bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hypoxia: Intriguing Feature in Cancer Cell Biology
Authors: Chowdhury Monalisa , Das Prasanta Kumar 
Published: February 2024

AbstractHypoxia, a key aspect of the tumor microenvironment, plays a vital role in cell proliferation, angiogenesis, metabolism, and the immune response within tumors. These factors collectively promote tumor advancement, aggressiveness, metastasis and result in a poor prognosis. Hypoxia inducible factor 1α (HIF‐1α), activated under low oxygen conditions, mediates many of these effects by altering drug target expression, metabolic regulation, and oxygen consumption. These changes promote cancer cell growth and survival. Hypoxic tumor cells develop aggressive traits and resistance to chemotherapy and radiotherapy, leading to increased mortality. Targeting hypoxic tumor offers a potential solution to overcome the challenges posed by tumor heterogeneity and can be used in designing diagnostic and therapeutic nanocarriers for various solid cancers. This concept provides an overview of the intricate relationship between hypoxia and the tumor microenvironment, highlighting its potential as a promising tool for cancer therapies. The article explores the development of hypoxia in cancer cells and its role in cancer progression, along with the latest advancements in hypoxia‐triggered cancer treatment.
The Therapeutic Potential in Cancer of Terpyridine‐Based Metal Complexes Featuring Group 11 Elements
Authors: Gil‐Moles María , Concepción Gimeno M. 
Published: February 2024

AbstractTerpyridine‐based complexes with group 11 metals emerge as potent metallodrugs in cancer therapy. This comprehensive review focuses on the current landscape of anticancer examples, particularly highlighting the mechanisms of action. While Cu(II) complexes, featuring diverse ancillary ligands, dominate the field, exploration of silver and gold species remains limited. These complexes exhibit significant cytotoxicity against various cancer cell lines with a commendable selectivity for non‐tumorigenic cells. DNA interactions, employing intercalation and groove binding, are pivotal and finely tuned through terpyridine ligand functionalization. In addition, copper complexes showcase nuclease activity, triggering apoptosis through ROS generation. Despite silver‘s high affinity for nitrogen donor atoms, its exploration is relatively sparse, with indications of acting as intercalating agents causing DNA hydrolytic cleavage. Gold(III) compounds, overshadowing gold(I) due to stability concerns, not only intercalate but also induce apoptosis and disrupt the mitochondrial membrane. Further investigations are needed to fully understand the mechanism of action of these compounds, highlighting the necessity of exploring additional biological targets for these promising metallodrugs.
Research on denoising and enhancing methods of medical images based on convolutional neural networks
Authors: Beibei Wu 
Published: February 2024

SummaryIn the process of modern medical diagnosis, medical image‐assisted diagnosis plays a very important role. However, the process of medical image acquisition, will be affected by various types and degrees of noise, and there will be a certain probability of producing strip artifacts, which will interfere with the doctor's diagnosis, analysis, and treatment of diseases to a certain extent. However, the traditional medical image denoising method will cause problems such as image edge blurring and detail loss, and it is difficult to achieve the balance between noise removal and detail information retention. Therefore, denoising medical images and improving the accuracy of denoising as much as possible have very important scientific research significance and clinical application value. Based on this, this article proposes a medical image denoising method based on a double residual convolutional neural network and compares it with traditional medical images denoising methods such as K‐SVD, BM3D, and PNLM3. Experimental results show that the medical image denoising method based on the double residual convolutional neural network proposed in this article has excellent performance.
Ambient ionisation mass spectrometry for drug and toxin analysis: A review of the recent literature
Authors: Henderson Alisha , Heaney Liam M. , Rankin‐Turner Stephanie 
Published: February 2024

AbstractAmbient ionisation mass spectrometry (AIMS) is a form of mass spectrometry whereby analyte ionisation occurs outside of a vacuum source under ambient conditions. This enables the direct analysis of samples in their native state, with little or no sample preparation and without chromatographic separation. The removal of these steps facilitates a much faster analytical process, enabling the direct analysis of samples within minutes if not seconds. Consequently, AIMS has gained rapid popularity across a diverse range of applications, in particular the analysis of drugs and toxins. Numerous fields rely upon mass spectrometry for the detection and identification of drugs, including clinical diagnostics, forensic chemistry, and food safety. However, all of these fields are hindered by the time‐consuming and laboratory‐confined nature of traditional techniques. As such, the potential for AIMS to resolve these challenges has resulted in a growing interest in ambient ionisation for drug and toxin analysis. Since the early 2000s, forensic science, diagnostic testing, anti‐doping, pharmaceuticals, environmental analysis and food safety have all seen a marked increase in AIMS applications, foreshadowing a new future for drug testing. In this review, some of the most promising AIMS techniques for drug analysis will be discussed, alongside different applications of AIMS published over a 5‐year period, to provide a summary of the recent research activity for ambient ionisation for drug and toxin analysis.
Evaluating genotyping‐in‐thousands by sequencing as a genetic monitoring tool for a climate sentinel mammal using non‐invasive and archival samples
Authors: Arpin Kate E. , Schmidt Danielle A. , Sjodin Bryson M. F. , Einfeldt Anthony L. , Galbreath Kurt , Russello Michael A. 
Published: February 2024

AbstractGenetic tools for wildlife monitoring can provide valuable information on spatiotemporal population trends and connectivity, particularly in systems experiencing rapid environmental change. Multiplexed targeted amplicon sequencing techniques, such as genotyping‐in‐thousands by sequencing (GT‐seq), can provide cost‐effective approaches for collecting genetic data from low‐quality and quantity DNA samples, making them potentially useful for long‐term wildlife monitoring using non‐invasive and archival samples. Here, we developed a GT‐seq panel as a potential monitoring tool for the American pika (Ochotona princeps) and evaluated its performance when applied to traditional, non‐invasive, and archival samples, respectively. Specifically, we optimized a GT‐seq panel (307 single nucleotide polymorphisms (SNPs)) that included neutral, sex‐associated, and putatively adaptive SNPs using contemporary tissue samples (n = 77) from the Northern Rocky Mountains lineage of American pikas. The panel demonstrated high genotyping success (94.7%), low genotyping error (0.001%), and excellent performance identifying individuals, sex, relatedness, and population structure. We subsequently applied the GT‐seq panel to archival tissue (n = 17) and contemporary fecal pellet samples (n = 129) collected within the Canadian Rocky Mountains to evaluate its effectiveness. Although the panel demonstrated high efficacy with archival tissue samples (90.5% genotyping success, 0.0% genotyping error), this was not the case for the fecal pellet samples (79.7% genotyping success, 28.4% genotyping error) likely due to the exceptionally low quality/quantity of recovered DNA using the approaches implemented. Overall, our study reinforced GT‐seq as an effective tool using contemporary and archival tissue samples, providing future opportunities for temporal applications using historical specimens. Our results further highlight the need for additional optimization of sample and genetic data collection techniques prior to broader‐scale implementation of a non‐invasive genetic monitoring tool for American pikas.
Experimental evolution of hybrid populations to identify Dobzhansky–Muller incompatibility loci
Authors: Szabo Nicole , Cutter Asher D. 
Published: February 2024

AbstractEpistatic interactions between loci that reduce fitness in interspecies hybrids, Dobzhansky–Muller incompatibilities (DMIs), contribute genetically to the inviability and infertility within hybrid populations. It remains a challenge, however, to identify the loci that contribute to DMIs as causes of reproductive isolation between species. Here, we assess through forward simulation the power of evolve‐and‐resequence (E&R) experimental evolution of hybrid populations to map DMI loci. We document conditions under which such a mapping strategy may be most feasible and demonstrate how mapping power is sensitive to biologically relevant parameters such as one‐way versus two‐way incompatibility type, selection strength, recombination rate, and dominance interactions. We also assess the influence of parameters under direct control of an experimenter, including duration of experimental evolution and number of replicate populations. We conclude that an E&R strategy for mapping DMI loci, and other cases of epistasis, can be a viable option under some circumstances for study systems with short generation times like Caenorhabditis nematodes.
Does size‐selective harvesting erode adaptive potential to thermal stress?
Authors: Sadler Daniel E. , van Dijk Stephan , Karjalainen Juha , Watts Phillip C. , Uusi‐Heikkilä Silva 
Published: February 2024

AbstractOverharvesting is a serious threat to many fish populations. High mortality and directional selection on body size can cause evolutionary change in exploited populations via selection for a specific phenotype and a potential reduction in phenotypic diversity. Whether the loss of phenotypic diversity that accompanies directional selection impairs response to environmental stress is not known. To address this question, we exposed three zebrafish selection lines to thermal stress. Two lines had experienced directional selection for (1) large and (2) small body size, and one was (3) subject to random removal of individuals with respect to body size (i.e. line with no directional selection). Selection lines were exposed to three temperatures (elevated, 34°C; ambient, 28°C; low, 22°C) to determine the response to an environmental stressor (thermal stress). We assessed differences among selection lines in their life history (growth and reproduction), physiological traits (metabolic rate and critical thermal max) and behaviour (activity and feeding behaviour) when reared at different temperatures. Lines experiencing directional selection (i.e. size selected) showed reduced growth rate and a shift in average phenotype in response to lower or elevated thermal stress compared with fish from the random‐selected line. Our data indicate that populations exposed to directional selection can have a more limited capacity to respond to thermal stress compared with fish that experience a comparable reduction in population size (but without directional selection). Future studies should aim to understand the impacts of environmental stressors on natural fish stocks.
Nickel‐Catalyzed Synthesis of Thioesters from Amides and Disulfides
Authors: Mai Wen‐Peng , Zhao Yu , Lv Ming‐Xiu , Zhang Wan‐Ru , Xiao Yong‐Mei , Yuan Jin‐Wei , Yang Liang‐Ru 
Published: February 2024

AbstractA novel nickel‐catalyzed practical and simple synthesis of thioesters from amides and disulfides has been developed. Diverse substituted aromatic amides are capable of coupling with diaryl or dialkyl disulfides via C−N/S−S bonds cleavage to produce the desirable thioesters in moderate to good yields. This procedure features cheap metals, novel and easily preparative substrates, providing a simple and practical access to a variety of thioesters without toxic thiols or CO gas.
LoRaSync: Energy efficient synchronization for scalable LoRaWAN
Authors: Chasserat Laurent , Accettura Nicola , Berthou Pascal 
Published: February 2024

AbstractLow‐power wide‐area networks connect a large number of battery‐powered wireless devices over long distances. Among them, long range wide area networks (LoRaWAN) implement a pure ALOHA medium access scheme to save device energy by minimizing the radio usage. However, frame collisions restrain the network scalability when the traffic load increases. In this context, synchronization can be used to exploit the available bandwidth more efficiently by controlling the timing of frame transmissions and reducing the collision probability. Such strategy allows to increase the network throughput at the cost of an extra energy demand due to the inherent overhead. In that, the entailed network scenario is still supposed to address low power applications. Therefore this article timely presents LoRaSync, an energy‐efficient synchronization scheme designed for LoRa networks of any size. An accurate clock drift model was established based on measurements made on real cheap devices, and leveraged to support the design of LoRaSync. Our mechanism has been used to evaluate the same ALOHA‐based random access but on a time‐slotted basis, thus increasing the maximum achievable throughput compared to the legacy access. Throughput and energy efficiency models are established to evaluate the performances of a LoRaSync‐operated network. These models are validated with a simulation environment mimicking large‐scale deployments, and then used to determine the most energy efficient slot size for any traffic load. As a final proof of concept, LoRaSync has been implemented and tested on a LoRa testbed to demonstrate the feasibility of our solution on real hardware.
Opposition‐based improved memetic algorithm for placement of concurrentInternet of Thingsapplications in fog computing
Authors: Malathy N. , Revathi T. 
Published: February 2024

AbstractThe transpiration of a new computing standard, Fog computing exploits the computing resources immediacy to the Internet of Things (IoT) devices and hence together with the cloud servers bestow the required resources promptly. Even though the fog server provides timely services due to the fewer resources it cannot tackle the resource‐consuming applications developed by more IoT devices. Hence the placement of applications in the fog computing paradigm with more cloud and fog servers is a major challenging problem. To overcome this problem and to minimize the execution time and energy consumption of the IoT applications in a computing platform consisting of numerous IoT devices, and several fog and cloud servers, a weighted cost model is proposed. The number of parallel jobs for concurrent execution is increased due to the heterogeneity of IoT applications, and hence a pre‐scheduling technique is presented to accomplish this. In addition to that, a novel application placement method using the Opposition‐based Improved Memetic Algorithm (OBIMA) is adapted to formulate the placement of parallel IoT operations. The experimental results show that the proposed model outperforms well compared to other state‐of‐the‐art methods.
Innovative Strategies for Photons Management on Ultrathin Silicon Solar Cells
Authors: Li Ning , Fratalocchi Andrea 
Published: February 2024

AbstractSilicon (Si), the eighth most common element in the known universe by mass and widely applied in the industry of electronics chips and solar cells, rarely emerges as a pure element in the Earth's crust. Optimizing its manufacturing can be crucial in the global challenge of reducing the cost of renewable energy modules and implementing sustainable development goals in the future. In the industry of solar cells, this challenge is stimulating studies of ultrathin Si‐based architectures, which are rapidly attracting broad attention. Ultrathin solar cells require up to two orders of magnitude less Si than conventional solar cells, and owning to a flexible nature, they are opening applications in different industries that conventional cells do not yet serve. Despite these attractive factors, a difficulty in ultrathin Si solar cells is overcoming the weak light absorption at near‐infrared wavelengths. The primary goal in addressing this problem is scaling up cost‐effective and innovative textures for anti‐reflection and light‐trapping with shallower depth junctions, which can offer similar performances to traditional thick modules. This review provides an overview of this area of research, discussing this field both as science and engineering and highlighting present progress and future outlooks.
Multitemporal landslide inventory and susceptibility map for the Arun River Basin, Nepal
Authors: Amatya Pukar , Emberson Robert , Kirschbaum Dalia 
Published: February 2024

AbstractThe transboundary Arun River Basin (ARB) spreads across Nepal and Tibet. Nearly 95% of the basin lies in Tibet through which the Pumqu River flows, forming the Arun River once it enters Nepal. The ARB has five large hydropower projects undergoing construction or planned for the future. Rainfall and earthquake‐induced landslides, landslide‐dammed lakes and landslide‐induced glacial lake outburst floods pose major risks to smooth operation of these projects. To safeguard upcoming hydropower projects, areas susceptible to landslides in the ARB must be identified. We used high‐resolution satellite imagery and open‐source tools to generate a multitemporal landslide inventory for the basin. The rigorously quality‐controlled inventory represents a yearly record of landslides from 2011 to 2020. A data‐driven approach was used to map areas susceptible to landslides within the ARB. The multitemporal landslide inventory combined with other readily available Earth observation‐based variables was used to create a landslide susceptibility map. The susceptibility analysis provides a valuable initial estimate of where landslides are likely to initiate. These landslide products could form the basis of more comprehensive local studies to inform hydropower project development.
Degradation protection and enhanced biocompatibility of Mg alloys pretreated with plasma proteins
Authors: Wei Xian , Meng Jiajia , Ma Sujie , Li Yanchun , Qing Hong , Peng Xubiao , Zhao Qing 
Published: February 2024

AbstractAfter implantation of the Mg alloy in the human body, the adsorption of plasma protein on surface will cause a series of cell reactions and affect the degradation of Mg alloys. Herein, in vitro biological reactions of the ZK60 and AZ31 Mg alloys are analyzed in plasma protein environment. Combined with mass spectrometry analysis of the type of adsorbed proteins, it is shown that proteins such as fibrinogen, vitronectin, fibronectin, and prothrombin are prone to get adsorbed on the surface of the alloys than other proteins, leading to the promotion of MG63 cell adhesion and proliferation. The effect of selected proteins (fibrinogen, fibronectin, and prothrombin) on degradation of ZK60 and AZ31 Mg alloys is investigated using immersion tests. The degradation of AZ31 Mg alloy is significantly restrained with the presence of proteins. This is due to the protein adsorption effect on the sample surface. The molecular dynamics simulation results indicate that both fibrinogen and fibronectin tend to adsorb onto the AZ31 rather than ZK60, forming a stable protein layer on the AZ31 Mg alloy retarding the degradation of the samples. As to ZK60 alloy, the addition of protein inhibits the degradation in the short term, however, the degradation increases after a long time of immersion. This phenomenon is particularly pronounced in fibronectin solution.
Novel machine learning insights into the QM7b and QM9 quantum mechanics datasets
Authors: Valdés Julio J. , Tchagang Alain B. 
Published: February 2024

AbstractThis paper (i) explores the internal structure of two quantum mechanics datasets (QM7b, QM9), composed of several thousands of organic molecules and described in terms of electronic properties, and (ii) further explores an inverse design approach to molecular design consisting of using machine learning methods to approximate the atomic composition of molecules, using QM9 data. Understanding the structure and characteristics of this kind of data is important when predicting the atomic composition from physical‐chemical properties in inverse molecular designs. Intrinsic dimension analysis, clustering, and outlier detection methods were used in the study. They revealed that for both datasets the intrinsic dimensionality is several times smaller than the descriptive dimensions. The QM7b data is composed of well‐defined clusters related to atomic composition. The QM9 data consists of an outer region predominantly composed of outliers, and an inner, core region that concentrates clustered inliner objects. A significant relationship exists between the number of atoms in the molecule and its outlier/inliner nature. The spatial structure exhibits a relationship with molecular weight. Despite the structural differences between the two datasets, the predictability of variables of interest for inverse molecular design is high. This is exemplified by models estimating the number of atoms of the molecule from both the original properties and from lower dimensional embedding spaces. In the generative approach the input is given by a set of desired properties of the molecule and the output is an approximation of the atomic composition in terms of its constituent chemical elements. This could serve as the starting region for further search in the huge space determined by the set of possible chemical compounds. The quantum mechanic's dataset QM9 is used in the study, composed of 133,885 small organic molecules and 19 electronic properties. Different multi‐target regression approaches were considered for predicting the atomic composition from the properties, including feature engineering techniques in an auto‐machine learning framework. High‐quality models were found that predict the atomic composition of the molecules from their electronic properties, as well as from a subset of only 52.6% size. Feature selection worked better than feature generation. The results validate the generative approach to inverse molecular design.
Constructing MRD codes by switching
Authors: Shi Minjia , Krotov Denis S. , Özbudak Ferruh 
Published: February 2024

AbstractMaximum rank‐distance (MRD) codes are (not necessarily linear) maximum codes in the rank‐distance metric space on ‐by‐ matrices over a finite field . They are diameter perfect and have the cardinality if . We define switching in MRD codes as the replacement of special MRD subcodes by other subcodes with the same parameters. We consider constructions of MRD codes admitting switching, such as punctured twisted Gabidulin codes and direct‐product codes. Using switching, we construct a huge class of MRD codes whose cardinality grows doubly exponentially in if the other parameters (, the code distance) are fixed. Moreover, we construct MRD codes with different affine ranks and aperiodic MRD codes.
Role of c‐Fos in DNA damage repair
Authors: Yao Haiyang , Wu Yilun , Zhong Yiming , Huang Chenxuan , Guo Zimo , Jin Yinpeng , Wang Xianli 
Published: February 2024

Abstractc‐Fos, a member of the immediate early gene, serves as a widely used marker of neuronal activation induced by various types of brain damage. In addition, c‐Fos is believed to play a regulatory role in DNA damage repair. This paper reviews the literature on c‐Fos' involvement in the regulation of DNA damage repair and indicates that genes of the Fos family can be induced by various forms of DNA damage. In addition, cells lacking c‐Fos have difficulties in DNA repair. c‐Fos is involved in tumorigenesis and progression as a proto‐oncogene that maintains cancer cell survival, which may also be related to DNA repair. c‐Fos may impact the repair of DNA damage by regulating the expression of downstream proteins, including ATR, ERCC1, XPF, and others. Nonetheless, the underlying mechanisms necessitate further exploration.