on a high-sugar and high-fat diet.

Aging entails the progressive decline in the body's self-regulation and functionality over time. Notably, obesity and aging exhibit parallel phenotypes, with obesity further accelerating the aging process across multiple dimensions and diminishing lifespan. In this study, we explored the impact of trans fatty acid (TFA) consumption on the overall health and lifespan of male Drosophila melanogaster under an isocaloric high-sugar and high-fat diet. Our results indicate that TFA intake results in a shortened lifespan, elevated body weight, and increased triglyceride levels in flies fed a high-sugar and high-fat diet with equivalent caloric intake. Additionally, TFA exposure induces oxidative stress, locomotor deficits, and damage to the intestinal barrier in flies. Collectively, chronic TFA consumption expedites the aging process and reduces the lifespan of male Drosophila melanogaster. These results contribute supplementary evidence regarding the adverse health effects associated with TFAs.

Targeted pathophysiological treatment of ischemic stroke using nanoparticle-based drug delivery system.

Ischemic stroke poses significant challenges in terms of mortality and disability rates globally. A key obstacle to the successful treatment of ischemic stroke lies in the limited efficacy of administering therapeutic agents. Leveraging the unique properties of nanoparticles for brain targeting and crossing the blood-brain barrier, researchers have engineered diverse nanoparticle-based drug delivery systems to improve the therapeutic outcomes of ischemic stroke. This review provides a concise overview of the pathophysiological mechanisms implicated in ischemic stroke, encompassing oxidative stress, glutamate excitotoxicity, neuroinflammation, and cell death, to elucidate potential targets for nanoparticle-based drug delivery systems. Furthermore, the review outlines the classification of nanoparticle-based drug delivery systems according to these distinct physiological processes. This categorization aids in identifying the attributes and commonalities of nanoparticles that target specific pathophysiological pathways in ischemic stroke, thereby facilitating the advancement of nanomedicine development. The review discusses the potential benefits and existing challenges associated with employing nanoparticles in the treatment of ischemic stroke, offering new perspectives on designing efficacious nanoparticles to enhance ischemic stroke treatment outcomes.

Human gingival mesenchymal stem cell-derived exosomes cross-regulate the Wnt/ß-catenin and NF-κB signalling pathways in the periodontal inflammation microenvironment.

AIM: To examine the immunomodulatory effect of exosomes originating from gingival mesenchymal stem cells (GMSC-Exo) on periodontal bone regeneration and its role in the regulation of the nuclear-factor kappaB (NF-κB) and Wnt/ß-catenin pathways in the periodontal inflammatory microenvironment. MATERIALS AND METHODS: First, periodontal ligament stem cells (PDLSCs) were treated with GMSC-Exo or Porphyromonas gingivalis-derived lipopolysaccharide (P.g-LPS) in vitro. Quantitative real-time PCR (qRT-PCR) and western blot were carried out to detect the expressions of osteogenic differentiation-related factors in cells. Further, PDLSCs were treated with P.g-LPS or inhibitors. The expression of NF-κB pathway-related factors as well as of Wnt/ß-catenin pathway-related factors were detected by qRT-PCR and western blot. RESULTS: GMSC-Exo treatment promoted the expression of osteogenic differentiation-related factors within PDLSCs in both normal and inflammatory environments. Further investigations showed that GMSC-Exo could also inhibit the P.g-LPS-induced activation of the NF-κB pathway, leading to the up-regulation of the Wnt/ß-catenin pathway. When the Wnt/ß-catenin signalling was blocked, the inhibitory effect of GMSC-Exo on the NF-κB pathway was abolished. CONCLUSIONS: GMSC-Exo could promote the osteogenic differentiation of PDLSCs. There could be mutually exclusive regulatory roles between the NF-κB and Wnt/ß-catenin signalling pathways in a periodontal inflammatory environment. GMSC-Exo exhibited an effective cross-regulation ability for both pathways.

Microalgae tolerant of boron stress and bioresources accumulation during the boron removal process.

Boron (B) industry and consuming produce large amounts of B-containing wastewater. Low tolerance of microorganisms and plants resulted in the biological removal of B was limited. Microalgae show high adaptability in adverse environments. Whether microalgae able to be utilized in B removal meanwhile produce bioresources, and the B tolerant mechanisms and regulation pathway of microalgae are unclear. In this study, the cell growth, B removal, and lipid/starch production of Chlorella regularis under different levels of B stress (0.5, 10, 25, and 50 mg/L) were examined. The mechanisms of signal perception and response were explored by transcriptome and network analysis. Microalgae tolerated 25 mg/L high B stress, cell growth showed no decline and biomass reach up to 4.5 g/L. Microalgae took in B with 3.35 mg/g and bonded them to protein and carbon components in cells, the B removal capability was higher than some special adsorbents. Microalgae produced 188.65 mg/(L∙d) lipids and 305.35 mg/(L∙d) starch. The mitogen-activated protein-kinase signaling pathway was involved in the B tolerance of microalgae and regulated B efflux, glycolysis, and lipid/starch accumulation to relieve B stress. This study provides potential biological technique for B removal in wastewater and promotes new insight into signal role in toxic pollutants biological treatment.

Risk Factors Associated With Misdiagnosis of Digital Glomus Tumor: A Retrospective Cohort Study.

OBJECTIVE: Glomus tumors are benign with unique triad of symptoms; however, the delayed diagnosis of these tumors is common. We investigated the possible risk factors for the misdiagnosis of digital glomus tumors, with an aim to treat these patients on time. METHODS: We conducted a retrospective cohort study of 104 patients with digital glomus tumors from October 2009 to February 2021. Data pertaining to sex, age, tumor locations, symptoms, imaging modalities, and clinical departments visited by the patients were extracted and analyzed through logistic regression. RESULTS: The duration of delayed diagnosis ranged from 3 months to 40 years (mean, 5.5 ± 6.5 years). The total misdiagnosis and recurrence rate are 34.6% and 3.8%, respectively. On the multivariate logistic regression, the misdiagnosis of digital glomus tumor was significantly associated with the clinical departments visited by the patients ( P < 0.001). The risk of misdiagnosis of nonhand surgery department visit is 179.741-fold higher than that of hand surgery department visit. CONCLUSIONS: The misdiagnosis rate of digital glomus tumor was closely related to the clinical departments visited by the patients. Hand surgeons are the first choice for the treatment of the tumor.

Enhanced mixing state of black carbon with nitrate in single particles during haze periods in Zhengzhou, China.

Black carbon (BC) plays an important role in air quality and climate change, which is closely associated with its mixing state and chemical compositions. In this work the mixing state of BC-containing single particles was investigated to explore the evolution process of ambient BC particles using a single particle aerosol mass spectrometer (SPAMS) in March 2018 in Zhengzhou, China. The BC-containing particles accounted for 61.4% of total detected ambient single particles and were classified into five types including BC-nitrate (BC-N, 52.3%) as the most abundant species, followed by BC-nitrate-sulfate (BC-NS, 22.4%), BCOC (16.8%), BC-fresh (BC-F, 4.5%) and BC-sulfate particles (BC-S, 4.0%). With enhancement of the ambient nitrate concentration, the relative peak area (RPA) of nitrate in BC-N and BCNS particles both increased, yet only the number fraction (Nf) of BCN particles increased while the Nf of BC-NS particles decreased, suggesting that the enhanced mixing state of BC with nitrate was mainly due to the increase in the ambient nitrate mass concentration. In addition, the Nf of BC-N decreased from 65.3% to 28.4% as the absorbing Ångström exponents (AAE) of eBC increased from 0.75 to 1.45, which indicated the reduction of light absorption ability of aged BC particles with the enhanced formation of BC-N particles. The results of this work indicated a change in the mixing state of BC particles due to the dominance of nitrate in PM2.5, which also influenced the optical properties of aged BC particles.

[Chemical constituents from Artocarpus incisus and their inhibitory effects on proliferation of synoviocytes in vitro].

The chemical constituents from the branches and leaves of Artocarpus incisus were isolated and purified via silica gel, ODS, and Sephadex LH-20 column chromatography as well as preparative HPLC. The chemical structures of all isolated compounds were identified in the light of their physicochemical properties, spectroscopic analyses, and comparisons of their physicochemical and spectroscopic data with the reported data in literature. As a result, 20 compounds were isolated and characterized from the 90% ethanol extract of the branches and leaves of A. incisus, which were identified as tephrosin(1), 6-hydroxy-6 a, 12 a-dehydrodeguelin(2), sarcolobin(3), lupiwighteone(4), 12-deoxo-12α-methoxyelliptone(5), 6 aα,12 aα-12 a-hydroxyelliptone(6), homopterocarpin(7), 3-hydroxy-8,9-dimethoxypterocarpan(8), pterocarpin(9), maackiain(10), medicarpin(11), calycosin(12), genistein(13), formononetin(14), 5-hydroxy-4',7-dimethoxy isoflavone(15), liquiritigenin(16), 4(15)-eudesmene-1ß,7α-diol(17), ent-4(15)-eudesmene-1ß,6α-diol(18), 1α-hydroxyisodauc-4-en-15-al(19), and guaianediol(20). Except compounds 13 and 16, all other compounds were isolated from the Artocarpus plants for the first time. Additionally, using MTS assay, compounds 1-20 were eva-luated for their anti-rheumatoid arthritis activities by measuring their anti-proliferative effects on synoviocytes in vitro. As a consequence, compounds 1-16 showed notable anti-rheumatoid arthritis activities, which displayed inhibitory effects on the proliferation of MH7 A synovial fibroblast cells, with the IC_(50) values in range of(9.86±0.09)-(218.07±1.96) µmol·L~(-1).

Bioactive prenylated coumarins as potential anti-inflammatory and anti-HIV agents from Clausena lenis.

The phytochemical study on the stems of Clausena lenis resulted in the isolation of three new prenylated coumarins, clauselenins A-C (1-3), together with nine known prenylated coumarins (4-12). The chemical structures of new prenylated coumarins (1-3) were elucidated by means of comprehensive spectral analyses and the known compounds (4-12) were determined by means of comparing their experimental spectral data with those described data in the literatures. All isolated prenylated coumarins were assessed for their anti-inflammatory effects together with anti-HIV activities in vitro. Prenylated coumarins 1-12 displayed remarkable inhibitory effects against nitric oxide (NO) production induced by lipopolysaccharide in mouse macrophage RAW 264.7 cells in vitro with the IC50 values which are comparable to hydrocortisone. Meanwhile, prenylated coumarins 1-12 exhibited considerable anti-HIV-1 reverse transcriptase (RT) activities possessing EC50 values in the range of 0.17-9.08 µM. These findings indicate that the isolation and identification of these prenylated coumarins with pronounced anti-inflammatory effects as well as anti-HIV activities separated from the stems of C. lenis could be of great significance to the development of new anti-inflammatory and anti-HIV agents and their potential applications in the pharmaceutical industry.

Structural characterization, antiproliferative and anti-inflammatory activities of alkaloids from the roots of Zanthoxylum austrosinense.

Three new carbazole alkaloids, zanthoaustrones A-C (1-3), as well as nine known compounds 4-12, were isolated and characterized from the roots of Zanthoxylum austrosinense Huang (Rutaceae). Their chemical structures were elucidated on the basis of extensive and comprehensive spectroscopic methods, while the known alkaloids were identified by the comparison of their observed spectroscopic data including NMR data, MS data and optical rotation values with the data described in the literature. Furthermore, the antiproliferative activities as well as the anti-inflammatory effects of all isolated alkaloids in vitro were evaluated. All obtained alkaloids 1-12 displayed notable antiproliferative activities against diverse human cancer cell lines exhibiting IC50 values in range of 0.85 ± 0.06 to 29.56 ± 0.17 µM, which is equivalent to the positive control (cisplatin) showing IC50 values ranging from 1.58 ± 0.09 to 28.69 ± 0.21 µM. Moreover, compounds 1-12 exhibited pronounced inhibitory activities on nitric oxide (NO) production with IC50 values displaying IC50 values in range of 0.89 ± 0.05 to 9.62 ± 0.15 µM, which is comparable to the positive control (hydrocortisone) holding an IC50 value of 4.06 ± 0.11 µM. These findings indicate that the separation and characterization of these alkaloids displaying significant antiproliferative activities together with anti-inflammatory effects from the roots of Z. austrosinense could be meaningful to the research and development of new anti-cancer drugs as well as anti-inflammatory agents.

A Power-Frequency Electric Field Sensor for Portable Measurement.

In this paper, a new type of electric field sensor is proposed for the health and safety protection of inspection staff in high-voltage environments. Compared with the traditional power frequency electric field measurement instruments, the portable instrument has some special performance requirements and, thus, a new kind of double spherical shell sensor is presented. First, the mathematical relationships between the induced voltage of the sensor, the output voltage of the measurement circuit, and the original electric field in free space are deduced theoretically. These equations show the principle of the proposed sensor to measure the electric field and the effect factors of the measurement. Next, the characteristics of the sensor are analyzed through simulation. The simulation results are in good agreement with the theoretical analysis. The influencing rules of the size and material of the sensor on the measurement results are summarized. Then, the proposed sensor and the matching measurement system are used in a physical experiment. After calibration, the error of the measurement system is discussed. Lastly, the directional characteristic of the proposed sensor is experimentally tested.

Genetic control and regulatory mechanisms of succinoglycan and curdlan biosynthesis in genus Agrobacterium.

Agrobacterium is a genus of gram-negative bacteria that can produce several typical exopolysaccharides with commercial uses in the food and pharmaceutical fields. In particular, succinoglycan and curdlan, due to their good quality in high yield, have been employed on an industrial scale comparatively early. Exopolysaccharide biosynthesis is a multiple-step process controlled by different functional genes, and various environmental factors cause changes in exopolysaccharide biosynthesis through regulatory mechanisms. In this mini-review, we focus on the genetic control and regulatory mechanisms of succinoglycan and curdlan produced by Agrobacterium. Some key functional genes and regulatory mechanisms for exopolysaccharide biosynthesis are described, possessing a high potential for application in metabolic engineering to modify exopolysaccharide production and physicochemical properties. This review may contribute to the understanding of exopolysaccharide biosynthesis and exopolysaccharide modification by metabolic engineering methods in Agrobacterium.

Influence and Correction from the Human Body on the Measurement of a Power-Frequency Electric Field Sensor.

According to the operating specifications of existing electric field measuring instruments, measuring technicians must be located far from the instruments to eliminate the influence of the human body occupancy on a spatial electric field. Nevertheless, in order to develop a portable safety protection instrument with an effective electric field warning function for working staff in a high-voltage environment, it is necessary to study the influence of an approaching human body on the measurement of an electric field and to correct the measurement results. A single-shaft electric field measuring instrument called the Type LP-2000, which was developed by our research team, is used as the research object in this study. First, we explain the principle of electric field measurement and describe the capacitance effect produced by the human body. Through a theoretical analysis, we show that the measured electric field value decreases as a human body approaches. Their relationship is linearly proportional. Then, the ratio is identified as a correction coefficient to correct for the influence of human body proximity. The conclusion drawn from the theoretical analysis is proved via simulation. The correction coefficient kb = 1.8010 is obtained on the basis of the linear fitting of simulated data. Finally, a physical experiment is performed. When no human is present, we compare the results from the Type LP-2000 measured with Narda EFA-300 and the simulated value to verify the accuracy of the Type LP-2000. For the case of an approaching human body, the correction coefficient kb* = 1.9094 is obtained by comparing the data measured with the Type LP-2000 to the simulated value. The correction coefficient obtained from the experiment (i.e., kb*) is highly consistent with that obtained from the simulation (i.e., kb). Two experimental programs are set; under these programs, the excitation voltages and distance measuring points are regulated to produce different electric field intensities. Using kb = 1.9094, the corrected measurement of electric field intensity can accurately reflect the original environmental electric field intensity, and the maximal error is less than 6% in all the data comparisons. These results verify the effectiveness of our proposed method.

Modeling annotator preference and stochastic annotation error for medical image segmentation.

Manual annotation of medical images is highly subjective, leading to inevitable annotation biases. Deep learning models may surpass human performance on a variety of tasks, but they may also mimic or amplify these biases. Although we can have multiple annotators and fuse their annotations to reduce stochastic errors, we cannot use this strategy to handle the bias caused by annotators' preferences. In this paper, we highlight the issue of annotator-related biases on medical image segmentation tasks, and propose a Preference-involved Annotation Distribution Learning (PADL) framework to address it from the perspective of modeling an annotator's preference and stochastic errors so as to produce not only a meta segmentation but also the annotator-specific segmentation. Under this framework, a stochastic error modeling (SEM) module estimates the meta segmentation map and average stochastic error map, and a series of human preference modeling (HPM) modules estimate each annotator's segmentation and the corresponding stochastic error. We evaluated our PADL framework on two medical image benchmarks with different imaging modalities, which have been annotated by multiple medical professionals, and achieved promising performance on all five medical image segmentation tasks. Code is available at https://github.com/Merrical/PADL.

UniMiSS+: Universal Medical Self-Supervised Learning From Cross-Dimensional Unpaired Data.

Self-supervised learning (SSL) opens up huge opportunities for medical image analysis that is well known for its lack of annotations. However, aggregating massive (unlabeled) 3D medical images like computerized tomography (CT) remains challenging due to its high imaging cost and privacy restrictions. In our pilot study, we advocated bringing a wealth of 2D images like chest X-rays as compensation for the lack of 3D data, aiming to build a universal medical self-supervised representation learning framework, called UniMiSS. Especially, we designed a pyramid U- like medical Transformer (MiT) as the backbone to make UniMiSS possible to perform SSL with both 2D and 3D images. Consequently, the predecessor UniMiSS has two obvious merits compared to current 3D-specific SSL: (1) more effective - superior to learning strong representations, benefiting from more and diverse data; and (2) more versatile - suitable for various downstream tasks without the restriction on the dimensionality barrier. Unfortunately, UniMiSS did not dig deeply into the intrinsic anatomy correlation between 2D medical images and 3D volumes due to the lack of paired multi-modal/dimension patient data. In this extension paper, we propose the UniMiSS+, in which we introduce the digitally reconstructed radiographs (DRR) technology to simulate X-ray images from a CT volume to access paired CT and X-ray data. Benefiting from the paired group, we introduce an extra pair- wise constraint to boost the cross-modality correlation learning, which also can be adopted as a cross-dimension regularization to further improve the representations. We conduct expensive experiments on multiple 3D/2D medical image analysis tasks, including segmentation and classification. The results show that the proposed UniMiSS+ achieves promising performance on various downstream tasks, not only outperforming the ImageNet pre-training and other advanced SSL counterparts substantially but also improving the predecessor UniMiSS pre-training. Code is available at: https://github.com/YtongXie/UniMiSS-code.

Rethinking masked image modelling for medical image representation.

Masked Image Modelling (MIM), a form of self-supervised learning, has garnered significant success in computer vision by improving image representations using unannotated data. Traditional MIMs typically employ a strategy of random sampling across the image. However, this random masking technique may not be ideally suited for medical imaging, which possesses distinct characteristics divergent from natural images. In medical imaging, particularly in pathology, disease-related features are often exceedingly sparse and localized, while the remaining regions appear normal and undifferentiated. Additionally, medical images frequently accompany reports, directly pinpointing pathological changes' location. Inspired by this, we propose Masked medical Image Modelling (MedIM), a novel approach, to our knowledge, the first research that employs radiological reports to guide the masking and restore the informative areas of images, encouraging the network to explore the stronger semantic representations from medical images. We introduce two mutual comprehensive masking strategies, knowledge-driven masking (KDM), and sentence-driven masking (SDM). KDM uses Medical Subject Headings (MeSH) words unique to radiology reports to identify symptom clues mapped to MeSH words (e.g., cardiac, edema, vascular, pulmonary) and guide the mask generation. Recognizing that radiological reports often comprise several sentences detailing varied findings, SDM integrates sentence-level information to identify key regions for masking. MedIM reconstructs images informed by this masking from the KDM and SDM modules, promoting a comprehensive and enriched medical image representation. Our extensive experiments on seven downstream tasks covering multi-label/class image classification, pneumothorax segmentation, and medical image-report analysis, demonstrate that MedIM with report-guided masking achieves competitive performance. Our method substantially outperforms ImageNet pre-training, MIM-based pre-training, and medical image-report pre-training counterparts. Codes are available at https://github.com/YtongXie/MedIM.

Formulation and characterization of nanocapsules loaded with roselle anthocyanins extract and enhancement of anthocyanins bioaccessibility.

Hibiscus sabdariffa L. (roselle) is a medicinal and edible plant which rich in anthocyanins with potent antioxidant properties. To enhance the stability of roselle anthocyanins, they were encapsulated in nanocapsules composed of carboxymethyl chitosan (CMC), chitosan hydrochloride (CHC), and ß-lactoglobulin (ß-Lg). In vitro simulated digestion assays evaluated the impact of various core-to-wall ratios and ß-Lg concentrations on the bioaccessibility of seven anthocyanins. Nanocapsules with a core-to-wall ratio of 1:2 and ß-Lg at 10 mg/mL exhibited the highest encapsulation efficiency (EE). Cyanidin-3-glucoside had the highest EE, while cyanidin-3-sambubioside showed the outstanding retention rate. Furthermore, simulated digestion experiments combined with molecular docking revealed that peonidin-3-glucoside and petunidin-3-glucoside likely interact with and bind to the outer ß-Lg layer of the nanocapsules, increasing their release during in vitro digestion. This study demonstrates that encapsulating roselle anthocyanins in CMC, CHC, and ß-Lg nanocapsules significantly enhances their bioaccessibility.

Consistency-guided Differential Decoding for Enhancing Semi-supervised Medical Image Segmentation.

Semi-supervised learning (SSL) has been proven beneficial for mitigating the issue of limited labeled data, especially on volumetric medical image segmentation. Unlike previous SSL methods which focus on exploring highly confident pseudo-labels or developing consistency regularization schemes, our empirical findings suggest that differential decoder features emerge naturally when two decoders strive to generate consistent predictions. Based on the observation, we first analyze the treasure of discrepancy in learning towards consistency, under both pseudo-labeling and consistency regularization settings, and subsequently propose a novel SSL method called LeFeD, which learns the feature-level discrepancies obtained from two decoders, by feeding such information as feedback signals to the encoder. The core design of LeFeD is to enlarge the discrepancies by training differential decoders, and then learn from the differential features iteratively. We evaluate LeFeD against eight state-of-the-art (SOTA) methods on three public datasets. Experiments show LeFeD surpasses competitors without any bells and whistles, such as uncertainty estimation and strong constraints, as well as setting a new state of the art for semi-supervised medical image segmentation. Code has been released at https://github.com/maxwell0027/LeFeD.

ReFs: A hybrid pre-training paradigm for 3D medical image segmentation.

Self-supervised learning (SSL) has achieved remarkable progress in medical image segmentation. The application of an SSL algorithm often follows a two-stage training process: using unlabeled data to perform label-free representation learning and fine-tuning the pre-trained model on the downstream tasks. One issue of this paradigm is that the SSL step is unaware of the downstream task, which may lead to sub-optimal feature representation for a target task. In this paper, we propose a hybrid pre-training paradigm that is driven by both self-supervised and supervised objectives. To achieve this, a supervised reference task is involved in self-supervised learning, aiming to improve the representation quality. Specifically, we employ the off-the-shelf medical image segmentation task as reference, and encourage learning a representation that (1) incurs low prediction loss on both SSL and reference tasks and (2) leads to a similar gradient when updating the feature extractor from either task. In this way, the reference task pilots SSL in the direction beneficial for the downstream segmentation. To this end, we propose a simple but effective gradient matching method to optimize the model towards a consistent direction, thus improving the compatibility of both SSL and supervised reference tasks. We call this hybrid pre-training paradigm reference-guided self-supervised learning (ReFs), and perform it on a large-scale unlabeled dataset and an additional reference dataset. The experimental results demonstrate its effectiveness on seven downstream medical image segmentation benchmarks.