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As a promising cancer treatment method, cold atmospheric plasma has received widespread attention in recent years. However, previous research has focused more on how to realize and expand the anti-cancer scope of plasma jet. There are also studies on the killing of small-scale cancer cells, but the effects of plasma jet on normal cells and normal cell clusters have been ignored. Therefore, we proposed a 50 µm sized micro-plasma jet device, and used the device to treat melanoma cells (A-375) and human glial cells (HA1800) to evaluate their anti-cancer effects and effects on normal cells. The experimental results show that this kind of micro-plasma jet device can effectively inactivate cancer cells in a short period of time, while having little effect on normal cells. This work provides a certain experimental basis for the application of fine plasma jet to clinically inactivate cancer cells. ARTICLE HIGHLIGHTS: HIGHLIGHTS • The experiment of apoptosis induced by plasma jet was carried out in this paper. A-375 and HA1800 were treated with plasma jet and subsequently characterized. • Based on the experimental results of this paper, it can be concluded that plasma jet has a significant role in inducing apoptosis. • The ability of plasma jet to induce apoptosis of cancer cells is much higher than that of normal cells. [ABSTRACT FROM AUTHOR]

To verify the existence of plasma with the potential to kill tumor cells, this paper designed a novel helium (He) micro-plasma jet array device and detected the concentration of typical long-lived reactive oxygen and nitrogen species (RONS) with oxidative activity generated by it. The paper described a new He micro-plasma jet array device consisting of nine flexible quartz capillaries with an inner diameter of 75 μm arranged in a 3 × 3 array. Sterilized ultrapure water (up water) was first treated with the He micro-plasma jet array device to activate it to form enriched RONS micro-plasma-activated water (μ-PAW), and then μ-PAW was added to the cell culture medium (with cells) to observe the proliferation of human glioma cells. The concentration of long-lived RONS, such as nitrate (NO3−), was detected according to Beer–Lambert's law in combination with UV spectrophotometry as well as a color development method. The MTT Cell Proliferation and Cytotoxicity Assay Kit combined with the Hoechst Staining Kit were used to assess the proliferation status of the cells. The results showed that the range of RONS concentration variation could be controlled in the order of micromoles (µmol), while plasma-induced tumor cell death is apoptosis that does not affect the surrounding environment. [ABSTRACT FROM AUTHOR]

The identification of phosphates holds significant importance in many physiological processes and disease diagnosis, and traditional detection techniques struggle to simultaneously detect and distinguish phosphates. The complexity of synthesizing sensing units restricts the construction of sensor arrays as well. In this study, a bifunctional dicopper chloride trihydroxide (Cu 2 Cl(OH) 3) nanozyme with conspicuous laccase- and peroxidase-like activities has been synthesized in basic deep eutectic solvents (DES). Exploiting the various regulatory impacts of multiple phosphates on the dual-enzyme mimicking activities, the sensor array based on the laccase mimic and peroxidase mimic properties of Cu 2 Cl(OH) 3 was designed, which has been successfully harnessed for the identification of eight phosphates (ATP, ADP, AMP, PPi, Pi, GTP, GDP, and GMP). This approach streamlines the creation of sensor arrays. Besides, the three simulated actual samples (healthy individuals, moderately ill patients, and severely ill patients) have been accurately distinguished. This work makes a substantial contribution to enhancing the highly effective construction of array channels and promoting discrimination of phosphates in intricate samples. [Display omitted] • A bifunctional Cu 2 Cl(OH) 3 nanozyme was successfully prepared in basic DES. • A sensor array was developed based on dual-enzyme mimetic activity of Cu 2 Cl(OH) 3. • Pattern recognition of eight phosphates can be achieved through LDA. • The sensor array has successfully distinguished three simulated actual samples. [ABSTRACT FROM AUTHOR]

In social advertising, the social platform host may run marketing campaigns for multiple competing clients simultaneously. In this case, each client comes up with a budget and an influence spread requirement. The host runs campaigns by allocating a set of seed nodes for each client. If the influence spread triggered by a seed set meets the requirement, the host can earn the budget from the corresponding client. In this paper, we study the problem of Profit Maximization, considering that different seeds incur different costs. Given all the clients' requirements met, we aim to find the optimal seed allocation with minimum cost. Under the competitive K-LT propagation model, we show the Profit Maximization problem is NP-hard and NP-hard to approximate with any factor. To find a feasible solution, we propose an effective algorithm that iteratively selects a candidate set and obtains an approximate allocation. The experimental results over a real-world dataset validate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Abstract Existing studies on influence maximization (IM) mainly focus on activating a set of influential users (seed nodes). Originated from the seed nodes' promotion actions (e.g., posting an advertising tweet) on social networks, a large influence spread might be triggered. However, in practice it is usually very expensive to have influential users posting original tweets in a promotional event. In contrast, it will incur much lower costs to have influential users reposting tweets and have ordinary users posting original tweets. Inspired by these observations, in this paper, we consider the Holistic Budgeted Influence Maximization (HBIM) problem, which maximizes the influence spread by deploying the budget to select seed nodes (for posting) and boost nodes (for reposting). To tackle the NP-hardness and non-submodularity of the problem, we devise two efficient algorithms with the data-dependent approximation ratios. Extensive experiments on real social networks demonstrate the efficiency and effectiveness of our proposed algorithms. [ABSTRACT FROM AUTHOR]

Abstract Existing works on influence maximization (IM) aim at finding influential online users as seed nodes. Originated from these seed nodes, large online influence spread can be triggered. However, such user-driven perspective limits the IM problem within the purely online environment. Due to the increasing interactions between the cyber world and the physical world, offline events in real world are showing more impact on online information spread. Most IM methods are totally unaware of the cyber–physical interactions and thus their effectiveness is limited when offline events are taken into account. To address this issue, in this paper we consider influence maximization from an online–offline interactive setting and propose the location-driven influence maximization (LDIM) problem. The LDIM problem aims to find the optimal offline deployment of locations and durations to hold events, so as to maximize the online influence spread. We propose a location-driven propagation (LDP) model to describe the online influence spread process triggered by offline events. Under the LDP model, we prove the LDIM problem is NP-hard and computing the objective function is #P-hard. Thus we develop a greedy algorithm over integer lattice and prove its 1 − 1 ∕ e approximation. To overcome the expensive time complexity, we further develop two algorithms with time complexity reduced successively while ensuring provable approximation ratios of 1 − 1 ∕ e − ε and 1 − 1 ∕ e − ε − ε ′ respectively. Experimental results on real datasets show the effectiveness of the proposed algorithms and the significant improvement on scalability. [ABSTRACT FROM AUTHOR]

Abstract Attention is the behavioral and cognitive process of selectively concentrating on small fraction of information while ignoring other perceivable information. Thus, user's attention will influence his decision on the consumption and consequently the performance of recommender systems. When presented a long list of recommendations, users will focus more on the items that draw their attention and skip others even if they suit their tastes. However, existing works in social recommendation mainly generate recommendations based on users' preference and overlook the importance of users' attention. To overcome this limitation, in this paper we propose a probabilistic model HTPF that considers both users' attention and preference in social recommendation. Besides, by analyzing four real-world social recommendation datasets, we observe that social relations have more explicit impact on users' attention than on their item rating values. That is, user's attention on items are easily affected by his social relations while his rating values seem somehow indifferent to social influence. It will be more effective to use social network information to infer users' attention rather than users' preference. Thus, different from existing methods that model the social influence on user's rating values, HTPF focuses on exploiting social influence on user's attention. Further, a scalable variational inference algorithm has been developed for our model to predict how the specific items will draw users' attention and how the items will suit users' tastes. Then HTPF generates the recommendation results based on the weighted combination of these two aspects. Extensive experiments conducted on large real-world datasets validate the superiority of our method over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The detection of endogenous phenolic compounds (EPs) in food is of great significance in elucidating their bioactivity and health effects. Here, a novel bifunctional vanillic acid-Cu (VA-Cu) nanozyme with peroxidase-like and laccase-like activities was successfully prepared. The peroxidase mimic behavior of VA-Cu nanozyme can catalyze 3,3',5,5'-tetramethylbenzidine (TMB) to generate oxidized TMB (oxTMB). Owing to the high reducing power of EPs, this process can be inhibited, and the degree of inhibition increases with the increase of reaction time. Additionally, owing to the outstanding laccase mimic behavior of the VA-Cu, it can facilitate the oxidation of various EPs, resulting in the formation of colored quinone imines, and the degree of catalysis increases with the increase of reaction time. Based on the interesting experimental phenomena mentioned above, a six-channel nanozyme sensor array (2 enzyme-mimic activities × 3 time points = 6 sensing channels) was constructed, successfully achieving discriminant analysis of nine EPs. In addition, the combination of artificial neural network (ANN) algorithms and sensor arrays has successfully achieved accurate identification and prediction of nine EPs in black tea, honey, and grape juice. Finally, a portable method for identifying EPs in food has been proposed by combining it with a smartphone.

Melanization reflects not only body color variation but also environmental plasticity. It is a strategy that helps insects adapt to environmental change. Different color morphs may have distinct life history traits, e.g., development time, growth rate, and body weight. The green slender planthopper Saccharosydne procerus (Matsumura) is the main pest of water bamboo (Zizania latifolia). This insect has two color morphs. The present study explored the influence of photoperiod and its interaction with temperature in nymph stage on adult melanism. Additionally, the longevity, fecundity, mating rate, and hatching rate of S. procerus were examined to determine whether the fitness of the insect was influenced by melanism under different temperature and photoperiod. The results showed that a greater number of melanic morphs occurred if the photoperiod was extremely long. A two-factor ANOVA showed that temperature and photoperiod both have a significant influence on melanism. The percentages of variation explained by these factors were 45.53 and 48.71%, respectively. Moreover, melanic morphs had greater advantages than non-melanic morphs under an environmental regime of high temperatures and a long photoperiod, whereas non-melanic morphs were better adapted to cold temperatures and a short photoperiod. These results cannot be explained by the thermal melanism hypothesis. Thus, it may be unavailable to seek to explain melanism in terms of only one hypothesis.