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Research background: Inflation has always been the core issue of economic research and there are many academic research achievements in this field. In recent years, global inflation has intensified, and many scholars focus on research in this field again, providing certain reference value for countries around the world to formulate corresponding macro policies. Purpose of the article: The five-year impact factors are used as the evaluation criteria in this paper, and 1,637 high-quality documents on inflation from 1906 to 2022 are collected from the Web of Science Core Collection database. Using bibliometrics, a comprehensive review of influential literature in the field of inflation is conducted to reveal the evolution and trends of the field. Methods: First, we focus on these high-quality documents about the descriptive statistical characteristics, high cited documents and high impact factor journals. Then, based on the visualization tool, the cooperative network of countries/regions, authors and institutions is depicted and the cooperative relationship between them is determined. At the same time, the most influential countries/regions, authors and institutions are identified by analyzing the citation structure. In addition, through thematic and keyword analysis, the topic hotspots and future research trends of high-quality literature in the field of inflation are deduced. Findings & value added: On the whole, the research on inflation in the United States is relatively mature, and has produced a large number of influential academic cooperation results. Finally, we have a series of discussions on the history of inflation in the United States and policy suggestions. In the future, governments of various countries, especially the United States, will still face certain challenges in how to formulate policies and measures to mitigate the impact of inflation.

RAB32 is a potential prognostic marker that is overexpressed in a variety of cancers. The purpose of this study was to investigate the expression and function of RAB32 in glioblastoma (GBM).The expression data of RAB32 were obtained by accessing TCGA, CGGA and GEPIA databases, which were verified by western blot and immunohistochemistry. The prognostic value of RAB32 methylation was carefully examined using cBioPortal and MethSurv. GSEA was used to analyze cancer-related signaling pathways that may be activated by high RAB32 expression. The correlation between RAB32 and GBM immunoinfiltration was studied by accessing TISIDB database.The effects of RAB32 on proliferation, migration and invasion of GBM cells were predicted by Colony Formation Assay, CCK-8 assay and Transwell assay. In this study, RAB32 expression was up-regulated in GBM compared to normal brain tissue. Survival analysis showed that high expression of RAB32 was an independent risk factor for overall survival in glioma patients. RAB32 methylation was negatively correlated with RAB32 expression, and the overall survival rate of RAB32 hypomethylated patients was lower than that of RAB32 hypermethylated patients.Through functional enrichment analysis, we found that the RAB32 overexpression significantly activated multiple signaling pathways. Immunoassay results showed that RAB32 expression was correlated with immune infiltration of tumor microenvironment. Knocking down the expression of RAB32 gene significantly inhibited the proliferation, migration and invasion of glioma cells. Our results show that RAB32 is a key factor affecting the prognosis of patients with GBM, and its targeting may provide a new treatment for patients with GBM.

Open Science is becoming a mainstream scientific ideology in psychology and related fields. However, researchers, especially early-career researchers (ECRs) in developing countries, are facing significant hurdles in engaging in Open Science and moving it forward. In China, various societal and cultural factors discourage ECRs from participating in Open Science, such as the lack of dedicated communication channels and the norm of modesty. To make the voice of Open Science heard by Chinese-speaking ECRs and scholars at large, the Chinese Open Science Network (COSN) was initiated in 2016. With its core values being grassroots-oriented, diversity, and inclusivity, COSN has grown from a small Open Science interest group to a recognized network both in the Chinese-speaking research community and the international Open Science community. So far, COSN has organized three in-person workshops, 12 tutorials, 48 talks, and 55 journal club sessions and translated 15 Open Science-related articles and blogs from English to Chinese. Currently, the main social media account of COSN (i.e., the WeChat Official Account) has more than 23,000 subscribers, and more than 1,000 researchers/students actively participate in the discussions on Open Science. In this article, we share our experience in building such a network to encourage ECRs in developing countries to start their own Open Science initiatives and engage in the global Open Science movement. We foresee great collaborative efforts of COSN together with all other local and international networks to further accelerate the Open Science movement.

Chimeric antigen receptor-T (CAR-T) therapy remains to be investigated in T-cell malignancies. CD7 is an ideal target for T-cell malignancies but is also expressed on normal T cells, which may cause CAR-T cell fratricide. Donor-derived anti-CD7 CAR-T cells using endoplasmic reticulum retention have shown efficacy in patients with T-cell acute lymphoblastic leukemia (ALL). Here we launched a phase I trial to explore differences between autologous and allogeneic anti-CD7 CAR-T therapies in T-cell ALL and lymphoma. Ten patients were treated and 5 received autologous CAR-T therapies. No dose-limiting toxicity or neurotoxicity was observed. Grade 1–2 cytokine release syndrome occurred in 7 patients, and grade 3 in 1 patient. Grade 1–2 graft-versus-host diseases were observed in 2 patients. Seven patients had bone marrow infiltration, and 100% of them achieved complete remission with negative minimal residual disease within one month. Two-fifths of patients achieved extramedullary or extranodular remission. The median follow-up was 6 (range, 2.7–14) months and bridging transplantation was not administrated. Patients treated with allogeneic CAR-T cells had higher remission rate, less recurrence and more durable CAR-T survival than those receiving autologous products. Allogeneic CAR-T cells appeared to be a better option for patients with T-cell malignancies.

Oridonin, a bioactive diterpenoid isolated from

In recent years, double hierarchy linguistic expression models have developed rapidly in the field of decision making because their rich semantics are closer to people’s actual language environment. However, the existing double hierarchy linguistic expression models are difficult to deal with incomplete or missing information application situations. For example, online reviews provide some references for consumers to make decisions, but the information of many reviews is not necessarily complete. Therefore, we try to solve this problem and propose the concept of interval probabilistic double hierarchy linguistic term set (IP-DHLTS). At the same time, in order to ensure stability under different criteria weights, we choose to combine the EDAS method to obtain the average solution based on two measures. To sum up, we develop the interval probabilistic double hierarchy linguistic EDAS method and solve a real case with the natural language processing basic techniques about the hotel online reviews. Finally, the feasibility of the proposed method is verified by comparison with other methods.

Y-chromosomal short tandem repeats (Y-STRs) are widely applied to evolutionary, genealogical, and kinship analyses of male linages in forensic studies, but these low to midrange mutated Y-STRs typically fail to separate related males from the same paternal lineage. Recently, rapidly mutating Y-STRs (RM Y-STRs) have been demonstrated to improve the differentiation of male relatives and individuals. The Microreader™ RM-Y ID System is a new RM Y-STR kit that is capable of simultaneously amplifying 17 RM Y-STRs. Herein, to verify the efficiency and accuracy of the Microreader™ RM-Y ID System, developmental validation was conducted, including PCR-based studies, sensitivity, stability, species specificity, mixture, stutter percentage, and precision studies. Full profiles could be obtained when the hematin concentration was 250 μM, humic acid concentration was 1500 ng/μl, and tannic acid concentration was 200 ng/μl. Full profiles of the mixture of males/males could be detected up to a ratio of 19:1, and full profiles of females/males could always be detected even at ratios up to 24,000:1. Moreover, the forensic characteristics of 250 DNA-confirmed father-son pairs were analysed. The results showed that these 17 RM Y-STRs had high power for forensic discrimination (HD = 1) in the Chinese Han population, and the mutation rates were in the range of 4 × 10−3 (95% CI 1.00 × 10−4 to 2.21 × 10−2, DYS464) to 8.8 × 10−2 (95% CI 5.60 × 10−2 to 1.30 × 10−1, DYF399S1), indicating that the kit was effective for RM Y-STR studies and absolute individualisation of interrelated male individuals.

The double hierarchy linguistic information is more close to people’s habits of expression and it can describe evaluation information more accurately, so it has been extensively utilized in multi-criteria decision-making (MCDM) problems in recent years. First of all, taking the advantage of the completeness of the double hierarchy linguistic information, this paper proposes the continuous interval-valued double hierarchy linguistic term set. It is vital that it not only can express more complex evaluation information but also can aggregate group opinions by continuous terms. At the same time, the gained and lost dominance score (GLDS) method is a outranking method that has emerged in recent years. It takes the perspective of individuals and groups more comprehensively, making the obtained ranking results more convincing and accurate. Therefore, we construct the GLDS method into the continuous interval-valued double hierarchy linguistic environment (named CIVDHL-GLDS method) based on the above considerations. Public transportation is an important part of urban transportation. A comprehensive and objective description of urban surface bus operation level is conductive to a more objective evaluation of urban traffic level. Afterwards, in order to justify its validity of our method, taking the performance evaluation of four subsidiaries of Chengdu Public Transport Group in Sichuan province as a case study. Finally, Some discussions and comparisons are provided to reveal our proposed method has some innovations and dominance.

Kyrgyz ethnic group is one of the nomads in China, with the majority in Xinjiang and a small part of them living in Heilongjiang province. Historically, they have went through five migrations westward due to the wars. The name "Kyrgyz" means 40 tribes, originating from the primary groups of Kyrgyz. However, it is a largely understudied population, especially from the Y chromosome. In this study, we used a previously validated high-resolution Y-chromosome single nucleotide polymorphisms (Y-SNPs) and short tandem repeats (Y-STRs) system to study Kyrgyz ethnic group. A total of 314 male samples of Kyrgyz ethnic group were genotyped by 173 Y-SNPs and 27 Y-STRs. After data analysis, the results unveiled that Kyrgyz ethnic group was a population with high percentage of both haplogroup C2a1a3a1d∼-F10091 (91/134) and R1a1a1b2a2-Z2124 (109/134), which has never been reported. This implied that Kyrgyz ethnic group might have gone through bottleneck effects twice, with these two main lineages left. Mismatch analysis indicated that the biggest mismatch number in haplogroup C2a1a3a1d∼-F10091 was 10, while that of haplogroup R1a1a1b2a2-Z2124 was 20. This huge difference reflected the different substructure in two lineages, suggesting that haplogroup C2a1a3a1d∼-F10091 might have the least admixture compared to the other two lineages. After admixture modelling with other datasets, the conclusion could be drawn that Kyrgyz ethnic group had great genetic affinity with Punjabi from Lahore, Pakistan, which supported that Kyrgyz ethnic group in China was close to central Asian.

Unmanned aerial vehicles (UAVs) are usually dispatched as mobile sinks to assist data collection in large-scale wireless sensor networks (WSNs). However, when considering the limitations of UAV’s mobility and communication capabilities in a large-scale WSN, some sensor nodes may run out of storage space as they fail to offload their data to the UAV for an extended period of time. To minimize the data loss caused by the above issue, a joint user scheduling and trajectory planning data collection strategy is proposed in this letter, which is formulated as a non-convex optimization problem. The problem is further divided into two sub-problems and solved sequentially. Simulation results show that the proposed strategy is more effective in minimizing data loss rate than other strategies.

In the past two decades, Y chromosome data has been generated for human population genetic studies. These Y chromosome datasets were produced with various testing methods and markers, thus difficult to combine them for a comprehensive analysis. In this study, we combine four human Y chromosomal datasets of Han, Tibetan, Hui, and Li ethnic groups. The dataset contains 27 microsatellites and 137 single nucleotide polymorphisms these populations share in common. We assembled a single dataset containing 2439 individuals from 25 nationwide populations in China. A systematic analysis of genetic distance and clustering was performed. To determine the gene flow of the studied population with worldwide populations, we modeled the ancestry informative markers. The reference panel was regarded as a mixture of South Asian (SAS), East Asian (EAS), European (EUR), African (AFR), and American (AMR) populations from 1000 Genomes data of Y chromosome using nonlinear data-fitting. We then calculated the admixture proportion of these four studied populations with 26 worldwide populations. The results showed that the Han and Hui have great genetic affinity, and Hui is the most admixed ethnic group, with 61.53% EAS, 34.65% SAS, 1.91% AFR, 1.56% AMR, and 0.04% EUR ancestry component (the AMR is highly admixed and thus should be ignored). All the other three ethnic groups contained more than 97% EAS ancestry component. The Li is the least admixed population in this study. The combined dataset in this study is the largest of this kind reported to date and proposes reference population data for use in future paternal genetic studies and forensic genealogical identification.

Cancer patients are prone to infections, but the mortality of fatal infections remains unclear. Understanding the patterns of fatal infections in patients with cancer is imperative. In this study, we report the characteristics, incidence, and predictive risk factors of fatal infections among a population-based cancer cohort. A total of 8,471,051 patients diagnosed with cancer between 1975 and 2016 were retrospectively identified from the Surveillance, Epidemiology, and End Results (SEER) program. The primary outcome was dying from fatal infections. Mortality rates and standardized mortality ratios (SMRs) adjusted for age, sex, race, and calendar year were calculated to characterize the relative risks of dying from fatal infections and to compare with the general population. Furthermore, cumulative mortality rates and the Cox regression models were applied to identify predictive risk factors of fatal infections. In cancer patients, the mortality rate of fatal infections was 260.1/100,000 person-years, nearly three times that of the general population [SMR, 2.92; 95% (confidence interval) CI 2.91–2.94]. Notably, a decreasing trend in mortality rate of fatal infections was observed in recent decades. SMRs of fatal infections were highest in Kaposi sarcoma (SMR, 162.2; 95% CI 159.4–165.1), liver cancer (SMR, 30.9; 95% CI 30.0–31.8), acute lymphocytic leukemia (SMR, 19.1; 95% CI 17.0–21.4), and acute myeloid leukemia (SMR, 13.3; 95% CI 12.4–14.3). Patients aged between 20 and 39 years old exhibited a higher cumulative mortality rate in the first few years after cancer diagnosis, whereas the cumulative mortality rate of those > 80 years old was rapidly increasing and became the highest approximately 3 years post-cancer diagnosis. Predictive risk factors of dying from fatal infections in cancer patients were the age of 20–39 or > 80 years, male sex, black race, diagnosed with cancer before 2000, unmarried status, advanced cancer stage, and not receiving surgery and radiotherapy, but receiving chemotherapy. Cancer patients were at high risks of dying from infectious diseases. Certain groups of cancer patients, including those aged between 20 and 39 or > 80 years, as well as those receiving chemotherapy, should be sensitized to the risk of fatal infections.

In this paper, the Johnson-Cook constitutive model is modified by considering the influence of hard point such as TiC and NbC in the matrix of Inconel718 on the deformation stress. The theoretical model of cutting specific energy in the main deformation zone of Inconel718 is modified based on the new constitutive model by combining the strain gradient theory. The effect of different cutting parameters on cutting specific energy is studied, and the effect of cutting specific energy on cutting deformation and the resulting dimensional effect are also analyzed. The research results show that cutting specific energy increases with the increase of cutting speed. With the increase of cutting thickness, the cutting specific energy reduced, and the trend is non-linear. The change of undeformed chip thickness will cause size effect. The cutting specific energy increases with the reduction of the thickness of undeformed chip, and the impact of the thickness of undeformed chip on the cutting specific energy becomes smaller and smaller as the speed increases. The existence of hard points makes the main deformation zone generate a large amount of heat energy and deformation energy, which leads to dimensional effects and makes the material be more prone to adiabatic shear instability, then leads to the increase of cutting specific energy. With the increase of cutting specific energy, the width of adiabatic shear band is narrowed, the degree of serration is aggravated, and the chip morphology is closer to the experimental results.

Background Infective endocarditis (IE) continues to be associated with great challenges. Embolic events (EE) are frequent and life-threatening complications in IE patients. It remains challenging to predict and assess the embolic risk in individual patients with IE accurately. Hypothesis Accurate prediction of embolization is critical in the early identification and treatment of risky and potentially embolic lesions in patients with IE. Methods We searched the PubMed, Web of Science, and Google Scholar databases using a range of related search terms, and reviewed the literatures about the pathogenesis and embolic predictors of IE. Results The development of IE and its complications is widely accepted as the result of complex interactions between microorganisms, valve endothelium, and host immune responses. The predictive value of echocardiographic characteristics is the most powerful for EE. In addition, both easily obtained blood biomarkers such as C-reactive protein, mean platelet volume, neutrophil-to-lymphocyte ratio, anti-β2-glycoprotein I antibodies, D-Dimer, troponin I, matrix metalloproteinases, and several microbiological or clinical characteristics might be promising as potential predictors of EE. Conclusion Our review provides a synthesis of current knowledge regarding the pathogenesis and predictors of embolism in IE along with a review of potentially emerging biomarkers.

Epitope molecularly imprinted polymers (EMIPs) are novel imprinted materials using short characteristic peptides as templates rather than entire proteins. To be specific, the amino acid sequence of the template peptide is the same as an exposed N- or C-terminus of a target protein, or its amino acid composition and sequence replicate a similar conformational arrangement as the same amino acid residues on the surface of the target protein. EMIPs have a good application prospect in protein research. Herein, we focus on classification of epitope imprinting techniques, methods of epitope immobilization on matrix materials including boronate affinity immobilization, covalent bonding immobilization, physical adsorption immobilization and metal ion chelation immobilization, and application of EMIPs in peptides, proteins, target imaging and target therapy fields. Finally, the main problems and future development are summarized.

In general, a two-dimensional (2D) moving trajectory can be reconstructed using data collected from a 6-axis IMU (inertial measurement unit), which consists of a 3-axis gyroscope and a 3-axis accelerometer. Since the power consumption of a gyroscope is significantly larger than that of an accelerometer, to be energy-efficient, it is more preferable to use two gyro-free IMUs as alternatives. The existing gyro-free trajectory reconstruction approach is an angular velocity (i.e. $\omega$ ) -based method. The design of an $\omega$ -free gyro-free approach remains as a research gap, which motivates this research work. In this paper, an $\omega$ -free accelerometer pair (OFAP) method is proposed, which can yield a lower accumulated error, has a lower power consumption, is more scalable, and can deliver the same trajectory reconstruction performance as the existing approach.

Patients with hematological malignancies might be at high risk for renal diseases as evidenced by earlier studies. We aim to investigate the mortality and risk factors of deaths due to renal diseases in this population. A total of 831 535 patients diagnosed with hematological malignancies in the Surveillance, Epidemiology, and End Results (SEER) database in the United States from 1975 to 2016 were identified. Standardized mortality ratio (SMR) was evaluated based on the general population's mortality data gathered by the National Center for Health Statistics. The mortality rate associated with renal diseases was 94.22/100 000 person-years among patients with hematological malignancies (SMR = 3.59; 95% CI, 3.48-3.70]). The highest mortality rate of dying from renal diseases was observed among multiple myeloma (MM) patients (307.99/100 000 person-years; SMR = 7.98; 95% CI, 7.49-8.50), followed by those with chronic myeloid leukemia (142.57/100 000 person-years; SMR = 6.54; 95% CI, 5.63-7.60) and chronic lymphocytic leukemia (103.66/100 000 person-years; SMR = 2.51; 95% CI, 2.27-2.77). The SMRs increased with time and were found to be the highest 10 years after cancer diagnosis. Independent predictors associated with death from renal diseases were found to be older age, male gender, blacks, unmarried, and MM, using the Cox proportional hazards model. We call for enhanced coordinated multidisciplinary care between hematologists and nephrologists to reduce the mortality rate of renal diseases among patients with hematological malignancies.

Nutritional decisions may be important for health, and yet identifying trustworthy sources of advice can be difficult to achieve. Many people turn to books for nutritional advice, making the contents of these books and the expertise of their authors relevant to public health. Here, the top 100 best-selling books were identified and assessed for both the claims they make in their summaries and the credentials of the authors. Weight loss was a common theme in the summaries of nutritional best-selling books. In addition to weight loss, 31 of the books promised to cure or prevent a host of diseases, including diabetes, heart disease, cancer, and dementia; however, the nutritional advice given to achieve these outcomes varied widely in terms of which types of foods should be consumed or avoided and this information was often contradictory between books. Recommendations regarding the consumption of carbohydrates, dairy, proteins, and fat in particular differed greatly between books. To determine the qualifications of each author in making nutritional claims, the highest earned degree and listed occupations of each author was researched and analyzed. Out of 83 unique authors, 33 had an M.D. or Ph.D degree. Twenty-eight of the authors were physicians, three were dietitians, and other authors held a wide range of jobs, including personal trainers, bloggers, and actors. Of 20 authors who had or claimed university affiliations, seven had a current university appointment that could be verified online in university directories. This study illuminates the range of the incongruous information being dispersed to the public and emphasizes the need for future efforts to improve the dissemination of sound nutritional advice.

Macroscopic functional connectomic analyses have identified sets of densely connected regions in the human brain, known as connectome hubs, which play a vital role in understanding network communication, cognitive processing, and brain disorders. However, anatomical locations of functional connectome hubs are largely inconsistent and less reproducible among extant reports, partly due to inadequate sample size and differences in image processing and network analysis. Moreover, the genetic signatures underlying the robust connectome hubs remain unknown. Here, we conduct the first worldwide voxelwise meta-connectomic analysis by pooling resting-state functional MRI data of 5,212 healthy young adults across 61 independent international cohorts with harmonized image processing and network analysis protocols. We identify highly consistent and reproducible functional connectome hubs that are spatially distributed in multiple heteromodal and unimodal regions, with the most robust findings mainly located in lateral parietal regions. These connectome hubs show unique, heterogeneous connectivity profiles and are critical for both intra- and inter-network communications. Using transcriptome data from the Allen Human Brain Atlas and BrainSpan Atlas as well as machine learning, we demonstrate that these robust hubs are significantly associated with a transcriptomic pattern dominated by genes involved in the neuropeptide signaling pathway, neurodevelopmental processes, and cellular metabolic processes. This pattern represents microstructural and metabolic substrates underlying the development and functioning of brain hubs. Together, these results highlight robustness of macroscopic connectome hubs of the human brain and their potential cellular and molecular underpinnings and have implications for understanding how brain hubs support the connectome organization in health and disease.

For patients with glioma located in or adjacent to the linguistic eloquent cortex, awake surgery with an emphasis on the preservation of language function is preferred. However, the brain network basis of postoperative linguistic functional outcomes remains largely unknown. In this work, 34 patients with left cerebral gliomas who underwent awake surgery were assessed for language function and resting-state network properties before and after surgery. We found that there were 28 patients whose language function returned to at least 80% of the baseline scores within 3 months after surgery or to 85% within 6 months after surgery. For these patients, the spontaneous recovery of language function synchronized with changes within the language and cognitive control networks, but not with other networks. Specifically, compared with baseline values, language functions and global network properties were the worst within 1 month after surgery and gradually recovered within 6 months after surgery. The recovery of connections was tumour location dependent and was attributed to both ipsihemispheric and interhemispheric connections. In contrast, for six patients whose language function did not recover well, severe network disruptions were observed before surgery and persisted into the chronic phase. This study suggests the synchronization of functional network normalization and spontaneous language recovery in postoperative patients with glioma.

Reading remains a preferred leisure activity fueling an exceptionally competitive publishing market: among more than three million books published each year, only a tiny fraction are read widely. It is largely unpredictable, however, which book will that be, and how many copies it will sell. Here we aim to unveil the features that affect the success of books by predicting a book’s sales prior to its publication. We do so by employing the Learning to Place machine learning approach, that can predicts sales for both fiction and nonfiction books as well as explaining the predictions by comparing and contrasting each book with similar ones. We analyze features contributing to the success of a book by feature importance analysis, finding that a strong driving factor of book sales across all genres is the publishing house. We also uncover differences between genres: for thrillers and mystery, the publishing history of an author (as measured by previous book sales) is highly important, while in literary fiction and religion, the author’s visibility plays a more central role. These observations provide insights into the driving forces behind success within the current publishing industry, as well as how individuals choose what books to read.

Among various data aggregation approaches proposed for wireless sensor networks (WSNs), the one based on compressive sensing (CS) has the merit of low traffic cost. The key step to design a CS-based data aggregation protocol is to construct a measurement matrix ${\Phi }$ based on the network structure and to assign each node a unique column vector of ${\Phi }$ . Assuming an expanded scenario, where some new nodes join in the network, the data aggregation scheme has to entirely re-generate a new matrix ${\Phi ^{\prime }}$ with a larger size to meet the node number and CS property simultaneously. Apparently, it is energy-consuming to reallocate the weight vectors from the new measurement matrix to all the nodes. Thus, we propose an approach which aims to keep the weight vectors of existing sensors unchanged but assign only optimized measurement vectors to the newly added nodes. In order to solve the relevant non-convex optimization problem, two efficient methods with good data aggregation performance are proposed. Numeric experiments validate the effectiveness of our proposed methods.

This paper studies an unmanned aerial vehicles (UAV) based data aggregation of wireless sensor networks (WSNs). Data aggregation approaches based on Compressive Sensing (CS) enjoy the benefits of having their traffic volume reduced. However, existing CS-based data aggregation schemes have drawbacks, such as errors in data reconstruction process and the large overheads in regenerating and broadcasting feasible measurement matrices when the network is scaled up. To this end, we propose a topology-aware data aggregation (TADA) protocol, which preserves the advantages of CS-based schemes while alleviating the aforementioned issues. Extensive performance comparisons demonstrate that the data reconstruction error rate of TADA is much lower than other CS-based schemes under test. More important, for most WSN applications, since the underlying physical layer prefers frames with small payloads, the proposed TADA utilizes less transmissions from sensors to UAV in each round of data aggregation, and thus can yields a lower energy consumption.

Jet electrochemical (Jet-EC) milling, using a movable nozzle as the cathode, can fabricate complicated structures flexibly. With the dissolution restricted to a limited area by the electrolyte jet, high removal localization and good surface quality can be obtained. However, the edge of the machined groove is usually subject to stray corrosion because of the low current density in the edge area. In this paper, it is confirmed that the jet shape, which is determined by the working parameters, has a notable influence on the edge condition. Through experiments and analysis, the interrelation between the working parameters, jet shape and edge condition in Jet-EC groove milling is established. It is found that the stray corrosion on the edge can be reduced significantly by adjusting the jet shape, and the necessary conditions to completely eliminate the stray corrosion are established. Furthermore, based on the conclusions, a groove with both a sharp edge and a smooth surface is fabricated via a two-pass process. A deep groove (1.208 mm) with a sharp edge is produced via multiple-pass milling with downward cathode feeding.

Data aggregation is one of the most important functions provided by wireless sensor networks (WSNs). Among a variety of data aggregation schemes, the coding-based approaches (such as Compressive sensing (CS) and other similar programs) can significantly reduce traffic quantity by encoding the raw sensed data using weight vectors. The critical feature to design a coding-based data aggregation protocol is to construct a weight/measurement matrix for the application scenario. After that, the sink node assigns the column of the matrix, which is treated as the weight vector during the encoding process, to each sensor node respectively. However, for a dynamic scenario where the number of sensor nodes changes frequently, the existing approaches have to reconfigure the network by regenerating the measurement matrix and allocating the new weight vectors for all the existing nodes, which causes a considerable energy consumption and affects the regular monitoring tasks. To solve this problem, we propose a Vandermonde matrix-based scalable data aggregation protocol (VSDA) , which preserves the advantages of coding-based schemes and addresses the issues mentioned above. In VSDA , as new nodes join into the scaled-up network, the original weight vectors owned by the original nodes do not need to regenerate the weight vectors entirely but add some new entries by itself at all. It outperforms the existing schemes by saving the energy in network scaling-up. Besides, we propose a concise hardware framework to quantify the data encoding process of VSDA , which provides a performance analysis process that is closer to practical application. The numeric tests validate the performance of VSDA compared with the existing schemes in several aspects, such as, the number of transmissions, energy consumption, and storage space showing the outperformance of VSDA scheme.

We propose a novel signal multiplexing and detection method for multiple-input multiple-output (MIMO) communication systems, especially when the number of transmitting and receiving antennas is limited. Inspired by the idea of Compressive Sensing (CS) which can recover a given signal vector from a vector of measurements with less dimensions, our proposed CS-based multiplexing scheme can deliver a modulated data vector with length l via a MIMO system with fewer transmitting/receiving antennas than l , offering higher multiplexing gain. On the receiving side, our proposed detection scheme has two steps, which resort the BCS algorithm and a Deep-Learning algorithm to recover the original modulated data vector. Analytical and simulation results show that the proposed multiplexing and detection method can achieve larger multiplexing gain while reserving good bit error rate (BER), offering a novel research paradigm to improve the utility rate of multiple antennas.

Electrochemical drilling (ECD) is an important method for producing small holes in difficult-to-machine materials such as titanium alloys and nickel-based alloys. However, the hollow tube shape often generates spikes at the hole bottoms, and these significantly influence the flow uniformity, machining accuracy, and process stability. This paper considers the hole bottom to be spiked, flat, or pitted depending on the equilibrium gap, which can be controlled with the machining parameters of applied voltage and electrode feed rate. Consequently, the spikes can be eliminated with a suitable choice of machining parameters. An electric-field model of ECD is established and the dynamic shaping of holes is simulated. This reveals that the machining parameters affect the equilibrium gap, whereupon the hole bottom shape is determined. Experiments verify the simulation results, and the relationships between the machining parameters (applied voltage and electrode feed rate) and (i) the equilibrium gap and (ii) the bottom shape are plotted. It is found that a low voltage with a high feed rate generates a small gap and a spiked bottom, whereas a high voltage with a low feed rate produces a large gap and a flat or pitted bottom. Finally, three typical parameter groups are selected with which to drill through holes. The machining-current signals at the moment of hole punching and the tool durability indicate that the ECD stability without a spike is better than that with a spike. Optimal parameters are beneficial for eliminating spikes and enhancing the ECD process performance.

The AGCU Expressmarker 20 + 20Y Kit is a newly devised short tandem repeat (STR) multiplex system that simultaneously analyses a set of 19 autosomal STR loci, 20 Y chromosomal STR loci and the amelogenin locus with six-dye fluorescent labelling. Here, the AGCU Expressmarker 20 + 20Y system was validated following the guidelines published by the Scientific Working Group on DNA Analysis Methods (SWGDAM), including PCR-condition, sensitivity, mixtures, species specificity, models of inhibition, precision, stutter percentage, concordance, population genetic studies, performance on three kinds of degraded DNA and a type of casework samples. The results indicated that the kit had high sensitivity when there was a small amount of DNA (0.0625 ng), more than one male (minor: major=1:19), or a mixture of males and females (male: female=1:32), models of inhibition (250 μM hematin, 1500 ng/μL humic acid and more than 100 ng/μL tannic acid) and degraded samples. The kit showed high precision level with standard deviation of allele size no more than 0.0930. Furthermore, this system was also tested in 444 random male samples of Chinese Han and Hui population, showing its high discrimination capability in Han and Hui population. Meanwhile, the system was applicable to the case of the AMELY abnormality. In short, the kit was verified and proved to be a robust, reliable and suitable tool for human identification and casework samples.

The polymerase chain reaction (PCR)-based gene targeting method, which can delete a specific gene or introduce tags, has been widely utilized to study gene function in fission yeast. One of the critical steps in this method is to design primers for amplifying DNA fragments of deletion or tagging modules and for checking the integration of those DNA fragments at designated loci. Although the primer design tool Pombe PCR Primer Program (PPPP) is available for Schizosaccharomyces pombe, there is no such publicly available application for the other three fission yeast species, S. cryophilus, S. japonicus, and S. octosporus. Likewise, no application enabling DNA/protein sequence retrieval for these three fission yeast species is available either. Therefore, access to such functionality would substantially assist in retrieval of gene sequences of interest and primer design in these fission yeast species. In this report, we describe two applications for fission yeast study: Yesprit and Yeaseq. Yesprit is a primer design tool for strain construction using the PCR-based method, and Yeaseq is a sequence viewer that can acquire the DNA/protein sequences of specific genes. Both tools can be run on the Windows, macOS, and Linux platforms. We believe that the Yesprit and Yeaseq will facilitate research using the four fission yeast species.

Micro-structure on metal surface can be created with high precision and good surface quality by ultrasonic-assisted electrochemical micromachining (USEMM). One of the prevalent material removal mechanisms in ultrasonic machining (UM) is cavitation erosion. However, the mechanism of material erosion is not clear and worth investigating. This study of the mechanical and chemical effects of the ultrasonic vibration in 6061 aluminium alloy is targeted to reveal the material processing mechanism in USEMM. Based on the built model, the velocity of micro-jet produced near the workpiece surface by ultrasonic cavitation reaches up to 350 m/s when bubble collapses computed by software MATLAB. The impact of micro jet produces plastic micro-pits on the metal surface and the convex peak around the edge of the pits, which is verified in ABAQUS software. The metallographic microscope and curves of the electrochemical polarization behaviour results indicate a significant grain refinement and a marked increase of anodic dissolution current, as well as a weaker resistance than the original workpiece in NaNO3 electrolyte during UM. The current-time curve during machining demonstrates the passive layer forms on the metal surface and then breaks down at the time of less than 0.0066s in USEMM. Micrographs of scanning electron microscope (SEM) of the machined surface in different stages show that many uniform and flat pits are formed in USEMM, compared with the local uneven pits in EMM.

Neuroimaging non-human primates (NHPs) is a growing, yet highly specialized field of neuroscience. Resources that were primarily developed for human neuroimaging often need to be significantly adapted for use with NHPs or other animals, which has led to an abundance of custom, in-house solutions. In recent years, the global NHP neuroimaging community has made significant efforts to transform the field towards more open and collaborative practices. Here we present the PRIMatE Resource Exchange (PRIME-RE), a new collaborative online platform for NHP neuroimaging. PRIME-RE is a dynamic community-driven hub for the exchange of practical knowledge, specialized analytical tools, and open data repositories, specifically related to NHP neuroimaging. PRIME-RE caters to both researchers and developers who are either new to the field, looking to stay abreast of the latest developments, or seeking to collaboratively advance the field . Pypreclin work was supported by the Fondation pour la Recherche Medicale (FRM grant number ECO20160736100 to JT), Fondation de France, Human Brain Project (Corticity project). RT and KH are supported by ANR-19-DATA-0025-01 NeuroWebLab. RBM is supported by the Biotechnology and Biological Sciences Research Council (BBSRC) UK [BB/N019814/1]. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust [203129/Z/16/Z]. Sí

Background The delayed diagnosis of neuroblastoma (NB) is common in China, which results in the prognosis of NB in China lagging behind that in developed countries. Methods A referral flowchart for suspected NB was implemented in nononcology clinics at Beijing Children's Hospital (BCH). Patients with symptoms of suspected NB were referred from nononcology clinics in BCH to oncology clinics and confirmed NB cases were regarded as referral group. The control group comprised patients initially diagnosed with NB who came directly to oncology clinics in BCH from other regions nationwide. The age at NB diagnosis was compared as primary outcome, and the 5-year overall survival (OS) and event-free survival (EFS) were compared via the Kaplan-Meier method and log-rank tests. Results In total, 3337 children with suspected NB were screened consecutively from 687 070 pediatric patients. Through examination of urine vanillylmandelic acid and homovanillic acid, or B-ultrasound, 102 of 3337 patients were referred to oncologists for comprehensive evaluations. Eventually, 29 referred patients were diagnosed as NB and the hospital-based diagnosis rate of NB was 4.2 per 100 000 visits. The median age at diagnosis in the referral group was 21.0 months, which was 9 months earlier than that of the control group (30.0 months, P = .026). The 5-year OS rate was 72.4% in the referral group, which was higher than that of the control group (66.7%) but without statistical significance (P = .664). Conclusion Delayed NB detection could be avoided by training pediatricians in nononcology clinics to detect suspected NB and refer these patients to oncologists.

Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20 s~10 min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction.

Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20s~10min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction.

Human brain plastically adapts to environmental demands. Here, we propose that naturally occuring plasticity in certain brain areas should be reflected by higher environmental influence and therefore lower heritability of the structure of those brain areas. Mesulam’s (1998) seminal overview proposed a hierarchy of plasticity, where higher-order multimodal areas should be more plastic than lower-order sensory areas. Using microstructural and functional gradients as proxies for Mesulam’s hierarchy, we seek to test whether these gradients predict heritability of brain structure. We test this model simultaneously across multiple measures of cortical structure and microstructure derived from structural magnet resonance imaging. We also account for multiple other explanations of heritability differences, such as signal-to-noise ratio and spatial autocorrelation. We estimated heritability of brain areas using 984 participants from the Human Connectome Project. Multi-level modelling of heritability differences demonstrated that heritability is explained by both signal quality, as well as by the primary microstructural gradient. Namely, sensory areas had higher heritability and limbic/heteromodal areas had lower heritability. Given the increasing availability of genetically informed imaging data, heritability could be a quick method assess brain plasticity.Highlights (up to 85 chars)Cortical areas vary in heritability. This is seen across structural measures.Heritability differences could be explained by plasticity, topography, or noise.We build a comprehensive model testing many explanations across 5 measures.Heritability is explained by noise and 1st structural gradient reflecting plasticity.Heritability could be a method to study brain plasticity.