Generation Step Research Articles

ІННОВАЦІЙНИЙ ПІДХІД У ПРОГНОЗУВАННІ ЧАСОВИХ РЯДІВ: ВІД ТРАДИЦІЙНИХ МЕТОДІВ ДО НОВАТОРСЬКОЇ МОДЕЛІ TIMESFM

Forecasting time series is crucial for companies as it enables them to make informed decisions using historical data and future predictions. Various approaches exist for time series forecasting, each tailored to different data characteristics and forecasting tasks. Currently, widely known models include ARIMA, LSTM networks, Prophet, and XGBoost. However, these models often require significant preprocessing time. To address this issue and leverage recent advancements in generative AI, Google introduced TimesFM, a decoder-only model specifically designed for time series forecasting. TimesFM utilizes transformer layers and a multi-layer perceptron block to transform time series fragments into tokens, enabling efficient forecasting with minimal generation steps. Synthetic and real-world data are combined for pretraining to capture fundamental temporal patterns and enhance model generalization. Evaluation demonstrates TimesFM's competitive performance across various benchmark time series datasets compared to traditional statistical methods and DL models.

Biomechanical evaluation of flash-frozen and cryo-sectioned papillary muscle samples by using sinusoidal analysis: cross-bridge kinetics and the effect of partial Ca2+ activation.

We examined the integrity of flash-frozen and cryo-sectioned cardiac muscle preparations (introduced by Feng and Jin, 2020) by assessing tension transients in response to sinusoidal length changes at varying frequencies (1-100Hz) at 25°C. Using 70-μm-thick sections, we isolated fiber preparations to study cross-bridge (CB) kinetics: preparations were activated by saturating Ca2+ as well as varying concentrations of ATP and phosphate (Pi). Our results showed that, compared to ordinary skinned fibers, in-series stiffness decreased to 1/2, which resulted in a decrease of isometric tension to 62%, but CB kinetics and Ca2+ sensitivity were little affected. The pCa study demonstrated that the rate constant of the force generation step (2πb) is proportionate to [Ca2+] at < 5μM, suggesting that the activation mechanism can be described by a simple second order reaction. We also found that tension, stiffness, and magnitude parameters are related to [Ca2+] by the Hill equation, with a cooperativity coefficient of 4-5, which is consistent with the fact that Ca2+ activation mechanisms involve cooperative multimolecular interactions. Our results support the long-held hypothesis that Process C (Phase 2) represents the CB detachment step, and Process B (Phase 3) represents the force generation step. Moreover, we discovered that constant H may represent the work-performing step in cardiac preparations. Our experiments demonstrate excellent CB kinetics with two well-defined exponentials that can be more distinguished than those found using ordinary skinned fibers. Flash-frozen and cryo-sectioned preparations are especiallysuitable for multi-institutionalcollaborations nationally and internationally because of their ease of transportation.

Oscillatory work and the step that generates force in single myofibrils from rabbit psoas.

The elementary molecular step that generates force by cross-bridges (CBs) in active muscles has been under intense investigation in the field of muscle biophysics. It is known that an increase in the phosphate (Pi) concentration diminishes isometric force in active fibers, indicating a tight coupling between the force generation step and the Pi release step. The question asked here is whether the force generation occurs before Pi release or after release. We investigated the effect of Pi on oscillatory work production in single myofibrils and found that Pi-attached state(s) to CBs is essential for its production. Oscillatory work is the mechanism that allows an insect to fly by beating its wings, and it also has been observed in skeletal and cardiac muscle fibers, implying that it is an essential feature of all striated muscle types. With our studies, oscillatory work disappears in the absence of Pi in experiments using myofibrils. This suggests that force is generated during a transition between steps of oscillatory work production, and that the states involved in force production must have Pi attached. With sinusoidal analysis, we obtained the kinetic constants around the Pi release steps, established a CB scheme, and evaluated force generated (and supported) by each CB state. Our results demonstrate that force is generated before Pi is released, and the same force is maintained after Pi is released. Stretch activation and/or delayed tension can also be explained with this CB scheme and forms the basis of force generation and oscillatory work production.

Rescue of Mycobacterium bovis DNA Obtained from Cultured Samples during Official Surveillance of Animal TB: Key Steps for Robust Whole Genome Sequence Data Generation.

Epidemiological surveillance of animal tuberculosis (TB) based on whole genome sequencing (WGS) of Mycobacterium bovis has recently gained track due to its high resolution to identify infection sources, characterize the pathogen population structure, and facilitate contact tracing. However, the workflow from bacterial isolation to sequence data analysis has several technical challenges that may severely impact the power to understand the epidemiological scenario and inform outbreak response. While trying to use archived DNA from cultured samples obtained during routine official surveillance of animal TB in Portugal, we struggled against three major challenges: the low amount of M. bovis DNA obtained from routinely processed animal samples; the lack of purity of M. bovis DNA, i.e., high levels of contamination with DNA from other organisms; and the co-occurrence of more than one M. bovis strain per sample (within-host mixed infection). The loss of isolated genomes generates missed links in transmission chain reconstruction, hampering the biological and epidemiological interpretation of data as a whole. Upon identification of these challenges, we implemented an integrated solution framework based on whole genome amplification and a dedicated computational pipeline to minimize their effects and recover as many genomes as possible. With the approaches described herein, we were able to recover 62 out of 100 samples that would have otherwise been lost. Based on these results, we discuss adjustments that should be made in official and research laboratories to facilitate the sequential implementation of bacteriological culture, PCR, downstream genomics, and computational-based methods. All of this in a time frame supporting data-driven intervention.

High Efficiency In vitro Whole Plant Regeneration via Desiccated Callus in Oryza sativa cv. MTU1010

For most people across the planet, rice is also considered as most beneficial crops in terms of fulfilling every day's energy and nutritional needs. Worldwide food safety is seriously threatened since severe limitations on rice output caused by both abiotic and biotic variables. Even though rice features much more developed regeneration mechanism than other agricultural crops, majority of desi cultivars continue to stay as impervious to genetic alterations and regeneration. Therefore, among preliminary steps in the transgenic plant generation necessitates to improve tissue culture procedures to produce viable plants via embryogenic calli mediated regeneration. In this study, we reporting that enhanced rate of plant regeneration was observed from mature seed-derived embryogenic calli of the indica rice cultivar MTU1010 following partially desiccation procedure. We studied the effects of several plant growth regulators on the efficiency of inducing embryogenic callus and regeneration of whole plantlets. The MS-CIM medium with 2.0 mg-1 2,4, D showed the highest callus induction percentage. Maximum rate of shoot regeneration (92%) was observed from 48hr partially dehydrated calli, when enriched with high cytokinin (2 mg/l kinetin) and lower auxin (0.5 mg/l NAA) concentrations. Calli that had been moderately dehydrated showed significantly greater regeneration rates than undehydrated calli. Upon being transferred onto a rooting medium, the well-rooted plantlets were shifted to shaded glass house for hardening. Hence, this approach is rather simple to apply and can be employed in genetic modification studies alongside other biotechnological endeavours.

SENSAAS-Flex: a joint optimization approach for aligning 3D shapes and exploring the molecular conformation space.

Popular shape-based alignment methods handle molecular flexibility by utilizing conformational ensembles to select the most fitted conformer. However, the initial conformer library generation step is computationally intensive and limiting to the overall alignment process. In this work, we describe a method to perform flexible alignment of two molecular shapes by optimizing the 3D conformation. SENSAAS-Flex, an add-on to the SENSAAS tool, is able to proceed from a limited set of initial conformers through an iterative process where additional conformational optimizations are made at the substructure level and constrained by the target shape. In self- and cross-alignment experiments, SENSAAS-Flex is able to reproduce the crystal structure geometry of ligands of the AstraZeneca Molecule Overlay Test set and PDBbind refined dataset. Our study shows that the point-based representation of molecular surfaces is appropriate in terms of shape constraint to sample the conformational space and perform flexible molecular alignments. The documentation and source code are available at https://chemoinfo.ipmc.cnrs.fr/Sensaas-flex/sensaas-flex-main.tar.gz.

Inosine induces stemness features in CAR-T cells and enhances potency

Adenosine (Ado) mediates immune suppression in the tumor microenvironment and exhausted CD8+ CAR-T cells express CD39 and CD73, which mediate proximal steps in Ado generation. Here, we sought to enhance CAR-T cell potency by knocking out CD39, CD73, or adenosine receptor 2a (A2aR) but observed only modest effects. In contrast, overexpression of Ado deaminase (ADA-OE), which metabolizes Ado to inosine (INO), induced stemness and enhanced CAR-T functionality. Similarly, CAR-T cell exposure to INO augmented function and induced features of stemness. INO induced profound metabolic reprogramming, diminishing glycolysis, increasing mitochondrial and glycolytic capacity, glutaminolysis and polyamine synthesis, and reprogrammed the epigenome toward greater stemness. Clinical scale manufacturing using INO generated enhanced potency CAR-T cell products meeting criteria for clinical dosing. These results identify INO as a potent modulator of CAR-T cell metabolism and epigenetic stemness programming and deliver an enhanced potency platform for cell manufacturing.

ASCF: Optimization of the Apriori Algorithm Using Spark-Based Cuckoo Filter Structure

Data mining is the process used for extracting hidden patterns from large databases using a variety of techniques. For example, in supermarkets, we can discover the items that are often purchased together and that are hidden within the data. This helps make better decisions which improve the business outcomes. One of the techniques that are used to discover frequent patterns in large databases is frequent itemset mining (FIM) that is a part of association rule mining (ARM). There are different algorithms for mining frequent itemsets. One of the most common algorithms for this purpose is the Apriori algorithm that deduces association rules between different objects which describe how these objects are related together. It can be used in different application areas like market basket analysis, student’s courses selection process in the E-learning platforms, stock management, and medical applications. Nowadays, there is a great explosion of data that will increase the computational time in the Apriori algorithm. Therefore, there is a necessity to run the data-intensive algorithms in a parallel-distributed environment to achieve a convenient performance. In this paper, optimization of the Apriori algorithm using the Spark-based cuckoo filter structure (ASCF) is introduced. ASCF succeeds in removing the candidate generation step from the Apriori algorithm to reduce computational complexity and avoid costly comparisons. It uses the cuckoo filter structure to prune the transactions by reducing the number of items in each transaction. The proposed algorithm is implemented on the Spark in-memory processing distributed environment to reduce processing time. ASCF offers a great improvement in performance over the other candidate algorithms based on Apriori, where it achieves a time of only 5.8% of the state-of-the-art approach on the retail dataset with a minimum support of 0.75%.

Improved Selective Deep-Learning-Based Clustering Ensemble

Clustering ensemble integrates multiple base clustering results to improve the stability and robustness of the single clustering method. It consists of two principal steps: a generation step, which is about the creation of base clusterings, and a consensus function, which is the integration of all clusterings obtained in the generation step. However, most of the existing base clustering algorithms used in the generation step are shallow clustering algorithms such as k-means. These shallow clustering algorithms do not work well or even fail when dealing with large-scale, high-dimensional unstructured data. The emergence of deep clustering algorithms provides a solution to address this challenge. Deep clustering combines the unsupervised commonality of deep representation learning to address complex high-dimensional data clustering, which has achieved excellent performance in many fields. In light of this, we introduce deep clustering into clustering ensemble and propose an improved selective deep-learning-based clustering ensemble algorithm (ISDCE). ISDCE exploits the deep clustering algorithm with different initialization parameters to generate multiple diverse base clusterings. Next, ISDCE constructs ensemble quality and diversity evaluation metrics of base clusterings to select higher-quality and rich-diversity candidate base clusterings. Finally, a weighted graph partition consensus function is utilized to aggregate the candidate base clusterings to obtain a consensus clustering result. Extensive experimental results on various types of datasets demonstrate that ISDCE performs significantly better than existing clustering ensemble approaches.

Generating Well-Typed Terms That Are Not “Useless”

Random generation of well-typed terms lies at the core of effective random testing of compilers for functional languages. Existing techniques have had success following a top-down type-oriented approach to generation that makes choices locally, which suffers from an inherent limitation: the type of an expression is often generated independently from the expression itself. Such generation frequently yields functions with argument types that cannot be used to produce a result in a meaningful way, leaving those arguments unused. Such "use-less" functions can hinder both performance, as the argument generation code is dead but still needs to be compiled, and effectiveness, as a lot of interesting optimizations are tested less frequently. In this paper, we introduce a novel algorithm that is significantly more effective at generating functions that use their arguments. We formalize both the "local" and the "nonlocal" algorithms as step-relations in an extension of the simply-typed lambda calculus with type and arguments holes, showing how delaying the generation of types for subexpressions by allowing nonlocal generation steps leads to "useful" functions.

Degradation of tannic acid in heterogeneous Fenton system and persulfate system using iron(III) modified attapulgite: Influencing factors and degradation mechanism analysis

The redox cycle of iron is a well-known rate-determining step for radical generation in advanced oxidation system. In this study, iron(III) modified attapulgite (Fe-ATP) was employed as a regulator to enhance the degradation of tannic acid (TA) in heterogeneous Fenton and persulfate (PS) systems. Results showed that under the conditions of pH 3 and 0.5 g·L−1 of Fe-ATP, the maximum removal rate of TA was 82.2% and the removal rate of total organic carbon (TOC) was 31.7% in heterogeneous Fenton system with 5 mM H2O2 and 90 min reaction, and the maximum removal rate of TA was 78.5% and removal rate of TOC was 28.9% in PS system with 1 g·L−1 Na2S2O8 and 60 min reaction. Among commonly inorganic anions (Cl-, NO3-, HCO3-, CO32-, SO42-, HPO42- and H2PO4-), only HPO42- and H2PO4- significantly inhibited the TA removal by Fe-ATP. Radical quenching tests indicated that hydroxyl radical (∙OH) and sulfate radical (SO4∙-) were the main active species in the heterogeneous Fenton system and PS system, respectively. Moreover, the TA removal rate could still reach 40% after four times of recycling. Hence, the Fe-ATP could be a good catalyzer for treating tannic acid contaminated waters in heterogeneous Fenton system and persulfate system.

A novel improvement in RSA algorithm

In this study, we propose a new cryptographic scheme based on the textbook RSA algorithm associating the concept of continued fractions. The algorithm is designed under the three primary steps of key generation, encryption, and decryption. The key generation of the system is improved to generate a large encryption key without affecting the decryption key. The use of continued fractions gives an additional advantage as it encrypts a string of plaintexts by encrypting only two integers which optimizes the time and the memory consumption. The encryption process and the decryption process are similar to that of the standard RSA algorithm, insomuch the implemented algorithm does not affect the security of the original RSA algorithm which depends on the factoring problem. In addition, as the algorithm uses a large encryption key, it has been proven that the system is secure against the Wiener’s attack.

GTR: An SQL Generator With Transition Representation in Cross-Domain Database Systems.

Recent studies have focused on using natural language (NL) to automatically retrieve useful data from database (DB) systems. As an important component of autonomous DB systems, the NL-to-SQL technique can assist DB administrators in writing high-quality SQL statements and make persons with no SQL background knowledge learn complex SQL languages. However, existing studies cannot deal with the issue that the expression of NL inevitably mismatches the implementation details of SQLs, and the large number of out-of-domain (OOD) words makes it difficult to predict table columns. In particular, it is difficult to accurately convert NL into SQL in an end-to-end fashion. Intuitively, it facilitates the model to understand the relations if a "bridge" transition representation (TR) is employed to make it compatible with both NL and SQL in the phase of conversion. In this article, we propose an automatic SQL generator with TR called GTR in cross-domain DB systems. Specifically, GTR contains three SQL generation steps: 1) GTR learns the relation between questions and DB schemas; 2) GTR uses a grammar-based model to synthesize a TR; and 3) GTR predicts SQL from TR based on the rules. We conduct extensive experiments on two commonly used datasets, that is, WikiSQL and Spider. On the testing set of the Spider and WikiSQL datasets, the results show that GTR achieves 58.32% and 71.29% exact matching accuracy which outperforms the state-of-the-art methods, respectively.

“Triple-Hop” nano-bomb combining CDT, PDT and immunetherapy for NIR-triggered cancer therapy

The development of non-interventional combination therapy with immune activation property provides a potential direction for solving the diversity, complexity and heterogeneity of malignant tumors. However, how to induce satisfactory immune activation by non-interventional treatment remains a great challenge. Herein, a “Triple-Hop” nano-bomb UCNPs@SiO2-psoralen-K2FeO4@lauric acid-RGD (USPFLR), was prepared and “detonated” after three steps. For step one, with NIR light as the “ignition” signal, USPFLR promoted the tumor to reach 40℃ and the phase transition of Lauric acid (LA) induced the release of K2FeO4 and psoralen, triggering H2O2/GSH depletion and ·OH /O2 production through multi-step cascade reactions of K2FeO4 and psoralen-involved photodynamic therapy (PDT). For step two, high ROS release and direct DNA damage caused by psoralen further significantly triggered ferroptosis pathway and cGAS-STING pathway, which promoted the maturation of dendritic cells (DCs) and induced immune activation and immune memory in the third step. Therefore, through the first step of O2 supply, GSH consumption and ROS generation, the second step of the activation of the ferroptosis pathway and cGAS-STING pathway and the third step of immune activation and immune memory, USPFLR nano-bombs successfully realized the “Triple-Hop” anti-tumor strategy, providing a referential application paradigm for tumor eradication and prevention of tumor recurrence.

Algorithmic model generation for multi-site multi-period planning of clean processes by P-graphs

Optimal clean process design requires strict constraints to enforce waste and byproduct management, all of which can be formulated in the language of mathematical programming. However, waste management and the utilization of by-products are often carried out in locations or periods other than the production process. The paper describes all modeling steps by P-graphs sufficient to represent raw material availability and production capacities in multiple time periods at multiple sites, as well as transportation and storage capacities of process materials and wastes. These steps are integrated into a single comprehensive model generation algorithm. For easier understanding, each model generation step is illustrated by a case study of planning a multi-site multi-period furniture production process alongside the recent challenges of energy supply and waste management. Finally, the case study of furniture production is analyzed under various circumstances to highlight the power of the proposed tools in daily production and transportation planning. Accordingly, the proposed method provides such alternative 5 best manufacturing and logistics plans that, in the event of a complete failure or overloading of one of the production capacities at either locations, there is still an alternative plan within a 3% profit decrease.

PH-shape: an adaptive persistent homology-based approach for building outline extraction from ALS point cloud data

ABSTRACT Building outline extraction from segmented point clouds is a critical step of building footprint generation. Existing methods for this task are often based on the convex hull and α-shape algorithm. There are also some methods using grids and Delaunay triangulation. The common challenge of these methods is the determination of proper parameters. While deep learning-based methods have shown promise in reducing the impact and dependence on parameter selection, their reliance on datasets with ground truth information limits the generalization of these methods. In this study, a novel unsupervised approach, called PH-shape, is proposed to address the aforementioned challenge. The methods of Persistence Homology (PH) and Fourier descriptor are introduced into the task of building outline extraction. The PH from the theory of topological data analysis supports the automatic and adaptive determination of proper buffer radius, thus enabling the parameter-adaptive extraction of building outlines through buffering and “inverse” buffering. The quantitative and qualitative experiment results on two datasets with different point densities demonstrate the effectiveness of the proposed approach in the face of various building types, interior boundaries, and the density variation in the point cloud data of one building. The PH-supported parameter adaptivity helps the proposed approach overcome the challenge of parameter determination and data variations and achieve reliable extraction of building outlines.

Archaeological site segmentation of ancient city walls based on deep learning and LiDAR remote sensing

Ancient city walls, one of the most notable distinguishing features of Chinese ancient cities, are military defenses constructed of rammed earth. The ancient city walls have considerable study value because they served as the city's boundary and a symbol of power. However, as a result of natural erosion and human activities, many sites have been ruined. Existing optical remote sensing technologies, LiDAR point cloud processing algorithms, and deep learning methods are inadequate for the extraction and segmentation of ancient city wall sites. The novel semantic segmentation method for ancient city wall sites is described in this paper that extracts sites at the pixel level from LiDAR remote sensing data based on deep learning. To begin, the point cloud data collected by airborne laser scanning is processed into DEM data, and the distribution of ancient city walls in the study area is obtained through archaeological survey and expert interpretation. The dataset for deep learning semantic segmentation is then generated using image cropping and data augmentation techniques. Third, implement a U-Net semantic segmentation framework for microtopographic archaeological sites, and predict ancient city wall sites in the testing region after model training. Finally, the deep learning results are optimized using the connected component analysis method, and prediction mistakes such as holes and noise are removed. Taking Jinancheng, the capital city of the Chu kingdom, as an example, the proposed method process can identify and extract the ancient city wall sites at the pixel level, where the evaluation metrics reach 94.12% (Precision) and 81.38% (IoU). The experiment results are excellent due to improvement strategies in the dataset generation, model training, and post-processing steps. Thus, this study is significant for the current survey and protection of ancient city wall sites. The source code will be freely available at https://github.com/wshunli/Open-CHAI-CityWalls.

An in Vitro Pilot Study of the Role of Factor IX As an Alternative to Bypassing Agents in the Treatment of Breakthrough Bleeds in Patients with Hemophilia a and Inhibitors Treated with Emicizumab

Introduction: Hemophilia A(HA) is an X linked disorder characterized by a deficiency in Factor VIII. 30% of patients with severe HA develop alloantibodies that inhibit the coagulant activity of FVIII. Emicizumab, a substitution therapy that bridges factor IX and X, is approved in patients with severe HA with and without inhibitors. Indeed, it is now the standard of care prophylactic therapy for patients with inhibitors. Breakthrough bleeds and perioperative management require the use of bypassing agents in addition to emicizumab, with recombinant VIIa deemed the safest of these options. Nevertheless there are still concerns about thrombotic risks and inability to monitor bypassing agents given to patients on emicizumab. Since the rate limiting step for FXa generation in the plasma of patients receiving emicizumab is the concentration of generated FIXa, we conducted an in vitro pilot study to evaluate the hemostatic effect of adding FIX to the plasma of patients on emicizumab prophylaxis. Methods: After informed consent was obtained, blood was collected in citrated tubes prefilled with 1.45mM of corn trypsin inhibitor(CTI) from 25 patients with HA receiving emicizumab prophylaxis. Aligning with previously published data, the added hemostatic effect of rFVIIa was studied in platelet rich plasma (PRP), while all other products were studied in platelet poor plasma (PPP). PRP was spiked with 0, 45, 90 and 270mg/kg of rFVIIa, and PPP was spiked with 0, 10, 20, 30 and 50 IU/kg of activated prothrombin complex concentrate (aPCC); 0, 25, 50 and 100 IU/kg of recombinant FIX(rFIX) and 0, 25, 50 and 100 IU/kg of plasma derived FIX(pdFIX). Thrombin generation was run on these spiked samples using the CAT method. Coagulation was triggered in PRP samples with tissue factor 1pM. PPP samples were activated using 1pM of TF and 1.5pM FIXa. Mean endogenous thrombin potential (ETP, area under the thrombin generation curve) values were compared between the different experiments and with samples obtained from controls. Baseline FIX concentrations were determined in these patients using bovine chromogenic assay. The mean plasma emicizumab concentration (+Standard Deviation) was 44.45 µg/mL (+15.83). Results: As shown in fig 1, ETP was significantly improved with increasing concentrations of aPCC, rFIX and pdFIX in PPP(p&amp;lt;0.005). Fig 2 shows that ETP normalized with the addition of rFVIIa 45mg/kg (p&amp;lt;0.0001) in most of the patients tested. Moreover, we did not see ETP above the upper limit of the normal range when FIX was added, while 50 IU/kg of aPCC and 270 mg/kg of rFVIIa clearly surpassed the upper limit of the normal range, suggesting a risk of hypercoagulability and thrombosis. FIX levels varied between 63.9% and 248.9% with a mean of 134.27%. ETP obtained after adding rFIX 100 IU/kg was significantly greater than that attained with pdFIX (p=0.027), that might be explained by the presence of the activated form of FIX in rFIX concentrates. No statistical significant difference between the doses of 50 IU/Kg of FIX and 100 IU/KG was observed (p&amp;gt;0.05), with a noted tendency to increased ETP with 100IU/KG dose. Conclusion: Our in vitro study demonstrates excess thrombin generation (and potential thrombosis risk) with higher concentrations of rFVIIa and aPCC that are used in the clinical care of patients. While addition of FIX at a range of doses appears to improve thrombin generation without exceeding the upper normal limits. These data suggest that FIX therapy may safely improve hemostasis in patients with HA and inhibitors receiving emicizumab prophylaxis. A clinical study looking at a starting dose of rFIX at 50 IU/KG for mild to moderate bleeds and 100 IU/Kg for more severe bleeds is warranted. In addition to the advantage of monitoring levels with FIX therapy, the longer half-life of FIX offers the potential advantage of controlling hemostasis with single dose administration. Future prospective clinical trials are needed to investigate the efficacy and safety of this therapy in this patient population.

Incorporation of N-doped biochar into zero-valent iron for efficient reductive degradation of neonicotinoids: mechanism and performance

The extensive use of neonicotinoids on food crops for pest management has resulted in substantial environmental contamination. It is imperative to develop an effective remediation material and technique as well as to determine the evolution pathways of products. Here, novel ball-milled nitrogen-doped biochar (NBC)-modified zero-valent iron (ZVI) composites (named MNBC-ZVI) were fabricated and applied to degrading neonicotinoids. Based on the characterization results, NBC incorporation introduced N-doped sites and new allying heterojunctions and achieved surface charge redistribution, rapid electron transfer, and higher hydrophobicity of ZVI particles. As a result, the interaction between ZVI particles and thiamethoxam (a typical neonicotinoid) was improved, and the adsorption–desorption and reductive degradation of thiamethoxam and ·H generation steps were optimized. MNBC-ZVI could rapidly degrade 100% of 10 mg·L−1 thiamethoxam within 360 min, its reduction rate constant was 12.1-fold greater than that of pristine ZVI, and the electron efficiency increased from 29.7% to 57.8%. This improved reactivity and selectivity resulted from increased electron transfer, enhanced hydrophobicity, and reduced accumulation of iron mud. Moreover, the degradation of neonicotinoids occurred mainly via nitrate reduction and dichlorination, and toxicity tests with degradation intermediates revealed that neonicotinoids undergo rapid detoxification. Remarkably, MNBC-ZVI also presented favorable tolerance to various anions, humic acid, wastewater and contaminated soil, as well as high reusability. This work offers an efficient and economic biochar-ZVI remediation technology for the rapid degradation and detoxification of neonicotinoids, significantly contributes to knowledge on the relevant removal mechanism and further advances the synthesis of highly reactive and environmentally friendly materials.Graphical

An Unsupervised Band Selection Method via Contrastive Learning for Hyperspectral Images

Band selection (BS) is an efficacious approach to reduce hyperspectral information redundancy while preserving the physical meaning of hyperspectral images (HSIs). Recently, deep learning-based BS methods have received widespread interest due to their ability to model the nonlinear relationship between bands, with existing methods typically relying on generative algorithms. However, the process of generating images with pixel-level detail required by generative algorithm-based BS methods is computationally expensive. To alleviate this issue, we propose a contrastive learning-based unsupervised BS architecture, termed ContrastBS, in this article. With the help of contrastive learning, the proposed architecture avoids the costly generation step in pixel space by learning to distinguish data at the abstract semantic level of the feature space. Specifically, ContrastBS combines an attention mechanism with contrastive learning to extract the importance of each band. Furthermore, we design a novel loss function, which is able to constrain the symmetric loss while ensuring attention to the most valuable bands, for the contrastive learning-based BS network. Experimental results indicate that ContrastBS has excellent classification performance and competitive time cost compared to the comparison methods.