bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of Charge–Discharging Behavior of Metal Oxide–Based Anode Electrocatalysts for Alkaline Water Electrolysis to Suppress Degradation due to Reverse Current
Authors: Oda Kazuaki , Kuroda Yoshiyuki , Mitsushima Shigenori
Published: February 2023

In the bipolar-type alkaline water electrolysis powered by renewable energy, electrocatalysts are degraded by repeated potential change associated with the generation of reverse current. If an electrode has large discharge capacity, the opposite electrode on the same bipolar plate is degraded by the reverse current. In this study, discharge capacity of various transition metal-based electrocatalysts was investigated to clarify the determining factors of electrocatalysts on the reverse current and durability. The discharge capacities from 1.5 to 0.5 V vs. RHE (Qdc,0.5) of electrocatalysts are proportional to the surface area in most cases. The proportionality coefficient, corresponding to the specific capacity, is 1.0 C·m–2 for Co3O4 and 2.3 C·m–2 for manganese-based electrocatalysts. The substitution of Co3+ in Co3O4 with Ni3+ increased Qdc,0.5. The upper limit of theoretical specific capacity for Co3O4 is estimated to be 1.699 C·m–2, meaning the former and latter cases correspond to 2- and 1-electron reactions, respectively, per a cation at the surface. The discharge capacities of the elctrocatalysts increased because of the dissolution and recrystallization of nickel and/or cobalt into metal hydroxides. The increase in the capacities of Co3O4 and NiCo2O4 during ten charge–discharge cycles was below 2–9% and 0.5–38%, respectively. Therefore, if a cathode electrocatalyst with relatively low redox durability is used on the one side of a bipolar plate, it is necessary to control optimum discharge capacity of the anode by changing surface area and constituent metal cations to minimize the generation of reverse current.

Graphical Abstract
Control of the post-infarct immune microenvironment through biotherapeutic and biomaterial-based approaches
Authors: Soni Shreya S. , D’Elia Arielle M. , Rodell Christopher B.
Published: February 2023

Ischemic heart failure (IHF) is a leading cause of morbidity and mortality worldwide, for which heart transplantation remains the only definitive treatment. IHF manifests from myocardial infarction (MI) that initiates tissue remodeling processes, mediated by mechanical changes in the tissue (loss of contractility, softening of the myocardium) that are interdependent with cellular mechanisms (cardiomyocyte death, inflammatory response). The early remodeling phase is characterized by robust inflammation that is necessary for tissue debridement and the initiation of repair processes. While later transition toward an immunoregenerative function is desirable, functional reorientation from an inflammatory to reparatory environment is often lacking, trapping the heart in a chronically inflamed state that perpetuates cardiomyocyte death, ventricular dilatation, excess fibrosis, and progressive IHF. Therapies can redirect the immune microenvironment, including biotherapeutic and biomaterial-based approaches. In this review, we outline these existing approaches, with a particular focus on the immunomodulatory effects of therapeutics (small molecule drugs, biomolecules, and cell or cell-derived products). Cardioprotective strategies, often focusing on immunosuppression, have shown promise in pre-clinical and clinical trials. However, immunoregenerative therapies are emerging that often benefit from exacerbating early inflammation. Biomaterials can be used to enhance these therapies as a result of their intrinsic immunomodulatory properties, parallel mechanisms of action (e.g., mechanical restraint), or by enabling cell or tissue-targeted delivery. We further discuss translatability and the continued progress of technologies and procedures that contribute to the bench-to-bedside development of these critically needed treatments.

Graphical Abstract
SimDCL: dropout-based simple graph contrastive learning for recommendation
Authors: Xu YuHao , Wang ZhenHai , Wang ZhiRu , Guo YunLong , Fan Rong , Tian HongYu , Wang Xing
Published: February 2023

Representation learning of users and items is the core of recommendation, and benefited from the development of graph neural network (GNN), graph collaborative filtering (GCF) for capturing higher order connectivity has been successful in the recommendation domain. Nevertheless, the matrix sparsity problem in collaborative filtering and the tendency of higher order embeddings to smooth in GNN limit further performance improvements. Contrastive learning (CL) was introduced into GCF and alleviated these problems to some extent. However, existing methods usually require graph perturbation to construct augmented views or design complex CL tasks, which limits the further development of CL-based methods in the recommendation. We propose a simple CL framework that does not require graph augmentation, but is based on dropout techniques to generate contrastive views to address the aforementioned problem. Specifically, we first added dropout operation to the GNN computation, and then fed the same batch of samples twice into the network for computation. Using the randomness of dropout, a pair of views with random noise was obtained, and maximizing the similarity of the view pairs is set as an auxiliary task to complement the recommendation. In addition, we made a simple modification to the computation of the GNN to alleviate the information loss due to embedding smoothing by means of cross-layer connected graph convolution computation. We named our proposed method as Simple Contrastive Learning Graph Neural Network based on dropout (SimDCL). Extensive experiments on five public datasets demonstrate the effectiveness of the proposed SimDCL, especially on the Amazon Books and Ta-Feng datasets, where our approach achieves 44% and 43% performance gains compared to baseline.
Technical problems and non destructive testing of rock bolt support systems in mines
Authors: Staniek Andrzej
Published: February 2023

The problem of proper assessment of the technical functionality of rock bolt support systems is still valid. Many research centers have undertaken efforts to diagnose and monitor the technical state of such a support system used in mines and tunneling. With that aim the method of quality assessment of grouted rock bolts was invented and a relevant apparatus was constructed. The method concerns non-destructive identification of discontinuity of a resin layer (grout) surrounding rock bolts. The method is based on an impact excitation of a rock bolt and uses modal analysis procedures. Assuming that the installed rock bolt acts as an oscillator, different lengths and positions of grouting discontinuity alter its modal parameters. The extraction of these modal parameters, of which a resonant frequency is seen as the most valued, enable the relevant identification of grout discontinuity. After constructing a prototype version and validating the results for known cases of resin discontinuity in an experimental coal mine, the apparatus fulfilling ATEX requirements was developed. Subsequently that version was also verified both in laboratory conditions and in an experimental coal mine. As necessary for proper identification of discontinuity length, the reference data base was developed and elaborated consisting of a very large number of finite element models (FE models), namely discontinuity cases. The models encountered different rock bolt lengths and diameters, different rock strata parameters and different positions and lengths of resin layers. Then the method was used in a working coal mine to monitor a technical state of rock bolt support system mounted to reinforce long underground openings. The data base was utilized as reference for investigated rock bolts.
Recent advances in the theory of thermoelasticity and the modified models for the nanobeams: a review
Authors: Kaur Iqbal , Singh Kulvinder , Craciun Eduard-Marius
Published: February 2023

The present study focuses on recent research on thermoelasticity theories as well as their associated reformed models related to the micro-/nano-beams/nano-bars. Our goal is to present an overview of the use and limitations of existing relevant theories. The properties of these theories were analyzed by many researchers in a variety of fields as well as different problems, providing insight into their characteristics. In this review, we discuss theory, techniques, formulation, as well as limitations for solving equations for micro-/nano-beams/nano-bars. In light of the fact that this review may be a useful tool for researchers who work in sensitive industries such as MEMS/NEMS/Resonators/Sensors.
Leveraging electronic data to expand infection detection beyond traditional settings and definitions (Part II/III)
Authors: Branch-Elliman Westyn , Sundermann Alexander J. , Wiens Jenna , Shenoy Erica S.
Published: February 2023

The rich and complex electronic health record presents promise for expanding infection detection beyond currently covered settings of care. Here, we review the “how to” of leveraging electronic data sources to expand surveillance to settings of care and infections that have not been the traditional purview of the National Healthcare Safety Network (NHSN), including a discussion of creation of objective and reproducible infection surveillance definitions. In pursuit of a ‘fully automated’ system, we also examine the promises and pitfalls of leveraging unstructured, free-text data to support infection prevention activities and emerging technological advances that will likely affect the practice of automated infection surveillance. Finally, barriers to achieving a completely ‘automated’ infection detection system and challenges with intra- and interfacility reliability and missing data are discussed.
Samsung in Vietnam: FDI, Business–Government Relations, Industrial Parks, and Skills Shortages
Authors: Sheldon Peter , Kwon Seung-Ho
Published: February 2023

Samsung has largely shifted its electronics manufacturing out of China, and mostly to Vietnam. In that process, it has, to date, avoided local labour market (LLM) difficulties faced by its subsidiaries in industrial parks in China: in particular, pervasive skill shortages among the electronics assembly workers on whom Samsung’s manufacturing depends. Labour retention problems in China had significantly challenged its commitment to invest in necessary employee training. By contrast, Samsung’s domination of foreign direct investment (FDI) within particular Vietnamese industrial parks has removed the inter-employer competition it experienced in China. A deeper explanation involves Samsung’s development – as Vietnam’s largest single source of FDI, and major producer of that country’s exports – of synergistic and symbiotic relationships with Vietnam’s national and subnational governments. These relationships have allowed Samsung to influence development strategies for industrial parks and their LLMs. Samsung has invests expansively in forms of education and training whose benefits extend beyond the areas in which its manufacturing facilities operate, to populations that the company does not employ. Providing benefits beyond typical success criteria for FDI in developing economies, Samsung has helped Vietnam’s party-state meet its own legitimacy needs with its internal constituencies, contributing to a broader practical consensus in favour of the party-state’s development policies. This article therefore widens the perspective of FDI to embed it in national and subnational business–government relations.
Bones without Flesh and (Trans)Gender without Bodies: Querying Desires for Trans Historicity
Authors: Everhart Avery Rose
Published: February 2023

In 2011, a 5,000-year-old “male” skeleton buried in a “female” way was discovered by an archaeological team just outside of modern-day Prague. This article queries the impulse to name such a discovery as evidence of transgender identity, and bodies, in an increasingly ancient past. To do so, it takes up the work of Denise Ferreira da Silva, Sylvia Wynter, and Hortense Spillers as a means to push back against the impetus to name such discoveries “transgender” in order to shore up the legitimacy of contemporary trans identity. Each of these three thinkers offers a different vantage point for rethinking such naming practices that push the reader to consider how desires to name and place “transgender” in a distant past papers over the violence of plantation slavery, global imperialism, and the Enlightenment's shift toward scientific reason. This article argues not that such anthropological discoveries should not be considered transgender, but rather that the desire for them to be, or become, transgender does not legitimate contemporary transgender identity, and may instead treat certain iterations of transness as spatially and temporally universal.
Pharmaceuticalised livelihoods: antibiotics and the rise of ‘Quick Farming’ in peri-urban Uganda
Authors: Kayendeke Miriam , Denyer-Willis Laurie , Nayiga Susan , Nabirye Christine , Fortané Nicolas , Staedke Sarah G , Chandler Clare IR
Published: February 2023

The ‘livestock revolution’ has seen the lives and livelihoods of peri-urban peoples increasingly intertwine with pigs and poultry across Africa in response to a rising demand for meat protein. This ‘revolution’ heralds the potential to address both poverty and nutritional needs. However, the intensification of farming has sparked concern, including for antibiotic misuse and its consequences for antimicrobial resistance (AMR). These changes reflect a micro-biopolitical conundrum where the agendas of microbes, farmers, publics, authorities and transnational agencies are in tension. To understand this requires close attention to the practices, principles and potentials held between these actors. Ethnographic research took place in a peri-urban district, Wakiso, in Uganda between May 2018 and March 2021. This included a medicine survey at 115 small- and medium-scale pig and poultry farms, 18 weeks of participant observation at six farms, 34 in-depth interviews with farmers and others in the local livestock sector, four group discussions with 38 farmers and 7 veterinary officers, and analysis of archival, media and policy documents. Wide-scale adoption of quick farming was found, an entrepreneurial phenomenon that sees Ugandans raising ‘exotic’ livestock with imported methods and measures for production, including antibiotics for immediate therapy, prevention of infections and to promote production and protection of livelihoods. This assemblage – a promissory assemblage of the peri-urban – reinforced precarity against which antibiotics formed a potential layer of protection. The paper argues that to address antibiotic use as a driver of AMR is to address precarity as a driver of antibiotic use. Reduced reliance on antibiotics required a level of biosecurity and economies of scale in purchasing insurance that appeared affordable only by larger-scale commercial producers. This study illustrates the risks – to finances, development and health – of expanding an entrepreneurial model of protein production in populations vulnerable to climate, infection and market dynamics.
Peace pacts and contentious politics: The Chico River Dam struggle in the Philippines, 1974–82
Authors: Göransson Markus Balázs
Published: February 2023

In the 1970s, communities of the Kalinga sub-ethnic group in the Cordillera Mountains in northern Philippines successfully halted the construction of a series of hydroelectric dams along their main waterway, the Chico River, which would have caused their displacement. Based on interviews and archival research, the article examines the role played by a Kalinga political institution known as the bodong or peace pact in the Kalingas’ mobilisation against the dam project, using an analytical framework drawn from Charles Tilly's and Sidney Tarrow's work on contentious politics.
Crowds and Crowd-Pleasing in Plato
Authors: Keum Tae-Yeoun
Published: February 2023

Plato's antipathy to crowds is a commonplace that reinforces a prevailing portrait of the Socratic method as a practice that centers on individuals, to the exclusion of crowds and the many. This canonical view, however, comes into tension with the tendency of Plato's Socrates to conduct his dialogues in the presence of collective audiences. I argue that Plato's position on crowds is at once more complex and more ambivalent than has been commonly accepted. I distinguish between two distinct lines of critique that Plato develops against crowds: the argument that the incentive structures that move crowds are unconducive to philosophy; and a more ambiguous argument that crowds tend not to be as amenable to control as their portrayal in the Athenian democratic imaginary seems to promise. Plato's depiction of Socratic practice can be understood as an effort to explore an alternative vision of crowd control.
Autonomous and Between Worlds: Musical Manifestos for Anti-Capitalist Futures in Post-Wall East Berlin
Authors: KELLY ELAINE
Published: February 2023

The opening of the Berlin Wall in November 1989 provided a spectacular climax for Samuel Huntington's third wave of democracy. It represented the triumph not only of a people over their oppressive regime but also, ultimately, of Western economic liberalism; the post-socialist democracy of German reunification was a singular one with consumerism at its core. In the early 1990s, many East German reform socialists resisted this inevitability, and exploited the temporary political and bureaucratic void that emerged in East Berlin as a space in which to imagine alternative democratic futures from the socialist past. In this article I explore the attempts by two very different East Berlin musical communities to incubate anti-capitalist worlds from the legacies – both official and unofficial – of the German Democratic Republic (GDR). First, I reflect on the autonomous musical worlds that were created by members of the GDR's alternative bands in occupied buildings in Prenzlauer Berg and Mitte. Second, I consider the articulation by the ZwischenWelt-Festival, a reincarnation of the GDR's Festival of Political Song, of a more outward-looking future for East Germany, based on the ideals of international solidarity.
Minjung Kayo: Imagining Democracy through Song in South Korea
Authors: LEE JUNG-MIN MINA
Published: February 2023

During South Korea's authoritarian period (1961–87), student activists employed songs to express their anti-government and pro-democratic views. Known as minjung kayo (people's songs), these protest songs can be traced to the modern American folk music embraced by South Korean youth in the 1960s. By the late 1980s, however, minjung kayo carried emphatically anti-American, nationalistic, and socialist tones, echoing the minjung ideals that strove to achieve authentic ‘Koreanness’. This article unravels the complexities underlying the process of minjung kayo's development into an emblem of the pro-democratic movement, which entailed a shift away from its initial reflective and poetic style inspired by American folk music (exemplified in the songs of Kim Min-ki) and a move towards the militant style influenced by the Marxist composer Hanns Eisler. It argues that minjung kayo embodied the complex relationship South Korean activists held with their colonial past and autocratic present, as well as visions of their democratic future.
A New Measure of Roughness Could Advance Earthquake Geophysics
Authors: Stanley Sarah
Published: February 2023

Scientists recently developed an alternative way to measure a rock’s roughness. It might help them understand the physics of faults.
Agriculture at the Center of the Aztec Horizon Calendar
Authors: Basilio Humberto
Published: February 2023

Horizon calendars were a key part of time measurement for pre-Hispanic cultures in the Basin of Mexico. A new study suggests that calendars were used to manage the agricultural cycle.
Small Shrubs May Have Played a Large Role in Decarbonizing the Ancient Atmosphere
Authors: Rodrigues Meghie
Published: February 2023

Vascular plants may have contributed to shaping Earth’s atmosphere long before trees evolved.
Biden Calls Climate Change “Existential Threat”
Authors: Cartier Kimberly
Published: February 2023

Wildfires, flooding, and green energy also appeared—briefly—in the president’s second State of the Union address.
Combination treatment of T1-44, a PRMT5 inhibitor with Vactosertib, an inhibitor of TGF-β signaling, inhibits invasion and prolongs survival in a mouse model of pancreatic tumors
Authors: Hong Eunji , Barczak Wojciech , Park Sujin , Heo Jin Sun , Ooshima Akira , Munro Shonagh , Hong Chang Pyo , Park Jinah , An Haein , Park Joon Oh , Park Seok Hee , La Thangue Nick B. , Kim Seong-Jin
Published: February 2023

Pancreatic ductal adenocarcinoma (PDAC) is the most lethal type of cancer and the third leading cause of cancer death with the lowest 5-year survival rate. Heterogeneity, difficulty in diagnosis, and rapid metastatic progression are the causes of high mortality in pancreatic cancer. Recent studies have shown that Protein arginine methyltransferase 5 (PRMT5) is overexpressed in pancreatic cancers, and these patients have a worse prognosis. Recently, PRMT5 as an anti-cancer target has gained considerable interest. In this study, we investigated whether inhibition of PRMT5 activity was synergistic with blockade of TGF-β1 signaling, which plays an important role in the construction of the desmoplastic matrix in pancreatic cancer and induces therapeutic vulnerability. Compared with T1-44, a selective inhibitor of PRMT5 activity, the combination of T1-44 with the TGF-β1 signaling inhibitor Vactosertib significantly reduced tumor size and surrounding tissue invasion and significantly improved long-term survival. RNA sequencing analysis of mouse tumors revealed that the combination of T1-44 and Vactosertib significantly altered the expression of genes involved in cancer progression, such as cell migration, extracellular matrix, and apoptotic processes. In particular, the expression of Btg2, known as a tumor suppressor factor in various cancers, was markedly induced by combination treatment. Ectopic overexpression of Btg2 inhibited the EMT response, blocking cell migration, and promoted cancer cell death. These data demonstrate that the combination therapy of T1-44 with Vactosertib is synergistic for pancreatic cancer, suggesting that this novel combination therapy has value in the treatment strategy of patients with pancreatic cancer.
Unique DUOX2+ACE2+ small cholangiocytes are pathogenic targets for primary biliary cholangitis
Authors: Li Xi , Li Yan , Xiao Jintao , Wang Huiwen , Guo Yan , Mao Xiuru , Shi Pan , Hou Yanliang , Zhang Xiaoxun , Zhao Nan , Zheng Minghua , He Yonghong , Ding Jingjing , Tan Ya , Liao Min , Li Ling , Peng Ying , Li Xuan , Pan Qiong , Xie Qiaoling , Li Qiao , Li Jianwei , Li Ying , Chen Zhe , Huang Yongxiu , Assis David N. , Cai Shi-Ying , Boyer James L. , Huang Xuequan , Tang Can-E , Liu Xiaowei , Peng Shifang , Chai Jin
Published: February 2023

Cholangiocytes play a crucial role in bile formation. Cholangiocyte injury causes cholestasis, including primary biliary cholangitis (PBC). However, the etiology of PBC remains unclear despite being characterized as an autoimmune disease. Using single-cell RNA sequencing (scRNA-seq), fluorescence-activated-cell-sorting, multiplex immunofluorescence (IF) and RNAscope analyses, we identified unique DUOX2+ACE2+ small cholangiocytes in human and mouse livers. Their selective decrease in PBC patients was associated with the severity of disease. Moreover, proteomics, scRNA-seq, and qPCR analyses indicated that polymeric immunoglobulin receptor (pIgR) was highly expressed in DUOX2+ACE2+ cholangiocytes. Serum anti-pIgR autoantibody levels were significantly increased in PBC patients, regardless of positive and negative AMA-M2. Spatial transcriptomics and multiplex IF revealed that CD27+ memory B and plasma cells accumulated in the hepatic portal tracts of PBC patients. Collectively, DUOX2+ACE2+ small cholangiocytes are pathogenic targets in PBC, and preservation of DUOX2+ACE2+ cholangiocytes and targeting anti-pIgR autoantibodies may be valuable strategies for therapeutic interventions in PBC.
High entropy liquid electrolytes for lithium batteries
Authors: Wang Qidi , Zhao Chenglong , Wang Jianlin , Yao Zhenpeng , Wang Shuwei , Kumar Sai Govind Hari , Ganapathy Swapna , Eustace Stephen , Bai Xuedong , Li Baohua , Wagemaker Marnix
Published: February 2023

High-entropy alloys/compounds have large configurational entropy by introducing multiple components, showing improved functional properties that exceed those of conventional materials. However, how increasing entropy impacts the thermodynamic/kinetic properties in liquids that are ambiguous. Here we show this strategy in liquid electrolytes for rechargeable lithium batteries, demonstrating the substantial impact of raising the entropy of electrolytes by introducing multiple salts. Unlike all liquid electrolytes so far reported, the participation of several anionic groups in this electrolyte induces a larger diversity in solvation structures, unexpectedly decreasing solvation strengths between lithium ions and solvents/anions, facilitating lithium-ion diffusivity and the formation of stable interphase passivation layers. In comparison to the single-salt electrolytes, a low-concentration dimethyl ether electrolyte with four salts shows an enhanced cycling stability and rate capability. These findings, rationalized by the fundamental relationship between entropy-dominated solvation structures and ion transport, bring forward high-entropy electrolytes as a composition-rich and unexplored space for lithium batteries and beyond.
ZBTB12 is a molecular barrier to dedifferentiation in human pluripotent stem cells
Authors: Han Dasol , Liu Guojing , Oh Yujeong , Oh Seyoun , Yang Seungbok , Mandjikian Lori , Rani Neha , Almeida Maria C. , Kosik Kenneth S. , Jang Jiwon
Published: February 2023

Development is generally viewed as one-way traffic of cell state transition from primitive to developmentally advanced states. However, molecular mechanisms that ensure the unidirectional transition of cell fates remain largely unknown. Through exact transcription start site mapping, we report an evolutionarily conserved BTB domain-containing zinc finger protein, ZBTB12, as a molecular barrier for dedifferentiation of human pluripotent stem cells (hPSCs). Single-cell RNA sequencing reveals that ZBTB12 is essential for three germ layer differentiation by blocking hPSC dedifferentiation. Mechanistically, ZBTB12 fine-tunes the expression of human endogenous retrovirus H (HERVH), a primate-specific retrotransposon, and targets specific transcripts that utilize HERVH as a regulatory element. In particular, the downregulation of HERVH-overlapping long non-coding RNAs (lncRNAs) by ZBTB12 is necessary for a successful exit from a pluripotent state and lineage derivation. Overall, we identify ZBTB12 as a molecular barrier that safeguards the unidirectional transition of metastable stem cell fates toward developmentally advanced states.
Astrocytic lactate dehydrogenase A regulates neuronal excitability and depressive-like behaviors through lactate homeostasis in mice
Authors: Yao Shan , Xu Min-Dong , Wang Ying , Zhao Shen-Ting , Wang Jin , Chen Gui-Fu , Chen Wen-Bing , Liu Jian , Huang Guo-Bin , Sun Wen-Juan , Zhang Yan-Yan , Hou Huan-Li , Li Lei , Sun Xiang-Dong
Published: February 2023

Alterations in energy metabolism are associated with depression. However, the role of glycolysis in the pathogenesis of depression and the underlying molecular mechanisms remain unexplored. Through an unbiased proteomic screen coupled with biochemical verifications, we show that the levels of glycolysis and lactate dehydrogenase A (LDHA), a glycolytic enzyme that catalyzes L-lactate production, are reduced in the dorsomedial prefrontal cortex (dmPFC) of stress-susceptible mice in chronic social defeat stress (CSDS) model. Conditional knockout of LDHA from the brain promotes depressive-like behaviors in both male and female mice, accompanied with reduced L-lactate levels and decreased neuronal excitability in the dmPFC. Moreover, these phenotypes could be duplicated by knockdown of LDHA in the dmPFC or specifically in astrocytes. In contrast, overexpression of LDHA reverses these phenotypic changes in CSDS-susceptible mice. Mechanistic studies demonstrate that L-lactate promotes neuronal excitability through monocarboxylic acid transporter 2 (MCT2) and by inhibiting large-conductance Ca2+-activated potassium (BK) channel. Together, these results reveal a role of LDHA in maintaining neuronal excitability to prevent depressive-like behaviors.
Promoting water dissociation for efficient solar driven CO2 electroreduction via improving hydroxyl adsorption
Authors: Chen Xin , Chen Junxiang , Chen Huayu , Zhang Qiqi , Li Jiaxuan , Cui Jiwei , Sun Yanhui , Wang Defa , Ye Jinhua , Liu Lequan
Published: February 2023

Exploring efficient electrocatalysts with fundamental understanding of the reaction mechanism is imperative in CO2electroreduction. However, the impact of sluggish water dissociation as proton source and the surface species in reaction are still unclear. Herein, we report a strategy of promoting protonation in CO2electroreduction by implementing oxygen vacancy engineering on Bi2O2CO3over which high Faradaic efficiency of formate (above 90%) and large partial current density (162 mA cm−2) are achieved. Systematic study reveals that the production rate of formate is mainly hampered by water dissociation, while the introduction of oxygen vacancy accelerates water dissociation kinetics by strengthening hydroxyl adsorption and reduces the energetic span of CO2electroreduction. Moreover, CO3* involved in formate formation as the key surface species is clearly identified by electron spin resonance measurements and designed in situ Raman spectroscopy study combined with isotopic labelling. Coupled with photovoltaic device, the solar to formate energy conversion efficiency reaches as high as 13.3%.
Planthopper salivary sheath protein LsSP1 contributes to manipulation of rice plant defenses
Authors: Huang Hai-Jian , Wang Yi-Zhe , Li Li-Li , Lu Hai-Bin , Lu Jia-Bao , Wang Xin , Ye Zhuang-Xin , Zhang Ze-Long , He Yu-Juan , Lu Gang , Zhuo Ji-Chong , Mao Qian-Zhuo , Sun Zong-Tao , Chen Jian-Ping , Li Jun-Min , Zhang Chuan-Xi
Published: February 2023

Salivary elicitors secreted by herbivorous insects can be perceived by host plants to trigger plant immunity. However, how insects secrete other salivary components to subsequently attenuate the elicitor-induced plant immunity remains poorly understood. Here, we study the small brown planthopper, Laodelphax striatellus salivary sheath protein LsSP1. Using Y2H, BiFC and LUC assays, we show that LsSP1 is secreted into host plants and binds to salivary sheath via mucin-like protein (LsMLP). Rice plants pre-infested with dsLsSP1-treated L. striatellus are less attractive to L. striatellus nymphs than those pre-infected with dsGFP-treated controls. Transgenic rice plants with LsSP1 overexpression rescue the insect feeding defects caused by a deficiency of LsSP1 secretion, consistent with the potential role of LsSP1 in manipulating plant defenses. Our results illustrate the importance of salivary sheath proteins in mediating the interactions between plants and herbivorous insects.
Variation of lightning-ignited wildfire patterns under climate change
Authors: Pérez-Invernón Francisco J. , Gordillo-Vázquez Francisco J. , Huntrieser Heidi , Jöckel Patrick
Published: February 2023

Lightning is the main precursor of natural wildfires and Long-Continuing-Current (LCC) lightning flashes are proposed to be the main igniters of lightning-ignited wildfires (LIW). Previous studies predict a change of the global occurrence rate and spatial pattern of total lightning. Nevertheless, the sensitivity of lightning-ignited wildfire occurrence to climate change is uncertain. Here, we investigate space-based measurements of LCC lightning associated with lightning ignitions and present LCC lightning projections under the Representative Concentration Pathway RCP6.0 for the 2090s by applying a recent LCC lightning parameterization based on the updraft strength in thunderstorms. We find a 41% global increase of the LCC lightning flash rate. Increases are largest in South America, the western coast of North America, Central America, Australia, Southern and Eastern Asia, and Europe, while only regional variations are found in northern polar forests, where fire risk can affect permafrost soil carbon release. These results show that lightning schemes including LCC lightning are needed to project the occurrence of lightning-ignited wildfires under climate change.
Quantum face recognition protocol with ghost imaging
Authors: Salari Vahid , Paneru Dilip , Saglamyurek Erhan , Ghadimi Milad , Abdar Moloud , Rezaee Mohammadreza , Aslani Mehdi , Barzanjeh Shabir , Karimi Ebrahim
Published: February 2023

Face recognition is one of the most ubiquitous examples of pattern recognition in machine learning, with numerous applications in security, access control, and law enforcement, among many others. Pattern recognition with classical algorithms requires significant computational resources, especially when dealing with high-resolution images in an extensive database. Quantum algorithms have been shown to improve the efficiency and speed of many computational tasks, and as such, they could also potentially improve the complexity of the face recognition process. Here, we propose a quantum machine learning algorithm for pattern recognition based on quantum principal component analysis, and quantum independent component analysis. A novel quantum algorithm for finding dissimilarity in the faces based on the computation of trace and determinant of a matrix (image) is also proposed. The overall complexity of our pattern recognition algorithm is $$O(N\,\log N)$$

O
(
N

log
N
)

—N is the image dimension. As an input to these pattern recognition algorithms, we consider experimental images obtained from quantum imaging techniques with correlated photons, e.g. “interaction-free” imaging or “ghost” imaging. Interfacing these imaging techniques with our quantum pattern recognition processor provides input images that possess a better signal-to-noise ratio, lower exposures, and higher resolution, thus speeding up the machine learning process further. Our fully quantum pattern recognition system with quantum algorithm and quantum inputs promises a much-improved image acquisition and identification system with potential applications extending beyond face recognition, e.g., in medical imaging for diagnosing sensitive tissues or biology for protein identification.
Image Turing test and its applications on synthetic chest radiographs by using the progressive growing generative adversarial network
Authors: Jang Miso , Bae Hyun-jin , Kim Minjee , Park Seo Young , Son A-yeon , Choi Se Jin , Choe Jooae , Choi Hye Young , Hwang Hye Jeon , Noh Han Na , Seo Joon Beom , Lee Sang Min , Kim Namkug
Published: February 2023

The generative adversarial network (GAN) is a promising deep learning method for generating images. We evaluated the generation of highly realistic and high-resolution chest radiographs (CXRs) using progressive growing GAN (PGGAN). We trained two PGGAN models using normal and abnormal CXRs, solely relying on normal CXRs to demonstrate the quality of synthetic CXRs that were 1000 × 1000 pixels in size. Image Turing tests were evaluated by six radiologists in a binary fashion using two independent validation sets to judge the authenticity of each CXR, with a mean accuracy of 67.42% and 69.92% for the first and second trials, respectively. Inter-reader agreements were poor for the first (κ = 0.10) and second (κ = 0.14) Turing tests. Additionally, a convolutional neural network (CNN) was used to classify normal or abnormal CXR using only real images and/or synthetic images mixed datasets. The accuracy of the CNN model trained using a mixed dataset of synthetic and real data was 93.3%, compared to 91.0% for the model built using only the real data. PGGAN was able to generate CXRs that were identical to real CXRs, and this showed promise to overcome imbalances between classes in CNN training.
Presenting rose odor during learning, sleep and retrieval helps to improve memory consolidation: a real-life study
Authors: Knötzele Jessica , Riemann Dieter , Frase Lukas , Feige Bernd , van Elst Ludger Tebartz , Kornmeier Jürgen
Published: February 2023

Improving our learning abilities is important for numerous aspects of our life. Several studies found beneficial effects of presenting cues (odor or sounds) during learning and during sleep for memory performance. A recent study applying a real-life paradigm indicated that additional odor cueing during a Final Test can further increase this cueing effect. The present online study builds on these findings with the following questions: (1) Can we replicate beneficial memory effects of additional odor cueing during tests? (2) How many odor cueing learning sessions and odor cueing nights of sleep maximize the learning success? (3) Can odor cueing also reduce the amount of forgetting over time? 160 Participants learned 40 German Japanese word pairs in four groups with separate experimental conditions over three days. Group N received no odor during the whole study. Group LS received odor cueing during learning and sleep, group LT during learning and testing and group LST during learning, sleep and testing. Participants performed intermediate tests after each learning session plus three final tests 1, 7 and 28 days after the last learning session. Results: (1) Group LST learned 8.5% more vocabulary words than the other groups overall. (2) This odor cueing effect increased across the three days of cued learning. (3) We found no clear evidence for effects of odor cueing on the forgetting dynamics. Our findings support the notion of a beneficial effect of odor cueing. They further suggest to use at least 3 days and nights of odor cueing. Overall, this study indicates that there is an easy, efficient and economical way to enhance memory performance in daily life.
Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features
Authors: Barough Siavash Shirzadeh , Safavi-Naini Seyed Amir Ahmad , Siavoshi Fatemeh , Tamimi Atena , Ilkhani Saba , Akbari Setareh , Ezzati Sadaf , Hatamabadi Hamidreza , Pourhoseingholi Mohamad Amin
Published: February 2023

We aimed to propose a mortality risk prediction model using on-admission clinical and laboratory predictors. We used a dataset of confirmed COVID-19 patients admitted to three general hospitals in Tehran. Clinical and laboratory values were gathered on admission. Six different machine learning models and two feature selection methods were used to assess the risk of in-hospital mortality. The proposed model was selected using the area under the receiver operator curve (AUC). Furthermore, a dataset from an additional hospital was used for external validation. 5320 hospitalized COVID-19 patients were enrolled in the study, with a mortality rate of 17.24% (N = 917). Among 82 features, ten laboratories and 27 clinical features were selected by LASSO. All methods showed acceptable performance (AUC > 80%), except for K-nearest neighbor. Our proposed deep neural network on features selected by LASSO showed AUC scores of 83.4% and 82.8% in internal and external validation, respectively. Furthermore, our imputer worked efficiently when two out of ten laboratory parameters were missing (AUC = 81.8%). We worked intimately with healthcare professionals to provide a tool that can solve real-world needs. Our model confirmed the potential of machine learning methods for use in clinical practice as a decision-support system.
Metal-induced oxidative stress and human plasma protein oxidation after SARS-CoV-2 infection
Authors: Aryal Baikuntha , Tillotson Joseph , Ok Kiwon , Stoltzfus Andrew T. , Michel Sarah L. J. , Rao V. Ashutosh
Published: February 2023

Pathogenesis of COVID-19 by SARS-CoV-2 resulted in a global pandemic and public health emergency in 2020. Viral infection can induce oxidative stress through reactive oxygen species (ROS). Inflammation and environmental stress are major sources of oxidative stress after infection. Micronutrients such as iron, copper, zinc, and manganese play various roles in human tissues and their imbalance in blood can impact immune responses against pathogens including SARS CoV-2. We hypothesized that alteration of free metal ions during infection and metal-catalyzed oxidation plays a critical role towards pathogenesis after infection. We analyzed convalescent and hospitalized COVID-19 patient plasma using orthogonal analytical techniques to determine redox active metal concentrations, overall protein oxidation, oxidative modifications, and protein levels via proteomics to understand the consequences of metal-induced oxidative stress in COVID-19 plasma proteins. Metal analysis using ICP-MS showed significantly greater concentrations of copper in COVID-19 plasma compared to healthy controls. We demonstrate significantly greater total protein carbonylation, other oxidative modifications, and deamidation of plasma proteins in COVID-19 plasma compared to healthy controls. Proteomics analysis showed that levels of redox active proteins including hemoglobulin were elevated in COVID-19 plasma. Molecular modeling concurred with potential interactions between iron binding proteins and SARS CoV-2 surface proteins. Overall, increased levels of redox active metals and protein oxidation indicate that oxidative stress-induced protein oxidation in COVID-19 may be a consequence of the interactions of SARS-CoV-2 proteins with host cell metal binding proteins resulting in altered cellular homeostasis.
The applicability of species sensitivity distributions to the development of generic doses for phytosanitary irradiation
Authors: Penca Cory , Beam Andrea L. , Bailey Woodward D.
Published: February 2023

Ionizing radiation is used as a phytosanitary treatment to prevent the introduction of pests through trade. Generic doses are a valuable means to increase the number of pest-commodity combinations that can be treated using phytosanitary irradiation. Generic doses allow for the treatment of the entire taxa for which the dose has been approved, allowing for the treatment of untested species. As such, the approval of a generic dose requires substantial supporting data and careful consideration of the risks involved. We adopt the Species Sensitivity Distribution (SSD) framework, already in widespread use in the field of ecotoxicology and environmental risk assessment, to evaluate generic doses for phytosanitary irradiation treatments. Parametric SSDs for Curculionidae and Tephritidae were developed using existing data on efficacious phytosanitary irradiation treatments. The resulting SSDs provided estimates of the taxa coverage expected by the generic dose, along with the margin of uncertainty. The SSD analysis lends support to the existing 150 Gy generic dose for Tephritidae and a proposed 175 Gy generic dose for Curculionidae. The quantitative estimates of risk produced by the SSD approach can be a valuable tool for phytosanitary rule making, improving the process for generic dose development and approval.
Growth response of kale (Brassica oleracea) and Nile tilapia (Oreochromis niloticus) under saline aqua-sandponics-vegeculture system
Authors: Kimera Fahad , Mugwanya Muziri , Dawood Mahmoud , Sewilam Hani
Published: February 2023

Salinity and freshwater scarcity are significant challenges affecting agriculture production worldwide. Sustaining food production in arid and semi-arid regions requires innovative, efficient, and low-cost technologies. Integrated aqua-vegeculture systems (IAVS) are promising technologies for cultivating vegetable crops and rearing fish and in a closed-loop system. The system utilizes fish effluents as crop fertilizers and recycles water for increased productivity. Hence, the current study aimed to investigate the response and productivity of kale (Brassica oleracea L.) grown at different brackish water salinities in an IAVS. The greenhouse experiment followed a completely randomized design with three salinity variants (i.e., 3000, 6000, and 9000 ppm) and control (freshwater, 400 ppm) with four replicates per treatment. The study results indicated that kale grown in a greenhouse could tolerate salinity levels of up to 6000 ppm without significantly compromising the plants’ growth, yield, and nutritional composition of leaves. Likewise, rearing Oreochromis niloticus at high water salinities did not negatively impact the water quality and the growth performance, survival, and feed utilization of fish. Overall, cultivating kale and rearing O. niloticus in IAVS in water salinities reaching up to 6000 ppm could be a sustainable agricultural strategy to increase food production in regions affected by freshwater scarcity.
Railway infrastructure maintenance efficiency improvement using deep reinforcement learning integrated with digital twin based on track geometry and component defects
Authors: Sresakoolchai Jessada , Kaewunruen Sakdirat
Published: February 2023

Railway maintenance is a complex and complicated task in the railway industry due to the number of its components and relationships. Ineffective railway maintenance results in excess cost, defective railway structure and components, longer possession time, poorer safety, and lower passenger comfort. Of the three main maintenance approaches, predictive maintenance is the trendy one, and is proven that it provides the highest efficiency. However, the implementation of predictive maintenance for the railway industry cannot be done without an efficient tool. Normally, railway maintenance is corrective when some things fail or preventive when maintenance is routine. A novel approach using an integration between deep reinforcement learning and digital twin is proposed in this study to improve the efficiency of railway maintenance which other techniques such as supervised and unsupervised learning cannot provide. In the study, Advantage Actor Critic (A2C) is used to develop a reinforcement learning model and agent to fulfill the need of the study. Real-world field data over four years and 30 km. is obtained and applied for developing the reinforcement learning model. Track geometry parameters, railway component defects, and maintenance activities are used as parameters to develop the reinforcement learning model. Rewards (or penalties) are calculated based on maintenance costs and occurring defects. The new breakthrough exhibits that using reinforcement learning integrated with digital twin can reduce maintenance activities by 21% and reduce the occurring defects by 68%. Novelties of the study are the use of A2C which is faster and provides better results than other traditional techniques such as Deep Q-learning (DQN), each track geometry parameter is considered without combining into a track quality index, filed data are used to develop the reinforcement learning model, and seven independent actions are included in the reinforcement learning model. This study is the world’s first to contribute a new guideline for applying reinforcement learning and digital twins to improve the efficiency of railway maintenance, reduce the number of defects, reduce the maintenance cost, reduce the possession time for railway maintenance, improve the overall safety of the railway operation, and improve the passenger comfort which can be seen from its results.