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In the contemporary era, lithium-ion batteries have gained considerable attention in various industries such as 3C products, electric vehicles and energy storage systems due to their exceptional properties. With the rapid progress in the energy storage sector, there is a growing demand for greater energy density in lithium-ion batteries. While the use of thick electrodes is a straightforward and effective approach to enhance the energy density of battery, it is hindered by the sluggish reaction dynamics and insufficient mechanical properties. Therefore, we comprehensively review recent advances in the field of thick electrodes for lithium-ion batteries to overcome the bottlenecks in the development of thick electrodes and achieve efficient fabrication for high-performance lithium-ion batteries. Initially, a systematic analysis is performed to identify the factors affecting the performance of the thick electrodes. the correlation between electrode materials, structural parameters, and performance is also investigated. Subsequently, the viable strategies for constructing thick electrodes with improved properties are summarize, including high throughput, high conductivity and low tortuosity, in both material development and structural design. In addition, recent advances in efficient fabrication methods for thick electrode fabrication are reviewed, with a comprehensive assessment of their merits, limitations, and applicable scenarios. Finally, a comprehensive overview of the multiscale design and manufacturing process for thick electrodes in lithium-ion batteries is provided, accompanied by valuable insights into design considerations that are crucial for future advances in this area.

Abstract Iron‐based pyrophosphates are attractive cathodes for sodium‐ion batteries due to their large framework, cost‐effectiveness, and high energy density. However, the understanding of the crystal structure is scarce and only a limited candidates have been reported so far. In this work, we found for the first time that a continuous solid solution, Na4−αFe2+α/2(P2O7)2 (0 ≤ α ≤ 1, could be obtained by mutual substitution of cations at center‐symmetric Na3 and Na4 sites while keeping the crystal building blocks of anionic P2O7 unchanged. In particular, a novel off‐stoichiometric Na3Fe2.5(P2O7)2 is thus proposed, and its structure, energy storage mechanism, and electrochemical performance are extensively investigated to unveil the structure–function relationship. The as‐prepared off‐stoichiometric electrode delivers appealing performance with a reversible discharge capacity of 83 mAh g−1, a working voltage of 2.9 V (vs. Na+/Na), the retention of 89.2% of the initial capacity after 500 cycles, and enhanced rate capability of 51 mAh g−1 at a current density of 1600 mA g−1. This research shows that sodium ferric pyrophosphate could form extended solid solution composition and promising phase is concealed in the range of Na4−αFe2+α/2(P2O7)2, offering more chances for exploration of new cathode materials for the construction of high‐performance SIBs.

In meta-analyses, unlike model-based methods, such as fixed- or random-effect models, the p-value combining methods are distribution-free and robust. How to appropriately and powerfully combine p-values obtained from various sources remains an important but challenging topic in statistical inference. For cases where all or a majority of the individual alternative hypotheses have the same but unknown direction, concordant tests based on one-sided p-values can substantially improve the detecting power. However, there exists no test that is uniformly most powerful; therefore, figuring out how to choose a robust and powerful test to combine one-sided p-values for a given data set is desirable. In this paper, we propose and study a class of gamma distribution-based concordant tests. Those concordant tests are optimal under specific conditions. An asymptotically optimal concordant test is also studied. The excellent performances of the proposed tests were demonstrated through a numeric simulation study and real data example.

Abstract Sodium‐ion battery (SIB) is considered as a revolutionary technology toward large‐scale energy storage applications. Developing cost‐effective cathode material as well as economical synthesis procedure is a key challenge for its commercialization. Herein, we develop a facile and economic strategy to simultaneously remove rust from the surface of carbon steel and achieve porous and hollow spherical Na4Fe3(PO4)2P2O7/C (HS‐NFPP/C). Benefiting from the desirable structure that fastens the electronic/ionic transportation and effectively accommodates the volume expansion/contraction during discharge/charge process, the as‐prepared cathode exhibits outstanding rate capability and ultralong cycle life. An extraordinarily high‐power density of 32.3 kW kg−1 with an ultrahigh capacity retention of 89.7% after 10 000 cycles are achieved. More significantly, the 3 Ah HC||HS‐NFPP/C full battery manifests impressive cycling stability. Therefore, this work provides an economical and sustainable approach for the massive production of high‐performance Na4Fe3(PO4)2P2O7 cathode, which can be potentially commercialized toward SIB applications.

This study prepared silicon oxide anode materials with nitrogen-doped carbon matrices (SiOx/C–N) through silicon-containing polyester thermal carbonization. Melamine was introduced as a nitrogen source during the experiment. This nitrogen doping process resulted in a porous structure in the carbon matrices, a fact confirmed by scanning electron microscopy (SEM). Pyridinic and quaternary nitrogen, but mainly tertiary nitrogen, were generated, as shown via X-ray photoelectron spectroscopy (XPS). Electrochemical tests confirmed that, as anode materials for a lithium-ion battery, SiOx/C–N provided better cycle stability, improved rate capability, and lower Li+ diffusion resistance. The best performance showed an activated capacity at 493.5 mAh/g, preserved at 432.8 mAh/g after the 100th cycle, with 87.7% total Columbic efficiency. Those without nitrogen doping gave 1126.7 mAh/g, 249.0 mAh/g, and 22.1%, respectively. The most noteworthy point was that, after 100 cycles, anodes without nitrogen doping were pulverized into fine powders (SEM); meanwhile, in the case of anodes with nitrogen doping, powders of a larger size (0.5–1.0 µm) formed, with the accumulation of surrounding cavities. We suggest that the formation of more prominent powders may have resulted from the more substantial nitrogen-doped carbon matrices, which prevented the anode from further breaking down to a smaller size. The volume expansion stress decreased when the powders decreased to nanosize, which is why the nanosized silicon anode materials showed better cycling stability. When the anodes were cracked into powders with a determined diameter, the stress from volume expansion decreased to a level at which the powders could preserve their shape, and the breakage of the powders was stopped. Hence, the diameters of the final reserved powders are contingent on the strength of the matrix. As reported, nitrogen-doped carbon matrices are more robust than those not doped with nitrogen. Thus, in our research, anodes with nitrogen-doped carbon matrices presented more large-diameter powders, as SEM confirmed. Anodes with nitrogen doping will not be further broken at a larger diameter. At this point, the SEI film will not show continuous breakage and formation compared to the anode without doping. This was validated by the lower deposition content of the SEI-film-related elements (phosphorous and fluorine) in the cycled anodes with nitrogen doping. The anode without nitrogen doping presented higher content, meaning that the SEI films were broken many times during lithiation/delithiation (EDS mapping).

Abstract Recently Liu and Xie proposed a p-value combination test based on the Cauchy distribution (CCT). They showed that when the significance levels are small, CCT can control type I error rate and the resulting p-value can be simply approximated using a Cauchy distribution. One very special and attractive property of CCT is that it is applicable to situations where the p-values to be combined are dependent. However, in this paper, we show that under some conditions the commonly used MinP test is much more powerful than CCT. In addition, under some other situations, CCT is powerless at all. Therefore, we should use CCT with caution. We also proposed new robust p-value combination tests using a second MinP/CCT to combine the dependent p-values obtained from CCT and MinP applied to the original p-values. We call the new tests MinP-CCT-MinP (MCM) and CCT-MinP-CCT (CMC). We study the performance of the new tests by comparing them with CCT and MinP using comprehensive simulation study. Our study shows that the proposed tests, MCM and CMC, are robust and powerful under many conditions, and can be considered as alternatives of CCT or MinP.

Abstract Lithium–ion battery (LIB) suffers from safety risks and narrow operational temperature range in despite the rapid drop in cost over the past decade. Subjected to the limited materials choices, it is not feasible to modify the cathode and anode to improve the battery's wide‐temperature performance, hence, optimizing the design of the electrolyte system has currently become the most feasible and economical way to broaden the operating temperature range of LIBs. Considering urgent demands for wide‐temperature LIBs and achieved enormously excited results about wide‐temperature electrolytes in recent years, a review about this topic and scope is timely and important at present. In this study, we first examine the physicochemical properties of current commercial electrolytes with emphasis on the variations of key parameters along with the temperature. After that, we give comprehensive overview of the employed strategies for separately improving the electrochemical performance of electrolytes toward low‐temperature, high‐temperature, and wide‐temperature applications. Furthermore, recent progress of ionic liquids and solid‐state electrolytes that are capable of working within wide temperature range is also summarized. We hope this review will provide deep understanding of the design principles of wide‐temperature electrolyte, and inspire more endeavors to conquer the practicability issue of LIBs in extreme environments.

Metformin has been suggested to reduce thyroid cancer incidence and to improve thyroid cancer prognosis. We aimed to evaluate the associations between metformin and thyroid cancer incidence and prognosis (metastasis/recurrence/progression-free survival). Cochrane Library, PubMed, ClinicalTrials.gov, and U.S. National Library of Medicine Clinical Trials were searched through the end of December 2021. Data were collected from original observational studies or clinical trials on the incidence or prognosis of thyroid carcinoma outcomes in type 2 diabetes mellitus (T2DM) patients with and without metformin use. Risk of bias in non-randomized studies of interventions (ROBINS-I) tool and Grading of Recommendations, and Assessment, Development and Evaluations (GRADE) approach were used to evaluate the risk of bias and quality of the body of evidence, respectively. In general, 4 studies were related to the thyroid cancer incidence, including 1,705,123 participants metformin users and non-users and yielding a total of 3,238 thyroid cancer events; 3 studies reported the prognosis of thyroid carcinoma based on a total of 4,972 individuals with primary thyroid carcinoma and comorbid type 2 diabetes, and the number of thyroid cancer prognosis cases ranged from 3 to 79. The overall risk of bias of the included studies ranged from moderate to serious. In the random-effects model, the summary relative risk (SRR) for thyroid cancer incidence was 0.743 (95% CI: 0.453-1.220; I2 = 88.7%, low certainty) comparing metformin users to non-users; and SRR for the prognosis of thyroid cancer was 0.504 (95% CI: 0.178-1.430; I2 = 57.5%, low certainty). Non-statistically significant negative associations between metformin use and incidence and prognosis of thyroid cancer were found in the current analysis, although the quantity and quality of the evidence were limited. Futher investigation is needed to evaluate the clinical benefits of metformin on thyroid cancer prevention and treatments.

Multivalent metal ion (Mg2+, Zn2+, Ca2+, and Al3+) batteries (MMIBs) emerged as promising technologies for large-scale energy storage systems in recent years due to the abundant metal reserves in the Earth’s crust and potentially low cost. However, the lack of high-performance electrode materials is still the main obstacle to the development of MMIBs. As a newly large family of two-dimensional transition metal carbides, nitrides, and carbonitrides, MXenes have attracted growing focus in the energy storage field because of their large specific surface area, excellent conductivity, tunable interlayer spaces, and compositional diversity. In particular, the multifunctional chemistry and superior hydrophilicity enable MXenes to serve not only as electrode materials but also as important functional components for heterojunction composite electrodes. Herein, the advances of MXene-based materials since its discovery for MMIBs are summarized, with an emphasis on the rational design and controllable synthesis of MXenes. More importantly, the fundamental understanding of the relationship between the morphology, structure, and function of MXenes is highlighted. Finally, the existing challenges and future research directions on MXene-based materials toward MMIBs application are critically discussed and prospected.

Large-scale energy storage using sodium ion batteries (SIBs) as a hub for the conversion of renewable energy has become a topic of great importance. However, the application of SIBs is hindered by low energy density arising from inferior capacity and operation voltage. In this regard, vanadium-based phosphate polyanions with multiple valence changes (III–V), high redox potential, abundant resources, spacious frame structure, and remarkable thermal stability are promising avenues to address this dilemma. In this review, following the principle of electronic structure and function relationship, we summarize the recent progress in phosphates, pyrophosphates, fluorophosphates, and mixed polyanions of vanadium-centered polyanionic materials for SIBs. This review may provide comprehensive understanding and guidelines to further construct high performance, low-cost sodium-ion batteries.

Sodium-ion battery has been widely studied because of its abundant sodium resources and expectable electrochemical performance. The unique tunnel Na0.44MnO2 has attracted wide attention as one of the available cathode materials because of its low cost, as well as long cycle stability and rate capability in the non-aqueous and aqueous batteries. During the past decades, much efforts have been made to improve the electrochemical performance of the Na0.44MnO2. This review concisely describes the research progress of Na0.44MnO2 cathode for both non-aqueous and aqueous sodium-ion batteries, mainly focuses on the crystal structure, morphology, charge–discharge mechanism and influence of different synthesis and modification methods on the morphology and electrochemical performance. In addition, the main opportunities and challenges in this field are briefly commented and discussed.

While neuroimaging and blood biomarker have been two of the most active areas of research in the neurotrauma community, these fields rarely intersect to delineate subconcussive brain injury. The aim of the study was to examine the association between diffusion MRI techniques [diffusion tensor imaging (DTI) and neurite orientation/dispersion density imaging (NODDI)] and brain-injury blood biomarker levels [tau, neurofilament-light (NfL), glial-fibrillary-acidic-protein (GFAP)] in high-school football players at their baseline, aiming to detect cumulative neuronal damage from prior seasons. Twenty-five football players were enrolled in the study. MRI measures and blood samples were obtained during preseason data collection. The whole-brain, tract-based spatial statistics was conducted for six diffusion metrics: fractional anisotropy (FA), mean diffusivity (MD), axial/radial diffusivity (AD, RD), neurite density index (NDI), and orientation dispersion index (ODI). Five players were ineligible for MRIs, and three serum samples were excluded due to hemolysis, resulting in 17 completed set of diffusion metrics and blood biomarker levels for association analysis. Our permutation-based regression model revealed that serum tau levels were significantly associated with MD and NDI in various axonal tracts; specifically, elevated serum tau levels correlated to elevated MD (p = 0.0044) and reduced NDI (p = 0.016) in the corpus callosum and surrounding white matter tracts (e.g., longitudinal fasciculus). Additionally, there was a negative association between NfL and ODI in the focal area of the longitudinal fasciculus. Our data suggest that high school football players may develop axonal microstructural abnormality in the corpus callosum and surrounding white matter tracts, such as longitudinal fasciculus. A future study is warranted to determine the longitudinal multimodal relationship in response to repetitive exposure to sports-related head impacts.

While awareness and understanding of concussion have improved drastically, post-concussion management in academic settings is still at its infancy. The aim of the study was to examine to what extent concussion influences academic performance and to whether there would be a difference in concussion effects on academic performance between high school and college students. This cross-sectional survey study included students, who were between 14 and 24 years old and sustained a sport-related concussion within the previous year. The study used a modified chain-referral sampling method, by distributing a questionnaire link to 3,000 randomly stratified athletic trainers, who worked in high school or college settings. These athletic trainers were then asked to forward the questionnaire link to students in their team, who have sustained a concussion within the previous year. The questionnaire recorded responses regarding demographics (age, sex, race/ethnicity, number of previous concussions); 22 concussion-related symptoms in a binary scale (presence/absence); perception of difficulties in math, reading, writing, computer use, and attention in a 5-point Likert scale; and asymptomatic duration of academic engagement in a 7-point Likert scale. There were 130 respondents with a history of concussion in the past year (n = 59 high school, n = 71 college). While recovering from concussion, significantly more college students (84.5%) reported “difficulty concentrating” than high school students (68.6%: p = 0.049). High -school students experienced more difficulty with math than college students (p = 0.002), whereas college students experienced more difficulty with reading (p = 0.013) and computer use (p = 0.026) than high school students. Asymptomatic duration of cognitive activity was influenced by age (p = 0.0004), where younger students were less tolerant in performing academic tasks after a concussion than older students. Our data indicate that concussions can induce negative symptoms in the academic setting regardless of age. The post-concussion difficulties in academic performance may be a grade-dependent manner, where concussions triggered difficulty in math among high school students and in reading and computer use among college students. It is clear that there is a need for guidelines and accommodations to support students with concussion in academic settings, and the guideline should reflect the age-dependent response to concussions.

Combining information (p-values) obtained from individual studies to test whether there is an overall effect is an important task in statistical data analysis. Many classical statistical tests, such as chi-square tests, can be viewed as being a p-value combination approach. It remains challenging to find powerful methods to combine p-values obtained from various sources. In this paper, we study a class of p-value combination methods based on gamma distribution. We show that this class of tests is optimal under certain conditions and several existing popular methods are equivalent to its special cases. An asymptotically and uniformly most powerful p-value combination test based on constrained likelihood ratio test is then studied. Numeric results from simulation study and real data examples demonstrate that the proposed tests are robust and powerful under many conditions. They have potential broad applications in statistical inference.

Aim: Students re-entering the academic setting after a concussion is commonly referred to as return-to-learn and, to date, very few studies have examined the return-to-learn aspect of concussion recovery. Methodology: Nine college-aged, full-time students who were diagnosed with concussions were monitored throughout their concussion recovery. The severity for five chief symptoms (headache, dizziness, difficulty concentrating, fatigue, anxiety) were recorded six-times per day through text messages, and daily phone calls recorded participant's behavioral traits. Results: We identified five behavioral variables which significantly influenced symptom resolution (music, sleep, physical activity, water and time) (p = 0.0004 to p = 0.036). Additionally, subjects reported math and computer-oriented courses as the most difficult (33 and 44%, respectively). Conclusion: We introduce a novel approach to monitor concussed students throughout their recovery, as well as factors that may influence concussion recovery process.

Abstract Background Although some nonparametric methods have been proposed in the literature to test for the equality of median survival times for censored data in medical research, in general they have inflated type I error rates, which make their use limited in practice, especially when the sample sizes are small. Methods In this paper, we propose a new nonparametric test with a simple test statistic to compare median survival times. Results The results from our comprehensive simulation study show the new test controls type I error rate very well under all situations considered, even for small sample sizes. In addition, it has comparable detecting power compared to existing methods. Another advantage of the proposed test is its relatively simple formula that requires less computation. Conclusions We propose a new statistical method for comparing survival curves based on their medians. The new method can be easily implemented and applied to censored event time data.

Pancreatic cancer is the third leading cause of cancer related deaths in the United States. Several dietary factors have been identified that modify pancreatic cancer risk, including low folate levels. In addition to nutrition and lifestyle determinants, folate status may be influenced by genetic factors such as single nucleotide polymorphisms (SNPs). In the present study, we investigated the association between folate levels, genetic polymorphisms in genes of the folate pathway, and pancreatic cancer.Serum and red blood cell (RBC) folate levels were measured in pancreatic cancer and control subjects. Genotypes were determined utilizing Taqman probes and SNP frequencies between cases and controls were assessed using Fisher's exact test. Logistic regression was used to estimate the odds ratio (OR) and corresponding 95% confidence intervals (CIs) to measure the association between genotypes and pancreatic cancer risk. The association between folate levels and SNP expression was calculated using one-way ANOVA.Mean RBC folate levels were significantly lower in pancreatic cancer cases compared to unrelated controls (508.4 ± 215.9 ng/mL vs 588.3 ± 229.2 ng/mL, respectively) whereas serum folate levels were similar. Irrespective of cancer status, several SNPs were found to be associated with altered serum folate concentrations, including the D919G SNP in methionine synthase (MTR), the L474F SNP in serine hydroxymethyl transferase 1 (SHMT1) and the V175M SNP in phosphatidyl ethanolamine methyltransferase (PEMT). Further, the V allele of the A222V SNP and the E allele of the E429A SNP in methylene tetrahydrofolate reductase (MTHFR) were associated with low RBC folate levels. Pancreatic cancer risk was found to be significantly lower for the LL allele of the L78R SNP in choline dehydrogenase (CHDH; OR = 0.29; 95% CI 0.12-0.76); however, it was not associated with altered serum or RBC folate levels.

Advanced Audio Coding (AAC), a standardized lossy compression scheme for digital audio, which was designed to be the successor of the MP3 format, generally achieves better sound quality than MP3 at similar bit rates. While AAC is also the default or standard audio format for many devices and AAC audio files may be presented as important digital evidences, the authentication of the audio files is highly needed but relatively missing. In this paper, we propose a scheme to expose tampered AAC audio streams that are encoded at the same encoding bit-rate. Specifically, we design a shift-recompression based method to retrieve the differential features between the re-encoded audio stream at each shifting and original audio stream, learning classifier is employed to recognize different patterns of differential features of the doctored forgery files and original (untouched) audio files. Experimental results show that our approach is very promising and effective to detect the forgery of the same encoding bit-rate on AAC audio streams. Our study also shows that shift recompression-based differential analysis is very effective for detection of the MP3 forgery at the same bit rate.

There are few carefully-designed studies investigating the safety of individual probiotics approved under Investigational New Drug policies.The primary aim of this prospective, double-blind placebo-controlled trial was to investigate if daily treatment of adults with Lactobacillus reuteri DSM 17938 (LR) for 2 months is safe and well-tolerated. Our secondary aim was to determine if LR treatment has immune effects as determined by regulatory T cell percentages, expression of toll-like receptors (TLR)-2 and -4 on circulating peripheral blood mononuclear cells (PMBCs), cytokine expression by stimulated PBMC, and intestinal inflammation as measured by fecal calprotectin.Forty healthy adults were randomized to a daily dose of 5 × 10(8) CFUs of LR (n = 30) or placebo (n = 10) for 2 months. Participants completed a daily diary card and had 7 clinic visits during treatment and observation.There were no severe adverse events (SAEs) and no significant differences in adverse events (AEs). There were no differences in PBMC subclasses, TLRs, or cytokine expression after treatment. The probiotic-treated group had a significantly higher fecal calprotectin level than the placebo group after 2 months of treatment: 50 µg/g (IQR 24-127 µg/g) vs. 17 µg/g (IQR 11-26 µg/g), p = 0.03, although values remained in the normal clinical range (0-162.9 µg/g). LR vials retained >10(8) CFUs viable organisms/ml.LR is safe and well tolerated in adults, without significant changes in immunologic markers. There was a small but significant increase in fecal calprotectin, perhaps indicating some element of immune recognition at the intestinal level.Clinical Trials.gov NCT00922727.

Microarray data has a high dimension of variables but available datasets usually have only a small number of samples, thereby making the study of such datasets interesting and challenging. In the task of analyzing microarray data for the purpose of, e.g., predicting gene-disease association, feature selection is very important because it provides a way to handle the high dimensionality by exploiting information redundancy induced by associations among genetic markers. Judicious feature selection in microarray data analysis can result in significant reduction of cost while maintaining or improving the classification or prediction accuracy of learning machines that are employed to sort out the datasets. In this paper, we propose a gene selection method called Recursive Feature Addition (RFA), which combines supervised learning and statistical similarity measures. We compare our method with the following gene selection methods: Support Vector Machine Recursive Feature Elimination (SVMRFE), Leave-One-Out Calculation Sequential Forward Selection (LOOCSFS), Gradient based Leave-one-out Gene Selection (GLGS). To evaluate the performance of these gene selection methods, we employ several popular learning classifiers on the MicroArray Quality Control phase II on predictive modeling (MAQC-II) breast cancer dataset and the MAQC-II multiple myeloma dataset. Experimental results show that gene selection is strictly paired with learning classifier. Overall, our approach outperforms other compared methods. The biological functional analysis based on the MAQC-II breast cancer dataset convinced us to apply our method for phenotype prediction. Additionally, learning classifiers also play important roles in the classification of microarray data and our experimental results indicate that the Nearest Mean Scale Classifier (NMSC) is a good choice due to its prediction reliability and its stability across the three performance measurements: Testing accuracy, MCC values, and AUC errors.

Complex I dysfunction is a common, heterogeneous cause of human mitochondrial disease having poorly understood pathogenesis. The extensive conservation of complex I composition between humans and Caenorhabditis elegans permits analysis of individual subunit contribution to mitochondrial functions at both the whole animal and mitochondrial levels. We provide the first experimentally-verified compilation of complex I composition in C. elegans, demonstrating 84% conservation with human complex I. Individual subunit contribution to mitochondrial respiratory capacity, holocomplex I assembly, and animal anesthetic behavior was studied in C. elegans by RNA interference-generated knockdown of nuclear genes encoding 28 complex I structural subunits and 2 assembly factors. Not all complex I subunits directly impact respiratory capacity. Subcomplex Ilambda subunits along the electron transfer pathway specifically control whole animal anesthetic sensitivity and complex II upregulation, proportionate to their relative impairment of complex I-dependent oxidative capacity. Translational analysis of complex I dysfunction facilitates mechanistic understanding of individual gene contribution to mitochondrial disease. We demonstrate that functional consequences of complex I deficiency vary with the particular subunit that is defective.