Single Atom Ag Bonding Between PF3T Nanocluster and TiO2 Leads the Ultra-Stable Visible-Light-Driven Photocatalytic H2 Production.

Atomic Ag cluster bonding is employed to reinforce the interface between PF3T nano-cluster and TiO2 nanoparticle. With an optimized Ag loading (Ag/TiO2 = 0.5 wt%), the Ag atoms will uniformly disperse on TiO2 thus generating a high density of intermediate states in the band gap to form the electron channel between the terthiophene group of PF3T and the TiO2 in the hybrid composite (denoted as T@Ag05-P). The former expands the photon absorption band width and the latter facilitates the core-hole splitting by injecting the photon excited electron (from the excitons in PF3T) into the conduction band (CB) of TiO2. These characteristics enable the high efficiency of H2 production to 16 580 µmol h-1 g-1 and photocatalysis stability without degradation under visible light exposure for 96 h. Compared to that of hybrid material without Ag bonding (TiO2@PF3T), the H2 production yield and stability are improved by 4.1 and 18.2-fold which shows the best performance among existing materials in similar component combination and interfacial reinforcement. The unique bonding method offers a new prospect to accelerate the development of photocatalytic hydrogen production technologies.

Structures and Electron Transport Paths in the Four Families of Flavin-Based Electron Bifurcation Enzymes.

Oxidoreductases facilitating electron transfer between molecules are pivotal in metabolic pathways. Flavin-based electron bifurcation (FBEB), a recently discovered energy coupling mechanism in oxidoreductases, enables the reversible division of electron pairs into two acceptors, bridging exergonic and otherwise unfeasible endergonic reactions. This chapter explores the four distinct FBEB complex families and highlights a decade of structural insights into FBEB complexes. In this chapter, we discuss the architecture, electron transfer routes, and conformational changes across all FBEB families, revealing the structural foundation that facilitate these remarkable functions.

Identification of key biomarkers for early warning of diabetic retinopathy using BP neural network algorithm and hierarchical clustering analysis.

Diabetic retinopathy is one of the most common microangiopathy in diabetes, essentially caused by abnormal blood glucose metabolism resulting from insufficient insulin secretion or reduced insulin activity. Epidemiological survey results show that about one third of diabetes patients have signs of diabetic retinopathy, and another third may suffer from serious retinopathy that threatens vision. However, the pathogenesis of diabetic retinopathy is still unclear, and there is no systematic method to detect the onset of the disease and effectively predict its occurrence. In this study, we used medical detection data from diabetic retinopathy patients to determine key biomarkers that induce disease onset through back propagation neural network algorithm and hierarchical clustering analysis, ultimately obtaining early warning signals of the disease. The key markers that induce diabetic retinopathy have been detected, which can also be used to explore the induction mechanism of disease occurrence and deliver strong warning signal before disease occurrence. We found that multiple clinical indicators that form key markers, such as glycated hemoglobin, serum uric acid, alanine aminotransferase are closely related to the occurrence of the disease. They respectively induced disease from the aspects of the individual lipid metabolism, cell oxidation reduction, bone metabolism and bone resorption and cell function of blood coagulation. The key markers that induce diabetic retinopathy complications do not act independently, but form a complete module to coordinate and work together before the onset of the disease, and transmit a strong warning signal. The key markers detected by this algorithm are more sensitive and effective in the early warning of disease. Hence, a new method related to key markers is proposed for the study of diabetic microvascular lesions. In clinical prediction and diagnosis, doctors can use key markers to give early warning of individual diseases and make early intervention.

Phylogenetic Diversity Indices from an Affine and Projective Viewpoint.

Phylogenetic diversity indices are commonly used to rank the elements in a collection of species or populations for conservation purposes. The derivation of these indices is typically based on some quantitative description of the evolutionary history of the species in question, which is often given in terms of a phylogenetic tree. Both rooted and unrooted phylogenetic trees can be employed, and there are close connections between the indices that are derived in these two different ways. In this paper, we introduce more general phylogenetic diversity indices that can be derived from collections of subsets (clusters) and collections of bipartitions (splits) of the given set of species. Such indices could be useful, for example, in case there is some uncertainty in the topology of the tree being used to derive a phylogenetic diversity index. As well as characterizing some of the indices that we introduce in terms of their special properties, we provide a link between cluster-based and split-based phylogenetic diversity indices that uses a discrete analogue of the classical link between affine and projective geometry. This provides a unified framework for many of the various phylogenetic diversity indices used in the literature based on rooted and unrooted phylogenetic trees, generalizations and new proofs for previous results concerning tree-based indices, and a way to define some new phylogenetic diversity indices that naturally arise as affine or projective variants of each other or as generalizations of tree-based indices.

Cyclic quantum teleportation of two-qubit entangled states by using six-qubit cluster state and six-qubit entangled state.

Cyclic quantum teleportation schemes requires at least the existence of three collaborators acting all as senders and receivers of quantum information, each one of them has an information to be transmitted to the next neighbour in a circular manner. Here, new cyclic quantum teleportation scheme is proposed for perfectly transmitting cyclically three arbitrary unknown two-qubit states ( α , ß and γ ) among the three collaborators. In this scheme, Alice can send to Bob the quantum information contained in her two-qubit state α and receive from Charlie the quantum information contained in the two-qubit state in his possession γ and similarly, Bob can transmit to Charlie the quantum information contained in his two-qubit state ß through a quantum channel of twelve-qubit state consisting of a six-qubit cluster state and a six-qubit entangled state by sequentially and cyclically performing Bell state measurements. Subsequently, each one of the three participants can afterwards retrieve his own desired two-qubit state using classical channel and by performing appropriate unitary Pauli operators and we have shown that our proposed scheme performs efficiently.

Cluster of Differentiation 36 (CD36) Preferentially Mediates Intestinal Absorption of Dietary Z-Astaxanthin and Especially 9-Z-Isomer via Higher Binding Affinity.

Variances in the biological functions of astaxanthin geometric isomers (i.e., all-E, Z) are related to their intestinal absorption, but the mechanism of isomer absorption mediated by transporters remains unclear. Here, models of in vitro cell overexpression, in situ intestinal perfusion, and in vivo mouse inhibition were employed to investigate the impact of cluster of differentiation 36 (CD36) on the absorption of astaxanthin isomers. Cells overexpressing CD36 notably enhanced the uptake of Z-astaxanthin, particularly the 9-Z-isomer (47.76%). The absorption rate and permeability of Z-astaxanthin surpassed that of the all-E-isomer by the in situ model. Furthermore, the addition of the CD36-specific inhibitor sulfo-N-succinimidyl oleate significantly reduced the absorption of Z-astaxanthin in the mouse duodenum and jejunum, especially the 9-Z-isomer (57.66%). Molecular docking and surface plasmon resonance techniques further validated that 9-Z-astaxanthin binds to more amino acids of CD36 with higher affinity and in a fast-binding, fast-dissociating mode, thus favoring transport. Our findings elucidate, for the first time, the mechanism of the CD36-mediated transmembrane transport of astaxanthin geometric isomers.

Progressive Neighbor-masked Contrastive Learning for Fusion-style Deep Multi-view Clustering.

Fusion-style Deep Multi-view Clustering (FDMC) can efficiently integrate comprehensive feature information from latent embeddings of multiple views and has drawn much attention recently. However, existing FDMC methods suffer from the interference of view-specific information for fusion representation, affecting the learning of discriminative cluster structure. In this paper, we propose a new framework of Progressive Neighbor-masked Contrastive Learning for FDMC (PNCL-FDMC) to tackle the aforementioned issues. Specifically, by using neighbor-masked contrastive learning, PNCL-FDMC can explicitly maintain the local structure during the embedding aggregation, which is beneficial to the common semantics enhancement on the fusion view. Based on the consistent aggregation, the fusion view is further enhanced by diversity-aware cluster structure enhancement. In this process, the enhanced cluster assignments and cluster discrepancies are employed to guide the weighted neighbor-masked contrastive alignment of semantic structure between individual views and the fusion view. Extensive experiments validate the effectiveness of the proposed framework, revealing its ability in discriminative representation learning and improving clustering performance.

Community health promotion and medical provision and impact on neonates (CHAMPION2) and support to rural India's public education system and impact on numeracy and literacy scores (STRIPES2): an update to the study protocol (v 11) for a cluster randomised trial in Madhya Pradesh, India.

BACKGROUND: This update outlines amendments to the CHAMPION2/STRIPES2 cluster randomised trial protocol primarily made due to the COVID-19 pandemic and nationwide lockdown in India in 2020. These amendments were in line with national guidelines for health research during the COVID-19 pandemic. METHODS: We did not change the original trial design, eligibility, and outcomes. Amendments were introduced to minimise the risk of COVID-19 transmission and ensure safety and wellbeing of trial staff, participants, and other villagers. CHAMPION2 intervention: participatory learning and action (PLA) and fixed day service (FDS) meeting were revised to incorporate social distancing and hygiene precautions. During the COVID-19 pandemic, PLA participation was limited to pregnant women and birthing partners. STRIPES2 intervention: before/after-school classes were halted for a period and then modified temporarily (reducing class sizes, and/or changing meeting places) with hygiene and safe distancing practices introduced. DATA COLLECTION: The research team gathered as much information as possible from participants by telephone. If the participant had no telephone or could not be contacted by telephone, data were collected in person. COVID-19 precautions: trial teams were trained on COVID-19 precautions and used personal protective equipment whilst in the villages for trial-related activities. After restarting the trial between June and September 2020 in a phased manner, some trial activities were suspended again in all the trial villages from April to June 2021 due to the second wave of COVID-19 cases and lockdown imposed in Satna, Madhya Pradesh. Trial timelines were also revised, with outcomes measured later than originally planned. TRIAL REGISTRATION: Clinical Trial Registry of India CTRI/2019/05/019296. Registered 23 May 2019. https://ctri.nic.in/Clinicaltrials/pmaindet2.php?EncHid=MzExOTg=&Enc=&userName=champion2 .

Binding of IscU and TusA to different but competing sites of IscS influences the activity of IscS and directs sulfur to the respective biomolecular synthesis pathway.

All sulfur transfer pathways generally have in common an l-cysteine desulfurase as the initial sulfur-mobilizing enzyme, which serves as a sulfur donor for the biosynthesis of numerous sulfur-containing biomolecules in the cell. In Escherichia coli, the housekeeping l-cysteine desulfurase IscS functions as a hub for sulfur transfer through interactions with several partner proteins, which bind at different sites on IscS. So far, the interaction sites of IscU, Fdx, CyaY, and IscX involved in iron sulfur (Fe-S) cluster assembly, TusA, required for molybdenum cofactor biosynthesis and mnm5s2U34 transfer RNA (tRNA) modifications, and ThiI, involved in both the biosynthesis of thiamine and s4U8 tRNA modifications, have been mapped. Previous studies have suggested that IscS partner proteins bind only one at a time, with the exception of Fe-S cluster assembly, which involves the formation of a ternary complex involving IscS, IscU, and one of CyaY, Fdx, or IscX. Here, we show that the affinity of TusA for IscS is similar to but lower than that of IscU and that these proteins compete for binding to IscS. We show that heterocomplexes involving the IscS dimer and single IscU and TusA molecules are readily formed and that binding of both TusA and IscU to IscS affects its l-cysteine desulfurase activity. A model is proposed in which the delivery of sulfur to different sulfur-requiring pathways is controlled by sulfur acceptor protein levels, IscS-binding affinities, and acceptor protein-modulated IscS desulfurase activity.IMPORTANCEIron-sulfur clusters are evolutionarily ancient prosthetic groups. The housekeeping l-cysteine desulfurase IscS functions as a central core for sulfur transfer through interactions with several partner proteins, which bind at different sites on each IscS monomer with different affinities and partially overlapping binding sites. We show that heterocomplexes involving the IscS dimer and single IscU and TusA molecules at each site of the dimer are formed, thereby influencing the activity of IscS.

Cluster Formation of Self-Assembled Triarylbismuthanes and Charge Transport Characterizations of Gold-Triarylbismuthane-Gold Junctions.

Organometallic molecules are promising for molecular electronic devices due to their potential to improve electrical conductance through access to complex orbital covalency that is not available to light-element organic molecules. However, studies of the formation of organometallic monolayers and their charge transport properties are scarce. Here, we report the cluster formation and charge transport properties of gold-triarylbismuthane-gold molecular junctions. We found that triarylbismuthane molecules with -CN anchoring groups form clusters during the creation of self-assembled submonolayers. This clustering is attributed to strong interactions between the bismuth (Bi) center and the nitrogen atom in the -CN group of adjacent molecules. Examination of the influence of -NH2 and -CN anchoring groups on junction conductance revealed that, despite a stronger binding energy between the -NH2 group and gold, the conductance per molecular unit (i.e., molecule for the -NH2 group and cluster for the -CN group) is higher with the -CN anchoring group. Further analysis showed that an increase in the number of -CN groups from one to three within the junctions leads to a decrease in conductance while increasing the size of the cluster. This demonstrates the significant effects of different anchoring groups and the impact of varying the number of -CN groups on both the charge transport and cluster formation. This study highlights the importance of selecting the appropriate anchoring group in the design of molecular junctions. Additionally, controlling the size and formation of clusters can be a strategic approach to engineering charge transport in molecular junctions.

Antibacterial activity and biosynthetic potential of Streptomyces sp. PBR19, isolated from forest rhizosphere soil of Assam.

An Actinomycetia isolate, designated as PBR19, was derived from the rhizosphere soil of Pobitora Wildlife Sanctuary (PWS), Assam, India. The isolate, identified as Streptomyces sp., shares a sequence similarity of 93.96% with its nearest type strain, Streptomyces atrovirens. This finding indicates the potential classification of PBR19 as a new taxon within the Actinomycetota phylum. PBR19 displayed notable antibacterial action against some ESKAPE pathogens. The ethyl acetate extract of PBR19 (EtAc-PBR19) showed the lowest minimum inhibitory concentration (MIC) of ≥ 0.195 µg/mL against Acinetobacter baumannii ATCC BAA-1705. A lower MIC indicates higher potency against the tested pathogen. Scanning electron microscope (SEM) findings revealed significant changes in the cytoplasmic membrane structure of the pathogen. This suggests that the antibacterial activity may be linked to the disruption of the microbial membrane. The predominant chemical compound detected in the EtAc-PBR19 was identified as phenol, 3,5-bis(1,1-dimethylethyl), comprising 48.59% of the area percentage. Additionally, PBR19 was found to contain the type II polyketide synthases (PKS type II) gene associated with antibiotic synthesis. The predicted gene product of PKSII was identified as the macrolide antibiotic Megalomicin A. The taxonomic distinctiveness, potent antibacterial effects, and the presence of a gene associated with antibiotic synthesis suggest that PBR19 could be a valuable candidate for further exploration in drug development and synthetic biology. The study contributes to the broader understanding of microbial diversity and the potential for discovering bioactive compounds in less-explored environments.

Nothing means anything if everything means something: exploring the issues of coping profiles and the person-centered approach.

BACKGROUND: Adopting a person-centered approach to coping potentially allows researchers to explore the multifaceted nature of the construct. However, this increasingly adopted approach also has limitations. Namely, employing cluster or latent profile analysis to investigate coping through a person-centered lens often brings a lack of generalizability and subjectivity in interpreting the generated profiles. As such, this study aimed to explore the impact of varied methodology in person-centered investigations of coping profiles. METHODS: 682 university students' (M = 21.3 years old, SD = 3.5) responses to the COPE Inventory were analyzed across item, subscale, and higher-order category levels using cluster and latent profile analysis to produce 6 finalized models for cross-method comparison. RESULTS: Throughout 19 analyses, approach coping, avoidance coping, low coping, and help-seeking profiles were consistently identified, alluding to the potential of universal coping trends. However, membership overlap across COPE structures and methodology was largely inconsistent, with individual participants classified into theoretically distinct profiles based on the methodology employed. CONCLUSION: While evidence suggests latent profile analysis provides a more rigorous approach, the significant impact of minor methodological variations urges a reevaluation of person-centered approaches and incorporation of multi-construct data to enhance the understanding of coping profiles.

Sample Size Calculation for an Individual Stepped-Wedge Randomized Trial.

In the individual stepped-wedge randomized trial (ISW-RT), subjects are allocated to sequences, each sequence being defined by a control period followed by an experimental period. The total follow-up time is the same for all sequences, but the duration of the control and experimental periods varies among sequences. To our knowledge, there is no validated sample size calculation formula for ISW-RTs unlike stepped-wedge cluster randomized trials (SW-CRTs). The objective of this study was to adapt the formula used for SW-CRTs to the case of individual randomization and to validate this adaptation using a Monte Carlo simulation study. The proposed sample size calculation formula for an ISW-RT design yielded satisfactory empirical power for most scenarios except scenarios with operating characteristic values near the boundary (i.e., smallest possible number of periods, very high or very low autocorrelation coefficient). Overall, the results provide useful insights into the sample size calculation for ISW-RTs.

Prefiltered component-based greedy (PreCoG) scan method.

The spatial distribution of disease cases can provide important insights into disease spread and its potential risk factors. Identifying disease clusters correctly can help us discover new risk factors and inform interventions to control and prevent the spread of disease as quickly as possible. In this study, we propose a novel scan method, the Prefiltered Component-based Greedy (PreCoG) scan method, which efficiently and accurately detects irregularly shaped clusters using a prefiltered component-based algorithm. The PreCoG scan method's flexibility allows it to perform well in detecting both regularly and irregularly-shaped clusters. Additionally, it is fast to apply while providing high power, sensitivity, and positive predictive value for the detected clusters compared to other scan methods. To confirm the effectiveness of the PreCoG method, we compare its performance to many other scan methods. Additionally, we have implemented this method in the smerc R package to make it publicly available to other researchers. Our proposed PreCoG scan method presents a unique and innovative process for detecting disease clusters and can improve the accuracy of disease surveillance systems.

A multifaceted risk management program to improve the reporting rate of patient safety incidents in primary care: a cluster-randomised controlled trial.

BACKGROUND: While patient safety incident reporting is of key importance for patient safety in primary care, the reporting rate by healthcare professionals remains low. This study aimed to assess the effectiveness of a risk management program in increasing the reporting rate within multiprofessional primary care facilities. METHODS: A nation-wide cluster-randomised controlled trial was performed in France, with each cluster defined as a primary care facility. The intervention included professional e-learning training, identification of a risk management advisor, and multidisciplinary meetings to address incident analysis. In the first observational period, a patient safety incident reporting system for professionals was implemented in all facilities. Then, facilities were randomised, and the program was implemented. Incidents were reported over the 15-month study period. Quasi-Poisson models were used to compare reporting rates. RESULTS: Thirty-five facilities (intervention, n = 17; control, n = 18) were included, with 169 and 232 healthcare professionals, respectively, involved. Overall, 7 out of 17 facilities carried out the entire program (41.2%), while 6 did not hold meetings (35.3%); 48.5% of professionals logged on to the e-learning website. The relative rate of incidents reported was 2.7 (95% CI = [0.84-11.0]; p = 0.12). However, a statistically significant decrease in the incident rate between the pre-intervention and post-intervention periods was observed for the control arm (HR = 0.2; 95% CI = [0.05-0.54]; p = 0.02), but not for the intervention arm (HR = 0.54; 95% CI = [0.2-1.54]; p = 0.23). CONCLUSION: This program didn't lead to a significant improvement in the patient safety incident reporting rate by professionals but seemed to sustain reporting over time. Considering that the program was fully implemented in only 41% of facilities, this highlights the difficulty of implementing such multidisciplinary programs in primary care despite its adaptation to the setting. A better understanding of how risk management is currently organized in these multiprofessional facilities is of key importance to improve patient safety in primary care. TRIAL REGISTRATIONS: The study has been registered at clinicaltrials.gov (NCT02403388) on 30 March 2015.

Fire protection priorities in the oak forests of Iran with an emphasis on vertebrate habitat preservation.

This study examines the impact of fire incidents on wildlife and habitats in the western oak forests of Iran (Zagros region). These forests are globally recognized for their exceptional biodiversity but are frequently threatened by wildfires. To achieve this, the study uses the space-time scan statistics permutation (STSSP) model to identify areas with a higher frequency of fires. The study also analyzes the effects of fires on the Zagros forests from 2000 to 2021 using remote-sensing MODIS data. Also, to understand the elements at risk of fire, burned areas were assessed based on the richness of vertebrate species, determined by the distribution of 88 vertebrate species. The results show that the annual fire rate in the Zagros forests is 76.2 (fire occurrences per year), calculated using the Poisson distribution. Findings show the highest fire rates are found in the northwest and a part of the south of the Zagros. The northwest of the Zagros also has the largest number of single fires and clusters, indicating a wide spatial distribution of fire in these regions. On the other side, it was unexpectedly found that these regions have the richest number of species and higher habitat value. The results demonstrate a significant correlation between the value of the habitat and the extent of burned areas (p < 0.05). The study also reveals that the greatest impact of fires is on small vertebrates. The overlap of frequent fire spots with the richest regions of Zagros oak forests in terms of vertebrate diversity emphasizes the need for strategic forest risk reduction planning, especially in these priority zones.

Influence of easing COVID-19 strategies following downgrading of the national infectious disease category on COVID-19 occurrence among hospitalized patients in Japan.

PURPOSE: We aimed to evaluate the influence of easing COVID-19 preventive measures following its downgrading to a common infectious disease on COVID-19 occurrence among hospitalized patients. METHODS: Nosocomial occurrence of COVID-19 was compared between periods with national infectious disease category 5 versus the preceding category 2 equivalent. Changes in the revised manual included a shorter duration of work restrictions for infected health care practitioners (HCPs); no work restriction for HCPs exposed to SARS-CoV-2 with a negative test on days 1, 3 and 5; discontinuation of universal pre-admission screening; and pre-emptive isolation of patients without screening. Wearing an N95 mask and face shield was required in procedure/care with moderate-to high-risk contact. RESULTS: Although the mean monthly number of infected HCPs increased from 8.1 to 12.7 in the category 5 period (p = 0.034) and that of pre-admission screening decreased to one-fourth, the COVID-19 incidence in hospitalized patients remained similar between the two study periods (1.60 ± 5.59/month versus 1.40 ± 2.63/month, p = 0.358). Clusters, defined as ≥3 COVID-19 patients on the ward, were experienced twice in the preceding period and only once in the category 5 period. The index cases causing nosocomial SARS-CoV-2 transmission mostly involved rehabilitation therapists in the preceding period; five of six index cases were patients in the category 5 period. Following the expanded indication for N95 masks, neither SARS-CoV-2 transmission to patients nor transmission from infected patients was observed in HCPs for 1 year. CONCLUSION: With sustained, enhanced standard precautions, easing prevention strategies could limit nosocomial SARS-CoV-2 infections.

Group-informed attentive framework for enhanced diabetes mellitus progression prediction.

The increasing prevalence of Diabetes Mellitus (DM) as a global health concern highlights the paramount importance of accurately predicting its progression. This necessity has propelled the use of deep learning's advanced analytical and predictive capabilities to the forefront of current research. However, this approach is confronted with significant challenges, notably the prevalence of incomplete data and the need for more robust predictive models. Our research aims to address these critical issues, leveraging deep learning to enhance the precision and reliability of diabetes progression predictions. We address the issue of missing data by first locating individuals with data gaps within specific patient clusters, and then applying targeted imputation strategies for effective data imputation. To enhance the robustness of our model, we implement strategies such as data augmentation and the development of advanced group-level feature analysis. A cornerstone of our approach is the implementation of a deep attentive transformer that is sensitive to group characteristics. This framework excels in processing a wide array of data, including clinical and physical examination information, to accurately predict the progression of DM. Beyond its predictive capabilities, our model is engineered to perform advanced feature selection and reasoning. This is crucial for understanding the impact of both individual and group-level factors on deep models' predictions, providing invaluable insights into the dynamics of DM progression. Our approach not only marks a significant advancement in the prediction of diabetes progression but also contributes to a deeper understanding of the multifaceted factors influencing this chronic disease, thereby aiding in more effective diabetes management and research.

Hexokinase 1 forms rings that regulate mitochondrial fission during energy stress.

Metabolic enzymes can adapt during energy stress, but the consequences of these adaptations remain understudied. Here, we discovered that hexokinase 1 (HK1), a key glycolytic enzyme, forms rings around mitochondria during energy stress. These HK1-rings constrict mitochondria at contact sites with the endoplasmic reticulum (ER) and mitochondrial dynamics protein (MiD51). HK1-rings prevent mitochondrial fission by displacing the dynamin-related protein 1 (Drp1) from mitochondrial fission factor (Mff) and mitochondrial fission 1 protein (Fis1). The disassembly of HK1-rings during energy restoration correlated with mitochondrial fission. Mechanistically, we identified that the lack of ATP and glucose-6-phosphate (G6P) promotes the formation of HK1-rings. Mutations that affect the formation of HK1-rings showed that HK1-rings rewire cellular metabolism toward increased TCA cycle activity. Our findings highlight that HK1 is an energy stress sensor that regulates the shape, connectivity, and metabolic activity of mitochondria. Thus, the formation of HK1-rings may affect mitochondrial function in energy-stress-related pathologies.

Research on the relation between hydro-chemical and geological characteristics in karst area: Case study in Zhong Liang Mountain, Southwest China.

Karst groundwater, which is one of most important drinking water sources, is vulnerable to be polluted as its closed hydraulic relation with surface water. Thus, it is very important to identify the groundwater source to control groundwater pollution. The Pearson correlation coefficient among major ions (Na + K+, Ca2+, Mg2+, HCO3 -, SO4 2-, and Cl-) was employed to deduce the groundwater types in Zhong Liang Mountain, Southwest China. Then, the combined method of principal component analysis and cluster analysis were employed to identify the groundwater sources in a typical karst region of southwest China. The results shown that (1) the high positive correlation between cations and anions indicated the water-rock reaction of Ca-HCO3, Ca-SO4, (Na + K)-Cl, and Mg-SO4. (2) The major two principal components that would represent water-rock reaction of CaSO4 and Ca-HCO3 would, respectively, explain 60.41% and 31.80% of groundwater information. (3) Based on the two principal components, 33 groundwater samples were clustered into eight groups through hierarchical clustering, each group has similar water-rock reaction. The findings would be employed to forecast the surge water, that was an important work for tunnel construction and operation. PRACTITIONER POINTS: The components of groundwater was highly correlated with water-rock reaction. The principal component analysis screens the types of groundwater. The cluster analysis identifies the groundwater sources.