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Abstract Background Marchiafava-Bignami disease (MBD) is an exceptionally rare condition, a fact that should pique the professional curiosity of medical practitioners. In recent years, case reports of this disease have been infrequent, and no comprehensive analysis or summary of the characteristics of the published cases has been conducted. Methods We collected the medical records of three patients treated at our hospital from March 2022 to March 2023. Furthermore, we searched PubMed for “case reports” from January 2017 to March 2023 and included 30 cases. By retrospectively analyzing these 33 cases, we summarized the characteristics of the disease. Results Based on our analysis, we found that MBD primarily affects middle-aged men and typically has an acute or subacute onset, with the primary clinical manifestations being disturbances of consciousness, speech disorders, cognitive impairment, and psychiatric or behavioral abnormalities, often leading to misdiagnosis of psychiatric disorders. Most patients have a history of alcohol consumption or malnutrition. Head CT or MRI revealed symmetric lesions in the corpus callosum, with the splenium being the most commonly affected area. Lesions might also involve white matter outside the corpus callosum, and a wider range of lesions suggested a poor prognosis. However, the prognosis is generally favorable with timely and adequate administration of B vitamins, providing reassurance to medical professionals and patients alike. Conclusion The early recognition and treatment of Marchiafava-Bignami disease are paramount, as they can significantly improve the prognosis. This underscores the critical need for prompt clinical intervention in the early stages of the disease, instilling a sense of urgency and significance in the work of medical professionals.

Abstract Objective The right posterior parietal cortex is the core brain region of emotional processing and executive control network in the human brain, and the function of the right posterior parietal cortex is decreased in patients with major depressive disorder. This study aims to preliminarily investigate whether the excitation of the right posterior parietal cortex by transcranial direct current stimulation (tDCS) could improve their clinical symptoms. Methods In this study, 12 patients with major depressive disorder were given tDCS treatment at Xuanwu Hospital of Capital Medical University and the First Hospital of Hebei Medical University. The stimulating electrode (anode) was placed on the patients’ right parietal cortex, whereas the reference electrode (cathode) was placed on the patients’ left mastoid. The stimulation intensity was set as 2.0 mA. The patients with depressive disorder were treated for 20 min at a time twice a day for 14 consecutive days. The severity of the clinical symptoms was evaluated using the Hamilton Depression Rating Scale‐17 (HDRS‐17) and the Hamilton Anxiety Rating Scale (HARS) at before and right after treatment. Results The HDRS‐17 scores of patients with depressive disorder decreased significantly following the tDCS treatment compared with those before treatment (p

For the federated learning scenarios with edge-end cooperation, edge servers and device terminals update their models and exchange gradient parameters frequently, and hence eavesdroppers can manipulate channel estimation through pilot spoofing to intercept the transmitted information and reduce the update efficiency of federated learning model.Therefore, a pilot attack detection algorithm with heterogeneous pilot energy estimation was proposed.Firstly, a federated learning pilot attack system model was constructed after the security of pilot attacks on data transmission had been analyzed.Then, a pilot attack detection method based on random segmentation and encryption methods was proposed to detect the pilot spoofing accurately and the contaminated channel could be recovered as well.Experimental results show that the proposed algorithm is more suitable for detecting pilot attacks with low transmit power and strong concealment compared to other existing algorithms.Furthermore, the data transmission rate of edge servers is improved significantly through the precoding based on the recovered channel state information.

The nuclear factor Y (NF-Y) gene family is a class of transcription factors that are widely distributed in eukaryotes and are involved in various biological processes. However, the NF-Y gene family members in watermelon, a valued and nutritious fruit, remain largely unknown and their functions have not been characterized. In the present study, 22 ClNF-Y genes in watermelon, 29 CsNF-Y genes in cucumber, and 24 CmNF-Y genes in melon were identified based on the whole-genome investigation and their protein properties, gene location, gene structure, motif composition, conserved domain, and evolutionary relationship were investigated. ClNF-YB9 from watermelon and its homologs in cucumber and melon were expressed specifically in seeds. Its expression remained low in the early stages of watermelon seed development, increased at 20 days after pollination (DAP), and peaked at 45–50 DAP. Moreover, the knockout mutant Clnf-yb9 exhibited abnormal leafy cotyledon phenotype, implying its critical role during seed formation. Finally, protein interaction assays showed that ClNF-YB9 interacts with all ClNF-YCs and the ClNF-YB9-YC4 heterodimer was able to recruit a ClNF-YA7 subunit to assemble a complete NF-Y complex, which may function in seed development. This study revealed the structure and evolutionary relationships of the NF-Y gene family in Cucurbitaceae and the novel function of ClNF-YB9 in regulating seed development in watermelon.

ABSTRACT Baicalein (BE) is a promising antifungal small-molecule compound with an extended antifungal spectrum, good synergy with fluconazole, and low toxicity, but its target protein and antifungal mechanism remain elusive. In this study, we found that BE can function against Candida albicans by disrupting glycolysis through targeting Eno1 and inhibiting its function. Eno1 acts as a key therapeutic target of the drug, as BE had no antifungal activity against the eno1 null mutant in a Galleria mellonella model of C. albicans infection. To investigate the mechanism of action, we solved the crystal structure of C. albicans Eno1(CaEno1) and then compared the difference between this structure and that of Eno1 from humans. The predicted primary binding site of BE on CaEno1 is between amino acids D261 and W274, with D263, S269, and K273 playing critical roles in the interaction with BE. Both positions S269 and K273 have different residues in the human Eno1 (hEno1). This finding suggests that BE may bind selectively to CaEno1, which would limit the potential for side effects in humans. Our findings demonstrate that Eno1 is a target protein of BE and thus may serve as a novel target for the development of antifungal therapeutics acting through the inhibition of glycolysis. IMPORTANCE Baicalein (BE) is a promising antifungal agent which has been well characterized, but its target protein is still undiscovered. The protein Eno1 plays a crucial role in the survival of Candida albicans. However, there are few antifungal agents which inhibit the functions of Eno1. Here, we found that BE can function against Candida albicans by disrupting glycolysis through targeting Eno1 and inhibiting its function. We further solved the crystal structure of C. albicans Eno1(CaEno1) and predicted that the primary binding site of BE on CaEno1 is between amino acids D261 and W274, with D263, S269, and K273 playing critical roles in the interaction with BE. Our findings will be helpful to get specific small-molecule inhibitors of CaEno1 and open the way for the development of new antifungal therapeutics targeted at inhibiting glycolysis.

Abstract Background Kinesin (KIN) as a motor protein is a versatile nano-machine and involved in diverse essential processes in plant growth and development. However, the kinesin gene family has not been identified in watermelon, a valued and nutritious fruit, and yet their functions have not been characterized. Especially, their involvement in early fruit development, which directly determines the size, shape, yield and quality of the watermelon fruit, remains unclear. Results In this study, we performed a whole-genome investigation and comprehensive analysis of kinesin genes in C. lanatus. In total, 48 kinesins were identified and categorized into 10 kinesin subfamilies groups based on phylogenetic analysis. Their uneven distribution on 11 chromosomes was revealed by distribution analysis. Conserved motif analysis showed that the ATP-binding motif of kinesins was conserved within all subfamilies, but not the microtubule-binding motif. 10 segmental duplication pairs genes were detected by the syntenic and phylogenetic approaches, which showed the expansion of the kinesin gene family in C. lanatus genome during evolution. Moreover, 5 ClKINs genes are specifically and abundantly expressed in early fruit developmental stages according to comprehensive expression profile analysis, implying their critical regulatory roles during early fruit development. Our data also demonstrated that the majority of kinesin genes were responsive to plant hormones, revealing their potential involvement in the signaling pathways of plant hormones. Conclusions Kinesin gene family in watermelon was comprehensively analyzed in this study, which establishes a foundation for further functional investigation of C. lanatus kinesin genes and provides novel insights into their biological functions. In addition, these results also provide useful information for understanding the relationship between plant hormone and kinesin genes in C. lanatus.

Abstract Automatic object segmentation has been a challenging task due to intensity inhomogeneity. The traditional way is to eliminate the intensity inhomogeneity, which causes the object to lose useful intensity information. The authors propose an adaptive level set method for the segmentation of intensity inhomogeneous images. Firstly, global and local features are utilised to collaboratively estimate the image, which devotes to compensating for intensity inhomogeneity. The local estimation retains detailed spatial information, and the global estimation mainly contains the regional information of the partitioned object. Then, during the construction of the energy functional, joint estimation is introduced to create the external energy. To acquire the precise location of the boundary, a weighting factor indicated by the gradient is introduced into the internal energy. Finally, after the numerical calculation of the energy functional by additive operator splitting algorithm, this method achieves the desired performance in terms of accuracy and robustness. Experimental results verify this method outperforms the comparative methods and can be applied to many real‐world scenarios.

Field Programmable Gate Array (FPGA) has become an efficient accelerator for convolutional neural network (CNN) inference due to its high performance and flexibility. To further improve the performance of CNN inference on FPGAs, an Intellectual Property core (IP core) called Deep Learning Processor Unit (DPU) is released by Xilinx. Unlike previous FPGA-based hardware designs focusing on specific functions or CNNs, the DPU IP supports ample basic functions of deep learning, and the developers can take advantage of DPUs to accelerate CNN inference conveniently. In DPU-based CNN acceleration platform, an encapsulated scheduler plays a crucial role in task scheduling between heterogeneous ARM and multiple DPUs. However, the current scheduler is unsatisfactory because its low schedule efficiency. This paper thus presents a high performance task assignment framework built upon Xilinx hybrid CPU-FPGA MPSoC devices. We first evaluate the main causes of low schedule efficiency problem. Then, we explore the scheduler rules and improve shedule efficiency through purposeful observations and analysis. Finally, we integrate our optimizations, and propose an efficient task assignment framework to maximize performance on DPU-based CNN acceleration platform. Experimental results on Xilinx Zynq UltraScale+ MPSoC zcu104 show that our efficient task assignment framework significantly boosts schedule efficiency for small-scale CNNs (from 36% to 70%), medium-scale CNNs (from 65% to 95%), and large-scale CNNs (from 77% to 99%) compared with original schedule strategy.

Abstract Background Tianshu capsule (TSC), a formula of traditional Chinese medicine, has been widely used in clinical practice for prophylactic treatment of headaches in China. However, former clinical trials of TSC were small, and lack of a standard set of diagnostic criteria to enroll patients. The study was conducted to re-evaluate the efficacy and safety of TSC post-marketing in an extending number of migraineurs who have diagnosed migraine with the International Classification of Headache Disorders, 3rd edition (beta version, ICHD-3β). Methods The study was a double-blind, randomized, placebo-controlled clinical trial that conducted at 20 clinical centers in China. At enrollment, patients between 18 and 65 years of age diagnosed with migraine were assigned to receive either TSC (4.08 g, three times daily) or a matched placebo according to a randomization protocol. The primary endpoint was a relative reduction of 50% or more in the frequency of headache attacks. The secondary outcomes included a reduction in the incidence of headache, the visual analogue scale of headache attacks, days of acute analgesic usage, and percentage of patients with a decrease of 50% or more in headache severity. Accompanying symptoms were also assessed. Results One thousand migraine patients were initially enrolled in the study, and 919 of them completed the trial. Following the 12-week treatment, significant improvement was observed in the TSC group concerning both primary and secondary outcomes. After therapy discontinuation, the gap between the TSC group and the placebo group in efficacy outcomes continued to increase. There were no severe adverse effects. Conclusions TSC is an effective, well-tolerated medicine for prophylactic treatment of migraine, and still have prophylactic effect after medicine discontinuation. Trial registration ClinicalTrials.gov Identifier: NCT02035111; Data of registration: 2014-01-10.

Considering that the data dissemination in multi-channel VANET (vehicular ad hoc network),a cooperative data dissemination scheduling algorithm was introduced for V2R(vehicle to roadside unit) and V2V(vehicle to vehicle).The algorithm created initial scheduling operators according to data requisition of vehicles.Then,initial collision graph and collision matrix were generated based on the conflict among initial scheduling operators.After proving the positive semidefinite of collision matrix,SDP (semidefinite programming) was used to channel allocation and collision graph creation.The algorithm then assigned weights for each data requisition according to dwell time and data volume of vehicles in RSU service region.Furthermore,it selected maximum weighted independent set of collision graph.The goal was to satisfy the most urgent data requisitions by V2R/V2V cooperate transmission.Transportation simulation results demonstrate that the proposed solution effectively promotes the service capacity by utilizes the multichannel of VANET and V2R/V2V transmission scheduling.

With data transparency and immutability, the blockchain can provide trustless and decentralized services for Internet of Things (IoT) applications. However, most blockchain-IoT networks, especially those with a private blockchain, are built on top of an infrastructure-based wireless network (i.e., using Wi-Fi access points or cellular base stations). Hence, they are still under the risk of Single-Point-of-Failure (SPoF) on the network layer, hindering the decentralization merit, for example, when the access points or base stations get failures. This paper presents an Optimized Link State Routing (OLSR) protocol-based solution for that issue in a private blockchain-IoT application. By decentralizing the underlying network with OLSR, the private blockchain network can avoid SPoF and automatically recover after a failure. Single blockchain connections can be extended to multiple ad hoc hops. Services over blockchain become flexible to fit various IoT scenarios. We show the effectiveness of our solution by constructing a private Ethereum blockchain network running on IoT devices (i.e., Raspberry Pi model 4) with environmental data sensing (i.e., Particular Matter (PM)). The IoT devices use OLSR to form an ad hoc network. The environment data are collected and propagated in transactions to a pre-loaded smart contract periodically. We then evaluate the IoT blockchain network’s recovery time when facing a link error. The evaluation results show that OLSR can automatically recover after the failure. We also evaluate the transaction-oriented latency and block-oriented latency, which indicates the blocks have a high transmission quality, while transactions are transferred individually.

Wireless Power Transfer (WPT) technology is considered as a promising approach to make Wireless Rechargeable Sensor Network (WRSN) work perpetually. In WRSN, a vehicle exists, termed a mobile charger, which can move close to sensor nodes and charge them wirelessly. Due to the mobile charger’s limited traveling distance and speed, not every node that needs to be charged may be serviced in time. Thus, in such scenario, how to make a route plan for the mobile charger to determine which nodes should be charged first is a critical issue related to the network’s Quality of Service (QoS). In this paper, we propose a mobile charger’s scheduling algorithm to mitigate the data loss of network by considering the node’s criticality in connectivity and energy. First, we introduce a novel metric named criticality index to measure node’s connectivity contribution, which is computed as a summation of node’s neighbor dissimilarity. Furthermore, to reflect the node’s charging demand, an indicator called energy criticality is adopted to weight the criticality index, which is a normalized ratio of the node’s consumed energy to its total energy. Then, we formulate an optimization problem with the objective of maximizing total weighted criticality indexes of nodes to construct a charging tour, subject to the mobile charger’s traveling distance constraint. Due to the NP-hardness of the problem, a heuristic algorithm is proposed to solve it. The heuristic algorithm includes three steps, which is spanning tree growing, tour construction and tour improvement. Finally, we compare the proposed algorithm to the state-of-art scheduling algorithms. The obtained results demonstrate that the proposed algorithm is a promising one.

This paper addresses the problem of estimation fusion in a distributed wireless sensor network (WSN) under the following conditions: (i) sensor noises are contaminated by outliers or gross errors; (ii) process noise and sensor noises are correlated; (iii) cross-correlation among local estimates is unknown. First, to attack the correlation and outliers, a correlated robust Kalman filtering (coR 2 KF) scheme with weighted matrices on innovation sequences is introduced as local estimator. It is shown that the proposed coR 2 KF takes both conventional Kalman filter and robust Kalman filter as a special case. Then, a novel version of our internal ellipsoid approximation fusion (IEAF) is used in the fusion center to handle the unknown cross-correlation of local estimates. The explicit solution to both fusion estimate and corresponding covariance is given. Finally, to demonstrate robustness of the proposed coR 2 KF and the effectiveness of IEAF strategy, a simulation example of tracking a target moving on noisy circular trajectories is included.

Human Elongator complex, which plays a key role in transcript elongation in vitro assay, is incredibly similar in either components or function to its yeast counterpart. However, there are only a few studies focusing on its target gene characterization in vivo. We studied the effect of down-regulation of the human elongation protein 3 (hELP3) on the expression of HSP70 through antisense strategy. Transfecting antisense plasmid p1107 into HeLa cells highly suppressed hELP3 expression, and substantially reduced expression of HSP70 mRNA and protein. Furthermore, chromatin immunoprecipitation assay (ChIP Assay) revealed that hElp3 participates in the transcription elongation of HSPA1A in HeLa cells. Finally, complementation and ChIP Assay in yeast showed that hElp3 can not only complement the growth and slow activation of HSP70 (SSA3) gene transcription, but also directly regulates the transcription of SSA3. On the contrary, these functions are lost when the HAT domain is deleted from hElp3. These data suggest that hElp3 can regulate the transcription of HSP70 gene, and the HAT domain of hElp3 is essential for this function. These findings now provide novel insights and evidence of the functions of hELP3 in human cells.