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Background/aimsThe aim of this study was to assess the ability of neuropsychological tests to differentiate autopsy-defined Alzheimer disease (AD) from subcortical ischemic vascular dementia (SIVD).MethodsFrom a sample of 175 cases followed longitudinally that underwent autopsy, we selected 23 normal controls (NC), 20 SIVD, 69 AD, and 10 mixed cases of dementia. Baseline neuropsychological tests, including Memory Assessment Scale word list learning test, control oral word association test, and animal fluency, were compared between the three autopsy-defined groups.ResultsThe NC, SIVD, and AD groups did not differ by age or education. The SIVD and AD groups did not differ by the Global Clinical Dementia Rating Scale. Subjects with AD performed worse on delayed recall (p < 0.01). A receiver operating characteristics analysis comparing the SIVD and AD groups including age, education, difference between categorical (animals) versus phonemic fluency (letter F), and the first recall from the word learning test distinguished the two groups with a sensitivity of 85%, specificity of 67%, and positive likelihood ratio of 2.57 (AUC = 0.789, 95% CI 0.69-0.88, p < 0.0001).ConclusionIn neuropathologically defined subgroups, neuropsychological profiles have modest ability to distinguish patients with AD from those with SIVD.

Sucrose is a commonly utilized nutritive sweetener in food and beverages due to its abundance in nature and low production costs. However, excessive intake of sucrose increases the risk of metabolic disorders, including diabetes and obesity. Therefore, there is a growing demand for the development of nonnutritive sweeteners with almost no calories. d‐Allulose is an ultra‐low‐calorie, rare six‐carbon monosaccharide with high sweetness, making it an ideal alternative to sucrose. In this study, we developed a cell factory for d‐allulose production from sucrose using Escherichia coli JM109 (DE3) as a chassis host. The genes cscA, cscB, cscK, alsE, and a6PP were co‐expressed for the construction of the synthesis pathway. Then, the introduction of ptsG‐F and knockout of ptsG, fruA, ptsI, and ptsH to reprogram sugar transport pathways resulted in an improvement in substrate utilization. Next, the carbon fluxes of the Embden‐Meyerhof‐Parnas and the pentose phosphate pathways were regulated by the inactivation of pfkA and zwf, achieving an increase in d‐allulose titer and yield of 154.2% and 161.1%, respectively. Finally, scaled‐up fermentation was performed in a 5 L fermenter. The titer of d‐allulose reached 11.15 g/L, with a yield of 0.208 g/g on sucrose. [ABSTRACT FROM AUTHOR]

The inherently intermittent feature of solar energy requires reliable energy conversion and storage systems for utilizing the most abundant solar energy. Phase change materials are potential solutions to store a large amount of heat produced by solar light. However, few of the phase change materials have the ability to efficiently convert solar energy into heat; additionally, phase change materials need to be encapsulated in porous substrates for enhancing their leaking resistance and photo-to-thermal performance. In this work, monolithic MXene aerogels, fabricated by Al3+ cross-linking and freeze-drying, were used as the encapsulation and photothermal materials. The composites phase change materials of MXene/polyethylene glycol can be made with a large polyethylene glycol loading above 90 wt% with the maximum of 97 wt%, owing to the large porosity of MXene aerogels. The low content of MXene has a limited impact on the phase transition temperature and enthalpy of polyethylene glycol, with an enthalpy retention rate ranging from 89.2 to 96.5% for 90–97 wt% polyethylene glycol loadings. MXene aerogels greatly improve the leaking resistance of polyethylene glycol above its melting point of 60 °C, even at 100 °C. The composites phase change materials also show outstanding cycling stability for 500 cycles of heat storage and release, retaining 97.7% of the heat storage capability. The optimized composite phase change material has a solar energy utilization of 93.5%, being superior to most of the reported results. Our strategy produces promising composite phase change materials for solar energy utilization using the MXene aerogels as the encapsulation and photothermal materials.

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Time-sensitive networking (TSN) is considered an ideal solution to meet the transmission needs of existing industrial production methods. The traffic scheduling problem of TSN is an NP-hard problem. The traditional traffic scheduling algorithms can lead to issues such as significant computational time consumption and traffic congestion, which are not conducive to the rapid and high-quality deployment of TSN. To simplify the complexity of TSN schedule table generation, the paper studies the scheduling problem of mixed critical traffic in TSN. Using a combinatorial packing traffic scheduling algorithm based on unoccupied space (CPTSA-US), a scheduling table for TSN traffic transmission is generated, proving the feasibility of transforming the TSN traffic scheduling problem into a packing problem. In addition, the initial packing algorithm and traditional traffic scheduling algorithm can cause traffic accumulation, seriously affecting network performance. This paper proposes a mixed-critical traffic scheduling algorithm based on free time domain (MCTSA-FTD), which further partitions the packing space transformed by the time domain. And performs multiple packing of traffic based on the partitioned packing space and generates the TSN schedule table according to the reverse transformation of the packing results. The simulation results show that compared to the CPTSA-US and the traditional traffic scheduling solution algorithm SMT (Satisfiability Modulo Theory), the schedule table generated by the MCTSA-FTD significantly improves the delay, jitter, and packet loss of BE flows in the network while ensuring the transmission requirements of TT flows. This can effectively enhance the transmission performance of the network. [ABSTRACT FROM AUTHOR]

The rapid advancement of artificial intelligence technology has significantly enhanced the intelligence of mobile robots, facilitating their widespread utilization in unmanned driving, smart home systems, and various other domains. As the scope, scale, and complexity of robot deployment continue to expand, there arises a heightened demand for enhanced computational power and real-time performance, with path planning emerging as a prominent research focus. In this study, we present an adaptive Lévy flight–rotation slime mold algorithm (LRSMA) for global robot motion planning, which incorporates LRSMA with the cubic Hermite interpolation. Unlike traditional methods, the algorithm eliminates the need for a priori knowledge of appropriate interpolation points. Instead, it autonomously detects the convergence status of LRSMA, dynamically increasing interpolation points to enhance the curvature of the motion curve when it surpasses the predefined threshold. Subsequently, it compares path lengths resulting from two different objective functions to determine the optimal number of interpolation points and the best path. Compared to LRSMA, this algorithm reduced the minimum path length and average processing time by (2.52%, 3.56%) and (38.89%, 62.46%), respectively, along with minimum processing times. Our findings demonstrate that this method effectively generates collision-free, smooth, and curvature-constrained motion curves with the least processing time. [ABSTRACT FROM AUTHOR]

The rice stem borer (RSB), Chilo suppressalis, a notorious rice pest in China, has evolved a high resistance level to commonly used insecticides. Tetraniliprole, a new anthranilic diamide insecticide, effectively controls multiple pests, including RSB. However, the potential resistance risk of RSB to tetraniliprole is still unknown. In this study, the tetraniliprole‐selection (Tet‐R) strain was obtained through 10 continuous generations of selection with tetraniliprole 30% lethal concentration (LC30). The realized heritability (h2) of the Tet‐R strain was 0.387, indicating that resistance of RSB to tetraniliprole developed rapidly under the continuous selection of tetraniliprole. The Tet‐R strain had a high fitness cost (relative fitness = 0.53). We established the susceptibility baseline of RSB to tetraniliprole (lethal concentration at LC50 = 0.727 mg/L) and investigated the resistance level of 6 field populations to tetraniliprole. All tested strains that had resistance to chlorantraniliprole exhibited moderate‐ to high‐level resistance to tetraniliprole (resistance ratio = 27.7−806.8). Detection of ryanodine receptor (RyR) mutations showed that the Y4667C, Y4667D, I4758M, and Y4891F mutations were present in tested RSB field populations. RyR mutations were responsible for the cross‐resistance between tetraniliprole and chlorantraniliprole. Further, the clustered regularly interspaced palindromic repeats (CRISPR) / CRISPR‐associated protein 9‐mediated genome‐modified flies were used to study the contribution of RyR mutations to tetraniliprole resistance. The order of contribution of a single RyR mutation to tetraniliprole resistance was Y4667D > G4915E > Y4667C ≈ I4758M > Y4891F. In addition, the I4758M and Y4667C double mutations conferred higher tetraniliprole resistance than single Y4667C mutations. These results can guide resistance management practices for diamides in RSB and other arthropods. [ABSTRACT FROM AUTHOR]