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A perturbation bound for Williamson's symplectic normal form under any unitarily invariant norm has been derived in Idel et al. (2017) [3]. We improve this bound to a tight one, and derive a few alternative forms of our new bound. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the study is to investigate the interactive influence of depression on left-behind (LB) and non-left-behind (NLB) children from the perspective of peer effects. The roles of teachers, parents, and friends are also explored. Methods: Data on 1817 children, 1817 parents, and 55 teachers were obtained from a field survey in December 2021. All students in the sample were randomly assigned to classrooms. A peer effect model and OLS methods were used to estimate the peer influence of depression. Robustness tests were conducted by randomly removing schools from the sample. Results: Depression was contagious among different groups of rural children, and the peer effect of the NLB children's depression played a dominant role. Both LB and NLB children were more affected by their NLB classmates' depression. LB children were not significantly affected by depression in other LB children. This conclusion remains robust after robustness testing. In addition, heterogeneity analysis showed that outgoing and cheerful teachers, effective parent–child communication and high-quality friendship all alleviated peer influence on depression. Conclusions: LB children have more severe depression than NLB children, but LB children are more affected by depression in their NLB peers. Policymakers should train teachers to engage in positive communication with students to improve mental health in children. In addition, this article recommends that children move and live with their parents when family conditions permit. [ABSTRACT FROM AUTHOR]

Electricity load prediction is an essential tool for power system planning, operation and management. The critical information it provides can be used by energy providers to maximise power system operation efficiency and minimise system operation costs. Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) are two suitable methods that have been successfully used for analysing time series problems. In this paper, the two algorithms are explored further for load prediction; two load prediction algorithms are developed and verified by using the half-hourly load data from the University of Warwick campus energy centre with four different prediction time horizons. The novelty lies in comparing and analysing the prediction accuracy of two intelligent algorithms with multiple time scales and in exploring better scenarios for their prediction applications. High-resolution load forecasting over a long range of time is also conducted in this paper. The MAPE values for the LSTM are 2.501%, 3.577%, 25.073% and 69.947% for four prediction time horizons delineated. For the SVM, the MAPE values are 2.531%, 5.039%, 7.819% and 10.841%, respectively. It is found that both methods are suitable for shorter time horizon predictions. The results show that LSTM is more capable of ultra-short and short-term forecasting, while SVM has a higher prediction accuracy in medium-term and long-term forecasts. Further investigation is performed via blind tests and the test results are consistent. [ABSTRACT FROM AUTHOR]

Highly purified and uniformly distributed carbon nanotube (CNT) network is one of the key issues for developing advanced nanocomposites. However, the challenge for mitigation of the aggregation of CNT bundles during the CNT film fabrication still remained. In this work, debundling and reorganization of CNT bundles have been realized simultaneously by using a high-temperature thermal treatment. The debundling of large CNT bundles into small ones started after they were heat treated under 1400 °C and uniformly distributed CNT networks with small bundles were obtained after heat treated under 1800 °C. The tensile strength of the CNT film significantly increased by about 64% compared to the pristine film. Microstructural observations showed that the iron nanoparticles started to evaporate at 1400 °C and were completely removed at 1800 °C. Hollow amorphous carbon shells wrapped on the nanoparticles were left inside the CNT networks after the removal of the iron nanoparticles. Interestingly, these amorphous carbon shells act as carbon source for the deposition of a carbon layer on the CNT surface when the heat treatment temperature was up to 2000 °C, and they were completely consumed after being treated at 2800 °C. The microstructural evolution process of CNT bundles during heat treatment suggests the simple high-temperature treatment strategy could be applied for fabrication of highly purified CNT networks with well-distributed small bundles, enabling the development of advanced multifunctional nanocomposites in the near future. A high-temperature heat treatment is used to investigate the microstructure evolution and mechanical properties of CNT films. Benefiting from the high-purity and uniformly distributed CNT networks with small bundles after heat treated under 1800 °C, the tensile strength of the CNT film has significantly increased by ∼64% compared to the pristine film. When heat treated under 2800 °C, the hollow amorphous carbon shells are completely consumed and act as carbon source for deposition of carbon layer on the CNT surface. [Display omitted] • Debundling of large CNT bundles was observed after a high temperature treatment. • A regenerative carbon layer was deposited on CNT surface under high temperature. • The microstructural evolution of CNT networks under high temperature was proposed. [ABSTRACT FROM AUTHOR]

Malathion is a toxic organophosphorus pesticide. When ingested by humans, it affects the nervous system and endangers health. Herein, a new type of aptamer-based sensor was developed for its determination. The results demonstrate that this sensor has high sensitivity and good selectivity and was successfully used for the determination of malathion in apples. The detection mechanism is as follows. The aptamer is adsorbed on the surface of gold nanoparticles (AuNPs) to prevent the aggregation of AuNPs in a high salt solution. After adding malathion, the aptamer specifically binds to malathion and is removed from the surface of the AuNPs. The quantity of aptamer adsorbed on the surface of AuNPs had a negative relationship with the malathion concentration. The unmodified AuNPs aggregated to different degrees in NaCl solution. In the Fehling reaction, various degrees of aggregated AuNPs catalyze the reduction to different size Cu2O and exhibit different resonance scattering intensities. An excellent linear response between the resonance scattering spectra intensity and malathion concentration was obtained. The limit of detection of the developed method was 5.24 ng/L (15.86 pM). The recovery and repeatability for the apple samples were satisfactory. [ABSTRACT FROM AUTHOR]

A novel approach to EMS infrastructure systems design, planning, and operations under long-term uncertainty is introduced based on multi-stage stochastic programming and decision rules, accounting for strategic flexibility (also known as real options – RO). Different from standard RO analysis, the approach mimics real-world decision-making by exercising flexibility based on conditional-go decision rules. The objective is to minimize the expected total costs over the system’s life cycle, and the outputs are the optimal initial configuration and decision rules. A flexible solution provides lower expected cost than stochastically optimal rigid solutions, especially valuable when required incident coverage rate is >95%. [ABSTRACT FROM AUTHOR]

[Display omitted] • The enzymatic cascade increased the selectivity of the sensor and reduced the reaction time. • Gold nanoclusters were synthesized for internal calibration to improve the accuracy of the sensor. • The constructed ratiometric fluorescence sensor for choline detection has two linear ranges of 0.5–20 μM and 20–100 μM and low LOD of 0.27 μM. • The fluorescent sensor was successfully applied to the detection of choline in human serum. Choline (ChO) is a biomarker of many diseases. Its accurate and sensitive detection is of great significance for the diseases diagnosis. Gold nanoclusters with bovine serum albumin as template (BSA-AuNCs) were synthesized by microwave-assisted method. A ratiometric fluorescence sensor was constructed based on BSA-AuNCs and 2,3-diaminophenazine, which was the oxidation product of o-phenylenediamine. Combined with cascade enzymatic reaction, it was used for the detection of choline. In the detection system, choline oxidase improved the selectivity of detection, and the peroxidase-like activity of molybdenum disulfide nanosheets shortened the detection time. The linear ranges of choline detection by ratiometric fluorescence sensor were 0.5–20 μM and 20–100 μM. The detection limit was 0.27 μM. The ratiometric fluorescence sensor was easy to operate and had good selectivity. Compared with single signal sensor, it can effectively reduce the error interference such as environmental change, and the detection result was more accurate. [ABSTRACT FROM AUTHOR]

As an emerging fluorescent nanomaterial, gold nanoclusters are of increasing interest in sensing and imaging because of their stable luminescence properties, large Stokes shift and low toxicity. Here, red-emitting gold nanoclusters stabilized by 6-Deoxy-6-mercapto-β-cyclodextrin (6-SH-CD@Au NCs) were successfully synthesized based on photo-reduction method for the detection of chlortetracycline. Under alkaline conditions, chlortetracycline transforms into iso-chlortetracycline (iso-CTC), which has a distinct blue fluorescence at 415 nm. The fluorescence intensity at 415 nm changed with increasing concentration of chlortetracycline and inner filter effect (IFE) of 6-SH-CD@Au NCs, while the fluorescence of the gold nanocluster at 670 nm decreased due to the IFE of iso-CTC. This fluorescent system detected chlortetracycline with good linearity in the concentration range of 5 nM-1 μM, and the limit of detection is 2.7 nM. The developed fluorescence sensor and test paper assay had been successfully applied for the detection of chlortetracycline in milk samples. The results demonstrated that the ratiometric sensor has advantages of simplicity, high sensitivity and specificity, cost-effectiveness and ease of operation. [Display omitted] • The novel photo-reduction strategy for the synthesis of gold clusters is a simple green process. • The method has good selectivity and sensitivity for chlortetracycline. • The fluorescent sensor was successfully applied to the detection of CTC in milk. [ABSTRACT FROM AUTHOR]

Alkaline phosphatase (ALP) is a kind of biological hydrolase which plays a key role in the normal growth of the body. Therefore, it is necessary to establish a simple and accurate method for quantitative detection of ALP. In this work, gold nanoclusters with bovine serum albumin as a template (BSA-AuNCs) were rapidly synthesized in 20 s based on microwave-assisted. Subsequently, manganese dioxide (MnO 2) was generated in situ on the surface of the gold nanoclusters which quenched the fluorescence of BSA-AuNCs. The quenching mechanism was explored and found that it was through the combined effect of fluorescence resonance energy transfer and inner filter effect. The quantitative detection of ALP was achieved through a simple enzymatic reaction and a redox reaction. The developed method was simple and convenient, and the detection limit for ALP was 1.5 mU mL−1. In addition, this fluorescence sensor had good selectivity and could accurately detect ALP in human serum, which demonstrated its potential applications in clinical diagnosis. [Display omitted] • Gold nanoclusters with strong red fluorescence were synthesized ultra-rapidly by microwave-assisted method. • Manganese dioxide grew in situ on the surface of BSA-gold nanoclusters. • The developed fluorescent sensor was low cost and the experimental operation was simple. • The fluorescent sensor was successfully applied to the detection of alkaline phosphatase in human serum. [ABSTRACT FROM AUTHOR]

• A novel fluorescence switching aptasensor for ultrasensitive detection of Kana was developed. • Aptamer as fluorescence switching was simple to fabricate. • The constructed aptasensor showed high selectivity and accuracy. • The limit of detection, accuracy and recovery rate of the developed fluorescence aptasensor were satisfied. We developed a fluorescent switching based on bovine serum albumin stabilized gold nanoclusters (BSA-AuNCs) and kanamycin (Kana) aptamer to detect Kana. Red fluorescence of BSA-AuNCs was turn off when Kana aptamer was assembled on the surface of BSA-AuNCs. However, after adding Kana, the specific aptamer detached from the surface of the BSA-AuNCs and formed the complex of aptamer-kanamycin, then the red fluorescence of BSA-AuNCs was turn on. The fluorescence intensity of BSA-AuNCs can be mediated by the amount of Kana aptamer. A good linear response for Kana was obtained in the concentration range 0.04 nM to 7.0 nM. The assay was used to detect Kana in milk matrix, and the accuracy and recovery of the method were satisfied. It is worth noting that the aptamer mediated BSA-AuNCs fluorescent switching was simple and easy to fabricate, which can provide a novel assay for the detection of small molecules. [ABSTRACT FROM AUTHOR]

Background: Several studies have shown increased levels of cerebrospinal fluid (CSF) synaptosomal-associated protein 25 (SNAP-25) in patients with Alzheimer's disease (AD). However, results have been inconsistent thus far.Objective: We conducted meta-analyses summarizing the associations of CSF SNAP-25 levels with AD to assess the utility of SNAP-25 as a novel biomarker for AD.Methods: We conducted a meta-analysis of differences in CSF SNAP-25 levels in patients with AD or mild cognitive impairment (MCI) and in cognitively healthy controls (HC). We calculated pooled correlation coefficients comparing SNAP-25 levels and total tau (T-tau) or hyperphosphorylated tau (P-tau) in CSF.Results: Eight studies enrolling 1,162 individuals (423 AD, 275 MCI, 464 HC) were included for quantitative analysis. Patients with AD (ratio of means [RoM] = 1.50, 95% confidence interval [CI]: 1.30,1.74) and MCI (RoM = 1.45, 95% CI: 1.12,1.87) had increased levels of CSF SNAP-25 as compared to HC. The difference in CSF SNAP-25 levels when comparing AD and MCI (RoM = 1.05, 95% CI: 0.96,1.14) was not statistically significant but showed a trend toward significance. Statistically significant correlations were found when comparing CSF SNAP-25 with CSF T-tau (Spearman correlation coefficient, ρ=0.78; ρ=0.66; ρ=0.69, respectively) and P-tau (ρ=0.77; ρ=0.70; ρ=0.62, respectively) levels in patients with AD, MCI, and HC.Conclusion: Increased CSF SNAP-25 levels differentiated patients with AD or MCI from controls, suggesting the utility of this biomarker in the early diagnosis of AD. [ABSTRACT FROM AUTHOR]

• The building materials of sensor were low cost and the experimental operation was simple. • The developed colorimetric sensor showed high sensitivity, selectivity and stability for GEN detection. • The detection limit, detection accuracy and recovery rate were satisfied. The residue of gentamicin (GEN) in food is a serious threat to human health. Therefore, it is urgent to develop a simple and highly sensitive method for effectively detecting GEN in food. In this study we proposed a lysine-functionalized gold nanoparticles (AuNPs) colorimetric sensor to detect GEN simply and quickly. The detection mechanism as follows: The interaction between lysine and AuNPs causes lysine to adsorb on the surface of AuNPs. After the addition of GEN, the strong hydrogen bond between GEN and lysine promotes the aggregation of AuNPs. The color of AuNPs changed from wine red to blue, which produced obvious color change. UV–vis spectrometer was used to detect it. The results showed that the colorimetric sensor had high selectivity and sensitivity for the detection of GEN, and the linear range and limit of detection were 5–60 nM and 1.22 nM, respectively. In addition, this method has been successfully applied to the quantitative detection of GEN in milk, and the accuracy and recovery were satisfied. [ABSTRACT FROM AUTHOR]

Objective: A large number of studies have found that the prevalence of cognitive impairment varies in different regions. However, data on cognitive impairment in the Chinese population is still lacking. The goal of this study was to assess the prevalence of cognitive impairment among the elderly in a region of China and explore the associated risk factors. Methods: We performed a population‐based cross‐sectional survey from April to June 2022. Residents come from three villages and six urban communities in the county‐level city of Liuyang in southern China (N = 3233) and the coverage rate of our study population reached 73%. Participants were assessed with a series of clinical examinations and neuropsychological measures. A total of 2598 participants were selected after filtering out those under 60 years old or with incomplete data. Patients with cognitive impairment included those with mild cognitive impairment (MCI) or dementia who met standard diagnostic criteria. Results: The prevalence of cognitive impairment, MCI, and dementia among participants aged 60 years and older were 21.48% (95% CI, 19.90–23.10), 15.70% (95% CI, 14.30–17.10), and 5.77 (95% CI, 4.90–6.70), respectively. And residents in villagers were more likely to have cognitive impairment than in urban communities (p < 0.001). Age growth and education level were independent influencing factors for cognitive impairment in all populations (p < 0.001). For lifestyles factors, both smoking and drinking reduced the risk of cognitive impairment (p < 0.05), but when further quantified, the link disappeared. Moreover, having cerebrovascular disease and severe vision impairment were risk factors (p < 0.05). Conclusion: A representative prevalence of cognitive impairment, MCI, and dementia was found in the elderly Han Chinese population in Southern China. And we further explored the role of known risk factors, particularly in physical activity, smoking, and alcohol consumption. [ABSTRACT FROM AUTHOR]

Background and Aims: Neuronal intranuclear inclusion disease (NIID) is a rare progressive neurodegenerative disorder mainly caused by abnormally expanded GGC repeats within the NOTCH2NLC gene. Most patients with NIID show polyneuropathy. Here, we aim to investigate diagnostic electrophysiological markers of NIID. Methods: In this retrospective dual‐center study, we reviewed 96 patients with NOTCH2NLC‐related NIID, 94 patients with genetically confirmed Charcot–Marie‐Tooth (CMT) disease, and 62 control participants without history of peripheral neuropathy, who underwent nerve conduction studies between 2018 and 2022. Results: Peripheral nerve symptoms were presented by 53.1% of patients with NIID, whereas 97.9% of them showed peripheral neuropathy according to electrophysiological examinations. Patients with NIID were characterized by slight demyelinating sensorimotor polyneuropathy; some patients also showed mild axonal lesions. Motor nerve conduction velocity (MCV) of the median nerve usually exceeded 35 m/s, and were found to be negatively correlated with the GGC repeat sizes. Regarding the electrophysiological differences between muscle weakness type (n = 27) and non‐muscle weakness type (n = 69) of NIID, nerve conduction abnormalities were more severe in the muscle weakness type involving both demyelination and axonal impairment. Notably, specific DWI subcortical lace sign was presented in only 33.3% of muscle weakness type, thus it was difficult to differentiate them from CMT. Combining age of onset, distal motor latency, and compound muscle action potential of the median nerve showed the optimal diagnostic performance to distinguish NIID from major CMT (AUC = 0.989, sensitivity = 92.6%, specificity = 97.4%). Interpretation: Peripheral polyneuropathy is common in NIID. Our study suggest that nerve conduction study is useful to discriminate NIID. [ABSTRACT FROM AUTHOR]

Aims: We mainly evaluate retinal alterations in Alzheimer's disease (AD) patients, investigate the associations between retinal changes with AD biomarkers, and explore an optimal machine learning (ML) model for AD diagnosis based on retinal thickness. Methods: A total of 159 AD patients and 299 healthy controls were enrolled. The retinal parameters of each participant were measured using optical coherence tomography (OCT). Additionally, cognitive impairment severity, brain atrophy, and cerebrospinal fluid (CSF) biomarkers were measured in AD patients. Results: AD patients demonstrated a significant decrease in the average, superior, and inferior quadrant peripapillary retinal nerve fiber layer, macular retinal nerve fiber layer, ganglion cell layer (GCL), inner plexiform layer (IPL) thicknesses, as well as total macular volume (TMV) (all p < 0.05). Moreover, TMV was positively associated with Mini‐Mental State Examination and Montreal Cognitive Assessment scores, IPL thickness was correlated negatively with the medial temporal lobe atrophy score, and the GCL thickness was positively correlated with CSF Aβ42/Aβ40 and negatively associated with p‐tau level. Based on the significantly decreased OCT variables between both groups, the XGBoost algorithm exhibited the best diagnostic performance for AD, whose four references, including accuracy, area under the curve, f1 score, and recall, ranged from 0.69 to 0.74. Moreover, the macular retinal thickness exhibited an absolute superiority for AD diagnosis compared with other enrolled variables in all ML models. Conclusion: We identified the retinal alterations in AD patients and found that macular thickness and volume were associated with AD severity and biomarkers. Furthermore, we confirmed that OCT combined with ML could serve as a potential diagnostic tool for AD. [ABSTRACT FROM AUTHOR]

Background: Some previous studies showed abnormal pathological and vascular changes in the retina of patients with Alzheimer's disease (AD). However, whether retinal microvascular density is a diagnostic indicator for AD remains unclear.Objective: This study evaluated the macular vessel density (m-VD) in the superficial capillary plexus and fovea avascular zone (FAZ) area in AD, explored their correlations with clinical parameters, and finally confirmed an optimal machine learning model for AD diagnosis.Methods: 77 patients with AD and 145 healthy controls (HCs) were enrolled. The m-VD and the FAZ area were measured using optical coherence tomography angiography (OCTA) in all participants. Additionally, AD underwent neuropsychological assessment, brain magnetic resonance imaging scan, cerebrospinal fluid (CSF) biomarker detection, and APOE ɛ4 genotyping. Finally, the performance of machine learning algorithms based on the OCTA measurements was evaluated by Python programming language.Results: The m-VD was noticeably decreased in AD compared with HCs. Moreover, m-VD in the fovea, superior inner, inferior inner, nasal inner subfields, and the whole inner ring declined significantly in mild AD, while it was more serious in moderate/severe AD. However, no significant difference in the FAZ was noted between AD and HCs. Furthermore, we found that m-VD exhibited a significant correlation with cognitive function, medial temporal atrophy and Fazekas scores, and APOE ɛ4 genotypes. No significant correlations were observed between m-VD and CSF biomarkers. Furthermore, results revealed the Adaptive boosting algorithm exhibited the best diagnostic performance for AD.Conclusion: Macular vascular density could serve as a diagnostic biomarker for AD. [ABSTRACT FROM AUTHOR]

Despite the similar clinical and pathological features between Niemann-Pick type C (NPC) disease and Alzheimer's disease (AD), few studies have investigated the role of NPC genes in AD. To elucidate the role of NPC genes in AD, we sequenced NPC1 and NPC2 in 1192 AD patients and 2412 controls. Variants were divided into common variants and rare variants according to minor allele frequency (MAF). Common variant (MAF≥0.01) based association analysis was conducted by PLINK 1.9. Gene-based aggregation testing of rare variants was performed by Sequence Kernel Association Test-Optimal (SKAT-O test), respectively. Age at onset (AAO) and mini-mental state examination (MMSE) association studies were also performed with PLINK 1.9. Six common variants were identified and exhibited no association with AD. Gene-based aggregation testing revealed that both NPC1 and NPC2 were not associated with AD risk. Additionally, AAO and MMSE association studies revealed that no common variants were linked with AD endophenotypes. Taken together, our study indicated that NPC1 and NPC2 may not be implicated in AD pathogenesis in the Chinese population. [ABSTRACT FROM AUTHOR]

A combination of simulation and experimentation is employed to explore the influence of the non-uniformity of energy distribution in nanosecond PSEB on the target ablation. Firstly, A spatiotemporally distributed heat source model is built using Monte Carlo computational method and an ablation phase transition model is established using molecular dynamics method. The results reveal the time evolution of multiple cavities in the sub-surface region, as well as the temperature and stress field distributions with electron beam irradiation. Phase explosion formation and lattice structure changes in the steel target during the ablation process is found for the first time. The PSEB modified AISI1045 steel samples is experimentally conducted. The characterization results show the changes in the crystal structure within AISI1045 steel, which is attributed to the formation of an amorphous layer resulting from grain refinement and structural defects, as well as the combined effects of thermal elastic stress waves, shock thermal stress waves and temperature gradient distribution. The experimentally obtained crystal phase changes are consistent with the simulation results, confirming the accuracy and validity of the modelling. The findings of this study provide a molecular-scale explanation for the formation of unique morphology and micro-structures during the PSEB ablated metal target. [ABSTRACT FROM AUTHOR]

Optimal store placement aims to identify the optimal location for a new brick-and-mortar store that can maximize its sale by analyzing and mining users' preferences from large-scale urban data. In recent years, the expansion of chain enterprises in new cities brings some challenges because of two aspects: (1) data scarcity in new cities, so most existing models tend to not work (i.e., overfitting), because the superior performance of these works is conditioned on large-scale training samples; (2) data distribution discrepancy among different cities, so knowledge learned from other cities cannot be utilized directly in new cities. In this article, we propose a task-adaptative model-agnostic meta-learning framework, namely, MetaStore, to tackle these two challenges and improve the prediction performance in new cities with insufficient data for optimal store placement, by transferring prior knowledge learned from multiple data-rich cities. Specifically, we develop a task-adaptative meta-learning algorithm to learn city-specific prior initializations from multiple cities, which is capable of handling the multimodal data distribution and accelerating the adaptation in new cities compared to other methods. In addition, we design an effective learning strategy for MetaStore to promote faster convergence and optimization by sampling high-quality data for each training batch in view of noisy data in practical applications. The extensive experimental results demonstrate that our proposed method leads to state-of-the-art performance compared with various baselines. [ABSTRACT FROM AUTHOR]

Abstract The rapid expansion of urban populations and concomitant increase in the generation of municipal solid waste (MSW) exert considerable pressure on the conventional centralized MSW management system and are beginning to exceed disposal capacities. To tackle this issue, the conventional centralized MSW management system is more likely to evolve toward a more decentralized system with smaller capacity waste treatment facilities that are integrated at different levels of the urban environment, e.g., buildings, districts, and municipalities. In addition, MSW can become an important urban resource to address the rising energy consumption through waste-to-energy (WTE) technologies capable of generating electricity, heat, and biogas. This shift toward the combined centralized-decentralized waste-to-energy management system (WtEMS) requires an adapted decision-support methodology (DSM) that can assist decision-makers in analyzing MSW generation across large urban territories and designing optimal long-term WtEMS. The proposed integrated DSM for WtEMS planning relies on: i) an MSW segregation and prediction methodology, ii) an optimization methodology for the deployment of multi-level urban waste infrastructure combining centralized and decentralized facilities, and iii) a multi-criterion sustainability framework for WtEMS assessment. The proposed DSM was tested on a case study that was located in Singapore. The proposed WtEMS not only reduced the total operational expenses by about 50%, but also increased revenues from electricity recovery by two times in comparison with the conventional MSW management system. It also allowed more optimal land use (capacity-land fragmentation was reduced by 74.8%) and reduced the size of the required transportation fleet by 15.3% in comparison with the conventional MSW system. The Global Warming Potential (GWP) was improved by about 18.7%. Highlights • Integrated Decision Support Methodology for Waste-to-Energy Management System planning for Municipal Solid Waste segregation and prediction. • Integrates methodologies for segregation and prediction, facilities deployment optimization, and multi-criterion sustainability analysis. • Case study shows 50% total operational expenses reduction, twice revenue increase from electricity recovery in comparison with conventional MSW. • Results show 74.8% reduction in capacity-land fragmentation, 15.3% reduction in required transportation fleet compared with conventional systems. • Results also show improved Global Warming Potential by about 18.7%. [ABSTRACT FROM AUTHOR]

Genetics plays an important role in progressive supranuclear palsy (PSP) and remains poorly understood. A detailed literature search identified 19 PSP-associated genes: MAPT, LRRK2, LRP10, DCTN1, GRN, NPC1, PARK, TARDBP, TBK1, BSN , GBA , STX6, EIF2AK3, MOBP, DUSP10, SLCO1A2, RUNX2, CXCR4 , and APOE. To date, genetic studies on PSP have focused on Caucasian population. The gaps in PSP genetic study on East Asian populations need to be filled. Exon and flanking regions of the PSP-associated genes were sequenced in 104 patients with PSP and 488 healthy controls. Common variant-based association analysis and gene-based association tests of rare variants were performed using PLINK 1.9 and the sequence kernel association test-optimal, respectively. Additionally, the association of APOE and MAPT genotypes with PSP was evaluated. The above association analyses were repeated among probable PSP patients. Finally, PLINK 1.9 was used to test variants associated with the onset age of PSP. A rare non-pathogenic variant of MAPT (c.425C > T,p.A142V) was detected in a PSP patient. No common variants were significantly associated with PSP. In both the rare-variant and the rare-damaging-variant groups, the combined effect for GBA reached statistical significance (p = 1.43 × 10−3, p = 4.98 × 10−4). The result between APOE, MAPT genotypes and PSP risk were inconsistent across all PSP group and probably PSP group. The pathogenic variant in MAPT were uncommon in PSP patients. Moreover, GBA gene was likely to increase the risk of PSP, and GBA -associated diseases were beyond α-synucleinopathies. The association between APOE, MAPT and PSP is still unclear among the non-Caucasian population. • Filling the gaps in PSP genetic study on East Asian populations. • Finding the association between GBA gene and PSP for the first time. • The pathogenic variant in MAPT were uncommon in PSP patients. [ABSTRACT FROM AUTHOR]

Abstract: High-titer serologically detected male (SDM) antibody fragments are essential for specific binding to the SDM antigen and promoting its application. The A8 clone previously obtained from an original phage antibody library was further affinity-matured by light- and high-chain shuffling respectively, to generate the end product B9 clone. The binding capacity of B9 phage Fabs to male splenocytes doubled the value of its parental A8 clone (determined using ELISA). Based on immunofluorescent staining, B9-Fabs mainly bound to the surface antigen of male splenocytes and recognized testicular cells. The resulting B9-Fabs detected a single protein (approximately 40 kDa determined using Western blot analysis of male splenocytes and testis); its high SDM antigen binding ability might have been because of mutation sites and varied lengths of the amino acid sequences in the complementarity determining regions-3 of the κ and Fd chains. The new recombinant clones of Fab that were phage-enhanced using chain shuffling were candidate molecules for investigating molecular mechanisms of SDM antigens specific binding and applications. [Copyright &y& Elsevier]